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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 05 November 2022
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Annual Energy Outlook 2016 with Projections to 2040 (Energy Information Administration, 2016); https://www.eia.gov/outlooks/aeo/pdf/0383(2016).pdf
Annual Energy Outlook 2018 with Projections to 2050 (Energy Information Administration, 2018); https://www.eia.gov/outlooks/aeo/pdf/AEO2018.pdf
New York City Mobility Report (New York City Department of Transportation, 2018); http://www.nyc.gov/html/dot/downloads/pdf/mobility-report-2018-print.pdf
NYC Green Dividend (City of New York, 2010); http://www.nyc.gov/html/dot/downloads/pdf/nyc_greendividend_april2010.pdf
Miller, S. 80x50 Policy White Paper Transportation (New York League of Conservation Voters, 2018); https://nylcvef.org/wp-content/uploads/2017/08/80X50PolicyWhitePaper_Transportation_061417_edits.pdf
The MTA Network: Public Transportation for the New York Region (Metropolitan Transportation Authority, 2018); http://web.mta.info/mta/network.htm
Moss, M. L., Sam, S. M. K., Levy, A. S. & Hernandez, J. Subway Ridership 1975–2015 (NYU Rudin Center for Transportation, 2017); https://wagner.nyu.edu/files/faculty/publications/State%20of%20Subway%20Ridership%20-%20Mar717.pdf
Nonattainment Areas for Criteria Pollutants (Green Book) (US Environmental Protection Agency, 2019); https://www.epa.gov/green-book
Why New York State Needs a Clean Transportation System (Union of Concerned Scientists, 2018); https://www.ucsusa.org/resources/why-new-york-state-needs-clean-transportation-system
McNeill, V. F. Addressing the global air pollution crisis: chemistry’s role. Trends Chem. 1, 5–8 (2019).
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).
Inventory of New York City Greenhouse Gas Emissions in 2014 (City of New York, 2016); https://www1.nyc.gov/assets/sustainability/downloads/pdf/publications/NYC_GHG_Inventory_2014.pdf
Inventory of New York City Greenhouse Gas Emissions in 2015 (City of New York, 2017); https://www1.nyc.gov/assets/dcas/downloads/pdf/energy/reportsandpublication/NYC_GHG_Inventory_2015_FINAL.pdf
New York City’s Roadmap to 80x50 (New York City, 2014); https://www1.nyc.gov/assets/sustainability/downloads/pdf/publications/New%20York%20City’s%20Roadmap%20to%2080%20x%2050.pdf
Yeh, S. et al. Detailed assessment of global transport-energy models’ structures and projections. Transp. Res. D Transp. Environ. 55, 294–309 (2017).
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).
Creutzig, F. et al. Transport: a roadblock to climate change mitigation? Urban mobility solutions foster climate mitigation. Science 350, 911–913 (2015).
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).
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); https://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=539956&Lab=CEMM
PlaNYC: New York City’s Pathways to Deep Carbon Reductions (City of New York, 2013); http://s-media.nyc.gov/agencies/planyc2030/pdf/nyc_pathways.pdf
Aligning NYC with the Paris Agreement (City of New York, 2017); http://www1.nyc.gov/assets/sustainability/downloads/pdf/publications/1point5-AligningNYCwithParisAgrmtFORWEB.pdf
Clean Energy Standard (New York State Energy Research and Development Authority, 2016); https://www.nyserda.ny.gov/All-Programs/Programs/Clean-Energy-Standard
Annual Energy Outlook 2020 with Projections to 2050 (Energy Information Administration, 2020); https://www.eia.gov/outlooks/aeo/section_appendices.php
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); https://www.govinfo.gov/content/pkg/FR-2010-05-07/pdf/2010-8159.pdf
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); https://www.govinfo.gov/content/pkg/FR-2012-10-15/pdf/2012-21972.pdf
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); https://www.govinfo.gov/content/pkg/FR-2010-05-07/pdf/2010-8159.pdf
Light-Duty Vehicles and Light-Duty Trucks: Clean Fuel Fleet Exhaust Emission Standards EPA-420-B-16-006 (US Environmental Protection Agency, 2016); https://www.epa.gov/greenvehicles/light-duty-vehicle-emissions#standards
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); https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-pollution-motor-vehicles-tier-3
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).
Loulou, R., Goldstein, G. & Noble, K. Documentation for the MARKAL Family of Models (International Energy Agency, 2004); http://iea-etsap.org/MrklDoc-I_StdMARKAL.pdf
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).
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).
Ghanadan, R. & Koomey, J. G. Using energy scenarios to explore alternative energy pathways in California. Energy Policy 33, 1117–1142 (2005).
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).
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).
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).
EIA-860 Detailed Data (Energy Information Administration, 2018); https://www.eia.gov/electricity/data/eia860/
Hughes-Cromwick, M. (ed.) Public Transportation Fact Book (American Public Transportation Association, 2018); https://www.apta.com/wp-content/uploads/Resources/resources/statistics/Documents/FactBook/2018-APTA-Fact-Book.pdf
Lenox, C. et al. EPA U.S. Nine-Region MARKAL Database Documentation EPA/600/B-13/2013 (US Environmental Protection Agency, 2013).
Exhaust Emission Rates for Heavy-Duty On-Road Vehicles in MOVES2014 (US Environmental Protection Agency, 2014); https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100NO46.pdf
Exhaust Emission Rates for Light-Duty On-Road Vehicles in MOVES2014 Final Report (US Environmental Protection Agency, 2014); https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100NNVN.pdf
National Emissions Inventory (US Environmental Protection Agency, 2014); https://www.epa.gov/air-emissions-inventories/national-emissions-inventory-nei
PLUTO and MapPLUTO (City of New York, 2015); http://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page
NYC Benchmarking Law Data (City of New York, 2018); https://www1.nyc.gov/html/gbee/html/plan/ll84.shtml
The Growth of App-Based Ride Services and Traffic, Travel and the Future of New York City (Schaller Consulting, 2017); http://www.schallerconsult.com/rideservices/automobility.pdf
Erdhardt, G. D. et al. Do transportation network companies decrease or increase congestion? Sci. Adv. https://advances.sciencemag.org/content/advances/5/5/eaau2670.full.pdf (2019).
The New Automobility: Lyft, Uber and the Future of American Cities (Schaller Consulting, 2018); http://www.schallerconsult.com/rideservices/automobility.pdf
Mayor de Blasio Puts into Effect For-Hire Vehicle Cruising Cap and Extends License Cap (City of New York, 2019); https://www1.nyc.gov/office-of-the-mayor/news/384-19/mayor-de-blasio-puts-effect-for-hire-vehicle-cruising-cap-extends-license-cap
Average Vehicle Occupancy Factors for Computing Travel Time Reliability Measures and Total Peak Hour Excessive Delay Metrics (US Department of Transportation, 2018); https://www.fhwa.dot.gov/tpm/guidance/avo_factors.pdf
Facts and Usage Statistics about Public Transit in New York City (Moovit, 2020); https://moovitapp.com/insights/en/Moovit_Insights_Public_Transit_Index_USA_NYCNJ-121
OneNYC 2050: Building a Strong and Fair City – Efficient Mobility (City of New York, 2013); https://onenyc.cityofnewyork.us/wp-content/uploads/2019/04/H_OneNYC_2050_Interior_r5_v2_EfficientMobility_190422_web.pdf
NY Solar Map (City University of New York, 2019); https://nysolarmap.com/
Energy Efficiency and Renewable Energy Potential Study of New York State (New York State Energy Research and Development Authority, 2019); https//www.nyserda.ny.gov/-/media/Files/EDPPP/Energy-Prices/Energy-Statistics/14-19-EE-RE-Potential-Study-Vol1.pdf
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.
The authors declare no competing interests.
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.
Model output data for fuel consumption in the transportation sector in petajoules for the STEADY-STATE, DEPENDENCE and REVOLUTON scenarios.
Model output data for fuel consumption per mode of transportation in the STEADY-STATE, DEPENDENCE and REVOLUTON scenarios.
Model output data for transportation CO2 emissions in MtCO2 in STEADY-STATE, and emissions changes in DEPENDENCE and REVOLUTON relative to STEADY-STATE.
Model output data for transportation sector NOx emissions in kt in STEADY-STATE, and emissions changes in DEPENDENCE and REVOLUTON relative to STEADY-STATE.
Model output data for unit CO2 emissions rate for light-duty vehicle types.
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.
About this article
Cite this article
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). https://doi.org/10.1038/s41560-020-00740-2
This article is cited by
Nature Energy (2022)
Nature Communications (2022)
International Journal of Environmental Science and Technology (2022)
The impact of high-tech product export trade on regional carbon performance in China: the mediating roles of industrial structure supererogation, low-carbon technological innovation, and human capital accumulation
Environmental Science and Pollution Research (2022)
How does transportation infrastructure affect urban carbon emissions? an empirical study based on 286 cities in China
Environmental Science and Pollution Research (2022)