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Transportation emissions scenarios for New York City under different carbon intensities of electricity and electric vehicle adoption rates


Like many cities around the world, New York City is establishing policies to reduce CO2 emissions from all energy sectors by 2050. Understanding the impact of varying degrees of electric vehicle adoption and CO2 intensities on emissions reduction in the city is critical. Here, using a technology-rich, bottom-up, energy system optimization model, we analyse the cost and air emissions impacts of New York City’s proposed CO2 reduction policies for the transportation sector through a scenario framework. Our analysis reveals that the electrification of light-duty vehicles at earlier periods is essential for deeper reductions in air emissions. When further combined with energy efficiency improvements, these actions contribute to CO2 reductions under the scenarios of more CO2-intense electricity. Substantial reliance on fossil fuels and a need for structural change pose challenges to cost-effective CO2 reductions in the transportation sector. Here we find that uncertainties associated with decarbonization of the electric grid have a minimum influence on the cost-effectiveness of CO2 reduction pathways for the transportation sector.

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Fig. 1: COMET-NYC structure.
Fig. 2: Fuel consumption in petajoules in the transportation sector.
Fig. 3: Fuel consumption trends in petajoules per mode of transportation.
Fig. 4: Transportation CO2 emissions changes relative to STEADY-STATE.
Fig. 5: Transportation NOx emissions changes relative to STEADY-STATE.
Fig. 6: Unit CO2 emissions rate for light-duty vehicle types across scenarios.
Fig. 7: Transportation NOx emissions changes relative to STEADY-STATE for sensitivity scenarios.

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Data availability

The data that support the plots in the manuscript are available at US EPA’s ScienceHub Data Repository ( Source data are provided with this paper.


  1. Annual Energy Outlook 2016 with Projections to 2040 (Energy Information Administration, 2016);

  2. Annual Energy Outlook 2018 with Projections to 2050 (Energy Information Administration, 2018);

  3. New York City Mobility Report (New York City Department of Transportation, 2018);

  4. NYC Green Dividend (City of New York, 2010);

  5. Miller, S. 80x50 Policy White Paper Transportation (New York League of Conservation Voters, 2018);

  6. The MTA Network: Public Transportation for the New York Region (Metropolitan Transportation Authority, 2018);

  7. Moss, M. L., Sam, S. M. K., Levy, A. S. & Hernandez, J. Subway Ridership 1975–2015 (NYU Rudin Center for Transportation, 2017);

  8. Nonattainment Areas for Criteria Pollutants (Green Book) (US Environmental Protection Agency, 2019);

  9. Why New York State Needs a Clean Transportation System (Union of Concerned Scientists, 2018);

  10. McNeill, V. F. Addressing the global air pollution crisis: chemistry’s role. Trends Chem. 1, 5–8 (2019).

    Article  Google Scholar 

  11. Cohen, A. J. et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the global burden of diseases study 2015. Lancet 389, 1907–1918 (2017).

    Article  Google Scholar 

  12. Inventory of New York City Greenhouse Gas Emissions in 2014 (City of New York, 2016);

  13. Inventory of New York City Greenhouse Gas Emissions in 2015 (City of New York, 2017);

  14. New York City’s Roadmap to 80x50 (New York City, 2014);’s%20Roadmap%20to%2080%20x%2050.pdf

  15. Yeh, S. et al. Detailed assessment of global transport-energy models’ structures and projections. Transp. Res. D Transp. Environ. 55, 294–309 (2017).

    Article  Google Scholar 

  16. Edelenbosch, O. Y. et al. Transport fuel demand responses to fuel price and income projections: comparison of integrated assessment models. Transp. Res. D Transp. Environ. 55, 310–321 (2017).

    Article  Google Scholar 

  17. Creutzig, F. et al. Transport: a roadblock to climate change mitigation? Urban mobility solutions foster climate mitigation. Science 350, 911–913 (2015).

    Article  Google Scholar 

  18. Sallis, J. F. et al. Use of science to guide city planning policy and practice: how to achieve healthy and sustainable future cities. Lancet 388, 2936–2947 (2016).

    Article  Google Scholar 

  19. Kaplan, P. O. & Isik, M. City-based Optimization Model for Energy Technologies: COMET - New York City Documentation (EPA 600/R-19/124) (US Environmental Protection Agency, 2020);

  20. PlaNYC: New York City’s Pathways to Deep Carbon Reductions (City of New York, 2013);

  21. Aligning NYC with the Paris Agreement (City of New York, 2017);

  22. Clean Energy Standard (New York State Energy Research and Development Authority, 2016);

  23. Annual Energy Outlook 2020 with Projections to 2050 (Energy Information Administration, 2020);

  24. Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards Final Rule RIN 2060–AP58 Washington, DC: 2010-05-07 (US Environmental Protection Agency, 2010);

  25. 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards RIN 2060–AQ54; RIN 2127–AK79 Washington, DC: 2012-10-15 (US Environmental Protection Agency, 2012);

  26. Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Final Rule RIN 2127–AK50 Washington, DC: 2010-05-07 (US Environmental Protection Agency, 2010);

  27. Light-Duty Vehicles and Light-Duty Trucks: Clean Fuel Fleet Exhaust Emission Standards EPA-420-B-16-006 (US Environmental Protection Agency, 2016);

  28. Control of Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle Emission and Fuel Standards RIN 2050-AQ86. Washington, DC: FR 2014-04-28 (US Environmental Protection Agency, 2014);

  29. Kaplan, P. O. & Witt, J. W. What is the role of distributed energy resources under scenarios of greenhouse gas reductions? A specific focus on combined heat and power systems in the industrial and commercial sectors. Appl. Energy 235, 83–94 (2018).

    Article  Google Scholar 

  30. Loulou, R., Goldstein, G. & Noble, K. Documentation for the MARKAL Family of Models (International Energy Agency, 2004);

  31. Leighty, W., Ogden, J. M. & Yang, C. Modelling transitions in the California light-duty vehicles sector to achieve deep reductions in transportation greenhouse gas emissions. Energy Policy 44, 52–67 (2012).

    Article  Google Scholar 

  32. McCollum, D., Yang, C., Yeh, S. & Ogden, J. Deep greenhouse gas reduction scenarios for California – strategic implications from the CA-TIMES energy-economic systems model. Energy Strategy Rev. 1, 19–32 (2012).

    Article  Google Scholar 

  33. Ghanadan, R. & Koomey, J. G. Using energy scenarios to explore alternative energy pathways in California. Energy Policy 33, 1117–1142 (2005).

    Article  Google Scholar 

  34. Lenox, C. & Kaplan, P. O. Role of natural gas in meeting an electric sector emissions reduction strategy and effects on greenhouse gas emissions. Energy Econ. 60, 460–468 (2016).

    Article  Google Scholar 

  35. Brown, K. E. et al. Evolution of the United States energy system and related emissions under varying social and technological development paradigms: plausible scenarios for use in robust decision making. Environ. Sci. Technol. 52, 8027–8038 (2018).

    Article  Google Scholar 

  36. Kaplan, P. O. & Kaldunski, B. An integrated approach to water & energy infrastructure decision making using the MARKAL framework: a case study of New York City. In Proc. ACEEE Summer Study on Energy Efficiency in Buildings at Pacific Grove, CA (ACEEE, 2016).

  37. EIA-860 Detailed Data (Energy Information Administration, 2018);

  38. Hughes-Cromwick, M. (ed.) Public Transportation Fact Book (American Public Transportation Association, 2018);

  39. Lenox, C. et al. EPA U.S. Nine-Region MARKAL Database Documentation EPA/600/B-13/2013 (US Environmental Protection Agency, 2013).

  40. Exhaust Emission Rates for Heavy-Duty On-Road Vehicles in MOVES2014 (US Environmental Protection Agency, 2014);

  41. Exhaust Emission Rates for Light-Duty On-Road Vehicles in MOVES2014 Final Report (US Environmental Protection Agency, 2014);

  42. National Emissions Inventory (US Environmental Protection Agency, 2014);

  43. PLUTO and MapPLUTO (City of New York, 2015);

  44. NYC Benchmarking Law Data (City of New York, 2018);

  45. The Growth of App-Based Ride Services and Traffic, Travel and the Future of New York City (Schaller Consulting, 2017);

  46. Erdhardt, G. D. et al. Do transportation network companies decrease or increase congestion? Sci. Adv. (2019).

  47. The New Automobility: Lyft, Uber and the Future of American Cities (Schaller Consulting, 2018);

  48. Mayor de Blasio Puts into Effect For-Hire Vehicle Cruising Cap and Extends License Cap (City of New York, 2019);

  49. Average Vehicle Occupancy Factors for Computing Travel Time Reliability Measures and Total Peak Hour Excessive Delay Metrics (US Department of Transportation, 2018);

  50. Facts and Usage Statistics about Public Transit in New York City (Moovit, 2020);

  51. OneNYC 2050: Building a Strong and Fair City – Efficient Mobility (City of New York, 2013);

  52. NY Solar Map (City University of New York, 2019);

  53. Energy Efficiency and Renewable Energy Potential Study of New York State (New York State Energy Research and Development Authority, 2019); https//

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This research was supported in part by an appointment to the Research Participation Program for the US EPA, Office of Research and Development, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and EPA. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the US EPA.

Author information

Authors and Affiliations



P.O.K. designed the research and led the modelling and analysis of results. M.I. and R.D. contributed to the study design. M.I. built the COMET model, processed the data, conducted the runs and performed scenario analysis. M.I., R.D. and P.O.K wrote the manuscript and analysed the results together. P.O.K. led the revisions to the manuscript with contributions from M.I. and R.D.

Corresponding author

Correspondence to P. Ozge Kaplan.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Energy thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–15, Figs. 1–21, Notes 1–10 and references.

Source data

Source Data Fig. 2

Model output data for fuel consumption in the transportation sector in petajoules for the STEADY-STATE, DEPENDENCE and REVOLUTON scenarios.

Source Data Fig. 3

Model output data for fuel consumption per mode of transportation in the STEADY-STATE, DEPENDENCE and REVOLUTON scenarios.

Source Data Fig. 4

Model output data for transportation CO2 emissions in MtCO2 in STEADY-STATE, and emissions changes in DEPENDENCE and REVOLUTON relative to STEADY-STATE.

Source Data Fig. 5

Model output data for transportation sector NOx emissions in kt in STEADY-STATE, and emissions changes in DEPENDENCE and REVOLUTON relative to STEADY-STATE.

Source Data Fig. 6

Model output data for unit CO2 emissions rate for light-duty vehicle types.

Source Data Fig. 7

Model output data for transportation NOx emissions changes in the BATTERY, TNC and MODESWITCH variations of the DEPENDENCE and REVOLUTION scenarios relative to STEADY-STATE in kt.

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Isik, M., Dodder, R. & Kaplan, P.O. Transportation emissions scenarios for New York City under different carbon intensities of electricity and electric vehicle adoption rates. Nat Energy 6, 92–104 (2021).

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