The GCAM-USA model was used to evaluate the incremental national and regional emission impacts of widespread electric vehicle adoption in the US through 2050. This dataset includes the model outputs that were used to develop figures and tables for the related manuscript. This dataset is associated with the following publication: Ou, Y., N. Kittner, S. Babaee, S.J. Smith, C. Nolte, and D. Loughlin. Evaluating long-term emission impacts of large-scale electric vehicle deployment in the US using a human-Earth systems model. Applied Energy. Elsevier B.V., Amsterdam, NETHERLANDS, 300: 117364, (2021).

This dataset provides the values used to develop the figures within the manuscript "Incorporating upstream emissions into electric sector nitrogen oxide reduction targets". Here is the abstract from that manuscript: Electricity production is a major source of air pollutants in the U.S. Policies to reduce these emissions can result in the power industry choosing to apply controls or switch to fuels with lower combustion emissions. However, the life cycle emissions associated with various fuels can differ considerably, potentially impacting the effectiveness of fuel switching. Life cycle emissions, which include emissions from extracting, processing, transporting, and distributing fuels, as well as manufacturing and constructing new generating capacity, have received less consideration in policy-making. Life cycle analysis allows quantification of these emissions such that they can be considered in decision-making. We examine a hypothetical electric sector emission reduction target for nitrogen oxides using the Global Change Assessment Model with U.S. state-level resolution. When only power plant emissions are considered in setting an emission reduction target, fuel switching leads to an increase in upstream emissions that offsets a portion of the targeted reductions. When fuel extraction, processing, and transport emissions are included under the reduction target, the resulting control strategy meets the required reductions and does so at lower cost. However, manufacturing and construction emissions increase, indicating that it may be beneficial to consider these sources as well. In the real world, life cycle-based approaches could be implemented by allowing industry to earn reduction credits by reducing upstream emissions. We discuss some of the limitations of such an approach, including the difficulty in identifying the location of upstream emissions, which may occur across regulatory authorities or even outside of the U.S. This dataset is associated with the following publication: Babaee, S., D. Loughlin, and O. Kaplan. Incorporating upstream emissions into electric sector nitrogen oxide reduction targets. Cleaner Engineering and Technology. Elsevier B.V., Amsterdam, NETHERLANDS, 1: 100017, (2020).

This dataset contains 2010 emissions of nitrogen oxides, sulfur dioxide, and fine particulate matter by sector and state as modeled using the GCAM-USA integrated assessment model, in comparison to the 2011 National Emissions Inventory (NEI). In addition, the dataset includes 2025 projections from both GCAM-USA and the NEI. The dataset includes data underlying the figures and tables in the following journal article: Wenjing Shi et al. (2017), Projecting state-level air pollutant emissions using an integrated assessment model: GCAM-USA. Applied Energy, in review. This dataset is associated with the following publication: Shi, W., Y. Ou, S. Smith, C. Ledna, C. Nolte, and D. Loughlin. Projecting state-level air pollutant emissions using an integrated assessment model: GCAM-USA.. Applied Energy. Elsevier B.V., Amsterdam, NETHERLANDS, 208: 511-521, (2017).

This file describes the dataset used in Ou et al., "Air pollution control strategies directly limiting national health damages in the US." This work used the Global Change Assessment Model (GCAM) with state-level representation of the U.S. energy system (GCAM-USA). GCAM and GCAM-USA are developed and released by the University of Maryland/Pacific Northwest National Laboratory Joint Global Change Research Center (JGCRI). For further details, see the GCAM documentation: jgcri.github.io/gcam-doc. The model source code is available at github.com/JGCRI/gcam-core. A modified version of GCAMv4.3 was used for this analysis. Source code and input data specific for this paper are available upon request. This dataset contains Excel spreadsheets and an R script that link to comma-separated values (CSV) files that were extracted from the model output. The spreadsheets and scripts show the data and reproduce each of the figures in the paper. This dataset is associated with the following publication: Ou, Y., J. West, S. Smith, C. Nolte, and D. Loughlin. Air pollution control strategies directly limiting national health damages in the US.. Nature Communications. Nature Publishing Group, London, UK, 11: 957, (2020).

This file includes light-duty vehicle (LDV) stock, sales, and energy and emissions data for the scenarios reported in "Electrification Pathways for U.S. Passenger Vehicles." Data is reported annually at the national level for the United States for each of the six scenarios described in the accompanying paper.

This data includes model results generated using the MARKAL model and the EPA_US_9r database. Several scenarios involving automated vehicles were modeled. Results include emissions, fuel use, and fuel production under a variety of regimes of vehicle automation. This dataset is associated with the following publication: Dodder, R. Energy and emissions implications of automated vehicles in the U.S. energy system - December 2019. Transportation Research Part D: Transport and Environment. Elsevier BV, AMSTERDAM, NETHERLANDS, 77: 132-147, (2019).

Inputs for the publicly available EPA modeling utility MOVES (MOtor Vehicle Emissions Simulator), used to estimate air pollution emissions from mobile sources. Please contact dec.sm.MOVES@dec.ny.gov with questions.

Inputs for the publicly available EPA modeling utility MOVES (MOtor Vehicle Emissions Simulator), used to estimate air pollution emissions from mobile sources. Please contact dec.sm.MOVES@dec.ny.gov with questions.

Inputs for the publicly available EPA modeling utility MOVES (MOtor Vehicle Emissions Simulator), used to estimate air pollution emissions from mobile sources. Please contact dec.sm.MOVES@dec.ny.gov with questions.

This data is associated with the manuscript "Evolution of the US energy system and related emissions under varying social and technological development paradigms: Plausible scenarios for use in robust decision making" which will be submitted to Environmental Science & Technology (ES&T). The research considers how the US energy system might evolve under four possible scenarios, including different technologies and different emission outcomes. This dataset is associated with the following publication: Brown, K., T. Hottle, R. Bandyopadhyay, S. Babaee, R. Dodder, O. Kaplan, C. Lenox, and D. Loughlin. Evolution of the US energy system and related emissions under varying social and technological development paradigms: Plausible scenarios for use in robust decision making. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 8027-8038, (2018).