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

Abstract

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.

Data availability

The data that support the plots in the manuscript are available at US EPA’s ScienceHub Data Repository (https://catalog.data.gov/dataset). Source data are provided with this paper.

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Acknowledgements

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

Authors

Contributions

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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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.

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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). https://doi.org/10.1038/s41560-020-00740-2

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