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

Economy-wide evaluation of CO2 and air quality impacts of electrification in the United States

Adopting electric end-use technologies instead of fossil-fueled alternatives, known as electrification, is an important economy-wide decarbonization strategy that also reduces criteria pollutant emissions and improves air quality. In this study, we evaluate CO₂ and air quality co-benefits of electrification scenarios by linking a detailed energy systems model and a full-form photochemical air quality model in the United States. We find that electrification can substantially lower CO₂ and improve air quality and that decarbonization policy can amplify these trends, which yield immediate and localized benefits. In particular, transport electrification can improve ozone and fine particulate matter (PM_2.5), though the magnitude of changes varies regionally. However, growing activity from non-energy-related PM_2.5 sources-such as fugitive dust and agricultural emissions-can offset electrification benefits, suggesting that additional measures beyond CO₂ policy and electrification are needed to meet air quality goals. We illustrate how commonly used marginal emissions approaches systematically underestimate reductions from electrification.

Evaluating long-term emission impacts of large-scale electric vehicle deployment in the US using a human-Earth systems model

While large-scale adoption of electric vehicles (EVs) globally would reduce carbon dioxide (CO2) and traditional air pollutant emissions from the transportation sector, emissions from the electric sector, refineries, and potentially other sources would change in response. Here, a multi-sector human-Earth systems model is used to evaluate the net long-term emission implications of large-scale EV adoption in the US over widely differing pathways of the evolution of the electric sector. Our results indicate that high EV adoption would decrease net CO2 emissions through 2050, even for a scenario where all electric sector capacity additions through 2050 are fossil fuel technologies. Greater net CO2 reductions would be realized for scenarios that emphasize renewables or decarbonization of electricity production. Net air pollutant emission changes in 2050 are relatively small compared to expected overall decreases from recent levels to 2050. States participating in the Regional Greenhouse Gas Initiative experience greater CO2 and air pollutant reductions on a percentage basis. These results suggest that coordinated, multi-sector planning can greatly enhance the climate and environmental benefits of EVs. Additional factors are identified that influence the net emission impacts of EVs, including the retirement of coal capacity, refinery operations under reduced gasoline demands, and price-induced fuel switching in residential heating and in the industrial sector.

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.

Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches

The transformation of the energy system is a highly complex process involving many dimensions. Energy system models help to understand the process and to define either target systems or policy measures. Insights derived from the social sciences are not sufficiently represented in energy system models, but address crucial aspects of the transformation process. It is, therefore, necessary to develop approaches to integrate results from social science studies into energy system models. Hence, as a result of an interdisciplinary discourse among energy system modellers, social scientists, psychologists, economists and political scientists, this article explains which aspects should be considered in the models, how the respective results can be collected and which aspects of integration into energy system models are conceivable to provide an overview for other modellers. As a result of the discourse, five facets are examined: Investment behaviour (market acceptance), user behaviour, local acceptance, technology innovation and socio-political acceptance. Finally, an approach is presented that introduces a compound of energy system models (with a focus on the macro and micro-perspective) as well as submodels on technology genesis and socio-political acceptance, which serves to gain a more fundamental knowledge of the transformation process.

Fine-Scale Analysis of the Energy-Land-Water Nexus: Nitrate Leaching Implications of Biomass Cofiring in the Midwestern United States

As scientists seek to better understand the linkages between energy, water, and land systems, they confront a critical question of scale for their analysis. Many studies exploring this nexus restrict themselves to a small area in order to capture fine-scale processes, whereas other studies focus on interactions between energy, water, and land over broader domains but apply coarse resolution methods. Detailed studies of a narrow domain can be misleading if the policy intervention considered is broad-based and has impacts on energy, land, and agricultural markets. Regional studies with aggregate low-resolution representations may miss critical feedbacks driven by the dynamic interactions between subsystems. This study applies a novel, gridded energy-land-water modeling system to analyze the local environmental impacts of biomass cofiring of coal power plants across the upper MISO region. We use this framework to examine the impacts of a hypothetical biomass cofiring technology mandate of coal-fired power plants using corn residues. We find that this scenario has a significant impact on land allocation, fertilizer applications, and nitrogen leaching. The effects also impact regions not involved in cofiring through agricultural markets. Further, some MISO coal-fired plants would cease generation because the competition for biomass increases the cost of this feedstock and because the higher operating costs of cofiring renders them uncompetitive with other generation sources. These factors are not captured by analyses undertaken at the level of an individual power plant. We also show that a region-wide analysis of this cofiring mandate would have registered only a modest increase in nitrate leaching (just +5% across the upper MISO region). Such aggregate analyses would have obscured the extremely large increases in leaching at particular locations, as much as +60%. Many of these locations are already pollution hotspots. Fine-scale analysis, nested within a broader framework, is necessary to capture these critical environmental interactions within the energy, land, and water nexus.

Optimizing the Chinese Electricity Mix for CO2 Emission Reduction: An Input-Output Linear Programming Model with Endogenous Capital

This study develops an input-output linear programming (IO-LP) model to identify a cost-effective strategy to reduce the economy-wide carbon dioxide (CO₂) emissions in China from 2020 to 2050 through a shift in the electricity generation mix. In particular, the fixed capital formation of electricity technologies (FCFE) is endogenized so that the capital-related CO₂ emissions of various generation technologies can be captured in the model. The modeling results show that low-carbon electricity, e.g., hydro, nuclear, wind, and solar, is associated with lower operation-related CO₂ emissions but higher capital-related CO₂ emissions compared to coal-fired electricity. A scenario analysis further reveals that a shift in the electricity generation mix could reduce the accumulated economy-wide CO₂ emissions in China by 20% compared to the business-as-usual (BAU) level and could help peak China's CO₂ emissions by 2030. The emission reduction is mainly due to a drop in operation-related CO₂ emissions of electricity, contributing to a decrease in accumulated economy-wide emissions by 21.4%. The infrastructure expansion of electricity, on the other hand, causes a rise in the accumulated emissions by 1.4%. The proposed model serves as an effective tool to identify the optimal technology choice in the electricity system with the consideration of both direct and indirect CO₂ emissions in the economy.

State-level drivers of future fine particulate matter mortality in the United States

Future fine particulate matter (PM₂.₅) concentrations and resulting health impacts will be largely determined by factors such as energy use, fuel choices, emission controls, state and national policies, and demographcs. In this study, a human-earth system model is used to estimate PM_2.5 mortality costs (PMMC) due to air pollutant emissions from each US state over the period 2015 to 2050, considering current major air quality and energy regulations. Contributions of various socioeconomic and energy factors to PMMC are quantified using the Logarithmic Mean Divisia Index. National PMMC are estimated to decrease 25% from 2015 to 2050, driven by decreases in energy intensity and PMMC per unit consumption of electric sector coal and transportation liquids. These factors together contribute 68% of the decrease, primarily from technology improvements and air quality regulations. States with greater population and economic growth, but with fewer clean energy resources, are more likely to face significant challenges in reducing future PMMC from their emissions. In contrast, states with larger projected decreases in PMMC have smaller increases in population and per capita GDP, and greater decreases in electric sector coal share and PMMC per unit fuel consumption.

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

Combined heat and power (CHP) is promoted as an economical, energy-efficient option for reducing air emissions, mitigating carbon emissions and reducing reliance on grid electricity. However, its potential benefits have only been analyzed within the context of the current energy system. To fully examine the viability of CHP as a clean-technology alternative, its growth must be analyzed considering how the energy sector may transform under the influence of various technological and policy drivers that are specifically geared toward limiting greenhouse gas (GHG) emissions. Scenarios were developed through a bottom-up technology model of the U.S. energy system to determine the impacts on CHP development and both system-wide and sectoral GHG and air pollutant emissions. Various scenarios were considered, from CO₂ emissions reductions in the electric generating units (EGU) sector to GHG reductions across the whole energy system while considering levels of CHP investment. The largest CHP investments were observed in scenarios that limited CO₂ emission from the EGU sector alone. The investments were scaled back in the scenarios that incorporated energy system level GHG reductions. The energy system level reduction scenarios yielded rapid transformation of the EGU sector towards zeroemissions technologies as reliance on electricity increases with the electrification of the many end-use sectors such as buildings, transportation and industrial sectors, reducing investment in CHP. The prime mover and fuel choice heavily influenced the air pollutant emissions resulting in trade-offs among pollutants including GHG emissions. The results suggest that CHP could play a role in a future low-carbon energy system, but that role diminishes as carbon reduction targets increase.

Energy and emissions implications of automated vehicles in the U.S. energy system

Vehicle automation has the potential to drastically transform transportation, with important implications for energy and the environment. There is considerable uncertainty regarding the impact of automation on travel demand and vehicle efficiency. We utilize the MARKet ALlocation (MARKAL) energy system model to examine four previously published scenarios that consider different effects of automation on efficiency and demand. We do not replicate detailed estimation of individual mechanisms but apply key outcomes from prior studies within a broader energy system framework. Our analysis adds insights on fuel switching, upstream impacts, and air emissions. MARKAL dynamically captures interactions between transportation and non-transportation sectors, which is important given that the revolutionary shifts from automation may invalidate static assumptions. Model results suggest that increasing travel demands from automation may boost fuel use and petroleum-based fuel prices, potentially increasing the market penetration of alternative-fuel vehicles. In contrast, dramatic efficiency improvements from automation could drive fuel prices lower, greatly reducing the competitiveness of alternative-fueled vehicles. Furthermore, these shifts could yield positive or negative environmental impacts. Some automation scenarios even resulted in counterintuitive results. For example, if high levels of efficiency improvement drive out alternative-fuel vehicles, such as battery electric and hybrids, a net worsening of air quality relative to the other scenarios could result. We also found system-level dynamics to be key. For example, reductions in liquid fuel prices led to increased consumption, and the resulting increase in air pollutant emissions offset a portion of the potential air quality benefits of automation.