Skip to main content

An official website of the United States government

Here’s how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( Lock Locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

NCA5 Logo
    • About This Report
    • Guide to the Report
    • Report Credits
    • Companion Podcast
    • Additional Resources
    • About this Report
    • Guide to this Report
    • OVERVIEW
    • Physical Science
    • 2. Climate Trends
    • 3. Earth Systems Processes
    • National Topics
    • 4. Water
    • 5. Energy
    • 6. Land
    • 7. Forests
    • 8. Ecosystems
    • 9. Coasts
    • 10. Oceans
    • 11. Agriculture
    • 12. Built Environment
    • 13. Transportation
    • 14. Air Quality
    • 15. Human Health
    • 16. Indigenous Peoples
    • 17. International
    • 18. Complex Systems
    • 19. Economics
    • 20. Social Systems and Justice
    • Regions
    • 21. Northeast
    • 22. Southeast
    • 23. US Caribbean
    • 24. Midwest
    • 25. Northern Great Plains
    • 26. Southern Great Plains
    • 27. Northwest
    • 28. Southwest
    • 29. Alaska
    • 30. Hawai'i and US-Affiliated Pacific Islands
    • Responses
    • 31. Adaptation
    • 32. Mitigation
    • Focus On
    • F1. Compound Events
    • F2. Western Wildfires
    • F3. COVID-19 and Climate Change
    • F4. Risks to Supply Chains
    • F5. Blue Carbon
    • Appendices
    • A1. Process
    • A2. Information Quality
    • A3. Scenarios and Datasets
    • A4. Indicators
    • A5. Glossary

    • All Figures
    • All Key Messages
    • View All Report Downloads
    • Download Full Chapter PDF
    • Download Chapter Handout
    • Download Chapter Figures (.zip)
    • Download Chapter Presentation Package
    • Descargar en Español
  • Art × Climate
  • NCA Atlas
  • EN ESPAÑOL
Mitigation
i

Fifth National Climate Assessment
32. Mitigation

  • SECTIONS
  • Introduction
  • 32.1. Net-Zero Emissions
  • 32.2. Achieving Emissions Reductions
  • 32.3. New Mitigation Options
  • 32.4. Health and Equity
  • 32.5. Mitigation at Many Scales
  • Traceable Accounts
  • References
Previous Chapter
View All Figures
Next Chapter
US greenhouse gas emissions have declined in recent decades but must decrease at a faster pace to meet mitigation targets and goals. Reaching net-zero emissions will involve improvements in energy efficiency, greater reliance on solar and wind energy, widespread electrification, and reliance on emerging technologies. Large reductions in emissions could improve human health and redress legacies of inequity.

INTRODUCTION

Mitigation refers to efforts to reduce emissions or to remove carbon from the atmosphere with the goal of avoiding or reducing the effects of climate change, which is different from adapting systems and activities to a changed climate (Ch. 31). To meet international climate goals, global carbon dioxide (CO2) emissions would need to reach net zero by around 2050 (KM 32.1).1

Authors
Federal Coordinating Lead Author
Rebecca S. Dodder, US Environmental Protection Agency
Chapter Lead Author
Steven J. Davis, University of California, Irvine
Agency Chapter Lead Author
David D. Turner, NOAA Global Systems Laboratory
Chapter Authors
Ines M.L Azevedo, Stanford University
Morgan Bazilian, Colorado School of Mines
John Bistline, Electric Power Research Institute
Sanya Carley, Indiana University
Christopher T. M. Clack, Vibrant Clean Energy LLC
Joseph E. Fargione, The Nature Conservancy
Emily Grubert, University of Notre Dame
Jason Hill, University of Minnesota
Adrienne L. Hollis, Hollis Environmental Consulting
Alan Jenn, University of California, Davis
Ryan A. Jones, Evolved Energy Research
Eric Masanet, University of California, Santa Barbara
Erin N. Mayfield, Dartmouth College
Matteo Muratori, National Renewable Energy Laboratory
Wei Peng, The Pennsylvania State University
Brittany C. Sellers, City of Orlando, Florida
Contributors
Technical Contributors
Jacques de Chalendar, Stanford University
Julianne DeAngelo, University of California, Irvine
Huilin Luo, The Pennsylvania State University
Tyler H. Ruggles, Carnegie Institution for Science
Jaxon Z. Stuhr, University of California, Santa Barbara
Review Editor
Michael Westphal, Pacific Northwest National Laboratory
USGCRP Coordinators
Christopher W. Avery, US Global Change Research Program / ICF
Reid A. Sherman, US Global Change Research Program / ICF
Recommended Citation

Davis, S.J., R.S. Dodder, D.D. Turner, I.M.L. Azevedo, M. Bazilian, J. Bistline, S. Carley, C.T.M. Clack, J.E. Fargione, E. Grubert, J. Hill, A.L. Hollis, A. Jenn, R.A. Jones, E. Masanet, E.N. Mayfield, M. Muratori, W. Peng, and B.C. Sellers, 2023: Ch. 32. Mitigation. In: Fifth National Climate Assessment. Crimmins, A.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, B.C. Stewart, and T.K. Maycock, Eds. U.S. Global Change Research Program, Washington, DC, USA. https://doi.org/10.7930/NCA5.2023.CH32

Download citation: BibTeX     |     RIS

Mitigation is the most cost-effective response to climate change, with potentially large benefits to economies (Ch. 19), social and economic equity (Ch. 12), human health (Chs. 13, 14, 15), food security (Ch. 6), and ecosystems (Chs. 7, 8). Modeling studies agree that large near-term decreases in greenhouse gases (GHGs) in the United States are feasible by improving energy efficiency, electrifying end uses of energy, and generating electricity from non-emitting energy sources such as solar and wind (KM 32.2). However, the optimal mix of technologies to reach net-zero emissions is not yet clear, and further research and development is needed to determine the best options for long-duration energy storage, non-emitting and dispatchable (sometimes called firm) sources of electricity, and net-zero options for aviation and long-distance freight transport, as well as carbon dioxide removal (KM 32.3). Actions to immediately and substantially reduce emissions are available, and can be supported by individual choices and decisions by multiple stakeholders (KM 32.5). Further, racial, economic, demographic, and geographic inequities and injustices are embedded within existing infrastructure and social systems, and mitigation will both influence and be influenced by equity, environmental, and economic factors (KM 32.4).

Art × Climate
Graphite on paper drawing in black and white shows a wind turbine against a cloudy sky.

Margaret Colarelli
Monument
(2017, graphite on paper)

Artist’s statement: “Monument” was inspired by a foggy drive along stretches of farmland. Striking, modern wind turbines are embedded in fields, hovering behind old houses and barns, jutting out from behind trees, lining highways, and pairing with the power lines they feed. They have changed the lived experience of farmers who now supply a vital source of energy along with the crops they raise. I hope to attune the viewer to see this changing landscape as hopeful progress. “Monument” honors the power and grace of these important sources of renewable energy.

View the full Art × Climate gallery.

Artworks and artists’ statements are not official Assessment products.

Successful Mitigation Means Reaching Net-Zero Emissions

Greenhouse gas emissions in the United States decreased by 12% between 2005 and 2019, mostly due to replacing coal-fired electricity generation with natural gas–fired and renewable generation . However, US net greenhouse gas emissions remain substantial and would have to decline by more than 6% per year on average, reaching net zero around midcentury, to meet current national climate targets and international temperature goals .

Mitigation Goals

To achieve the Paris Agreement (an international treaty on climate change) goal of limiting global warming to well below 2°C above preindustrial levels and pursuing efforts to limit global warming to 1.5°C above preindustrial levels, global CO2 emissions need to reach net zero around 2050 and remain net zero or net negative afterward.2 Thus, US CO2 emissions reaching net zero around midcentury would be consistent with Paris goals, although a wide range of trajectories is possible based on considerations of international equity, burden-sharing, costs, and policy assumptions.3,4,5 This chapter addresses pathways and options for mitigation of US emissions from all sectors consistent with national and international climate goals.

As part of the Paris Agreement, countries communicate nationally determined contributions (NDCs)—emissions-reduction targets that they intend to achieve. The latest NDC communicated by the United States to the United Nations Framework Convention on Climate Change (UNFCCC) Secretariat sets an economy-wide target of reducing all its net GHG emissions (not only CO2) by 50%–52% below 2005 levels in 2030, or roughly –6% per year beginning in 2022, putting the country on a path to achieve the goal of reaching net-zero GHG emissions by no later than 2050 (Figure 32.1).7 In addition, 24 states and Washington, DC, have their own reduction targets (KM 32.5).

URL
Alternative text
US Greenhouse Gas Emissions by Sector with 2030 and 2050 Goals Added
A stacked area and line chart illustrate historical US greenhouse gas emissions by economic sector, plus future emissions goals, as described in the text and caption. Y-axis values range from negative 1,000 to positive 8,000 million metric tons of carbon dioxide equivalent (abbreviated CO2-eq). Colored areas represent the following: net emissions (black solid line); US territories (purple); residential (orange); commercial (light teal); agricultural (light green); industry (gray): electricity generation (yellow); transportation (blue); and land use (dark teal). A dashed black line continues from the solid black line in 2019, showing the following emissions reduction goals indicated by black dots and labels: 50 to 52 percent below 2005 levels in 2030, at just under 3,000 million metric tons, and net-zero in 2050, at 0 million metric tons. Values for each sector from bottom to top follow. Land use emissions are negative, increasing from about negative 900 in 1990 to about negative 700 in 2019. Values for all other sectors are positive, with emissions for transportation, electricity generation, and industry each in the 1,200 to 2,300 million metric tons range. Agricultural, commercial, and residential emissions are each in the 300 to 700 million metric tons range. Emissions from US territories are less than 100 million metric tons. Net emissions increase from about 5,500 million metric tons in 1990 to about 6,600 million metric tons in 2007, then decrease to about 5,800 million metric tons in 2019. Electricity generation shows a peak around 2007 and a decline thereafter, which is the largest driver of the decline in net emissions.
US emissions will need to decrease rapidly to reach levels consistent with international climate targets.
Figure 32.1. Figure shows US annual greenhouse gas emissions and sinks from 2005 to 2019, as well as future targets for achieving the US nationally determined contribution under the Paris Agreement. US territories—including American Sāmoa, Commonwealth of the Northern Mariana Islands, Federated States of Micronesia, Guam, Republic of the Marshall Islands, and Republic of Palau—contribute minor emissions (not visible) that are not broken down by sector. CO2-eq = carbon dioxide equivalent. Adapted from DOS and EOP 2021.7

Major Trends

Between 1990 and 2019, US CO2 and nitrous oxide (N2O) emissions increased by approximately 3% in each case. Emissions of fluorinated gases increased by 86%, and methane (CH4) emissions decreased by 15%.8 Although the latest EPA inventory reports emissions through 2021,9 this chapter focuses on trends in emissions to 2019 because the COVID-19 pandemic caused substantial but largely temporary changes in energy-related emissions worldwide (see, e.g., Davis et al. 2022; Liu et al. 202010,11). The EPA estimates that US carbon dioxide equivalent (CO2-eq) GHG emissions were about 6.6 billion metric tons (or gigatons; Gt) in 2019, 2% more than in 1990.8,12 The sources of these emissions are primarily electricity generation, transportation, and combustion of fuels in other sectors (i.e., commercial, residential, and industrial), with smaller contributions from agriculture, industrial processes, and waste (Figure 32.1). Major sinks were land-use change and especially forests, which resulted in net uptake of 0.7 Gt of CO2 in 2019. Net GHG emissions from all sources and sinks were thus 5.8 Gt of CO2-eq in 2019.8,9,12

Between 2005 and 2019, US GHG emissions decreased by 12%, mainly because of reductions in electricity generation emissions. Indeed, since 2017, the largest share of GHG emissions has come from the transportation sector (Figure 32.1). Estimates include emissions occurring within all US territories, as annually reported to the UNFCCC Secretariat by the EPA. Independent estimates by other scientific bodies and researchers are similar but not identical.13,14,15,16

Sector-Specific Trends and Drivers

Between 1990 and 2019, economic and population growth have acted to increase US energy-related emissions but have been counterbalanced by reductions in both the energy used per dollar of GDP (or “energy intensity of economic activity”) and the CO2 emissions per unit of energy used (or “emissions intensity of energy”).17 In particular, decreases in energy emissions since 2007 were driven by a steady and substantial fall in CO2 emissions per unit of energy consumed from a maximum of 59 million metric tons (megatons; Mt) of CO2 per exajoule (1018 joules) of energy consumed in 2007 to 51 Mt per exajoule in 2019 (Figure 32.2).

URL
Alternative text
Changes in Drivers for Energy-Related Greenhouse Gas (GHG) Emissions
A time series graph shows changes in drivers for energy-related greenhouse gas (abbreviated GHG) emissions from 1990 to 2020, as described in the text and caption. Y-axis values range from 40 to 160. The drivers, indexed at 100 in 1990, are GDP per capita (orange line), population (blue line), GHG emissions (pink line), emissions intensity (brown line), and energy intensity (green line). GDP per capita and population both show steady increases, with values in 2020 of about 150 and 130, respectively. GHG emissions show an increase through 2007 then a decrease, with a value in 2019 of just over 100. Emissions intensity and energy intensity show steady decreases, with values of about 85 in 2019 and 55 in 2020, respectively.
US greenhouse gas emissions have dropped even as population and economic activity (as measured by GDP) have climbed.
Figure 32.2. Energy-related greenhouse gas (GHG) emissions in the United States have declined since 2007 (pink) despite rising population (blue) and economic activity measured by the GDP (orange). Behind the decreasing trend are gradual reductions in energy use per dollar of GDP (energy intensity, green) and large decreases in GHG emissions per unit of energy produced (emissions intensity, brown). Figure credit: Stanford University.

Electricity Sector Emissions

GHG emissions from the electricity sector in 2019 were 1,629 Mt of CO2-eq, or 30% of energy-related emissions.8 Decreases in US energy-related emissions since 2007 mostly reflect changes in the electricity sector, especially the retirement and reduced use of coal-fired power plants and corresponding increases in lower-cost electricity from natural gas–fired power plants (and to a lesser extent renewable technologies; Figure 32.3b). US emissions from electricity generation in 2019 were roughly 40% below 2005 levels (Figure 32.3a).

URL
Alternative text
Trends in Electricity Generation by Source and Related CO2 Emissions
Two time series graphs show trends in electricity-related carbon dioxide (abbreviated CO2) emissions and electricity generation by source as described in the text and caption. The left graph shows electricity-related CO2 emissions. Y-axis values range from 0 to 2,500 million metric tons of CO2 equivalent. Electricity-related emissions rose from about 2,300 million metric tons in 2000 to just under 2,500 million metric tons in around 2005, then steadily decreased to about 1,500 million metric tons in 2020. The right graph shows electricity generation by source. Y-axis values range from 0 to 4,500 megawatt hours. Sources are natural gas (green line), coal (light blue line), nuclear (magenta line) hydro (dark blue line), wind (yellow line), solar (purple line), other (turquoise line), and other renewables (gray line). The total from all sources is also shown (black line). Between 2001 and 2021, electricity generation from natural gas rose from about 650 to just over 1,500 megawatt hours. Coal has decreased from about 1,900 to about 900 megawatt hours. Electricity generation from nuclear and hydro has remained relatively constant at about 750 and about 250, respectively. Electricity generation from wind has increased from near zero to about 400, while solar has grown from about 0 to about 200. Generation from the other and other renewables categories have 100 megawatt hours across the period. Total electricity generation has increased from about 3,750 megawatt hours in 2001 to about 4,150 megawatt hours in 2021.
Carbon dioxide (CO2) emissions from electricity generation decreased by almost 40% between 2005 and 2020.
Figure 32.3. The decrease in electricity-related emissions between 2000 and 2020 (a) can be explained by the decline in high-emitting coal generation and the growth in generation from lower-emitting (natural gas) and non-emitting (wind and solar) sources (b). CO2-eq = carbon dioxide equivalent. Adapted from Scott Institute for Energy Innovation 201718 [CC BY-SA 4.0].

Transportation Sector Emissions

GHG emissions from the transportation sector in 2019 were 1,874 Mt of CO2-eq.8 Most transportation emissions are CO2 emissions from combustion of gasoline (59.6%, mostly for light trucks and cars), diesel (26.4%, mostly for heavy trucks, buses, and trains), and jet fuel (9.8%; Figure 32.4). In contrast to electricity sector emissions, transportation emissions increased by 23% between 1990 and 2018, largely reflecting 49% growth in demand for passenger vehicle transport over the period (measured in passenger-kilometers, or the distance traveled in km multiplied by the number of passengers), which was partially offset by a 22% decrease in energy required per passenger-kilometer. Over the same 1990–2018 time period, demand for heavy trucks (measured in vehicle-kilometer, or the total distance traveled by the truck fleet) more than doubled, and improvements in energy per vehicle-kilometer were more modest (an 8.6% decrease in energy required per vehicle-kilometer).

URL
Alternative text
Trends in Transportation Emissions and Underlying Drivers
Two stacked area charts and three time series graphs show trends in transportation emissions and underlying drivers, as described in the text and caption. The top left chart shows greenhouse gas (abbreviated GHG) emissions by the following modes: motorcycles (yellow), buses (gray), pipelines (pink), rail (brown), water (purple), air (orange), heavy trucks (green), light trucks (red), and cars (blue). Cars contribute the most GHG emissions, followed by light trucks and heavy trucks, then air. The remaining modes of transportation contribute relatively little. The top right chart shows carbon dioxide (abbreviated CO2) emissions from fuel combustion for the following categories: LPG (gray), electricity (pink), aviation gasoline (brown), residential fuel oil (purple), natural gas (yellow), jet fuel (green), diesel (red), and gasoline (blue). Gasoline contributes over half the total CO2 emissions from fuel combustion, followed by diesel, then jet fuel. The remaining categories contribute relatively little. Y-axis values for both stacked area charts range from 0.0 to 2.0 billion metric tons of carbon dioxide equivalent (abbreviated CO2-eq). For both charts, emissions for most categories rise steadily from 1990 through 2007, then decline through 2012, then rise again through 2019. There is an overall increase in emissions for all categories since 1990. The bottom left graph shows the annual change in emissions (blue line), energy (yellow line), and emissions relative to energy (magenta line) for the transportation sector. Values are indexed at 100 in 1990. Y-axis labels are 100 and 120. Energy values mirror emissions, increasing to their peak values in 2007, then decreasing through 2012, then increasing through 2018 and 2019, respectively, to values of just above 120. Emissions relative to energy show little overall change across the entire time period, with 2018 values being just below 100. The bottom center and right graphs show the annual change in emissions (blue line), demand (green line), energy relative to demand (light pink line), and emissions relative to energy (magenta line) for passenger vehicles and heavy trucks, respectively. Values are indexed at 100 in 1990. Y-axis labels range from 80 to 140 for passenger vehicles and from 100 to 200 for heavy trucks. For passenger vehicles, demand increases steadily from 1990 to 2018 to a value of just below 150, whereas emissions increase through the mid 2000s but then decrease, ending at a value of just below 120 in 2019. Energy relative to demand decreases throughout the period, with a value of less than 80 in 2018, and emissions relative to energy are relatively constant, with a value of 100 in 2018. For heavy trucks, demand follows a similar upward trend to emissions, reaching values of just above 200 in 2018 and just below 200 in 2019, respectively. Energy relative to demand and emissions relative to energy show little overall change across the entire time period, with 2018 values of around 90 and 100, respectively.
Transportations emissions fell from 2007–2012 but have climbed since then.
Figure 32.4. The figure shows US greenhouse gas emissions by transportation mode. Emissions from transportation decreased starting in 2007 (a), reflecting decreases in carbon dioxide (CO2) emissions from fuel combustion (b), but both have increased again since 2012. The trend was consistent across different types of vehicles, driven by a drop in demand during a period of recession and high fuel prices (c, d, e). Passenger vehicles include cars, light trucks, and buses. LPG refers to liquified petroleum gas or propane; CO2-eq = carbon dioxide equivalent. Figure credit: Stanford University.

Residential- and Commercial-Building Sector Emissions

Direct GHG emissions from residential and commercial buildings were 699 Mt CO2-eq in 2019. Since 1990, direct emissions from US residential and commercial buildings (i.e., excluding electricity) have risen by roughly 14% (Figure 32.5). The increase is primarily related to steady growth of fugitive emissions of fluorinated gases from building cooling systems.9 Over the same period, energy efficiency improvements and increasing electrification have kept flat direct CO2 emissions from onsite fuel combustion despite 50% increases in both residential and commercial building floor area (Figure 32.5).19,20

URL
Alternative text
Trends in Residential- and Commercial-Building Emissions and Intensities
Two graphs show trends in residential and commercial emissions and intensities, as described in the text and caption. At left, a stacked area chart shows direct carbon dioxide (abbreviated CO2) emissions from residential CO2 fossil fuel combustion (blue), commercial CO2 fossil fuel combustion (orange), use of fluorinated gases (gray), and fossil fuel combustion from other greenhouse gases (yellow). Y-axis values range from zero to 800 million metric tons of carbon dioxide equivalent (abbreviated CO2-eq). Residential CO2 fossil fuel combustion contributes the greatest amount of emissions. Emissions fluctuate over the time period, with a slight overall decline from just over 300 million metric tons in 1990 to about 300 million metric tons in 2020. Emissions from commercial CO2 fossil fuel combustion also fluctuate, but show little overall change at about 225 million metric tons in both 1990 and 2020. Emissions from the use of fluorinated gases are negligible in 1990, but increase over the period, with a value of about 100 in 2020. Emissions due to fossil fuel combustion from other greenhouse gases are very small relative to the other categories. At right, a bar graph compares direct emissions intensity and floor area of US commercial (orange bars) and residential (blue bars) buildings for the years 1990, 2000, 2010, and 2020. The left y-axis shows direct emissions intensity in units of kilograms of carbon dioxide equivalent per square meter, with values ranging from 0 to 50. A line graph is overlaid on the bars, comparing the emissions intensity of commercial (orange circles and lines) and residential (blue circles and lines) floor area. The right y-axis shows floor area in billions of square meters, with values ranging from 0 to 25. For commercial buildings (orange bars), the direct emissions intensity for 1990, 2000, 2010, and 2020 is about 40, 40, 30, and 27 kilograms, respectively. For residential buildings (blue bars), the direct emissions intensity for the same years is about 26, 23, 20, and 16 kilograms. Commercial floor area is about 6, 6, 8, and 9 billion square meters for 1990, 2000, 2010, and 2020, respectively. Residential floor area for the same years is about 14, 16, 18, and 21, billion square meters.
Overall greenhouse gas emissions from buildings have climbed despite small declines in CO2 emissions from onsite combustion of fossil fuels.
Figure 32.5. Carbon dioxide (CO2) emissions from onsite fuel combustion in US buildings have decreased modestly since 2005 (a), driven by decreasing fuel-related emissions per building floor area (b), but overall related greenhouse gas emissions from buildings have increased over the same period due to both growth in the size of floor area (lines in panel b) and increasing levels of fluorinated gases escaping from building cooling systems (gray area in panel a). CO2-eq = carbon dioxide equivalent. Figure credit: University of California, Santa Barbara.

Industrial Sector Emissions

GHG emissions from the industrial sector were 1,568 Mt of CO2-eq in 2019.9 Direct emissions from the industrial sector, including onsite fuel combustion as well as all process and fugitive GHG emissions (e.g., emissions from calcination of limestone in cement production and methane leakage from oil and gas infrastructure), decreased by 14% between 1990 and 2020, primarily due to decreases in total fossil fuel combustion, fluorinated gas production and use, and metals-related process emissions (Figure 32.6). The manufacturing sector (i.e., production of goods and materials) is the largest source of direct emissions within the overall industrial sector and accounts for substantial electricity sector emissions related to purchases of power and heat (Figure 32.6b). Six key manufacturing subsectors (petroleum refining, chemicals, cement, iron and steel, aluminum, and forest products) account for around 70% of all emissions attributable to the manufacturing sector (Figure 32.6b).

Between 1994 and 2018, electricity-related emissions from US manufacturing have decreased by about 32% due to improved process efficiencies, deployment of combined heat and power systems, and decarbonization of purchased electricity. However, direct emissions from onsite fossil fuel combustion have decreased by only 10% over the same period and now account for about three-quarters of direct manufacturing emissions (Figure 32.6b).

URL
Alternative text
Trends in Industry Emissions
Two graphs show trends in industry emissions as described in the text and caption. At left, a stacked area chart shows industry emissions by sector for 1990 through 2020. The y-axis shows millions of metric tons of carbon dioxide equivalent, with values ranging from 0 to 1,800. Total emissions were at about 1.6 billion metric tons in 1990, dipped to about 1.3 billion around 2008, then generally increased to about 1.5 billion before dipping to around 1.4 in 2020. Fossil fuel combustion (orange) contributes the largest portion at about 800 million. Contributions from other sectors in descending order are from natural gas and petroleum systems (dark blue), manufacturing process emissions (light blue), other industrial categories (green), coal mining (yellow), and production and use of fluorinated gases (gray). A stacked bar graph at right compares manufacturing emissions in 1994 and 2018. For each year, three bars show total emissions (from left to right) from fuel combustion, electricity, and processes. The y-axis shows emissions in millions of metric tons of carbon dioxide equivalent, with values ranging from 0 to 700. Bars are subdivided to show contributions from the following sectors: aluminum (light purple), pulp and paper (green), cement (dark purple), petroleum refining (yellow), iron and steel (orange), chemicals (dark blue), and other manufacturing (turquoise). Between 1994 and 2018, total emissions from fuel combustion declined from about 750 to about 575 million metric tons. Total emissions from electricity-related emissions decreased from about 500 to about 325 million metric tons. Total emissions from manufacturing processes decreased from about 250 to about 175 million metric tons.
Greenhouse gas emissions from US industry, including manufacturing, have declined in recent decades.
Figure 32.6. Greenhouse gas emissions from US industry have declined modestly since 2005 across all sources (a). Panel b shows the breakdown of industry emissions in 1994 and 2018 by key manufacturing subsectors such as chemicals, iron and steel, pulp and paper, and aluminum. Data on intensity of industry emissions over time are not available. CO2-eq = carbon dioxide equivalent. Figure credit: University of California, Santa Barbara.

Land-Related Emissions

Annual US GHG emissions related to land use in 2019 can be split into emissions of 615 Mt of CO2-eq from agriculture and uptake of 704 Mt of CO2-eq by other land use and land-use change (including forests; Figure 32.7). Thus in 2019, there was a net land-related uptake (i.e., negative emissions) of 90 Mt CO2-eq. Forests take up carbon, but the amount of carbon sequestered by US forest land has decreased from 816 Mt CO2 in 1990 to 638 Mt CO2 in 20199 due to a combination of drought, wildfire, and disturbances by insects and disease (Box 7.2; KM 6.1).21,22,23 Agricultural emissions (excluding fuel combustion) increased slightly from 548 Mt CO2-eq in 1990 to 615 Mt CO2-eq in 2019. The net uptake of 90 Mt CO2-eq from US lands in 2019 represents a 73% decrease from the uptake of 333 Mt CO2-eq in 1990.9

URL
Alternative text
Trends in Land-Use Greenhouse Gas Emissions and Underlying Drivers
Two graphs illustrate trends in land-use emissions and underlying drivers, as described in the text and caption. At left, a stacked area chart shows emissions by land use for 1990 to 20201. The y-axis shows millions of metric tons of carbon dioxide equivalent, with values ranging from negative 1,000 to positive 800. A blank line shows net emissions. Emissions and uptake are shown for crops (orange), livestock (yellow), forests (green), and land-use change and other land use (khaki green). Crops and livestock both show positive emissions, with livestock values increasing from about 300 million metric tons to nearly 400. Crops remained fairly steady at about 200. Forest shows large negative emissions, that is, uptake, although this has declined from about negative 800 to around negative 700. Land-use shows fairly consistent negative emissions of about negative 100. A black line shows net emissions, which are negative over the whole period but have been increasing, from about negative 350 million to around negative 200. The time series graph on the right shows trends in key drivers of land-use emissions. The y-axis shows percent change relative to 1990, with values ranging from negative 60 to positive 60. A legend shows the following drivers: land-use emissions (dark blue line), population (red), per-capita agricultural production (gray), land-use intensity of agricultural production (orange), and emissions intensity of land use turquoise). Both emissions and emissions intensity show large and nearly identical increases, climbing rapidly to about 40% by 2005 and increasing somewhat more slowly to about 50 percent by 2017. Population also shows a steady increase, reaching nearly 30 percent by 2017. Per-capita agricultural production is highly variable, but increased in recent years to about 20%. Land-use intensity of agricultural production shows a fairly steady decrease, reaching about negative 30 percent by 2017.
US forests sequester more carbon than is emitted by agriculture, but the forest sink has weakened in recent decades.
Figure 32.7. Net greenhouse gas emissions from US land use are negative, meaning the carbon taken up by forests is greater than agricultural emissions (a). However, this net sink has weakened since 2005, driven by increases in the emissions intensity of land use (light blue curve in panel b) and despite decreases in the land-use intensity of agricultural production (yellow curve in panel b). CO2-eq = carbon dioxide equivalent. Figure credit: University of California, Irvine.


We Know How to Drastically Reduce Emissions

A US energy system with net-zero emissions would rely on widespread improvements in energy efficiency, substantial electricity generation from solar and wind energy, and widespread electrification of transportation and heating . Low-carbon fuels would still be needed for some transport and industry applications that are difficult to electrify . Land-related emissions in the US could be reduced by increasing the efficiency of food systems and improving agricultural practices and by protecting and restoring natural lands . Across all sectors, many of these options are economically feasible now .

Established Opportunities to Reduce Energy-Related Emissions

In modeling studies, deeply decarbonized and net-zero-emissions energy systems share several common characteristics, but regional approaches may depend on differences in resources,24,25 industrial bases,26 existing infrastructure,27,28 geography,29 governance and politics,30 public acceptance,31 and broader policy priorities.32

Improve Energy Efficiency

Improving energy efficiency means supplying the same level of end-use services or output while using less energy. Efficiency of buildings and appliances can be improved by design or retrofits (e.g., better insulation),33 as well as by optimizing control and management of devices (e.g., HVAC and lighting; KM 12.3; Figure 5.5).34 Further efficiency gains are available in the transportation sector: urban design can reduce travel demands;35,36 public and active transportation modes can greatly reduce energy use per passenger-mile;37,38 and advanced engines, electrification, reducing the weight of vehicles, and aerodynamic improvements can reduce energy use per passenger-mile (KM 13.3).39,40,41 In model scenarios of energy systems that successfully reach net-zero CO2 emissions, total US energy use often decreases relative to current levels, despite economic and population growth.42,43

One of the concerns with energy efficiency is whether it can induce rebound effects of different types. Studies have shown that the direct rebound effect (i.e., use of more of a good or service as it becomes more affordable) is low in the context of energy goods and services, but there is more uncertainty regarding indirect rebound effects (i.e., how an increase in energy efficiency of a good or service may lead to a change in the use of other goods and services or changes in the overall economy).44,45

Decarbonize the Electricity Sector

Options for reducing electricity system emissions include variable renewables (e.g., solar and wind resources, which are not available on demand; KM 5.3), dispatchable or “firm” renewables (e.g., biomass, hydropower, and geothermal, which can be available on demand), and other low-emitting dispatchable resources (e.g., nuclear and carbon capture and storage [CCS]–equipped fossil-fired generators); energy storage technologies; improved transmission (both upgrading conductors and new rights-of-way); and demand management. The rate and scale at which these technologies may be deployed in the future depend on the uncertain trajectories of their costs and energy markets, as well as a host of non-economic factors (KM 32.4).46,47,48,49,50,51,52,53,54

However, given their plummeting costs (Figure 32.8a, b) and growing policy support (e.g., the Inflation Reduction Act of 202255), variable renewable-energy resources—especially wind and photovoltaic solar generation—are expected to play central roles in decarbonizing electricity systems across the United States. Energy system models project that the capacity of wind and solar would need to increase 2 to 10 times faster each year than maximum historical rates (Figure 32.9b) to reach the 2030 target of halving economy-wide GHG emissions and midcentury net-zero targets (Figure 32.1).42,43 In such scenarios, expansion of energy storage generally supports greater reliance on wind and solar (Figure 32.1).42,43

URL
Alternative text
Historical Trends in Costs and Capacity of Low-Carbon Energy Technologies in the United States
Six time series bar charts show historical trends in the unit costs and capacity of low-carbon energy technologies in the United States, as described in the caption. The top left and top center graphs show the average levelized cost of energy (abbreviated LCOE) for wind and solar, respectively, from 2010 to 2020. Y-axis values for both graphs range from 0 to 240 dollars per megawatt-hour. Average wind LCOE shows a gradual decrease over the time period from about 90 to just under 40 dollars per megawatt-hour, while average solar LCOE shows a steep decrease from about 220 to 130 dollars between 2010 and 2012 and then a steady decrease to about 30 dollars in 2020. The top right graph shows average electric vehicle (abbreviated EV) battery cost from 2010 to 2022. Y-axis values range from 0 to 1,000 dollars per kilowatt-hour. EV battery costs show a steep decline from about 1,000 dollars per kilowatt-hour in 2010 to about 500 dollars in 2012, followed by a slower decrease to less than 200 dollars in 2022. The bottom left graph shows cumulative onshore wind capacity from 2010 to 2020. Y-axis values range from 0 to 140 gigawatts. Between 2010 and 2020, cumulative wind capacity shows a steady increase from about 40 to about 120 gigawatts. The bottom center graph shows cumulative US utility-scale solar capacity from 2010 to 2021. Y-axis values range from 0 to 40 gigawatts. Between 2010 and 2015, cumulative solar capacity shows a gradual increase from nearly 0 to about 8 gigawatts and then increases rapidly to about 38 gigawatts by 2021. The bottom right graph shows cumulative US EV sales from 2010 to 2022. Y-axis values range from 0 to 3.5 million vehicles. Cumulative EV sales increase from 0 in 2010 to more than 1 million units in 2018 and then increase much more rapidly to about 3.25 million in 2022.
Costs of renewable energy sources and electric vehicle batteries have declined as their cumulative deployment has increased.
Figure 32.8. Costs of onshore wind (a), solar photovoltaics (b), and electric vehicle (EV) batteries (c) have decreased sharply since 2000 (data shown here start in 2010), as the cumulative capacities of wind and solar generation (d, e) and the cumulative number of EVs sold (f) have increased. Figure credit: Electric Power Research Institute, National Renewable Energy Laboratory, NOAA NCEI, and CISESS NC.
URL
Alternative text
Historical and Projected Net-Zero Annual Capacity Additions by Technology for the US Under Net-Zero Scenarios
Two horizontal bar charts show historical (top) and projected (bottom) net-zero annual capacity additions by technology for the US under net-zero scenarios, as described in the text and caption. Bar lengths indicate capacity in gigawatts per year of added capacity, with values ranging from 0 to 250. Color-coded bar segments show the breakdown by source, with both charts showing gas (dark blue), nuclear (orange), hydro (light blue), coal (green), other (light gray), wind (yellow), solar (deep blue), and storage (dark red). The bottom chart includes gas carbon capture and storage (dark gray) and bio carbon capture and storage (brown). The top panel shows annual data for 1950 to 2021, with capacity additions generally around 25 gigawatts or less, with a single peak above 50 in 2002. The 1950s and 1960s show mostly gas and coal additions. Nuclear additions became common in the 1970s and 1980s. The early 2000s saw large increases in capacity, almost exclusively from gas. Solar and wind begin to emerge thereafter and become the primary sources of additions through 2021. The bottom chart shows projected additions for various scenarios, based on annual average values for 2020 to 2050. The scenarios are drawn from three publications as indicated to the right of the chart. The scenarios are as follows: 1) a scenario from Jacobsen et al. 2015 shows nearly 250 gigawatts per year of additions, with a significant storage component as well as large wind and solar additions. 2) Five scenarios from Larson et al. 2020 show additions ranging from 50 to about 175 gigawatts per year, with mostly wind and solar additions, some storage and bio carbon capture and storage, and some other sources. These are labeled 100% renewable energy, renewable constrained, high biomass, less-high electrification, and high electrification. 3) Six scenarios from Williams et al. 2021 show additions generally ranging between about 75 and 125 gigawatts per year, with one scenario at about 175 gigawatts. All involve primarily wind and solar, along with storage and some gas. These are labeled net negative, low demand, low land, 100% renewable energy, delayed electrification, and central.
To reach net zero, the US will need to add more electricity-generating capacity in each of the next 30 years than we have added historically.
Figure 32.9. Since 1950, increases in US electricity-generating capacity have exceeded 50 gigawatts (GW) in only one year, 2002 (a). In comparison, scenarios of net-zero-emissions energy systems produced by models project average increases in electricity-generating capacity of more than 50 GW per year every year between 2020 and 2050 (b). CCS refers to carbon capture and storage. See Jacobson et al. 2015; Larson et al. 2020; and Williams et al. 2021.50,53,60 Adapted from Bistline 202161 with permission from Elsevier (https://www.sciencedirect.com/journal/joule).

The same model scenarios consistently project the rapid decline of coal-fired electricity generation in decarbonized systems to near zero by 2030 (Figure 32.10).42,43 In contrast, natural gas–fired electricity generation declines more slowly in most of these net-zero emissions scenarios, facilitating penetration of variable renewables but operating less frequently over time unless equipped with CCS.53,56,57

URL
Alternative text
Projected Coal and Solar/Wind Electricity Generation
Two box and whisker plots show projected coal and solar/wind electricity generation, as described in the caption. Both charts show data for 2020, 2030, 2040, and 2050. The chart at left shows electricity generation from unabated coal (without carbon capture and storage). The y-axis shows terawatt-hours, with values ranging from 0 to 1,200. Values are as follows. For 2020, a very narrow range of nearly 800 terawatt hours, with a few outliers shown as black dots. For 2030, a much broader range from 0 to about 225. For 2040, a narrow range of from 0 to about 25. For 2050, an even narrower range of 0 to about 15. The chart at right shows electricity generation from solar and wind, with the y-axis showing terawatt-hours, with values ranging from 0 to 14,000. Values are as follows. For 2020, a narrow range of about 500. For 2030, a wider range centered around 2,000. For 2040, an even wider range, from about 3,000 to about 5,000, with the 10th to 90th percentile range stretching from less than 2,000 to nearly 8,000. For 2050, the range is from about 4,000 to about 6,000, with the 10th to 90th percentile range stretching from less than 3,000 to more than 8,000.
Models project a steep decline in coal-generated electricity and increases in renewables.
Figure 32.10. Across net-zero scenarios produced by models, median US coal electricity generation (thick horizontal lines) is expected to decrease sharply between 2020 and 2030 (a). Meanwhile, in the same scenarios, median solar and wind generation would increase steadily between 2020 and 2050 (b). Plots show individual scenarios as points, the 25th–75th percentile ranges as rectangles, and the 10th–90th percentile ranges as thin vertical lines. The mean of each set of scenarios is represented by an X. Figure credit: University of California, Irvine, and Electric Power Research Institute.

Finally, net-zero CO2 emissions scenarios often maintain—but do not greatly expand—existing nuclear and hydropower capacity in the absence of significant cost declines (such as improved economics from small modular designs; KM 5.3) and/or constraints on the deployment of other technologies.42,43,58 In contrast, both transmission infrastructure (i.e., power lines) and international and interregional transfers of electricity often increase in decarbonization scenarios, although the scale of such increases varies.42,43,59

Electrify Energy End Uses

As electricity systems are decarbonized, energy model scenarios consistently project widespread electrification of energy end uses such as on-road transportation and heat for buildings and industry (KM 5.1).42,43,50,52,53,54,62 Electricity may also be used to produce low-carbon fuels, such as hydrogen and e-fuels (liquid fuels produced by combining carbon captured from the atmosphere with hydrogen produced by electrolysis), for difficult-to-electrify applications (Box 32.1).63 The share of US final energy demands (i.e., energy used) met by electricity in net-zero-emissions energy systems will depend on the costs of low-carbon alternatives such as biofuels and hydrogen, but estimates range from 30%–60% in 2050, up from about 20% today (Figure 32.11).42,43

In transportation, light-duty electric vehicles (EVs) have had policy support (e.g., tax refunds) at both the state and federal level for a long time, and new EV sales have increased in recent years (Figure 32.8).64,65,66,67 The EV share of new light-duty vehicle sales in the US is expected to grow quickly,67,68 which is the case in model scenarios that reach net-zero emissions by midcentury.42,43 Many medium- and heavy-duty vehicles can also be electrified,69 although some applications (e.g., long-distance trips) may present special challenges.70,71,72,73,74 Decarbonization of the most difficult-to-electrify transportation sectors (e.g., aviation, international shipping) may require liquid biofuels or fuels synthesized using electrolytic hydrogen and carbon captured from the atmosphere.75,76

Insofar as electricity is generated from non-emitting sources, electrification of space and water heating would drastically reduce direct emissions from residential and commercial buildings in the United States (where these end uses account for the bulk of natural gas and oil consumption).77,78,79 Similarly, most industrial energy demand could be electrified using existing technologies,80 although achieving net-zero emissions in some industries may present special challenges81,82,83,84—particularly related to the costs of supplying high-temperature heat with electricity85 and/or fundamental changes in processes such as switching to direct reduction of iron ore with electrolytic hydrogen or installing carbon capture and storage on thousands of cement kilns worldwide.86

URL
Alternative text
Characteristics of US Energy Systems in Climate Mitigation Scenarios
Three box and whisker charts show characteristics of US energy systems in climate mitigation scenarios, as described in the text and caption. All three charts show data for 2020, 2030, and 2050. At left, a chart shows electrification, with the y-axis showing percent of final energy, with values ranging from 0 to 70 percent. For 2020, the range is very narrow at about 20 percent. For 2030, the estimate is similarly narrow at about 25 percent. For 2040, the estimate ranges from more than 30 to about 45 percent, with the 10th to 90th percentile range stretching from less than 30 to more than 50 percent. For 2050, the range is from about 45 to more than 55, with the 10th to 90th percentile range stretching from less than 30 to about 65 percent. The middle chart shows electricity decarbonization. The y-axis is in million metric tons of carbon dioxide equivalent per exajoule, with values ranging from 0 to 140. For 2020, the estimate ranges from 100 to about 110. For 2030, the range is much lower, from about 25 to about 45, with the 10th to 90th percentile range stretching from less than 20 to more than 60. For 2040, the range is narrower, from less than 10 to about 20 percent, with the 10th to 90th percentile ranging from 0 to about 25. For 2050, the range is even lower, from just above 0 to about 7, and the 90th percentile is at about 10. The chart at right shows per capita energy use. The y-axis shows gigajoules per person, with values ranging from 0 to 250. For 2020, the range is from about 190 to about 215. For 2030, it decreases to about 175 to 185. For 2040, it decreases again to about 140 to about 150, with the 10th to 90th percentile ranging from about 130 to about 165. For 2050, the range is much narrower, centered around 135, with the 10th to 90th percentile ranging from about 130 to 140, although individual estimates shown as green dots range from just above 100 to about 160.
Net-zero model scenarios show large increases in electrical energy, accompanied by decarbonization of electricity sources and modest decreases in overall energy use per person.
Figure 32.11. Across net-zero model scenarios, between 2020 and 2050 the median share of all energy used (thick horizontal lines) by end consumers that is electricity increases (a), the median carbon intensity of electricity decreases (b), and the median energy per capita decreases modestly (c). Plots show individual scenarios as points, the 25th–75th percentile ranges as rectangles, and the 10th–90th percentile ranges as thin vertical lines. The mean of each set of scenarios is represented by an X. CO2 = carbon dioxide. Figure credit: University of California, Irvine; Electric Power Research Institute; and Evolved Energy Research.

Established Opportunities to Reduce Land-Related Emissions

Despite increasing demand for food and the headwinds of climate change impacts on agriculture, there are multiple options for decreasing land-use emissions and protecting and enhancing terrestrial carbon sinks (Ch. 11).

Use Most-Productive Land for Agriculture

Agriculture requires more land, by far, than any other human activity.87 One way to reduce the land required to grow food is to continue farming the most productive lands (those that grow more crops per land area). Removing the most productive areas from cultivation would lead to an increase in the overall land area required for agriculture.88 Loss of productive US farmland to sprawl or even restoration could thus lead to substantial land-use change and related GHG emissions elsewhere (e.g., if demanded agricultural goods are imported from other regions).89,90 For these reasons, studies have suggested mitigation efforts should prioritize restoration of marginal (i.e., not the most productive) lands.91

Reduce Food Waste

More than a third of all food in the US is currently wasted, more than 40% of which is food discarded by retailers and consumers (Box 11.2; Figure 11.12).92,93 Multiyear campaigns (from 4 to 11 years long) in five other developed countries successfully reduced food waste per person by 8%–29% through public education and public and private initiatives.92 Assuming similar reductions could be achieved, US agricultural land and land-related GHG emissions could be reduced by 4%–13%.

Shift Diets

GHG emissions produced during food production, distribution, transportation, and sale at retail or restaurants vary across different foods, so that different diets will entail different levels of life-cycle GHG emissions.94,95,96,97 In particular, although meat is a good source of protein and micronutrients, it generally produces more emissions per calorie than plant-based foods because energy is lost at each trophic level.94 Emissions related to meat production also vary: for example, ruminant animals usually produce much more GHG emissions per calorie of meat and per gram of protein than poultry (Figure 11.8).98 By reducing demand for emissions-intensive food, shifts to pescatarian, vegetarian, vegan, Mediterranean, or “flexitarian” (less meat consumption but not strictly vegetarian) diets can reduce land-related GHG emissions while providing direct health benefits (Figure 32.12),95,99,100,101 although analyses and models differ as to the level of future food demand related to such diets and other socioeconomic changes.102 Shifting diets and associated changes in agricultural practices have implications for land-use change as well as supply chains, air pollution, and human health.103 Consideration of energy and other inputs per unit of production and the resulting impacts on net GHG emissions is important for comparison of different dietary choices.

However, 10.4% of American households are food insecure (Box 11.1),104 so any approach to reduce the consumption of higher-emissions foods that results in higher food prices could disproportionately harm these households. Instead, policies might encourage less emissions-intensive diets while also reducing food costs and increasing consumer choice by making a diversity of plant-based and other lower-emissions, nutritious, and affordable options more widely available.

URL
Alternative text
Emissions Reductions and Related Health Benefits from Dietary Shifts
Two stacked bar charts show emissions reductions and related health benefits from dietary shifts, as described in the text and caption. In both charts, bar lengths show total values and color-coded segments indicate the relative contributions from the following diet components: red meat (red), carbon sequestration (green), dairy (light blue), poultry (dark blue), grains (turquoise), fruits and vegetables (orange), coffee, tea, cocoa, and spices (purple), oils (dark pink), alcohol (black), other (legumes, nuts and seeds, eggs, sugar; yellow), fish (light pink), and roots (medium pink). Another category is shown in the right panel only, consisting of other (legumes, nuts and seeds, eggs, sugar) plus roots (gray). The left chart shows emissions by diet type. The y-axis shows emissions in millions of metric tons of carbon dioxide equivalent, with values ranging from negative 1,200 to 1,000. For the current diet (far left), all emissions are positive, with a total of more than 800 million metric tons. Meat accounts for nearly 400 and dairy about 200. The other diets (from left to right) are NDG, EAT-Lancet, pescetarian, vegetarian, and vegan. All show large negative emissions values for carbon sequestration, ranging from about negative 700 to about negative 1,000. Positive emissions decrease to values from about 650 for NDG to between about 300 and about 425 for the other diets. Red meat and dairy drop to much smaller values, with varying mixes of other sources. The right chart shows health benefits, in terms of annual premature deaths attributed to total small particulate matter from food production by diet type. The y-axis shows mortality in deaths per year, with values ranging from 0 to 20,000. The chart shows total values for current diet and three others (from left to right): EAT-Lancet, vegetarian, and vegan. For current diet, mortality is more than 15,000 deaths per year, with red meat contributing to more than 11,000 of that total and dairy contributing more than 2,000. For the EAT-Lancet diet, values drop to less than 5,000. Values drop to about 3,500 for a vegetarian diet and about 3,000 for a vegan diet.
Changes in American diets could decrease US land-use greenhouse gas emissions, increase carbon sequestration, and reduce air pollution.
Figure 32.12. Studies have estimated potential reductions in greenhouse gas (GHG) emissions (a) and air pollution-related deaths (b) if the shares of foods in Americans’ current (average) diet were to shift. Although the specific changes in diet vary across studies, all would reduce GHG and pollution emissions as well as enhance carbon sequestration relative to the current diet. EAT-Lancet refers to a “flexitarian” diet that is mostly plant-based but includes modest amounts of fish, meat, and dairy foods. NDG refers to government-endorsed national dietary guidelines. PM2.5 refers to particulate matter 2.5 micrometers or smaller in diameter. CO2-eq = carbon dioxide equivalent. Figure credits: (a) University of Minnesota, NOAA NCEI, and CISESS NC; (b) adapted from Domingo et al. 2021103 [CC BY-NC-ND 4.0].

Improve Management of Croplands and Pasture

There are numerous opportunities to decrease the intensity of emissions (and/or increase sequestration; see Box 32.2) of croplands and pasture, including 1) improving soil health, 2) improving nitrogen fertilizer management, 3) increasing the number of trees and other perennials on the landscape (e.g., by agroforestry; see Ch. 11),105,106,107 and 4) avoiding methane emissions. Soil health and carbon sequestration can also be improved by amendments (including biochar; Figure 11.5), cover crops, reduced tillage,108 and diversification of crop rotations.109 Careful and sustained implementation of these practices can increase not only soil carbon but also yields, resilience, and profitability.

Better aligning the timing and amount of fertilization with plants’ needs can reduce fertilizer use110,111 and thereby also reduce both nitrous oxide (N2O) emissions from the soil and fossil fuel emissions from fertilizer production. Fertilizers with synthetic nitrification inhibitors can further reduce N2O emissions.112 Increased fertilizer efficiency and inhibition of nitrification processes in soil together can reduce N2O emissions by roughly 50%.113,114

There are also feasible options for reducing agricultural (livestock and rice) and waste (landfill and wastewater) sources of methane emissions.115 Methane is a relatively short-lived GHG that has contributed to at least 25% of climate warming to date.116,117 Consequently, technically feasible near-term methane emissions reductions could slow global decadal warming by 30%, avoiding a quarter degree Celsius of warming by midcentury.115 In addition to land-related sources of methane, there are large reductions possible from the oil and gas sector,118 primarily by repairing leaks at little or no net cost115 and ideally prioritizing disproportionately large sources (i.e., super-emitters).119,120,121

Avoid Conversion and Monitor Carbon Fluxes on Unmanaged Land

Between 50 and 150 Mt of annual CO2 emissions could be avoided by stopping conversions of unmanaged land in the United States (i.e., natural forests, grasslands, wetlands, or other ecosystems where there has been no substantial human influence or intervention).122 Strategies for stopping such conversions include densification of already-developed areas, zoning, and property tax incentives, as well as land protection such as conservation easements and public parks.123,124,125,126 Related to this opportunity, the recent decrease in carbon sequestration by US forests (KM 32.1) is a concern. Further weakening of this carbon sink would make reaching net zero proportionally that much more difficult. Improved monitoring of forest carbon fluxes and their drivers is therefore important, including those on unmanaged land and in boreal Alaska (KM 7.2).127,128,129


To Reach Net-Zero Emissions, Additional Mitigation Options Need to Be Explored

Although many mitigation options are currently available and cost-effective, the level and types of energy technologies and carbon management in net-zero-emissions energy systems depend on still-uncertain technological progress, public acceptance, consumer choice, and future developments in institutions, markets, and policies . Attractive targets for further research, development, and demonstration include carbon capture, utilization, and storage; long-duration energy storage; low-carbon fuels and feedstocks; demand management; next-generation electricity transmission; carbon dioxide removal; modern foods; and interventions to reduce industry and agricultural emissions .

Potential Opportunities to Reduce Energy-Related Emissions

There are many uncertainties and outstanding questions related to mitigation of energy-related emissions. These uncertainties are reflected by the large differences in the scale and mix of energy sources and use as well as carbon management across modeled net-zero-emissions energy systems, which highlight potential mitigation opportunities.

The Mix of Electricity Sources in Net-Zero-Emissions Energy Systems

In recently modeled net-zero-emissions US energy systems, the share of electricity demand met by variable renewables—as opposed to firm sources—varied from 45%–89% depending on the availability of energy storage, transmission, and the mix of solar and wind.42,43 Although grid managers are gaining experience planning and operating electricity systems with large amounts of solar and wind generation, questions persist as to the maximum share of these resources that should be included in reliable and resilient decarbonized systems130 and the best approaches for dealing with their natural variability.131 Large shares of variable renewables can be incorporated in electricity grids through the use of 1) batteries, hydrogen, and other types of energy storage; 2) transmission and interregional transfers of electricity; 3) firm low-carbon electricity sources; and 4) greater demand-side responses. The costs and effectiveness of these approaches for managing variability differ and are related to the spatial and temporal variability of solar and wind resources,25,76,132,133,134,135,136,137 in addition to a host of non-cost factors (KM 32.4). Moreover, energy sources and technologies will interact in complex ways to fulfill the different functions in electricity systems (e.g., providing energy, capacity, and ancillary services over different timescales), depending on their relative costs and system benefits, policy stringency and design, geophysical resources and infrastructure, environmental co-benefits, and societal preferences.56,57,138 Further research, development, and demonstration of technologies and approaches are needed to resolve uncertainties, identify key sensitivities, and clarify the most attractive options for providing reliable, resilient, and affordable electricity in net-zero-emissions energy systems (Figure 5.6).

Alternative Fuels for Difficult-to-Electrify Sectors

As with electricity, there is considerable uncertainty about the scale and mix of other energy carriers (e.g., hydrogen, bioenergy, e-fuels) that may be needed by difficult-to-electrify sectors such as long-distance transportation of freight, long-haul aviation, high-temperature industrial heating, and space heating in very cold climates.76,85,139,140,141 Hydrogen, ammonia, alcohols, and carbon-based fuels (e.g., methane, petroleum, methanol) can all be produced with low and eventually net-zero CO2 emissions (Box 32.1). However, it is not clear whether producing and burning these fuels would be lower in cost and more sustainable than continuing to use fossil fuels and managing the related emissions through CCS or carbon dioxide removal (CDR; removal of CO2 from the atmosphere).63 Here again, further research, development, and demonstration of technologies will help reveal critical dependencies and trade-offs and clarify the most sustainable and cost-effective pathways to net-zero-emissions fuels.

Box 32.1. Hydrogen

Hydrogen is an energy carrier that could link together multiple energy sectors (known as sector coupling) and facilitate high shares of variable wind and solar generation in electricity systems.142,143 Multiple processes can produce hydrogen—including electrolysis, which uses electricity to split water into hydrogen gas (H2) and oxygen gas (O2). These processes represent potential links between the electricity sector, fuels for transportation and industry, and feedstock for chemical materials.

Some electrolyzers (e.g., proton exchange membrane) can also be ramped up and down in seconds144,145 to help manage electricity demand in energy systems with variable electricity sources.143,146 Other means of producing hydrogen with low or no CO2 emissions, such as methane or biomass pyrolosis and steam methane reforming (SMR) with carbon capture, utilization, and storage (CCUS),143,147,148 may also contribute to decarbonization if life-cycle GHG emissions can be kept low,149,150 but these will not facilitate sector coupling or act as flexible electricity demand.

Global hydrogen demand was 90 Mt in 2020 and was supplied almost exclusively by fossil fuel feedstocks: 59% by natural gas without CCUS, 19% by coal, 21% from by-product processes that often contain a mixture of other gases, and less than 1% each of natural gas with CCUS, oil, and electricity (Figure 32.13).151 Petrochemical processes were the largest sources of by-product hydrogen. Reflecting its fossil origin, hydrogen production in 2020 accounted for 900 Mt of CO2 emissions.151 Of all hydrogen produced in 2020, 44% was used in refineries, 37% in ammonia production, 14% in methanol production, and 6% in the direct reduction of iron, with other demands accounting for less than 1%.151

As noted in the discussion of alternative fuels (KM 32.3), hydrogen may help to decarbonize difficult-to-electrify end uses such as long-distance transport of freight and aviation, for which energy density is critical.75,76 However, pressurizing or storing hydrogen in liquid phase for transport and storage adds additional costs and requires heavy storage tanks.76,143 When used, hydrogen can either be oxidized in fuel cells or combusted in gas turbines152,153 to produce electricity (or thrust) in a power-to-gas-to-power loop. Although substantial energy is lost in this loop, it allows shifting electricity in time from when it is readily available to when it is needed most.132,135

A key challenge is the current high cost of producing hydrogen from zero- or low-emitting processes. Hydrogen produced from carbon-emitting SMR can cost in the range of $1–$2.50/kg H2, much lower than the more than $4/kg H2 achievable with current electrolysis technology and wind or solar power.154,155 The US Department of Energy’s Hydrogen Shot program has set a goal of achieving clean hydrogen production for $1/kg H2 within a decade by reducing both electrolyzer and wind- and solar-electricity costs (KM 5.3).154 There are also challenges of leakage from and embrittlement of infrastructure not originally designed for hydrogen, such as natural gas pipelines, which create concerns about safety,143,156 the potential for increases in air pollutants such as nitrogen oxides if hydrogen is combusted,157,158 and the climate-warming influence of fugitive hydrogen.159,160 However, at low concentrations, hydrogen can be safely injected into natural gas pipelines and used in conventional home appliances.161,162,163,164,165

URL
Alternative text
Hydrogen Production by Source and End Uses in 2021 and 2050
Two Sankey diagrams show hydrogen production by source and end uses in 2021 and 2050, as described in the text and caption. The top chart shows the current energy system as of 2021. The input at left is pipeline gas blend, showing an amount of 1.73 exajoules. This leads to production of 1.32 exajoules of hydrogen and losses of 0.41 exajoules. From the hydrogen production, outputs are 0.96 exajoules to industry and 0.36 to fuel refining. The bottom chart shows an illustrative least-cost net-zero pathway for 2050, showing much larger inputs and outputs. Inputs include 10.24 exajoules from electricity, 4.38 from biomass, and 0.91 from pipeline gas blend. About two-thirds of the electricity output flows to hydrogen production and one third to losses. About half the biomass output flows to hydrogen production and half to losses. About two thirds of pipeline gas blend goes to hydrogen production and the rest to losses. Hydrogen production is 9.64 exajoules and losses are 5.89 exajoules. Hydrogen production flows to 4.62 exajoules for fuel refining, 2.96 for industry, and 2.00 for transportation.
Energy model scenarios show that the magnitude, sources, and uses of hydrogen will change substantially by 2050.
Figure 32.13. Curves in the figure show how hydrogen is produced (left), lost to waste (middle) and used (right) currently (a) and in an illustrative 2050 scenario (b). The thickness of the curves represents the amount of hydrogen in each category. Today, most hydrogen is produced by steam reforming of natural gas (SMR) and used by the chemical industry (especially for making fertilizer). In the depicted net-zero emissions scenario, by 2050 the largest source has become electricity, and fuel refining has become the largest use. BECCS-H2 refers to hydrogen produced from biomass feedstocks with carbon capture and storage; EJ = exajoule. Adapted with permission from Haley et al. 2022.166

Carbon Management

Most model scenarios that reach net-zero emissions in the United States entail substantial use of CDR technologies, not as a replacement for emissions reductions but instead to offset continuing emissions from the most difficult-to-decarbonize sectors and processes, such as aviation and cement making (sources of emissions that may be much more expensive to eliminate), to offset non-energy GHG emissions, and to reduce GHG concentrations in the atmosphere. The degree and form of CDR deployment, including the balance between industrial carbon capture and intentional enhancement of natural carbon sinks, remain highly uncertain, however, and depend on technological readiness, economics, public acceptance, and institutional and political considerations (Box 32.2).

Box 32.2. Carbon Dioxide Removal

The most recent modeling studies of net-zero emissions scenarios for the United States consistently project that some quantity of carbon dioxide removal (CDR) from the atmosphere will be needed to offset any residual greenhouse gas (GHG) emissions.42,43 The scale of CDR called for in these scenarios ranges from 0.8–2.9 gigatons (Gt) of carbon dioxide (CO2; median is 1.6 Gt) in scenarios that reach net-zero CO2 emissions by 2050 (Figure 32.14).

CDR options fall into two categories according to whether they enhance uptake of atmospheric CO2 by biological processes or by chemical processes, each of which can be further disaggregated depending on where the processes occur (e.g., on land, in the ocean, or in industrial facilities).167,168 Different approaches have different biophysical and economic limits to scale,169 as well as different concerns related to equity and environmental justice,170,171 environmental impacts,172 permanence or durability of removal (i.e., the timescale of sequestration and its reversibility),173,174,175 and additionality (i.e., the removal would not have occurred without human intervention).167,176

Current energy models are relatively simplistic in their representation of CDR, typically including only 1) bioenergy with carbon capture and storage (BECCS), 2) afforestation/reforestation, and 3) industrial direct air capture (DAC). Among these methods, the on-land biological options (BECCS and afforestation/reforestation) are the most prevalent in net-zero emissions scenarios; BECCS dominates if underground carbon sequestration is allowed (Figure 32.14).

Most scenarios use DAC sparingly, owing to its cost and energy requirements (Figure 32.14), but recent studies have highlighted potential cost reductions.177 Evaluations of natural climate solutions meanwhile suggest that reforestation represents the largest opportunity for land-based mitigation.178 Up to 128 million acres of land in the US are reforestable and could sequester 200–500 million metric tons (Mt) of CO2 per year179,180 given substantial investments in the reforestation supply chain.181

However, a variety of other biological CDR options are being explored and could be cost-effective at carbon prices of $50–$100/Mt CO2: improved management of rangeland and pasture might sequester 0.05–0.74 Mt of CO2 per acre per year, or a total of 49–490 Mt of CO2 per year, given the roughly 655 million acres of US grazing land.122,182,183,184,185 Improved management of cropland soil (e.g., applying biochar, cover crops, or no-till) might sequester 150–250 Mt of CO2 in the United States each year.122,186,187,188,189 In forests, extending timber rotations, removing competing vegetation, and selective harvesting could remove 160–315 Mt CO2 per year.180,190,191,192,193,194,195,196,197 Finally, rewetting drained US wetlands198,199 and reconnecting salt marshes to the ocean (which reduces methane emissions)200 could remove 9 Mt of CO2 per year.122 These options are discussed further in other chapters (KMs 6.3, 9.2, 11.1; Boxes 7.2, 30.5; Focus on Blue Carbon).

Although less mature, a growing body of research is also focusing on ocean-based CDR,201 including ocean fertilization,202 artificial upwelling and downwelling, seaweed farming,203 marine restoration, ocean alkalinity enhancement,204 and electrochemical engineering approaches (see, e.g., KM 10.3).

Additional research could reduce the uncertainty related to these estimates; establish robust monitoring, reporting, and verification protocols; and help to prioritize types and locations for CDR based on co-benefits and trade-offs. A related area of research is the Earth system response to large-scale CDR (i.e., negative emissions); a growing body of literature has shown that emitting GHGs and then removing them from the atmosphere is not the same as not emitting the GHGs at all.205,206,207

Reducing sunlight reaching the Earth’s surface, or solar radiation modification (SRM), is sometimes discussed alongside CDR because both are intentional interventions in the climate system.167,208 SRM is not mitigation as defined in this chapter; the effectiveness, costs, environmental trade-offs, and geopolitical implications of SRM are uncertain, and further research on these topics is either underway or may be merited (KM 17.2). Moreover, some scientists and policymakers emphasize that the risks of SRM should be considered in the context of the many risks of continued climate change.209

URL
Alternative text
Scale and Type of Carbon Dioxide Removal in US Net-Zero Emissions Scenarios
Three box and whisker plots show the scale and type of carbon dioxide removal in US net-zero emissions scenarios, as described in the text and caption. All three charts show data for 2020, 2030, 2040, and 2050. The left chart shows net carbon sequestration from direct air capture in US net-zero scenarios. The y-axis shows million metric tons of carbon dioxide per year, with values ranging from 0 to 1,600. Values are at zero for 2020, 2030, and 2040, although some scenarios show values of up to 100 million metric tons for 2040. For 2050, values range from zero to about 225, and the 95th percentile extends up to almost 600. The middle chart shows net carbon sequestration from bioenergy with carbon capture and storage in US net-zero scenarios. Y-axis values show millions of metric tons of carbon dioxide per year, with values ranging from 0 to 1,000. Values for 2020 are zero, increase to about 25 in 2030, range from about 50 to 250 in 2040, and range from nearly 300 to more than 600 in 2050. Individual scenarios range up to more than 200 for 2030 and more than 800 for 2040 and 2050. The right chart shows net carbon sequestration from land use, land change, and forestry in US net-zero scenarios. The y-axis shows millions of metric tons of carbon dioxide sequestration per year, with values ranging from 0 to 1,400. For 2020, the range is from about 450 to 800, with a 10th percentile range extending down to zero. For 2030, the range is 800 to about 950, with individual scenarios reaching near zero, but the 10th to 90th percentile range runs from about 750 to about 1,150. Values for 2040 are similar but with only one scenario near zero. Values for 2050 range from 800 to about 1,150. The 10th to 90th percentile range stretches from about 650 to more than 1,200.
Net-zero emissions scenarios project substantial carbon dioxide removal by 2050, although the type and quantities used in the scenarios vary considerably.
Figure 32.14. Annual carbon dioxide (CO2) removals increase between 2020 and 2050 in scenarios that reach net zero by 2050, including nature-based sequestration on land (c), bioenergy with carbon capture and storage (b), and—after 2040—direct air capture (a). Median sequestration (thick horizontal lines) by land use, land-use change, and forestry increases less dramatically in scenarios. Plots show individual scenarios as points, the 25th–75th percentile ranges as rectangles, and the 10th–90th percentile ranges as thin vertical lines. The mean of each set of scenarios is represented by an X. Figure credit: University of California, Irvine.

Changes in Transportation Modes and Behavior

Uncertain changes in mobility and travel behavior could facilitate or hinder mitigation. For example, autonomous vehicles are rapidly evolving but still need to overcome challenges of consistent safety measures, standardization of technology liability, and security and privacy concerns.210,211 Studies have shown that autonomous vehicles could increase or decrease energy use and GHG emissions depending on the conditions of adoption and use.212,213,214 New mobility services (e.g., ride-hailing or transit services with a monthly subscription) are becoming widespread and have the potential to transform current patterns of travel behavior, but they still face challenges of cost-competitiveness and consumer acceptance.215,216,217 And as with automation, these mobility services may reduce emissions under a limited set of conditions (e.g., electrification and shared use cases).214,218,219,220

Sector Coupling

The integration of different parts of energy systems, sometimes referred to as sector coupling, involves coordinated planning, operations, and markets for electricity, fuels, and thermal resources to meet end-use service demands. Linking energy industries, processes, and geographies could lower costs, reduce environmental impacts, and increase the reliability of low-carbon energy systems.76,221,222

Potential Opportunities to Reduce Land-Related Emissions

Modern Foods

Recent innovations aim to increase food choices with plant-based and cultured meat223,224,225 and foods synthesized chemically without photosynthetic inputs226,227 that may be able to displace demand for foods with substantially higher emissions per calorie. However, the potential benefits will depend on the scalability and public demand for such products.

Interventions to Reduce Methane and Nitrous Oxide Emissions

There are a number of options for reducing non-CO2 GHG emissions from agriculture whose potential remains uncertain. Ruminant feed supplements may suppress methane emissions (although some such supplements have not yet been approved for use in the United States).228,229 Methane from manure lagoons can be captured and used for bioenergy or reduced through flaring.230 Seasonally flooded rice paddies can undergo temporary drainage to reduce emissions by about 40%.231 And crops may be bred to produce root exudates that inhibit nitrification and thereby reduce N2O emissions from croplands.232


Mitigation Can Be Sustainable, Healthy, and Fair

Large reductions in US greenhouse gas emissions could have substantial benefits for human health and well-being . Mitigation is expected to affect pollution, the use of land and water resources, the labor force, and the affordability, reliability, and security of energy and food . An equitable and sustainable transition to net-zero-emissions energy and food systems in the United States could help redress legacies of inequity, racism, and injustice while maximizing overall benefits to our economy and environment .

A number of important dimensions rarely represented in mitigation scenarios may nonetheless determine the pace, feasibility, likelihood, efficacy, and cost-effectiveness of mitigation opportunities.

Air Pollution

Air pollutants that impact human health are often co-emitted with greenhouse gases. Exposure to ambient fine particulate matter (PM2.5) and ozone, which are among the largest risk factors for disease, causes 60,000–300,000 excess deaths per year in the United States (KM 14.5),233,234,235,236,237,238,239 with health effects observed at concentrations below the current national standard.240,241,242 Racial, ethnic, and socioeconomic disparities in air-pollution exposure are well documented235,243,244,245,246,247,248 and have persisted despite overall decreases in air pollution.235,241

FOCUS ON

COVID-19 and Climate Change

Climate change can increase the likelihood of pandemics like COVID-19 and worsen their impacts. Climate-driven changes in ecosystems increase the risk of emerging infectious diseases by altering interactions among humans, pathogens, and animals and changing social and biological susceptibility to infection.

Read More

Transitioning to a net-zero-emissions energy system has the potential to generate substantial air pollution benefits. Estimates of cumulative net benefits by 2050 range from roughly 200,000 to 2,000,000 avoided deaths,234,249,250,251 the monetized value (i.e., statistical value) of which could exceed the total expected costs of the transition to net zero.50,252 However, the distribution and magnitude of air pollution benefits over the transition period depend on the pace of electrification, technology selection, and siting decisions,50,253,254,255 especially regarding retirement of fossil fuel power plants (Figure 32.15)255,256,257,258,259,260,261 and vehicle electrification.262,263 Electrification of heating,257,264 reduction in fossil fuel production, electrification of the industrial sector, and shifting diets103 can also all generate meaningful air pollution benefits. Carbon capture and hydrogen technologies may also reduce air pollutant emissions, although it is not yet clear by how much.

It is also possible that mitigation efforts could increase air pollution at local and regional scales, for example, due to increases in bioenergy, residential wood heating, and domestic manufacturing to meet demands for materials and products (e.g., Gallagher and Holloway 2020; Commane and Schiferl 2022253,265).

URL
Alternative text
Health Co-benefits of Strategic Power Plant Retirements
Three maps of the contiguous United States show the health co-benefits of strategic power plant retirements, as described in the text and caption. Color shading by county indicates annual health damages incurred in millions of dollars, with values ranging from 0 to 1 (yellow) to 20 to 1,000 (dark red). Circles indicate locations of coal generation, with circle size indicating annual generation in terawatt hours, ranging from 5 to 10 for the smallest circle to more than 20 for the largest circle. The map at top left shows a baseline scenario for 2017. Annual health damages are in the 4 to 8 range or much higher across virtually all of the eastern half of the contiguous US, as well as in some areas of the Southwest, particularly southern California and Arizona, and isolated areas in the Northwest. Coal generation is also indicated at high density in the eastern half of the map, and is fairly common across the Northern and Southern Great Plains and northern parts of the Southwest. The map at top right is labeled climate-only scenario. Annual health damages are significantly lower in the eastern half of the country, although high values are still shown in heavily populated areas. Coal generation is less common as well, although still common in many areas, particularly the southern portion of the Midwest and northern portion of the Southeast. The bottom map is labeled climate and health scenario, and annual damages are even lower, with values dropping to the 1 to 4 range across most of the eastern part of the country and with smaller areas of higher values. The amounts of coal generation are also significantly lower, although still widespread across much of the country.
Shutting down coal-fired power plants would produce both health and climate benefits.
Figure 32.15. Blue circles show the location and size of US coal-fired power plants in 2017 (a) and in two scenarios: one in which the fewest plants are retired to reduce CO2 emissions by a fixed amount (b) and one in which plants are retired not only to achieve the same CO2 reduction but also to avoid health damages as much as possible (c). Not surprisingly, estimated health damages (red shading) are greatly reduced in the future scenario that prioritizes health. Annual generation from coal power plants (in terawatt-hours) and corresponding annualized health damages (in millions of dollars) from each scenario are both summarized by county. Baseline shows results based on 2017 continuous emissions monitoring systems data, while optimization results shown represent the climate-only and climate-plus-health scenarios. Health damages are shown by the county in which those damages occur; legend breaks are based on quintiles of the data. Although this analysis included only the continental US, its conclusions are consistent with similar analyses in other regions: substantial health benefits would be expected from retiring coal electricity anywhere. Adapted with permission from Sergi et al. 2020.255

Siting and Land Use

Net-zero-emissions energy systems may require large land areas, with land requirements in rough proportion to the share of wind and solar energy. Cumulative US land use for solar and wind energy in recent net-zero scenarios ranges from about 250,000 to more than 1 million square kilometers (including the entire area of solar and wind farms),42,43,53 with solar concentrated in the Northeast and Southeast and wind in the Midwest, Great Plains, and Texas (KM 6.3).50 Even at the low end of this range, the projected scale of land use is massive, and may face public opposition. For example, the visual impact and competition for land of such extensive systems would need to gain and maintain the support of many communities; this recently has been a challenge in the siting of solar and wind projects.266,267 Similar challenges may apply to siting and demonstration of other infrastructure regardless of its land footprint, such as new electricity transmission,266 CCS,268 and CDR.269 Others express concern over the potential environmental impacts of solar and wind farms, including land-cover change, loss of plant and animal habitats, barriers to migration and collision deaths of birds and bats,270,271,272 and competition for land between agriculture and renewables.273 Notably, competition with agriculture has also long been a concern about bioenergy, which may be alleviated if demand for corn ethanol decreases due to electrification of transport.274,275 Researchers have thus begun developing pathways that take some of these concerns and constraints into account,50,276 as well as identifying changes in governance and administrative law that may help streamline siting processes;277 however, siting may prove a key obstacle for renewables-based net-zero-emissions systems.278 Engagement with community groups and stakeholders early in the planning process has the potential to reduce project delays and cancellations.279

Water Use

The water requirements of net-zero-emissions energy systems could be lower than current consumption,280 largely because wind and solar require little water (Figure 32.16; KM 5.1).281,282,283,284 However, some processes for energy conversion and carbon management, such as electrolysis for hydrogen production, chemical synthesis of hydrocarbons (e.g., by the Fischer-Tropsch process), and CCS, are water intensive and could offset water savings from fuel switching. Ultimately, water use (and related quality), temporal, and locational needs, depend heavily on the mix of resources and processes used to achieve net-zero emissions.285,286,287

URL
Alternative text
Land and Water Requirements of Energy Sources
Two bar graphs show land and water requirements of energy sources, as described in the text and caption. The top graph shows water consumption intensity for thirteen energy sources. The y-axis shows cubic meters per gigajoule, with values ranging from 0 to 3.5. Values are as follows: Geothermal, about 3.1 cubic meters per gigajoule. Ethanol, about 2.9. Hydropower, about 2.6. Biodiesel, about 2.4. Solar thermal, about 1. Uranium, about 0.6. Coal, about 0.4. Solid biomass and refuse-dervied fuels, about 0.2. Oil, natural gas, and biogas are less than about 0.1. Wind and solar photovoltaic are both about zero. The bottom graph shows the land-use intensity of electricity for 12 land-use categories. The Y-axis shows average land-use intensity in units of hectares per terawatt-hour per year, with values ranging from 0 to 160,000. Values for each category are as follows: 160,000 hectares per terawatt-hour per year for dedicated biomass; about 15,000 for wind (spacing) and hydropower; less than about 3,,000 for ground-mounted photovoltaic, solar concentrating solar power, natural gas (spacing) and coal; and at or near 0 for natural gas (footprint), wind (footprint), residue biomass, geothermal, and nuclear.
Different sources of energy entail more or less water and land use.
Figure 32.16. Bars depict water-consumption intensity (a) and land-use intensity of electricity (LUIE) for the US in 2014 (b) related to different electricity sources. Wind and solar use less water than any of the other energy sources but more land area than nuclear, geothermal, or fossil sources. (a) Adapted with permission from Grubert and Sanders 2018;281 (b) adapted from Lovering et al. 2022288 [CC BY 4.0].

Labor

The productivity, supply, and disposition of labor, in addition to national discourse and community-level support and concern regarding labor, has the potential to accelerate or constrain mitigation efforts. Nearly 8 million Americans were directly employed in energy-related jobs in 2021, comprising roughly 5% of the total labor force.289,290 Of those 8 million energy-related jobs, approximately 41% were in net-zero-emissions-aligned areas in 2022.291 Energy-related jobs tend to be geographically concentrated in certain states and communities (Figure 32.17). More than 10% of the labor force in 150 counties (of 3,142) is directly employed in energy-related jobs289,292,293—often the production of coal, oil, and gas—but employment in mitigation-related activities is growing and is already high in many counties (e.g., energy efficiency in Vermont, wind installations in the Southern Great Plains; KM 26.2).

Reaching net-zero emissions in the United States by 2050 would generate jobs related to manufacturing and deployment of new infrastructure but reduce fossil fuel–related jobs.294 Many analyses find that employment and wage losses in fossil fuel sectors would be entirely offset (in aggregate) by increases in low-carbon resource industries.294,295,296,297,298 The number and local distribution of mitigation-related jobs will depend on the ultimate mix of energy sources, siting and investment decisions, labor supply constraints, the extent of domestic manufacturing, and political bargaining; however, decarbonization could lead to long-term expansion in the energy workforce in most states, even when accounting for increased worker productivity (which is often an underlying assumption in technology cost projections). Large-scale and sustained workforce development programs, high-road labor practices and policies, and corresponding federal support could accelerate a transition to net-zero emissions.294,299

However, there is already evidence of hiring difficulties in energy labor markets,292 portending labor supply bottlenecks in the absence of counteracting policies. Although there is public support for employment benefits related to climate mitigation,295 there is also evidence of mistrust associated with historical energy-related job creation narratives.300 Moreover, there are existing racial and gender disparities in the energy workforces.292

Meanwhile, despite policy and political discourse regarding just transitions for fossil fuel workers,295,301,302 many fossil fuel–dependent communities have experienced large declines in employment.27,303,304 Moreover, former fossil fuel workers often relocate because their skills are not always transferable to other local industries, and nearby communities lose tax revenues that support public infrastructure and social services (KM 26.2).305,306 Going forward, domestic policies that consider when and where workforces in declining energy industries could fill new jobs in emerging energy sectors (e.g., natural gas and carbon capture supply chains; coal mining; and solar manufacturing) have the potential to moderate labor supply bottlenecks, concentrated unemployment, and low-carbon boom-and-bust cycles. Where there is flexibility in siting of infrastructure and allocation of funding, such funds might also be leveraged to build political support and more equitably distribute costs and benefits. For example, provisions in the Inflation Reduction Act of 2022 offer enhanced tax credits to clean energy projects that pay prevailing wages to workers and use registered apprentices,291 that manufacture and source materials domestically,307 and/or that are located in “energy communities” defined by thresholds in the share of fossil fuel–related jobs.308

URL
Alternative text
Energy Employment from 2020 to 2050 for Alternative Net-Zero Pathways
Three stacked bar and line graphs show energy employment for 2020 through 2050 under three alternative zet-zero pathways, as described in the caption. The y axis shows net employment, ranging from a decrease of 1 million jobs below the zero axis to an increase of 7 million jobs above the zero axis. Shaded bar segments show the annual jobs gains or losses for 9 sectors: wind, solar, nuclear, grid, carbon dioxide (abbreviated C O 2), biomass, coal, natural gas, and oil. Black lines show the net change in jobs. Under a high electrification scenario (left chart), net jobs increase from 0 to about 2.8 million by 2050. The largest increases are from the grid sector, which accounts for nearly 1.5 jobs by 2050, as well as wind and solar at around 0.5 million each. Biomass jobs also show a small increase. Losses of about 0.5 million by 2050 are in the natural gas and oil sectors. A 100 percent renewable pathway shows slightly larger job losses of about 0.8 million total for natural gas and oil, but much larger increases in grid, wind, and solar, resulting in about 6 million new jobs for a net increase of more than 5 million jobs. For a renewable constrained pathway, job losses are smaller at less than 0.5 million, but job increases are also much smaller at about 1.1 million, resulting in a net gain of about 0.5 million. In this scenario, increases are mainly in the grid and nuclear sectors, with smaller increases in wind, solar, and biomass.
A shift toward renewables is projected to increase the total number of jobs in the energy sector.
Figure 32.17. Despite decreases in the number of fossil fuel–related jobs, the overall number of energy jobs (specifically those involved in the supply of energy) is generally projected to increase in net-zero-emissions energy scenarios between 2020 and 2050, although by much more in some scenarios than in others. These particular scenarios are from Larson et al. 202150 and span a range of energy futures in which nearly all buildings and transport are electrified but there are no constraints (a), renewables produce 100% of energy (b), or renewables produce much less energy and nuclear and fossil energy with carbon capture and storage are prevalent (c). CO2 = carbon dioxide. Adapted with permission from Jenkins et al. 2021.309

Energy Equity and Environmental Justice

Social inequities in the United States are rooted in systemic discriminatory practices, such as redlining, that marginalize communities based on race or socioeconomics. Social equity involves several energy- and climate-related aspects of recognition, procedural, and distributional justice (KMs 23.4, 27.3).310,311 In the context of energy and climate decision-making, recognition justice refers to an understanding that certain individuals and groups are presently bearing, and have historically borne, disparate burdens related to our collective energy systems and may therefore require extra resources or mitigation efforts. Procedural justice considers who is involved and has influence in energy and climate decision-making processes, with the goal of ensuring that those who want to be included in decision-making processes—and especially those who will be affected by the outcomes—are meaningfully engaged through fair and inclusive procedures (see, e.g., KM 30.3 regarding mitigation informed by Indigenous Knowledge). Distributional justice refers to the allocation of benefits and burdens based on geography and sociodemographics, with the objective that no single population receives a disproportionate share of energy or climate harms (e.g., energy-related air pollution; KM 14.3) or benefits (e.g., access to low-carbon and efficient energy technologies or clean-tech jobs).

The disproportionate public health burdens of energy systems on communities of color and/or low-income communities, such as from vehicle emissions and power plants, have been extensively documented (Figure 32.18).312,313,314,315 Energy insecurity (e.g., regularly struggling to pay energy bills) also disproportionately affects low-income households, communities of color, rural and Indigenous communities, families with children, and older adults (Ch. 16).314,316,317,318,319,320,321 This disproportionate burden of energy insecurity reflects that Black Americans, for example, are more likely to live in older homes that are less energy-efficient.318,319,322 Moreover, redlined areas often lack trees and green spaces to mitigate the urban heat island effect and thus experience higher summer temperatures than surrounding urban areas,323,324,325 which in turn increases energy demands and burdens326 and makes residents more susceptible to the adverse health effects of extreme heat (KM 15.3).326,327

Although environmental impacts and energy insecurity are not borne proportionately across social groups, it is possible to pursue mitigation options that also redress current and historical injustices. For example, low-income communities and communities of color could experience disproportionate improvements in air pollution.252,260 Energy equity considerations also include access to sufficient energy services,328,329 as well as reductions in energy burden or energy poverty,322,330,331 and the upfront costs of energy efficiency and low-carbon technologies.332 Mitigation efforts that increase the availability and affordability of energy services (including safe and comfortable temperatures) could improve energy equity outcomes. For example, improving thermal efficiency of buildings would both reduce energy costs and help to maintain safe indoor thermal temperatures in the absence of functional air-conditioning.333

Studies have found that low-carbon and efficient technologies (e.g., electric vehicles, solar panels, battery storage, and LED lightbulbs) tend to be disproportionately owned by—and the financial incentives for such are received by—higher-income, more educated, and White households.312,313,314,334 Job opportunities in clean energy have also tended to exclude women and people of color.335 Moreover, insofar as mitigation increases energy costs, more households will experience energy poverty, and energy inequities may get worse.336,337 In addition, changes in the type, timing, and cost of energy needed to provide safe and comfortable temperatures under climate change and anticipated electrification patterns may exacerbate health risks, financial energy burdens, and other measures of energy equity.328,336

URL
Alternative text
Inequitable Air Quality Within Historically Redlined Neighborhoods
Four maps and a bar chart illustrate inequitable air quality within historically redlined neighborhoods, as described in the caption. The maps at left show redlining maps drawn in the 1930s for Philadelphia (top left), Chicago (top right), Los Angeles (bottom left), and Detroit (bottom right), with colors indicating the following home owners’ loan corporation grades: A (green), B (blue), C (yellow), and D (red) for areas in each city. The box plot at right shows air pollution in 2010 by redlining grade. The y-axis shows population weighted concentrations of nitrogen dioxide in parts per billion, with values ranging from 0 to 25. Bars are shown for each of the four grades used in the maps, with the same color coding. Median and average values and ranges are lowest for A, higher for B, even higher for C, and highest for D. The size of the 25th to 75th percentile ranges also increases with letter grade. Median values are as follows: For grade A, about 10. For B, about 12. For C, about 13.5. For D, about 16.5.
Communities redlined in the 1930s experience more air pollution today.
Figure 32.18. The Home Owners’ Loan Corporation (HOLC) grades (A [“best”], B, C, and D [“hazardous,” i.e., redlined]) from the 1930s (which effectively denied Black and minority groups access to lending institutions) still corresponded to greater levels of air pollution in 2010. Panel (a) shows redlining maps of neighborhoods based on 1930s HOLC grade classifications for four US cities. Panel (b) shows the population-weighted distribution of nitrogen dioxide (NO2) levels (measured as concentration in parts per billion [ppb]) for 2010 across 202 census tracts in the contiguous US. Horizontal lines indicate medians, points indicate averages, and bars indicate 25th to 75th percentiles. Adapted with permission from Lane et al. 2022.315

Supply Chains, Energy Security, and Geopolitics

Climate mitigation efforts may drastically increase domestic and global demand for products (e.g., solar photovoltaics, batteries, electric motors, wind turbines) and metal and mineral resources (e.g., lithium, nickel, cobalt, copper), which may have implications for supply security, markets, advanced manufacturing (e.g., robotics and EVs), geopolitics, and mining (Focus on Risks to Supply Chains).338,339,340,341 Moreover, in the United States there are currently 50 listed critical minerals (up from 35 in 2018),342,343 defined as those essential to economic or national security and whose supply chains are vulnerable to disruption (Figure 32.19). With increased demand as the system decarbonizes, there could be near-term shortages in several minerals and metals. Note that a series of executive orders anticipates this challenge and calls for monitoring and reduction in US dependence on imported critical materials, for example, by increased recycling (e.g., Executive Order 13817, “A Federal Strategy to Ensure Secure and Reliable Supplies of Critical Minerals,”344 and Executive Order 13953, “Addressing the Threat to the Domestic Supply Chain from Reliance on Critical Minerals from Foreign Adversaries and Supporting the Domestic Mining and Processing Industries”345) and more resilient supply chains generally (Executive Order 14017, “America’s Supply Chains”; 6 see also Focus on Risks to Supply Chains; KMs 17.2, 18.1).

URL
Alternative text
Increasing Reliance on Imported Nonfuel Minerals
Two bar charts show increasing reliance on imported nonfuel minerals. The top chart shows United States net import reliance on nonfuel mineral commodities. The y axis shows the number of nonfuel mineral commodities, with values ranging from 0 to 25. Values are shown for 5 time periods: 1979, 1989, 1999, 2009, and 2019. Individual bars for each year show values for 4 percentages of net import reliance (from left to right): less than 0 to 25 percent (blue), 25 to 50 percent (orange), 50 to 75 percent (gray), and 75 to 100 percent or more (yellow). The 0 to 25 percent category increased from about 18 in 1979 to more than 20 commodities in 1989 and 1999 before dropping back to about 17 in 2009 and 2019. Values for 25 to 50 percent have generally increased from about 2.5 in 1979 to more than 5 in 2019. The 50 to 75 percent category has generally decreased from about 7 in 1979 to about 2 in 2019. The 75 to 100 percent category increased steadily from less than 4 in 1979 to about 14 in 2019. The bottom chart shows 2019 nonfuel mineral commodities, sorted by net import reliance. The y axis shows percent net import reliance, with values ranging from negative 300 to 100. Bars are color-coded using the same percentage categories as in the top chart. Values are shown for 39 minerals. Seven blue bars show minerals with negative import reliance. Ten other blue bars show values between 0 and about 25 percent. Six minerals are shown with orange bars and values between 25 and 50 percent. Two are in the gray category of 50 to 70 percent. Fourteen are yellow bars, indicating 75 to 100 percent or more.
The US has grown increasingly dependent on imported minerals.
Figure 32.19. Panel (a) shows that the US has become increasingly dependent on imports of 39 nonfuel mineral commodities since 1979; commodities of which 75%–100% is imported (yellow bars) are increasing in number, and commodities of which less than 25% is imported (blue bars) are decreasing in number. Panel (b) shows the specific commodities and the degree of import reliance for each in 2019. Figure credits: (a) adapted from Fortier et al. 2015;346 (b) University of California, Irvine.


Governments, Organizations, and Individuals Can Act to Reduce Emissions

Mitigation efforts can be supported by a range of actors and actions, from choices made by individuals to decisions made by businesses and local, Tribal, state, and national governments . Actions with significant near-term potential include sector-based policies accelerating deployment of low-carbon technologies, city-level efforts to promote public transportation and improve building efficiency, and individual behavioral changes to reduce energy demand and meat consumption .

A wide range of actors across the US have been involved in efforts to accelerate clean energy transition and mitigate GHG emissions, including new legislation; rules, regulations, and executive orders; and voluntary actions. For example,

  • the US has committed under the Paris Agreement to reduce GHG emissions by 50%–52% in 2030 relative to 2005;

  • through the Bipartisan Infrastructure Law and the Inflation Reduction Act and relevant programs, there are federal subsidies to clean energy businesses and for household purchases of EVs and heat pumps;347

  • 25 states,348 675 cities, 300 universities, and hundreds of companies have announced net-zero-emissions targets; and

  • bottom-up coalitions such as the America Is All In initiative have support from subnational leaders who represent a constituency of more than half the US population (see, e.g., KMs 21.4, 30.3).

Since 2018, the total number of state-level mitigation activities has increased by 83%, and 169 more cities have introduced emissions reduction targets since then (Figure 32.20; see also Ch. 12).349

URL
Alternative text
Mitigation-Related Activities at the State and City Levels
A map of the United States shows the number of mitigation-related activities taken at the state and city levels. A legend shows the number of mitigation activities ranging from 8 or fewer (light yellow) to 20 or more (dark blue). Diagonal lines indicate states with emissions-reductions targets, and orange circles indicate cities with emissions-reduction targets. States with more than 20 activities are Washington, California, Hawai‘i, Colorado, Massachusetts, New York, Connecticut, New Jersey, and Maryland. States with 16 to 20 activities are Oregon, Arizona, New Mexico, Minnesota, Wisconsin, Illinois, Ohio, Virginia, Vermont, and Maine. States with 12 to 16 activities are Montana, Nevada, Utah, Texas, Iowa, Missouri, Michigan, Pennsylvania, New Hampshire, Rhode Island, and Delaware. States or territories with 8 to 12 are Oklahoma, Arkansas, Louisiana, Indiana, Kentucky, North Carolina, South Carolina, Georgia, Florida, and Puerto Rico and US Virgin Islands. Those with less than 8 are Alaska, Idaho, Wyoming, North and South Dakota, Nebraska, Kansas, West Virginia, Tennessee, Mississippi, Alabama, the Commonwealth of the Northern Mariana Islands, and Guam. States with emissions-reductions targets are Washington, Oregon, California, Nevada, Montana, Colorado, Minnesota, Wisconsin, Illinois, Louisiana, Michigan, Maine, Vermont, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, Pennsylvania, Delaware, Maryland, Virginia, and North Carolina. Many cities show emissions reductions targets. Of the states, only Alaska shows no cities with such a plan, and the Northern Great Plains shows only two plans in Idaho.
Many states and cities have taken action to reduce greenhouse gas emissions.
Figure 32.20. Shading indicates the number of mitigation activities taken by each state, and orange circles indicate cities with emissions-reduction targets (as of April 2023). Almost every region has taken some action, with hotspots of activity in the Northeast, Southwest, Colorado, Hawaiʻi, and along the West Coast. See Figure 32.21 for examples of the types of actions taken. Figure credit: The Pennsylvania State University, NOAA NCEI, and CISESS NC.

The pathways toward achieving these goals often include a broad collection of measures and policies, including investments in infrastructure and clean technologies that will require substantial capital, financial backing, and resource allocation. The feasibility and impact of these measures are dependent on local and regional factors, which are often reflected in more granular sector- or economy-specific mitigation targets and actions (see Figure 32.21).350

URL
Alternative text
Adoption Rate of Various Forms of Policy Instruments and Climate Action
A horizontal bar graph shows the adoption rate of various forms of policy instruments and climate action at state and city levels. The x-axes show relative percentage of adoption, ranging from 1 to 100%. Bar length shows the percentage, and the bars are color-coded by category: emissions targets (red), regulatory policy (no federal policy; light blue), regulatory policy (with federal policy; light blue with hatching), financial incentive (with no federal policy) (orange), and financial incentive (with federal policy; orange with hatching). There are 42 categories listed. Emissions reductions are listed first, and show about 45 percent adoption at the state level and almost 40 percent at the city level. The remaining categories are listed in descending order based on state-level adoption. Those with more than 50 percent adoption are building energy code, net metering, interconnection, solar/wind access policy, renewables portfolio standard, energy standards for public buildings, property tax incentive, loan program, sales tax incentive, PACE financing, grant program, and energy efficiency resource standard. Six state-level categories show hatching that indicates “with federal policy.” At the city level, only the rebate program category shows more than 50 percent adoption. Categories with more than 10 percent are loan program, PACE financing, and grant program. Three city-level categories show federal policy hatching.
States and cities have adopted a range of climate actions and policies.
Figure 32.21. Bars show the percentages of states (left) and cities (right) that have announced emissions targets (tracked by the United Nations Framework Convention on Climate Change Non-state Actor Zone for Climate Action dataset) or adopted the selected clean energy policies (tracked in North Carolina State University’s Database of State Incentives for Renewables & Efficiency dataset) as of April 2023. The color of the bars indicates the type of policy, and hashing denotes that the policy action is also being adopted or announced by the Federal Government. PACE stands for property assessed clean energy. Figure credit: The Pennsylvania State University, NOAA NCEI, and CISESS NC.

To this end, nearly 40 states have introduced renewable portfolio standards or voluntary renewable energy goals, which further guide and codify decarbonization efforts within the energy sector and induce incremental shifts toward increased penetration of renewable electricity (KM 32.1). Similarly, more than 30 local governments have enacted requirements for energy efficiency, ranging from building codes and benchmarking ordinances to establishing performance standards (see, e.g., KM 12.3). With federal corporate average fuel economy (CAFE) standards in place for vehicles, local transportation-sector efforts are often focused on behavioral mode-shift goals, such as promoting clean and public transport options and reducing vehicle-miles traveled. The proposed federal Agriculture Resilience Act is designed to address the adaptive needs of US farmers and consumers as a result of a changing climate, as well as to reduce the emissions associated with agricultural production.351 In addition, the Securities and Exchange Commission is in the process of finalizing new rules that would require public companies to disclose greenhouse gas emissions related to their operations and supply chains, as well as climate risks to their business.352 Such rules would build on the voluntary reporting and reduction efforts of corporations under the Carbon Disclosure Project; Science Based Targets initiative; and Environmental, Social, and Corporate Governance frameworks and will need to be supported by improved accounting protocols and focused scientific research.175,353,354,355,356

Art × Climate
Pencil drawing shows a globe, with the top half in bright greens and blues and featuring solar panels and wind turbines, and the bottom half obscured by a yellowish haze, with cars and factories spewing smoke. In the lower half a large human hand holds an eraser that is clearing up the pollution, while in the top half another hand is drawing with a pencil.

Ritika S.
Youth Entry, Grade 8

Redrawing The Earth
(2023, colored pencil)

Artist’s statement: In my art, I try to convey that we can help reverse the effects of climate change. One hand is erasing the pollution caused by industrialization the world over, and the other is redrawing actions to restore the Earth’s beauty. I have always tried to help out the Earth, through stream clean ups and more. I hope people learn from my art that they can help change the world by just doing simple things like driving less, not littering, and maybe even setting up solar panels or wind turbines. The effects of climate change are only in our hands, so we should do whatever we can to help.

View the full Art × Climate gallery.

Artworks and artists’ statements are not official Assessment products.

Beyond goal-setting and implementing regulatory measures, the enabling of financial mechanisms is often a core element of mitigation strategy. Regional cap-and-trade programs utilize a system of accountability and performance to incentivize emissions reductions at the electricity-generation level. Meanwhile, federal subsidies, such as those provided to clean energy businesses and tax credits for electric vehicle purchases, can bolster behavior change.55 By enabling access to financial capital—whether within the government, commercial, or residential sectors—investments in infrastructure and the built environment, as well as research and development, may further drive these advances.

Available mitigation strategies vary in terms of emissions-reduction potential and costs (Figure 32.22), as well as in environmental, technical, and social implications (Figure 32.23). However, with the advancement of measurement technologies and insights gained from the deployment of various actions taken in vastly different environments, there is now more empirical evidence to inform strategy design for a given community (see regional chapters for examples of state, city, community, and Tribal mitigation actions; e.g., Box 21.1; KM 30.3). Additionally, more jurisdictions are adopting community-driven and holistic approaches to climate action planning, incorporating practices that address equitable access to information (including considerations for languages used and internet access) and events (including transportation vouchers, food and childcare provisions, and payment for subject-matter expertise to community members with lived experience), with a goal of improving and increasing capacity and ability to influence decision-making and, ultimately, assisting elected leaders in making the best-informed and most-impactful decisions for their unique communities.357,358,359

URL
Alternative text
Potential Emissions Reductions by Action, for the Year 2050
A horizontal bar chart shows potential emissions reductions by action for the year 2050. The x-axes show emissions savings in millions of metric tons of carbon dioxide equivalent, with values ranging from 0 to 900. Bar length shows the emissions savings, with the following color-coded segments indicating the breakdown of savings by marginal cost in dollars per ton of carbon dioxide equivalent: 0 to 49 in blue, 50 to 99 in light orange, 100 to 149 in dark orange, 150 to 199 in red, and 200 to 250 in dark red. The chart shows 44 actions. These are grouped into the categories non-carbon dioxide, incremental land sink, direct air capture with sequestration, zero-carbon fuels, vehicles, hydrogen production, electricity, and buildings and industry. Direct air capture with sequestration shows the largest potential at more than 800 million metric tons of carbon dioxide equivalent in savings, but virtually the entire bar is in dark red (200 to 250 marginal cost category), with a note indicating that this has large potential for emissions reductions but at a high marginal cost. By contrast, solar voltaic has a potential of about 450, but with more than 300 of this in the 0 to 49 marginal cost category, with a not indicating that this reflects less potential for emissions reductions than direct air capture, but at relatively low marginal costs, especially up to about 350 million metric tons of carbon dioxide equivalent. Other high potential but high marginal cost examples include reforestation (more than 300), avoided land conversion (more than 100), and crops and grazing (more than 100). Examples with high potential and varying marginal costs include pyrolysis with carbon capture (almost 400), power to liquids (more than 350), and new advanced nuclear (at nearly 400). Examples with high potential and lower marginal cost are onshore wind, medium quality (more than 300), onshore wind, high quality (almost 300), and solar voltaic (about 450).
The size and cost of emissions reductions depend on available technologies and the source of related emissions.
Figure 32.22. Energy system, land-sector, and non-CO2 (carbon dioxide) mitigation options for the year 2050 are shown along with estimated marginal costs, excluding the impact of policy incentives. The sum of the mitigation options shown results in net-negative CO2-eq (carbon dioxide equivalent) emissions in the United States, not only demonstrating the possibility of reaching net-zero emissions using a combination of these actions but also highlighting a large range of costs for such actions (costs as of 2021). Mitigation options from conservation and lifestyle change are not assessed due to the difficulty in assessing costs for these measures. H2 = hydrogen. Adapted with permission from Farbes et al. 2021360 and Figure SPM.7 in IPCC 2022.361
URL
Alternative text
US Company-Level Mitigation Actions
A heatmap chart shows company-level mitigation actions for the United States. The chart consists of 9 rows listing types of actions and 12 columns listing sectors. Blue shading indicates percentages of companies for each combination of action and sector, with values ranging from 0 to 20 (very light blue) to 80 to 100 (dark blue). Sectors are manufacturing; agriculture and natural resources; transport; service, culture, and media; building and construction; chemical industries; commerce and commercial products; energy; public service; health services; mining; and high tech. Two categories with the highest percentages across sectors are grouped under the heading commitments or participation in initiatives. First, emissions reduction commitment or emissions inventory shows 80 to 100 percent participation for 8 sectors, 40 to 80 for 3 others, and 20 to 40 for one sector. Second, participation in relevant initiatives shows 40 percent or more for all but 2 sectors. The remaining actions are listed under the heading actions undertaken. Two actions, low carbon supply chain and public engagement, have 40 percent or more participation for at least 9 sectors. Energy efficiency improvement and clean energy also show some sectors with 20 or 40 or more percent participation. Circular economy, company behavior change, and reducing process emissions show 0 to 20 percent for all sectors.
A majority of US companies have made mitigation commitments, inventoried emissions, or participated in initiatives, but fewer are taking action.
Figure 32.23. As of April 2023, a majority of US companies across many sectors have committed to reducing emissions or conducted emissions inventories, and many have participated in mitigation initiatives (top). Percentages are smaller in terms of actions taken (bottom). For example, many companies are involved in energy efficiency improvements, efforts to reduce supply chain emissions, and public engagement efforts. But 20% or fewer across all sectors are reported as reducing process emissions or effecting company behavior changes. Figure credit: The Pennsylvania State University, NOAA NCEI, and CISESS NC.

Box 32.3. Orlando Case Study: Mitigation in the Country’s Most Visited City

In the five decades since the opening of Walt Disney World, Orlando has become the most highly visited city in the United States. As a result, this community faces the unique challenge of managing the costs, demands, and emissions of more than 75 million annual visitors, or nearly 300 visitors for each individual resident.362,363 To address these impacts, local governments have adopted an ambitious, socially inclusive, and innovative climate strategy.

The prevalence of resort and multifamily developments, for example, has led to the adoption of energy efficiency requirements for commercial buildings and a community-wide commitment to 100% renewable energy to drive the decarbonization of the local building stock. Meanwhile, to address the needs of local residents, many of whom work in lower-wage jobs associated with the tourism industry, an energy-burden analysis was conducted to identify the neighborhoods most in need of assistance.

As the largest rental car market in the world, the region has served as a proving ground for enhanced electric and autonomous vehicle piloting,364,365 as well as the adoption of an electric vehicle readiness policy.366 Research efforts have focused on public safety when various modes of transportation, such as single-passenger vehicles (more likely to be utilized by visitors and more affluent residents), are active in the same vicinity as buses, cyclists, and pedestrians.

Enhanced waste-reduction efforts previously included an anaerobic digestion facility that utilizes the gray water and food scraps from the Disney parks and resorts to generate biogas, a renewable energy source that is used to power these same facilities. In tandem with this localized solution, waste avoidance and gleaning programs (e.g., improved collection of excess produce and perishables from farms, retailers, and restaurants)367 provide options for those who are food insecure.

Together, these mitigation strategies serve to protect the local environment, enhance the quality of life for local residents, and showcase a variety of solutions to the nearly 76 million guests who visit the region each year.


TRACEABLE ACCOUNTS

Process Description

Based on their own experience, nominations, and relevant recent literature, the chapter lead author and federal coordinating lead author discussed and selected a set of experts to invite as authors, seeking diverse representation of topical expertise, disciplinary perspectives, career stages, professional backgrounds, geographies, and demographics. Of 25 invitations, 16 were accepted, forming an author team with the requisite expertise to cover the chapter topics and provide a good balance of other characteristics. The author team began meeting regularly as a group and then divided into smaller working groups focused on different key topic areas, which also met regularly (all meetings were virtual, except for the in-person All-Author Meeting held in Washington, DC, in April 2023). During these meetings, the team worked together to develop key topic areas for the chapter, identify key literature and sources, and plan syntheses and figures for the chapter. The team also planned the public engagement workshop for the chapter and afterward discussed inputs and feedbacks from that workshop.


KEY MESSAGES

KEY MESSAGE 32.1

Successful Mitigation Means Reaching Net-Zero Emissions

Greenhouse gas emissions in the United States decreased by 12% between 2005 and 2019, mostly due to replacing coal-fired electricity generation with natural gas–fired and renewable generation . However, US net greenhouse gas emissions remain substantial and would have to decline by more than 6% per year on average, reaching net zero around midcentury, to meet current national climate targets and international temperature goals .

Read about Confidence and Likelihood

Description of Evidence Base

The assessment and summary of the sources and trends of US greenhouse gas emissions relies primarily on inventories and estimates from the EPA,8,9 supplemented by socioeconomic, energy activity, and agricultural production data from official sources such as the US Energy Information Administration (EIA)19,368,369,370,371 and the World Bank.372,373 EPA estimates of energy-related emissions are primarily based on tracked masses and volumes of combusted fuels (and in some case continuous emissions monitoring at point sources) publicly reported to the EIA, EPA, or Bureau of Transportation Statistics. EPA estimates of land sector (i.e., land use, land-use change, and agricultural) emissions are primarily based on activity data (e.g., area of land converted, number and kinds of livestock, mass of fertilizer applied) and associated emissions factors that have been developed based on numerous case studies.12,374 Federal- and state-level greenhouse gases (GHG) targets were compiled from publicly available sources and are not uncertain.

Major Uncertainties and Research Gaps

Although there is no uncertainty as to the current emissions targets and it is well-established that global warming will be proportional to cumulative carbon dioxide (CO2) emissions (e.g., Matthews et al. 2009375), there is relatively little scientific literature and relatively few national and international goals that address long-term management of the climate after net-zero emissions have been achieved and into the 22nd century.376

Estimates of agricultural and fugitive non-CO2 GHG emissions have greater uncertainty because they are spatially heterogenous “area” sources that are more challenging to measure directly,98,377 as evidenced by discrepancies between “top-down” estimates of global methane emissions based on measurements of the atmosphere and “bottom-up” estimates based on activity data such as number and kinds of livestock and extent of rice cultivation.378,379 For this reason, these are active areas of research, and analysts are bringing to bear a variety of different and innovative tools and methods to reduce the uncertainty and prioritize mitigation efforts (e.g., Liu et al. 2022; Norooz Oliaee et al. 2022; Conrad et al. 2023380,381,382).

Description of Confidence and Likelihood

Based on the multiple sources of high-quality energy system data, the authors have very high confidence in both the overall magnitude of energy-related US GHG emissions from each major source and their relative changes over time. There is also broad agreement among dynamic vegetation models, bookkeeping models of land-use change, and atmospheric observations as to the magnitude of the US land sink in recent years,52 but the sink has been decreasing23 and future uptake by US forests will depend on management and climate change impacts, both of which are uncertain.22,181,383,384 Given current emissions levels and stated goals, however, the required rate of decrease is not in question. For these reasons, we have very high confidence in the statements made in the Key Message.

KEY MESSAGE 32.2

We Know How to Drastically Reduce Emissions

A US energy system with net-zero emissions would rely on widespread improvements in energy efficiency, substantial electricity generation from solar and wind energy, and widespread electrification of transportation and heating . Low-carbon fuels would still be needed for some transport and industry applications that are difficult to electrify . Land-related emissions in the US could be reduced by increasing the efficiency of food systems and improving agricultural practices and by protecting and restoring natural lands . Across all sectors, many of these options are economically feasible now .

Read about Confidence and Likelihood

Description of Evidence Base

The assessment of established options for reducing energy-related GHG emissions reflects a large body of literature and recent energy-system modeling,61,385 including a database of 40 US net-zero emissions scenarios.42,43 Although there are substantial differences in the cost-effective energy systems modeled in these scenarios depending on model design and key assumptions, the Key Message and text emphasize characteristics that are robust across most, if not all, of the scenarios.48,51,131,386,387,388

Major Uncertainties and Research Gaps

The assessment of established options for reducing land-related GHG emissions reflects a substantial literature, but there are few quantitative scenarios to support potential reductions.97,99,122,178,186,192,389,390,391 Instead, potential reductions are often extrapolated from the localized studies that are available. Further research is warranted to test key sensitivities in energy model scenarios and to quantitatively assess factors beyond cost, such as social and political acceptance of (or opposition to) changes in use of land and water resources and adoption of energy technologies, and the associated distribution of benefits and impacts (as well as other non-cost factors discussed in KM 32.4).

Description of Confidence and Likelihood

Across 40 of the most recent and detailed energy system scenarios of net-zero US emissions, produced by 14 independent models and assuming a wide range of costs and constraints, the share of final energy met by electricity increases from about 20% today to 43%–57% by 2050 (the 25th–75th percentile range; Figure 32.11), and solar and wind are consistently major sources of energy, typically ranging from 57%–80% of primary energy by 2050 (the 25th–75th percentile range; Figure 32.10). Yet fuels continue to be used across those scenarios for some transportation and industry applications. The robustness of these numbers despite many methodological differences gives us high confidence in the energy-related statements in the Key Message.

A large literature also supports the opportunities for large reductions in land-related emissions, giving us high confidence in the land-related statement in the Key Message.97,99,122,178,186,192,389,390,391

Current costs of technologies such as solar, wind, and electric vehicles and the projected large-scale deployment of these technologies in cost-optimized energy system models,42,43,46,67,392 as well as many studies demonstrating the potential cost savings of energy efficiency improvements,393,394 optimization of agricultural inputs,395 shifts in diet,97,99,396 and repair of leaky infrastructure, all give the authors similarly high confidence that many mitigation options are now cost-effective.

KEY MESSAGE 32.3

To Reach Net-Zero Emissions, Additional Mitigation Options Need to Be Explored

Although many mitigation options are currently available and cost-effective, the level and types of energy technologies and carbon management in net-zero-emissions energy systems depend on still-uncertain technological progress, public acceptance, consumer choice, and future developments in institutions, markets, and policies . Attractive targets for further research, development, and demonstration include carbon capture, utilization, and storage; long-duration energy storage; low-carbon fuels and feedstocks; demand management; next-generation electricity transmission; carbon dioxide removal; modern foods; and interventions to reduce industry and agricultural emissions .

Read about Confidence and Likelihood

Description of Evidence Base

The assessment of potential options for reducing energy-related GHG emissions reflects a large body of literature and recent energy system modeling, including a database of 40 US net-zero emissions scenarios,42,43 but this Key Message highlights that the scale and mix of energy technologies and mitigation options remain sensitive to assumed—and yet uncertain—costs and constraints. Similarly, the potential options for reducing land-related GHG emissions presented in this Key Message are not as well studied, and there is open debate about the efficacy and/or cost-effectiveness of, for example, different energy storage technologies,397,398 advanced nuclear technology,399,400 and carbon management options,53,169,401,402 as well as future agricultural productivity.403,404

Major Uncertainties and Research Gaps

We assign medium confidence to the list of attractive targets for further research, development, and demonstration because existing literature either disagrees as to the potential of these technologies or only a few studies have made the case that they have great potential. Where analyses disagree, it may be because their findings depend on assumptions regarding deeply uncertain aspects of economic development, human behavior, or technological innovation. In general, additional research is needed to quantitatively assess a greater number of emerging energy technologies and land management options, and especially work that incorporates the various non-cost factors discussed in Key Message 32.4.

Description of Confidence and Likelihood

We have high confidence that we do not yet know which net-zero-emissions energy system will be cost-optimal (or socially and politically acceptable) and that we do not know the ideal types or scales of carbon management to support net-zero emissions and sustainability more broadly.405,406 This is because there is substantial variation in the type and scale of energy and carbon management technologies deployed in model scenarios, long-term projections of technology costs span large ranges, and the social and political support for different mitigation efforts is unclear. Although the effectiveness and scalability of some of the approaches to reduce land-related non-CO2 emissions remain uncertain (e.g., soil amendments, livestock feed supplements), other options are becoming clear, such as managing manure, cover cropping, and decreasing nitrogen fertilizer applications. Thus, we have medium confidence as to the options for reducing these land-related non-CO2 emissions.

KEY MESSAGE 32.4

Mitigation Can Be Sustainable, Healthy, and Fair

Large reductions in US greenhouse gas emissions could have substantial benefits for human health and well-being . Mitigation is expected to affect pollution, the use of land and water resources, the labor force, and the affordability, reliability, and security of energy and food . An equitable and sustainable transition to net-zero-emissions energy and food systems in the United States could help redress legacies of inequity, racism, and injustice while maximizing overall benefits to our economy and environment .

Read about Confidence and Likelihood

Description of Evidence Base

The assessment of historical and future impacts of energy systems on, for example, water,280,281,284,285 air pollution,103,235,246,247,254,255 energy security,32,340 labor,234,248,294,299,306 and energy equity and environmental justice218,252,260,301,310,330 is based on a diverse and rapidly growing academic literature as cited in the chapter.

Major Uncertainties and Research Gaps

As mentioned in regard to other Key Messages, there is a lack of specific qualitative and quantitative analyses and decision-making tools regarding how mitigation may affect and be affected by energy equity, environmental justice, land use, labor, water, air pollution, and energy security in different places, times, and social, demographic, and political contexts.336 There is also a lack of analyses and tools to reflect interacting technological, social, political, and environmental uncertainties and choices to inform multistakeholder decision-making.407

Description of Confidence and Likelihood

An extensive literature demonstrates the potential health benefits of climate mitigation, especially in regard to related decreases in air pollution. Fewer but still numerous studies have shown that the cost and resource savings or net social benefits of many mitigation options can accrue to specific populations. We therefore have high confidence in the potential benefits to human health and well-being, including specific environmental and socioeconomic effects. However, the available research also gives us high confidence that the benefits of mitigation may be distributed unevenly in the absence of proactive efforts to ensure fairness.

KEY MESSAGE 32.5

Governments, Organizations, and Individuals Can Act to Reduce Emissions

Mitigation efforts can be supported by a range of actors and actions, from choices made by individuals to decisions made by businesses and local, Tribal, state, and national governments . Actions with significant near-term potential include sector-based policies accelerating deployment of low-carbon technologies, city-level efforts to promote public transportation and improve building efficiency, and individual behavioral changes to reduce energy demand and meat consumption .

Read about Confidence and Likelihood

Description of Evidence Base

Our assessment of possible actors and mitigation actions is drawn from both the actions represented in models and studies by researchers,408,409,410,411,412 as well as reports and databases that have compiled lists of past actions taken (e.g., the Center for Climate and Energy Solutions State Climate Policy Maps, North Carolina State University Database of State Incentives for Renewables & Efficiency, CDP States and Regions Climate Tracker, and United Nations Framework Convention on Climate Change Non-state Actor Zone for Climate Action dataset).348,413,414,415

Major Uncertainties and Research Gaps

No jurisdiction has yet transitioned from a fossil-based economy to a deeply decarbonized or net-zero-emissions one. Moreover, actions to start down that road may be different from those that reach the end of it.416,417 Future research may productively explore the limits of actions by certain groups or jurisdictions, and seek to assess where collaborations are necessary and most valuable to support mitigation.417,418

Description of Confidence and Likelihood

Public commitments made and actions already taken (as tracked by the sources cited in the evidence base above) give us high confidence that mitigation can be supported by a wide range of actors in a wide variety of ways. Historical progress in reducing emissions (e.g., US electricity emissions since 2007) and forward-looking modeling analyses give us similarly high confidence that substantial near-term potential in the US lies in actions to boost low-carbon technologies,51,256,387,419,420,421 moderate use of internal combustion vehicles,66,67,69 improved building efficiency,33,34 and diet shifts.97,99,396

REFERENCES

  1. Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, M. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, R. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, F.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, V. Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, J.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, J. Kossin, S. Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, T. Ngo-Duc, F. Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallée, B.H. Samset, S. Sathyendranath, S.I. Seneviratne, A.A. Sörensson, S. Szopa, I. Takayabu, A.-M. Tréguier, B. van den Hurk, R. Vautard, K. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical summary. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, Eds. Cambridge University Press, Cambridge, UK and New York, NY, USA, 33−144. https://doi.org/10.1017/9781009157896.002
  2. IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Shukla, P.R., J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, and J. Malley, Eds. Cambridge University Press, Cambridge, UK and New York, NY, USA. https://doi.org/10.1017/9781009157926
  3. Feijoo, F., G. Iyer, M. Binsted, and J. Edmonds, 2020: US energy system transitions under cumulative emissions budgets. Climatic Change, 162 (4), 1947–1963. https://doi.org/10.1007/s10584-020-02670-0
  4. Schaeffer, R., A. Köberle, H.L. van Soest, C. Bertram, G. Luderer, K. Riahi, V. Krey, D.P. van Vuuren, E. Kriegler, S. Fujimori, W. Chen, C. He, Z. Vrontisi, S. Vishwanathan, A. Garg, R. Mathur, S. Shekhar, K. Oshiro, F. Ueckerdt, G. Safonov, G. Iyer, K. Gi, and V. Potashnikov, 2020: Comparing transformation pathways across major economies. Climatic Change, 162 (4), 1787–1803. https://doi.org/10.1007/s10584-020-02837-9
  5. van Soest, H.L., M.G.J. den Elzen, and D.P. van Vuuren, 2021: Net-zero emission targets for major emitting countries consistent with the Paris Agreement. Nature Communications, 12 (1), 2140. https://doi.org/10.1038/s41467-021-22294-x
  6. Executive Office of the President, 2021: Executive Order 14017: America’s supply chains. Federal Register, 86 (38), 11849–11854. https://www.federalregister.gov/documents/2021/03/01/2021-04280/americas-supply-chains
  7. DOS and EOP, 2021: The Long-Term Strategy of the United States: Pathways to Net-Zero Greenhouse Gas Emissions by 2050. U.S. Department of State and U.S. Executive Office of the President, Washington, DC. https://www.whitehouse.gov/wp-content/uploads/2021/10/us-long-term-strategy.pdf
  8. EPA, 2022: Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2020. EPA 430-R-22-003. U.S. Environmental Protection Agency. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2020
  9. EPA, 2023: Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2021. EPA 430-R-23-002. U.S. Environmental Protection Agency. https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissions-and-sinks-1990-2021
  10. Davis, S.J., Z. Liu, Z. Deng, B. Zhu, P. Ke, T. Sun, R. Guo, C. Hong, B. Zheng, Y. Wang, O. Boucher, P. Gentine, and P. Ciais, 2022: Emissions rebound from the COVID-19 pandemic. Nature Climate Change, 12 (5), 412–414. https://doi.org/10.1038/s41558-022-01332-6
  11. Liu, Z., P. Ciais, Z. Deng, S.J. Davis, B. Zheng, Y. Wang, D. Cui, B. Zhu, X. Dou, P. Ke, T. Sun, R. Guo, H. Zhong, O. Boucher, F.-M. Bréon, C. Lu, R. Guo, J. Xue, E. Boucher, K. Tanaka, and F. Chevallier, 2020: Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production. Scientific Data, 7 (1), 392. https://doi.org/10.1038/s41597-020-00708-7
  12. IPCC, 2006: 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Eggleston, S., L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, Eds. Institute for Global Environmental Strategies, Hayama, Japan. https://www.ipcc.ch/report/2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/
  13. Crippa, M., D. Guizzardi, E. Pisoni, E. Solazzo, A. Guion, M. Muntean, A. Florczyk, M. Schiavina, M. Melchiorri, and A.F. Hutfilter, 2021: Global anthropogenic emissions in urban areas: patterns, trends, and challenges. Environmental Research Letters, 16 (7), 074033. https://doi.org/10.1088/1748-9326/ac00e2
  14. Friedlingstein, P., M. O'Sullivan, M.W. Jones, R.M. Andrew, L. Gregor, et al., 2022: Global carbon budget 2022. Earth System Science Data, 14 (11), 4811–4900. https://doi.org/10.5194/essd-14-4811-2022
  15. IEA, 2023: Greenhouse Gas Emissions from Energy. International Energy Agency. https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy
  16. Liu, Z., P. Ciais, Z. Deng, R. Lei, S.J. Davis, S. Feng, B. Zheng, D. Cui, X. Dou, B. Zhu, R. Guo, P. Ke, T. Sun, C. Lu, P. He, Y. Wang, X. Yue, Y. Wang, Y. Lei, H. Zhou, Z. Cai, Y. Wu, R. Guo, T. Han, J. Xue, O. Boucher, E. Boucher, F. Chevallier, K. Tanaka, Y. Wei, H. Zhong, C. Kang, N. Zhang, B. Chen, F. Xi, M. Liu, F.-M. Bréon, Y. Lu, Q. Zhang, D. Guan, P. Gong, D.M. Kammen, K. He, and H.J. Schellnhuber, 2020: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nature Communications, 11 (1), 5172. https://doi.org/10.1038/s41467-020-18922-7
  17. Feng, K., S.J. Davis, L. Sun, and K. Hubacek, 2015: Drivers of the US CO2 emissions 1997–2013. Nature Communications, 6 (1), 7714. https://doi.org/10.1038/ncomms8714
  18. Scott Institute for Energy Innovation, 2017: Power Sector Carbon Index. Carnegie Mellon University, Pittsburgh, PA. https://www.emissionsindex.org
  19. EIA, 2015: Residential Energy Consumption Survey. U.S. Energy Information Administration. https://www.eia.gov/consumption/residential/data/2015/
  20. Goldstein, B., D. Gounaridis, and J.P. Newell, 2020: The carbon footprint of household energy use in the United States. Proceedings of the National Academy of Sciences of the United States of America, 117 (32), 19122–19130. https://doi.org/10.1073/pnas.1922205117
  21. Anderegg, W.R.L., O.S. Chegwidden, G. Badgley, Anna T. Trugman, D. Cullenward, J.T. Abatzoglou, Jeffrey A. Hicke, J. Freeman, and J.J. Hamman, 2022: Future climate risks from stress, insects and fire across US forests. Ecology Letters, 25 (6), 1510–1520. https://doi.org/10.1111/ele.14018
  22. Domke, G.M., S.N. Oswalt, B.F. Walters, and R.S. Morin, 2020: Tree planting has the potential to increase carbon sequestration capacity of forests in the United States. Proceedings of the National Academy of Sciences of the United States of America, 117 (40), 24649–24651. https://doi.org/10.1073/pnas.2010840117
  23. Quirion, B.R., G.M. Domke, B.F. Walters, G.M. Lovett, J.E. Fargione, L. Greenwood, K. Serbesoff-King, J.M. Randall, and S. Fei, 2021: Insect and disease disturbances correlate with reduced carbon sequestration in forests of the contiguous United States. Frontiers in Forests and Global Change, 4, 716582. https://doi.org/10.3389/ffgc.2021.716582
  24. Lopez, A., B. Roberts, D. Heimiller, N. Blair, and G. Porro, 2012: U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. NREL/TP-6A20-51946. U.S. Department of Energy, National Renewable Energy Laboratory, Golden, CO. https://www.nrel.gov/docs/fy12osti/51946.pdf
  25. Shaner, M.R., S.J. Davis, N.S. Lewis, and K. Caldeira, 2018: Geophysical constraints on the reliability of solar and wind power in the United States. Energy and Environmental Science, 11 (4), 914–925. https://doi.org/10.1039/c7ee03029k
  26. Olson-Hazboun, S.K., P.D. Howe, and A. Leiserowitz, 2018: The influence of extractive activities on public support for renewable energy policy. Energy Policy, 123, 117–126. https://doi.org/10.1016/j.enpol.2018.08.044
  27. Grubert, E., 2020: Fossil electricity retirement deadlines for a just transition. Science, 370 (6521), 1171–1173. https://doi.org/10.1126/science.abe0375
  28. Shearer, C., D. Tong, R. Fofrich, and S.J. Davis, 2020: Committed emissions of the U.S. power sector, 2000–2018. AGU Advances, 1 (3), e2020AV000162. https://doi.org/10.1029/2020av000162
  29. Tong, D., D.J. Farnham, L. Duan, Q. Zhang, N.S. Lewis, K. Caldeira, and S.J. Davis, 2021: Geophysical constraints on the reliability of solar and wind power worldwide. Nature Communications, 12 (1), 6146. https://doi.org/10.1038/s41467-021-26355-z
  30. Stokes, L.C. and H.L. Breetz, 2018: Politics in the U.S. energy transition: Case studies of solar, wind, biofuels and electric vehicles policy. Energy Policy, 113, 76–86. https://doi.org/10.1016/j.enpol.2017.10.057
  31. Noblet, C.L., M.F. Teisl, K. Evans, M.W. Anderson, S. McCoy, and E. Cervone, 2015: Public preferences for investments in renewable energy production and energy efficiency. Energy Policy, 87, 177–186. https://doi.org/10.1016/j.enpol.2015.09.003
  32. Manley, D.K., V.A. Hines, M.W. Jordan, and R.E. Stoltz, 2013: A survey of energy policy priorities in the United States: Energy supply security, economics, and the environment. Energy Policy, 60, 687–696. https://doi.org/10.1016/j.enpol.2013.04.061
  33. Belussi, L., B. Barozzi, A. Bellazzi, L. Danza, A. Devitofrancesco, C. Fanciulli, M. Ghellere, G. Guazzi, I. Meroni, F. Salamone, F. Scamoni, and C. Scrosati, 2019: A review of performance of zero energy buildings and energy efficiency solutions. Journal of Building Engineering, 25, 100772. https://doi.org/10.1016/j.jobe.2019.100772
  34. Chen, S., G. Zhang, X. Xia, S. Setunge, and L. Shi, 2020: A review of internal and external influencing factors on energy efficiency design of buildings. Energy and Buildings, 216, 109944. https://doi.org/10.1016/j.enbuild.2020.109944
  35. Holz-Rau, C. and J. Scheiner, 2019: Land-use and transport planning – A field of complex cause-impact relationships. Thoughts on transport growth, greenhouse gas emissions and the built environment. Transport Policy, 74, 127–137. https://doi.org/10.1016/j.tranpol.2018.12.004
  36. Tayarani, M., A. Poorfakhraei, R. Nadafianshahamabadi, and G. Rowangould, 2018: Can regional transportation and land-use planning achieve deep reductions in GHG emissions from vehicles? Transportation Research Part D: Transport and Environment, 63, 222–235. https://doi.org/10.1016/j.trd.2018.05.010
  37. Neves, A. and C. Brand, 2019: Assessing the potential for carbon emissions savings from replacing short car trips with walking and cycling using a mixed GPS-travel diary approach. Transportation Research Part A: Policy and Practice, 123, 130–146. https://doi.org/10.1016/j.tra.2018.08.022
  38. Oeschger, G., P. Carroll, and B. Caulfield, 2020: Micromobility and public transport integration: The current state of knowledge. Transportation Research Part D: Transport and Environment, 89, 102628. https://doi.org/10.1016/j.trd.2020.102628
  39. Hannan, M.A., M.M. Hoque, A. Hussain, Y. Yusof, and P.J. Ker, 2018: State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applications: Issues and recommendations. IEEE Access, 6, 19362–19378. https://doi.org/10.1109/access.2018.2817655
  40. Leach, F., G. Kalghatgi, R. Stone, and P. Miles, 2020: The scope for improving the efficiency and environmental impact of internal combustion engines. Transportation Engineering, 1, 100005. https://doi.org/10.1016/j.treng.2020.100005
  41. Li, Z., A. Khajepour, and J. Song, 2019: A comprehensive review of the key technologies for pure electric vehicles. Energy, 182, 824–839. https://doi.org/10.1016/j.energy.2019.06.077
  42. Browning, M., J. McFarland, J. Bistline, G. Boyd, M. Muratori, M. Binsted, C. Harris, T. Mai, G. Blanford, J. Edmonds, A.A. Fawcett, O. Kaplan, and J. Weyant, 2023: Net-zero CO2 by 2050 scenarios for the United States in the Energy Modeling Forum 37 study. Energy and Climate Change, 4, 100104. https://doi.org/10.1016/j.egycc.2023.100104
  43. Huppmann, D., J. Bistline, J. DeAngelo, R. Jones, J. McFarland, J. Weyant, and S.J. Davis, 2023: NCA5 Scenario Explorer and Data hosted by IIASA. Mitigation Chapter of the Fifth National Climate Assessment and the International Institute for Applied Systems Analysis. Vienna, Austria.
  44. Azevedo, I.M.L., 2014: Consumer end-use energy efficiency and rebound effects. Annual Review of Environment and Resources, 39 (1), 393–418. https://doi.org/10.1146/annurev-environ-021913-153558
  45. Saunders, H.D., J. Roy, I.M.L. Azevedo, D. Chakravarty, S. Dasgupta, S. de la Rue du Can, A. Druckman, R. Fouquet, M. Grubb, B. Lin, R. Lowe, R. Madlener, D.M. McCoy, L. Mundaca, T. Oreszczyn, S. Sorrell, D. Stern, K. Tanaka, and T. Wei, 2021: Energy efficiency: What has research delivered in the last 40 years? Annual Review of Environment and Resources, 46 (1), 135–165. https://doi.org/10.1146/annurev-environ-012320-084937
  46. Azevedo, I., C. Bataille, J. Bistline, L. Clarke, and S. Davis, 2021: Net-zero emissions energy systems: What we know and do not know. Energy and Climate Change, 2, 100049. https://doi.org/10.1016/j.egycc.2021.100049
  47. Clack, C.T.M., A. Choukulkar, B. Coté, and S.A. McKee, 2021: A Plan for Economy-Wide Decarbonization of the United States. Vibrant Clean Energy, Boulder, CO, 18 pp. https://www.vibrantcleanenergy.com/wp-content/uploads/2021/10/US-Econ-Decarb_CCSA.pdf
  48. DeAngelo, J., I. Azevedo, J. Bistline, L. Clarke, G. Luderer, E. Byers, and S.J. Davis, 2021: Energy systems in scenarios at net-zero CO2 emissions. Nature Communications, 12 (1), 6096. https://doi.org/10.1038/s41467-021-26356-y
  49. Jenkins, J.D., Z. Zhou, R. Ponciroli, R.B. Vilim, F. Ganda, F. de Sisternes, and A. Botterud, 2018: The benefits of nuclear flexibility in power system operations with renewable energy. Applied Energy, 222, 872–884. https://doi.org/10.1016/j.apenergy.2018.03.002
  50. Larson, E., C. Greig, J. Jenkins, E. Mayfield, A. Pascale, C. Zhang, J. Drossman, R. Williams, S. Pacala, R. Socolow, E. Baik, R. Birdsey, R. Duke, R. Jones, B. Haley, E. Leslie, K. Paustian, and A. Swan, 2021: Final Report Summary—Net-Zero America: Potential Pathways, Infrastructure, and Impacts. Princeton University, Princeton, NJ. https://netzeroamerica.princeton.edu/the-report
  51. Murray, B.C., J. Bistline, J. Creason, E. Wright, A. Kanudia, and F. de la Chesnaye, 2018: The EMF 32 study on technology and climate policy strategies for greenhouse gas reductions in the U.S. electric power sector: An overview. Energy Economics, 73, 286–289. https://doi.org/10.1016/j.eneco.2018.03.007
  52. Pacala, S.W., G.C. Hurtt, D. Baker, P. Peylin, R.A. Houghton, R.A. Birdsey, L. Heath, E.T. Sundquist, R.F. Stallard, P. Ciais, P. Moorcroft, J.P. Caspersen, E. Shevliakova, B. Moore, G. Kohlmaier, E. Holland, M. Gloor, M.E. Harmon, S.M. Fan, J.L. Sarmiento, C.L. Goodale, D. Schimel, and C.B. Field, 2001: Consistent land- and atmosphere-based U.S. carbon sink estimates. Science, 292 (5525), 2316–2320. https://doi.org/10.1126/science.1057320
  53. Williams, J.H., R.A. Jones, B. Haley, G. Kwok, J. Hargreaves, J. Farbes, and M.S. Torn, 2021: Carbon-neutral pathways for the United States. AGU Advances, 2 (1), e2020AV000284. https://doi.org/10.1029/2020av000284
  54. Bistline, J.E.T. and G.J. Blanford, 2021: The role of the power sector in net-zero energy systems. Energy and Climate Change, 2, 100045. https://doi.org/10.1016/j.egycc.2021.100045
  55. Inflation Reduction Act of 2022. 117th Congress, Pub. L. No. 117-169, 136 Stat. 1818, August 16, 2022. https://www.congress.gov/bill/117th-congress/house-bill/5376/text
  56. Cole, W.J., D. Greer, P. Denholm, A.W. Frazier, S. Machen, T. Mai, N. Vincent, and S.F. Baldwin, 2021: Quantifying the challenge of reaching a 100% renewable energy power system for the United States. Joule, 5 (7), 1732–1748. https://doi.org/10.1016/j.joule.2021.05.011
  57. Bistline, J.E.T. and G.J. Blanford, 2021: Impact of carbon dioxide removal technologies on deep decarbonization of the electric power sector. Nature Communications, 12 (1), 3732. https://doi.org/10.1038/s41467-021-23554-6
  58. EPRI, 2022: Nuclear Energy in Long-Term System Models: A Multi-Model Perspective. Electric Power Research Institute, 136 pp. https://www.epri.com/research/products/000000003002023697
  59. Bloom, A., L. Azar, J. Caspary, D. Lew, N. Miller, A. Silverstein, J. Simonelli, and R. Zavadil, 2021: Transmission Planning for 100% Clean Electricity. Energy Systems Integration Group, 29 pp. https://www.esig.energy/wp-content/uploads/2021/02/Transmission-Planning-White-Paper.pdf
  60. Jacobson, M.Z., M.A. Delucchi, M.A. Cameron, and B.A. Frew, 2015: Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes. Proceedings of the National Academy of Sciences of the United States of America, 112 (49), 15060–15065. https://doi.org/10.1073/pnas.1510028112
  61. Bistline, J.E.T., 2021: Roadmaps to net-zero emissions systems: Emerging insights and modeling challenges. Joule, 5 (10), 2551–2563. https://doi.org/10.1016/j.joule.2021.09.012
  62. Mai, T.T., P. Jadun, J.S. Logan, C.A. McMillan, M. Muratori, D.C. Steinberg, L.J. Vimmerstedt, B. Haley, R. Jones, and B. Nelson, 2018: Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. NREL/TP-6A20-71500. U.S. Department of Energy, National Renewable Energy Laboratory, Golden, CO. https://doi.org/10.2172/1459351
  63. Ueckerdt, F., C. Bauer, A. Dirnaichner, J. Everall, R. Sacchi, and G. Luderer, 2021: Potential and risks of hydrogen-based e-fuels in climate change mitigation. Nature Climate Change, 11 (5), 384–393. https://doi.org/10.1038/s41558-021-01032-7
  64. Hardman, S., 2019: Understanding the impact of reoccurring and non-financial incentives on plug-in electric vehicle adoption – A review. Transportation Research Part A: Policy and Practice, 119, 1–14. https://doi.org/10.1016/j.tra.2018.11.002
  65. Jenn, A., I.L. Azevedo, and P. Ferreira, 2013: The impact of federal incentives on the adoption of hybrid electric vehicles in the United States. Energy Economics, 40, 936–942. https://doi.org/10.1016/j.eneco.2013.07.025
  66. Jenn, A., K. Springel, and A.R. Gopal, 2018: Effectiveness of electric vehicle incentives in the United States. Energy Policy, 119, 349–356. https://doi.org/10.1016/j.enpol.2018.04.065
  67. Muratori, M., M. Alexander, D. Arent, M. Bazilian, P. Cazzola, E.M. Dede, J. Farrell, C. Gearhart, D. Greene, A. Jenn, M. Keyser, T. Lipman, S. Narumanchi, A. Pesaran, R. Sioshansi, E. Suomalainen, G. Tal, K. Walkowicz, and J. Ward, 2021: The rise of electric vehicles—2020 status and future expectations. Progress in Energy, 3 (2), 022002. https://doi.org/10.1088/2516-1083/abe0ad
  68. Rietmann, N., B. Hügler, and T. Lieven, 2020: Forecasting the trajectory of electric vehicle sales and the consequences for worldwide CO2 emissions. Journal of Cleaner Production, 261, 121038. https://doi.org/10.1016/j.jclepro.2020.121038
  69. Tong, F., A. Jenn, D. Wolfson, C.D. Scown, and M. Auffhammer, 2021: Health and climate impacts from long-haul truck electrification. Environmental Science & Technology, 55 (13), 8514–8523. https://doi.org/10.1021/acs.est.1c01273
  70. Borlaug, B., M. Muratori, M. Gilleran, D. Woody, W. Muston, T. Canada, A. Ingram, H. Gresham, and C. McQueen, 2021: Heavy-duty truck electrification and the impacts of depot charging on electricity distribution systems. Nature Energy, 6 (6), 673–682. https://doi.org/10.1038/s41560-021-00855-0
  71. Forrest, K., M. Mac Kinnon, B. Tarroja, and S. Samuelsen, 2020: Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sectors in California. Applied Energy, 276, 115439. https://doi.org/10.1016/j.apenergy.2020.115439
  72. Phadke, A.A., A. Khandekar, N. Abhyankar, D. Wooley, and D. Rajagopal, 2021: Why Regional and Long-Haul Trucks are Primed for Electrification Now. U.S. Department of Energy, Lawrence Berkeley National Laboratory, Berkeley, CA. https://eta-publications.lbl.gov/publications/why-regional-and-long-haul-trucks-are
  73. Smith, D., R. Graves, B. Ozpineci, P.T. Jones, J. Lustbader, K. Kelly, K. Walkowicz, A. Birky, G. Payne, C. Sigler, and J. Mosbacher, 2020: Medium- and Heavy-Duty Vehicle Electrification: An Assessment of Technology and Knowledge Gaps. ORNL/SPR-2020/7. U.S. Department of Energy, Oak Ridge National Laboratory and National Renewable Energy Laboratory. https://info.ornl.gov/sites/publications/files/pub136575.pdf
  74. Tong, F., D. Wolfson, A. Jenn, C.D. Scown, and M. Auffhammer, 2021: Energy consumption and charging load profiles from long-haul truck electrification in the United States. Environmental Research: Infrastructure and Sustainability, 1 (2), 025007. https://doi.org/10.1088/2634-4505/ac186a
  75. Bergero, C., G. Gosnell, D. Gielen, S. Kang, M. Bazilian, and S.J. Davis, 2023: Pathways to net-zero emissions from aviation. Nature Sustainability, 6 (4), 404–414. https://doi.org/10.1038/s41893-022-01046-9
  76. Davis, S.J., N.S. Lewis, M. Shaner, S. Aggarwal, D. Arent, I.L. Azevedo, S.M. Benson, T. Bradley, J. Brouwer, Y.-M. Chiang, C.T.M. Clack, A. Cohen, S. Doig, J. Edmonds, P. Fennell, C.B. Field, B. Hannegan, B.-M. Hodge, M.I. Hoffert, E. Ingersoll, P. Jaramillo, K.S. Lackner, K.J. Mach, M. Mastrandrea, J. Ogden, P.F. Peterson, D.L. Sanchez, D. Sperling, J. Stagner, J.E. Trancik, C.-J. Yang, and K. Caldeira, 2018: Net-zero emissions energy systems. Science, 360 (6396), 9793. https://doi.org/10.1126/science.aas9793
  77. Deason, J. and M. Borgeson, 2019: Electrification of buildings: Potential, challenges, and outlook. Current Sustainable/Renewable Energy Reports, 6 (4), 131–139. https://doi.org/10.1007/s40518-019-00143-2
  78. Mahone, A., C. Li, Z. Subin, M. Sontag, G. Mantegna, A. Karolides, A.K.A. German, and P. Morris, 2019: Residential Building Electrification in California: Consumer Economics, Greenhouse Gases and Grid Impacts. Energy and Environmental Economics, San Francisco, CA. https://www.ethree.com/wp-content/uploads/2019/04/E3_Residential_Building_Electrification_in_California_April_2019.pdf
  79. Tarroja, B., F. Chiang, A. AghaKouchak, S. Samuelsen, S.V. Raghavan, M. Wei, K. Sun, and T. Hong, 2018: Translating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California. Applied Energy, 225, 522–534. https://doi.org/10.1016/j.apenergy.2018.05.003
  80. Madeddu, S., F. Ueckerdt, M. Pehl, J. Peterseim, M. Lord, K.A. Kumar, C. Krüger, and G. Luderer, 2020: The CO2 reduction potential for the European industry via direct electrification of heat supply (power-to-heat). Environmental Research Letters, 15 (12), 124004. https://doi.org/10.1088/1748-9326/abbd02
  81. Bataille, C., M. Åhman, K. Neuhoff, L.J. Nilsson, M. Fischedick, S. Lechtenböhmer, B. Solano-Rodriquez, A. Denis-Ryan, S. Stiebert, H. Waisman, O. Sartor, and S. Rahbar, 2018: A review of technology and policy deep decarbonization pathway options for making energy-intensive industry production consistent with the Paris Agreement. Journal of Cleaner Production, 187, 960–973. https://doi.org/10.1016/j.jclepro.2018.03.107
  82. Bataille, C.G.F., 2020: Physical and policy pathways to net-zero emissions industry. WIREs Climate Change, 11 (2), e633. https://doi.org/10.1002/wcc.633
  83. Denis-Ryan, A., C. Bataille, and F. Jotzo, 2016: Managing carbon-intensive materials in a decarbonizing world without a global price on carbon. Climate Policy, 16 (sup1), S110–S128. https://doi.org/10.1080/14693062.2016.1176008
  84. Rissman, J., C. Bataille, E. Masanet, N. Aden, W.R. Morrow, N. Zhou, N. Elliott, R. Dell, N. Heeren, B. Huckestein, J. Cresko, S.A. Miller, J. Roy, P. Fennell, B. Cremmins, T. Koch Blank, D. Hone, E.D. Williams, S. de la Rue du Can, B. Sisson, M. Williams, J. Katzenberger, D. Burtraw, G. Sethi, H. Ping, D. Danielson, H. Lu, T. Lorber, J. Dinkel, and J. Helseth, 2020: Technologies and policies to decarbonize global industry: Review and assessment of mitigation drivers through 2070. Applied Energy, 266, 114848. https://doi.org/10.1016/j.apenergy.2020.114848
  85. Thiel, G.P. and A.K. Stark, 2021: To decarbonize industry, we must decarbonize heat. Joule, 5 (3), 531–550. https://doi.org/10.1016/j.joule.2020.12.007
  86. Fennell, P., J. Driver, C. Bataille, and S.J. Davis, 2022: Cement and steel—Nine steps to net zero. Nature, 603, 574–577. https://doi.org/10.1038/d41586-022-00758-4
  87. Winkler, K., R. Fuchs, M. Rounsevell, and M. Herold, 2021: Global land use changes are four times greater than previously estimated. Nature Communications, 12 (1), 2501. https://doi.org/10.1038/s41467-021-22702-2
  88. Searchinger, T.D., S. Wirsenius, T. Beringer, and P. Dumas, 2018: Assessing the efficiency of changes in land use for mitigating climate change. Nature, 564 (7735), 249–253. https://doi.org/10.1038/s41586-018-0757-z
  89. Hong, C., H. Zhao, Y. Qin, J.A. Burney, J. Pongratz, K. Hartung, Y. Liu, F.C. Moore, R.B. Jackson, Q. Zhang, and S.J. Davis, 2022: Land-use emissions embodied in international trade. Science, 376 (6593), 597–603. https://doi.org/10.1126/science.abj1572
  90. Mathews, J.A. and H. Tan, 2009: Biofuels and indirect land use change effects: The debate continues. Biofuels, Bioproducts and Biorefining, 3 (3), 305–317. https://doi.org/10.1002/bbb.147
  91. Mosier, S., S.C. Córdova, and G.P. Robertson, 2021: Restoring soil fertility on degraded lands to meet food, fuel, and climate security needs via perennialization. Frontiers in Sustainable Food Systems, 5, 706142. https://doi.org/10.3389/fsufs.2021.706142
  92. EPA, 2021: From Farm to Kitchen: The Environmental Impacts of U.S. Food Waste. EPA 600-R21 171. U.S. Environmental Protection Agency, Office of Research and Development. https://www.epa.gov/land-research/farm-kitchen-environmental-impacts-us-food-waste
  93. Gustavsson, J., C. Cederberg, U. Sonesson, R. van Otterdijk, and A. Meybeck, 2011: Global Food Losses and Food Waste—Extent, Causes and Prevention. Food and Agriculture Organization of the United Nations, Rome, Italy. https://www.fao.org/3/i2697e/i2697e.pdf
  94. Godfray, H.C.J., P. Aveyard, T. Garnett, J.W. Hall, T.J. Key, J. Lorimer, R.T. Pierrehumbert, P. Scarborough, M. Springmann, and S.A. Jebb, 2018: Meat consumption, health, and the environment. Science, 361 (6399), 5324. https://doi.org/10.1126/science.aam5324
  95. Poore, J. and T. Nemecek, 2018: Reducing food’s environmental impacts through producers and consumers. Science, 360 (6392), 987–992. https://doi.org/10.1126/science.aaq0216
  96. Tilman, D. and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515 (7528), 518–522. https://doi.org/10.1038/nature13959
  97. Willett, W., J. Rockström, B. Loken, M. Springmann, T. Lang, S. Vermeulen, T. Garnett, D. Tilman, F. DeClerck, A. Wood, M. Jonell, M. Clark, L.J. Gordon, J. Fanzo, C. Hawkes, R. Zurayk, J.A. Rivera, W. De Vries, L. Majele Sibanda, A. Afshin, A. Chaudhary, M. Herrero, R. Agustina, F. Branca, A. Lartey, S. Fan, B. Crona, E. Fox, V. Bignet, M. Troell, T. Lindahl, S. Singh, S.E. Cornell, K. Srinath Reddy, S. Narain, S. Nishtar, and C.J.L. Murray, 2019: Food in the Anthropocene: The EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet, 393 (10170), 447–492. https://doi.org/10.1016/s0140-6736(18)31788-4
  98. Hong, C., J.A. Burney, J. Pongratz, J.E.M.S. Nabel, N.D. Mueller, R.B. Jackson, and S.J. Davis, 2021: Global and regional drivers of land-use emissions in 1961–2017. Nature, 589 (7843), 554–561. https://doi.org/10.1038/s41586-020-03138-y
  99. Clark, M.A., N.G.G. Domingo, K. Colgan, S.K. Thakrar, D. Tilman, J. Lynch, I.L. Azevedo, and J.D. Hill, 2020: Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science, 370 (6517), 705–708. https://doi.org/10.1126/science.aba7357
  100. Schmidinger, K. and E. Stehfest, 2012: Including CO2 implications of land occupation in LCAs—Method and example for livestock products. The International Journal of Life Cycle Assessment, 17 (8), 962–972. https://doi.org/10.1007/s11367-012-0434-7
  101. Springmann, M., L. Spajic, M.A. Clark, J. Poore, A. Herforth, P. Webb, M. Rayner, and P. Scarborough, 2020: The healthiness and sustainability of national and global food based dietary guidelines: Modelling study. BMJ, 370, m2322. https://doi.org/10.1136/bmj.m2322
  102. Falcon, W.P., R.L. Naylor, and N.D. Shankar, 2022: Rethinking global food demand for 2050. Population and Development Review, 48 (4), 921–957. https://doi.org/10.1111/padr.12508
  103. Domingo, N.G.G., S. Balasubramanian, S.K. Thakrar, M.A. Clark, P.J. Adams, J.D. Marshall, N.Z. Muller, S.N. Pandis, S. Polasky, A.L. Robinson, C.W. Tessum, D. Tilman, P. Tschofen, and J.D. Hill, 2021: Air quality–related health damages of food. Proceedings of the National Academy of Sciences of the United States of America, 118 (20), e2013637118. https://doi.org/10.1073/pnas.2013637118
  104. Coleman-Jensen, A., M.P. Rabbitt, C.A. Gregory, and A. Singh, 2022: Household Food Security in the United States in 2021. ERR-309. U.S. Department of Agriculture, Economic Research Service. https://www.ers.usda.gov/publications/pub-details/?pubid=104655
  105. Khaleel, A.A., T.J. Sauer, and J.C. Tyndall, 2020: Changes in deep soil organic carbon and soil properties beneath tree windbreak plantings in the U.S. Great Plains. Agroforestry Systems, 94 (2), 565–581. https://doi.org/10.1007/s10457-019-00425-0
  106. Osorio, R.J., C.J. Barden, and I.A. Ciampitti, 2019: GIS approach to estimate windbreak crop yield effects in Kansas–Nebraska. Agroforestry Systems, 93 (4), 1567–1576. https://doi.org/10.1007/s10457-018-0270-2
  107. Schoeneberger, M., G. Bentrup, H. de Gooijer, R. Soolanayakanahally, T. Sauer, J. Brandle, X. Zhou, and D. Current, 2012: Branching out: Agroforestry as a climate change mitigation and adaptation tool for agriculture. Journal of Soil and Water Conservation, 67 (5), 128A–136A. https://doi.org/10.2489/jswc.67.5.128a
  108. Meurer, K.H.E., N.R. Haddaway, M.A. Bolinder, and T. Kätterer, 2018: Tillage intensity affects total SOC stocks in boreo-temperate regions only in the topsoil—A systematic review using an ESM approach. Earth-Science Reviews, 177, 613–622. https://doi.org/10.1016/j.earscirev.2017.12.015
  109. Tamburini, G., R. Bommarco, T.C. Wanger, C. Kremen, M.G.A. Van der Heijden, M. Liebman, and S. Hallin, 2020: Agricultural diversification promotes multiple ecosystem services without compromising yield. Science Advances, 6 (45), 1715. https://doi.org/10.1126/sciadv.aba1715
  110. Sela, S., H.M. van Es, B.N. Moebius-Clune, R. Marjerison, J. Melkonian, D. Moebius-Clune, R. Schindelbeck, and S. Gomes, 2016: Adapt-N outperforms grower-selected nitrogen rates in northeast and midwestern United States strip trials. Agronomy Journal, 108 (4), 1726–1734. https://doi.org/10.2134/agronj2015.0606
  111. Venterea, R.T., J.A. Coulter, and M.S. Dolan, 2016: Evaluation of intensive “4R” strategies for decreasing nitrous oxide emissions and nitrogen surplus in rainfed corn. Journal of Environmental Quality, 45 (4), 1186–1195. https://doi.org/10.2134/jeq2016.01.0024
  112. Ruser, R. and R. Schulz, 2015: The effect of nitrification inhibitors on the nitrous oxide (N2O) release from agricultural soils—A review. Journal of Plant Nutrition and Soil Science, 178 (2), 171–188. https://doi.org/10.1002/jpln.201400251
  113. Eagle, A.J., L.P. Olander, K.L. Locklier, J.B. Heffernan, and E.S. Bernhardt, 2017: Fertilizer management and environmental factors drive N2O and NO3 losses in corn: A meta-analysis. Soil Science Society of America Journal, 81 (5), 1191–1202. https://doi.org/10.2136/sssaj2016.09.0281
  114. Muller, J., D. De Rosa, J. Friedl, M. De Antoni Migliorati, D. Rowlings, P. Grace, and C. Scheer, 2023: Combining nitrification inhibitors with a reduced N rate maintains yield and reduces N2O emissions in sweet corn. Nutrient Cycling in Agroecosystems, 125 (2), 107–121. https://doi.org/10.1007/s10705-021-10185-y
  115. Ocko, I.B., T. Sun, D. Shindell, M. Oppenheimer, A.N. Hristov, S.W. Pacala, D.L. Mauzerall, Y. Xu, and S.P. Hamburg, 2021: Acting rapidly to deploy readily available methane mitigation measures by sector can immediately slow global warming. Environmental Research Letters, 16 (5), 054042. https://doi.org/10.1088/1748-9326/abf9c8
  116. IPCC, 2021: Summary for policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, Eds. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3−32. https://doi.org/10.1017/9781009157896.001
  117. Ocko, I.B., V. Naik, and D. Paynter, 2018: Rapid and reliable assessment of methane impacts on climate. Atmospheric Chemistry and Physics, 18 (21), 15555–15568. https://doi.org/10.5194/acp-18-15555-2018
  118. Aydin, G., I. Karakurt, and K. Aydiner, 2012: Analysis and mitigation opportunities of methane emissions from the energy sector. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 34 (11), 967–982. https://doi.org/10.1080/15567031003716725
  119. Kang, M., S. Christian, M.A. Celia, D.L. Mauzerall, M. Bill, A.R. Miller, Y. Chen, M.E. Conrad, T.H. Darrah, and R.B. Jackson, 2016: Identification and characterization of high methane-emitting abandoned oil and gas wells. Proceedings of the National Academy of Sciences of the United States of America, 113 (48), 13636–13641. https://doi.org/10.1073/pnas.1605913113
  120. Lauvaux, T., C. Giron, M. Mazzolini, A. d’Aspremont, R. Duren, D. Cusworth, D. Shindell, and P. Ciais, 2022: Global assessment of oil and gas methane ultra-emitters. Science, 375 (6580), 557–561. https://doi.org/10.1126/science.abj4351
  121. Omara, M., N. Zimmerman, M.R. Sullivan, X. Li, A. Ellis, R. Cesa, R. Subramanian, A.A. Presto, and A.L. Robinson, 2018: Methane emissions from natural gas production sites in the United States: Data synthesis and national estimate. Environmental Science & Technology, 52 (21), 12915–12925. https://doi.org/10.1021/acs.est.8b03535
  122. Fargione, J.E., S. Bassett, T. Boucher, S.D. Bridgham, R.T. Conant, S.C. Cook-patton, P.W. Ellis, A. Falcucci, J.W. Fourqurean, T. Gopalakrishna, H. Gu, B. Henderson, M.D. Hurteau, K.D. Kroeger, T. Kroeger, T.J. Lark, S.M. Leavitt, G. Lomax, R.I. McDonald, J.P. Megonigal, D.A. Miteva, C.J. Richardson, J. Sanderman, D. Shoch, S.A. Spawn, J.W. Veldman, C.A. WIlliams, P.B. Woodbury, C. Zganjar, M. Baranski, R.A. Houghton, E. Landis, E. McGlynn, W.H. Schlesinger, J.V. Siikamakiariana, E. Sutton-Grierand, and B.W. Griscom, 2018: Natural climate solutions for the United States. Science Advances, 4 (11), 1869. https://doi.org/10.1126/sciadv.aat1869
  123. APA, 2012: APA Policy Guide on Smart Growth. American Planning Association. https://www.planning.org/policy/guides/adopted/smartgrowth.htm
  124. Broitman, D. and E. Koomen, 2015: Residential density change: Densification and urban expansion. Computers, Environment and Urban Systems, 54, 32–46. https://doi.org/10.1016/j.compenvurbsys.2015.05.006
  125. Butler, B.J., P.F. Catanzaro, J.L. Greene, J.H. Hewes, M.A. Kilgore, D.B. Kittredge, Z. Ma, and M.L. Tyrrell, 2012: Taxing family forest owners: Implications of federal and state policies in the United States. Journal of Forestry, 110 (7), 371–380. https://doi.org/10.5849/jof.11-097
  126. Cathcart, J.F., J.D. Kline, M. Delaney, and M. Tilton, 2007: Carbon storage and Oregon’s land-use planning program. Journal of Forestry, 105 (4), 167–172. https://doi.org/10.1093/jof/105.4.167
  127. Harris, N.L., D.A. Gibbs, A. Baccini, R.A. Birdsey, S. de Bruin, M. Farina, L. Fatoyinbo, M.C. Hansen, M. Herold, R.A. Houghton, P.V. Potapov, D.R. Suarez, R.M. Roman-Cuesta, S.S. Saatchi, C.M. Slay, S.A. Turubanova, and A. Tyukavina, 2021: Global maps of twenty-first century forest carbon fluxes. Nature Climate Change, 11 (3), 234–240. https://doi.org/10.1038/s41558-020-00976-6
  128. Lister, A.J., H. Andersen, T. Frescino, D. Gatziolis, S. Healey, L.S. Heath, G.C. Liknes, R. McRoberts, G.G. Moisen, M. Nelson, R. Riemann, K. Schleeweis, T.A. Schroeder, J. Westfall, and B.T. Wilson, 2020: Use of remote sensing data to improve the efficiency of national forest inventories: A case study from the United States National Forest Inventory. Forests, 11 (12). https://doi.org/10.3390/f11121364
  129. Zheng, B., P. Ciais, F. Chevallier, H. Yang, J.G. Canadell, Y. Chen, I.R. van der Velde, I. Aben, E. Chuvieco, S.J. Davis, M. Deeter, C. Hong, Y. Kong, H. Li, H. Li, X. Lin, K. He, and Q. Zhang, 2023: Record-high CO2 emissions from boreal fires in 2021. Science, 379 (6635), 912–917. https://doi.org/10.1126/science.ade0805
  130. Smith, O., O. Cattell, E. Farcot, R.D. O’Dea, and K.I. Hopcraft, 2022: The effect of renewable energy incorporation on power grid stability and resilience. Science Advances, 8 (9), 6734. https://doi.org/10.1126/sciadv.abj6734
  131. Clarke, L., Y.-M. Wei, A. De La Vega Navarro, A. Garg, A.N. Hahmann, S. Khennas, I.M.L. Azevedo, A. Löschel, A.K. Singh, L. Steg, G. Strbac, and K. Wada, 2022: Ch. 6. Energy systems. In: IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Shukla, P.R., J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, and J. Malley, Eds. Cambridge University Press, Cambridge, UK and New York, NY. https://doi.org/10.1017/9781009157926.008
  132. Dowling, J.A., K.Z. Rinaldi, T.H. Ruggles, S.J. Davis, M. Yuan, F. Tong, N.S. Lewis, and K. Caldeira, 2020: Role of long-duration energy storage in variable renewable electricity systems. Joule, 4 (9), 1907–1928. https://doi.org/10.1016/j.joule.2020.07.007
  133. Jenkins, J.D. and N.A. Sepulveda, 2021: Long-duration energy storage: A blueprint for research and innovation. Joule, 5 (9), 2241–2246. https://doi.org/10.1016/j.joule.2021.08.002
  134. Sepulveda, N.A., J.D. Jenkins, F.J. de Sisternes, and R.K. Lester, 2018: The role of firm low-carbon electricity resources in deep decarbonization of power generation. Joule, 2 (11), 2403–2420. https://doi.org/10.1016/j.joule.2018.08.006
  135. Sepulveda, N.A., J.D. Jenkins, A. Edington, D.S. Mallapragada, and R.K. Lester, 2021: The design space for long-duration energy storage in decarbonized power systems. Nature Energy, 6 (5), 506–516. https://doi.org/10.1038/s41560-021-00796-8
  136. Shan, R., J. Reagan, S. Castellanos, S. Kurtz, and N. Kittner, 2022: Evaluating emerging long-duration energy storage technologies. Renewable and Sustainable Energy Reviews, 159, 112240. https://doi.org/10.1016/j.rser.2022.112240
  137. Tong, F., M. Yuan, N.S. Lewis, S.J. Davis, and K. Caldeira, 2020: Effects of deep reductions in energy storage costs on highly reliable wind and solar electricity systems. iScience, 23 (9), 101484. https://doi.org/10.1016/j.isci.2020.101484
  138. Jayadev, G., B.D. Leibowicz, and E. Kutanoglu, 2020: U.S. electricity infrastructure of the future: Generation and transmission pathways through 2050. Applied Energy, 260, 114267. https://doi.org/10.1016/j.apenergy.2019.114267
  139. Fennell, P.S., S.J. Davis, and A. Mohammed, 2021: Decarbonizing cement production. Joule, 5 (6), 1305–1311. https://doi.org/10.1016/j.joule.2021.04.011
  140. Sutherland, B.R., 2020: Sustainably heating heavy industry. Joule, 4 (1), 14–16. https://doi.org/10.1016/j.joule.2019.12.020
  141. Waite, M. and V. Modi, 2020: Electricity load implications of space heating decarbonization pathways. Joule, 4 (2), 376–394. https://doi.org/10.1016/j.joule.2019.11.011
  142. Brown, T., D. Schlachtberger, A. Kies, S. Schramm, and M. Greiner, 2018: Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system. Energy, 160, 720–739. https://doi.org/10.1016/j.energy.2018.06.222
  143. Griffiths, S., B.K. Sovacool, J. Kim, M. Bazilian, and J.M. Uratani, 2021: Industrial decarbonization via hydrogen: A critical and systematic review of developments, socio-technical systems and policy options. Energy Research & Social Science, 80, 102208. https://doi.org/10.1016/j.erss.2021.102208
  144. Gusain, D., M. Cvetković, R. Bentvelsen, and P. Palensky, 2020: Technical assessment of large scale PEM electrolyzers as flexibility service providers. In: 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE). Delft, Netherlands, 17–19 June 2020. IEEE. https://doi.org/10.1109/isie45063.2020.9152462
  145. Reissner, R., S. You, C. Bourasseau, P. Marcuello, V. Lacroix, G. Lavaille, D.A. Greenhalgh, L. Abadia, C. Imboden, and M. Bornstein, 2019: Unified and standardized qualifying tests of electrolysers for grid services. 3rd European Grid Service Markets Symposium, Lucerne, Switzerland, 3–4 July 2019. https://elib.dlr.de/129857/2/g0502-paper.pdf
  146. Ruggles, T.H., J.A. Dowling, N.S. Lewis, and K. Caldeira, 2021: Opportunities for flexible electricity loads such as hydrogen production from curtailed generation. Advances in Applied Energy, 3, 100051. https://doi.org/10.1016/j.adapen.2021.100051
  147. Demirbas, A. and G. Arin, 2002: An overview of biomass pyrolysis. Energy Sources, 24 (5), 471–482. https://doi.org/10.1080/00908310252889979
  148. Dincer, I. and C. Acar, 2015: Review and evaluation of hydrogen production methods for better sustainability. International Journal of Hydrogen Energy, 40 (34), 11094–11111. https://doi.org/10.1016/j.ijhydene.2014.12.035
  149. Antonini, C., K. Treyer, A. Streb, M. van der Spek, C. Bauer, and M. Mazzotti, 2020: Hydrogen production from natural gas and biomethane with carbon capture and storage–A techno-environmental analysis. Sustainable Energy & Fuels, 4 (6), 2967–2986. https://doi.org/10.1039/d0se00222d
  150. Howarth, R.W. and M.Z. Jacobson, 2021: How green is blue hydrogen? Energy Science & Engineering, 9 (10), 1676–1687. https://doi.org/10.1002/ese3.956
  151. IEA, 2021: Global Hydrogen Review 2021. International Energy Agency. https://www.iea.org/reports/global-hydrogen-review-2021
  152. Chiesa, P., G. Lozza, and L. Mazzocchi, 2005: Using hydrogen as gas turbine fuel. Journal of Engineering for Gas Turbines and Power, 127 (1), 73–80. https://doi.org/10.1115/1.1787513
  153. Leicher, J., T. Nowakowski, A. Giese, and K. Görner, 2017: Power-to-gas and the consequences: Impact of higher hydrogen concentrations in natural gas on industrial combustion processes. Energy Procedia, 120, 96–103. https://doi.org/10.1016/j.egypro.2017.07.157
  154. DOE, 2021: Hydrogen Shot. U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. https://www.energy.gov/eere/fuelcells/hydrogen-shot
  155. IEA, 2019: The Future of Hydrogen: Seizing Today’s Opportunities. International Energy Agency. https://www.iea.org/reports/the-future-of-hydrogen
  156. Pasman, H.J. and W.J. Rogers, 2010: Safety challenges in view of the upcoming hydrogen economy: An overview. Journal of Loss Prevention in the Process Industries, 23 (6), 697–704. https://doi.org/10.1016/j.jlp.2010.06.002
  157. Stępień, Z., 2021: A comprehensive overview of hydrogen-fueled internal combustion engines: Achievements and future challenges. Energies, 14 (20). https://doi.org/10.3390/en14206504
  158. York, W.D., W.S. Ziminsky, and E. Yilmaz, 2013: Development and testing of a low NOx hydrogen combustion system for heavy duty gas turbines. Proceedings of the ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. Volume 2: Combustion, Fuels and Emissions, Parts A and B, Copenhagen, Denmark, 11–15 June 2012. ASME, 1395–1405. https://doi.org/10.1115/gt2012-69913
  159. Ocko, I.B. and S.P. Hamburg, 2022: Climate consequences of hydrogen emissions. Atmospheric Chemistry and Physics, 22 (14), 9349–9368. https://doi.org/10.5194/acp-22-9349-2022
  160. Prather, M.J., 2003: An environmental experiment with H2? Science, 302 (5645), 581–582. https://doi.org/10.1126/science.1091060
  161. Nguyen, T.T., J.S. Park, W.S. Kim, S.H. Nahm, and U.B. Beak, 2020: Environment hydrogen embrittlement of pipeline steel X70 under various gas mixture conditions with in situ small punch tests. Materials Science and Engineering: A, 781, 139114. https://doi.org/10.1016/j.msea.2020.139114
  162. Sun, M., X. Huang, Y. Hu, and S. Lyu, 2022: Effects on the performance of domestic gas appliances operated on natural gas mixed with hydrogen. Energy, 244, 122557. https://doi.org/10.1016/j.energy.2021.122557
  163. Wu, X., H. Zhang, M. Yang, W. Jia, Y. Qiu, and L. Lan, 2022: From the perspective of new technology of blending hydrogen into natural gas pipelines transmission: Mechanism, experimental study, and suggestions for further work of hydrogen embrittlement in high-strength pipeline steels. International Journal of Hydrogen Energy, 47 (12), 8071–8090. https://doi.org/10.1016/j.ijhydene.2021.12.108
  164. Zhao, Y., V. McDonell, and S. Samuelsen, 2019: Experimental assessment of the combustion performance of an oven burner operated on pipeline natural gas mixed with hydrogen. International Journal of Hydrogen Energy, 44 (47), 26049–26062. https://doi.org/10.1016/j.ijhydene.2019.08.011
  165. Zhao, Y., V. McDonell, and S. Samuelsen, 2019: Influence of hydrogen addition to pipeline natural gas on the combustion performance of a cooktop burner. International Journal of Hydrogen Energy, 44 (23), 12239–12253. https://doi.org/10.1016/j.ijhydene.2019.03.100
  166. Haley, B., R.A. Jones, J.H. Williams, G. Kwok, J. Farbes, J. Hargreaves, K. Pickrell, D. Bentz, A. Waddell, and E. Leslie, 2022: Annual Decarbonization Perspective: Carbon-Neutral Pathways for the United States 2022. Evolved Energy Research. https://www.evolved.energy/post/adp2022
  167. Caldeira, K., G. Bala, and L. Cao, 2013: The science of geoengineering. Annual Review of Earth and Planetary Sciences, 41 (1), 231–256. https://doi.org/10.1146/annurev-earth-042711-105548
  168. National Academies of Sciences, Engineering, and Medicine, 2019: Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. The National Academies Press, Washington, DC, 510 pp. https://doi.org/10.17226/25259
  169. Smith, P., S.J. Davis, F. Creutzig, S. Fuss, J. Minx, B. Gabrielle, E. Kato, R.B. Jackson, A. Cowie, E. Kriegler, D.P. van Vuuren, J. Rogelj, P. Ciais, J. Milne, J.G. Canadell, D. McCollum, G. Peters, R. Andrew, V. Krey, G. Shrestha, P. Friedlingstein, T. Gasser, A. Grubler, W.K. Heidug, M. Jonas, C.D. Jones, F. Kraxner, E. Littleton, J. Lowe, J.R. Moreira, N. Nakicenovic, M. Obersteiner, A. Patwardhan, M. Rogner, E. Rubin, A. Sharifi, A. Torvanger, Y. Yamagata, J. Edmonds, and C. Yongsung, 2016: Biophysical and economic limits to negative CO2 emissions. Nature Climate Change, 6, 42–50. https://doi.org/10.1038/nclimate2870
  170. Batres, M., F.M. Wang, H. Buck, R. Kapila, U. Kosar, R. Licker, D. Nagabhushan, E. Rekhelman, and V. Suarez, 2021: Environmental and climate justice and technological carbon removal. The Electricity Journal, 34 (7), 107002. https://doi.org/10.1016/j.tej.2021.107002
  171. Lee, K., C. Fyson, and C.-F. Schleussner, 2021: Fair distributions of carbon dioxide removal obligations and implications for effective national net-zero targets. Environmental Research Letters, 16 (9), 094001. https://doi.org/10.1088/1748-9326/ac1970
  172. Fuhrman, J., C. Bergero, M. Weber, S. Monteith, F.M. Wang, A.F. Clarens, S.C. Doney, W. Shobe, and H. McJeon, 2023: Diverse carbon dioxide removal approaches could reduce impacts on the energy–water–land system. Nature Climate Change, 13 (4), 341–350. https://doi.org/10.1038/s41558-023-01604-9
  173. Chiquier, S., P. Patrizio, M. Bui, N. Sunny, and N. Mac Dowell, 2022: A comparative analysis of the efficiency, timing, and permanence of CO2 removal pathways. Energy and Environmental Science, 15 (10), 4389–4403. https://doi.org/10.1039/d2ee01021f
  174. Fankhauser, S., S.M. Smith, M. Allen, K. Axelsson, T. Hale, C. Hepburn, J.M. Kendall, R. Khosla, J. Lezaun, E. Mitchell-Larson, M. Obersteiner, L. Rajamani, R. Rickaby, N. Seddon, and T. Wetzer, 2022: The meaning of net zero and how to get it right. Nature Climate Change, 12 (1), 15–21. https://doi.org/10.1038/s41558-021-01245-w
  175. Novick, K.A., S. Metzger, W.R.L. Anderegg, M. Barnes, D.S. Cala, K. Guan, K.S. Hemes, D.Y. Hollinger, J. Kumar, M. Litvak, D. Lombardozzi, C.P. Normile, P. Oikawa, B.R.K. Runkle, M. Torn, and S. Wiesner, 2022: Informing nature-based climate solutions for the United States with the best-available science. Global Change Biology, 28 (12), 3778–3794. https://doi.org/10.1111/gcb.16156
  176. Sedjo, R. and B. Sohngen, 2012: Carbon sequestration in forests and soils. Annual Review of Resource Economics, 4 (1), 127–144. https://doi.org/10.1146/annurev-resource-083110-115941
  177. McQueen, N., K.V. Gomes, C. McCormick, K. Blumanthal, M. Pisciotta, and J. Wilcox, 2021: A review of direct air capture (DAC): Scaling up commercial technologies and innovating for the future. Progress in Energy, 3 (3), 032001. https://doi.org/10.1088/2516-1083/abf1ce
  178. Griscom, B.W., J. Adams, P.W. Ellis, R.A. Houghton, G. Lomax, D.A. Miteva, W.H. Schlesinger, D. Shoch, J.V. Siikamäki, P. Smith, P. Woodbury, C. Zganjar, A. Blackman, J. Campari, R.T. Conant, C. Delgado, P. Elias, T. Gopalakrishna, M.R. Hamsik, M. Herrero, J. Kiesecker, E. Landis, L. Laestadius, S.M. Leavitt, S. Minnemeyer, S. Polasky, P. Potapov, F.E. Putz, J. Sanderman, M. Silvius, E. Wollenberg, and J. Fargione, 2017: Natural climate solutions. Proceedings of the National Academy of Sciences of the United States of America, 114 (44), 11645–11650. https://doi.org/10.1073/pnas.1710465114
  179. Cook-Patton, S.C., T. Gopalakrishna, A. Daigneault, S.M. Leavitt, J. Platt, S.M. Scull, O. Amarjargal, P.W. Ellis, B.W. Griscom, J.L. McGuire, S.M. Yeo, and J.E. Fargione, 2020: Lower cost and more feasible options to restore forest cover in the contiguous United States for climate mitigation. One Earth, 3 (6), 739–752. https://doi.org/10.1016/j.oneear.2020.11.013
  180. Van Winkle, C., J. Baker, D. Lapidus, S.B. Ohrel, J. Steller, G. Latta, and D. Birur, 2017: Us Forest Sector Greenhouse Mitigation Potential and Implications for Nationally Determined Contributions. RTI Press Publication No. OP-0033-1705. RTI Press, Research Triangle Park, NC. https://doi.org/10.3768/rtipress.2017.op.0033.1705
  181. Fargione, J., D.L. Haase, O.T. Burney, O.A. Kildisheva, G. Edge, S.C. Cook-Patton, T. Chapman, A. Rempel, M.D. Hurteau, K.T. Davis, S. Dobrowski, S. Enebak, R. De La Torre, A.A.R. Bhuta, F. Cubbage, B. Kittler, D. Zhang, and R.W. Guldin, 2021: Challenges to the reforestation pipeline in the United States. Frontiers in Forests and Global Change, 4, 629198. https://doi.org/10.3389/ffgc.2021.629198
  182. Bigelow, D.P. and A. Borchers, 2017: Major Uses of Land in the United States, 2012. EIB-178. U.S. Department of Agriculture, Economic Research Service. https://www.ers.usda.gov/publications/pub-details/?pubid=84879
  183. Conant, R.T., C.E.P. Cerri, B.B. Osborne, and K. Paustian, 2017: Grassland management impacts on soil carbon stocks: A new synthesis. Ecological Applications, 27 (2), 662–668. https://doi.org/10.1002/eap.1473
  184. McSherry, M.E. and M.E. Ritchie, 2013: Effects of grazing on grassland soil carbon: A global review. Global Change Biology, 19 (5), 1347–1357. https://doi.org/10.1111/gcb.12144
  185. Sanderson, J.S., C. Beutler, J.R. Brown, I. Burke, T. Chapman, R.T. Conant, J.D. Derner, M. Easter, S.D. Fuhlendorf, G. Grissom, J.E. Herrick, D. Liptzin, J.A. Morgan, R. Murph, C. Pague, I. Rangwala, D. Ray, R. Rondeau, T. Schulz, and T. Sullivan, 2020: Cattle, conservation, and carbon in the western Great Plains. Journal of Soil and Water Conservation, 75 (1), 5A–12A. https://doi.org/10.2489/jswc.75.1.5a
  186. Bossio, D.A., S.C. Cook-Patton, P.W. Ellis, J. Fargione, J. Sanderman, P. Smith, S. Wood, R.J. Zomer, M. Unger, I.M. Emmer, and B.W. Griscom, 2020: The role of soil carbon in natural climate solutions. Nature Sustainability, 3, 391–398. https://doi.org/10.1038/s41893-020-0491-z
  187. Cai, A., T. Han, T. Ren, J. Sanderman, Y. Rui, B. Wang, P. Smith, M. Xu, and Y.e. Li, 2022: Declines in soil carbon storage under no tillage can be alleviated in the long run. Geoderma, 425, 116028. https://doi.org/10.1016/j.geoderma.2022.116028
  188. Lessmann, M., G.H. Ros, M.D. Young, and W. de Vries, 2022: Global variation in soil carbon sequestration potential through improved cropland management. Global Change Biology, 28 (3), 1162–1177. https://doi.org/10.1111/gcb.15954
  189. Poeplau, C. and A. Don, 2015: Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis. Agriculture, Ecosystems & Environment, 200, 33–41. https://doi.org/10.1016/j.agee.2014.10.024
  190. D’Amato, A.W., J.B. Bradford, S. Fraver, and B.J. Palik, 2011: Forest management for mitigation and adaptation to climate change: Insights from long-term silviculture experiments. Forest Ecology and Management, 262 (5), 803–816. https://doi.org/10.1016/j.foreco.2011.05.014
  191. Jackson, R.B. and J.S. Baker, 2010: Opportunities and constraints for forest climate mitigation. BioScience, 60 (9), 698–707. https://doi.org/10.1525/bio.2010.60.9.7
  192. Kaarakka, L., M. Cornett, G. Domke, T. Ontl, and L.E. Dee, 2021: Improved forest management as a natural climate solution: A review. Ecological Solutions and Evidence, 2 (3), e12090. https://doi.org/10.1002/2688-8319.12090
  193. Lee, T.D., S.E. Eisenhaure, and I.P. Gaudreau, 2017: Pre-logging treatment of invasive glossy buckthorn (Frangula alnus mill.) promotes regeneration of eastern white pine (Pinus strobus L.). Forests, 8 (1), 16. https://doi.org/10.3390/f8010016
  194. Moss, S.A. and E. Heitzman, 2013: The economic impact of timber harvesting practices on NIPF properties in West Virginia. In: Proceedings, 18th Central Hardwood Forest Conference, Miller, G.W., T.M. Schuler, K.W. Gottschalk, J.R. Brooks, S.T. Grushecky, B.D. Spong, and J.S. Rentch, Eds. Morgantown, WV, 26–28 March 2012. U.S. Department of Agriculture, Forest Service, Northern Research Station, 129–141. https://www.fs.usda.gov/research/treesearch/44060
  195. Ontl, T.A., M.K. Janowiak, C.W. Swanston, J. Daley, S. Handler, M. Cornett, S. Hagenbuch, C. Handrick, L. McCarthy, and N. Patch, 2020: Forest management for carbon sequestration and climate adaptation. Journal of Forestry, 118 (1), 86–101. https://doi.org/10.1093/jofore/fvz062
  196. Powers, M.D., R.K. Kolka, J.B. Bradford, B.J. Palik, S. Fraver, and M.F. Jurgensen, 2012: Carbon stocks across a chronosequence of thinned and unmanaged red pine (Pinus resinosa) stands. Ecological Applications, 22 (4), 1297–1307. https://doi.org/10.1890/11-0411.1
  197. Schuler, T.M., M. Thomas-Van Gundy, J.P. Brown, and J.K. Wiedenbeck, 2017: Managing Appalachian hardwood stands using four management practices: 60-year results. Forest Ecology and Management, 387, 3–11. https://doi.org/10.1016/j.foreco.2016.08.019
  198. Byrd, K., J. Ratliff, N. Bliss, A. Wein, B. Sleeter, T. Sohl, and Z. Li, 2015: Quantifying climate change mitigation potential in the United States Great Plains wetlands for three greenhouse gas emission scenarios. Mitigation and Adaptation Strategies for Global Change, 20 (3), 439–465. https://doi.org/10.1007/s11027-013-9500-0
  199. Byrd, K.B., L. Ballanti, N. Thomas, D. Nguyen, J.R. Holmquist, M. Simard, and L. Windham-Myers, 2018: A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States. ISPRS Journal of Photogrammetry and Remote Sensing, 139, 255–271. https://doi.org/10.1016/j.isprsjprs.2018.03.019
  200. Kroeger, K.D., S. Crooks, S. Moseman-Valtierra, and J. Tang, 2017: Restoring tides to reduce methane emissions in impounded wetlands: A new and potent Blue Carbon climate change intervention. Scientific Reports, 7 (1), 11914. https://doi.org/10.1038/s41598-017-12138-4
  201. National Academies of Sciences, Engineering, and Medicine, 2022: A Research Strategy for Ocean-based Carbon Dioxide Removal and Sequestration. The National Academies Press, Washington, DC, 322 pp. https://doi.org/10.17226/26278
  202. Lampitt, R.S., E.P. Achterberg, T.R. Anderson, J.A. Hughes, M.D. Iglesias-Rodriguez, B.A. Kelly-Gerreyn, M. Lucas, E.E. Popova, R. Sanders, J.G. Shepherd, D. Smythe-Wright, and A. Yool, 2008: Ocean fertilization: A potential means of geoengineering? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366 (1882), 3919–3945. https://doi.org/10.1098/rsta.2008.0139
  203. DeAngelo, J., B.T. Saenz, I.B. Arzeno-Soltero, C.A. Frieder, M.C. Long, J. Hamman, K.A. Davis, and S.J. Davis, 2023: Economic and biophysical limits to seaweed farming for climate change mitigation. Nature Plants, 9 (1), 45–57. https://doi.org/10.1038/s41477-022-01305-9
  204. Bach, L.T., S.J. Gill, R.E.M. Rickaby, S. Gore, and P. Renforth, 2019: CO2 removal with enhanced weathering and ocean alkalinity enhancement: Potential risks and co-benefits for marine pelagic ecosystems. Frontiers in Climate, 1, 7. https://doi.org/10.3389/fclim.2019.00007
  205. Drouet, L., V. Bosetti, S.A. Padoan, L. Aleluia Reis, C. Bertram, F. Dalla Longa, J. Després, J. Emmerling, F. Fosse, K. Fragkiadakis, S. Frank, O. Fricko, S. Fujimori, M. Harmsen, V. Krey, K. Oshiro, L.P. Nogueira, L. Paroussos, F. Piontek, K. Riahi, P.R.R. Rochedo, R. Schaeffer, J.y. Takakura, K.-I. van der Wijst, B. van der Zwaan, D. van Vuuren, Z. Vrontisi, M. Weitzel, B. Zakeri, and M. Tavoni, 2021: Net zero-emission pathways reduce the physical and economic risks of climate change. Nature Climate Change, 11 (12), 1070–1076. https://doi.org/10.1038/s41558-021-01218-z
  206. Jones, C.D., P. Ciais, S.J. Davis, P. Friedlingstein, T. Gasser, G.P. Peters, J. Rogelj, D.P. van Vuuren, J.G. Canadell, A. Cowie, R.B. Jackson, M. Jonas, E. Kriegler, E. Littleton, J.A. Lowe, J. Milne, G. Shrestha, P. Smith, A. Torvanger, and A. Wiltshire, 2016: Simulating the Earth system response to negative emissions. Environmental Research Letters, 11 (9), 095012. https://doi.org/10.1088/1748-9326/11/9/095012
  207. Koven, C.D., B.M. Sanderson, and A.L.S. Swann, 2023: Much of zero emissions commitment occurs before reaching net zero emissions. Environmental Research Letters, 18 (1), 014017. https://doi.org/10.1088/1748-9326/acab1a
  208. National Academies of Sciences, Engineering, and Medicine, 2021: Reflecting Sunlight: Recommendations for Solar Geoengineering Research and Research Governance. The National Academies Press, Washington, DC, 328 pp. https://doi.org/10.17226/25762
  209. OSTP, 2023: Congressionally-mandated report on solar radiation modification. White House Office of Science and Technology Policy, Washington, DC, June 30, 2023. https://www.whitehouse.gov/ostp/news-updates/2023/06/30/congressionally-mandated-report-on-solar-radiation-modification/
  210. Fagnant, D.J. and K. Kockelman, 2015: Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. https://doi.org/10.1016/j.tra.2015.04.003
  211. Motamedi, S., P. Wang, T. Zhang, and C.-Y. Chan, 2020: Acceptance of full driving automation: Personally owned and shared-use concepts. Human Factors, 62 (2), 288–309. https://doi.org/10.1177/0018720819870658
  212. Brown, K.E. and R. Dodder, 2019: Energy and emissions implications of automated vehicles in the U.S. energy system. Transportation Research Part D: Transport and Environment, 77, 132–147. https://doi.org/10.1016/j.trd.2019.09.003
  213. Massar, M., I. Reza, S.M. Rahman, S.M.H. Abdullah, A. Jamal, and F.S. Al-Ismail, 2021: Impacts of autonomous vehicles on greenhouse gas emissions—Positive or negative? International Journal of Environmental Research and Public Health, 18 (11), 5567. https://doi.org/10.3390/ijerph18115567
  214. Sheppard, C.J.R., A.T. Jenn, J.B. Greenblatt, G.S. Bauer, and B.F. Gerke, 2021: Private versus shared, automated electric vehicles for U.S. personal mobility: Energy use, greenhouse gas emissions, grid integration, and cost impacts. Environmental Science and Technology, 55 (5), 3229–3239. https://doi.org/10.1021/acs.est.0c06655
  215. Abduljabbar, R.L., S. Liyanage, and H. Dia, 2021: The role of micro-mobility in shaping sustainable cities: A systematic literature review. Transportation Research Part D: Transport and Environment, 92, 102734. https://doi.org/10.1016/j.trd.2021.102734
  216. Hidaka, K. and T. Shiga, 2018: Forecasting travel demand for new mobility services employing autonomous vehicles. Transportation Research Procedia, 34, 139–146. https://doi.org/10.1016/j.trpro.2018.11.025
  217. Kamargianni, M., W. Li, M. Matyas, and A. Schäfer, 2016: A critical review of new mobility services for urban transport. Transportation Research Procedia, 14, 3294–3303. https://doi.org/10.1016/j.trpro.2016.05.277
  218. Fleming, K.L., 2018: Social equity considerations in the new age of transportation: Electric, automated, and shared mobility. Journal of Science Policy & Governance, 13 (1). https://www.sciencepolicyjournal.org/uploads/5/4/3/4/5434385/fleming.pdf
  219. Jenn, A., 2020: Emissions benefits of electric vehicles in Uber and Lyft ride-hailing services. Nature Energy, 5 (7), 520–525. https://doi.org/10.1038/s41560-020-0632-7
  220. Taiebat, M. and M. Xu, 2019: Synergies of four emerging technologies for accelerated adoption of electric vehicles: Shared mobility, wireless charging, vehicle-to-grid, and vehicle automation. Journal of Cleaner Production, 230, 794–797. https://doi.org/10.1016/j.jclepro.2019.05.142
  221. Arent, D.J., C. Barrows, S. Davis, G. Grim, J. Schaidle, B. Kroposki, M. Ruth, and B. Van Zandt, 2022: Integration of energy systems. MRS Bulletin, 46 (12), 1139–1152. https://doi.org/10.1557/s43577-021-00244-8
  222. Ramsebner, J., R. Haas, A. Ajanovic, and M. Wietschel, 2021: The sector coupling concept: A critical review. WIREs Energy and Environment, 10 (4), e396. https://doi.org/10.1002/wene.396
  223. Rodríguez Escobar, M.I., E. Cadena, T.T. Nhu, M. Cooreman-Algoed, S. De Smet, and J. Dewulf, 2021: Analysis of the cultured meat production system in function of its environmental footprint: Current status, gaps and recommendations. Foods, 10 (12), 2941. https://doi.org/10.3390/foods10122941
  224. Rubio, N.R., N. Xiang, and D.L. Kaplan, 2020: Plant-based and cell-based approaches to meat production. Nature Communications, 11 (1), 6276. https://doi.org/10.1038/s41467-020-20061-y
  225. Tubb, C. and T. Seba, 2021: Rethinking food and agriculture 2020-2030: The second domestication of plants and animals, the disruption of the cow, and the collapse of industrial livestock farming. Industrial Biotechnology, 17 (2), 57–72. https://doi.org/10.1089/ind.2021.29240.ctu
  226. García Martínez, J.B., K.A. Alvarado, and D.C. Denkenberger, 2022: Synthetic fat from petroleum as a resilient food for global catastrophes: Preliminary techno-economic assessment and technology roadmap. Chemical Engineering Research and Design, 177, 255–272. https://doi.org/10.1016/j.cherd.2021.10.017
  227. MacDougall, A.H., J. Rogelj, and P. Withey, 2021: Estimated climate impact of replacing agriculture as the primary food production system. Environmental Research Letters, 16 (12), 125010. https://doi.org/10.1088/1748-9326/ac3aa5
  228. Almeida, A.K., R.S. Hegarty, and A. Cowie, 2021: Meta-analysis quantifying the potential of dietary additives and rumen modifiers for methane mitigation in ruminant production systems. Animal Nutrition, 7 (4), 1219–1230. https://doi.org/10.1016/j.aninu.2021.09.005
  229. Roque, B.M., M. Venegas, R.D. Kinley, R. de Nys, T.L. Duarte, X. Yang, and E. Kebreab, 2021: Red seaweed (Asparagopsis taxiformis) supplementation reduces enteric methane by over 80 percent in beef steers. PLoS ONE, 16 (3), e0247820. https://doi.org/10.1371/journal.pone.0247820
  230. Pape, D., J. Lewandrowski, R. Steele, D. Man, M. Riley-Gilbert, K. Moffroid, and S. Kolansky, 2016: Managing Agricultural Land for Greenhouse Gas Mitigation Within the United States. ICF International. https://www.usda.gov/sites/default/files/documents/White_Paper_WEB71816.pdf
  231. Souza, R., J. Yin, and S. Calabrese, 2021: Optimal drainage timing for mitigating methane emissions from rice paddy fields. Geoderma, 394, 114986. https://doi.org/10.1016/j.geoderma.2021.114986
  232. Subbarao, G.V. and T.D. Searchinger, 2021: A “more ammonium solution” to mitigate nitrogen pollution and boost crop yields. Proceedings of the National Academy of Sciences of the United States of America, 118 (22), e2107576118. https://doi.org/10.1073/pnas.2107576118
  233. Lelieveld, J., K. Klingmüller, A. Pozzer, R.T. Burnett, A. Haines, and V. Ramanathan, 2019: Effects of fossil fuel and total anthropogenic emission removal on public health and climate. Proceedings of the National Academy of Sciences of the United States of America, 116 (15), 7192–7197. https://doi.org/10.1073/pnas.1819989116
  234. Shindell, D., M. Ru, Y. Zhang, K. Seltzer, G. Faluvegi, L. Nazarenko, G.A. Schmidt, L. Parsons, A. Challapalli, L. Yang, and A. Glick, 2021: Temporal and spatial distribution of health, labor, and crop benefits of climate change mitigation in the United States. Proceedings of the National Academy of Sciences of the United States of America, 118 (46), e2104061118. https://doi.org/10.1073/pnas.2104061118
  235. Tessum, C.W., J.S. Apte, A.L. Goodkind, N.Z. Muller, K.A. Mullins, D.A. Paolella, S. Polasky, N.P. Springer, S.K. Thakrar, J.D. Marshall, and J.D. Hill, 2019: Inequity in consumption of goods and services adds to racial–ethnic disparities in air pollution exposure. Proceedings of the National Academy of Sciences of the United States of America, 116 (13), 6001–6006. https://doi.org/10.1073/pnas.1818859116
  236. Tessum, C.W., J.D. Hill, and J.D. Marshall, 2017: InMAP: A model for air pollution interventions. PLoS ONE, 12 (4), e0176131. https://doi.org/10.1371/journal.pone.0176131
  237. Thakrar, S.K., S. Balasubramanian, P.J. Adams, I.M.L. Azevedo, N.Z. Muller, S.N. Pandis, S. Polasky, C.A. Pope, A.L. Robinson, J.S. Apte, C.W. Tessum, J.D. Marshall, and J.D. Hill, 2020: Reducing mortality from air pollution in the United States by targeting specific emission sources. Environmental Science & Technology Letters, 7 (9), 639–645. https://doi.org/10.1021/acs.estlett.0c00424
  238. Vohra, K., A. Vodonos, J. Schwartz, E.A. Marais, M.P. Sulprizio, and L.J. Mickley, 2021: Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem. Environmental Research, 195, 110754. https://doi.org/10.1016/j.envres.2021.110754
  239. Murray, C.J.L., A.Y. Aravkin, P. Zheng, C. Abbafati, K.M. Abbas, et al., 2020: Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396 (10258), 1223–1249. https://doi.org/10.1016/s0140-6736(20)30752-2
  240. Brook, R.D., S. Rajagopalan, C.A. Pope, J.R. Brook, A. Bhatnagar, A.V. Diez-Roux, F. Holguin, Y. Hong, R.V. Luepker, M.A. Mittleman, A. Peters, D. Siscovick, S.C. Smith, L. Whitsel, and J.D. Kaufman, 2010: Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation, 121 (21), 2331–2378. https://doi.org/10.1161/cir.0b013e3181dbece1
  241. Crouse, D.L., P.A. Peters, A. van Donkelaar, M.S. Goldberg, P.J. Villeneuve, O. Brion, S. Khan, D.O. Atari, M. Jerrett, C.A. Pope, M. Brauer, J.R. Brook, R.V. Martin, D. Stieb, and R.T. Burnett, 2012: Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: A Canadian national-level cohort study. Environmental Health Perspectives, 120 (5), 708–714. https://doi.org/10.1289/ehp.1104049
  242. Pope, C.A. and D.W. Dockery, 2006: Health effects of fine particulate air pollution: Lines that connect. Journal of the Air & Waste Management Association, 56 (6), 709–742. https://doi.org/10.1080/10473289.2006.10464485
  243. Hajat, A., C. Hsia, and M.S. O’Neill, 2015: Socioeconomic disparities and air pollution exposure: A global review. Current Environmental Health Reports, 2 (4), 440–450. https://doi.org/10.1007/s40572-015-0069-5
  244. Liu, J., L.P. Clark, M.J. Bechle, A. Hajat, S.Y. Kim, A.L. Robinson, L. Sheppard, A.A. Szpiro, and J.D. Marshall, 2021: Disparities in air pollution exposure in the United States by race/ethnicity and income, 1990–2010. Environmental Health Perspectives, 129 (12), 127005. https://doi.org/10.1289/ehp8584
  245. Mikati, I., A.F. Benson, T.J. Luben, J.D. Sacks, and J. Richmond-Bryant, 2018: Disparities in distribution of particulate matter emission sources by race and poverty status. American Journal of Public Health, 108 (4), 480–485. https://doi.org/10.2105/ajph.2017.304297
  246. Tessum, C.W., D.A. Paolella, S.E. Chambliss, J.S. Apte, J.D. Hill, and J.D. Marshall, 2021: PM2.5 polluters disproportionately and systemically affect people of color in the United States. Science Advances, 7 (18), 4491. https://doi.org/10.1126/sciadv.abf4491
  247. Thind, M.P.S., C.W. Tessum, I.L. Azevedo, and J.D. Marshall, 2019: Fine particulate air pollution from electricity generation in the US: Health impacts by race, income, and geography. Environmental Science & Technology, 53 (23), 14010–14019. https://doi.org/10.1021/acs.est.9b02527
  248. Mayfield, E.N., J.L. Cohon, N.Z. Muller, I.M.L. Azevedo, and A.L. Robinson, 2019: Quantifying the social equity state of an energy system: Environmental and labor market equity of the shale gas boom in Appalachia. Environmental Research Letters, 14 (12), 124072. https://doi.org/10.1088/1748-9326/ab59cd
  249. Markandya, A., J. Sampedro, S.J. Smith, R. Van Dingenen, C. Pizarro-Irizar, I. Arto, and M. González-Eguino, 2018: Health co-benefits from air pollution and mitigation costs of the Paris Agreement: A modelling study. The Lancet Planetary Health, 2 (3), e126–e133. https://doi.org/10.1016/s2542-5196(18)30029-9
  250. Vandyck, T., K. Keramidas, A. Kitous, J.V. Spadaro, R. Van Dingenen, M. Holland, and B. Saveyn, 2018: Air quality co-benefits for human health and agriculture counterbalance costs to meet Paris Agreement pledges. Nature Communications, 9 (1), 4939. https://doi.org/10.1038/s41467-018-06885-9
  251. Zhang, Y., S.J. Smith, J.H. Bowden, Z. Adelman, and J.J. West, 2017: Co-benefits of global, domestic, and sectoral greenhouse gas mitigation for US air quality and human health in 2050. Environmental Research Letters, 12 (11), 114033. https://doi.org/10.1088/1748-9326/aa8f76
  252. Zhu, S., M. Mac Kinnon, A. Carlos-Carlos, S.J. Davis, and S. Samuelsen, 2022: Decarbonization will lead to more equitable air quality in California. Nature Communications, 13 (1), 5738. https://doi.org/10.1038/s41467-022-33295-9
  253. Gallagher, C.L. and T. Holloway, 2020: Integrating air quality and public health benefits in U.S. decarbonization strategies. Frontiers in Public Health, 8, 563358. https://doi.org/10.3389/fpubh.2020.563358
  254. Mayfield, E.N., 2022: Phasing out coal power plants based on cumulative air pollution impact and equity objectives in net zero energy system transitions. Environmental Research: Infrastructure and Sustainability, 2 (2), 021004. https://doi.org/10.1088/2634-4505/ac70f6
  255. Sergi, B.J., P.J. Adams, N.Z. Muller, A.L. Robinson, S.J. Davis, J.D. Marshall, and I.L. Azevedo, 2020: Optimizing emissions reductions from the U.S. power sector for climate and health benefits. Environmental Science & Technology, 54 (12), 7513–7523. https://doi.org/10.1021/acs.est.9b06936
  256. Barbose, G., R. Wiser, J. Heeter, T. Mai, L. Bird, M. Bolinger, A. Carpenter, G. Heath, D. Keyser, J. Macknick, A. Mills, and D. Millstein, 2016: A retrospective analysis of benefits and impacts of U.S. renewable portfolio standards. Energy Policy, 96, 645–660. https://doi.org/10.1016/j.enpol.2016.06.035
  257. Buonocore, J.J., P. Luckow, G. Norris, J.D. Spengler, B. Biewald, J. Fisher, and J.I. Levy, 2016: Health and climate benefits of different energy-efficiency and renewable energy choices. Nature Climate Change, 6 (1), 100–105. https://doi.org/10.1038/nclimate2771
  258. Dimanchev, E.G., S. Paltsev, M. Yuan, D. Rothenberg, C.W. Tessum, J.D. Marshall, and N.E. Selin, 2019: Health co-benefits of sub-national renewable energy policy in the US. Environmental Research Letters, 14 (8), 085012. https://doi.org/10.1088/1748-9326/ab31d9
  259. Millstein, D., R. Wiser, M. Bolinger, and G. Barbose, 2017: The climate and air-quality benefits of wind and solar power in the United States. Nature Energy, 2 (9), 17134. https://doi.org/10.1038/nenergy.2017.134
  260. Qiu, M., C.M. Zigler, and N.E. Selin, 2022: Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States. Science Advances, 8 (48), 8762. https://doi.org/10.1126/sciadv.abn8762
  261. Siler-Evans, K., I.L. Azevedo, M.G. Morgan, and J. Apt, 2013: Regional variations in the health, environmental, and climate benefits of wind and solar generation. Proceedings of the National Academy of Sciences of the United States of America, 110 (29), 11768–11773. https://doi.org/10.1073/pnas.1221978110
  262. Grabow, M.L., S.N. Spak, T. Holloway, B. Stone, Jr. , A.C. Mednick, and J.A. Patz, 2012: Air quality and exercise-related health benefits from reduced car travel in the midwestern United States. Environmental Health Perspectives, 120 (1), 68–76. https://doi.org/10.1289/ehp.1103440
  263. Tong, F. and I.M.L. Azevedo, 2020: What are the best combinations of fuel-vehicle technologies to mitigate climate change and air pollution effects across the United States? Environmental Research Letters, 15 (7), 074046. https://doi.org/10.1088/1748-9326/ab8a85
  264. Deetjen, T.A., L. Walsh, and P. Vaishnav, 2021: US residential heat pumps: The private economic potential and its emissions, health, and grid impacts. Environmental Research Letters, 16 (8), 084024. https://doi.org/10.1088/1748-9326/ac10dc
  265. Commane, R. and L.D. Schiferl, 2022: Climate mitigation policies for cities must consider air quality impacts. Chem, 8 (4), 910–923. https://doi.org/10.1016/j.chempr.2022.02.006
  266. Gross, S., 2020: Renewables, Land Use, and Local Opposition in the United States. The Brookings Institution, 24 pp. https://www.brookings.edu/research/renewables-land-use-and-local-opposition-in-the-united-states/
  267. Mulvaney, D., 2017: Identifying the roots of Green Civil War over utility-scale solar energy projects on public lands across the American Southwest. Journal of Land Use Science, 12 (6), 493–515. https://doi.org/10.1080/1747423x.2017.1379566
  268. Nielsen, J.A.E., K. Stavrianakis, and Z. Morrison, 2022: Community acceptance and social impacts of carbon capture, utilization and storage projects: A systematic meta-narrative literature review. PLoS ONE, 17 (8), e0272409. https://doi.org/10.1371/journal.pone.0272409
  269. Cox, E., E. Spence, and N. Pidgeon, 2020: Public perceptions of carbon dioxide removal in the United States and the United Kingdom. Nature Climate Change, 10 (8), 744–749. https://doi.org/10.1038/s41558-020-0823-z
  270. Hernandez, R.R., S.B. Easter, M.L. Murphy-Mariscal, F.T. Maestre, M. Tavassoli, E.B. Allen, C.W. Barrows, J. Belnap, R. Ochoa-Hueso, S. Ravi, and M.F. Allen, 2014: Environmental impacts of utility-scale solar energy. Renewable and Sustainable Energy Reviews, 29, 766–779. https://doi.org/10.1016/j.rser.2013.08.041
  271. Hernandez, R.R., M.K. Hoffacker, M.L. Murphy-Mariscal, G.C. Wu, and M.F. Allen, 2015: Solar energy development impacts on land cover change and protected areas. Proceedings of the National Academy of Sciences of the United States of America, 112 (44), 13579–13584. https://doi.org/10.1073/pnas.1517656112
  272. Laranjeiro, T., R. May, and F. Verones, 2018: Impacts of onshore wind energy production on birds and bats: Recommendations for future life cycle impact assessment developments. The International Journal of Life Cycle Assessment, 23 (10), 2007–2023. https://doi.org/10.1007/s11367-017-1434-4
  273. Hall, P.K., W. Morgan, and J. Richardson, 2022: Land Use Conflicts Between Wind and Solar Renewable Energy and Agricultural Uses. The National Agricultural Law Center. https://researchrepository.wvu.edu/cgi/viewcontent.cgi?article=1104&context=law_faculty
  274. Hill, J., 2022: The sobering truth about corn ethanol. Proceedings of the National Academy of Sciences of the United States of America, 119 (11), e2200997119. https://doi.org/10.1073/pnas.2200997119
  275. Lark, T.J., N.P. Hendricks, A. Smith, N. Pates, S.A. Spawn-Lee, M. Bougie, E.G. Booth, C.J. Kucharik, and H.K. Gibbs, 2022: Environmental outcomes of the US renewable fuel standard. Proceedings of the National Academy of Sciences of the United States of America, 119 (9), e2101084119. https://doi.org/10.1073/pnas.2101084119
  276. Wu, G.C., E. Leslie, O. Sawyerr, D.R. Cameron, E. Brand, B. Cohen, D. Allen, M. Ochoa, and A. Olson, 2020: Low-impact land use pathways to deep decarbonization of electricity. Environmental Research Letters, 15 (7), 074044. https://doi.org/10.1088/1748-9326/ab87d1
  277. Gerrard, M.B. and E. McTiernan, 2020: New York’s new statute on siting renewable energy facilities. New York Law Journal, 263 (93). https://scholarship.law.columbia.edu/faculty_scholarship/3026
  278. Moch, J.M. and H. Lee, 2022: The Challenges of Decarbonizing the U.S. Electric Grid by 2035. Harvard University, Harvard Kennedy School, Belfer Center, Cambridge, MA. https://www.belfercenter.org/publication/challenges-decarbonizing-us-electric-grid-2035
  279. Susskind, L., J. Chun, A. Gant, C. Hodgkins, J. Cohen, and S. Lohmar, 2022: Sources of opposition to renewable energy projects in the United States. Energy Policy, 165, 112922. https://doi.org/10.1016/j.enpol.2022.112922
  280. Grubert, E. and A. Marshall, 2022: Water for energy: Characterizing co-evolving energy and water systems under twin climate and energy system nonstationarities. WIREs Water, 9 (2), e1576. https://doi.org/10.1002/wat2.1576
  281. Grubert, E. and K.T. Sanders, 2018: Water use in the United States energy system: A national assessment and unit process inventory of water consumption and withdrawals. Environmental Science & Technology, 52 (11), 6695–6703. https://doi.org/10.1021/acs.est.8b00139
  282. Peer, R.A.M., E. Grubert, and K.T. Sanders, 2019: A regional assessment of the water embedded in the US electricity system. Environmental Research Letters, 14 (8), 084014. https://doi.org/10.1088/1748-9326/ab2daa
  283. Qin, Y., N.D. Mueller, S. Siebert, R.B. Jackson, A. AghaKouchak, J.B. Zimmerman, D. Tong, C. Hong, and S.J. Davis, 2019: Flexibility and intensity of global water use. Nature Sustainability, 2 (6), 515–523. https://doi.org/10.1038/s41893-019-0294-2
  284. Tarroja, B., R.A.M. Peer, K.T. Sanders, and E. Grubert, 2020: How do non-carbon priorities affect zero-carbon electricity systems? A case study of freshwater consumption and cost for Senate Bill 100 compliance in California. Applied Energy, 265, 114824. https://doi.org/10.1016/j.apenergy.2020.114824
  285. Grubert, E., 2023: Water consumption from electrolytic hydrogen in a carbon-neutral US energy system. Cleaner Production Letters, 4, 100037. https://doi.org/10.1016/j.clpl.2023.100037
  286. Lampert, D.J., H. Cai, and A. Elgowainy, 2016: Wells to wheels: Water consumption for transportation fuels in the United States. Energy & Environmental Science, 9 (3), 787–802. https://doi.org/10.1039/c5ee03254g
  287. Rosa, L., D.L. Sanchez, G. Realmonte, D. Baldocchi, and P. D'Odorico, 2021: The water footprint of carbon capture and storage technologies. Renewable and Sustainable Energy Reviews, 138, 110511. https://doi.org/10.1016/j.rser.2020.110511
  288. Lovering, J., M. Swain, L. Blomqvist, and R.R. Hernandez, 2022: Land-use intensity of electricity production and tomorrow’s energy landscape. PLoS ONE, 17 (7), e0270155. https://doi.org/10.1371/journal.pone.0270155
  289. BLS, 2022: Local Area Unemployment Statistics. U.S. Bureau of Labor Statistics. https://www.bls.gov/lau/home.htm
  290. DOE, 2022: United States Energy and Employment Report 2022. U.S. Department of Energy. https://www.energy.gov/sites/default/files/2022-06/USEER%202022%20National%20Report_1.pdf
  291. DOL, n.d.: Prevailing Wage and the Inflation Reduction Act. U.S. Department of Labor, accessed May 9, 2023. https://www.dol.gov/agencies/whd/ira
  292. NASEO and EFI, 2020: The 2019 U.S. Energy & Employment Report. National Association of State Energy Officials and Energy Futures Initiative. https://www.naseo.org/data/sites/1/documents/publications/useer-2019-us-energy-employment-report1.pdf
  293. Mayfield, E.N., J.L. Cohon, N.Z. Muller, I.M.L. Azevedo, and A.L. Robinson, 2019: Cumulative environmental and employment impacts of the shale gas boom. Nature Sustainability, 2 (12), 1122–1131. https://doi.org/10.1038/s41893-019-0420-1
  294. Mayfield, E., J. Jenkins, E. Larson, and C. Greig, 2021: Labor Pathways to Achieve Net-Zero Emissions in the United States by Mid-Century. USAEE Working Paper No. 21-494. U.S. Association for Energy Economics, 83 pp. https://doi.org/10.2139/ssrn.3834083
  295. Bergquist, P., M. Mildenberger, and L.C. Stokes, 2020: Combining climate, economic, and social policy builds public support for climate action in the US. Environmental Research Letters, 15 (5), 054019. https://doi.org/10.1088/1748-9326/ab81c1
  296. Blyth, W., J. Speirs, and R. Gross, 2014: Low carbon jobs: The evidence for net job creation from policy support for energy efficiency and renewable energy. In: BIEE 10th Academic Conference. 17–18 September 2014. U.K. Energy Research Centre, 31 pp. https://www.biee.org/wpcms/wp-content/uploads/Speirs-Low-carbon-jobs-The-evidence-for-net-job-creation.pdf
  297. Pollin, R. and S. Chakraborty, 2020: Job Creation Estimates Through Proposed Economic Stimulus Measures. University of Massachusetts, Political Economy Research Institute, Amherst, MA. https://peri.umass.edu/publication/item/1297-job-creation-estimates-through-proposed-economic-stimulus-measures
  298. Wei, M., S. Patadia, and D.M. Kammen, 2010: Putting renewables and energy efficiency to work: How many jobs can the clean energy industry generate in the US? Energy Policy, 38 (2), 919–931. https://doi.org/10.1016/j.enpol.2009.10.044
  299. Mayfield, E. and J. Jenkins, 2021: Influence of high road labor policies and practices on renewable energy costs, decarbonization pathways, and labor outcomes. Environmental Research Letters, 16 (12), 124012. https://doi.org/10.1088/1748-9326/ac34ba
  300. Bergquist, P., S. Ansolabehere, S. Carley, and D. Konisky, 2020: Backyard voices: How sense of place shapes views of large-scale energy transmission infrastructure. Energy Research & Social Science, 63, 101396. https://doi.org/10.1016/j.erss.2019.101396
  301. Chapman, A.J., B.C. McLellan, and T. Tezuka, 2018: Prioritizing mitigation efforts considering co-benefits, equity and energy justice: Fossil fuel to renewable energy transition pathways. Applied Energy, 219, 187–198. https://doi.org/10.1016/j.apenergy.2018.03.054
  302. Henry, M.S., M.D. Bazilian, and C. Markuson, 2020: Just transitions: Histories and futures in a post-COVID world. Energy Research & Social Science, 68, 101668. https://doi.org/10.1016/j.erss.2020.101668
  303. Cha, J.M., 2020: A just transition for whom? Politics, contestation, and social identity in the disruption of coal in the Powder River Basin. Energy Research and Social Science, 69, 101657. https://doi.org/10.1016/j.erss.2020.101657
  304. Pai, S., H. Zerriffi, J. Jewell, and J. Pathak, 2020: Solar has greater techno-economic resource suitability than wind for replacing coal mining jobs. Environmental Research Letters, 15 (3), 034065. https://doi.org/10.1088/1748-9326/ab6c6d
  305. Carley, S., T.P. Evans, and D.M. Konisky, 2018: Adaptation, culture, and the energy transition in American coal country. Energy Research & Social Science, 37, 133–139. https://doi.org/10.1016/j.erss.2017.10.007
  306. Pollin, R. and B. Callaci, 2019: The economics of just transition: A framework for supporting fossil fuel-dependent workers and communities in the United States. Labor Studies Journal, 44 (2), 93–138. https://doi.org/10.1177/0160449x18787051
  307. CRS, 2022: Proposed Tax Preference for Domestic Content in Energy Infrastructure. CRS Report IN11983. Congressional Research Service. https://crsreports.congress.gov/product/pdf/in/in11983
  308. NETL, 2022: Energy Community Tax Credit Bonus. U.S. Department of Energy, National Energy Technology Laboratory. https://energycommunities.gov/energy-community-tax-credit-bonus/
  309. Jenkins, J.D., E.N. Mayfield, E.D. Larson, S.W. Pacala, and C. Greig, 2021: Mission net-zero America: The nation-building path to a prosperous, net-zero emissions economy. Joule, 5 (11), 2755–2761. https://doi.org/10.1016/j.joule.2021.10.016
  310. Carley, S. and D.M. Konisky, 2020: The justice and equity implications of the clean energy transition. Nature Energy, 5 (8), 569–577. https://doi.org/10.1038/s41560-020-0641-6
  311. Jenkins, K., D. McCauley, R. Heffron, H. Stephan, and R. Rehner, 2016: Energy justice: A conceptual review. Energy Research & Social Science, 11, 174–182. https://doi.org/10.1016/j.erss.2015.10.004
  312. Banzhaf, S., L. Ma, and C. Timmins, 2019: Environmental justice: The economics of race, place, and pollution. Journal of Economic Perspectives, 33 (1), 185–208. https://doi.org/10.1257/jep.33.1.185
  313. Fefferman, N., C.-F. Chen, G. Bonilla, H. Nelson, and C.-P. Kuo, 2021: How limitations in energy access, poverty, and socioeconomic disparities compromise health interventions for outbreaks in urban settings. iScience, 24 (12), 103389. https://doi.org/10.1016/j.isci.2021.103389
  314. Hernández, D., 2016: Understanding ‘energy insecurity’ and why it matters to health. Social Science & Medicine, 167, 1–10. https://doi.org/10.1016/j.socscimed.2016.08.029
  315. Lane, H.M., R. Morello-Frosch, J.D. Marshall, and J.S. Apte, 2022: Historical redlining is associated with present-day air pollution disparities in U.S. cities. Environmental Science & Technology Letters, 9 (4), 345–350. https://doi.org/10.1021/acs.estlett.1c01012
  316. Brown, M., A. Soni, M. Lapsa, and K. Southworth, 2020: Low-Income Energy Affordability: Conclusions from a Literature Review. ORNL/TM-2019/1150. U.S. Department of Energy, Oak Ridge National Laboratory. https://info.ornl.gov/sites/publications/files/pub124723.pdf
  317. Drehobl, A. and L. Ross, 2016: Lifting the High Energy Burden in America’s Largest Cities: How Energy Efficiency Can Improve Low Income and Underserved Communities. American Council for an Energy Efficient Economy. https://www.aceee.org/sites/default/files/publications/researchreports/u1602.pdf
  318. Drehobl, A., L. Ross, and R. Ayala, 2020: How High Are Household Energy Burdens? An Assessment of National and Metropolitan Energy Burden across the United States. American Council for an Energy-Efficient Economy, Washington, DC. https://www.aceee.org/sites/default/files/pdfs/u2006.pdf
  319. Lewis, J., D. Hernández, and A.T. Geronimus, 2020: Energy efficiency as energy justice: Addressing racial inequities through investments in people and places. Energy Efficiency, 13 (3), 419–432. https://doi.org/10.1007/s12053-019-09820-z
  320. Reames, T.G., 2016: A community-based approach to low-income residential energy efficiency participation barriers. Local Environment, 21 (12), 1449–1466. https://doi.org/10.1080/13549839.2015.1136995
  321. Ross, L., A. Drehobl, and B. Stickles, 2018: The High Cost of Energy in Rural America: Household Energy Burdens and Opportunities for Energy Efficiency. American Council for an Energy-Efficient Economy, Washington DC. https://www.aceee.org/sites/default/files/publications/researchreports/u1806.pdf
  322. Bednar, D.J. and T.G. Reames, 2020: Recognition of and response to energy poverty in the United States. Nature Energy, 5 (6), 432–439. https://doi.org/10.1038/s41560-020-0582-0
  323. Benz, S.A. and J.A. Burney, 2021: Widespread race and class disparities in surface urban heat extremes across the United States. Earth's Future, 9 (7), e2021EF002016. https://doi.org/10.1029/2021ef002016
  324. Benz, S.A., S.J. Davis, and J.A. Burney, 2021: Drivers and projections of global surface temperature anomalies at the local scale. Environmental Research Letters, 16 (6), 064093. https://doi.org/10.1088/1748-9326/ac0661
  325. McDonald, R.I., T. Biswas, C. Sachar, I. Housman, T.M. Boucher, D. Balk, D. Nowak, E. Spotswood, C.K. Stanley, and S. Leyk, 2021: The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities. PLoS ONE, 16 (4), e0249715. https://doi.org/10.1371/journal.pone.0249715
  326. Hoffman, J.S., V. Shandas, and N. Pendleton, 2020: The effects of historical housing policies on resident exposure to intra-urban heat: A study of 108 US urban areas. Climate, 8 (1), 12. https://doi.org/10.3390/cli8010012
  327. Plumer, B. and N. Popovich, 2020: How decades of racist housing policy left neighborhoods sweltering. The New York Times, August 24, 2020. https://www.nytimes.com/interactive/2020/08/24/climate/racism-redlining-cities-global-warming.html
  328. Chen, M., G.A. Ban-Weiss, and K.T. Sanders, 2020: Utilizing smart-meter data to project impacts of urban warming on residential electricity use for vulnerable populations in Southern California. Environmental Research Letters, 15 (6), 064001. https://doi.org/10.1088/1748-9326/ab6fbe
  329. Sherwin, E.D. and I.M.L. Azevedo, 2020: Characterizing the association between low-income electric subsidies and the intra-day timing of electricity consumption. Environmental Research Letters, 15 (9), 094089. https://doi.org/10.1088/1748-9326/aba030
  330. Cong, S., D. Nock, Y.L. Qiu, and B. Xing, 2022: Unveiling hidden energy poverty using the energy equity gap. Nature Communications, 13 (1), 2456. https://doi.org/10.1038/s41467-022-30146-5
  331. Scheier, E. and N. Kittner, 2022: A measurement strategy to address disparities across household energy burdens. Nature Communications, 13 (1), 288. https://doi.org/10.1038/s41467-021-27673-y
  332. Gillingham, K., R.G. Newell, and K. Palmer, 2009: Energy efficiency economics and policy. Annual Review of Resource Economics, 1 (1), 597–620. https://doi.org/10.1146/annurev.resource.102308.124234
  333. Baniassadi, A., D.J. Sailor, E.S. Krayenhoff, A.M. Broadbent, and M. Georgescu, 2019: Passive survivability of buildings under changing urban climates across eight US cities. Environmental Research Letters, 14 (7), 074028. https://doi.org/10.1088/1748-9326/ab28ba
  334. Sunter, D.A., S. Castellanos, and D.M. Kammen, 2019: Disparities in rooftop photovoltaics deployment in the United States by race and ethnicity. Nature Sustainability, 2 (1), 71–76. https://doi.org/10.1038/s41893-018-0204-z
  335. Welton, S. and J.B. Eisen, 2019: Clean energy justice: Charting an emerging agenda. Harvard Environmental Law Review, 43, 307–371. https://scholarship.law.upenn.edu/faculty_scholarship/2842
  336. Carley, S., T.P. Evans, M. Graff, and D.M. Konisky, 2018: A framework for evaluating geographic disparities in energy transition vulnerability. Nature Energy, 3 (8), 621–627. https://doi.org/10.1038/s41560-018-0142-z
  337. Jessel, S., S. Sawyer, and D. Hernández, 2019: Energy, poverty, and health in climate change: A comprehensive review of an emerging literature. Frontiers in Public Health, 7, 357. https://doi.org/10.3389/fpubh.2019.00357
  338. IEA, 2021: The Role of Critical Minerals in Clean Energy Transitions. International Energy Agency. https://www.iea.org/reports/the-role-of-critical-minerals-in-clean-energy-transitions
  339. Lee, J., M. Bazilian, and S. Hastings-Simon, 2021: The material foundations of a low-carbon economy. One Earth, 4 (3), 331–334. https://doi.org/10.1016/j.oneear.2021.02.015
  340. Lee, J., M. Bazilian, B. Sovacool, K. Hund, S.M. Jowitt, T.P. Nguyen, A. Månberger, M. Kah, S. Greene, C. Galeazzi, K. Awuah-Offei, M. Moats, J. Tilton, and S. Kukoda, 2020: Reviewing the material and metal security of low-carbon energy transitions. Renewable and Sustainable Energy Reviews, 124, 109789. https://doi.org/10.1016/j.rser.2020.109789
  341. Sovacool, B.K., S.H. Ali, M. Bazilian, B. Radley, B. Nemery, J. Okatz, and D. Mulvaney, 2020: Sustainable minerals and metals for a low-carbon future. Science, 367 (6437), 30–33. https://doi.org/10.1126/science.aaz6003
  342. Nassar, N.T. and S.M. Fortier, 2021: Methodology and Technical Input for the 2021 Review and Revision of the U.S. Critical Minerals List. USGS Open-File Report 2021–1045. U.S. Geological Survey, 31 pp. https://doi.org/10.3133/ofr20211045
  343. U.S. Geological Survey, 2022: 2022 final list of critical minerals. Federal Register, 87 (37), 10381–10382. https://www.federalregister.gov/documents/2022/02/24/2022-04027/2022-final-list-of-critical-minerals
  344. Executive Office of the President, 2017: Executive Order 13817: A federal strategy to ensure secure and reliable supplies of critical minerals. Federal Register, 82 (246), 60835–60837. https://www.federalregister.gov/documents/2017/12/26/2017-27899/a-federal-strategy-to-ensure-secure-and-reliable-supplies-of-critical-minerals
  345. Executive Office of the President, 2020: Executive Order 13953: Addressing the threat to the domestic supply chain from reliance on critical minerals from foreign adversaries and supporting the domestic mining and processing industries. Federal Register, 85 (193), 62539–62544. https://www.federalregister.gov/documents/2020/10/05/2020-22064/addressing-the-threat-to-the-domestic-supply-chain-from-reliance-on-critical-minerals-from-foreign
  346. Fortier, S.M., J.H. DeYoung Jr., E.S. Sangine, and E.K. Schnebele, 2015: Comparison of U.S. Net Import Reliance for Nonfuel Mineral Commodities—A 60-Year Retrospective (1954–1984–2014). Fact Sheet 2015–3082. U.S. Geological Survey, 4 pp. https://doi.org/10.3133/fs20153082
  347. DOE, 2022: The Inflation Reduction Act Drives Significant Emissions Reductions and Positions America to Reach our Climate Goals. DOE/OP-0018. U.S. Department of Energy, Office of Policy, 6 pp. https://www.energy.gov/sites/default/files/2022-08/8.18%20InflationReductionAct_Factsheet_Final.pdf
  348. C2ES, 2023: State Climate Policy Maps. Center for Climate and Energy Solutions. https://www.c2es.org/content/state-climate-policy/
  349. UNFCCC, 2022: Actor Tracking. United Nations Framework Convention on Climate Change. https://climateaction.unfccc.int/actors
  350. Peñasco, C., L.D. Anadón, and E. Verdolini, 2021: Systematic review of the outcomes and trade-offs of ten types of decarbonization policy instruments. Nature Climate Change, 11 (3), 257–265. https://doi.org/10.1038/s41558-020-00971-x
  351. Agriculture Resilience Act. H.R. 2803, 117th Congress, 2021. https://www.congress.gov/bill/117th-congress/house-bill/2803/text
  352. SEC, 2022: SEC proposes rules to enhance and standardize climate-related disclosures for investors. U.S. Securities and Exchange Commission, Washington, DC, March 21, 2022. https://www.sec.gov/news/press-release/2022-46
  353. Bjørn, A., S.M. Lloyd, M. Brander, and H.D. Matthews, 2022: Renewable energy certificates threaten the integrity of corporate science-based targets. Nature Climate Change, 12 (6), 539–546. https://doi.org/10.1038/s41558-022-01379-5
  354. Curtis, Q., J. Fisch, and A.Z. Robertson, 2021: Do ESG funds deliver on their promises? Michigan Law Review, 120 (3). https://doi.org/10.36644/mlr.120.3.esg
  355. GHG Protocol, 2022: Land Sector and Removals Guidance. Greenhouse Gas Protocol. https://ghgprotocol.org/land-sector-and-removals-guidance
  356. Oldfield, E.E., A.J. Eagle, R.L. Rubin, J. Rudek, J. Sanderman, and D.R. Gordon, 2022: Crediting agricultural soil carbon sequestration. Science, 375 (6586), 1222–1225. https://doi.org/10.1126/science.abl7991
  357. Amorim-Maia, A.T., I. Anguelovski, E. Chu, and J. Connolly, 2022: Intersectional climate justice: A conceptual pathway for bridging adaptation planning, transformative action, and social equity. Urban Climate, 41, 101053. https://doi.org/10.1016/j.uclim.2021.101053
  358. Simon, K., G. Diprose, and A.C. Thomas, 2020: Community-led initiatives for climate adaptation and mitigation. Kōtuitui: New Zealand Journal of Social Sciences Online, 15 (1), 93–105. https://doi.org/10.1080/1177083x.2019.1652659
  359. Troxler, T.G., A.C. Clement, Y. Arditi-Rocha, G. Beesing, M. Bhat, J. Bolson, C. Cabán-Alemán, K. Castillo, O. Collins, M. Cruz, A. Dodd, S.D. Evans, A.L. Fleming, C. Genatios, J. Gilbert, A. Hernandez, C. Holder, M. Ilcheva, E. Kelly, and E. Wheaton, 2021: A system for resilience learning: Developing a community-driven, multi-sector research approach for greater preparedness and resilience to long-term climate stressors and extreme events in the Miami metropolitan region. Journal of Extreme Events, 08 (03), 2150019. https://doi.org/10.1142/s2345737621500196
  360. Farbes, J., B. Haley, and R. Jones, 2021: Marginal Abatement Cost Curves for U.S. Net-Zero Energy Systems: A Systems Approach. Evolved Energy Research, 52 pp. https://www.evolved.energy/post/mac2-0
  361. IPCC, 2022: Summary for policymakers. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Pörtner, H.-O., D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, and B. Rama, Eds. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3–33. https://doi.org/10.1017/9781009325844.001
  362. Visit Florida, 2022: Florida Visitor Estimates [Webpage]. https://www.visitflorida.org/resources/research/
  363. Visit Orlando, 2019: Orlando announces record 75 million visitors, solidifies ranking as No. 1 U.S. travel destination. Visit Orlando, Orlando, FL, May 9, 2019. https://www.visitorlando.com/media/press-releases/post/orlando-announces-record-75-million-visitors-solidifies-ranking-as-no-1-u-s-travel-destination/
  364. FDOT, 2022: Central Florida Autonomous Vehicle Proving Ground. Florida Department of Transportation. https://www.fdot.gov/traffic/teo-divisions.shtm/cav-ml-stamp/cv/maplocations/cf-av.shtm
  365. Ponnaluri, R., F. Heery, and V.Y. Tillander, 2017: The Florida connected and automated vehicle initiative: A focus on deployment. ITE Journal, 87 (10). https://trid.trb.org/view/1484058
  366. MetroPlan Orlando, 2020: MetroPlan Orlando CAV Readiness Study: Final Report. MetroPlan Orlando. https://metroplanorlando.gov/wp-content/uploads/MetroPlan-CAV-Readiness-7.1.20-Final.pdf
  367. USDA, 2009: Let’s Glean! United We Serve Toolkit. U.S. Department of Agriculture, 8 pp. https://www.usda.gov/sites/default/files/documents/usda_gleaning_toolkit.pdf
  368. EIA, 1992: Housing Characteristics 1990. DOE/EIA-0314(90). U.S. Energy Information Administration, Washington, DC. https://www.eia.gov/consumption/residential/index.php
  369. EIA, 1997: Manufacturing Energy Consumption Survey 1994. U.S. Energy Information Administration. https://www.eia.gov/consumption/manufacturing/data/1994/index.php?view=data
  370. EIA, 2021: 2018 Manufacturing Energy Consumption Survey. U.S. Energy Information Administration. https://www.eia.gov/consumption/manufacturing/data/2018/
  371. EIA, 2022: State Energy Data System (SEDS). U.S. Energy Information Administration. https://www.eia.gov/state/seds/
  372. The World Bank, 2021: World Development Indicators. World Bank Group.
  373. The World Bank, 2022: World Development Indicators. World Bank Group.
  374. IPCC, 2019: 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Calvo Buendia, E., K. Tanabe, A. Kranjc, J. Baasansuren, M. Fukuda, S. Ngarize, A. Osako, Y. Pyrozhenko, P. Shermanau, and S. Federici, Eds. Intergovernmental Panel on Climate Change, Switzerland. https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html
  375. Matthews, H.D., N.P. Gillett, P.A. Stott, and K. Zickfeld, 2009: The proportionality of global warming to cumulative carbon emissions. Nature, 459 (7248), 829–832. https://doi.org/10.1038/nature08047
  376. Lyon, C., E.E. Saupe, C.J. Smith, D.J. Hill, A.P. Beckerman, L.C. Stringer, R. Marchant, J. McKay, A. Burke, P. O’Higgins, A.M. Dunhill, B.J. Allen, J. Riel-Salvatore, and T. Aze, 2022: Climate change research and action must look beyond 2100. Global Change Biology, 28 (2), 349–361. https://doi.org/10.1111/gcb.15871
  377. McGlynn, E., S. Li, M. F. Berger, M. Amend, and K. L. Harper, 2022: Addressing uncertainty and bias in land use, land use change, and forestry greenhouse gas inventories. Climatic Change, 170 (1), 5. https://doi.org/10.1007/s10584-021-03254-2
  378. Nisbet, E.G., R.E. Fisher, D. Lowry, J.L. France, G. Allen, S. Bakkaloglu, T.J. Broderick, M. Cain, M. Coleman, J. Fernandez, G. Forster, P.T. Griffiths, C.P. Iverach, B.F.J. Kelly, M.R. Manning, P.B.R. Nisbet-Jones, J.A. Pyle, A. Townsend-Small, A. al-Shalaan, N. Warwick, and G. Zazzeri, 2020: Methane mitigation: Methods to reduce emissions, on the path to the Paris Agreement. Reviews of Geophysics, 58 (1), e2019RG000675. https://doi.org/10.1029/2019rg000675
  379. Saunois, M., A.R. Stavert, B. Poulter, P. Bousquet, J.G. Canadell, R.B. Jackson, P.A. Raymond, E.J. Dlugokencky, S. Houweling, P.K. Patra, P. Ciais, V.K. Arora, D. Bastviken, P. Bergamaschi, D.R. Blake, G. Brailsford, L. Bruhwiler, K.M. Carlson, M. Carrol, S. Castaldi, N. Chandra, C. Crevoisier, P.M. Crill, K. Covey, C.L. Curry, G. Etiope, C. Frankenberg, N. Gedney, M.I. Hegglin, L. Höglund-Isaksson, G. Hugelius, M. Ishizawa, A. Ito, G. Janssens-Maenhout, K.M. Jensen, F. Joos, T. Kleinen, P.B. Krummel, R.L. Langenfelds, G.G. Laruelle, L. Liu, T. Machida, S. Maksyutov, K.C. McDonald, J. McNorton, P.A. Miller, J.R. Melton, I. Morino, J. Müller, F. Murguia-Flores, V. Naik, Y. Niwa, S. Noce, S. O'Doherty, R.J. Parker, C. Peng, S. Peng, G.P. Peters, C. Prigent, R. Prinn, M. Ramonet, P. Regnier, W.J. Riley, J.A. Rosentreter, A. Segers, I.J. Simpson, H. Shi, S.J. Smith, L.P. Steele, B.F. Thornton, H. Tian, Y. Tohjima, F.N. Tubiello, A. Tsuruta, N. Viovy, A. Voulgarakis, T.S. Weber, M. van Weele, G.R. van der Werf, R.F. Weiss, D. Worthy, D. Wunch, Y. Yin, Y. Yoshida, W. Zhang, Z. Zhang, Y. Zhao, B. Zheng, Q. Zhu, Q. Zhu, and Q. Zhuang, 2020: The global methane budget 2000–2017. Earth System Science Data, 12 (3), 1561–1623. https://doi.org/10.5194/essd-12-1561-2020
  380. Conrad, B.M., D.R. Tyner, and M.R. Johnson, 2023: Robust probabilities of detection and quantification uncertainty for aerial methane detection: Examples for three airborne technologies. Remote Sensing of Environment, 288, 113499. https://doi.org/10.1016/j.rse.2023.113499
  381. Liu, M.J., K.N. Izquierdo, and D.S. Prince, 2022: Intelligent monitoring of fugitive emissions—Comparison of continuous monitoring with intelligent analytics to other emissions monitoring technologies. The APPEA Journal, 62, 56–65. https://doi.org/10.1071/aj21116
  382. Norooz Oliaee, J., N.A. Sabourin, S.A. Festa-Bianchet, J.A. Gupta, M.R. Johnson, K.A. Thomson, G.J. Smallwood, and P. Lobo, 2022: Development of a sub-ppb resolution methane sensor using a GaSb-based DFB diode laser near 3270 nm for fugitive emission measurement. ACS Sensors, 7 (2), 564–572. https://doi.org/10.1021/acssensors.1c02444
  383. Hurtt, G.C., S.W. Pacala, P.R. Moorcroft, J. Caspersen, E. Shevliakova, R.A. Houghton, and B. Moore, 2002: Projecting the future of the U.S. carbon sink. Proceedings of the National Academy of Sciences of the United States of America, 99 (3), 1389–1394. https://doi.org/10.1073/pnas.012249999
  384. Wu, C., S.R. Coffield, M.L. Goulden, J.T. Randerson, A.T. Trugman, and W.R.L. Anderegg, 2023: Uncertainty in US forest carbon storage potential due to climate risks. Nature Geoscience, 16 (5), 422–429. https://doi.org/10.1038/s41561-023-01166-7
  385. Kang, J.-N., Y.-M. Wei, L.-C. Liu, R. Han, B.-Y. Yu, and J.-W. Wang, 2020: Energy systems for climate change mitigation: A systematic review. Applied Energy, 263, 114602. https://doi.org/10.1016/j.apenergy.2020.114602
  386. Bataille, C., H. Waisman, M. Colombier, L. Segafredo, and J. Williams, 2016: The deep decarbonization pathways project (DDPP): Insights and emerging issues. Climate Policy, 16 (sup1), S1–S6. https://doi.org/10.1080/14693062.2016.1179620
  387. Bistline, J.E., E. Hodson, C.G. Rossmann, J. Creason, B. Murray, and A.R. Barron, 2018: Electric sector policy, technological change, and U.S. emissions reductions goals: Results from the EMF 32 model intercomparison project. Energy Economics, 73, 307–325. https://doi.org/10.1016/j.eneco.2018.04.012
  388. Arent, D.J., P. Green, Z. Abdullah, T. Barnes, S. Bauer, A. Bernstein, D. Berry, J. Berry, T. Burrell, B. Carpenter, J. Cochran, R. Cortright, M. Curry-Nkansah, P. Denholm, V. Gevorian, M. Himmel, B. Livingood, M. Keyser, J. King, B. Kroposki, T. Mai, M. Mehos, M. Muratori, S. Narumanchi, B. Pivovar, P. Romero-Lankao, M. Ruth, G. Stark, and C. Turchi, 2022: Challenges and opportunities in decarbonizing the U.S. energy system. Renewable and Sustainable Energy Reviews, 169, 112939. https://doi.org/10.1016/j.rser.2022.112939
  389. Costa Jr., C., E. Wollenberg, M. Benitez, R. Newman, N. Gardner, and F. Bellone, 2022: Roadmap for achieving net-zero emissions in global food systems by 2050. Scientific Reports, 12 (1), 15064. https://doi.org/10.1038/s41598-022-18601-1
  390. Robertson, G.P., S.K. Hamilton, K. Paustian, and P. Smith, 2022: Land-based climate solutions for the United States. Global Change Biology, 28 (16), 4912–4919. https://doi.org/10.1111/gcb.16267
  391. Roe, S., C. Streck, M. Obersteiner, S. Frank, B. Griscom, L. Drouet, O. Fricko, M. Gusti, N. Harris, T. Hasegawa, Z. Hausfather, P. Havlík, J. House, G.-J. Nabuurs, A. Popp, M.J.S. Sánchez, J. Sanderman, P. Smith, E. Stehfest, and D. Lawrence, 2019: Contribution of the land sector to a 1.5 °C world. Nature Climate Change, 9 (11), 817–828. https://doi.org/10.1038/s41558-019-0591-9
  392. Denholm, P., P. Brown, W. Cole, T. Mai, and B. Sergi, 2022: Examining Supply-Side Options to Achieve 100% Clean Electricity by 2035. NREL/TP6A40-81644. U.S. Department of Energy, National Renewable Energy Laboratory, Golden, CO. https://doi.org/10.2172/1885591
  393. Rosenow, J. and N. Eyre, 2022: Reinventing energy efficiency for net zero. Energy Research & Social Science, 90, 102602. https://doi.org/10.1016/j.erss.2022.102602
  394. IEA, 2021: Net Zero by 2050: A Roadmap for the Global Energy Sector. International Energy Agency, Paris, France. https://www.iea.org/reports/net-zero-by-2050
  395. Clark, M. and D. Tilman, 2017: Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environmental Research Letters, 12 (6), 064016. https://doi.org/10.1088/1748-9326/aa6cd5
  396. Stehfest, E., L. Bouwman, D.P. van Vuuren, M.G.J. den Elzen, B. Eickhout, and P. Kabat, 2009: Climate benefits of changing diet. Climatic Change, 95 (1), 83–102. https://doi.org/10.1007/s10584-008-9534-6
  397. Blair, N., C. Augustine, W. Cole, P. Denholm, W. Frazier, M. Geocaris, J. Jorgenson, K. McCabe, K. Podkaminer, A. Prasanna, and B. Sigrin, 2022: Storage Futures Study: Key Learnings for the Coming Decades. NREL/TP-7A40-81779. U.S. Department of Energy, National Renewable Energy Laboratory, Golden, CO. https://www.nrel.gov/docs/fy22osti/81779.pdf
  398. Comello, S. and S. Reichelstein, 2019: The emergence of cost effective battery storage. Nature Communications, 10 (1), 2038. https://doi.org/10.1038/s41467-019-09988-z
  399. Bistline, J., S. Bragg-Sitton, W. Cole, B. Dixon, E. Eschmann, J. Ho, A. Kwon, L. Martin, C. Murphy, C. Namovicz, and A. Sowder, 2023: Modeling nuclear energy’s future role in decarbonized energy systems. iScience, 26 (2), 105952. https://doi.org/10.1016/j.isci.2023.105952
  400. Duan, L., R. Petroski, L. Wood, and K. Caldeira, 2022: Stylized least-cost analysis of flexible nuclear power in deeply decarbonized electricity systems considering wind and solar resources worldwide. Nature Energy, 7 (3), 260–269. https://doi.org/10.1038/s41560-022-00979-x
  401. Carton, W., A. Asiyanbi, S. Beck, H.J. Buck, and J.F. Lund, 2020: Negative emissions and the long history of carbon removal. WIREs Climate Change, 11 (6), e671. https://doi.org/10.1002/wcc.671
  402. Fuss, S., W.F. Lamb, M.W. Callaghan, J. Hilaire, F. Creutzig, T. Amann, T. Beringer, W. de Oliveira Garcia, J. Hartmann, T. Khanna, G. Luderer, G.F. Nemet, J. Rogelj, P. Smith, J.L.V. Vicente, J. Wilcox, M. del Mar Zamora Dominguez, and J.C. Minx, 2018: Negative emissions—Part 2: Costs, potentials and side effects. Environmental Research Letters, 13 (6), 063002. https://doi.org/10.1088/1748-9326/aabf9f
  403. Jägermeyr, J., C. Müller, A.C. Ruane, J. Elliott, J. Balkovic, O. Castillo, B. Faye, I. Foster, C. Folberth, J.A. Franke, K. Fuchs, J.R. Guarin, J. Heinke, G. Hoogenboom, T. Iizumi, A.K. Jain, D. Kelly, N. Khabarov, S. Lange, T.-S. Lin, W. Liu, O. Mialyk, S. Minoli, E.J. Moyer, M. Okada, M. Phillips, C. Porter, S.S. Rabin, C. Scheer, J.M. Schneider, J.F. Schyns, R. Skalsky, A. Smerald, T. Stella, H. Stephens, H. Webber, F. Zabel, and C. Rosenzweig, 2021: Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nature Food, 2 (11), 873–885. https://doi.org/10.1038/s43016-021-00400-y
  404. Stehfest, E., W.-J. van Zeist, H. Valin, P. Havlik, A. Popp, P. Kyle, A. Tabeau, D. Mason-D’Croz, T. Hasegawa, B.L. Bodirsky, K. Calvin, J.C. Doelman, S. Fujimori, F. Humpenöder, H. Lotze-Campen, H. van Meijl, and K. Wiebe, 2019: Key determinants of global land-use projections. Nature Communications, 10 (1), 2166. https://doi.org/10.1038/s41467-019-09945-w
  405. Strefler, J., E. Kriegler, N. Bauer, G. Luderer, R.C. Pietzcker, A. Giannousakis, and O. Edenhofer, 2021: Alternative carbon price trajectories can avoid excessive carbon removal. Nature Communications, 12 (1), 2264. https://doi.org/10.1038/s41467-021-22211-2
  406. Workman, M., K. Dooley, G. Lomax, J. Maltby, and G. Darch, 2020: Decision making in contexts of deep uncertainty—An alternative approach for long-term climate policy. Environmental Science & Policy, 103, 77–84. https://doi.org/10.1016/j.envsci.2019.10.002
  407. Peng, W., G. Iyer, V. Bosetti, V. Chaturvedi, J. Edmonds, A.A. Fawcett, S. Hallegatte, D.G. Victor, D. van Vuuren, and J. Weyant, 2021: Climate policy models need to get real about people—Here’s how. Nature, 594 (7862), 174–176. https://doi.org/10.1038/d41586-021-01500-2
  408. Brown, M.A., P. Dwivedi, S. Mani, D. Matisoff, J.E. Mohan, J. Mullen, M. Oxman, M. Rodgers, R. Simmons, B. Beasley, and L. Polepeddi, 2021: A framework for localizing global climate solutions and their carbon reduction potential. Proceedings of the National Academy of Sciences of the United States of America, 118 (31), e2100008118. https://doi.org/10.1073/pnas.2100008118
  409. Daley, D.M., T.D. Abel, M. Stephan, S. Rai, and E. Rogers, 2023: Can polycentric governance lower industrial greenhouse gas emissions: Evidence from the United States. Environmental Policy and Governance. https://doi.org/10.1002/eet.2051
  410. Hultman, N.E., Clarke, L., , C. Frisch, K. Kennedy, H. McJeon, T. Cyrs, P. Hansel, P. Bodnar, M. Manion, M.R. Edwards, R. Cui, C. Bowman, J. Lund, M.I. Westphal, A. Clapper, J. Jaeger, A. Sen, J. Lou, D. Saha, W. Jaglom, K. Calhoun, K. Igusky, J. deWeese, K. Hammoud, J.C. Altimirano, M. Dennis, C. Henderson, G. Zwicker, and J. O'Neill, 2020: Fusing subnational with national climate action is central to decarbonization: The case of the United States. Nature Communications, 11 (1). https://doi.org/10.1038/s41467-020-18903-w
  411. Peng, W., G. Iyer, M. Binsted, J. Marlon, L. Clarke, J.A. Edmonds, and D.G. Victor, 2021: The surprisingly inexpensive cost of state-driven emission control strategies. Nature Climate Change, 11 (9), 738–745. https://doi.org/10.1038/s41558-021-01128-0
  412. Wakiyama, T. and E. Zusman, 2021: The impact of electricity market reform and subnational climate policy on carbon dioxide emissions across the United States: A path analysis. Renewable and Sustainable Energy Reviews, 149, 111337. https://doi.org/10.1016/j.rser.2021.111337
  413. CDP, 2023: States and Regions Climate Tracker. CDP Worldwide. https://www.cdp.net/en/research/global-reports/states-and-regions-climate-action-tracker
  414. NC Clean Energy Technology Center, 2022: Database of State Incentives for Renewables & Efficiency. NC State University, NC Clean Energy Technology Center, accessed August 19, 2022. https://www.dsireusa.org/
  415. UNFCCC, 2023: Global Climate Action. United Nations Framework Convention on Climate Change. https://climateaction.unfccc.int/
  416. ETC, 2018: Mission Possible: Reaching Net-Zero Carbon Emissions from Harder-to-Abate Sectors. Energy Transitions Commission. https://www.energy-transitions.org/publications/mission-possible/
  417. Grubert, E. and S. Hastings-Simon, 2022: Designing the mid-transition: A review of medium-term challenges for coordinated decarbonization in the United States. WIREs Climate Change, 13 (3), e768. https://doi.org/10.1002/wcc.768
  418. Baker, E., A.P. Goldstein, and I.M.L. Azevedo, 2021: A perspective on equity implications of net zero energy systems. Energy and Climate Change, 2, 100047. https://doi.org/10.1016/j.egycc.2021.100047
  419. Johnson, J.X. and J. Novacheck, 2015: Emissions reductions from expanding state-level renewable portfolio standards. Environmental Science & Technology, 49 (9), 5318–5325. https://doi.org/10.1021/es506123e
  420. Wiser, R., T. Mai, D. Millstein, G. Barbose, L. Bird, J. Heeter, D. Keyser, V. Krishnan, and J. Macknick, 2017: Assessing the costs and benefits of US renewable portfolio standards. Environmental Research Letters, 12 (9), 094023. https://doi.org/10.1088/1748-9326/aa87bd
  421. Young, D. and J. Bistline, 2018: The costs and value of renewable portfolio standards in meeting decarbonization goals. Energy Economics, 73, 337–351. https://doi.org/10.1016/j.eneco.2018.04.017

Previous Chapter
View All Figures
Next Chapter

Likelihood

Virtually Certain Very Likely Likely As Likely as Not Unlikely Very Unikely Exceptionally Unlikely
99%–100% 90%–100% 66%–100% 33%–66% 0%–33% 0%–10% 0%–1%

Confidence Level

Very High High Medium Low
  • Strong evidence (established theory, multiple sources, well-documented and accepted methods, etc.)
  • High consensus
  • Moderate evidence (several sources, some consistency, methods vary and/or documentation limited, etc.)
  • Medium consensus
  • Suggestive evidence (a few sources, limited consistency, methods emerging, etc.)
  • Competing schools of thought
  • Inconclusive evidence (limited sources, extrapolations, inconsistent findings, poor documentation and/or methods not tested, etc.)
  • Disagreement or lack of opinions among experts

GlobalChange.gov is made possible by our participating agencies

Department of Agriculture Department of Commerce Department of Defense Department of Energy Department of Health and Human Services Department of Homeland Security Department of Interior Department of State Department of Transportation Environmental Protection Agency NASA National Science Foundation Smithsonian Institute Agency for International Development
  • About USGCRP
  • FOIA requests
  • No FEAR Act
  • Accessibility
  • Privacy Policy
  • Copyright
  • Contact Us
  • Site Map
Looking for U.S. government information and services?
Visit USA.gov