Climate Policy

Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk

Abstract: Nuclear power use in the United States is projected to decline over the coming decades. We explore how nuclear phase-outs could affect air pollution, climate, and health with both existing and alternative grid infrastructure. We develop an energy grid dispatch model to estimate the emissions of CO2, NOx, and SO2 from each electricity generating unit, coupling these emissions with a chemical transport model to calculate impacts on ground-level ozone and fine particulate matter (PM2.5). 

Our yearlong scenario removing nuclear power results in compensation by coal, gas, and oil, leading to increased emissions. We find the resulting changes in PM2.5 and ozone lead to an additional 5230 annual mortalities. Changes in CO2 emissions lead to an order of magnitude higher mortalities throughout the 21st century, incurring $50.4-$220.2 of damages from one year of emissions. A scenario exploring simultaneous closures of nuclear and coal plants shifts the distribution of health impacts, and a scenario allowing for increased penetration of renewables reduces health impacts. Inequities in exposure to pollution are persistent across all scenarios– Black or African American people are exposed to the highest relative levels of pollution, even if renewable capacity is expanded.

Rapid estimation of climate-air quality interactions in integrated assessment using a response surface model

Abstract: Air quality and climate change are substantial and linked sustainability challenges, and there is a need for improved tools to assess the implications of addressing these challenges together. Due to the high computational cost of accurately assessing these challenges, integrated assessment models (IAMs) used in policy development often use global- or regional-scale marginal response factors to calculate air quality impacts of climate scenarios.

We bridge the gap between IAMs and high-fidelity simulation by developing a computationally-efficient approach to quantify how combined climate and air quality interventions affect air quality outcomes, including capturing spatial heterogeneity and complex atmospheric chemistry. We fit individual response surfaces to high-fidelity model simulation output for 1,525 locations worldwide under a variety of perturbation scenarios. Our approach captures known differences in atmospheric chemical regimes and can be straightforwardly implemented in IAMs, enabling researchers to rapidly estimate how air quality in different locations, and related equity-based metrics, will respond to large-scale changes in emissions policy. 

We find that the sensitivity of air quality to climate change and air pollutant emissions reductions differs in sign and magnitude by region, suggesting that calculations of “co-benefits” of climate policy that do not account for the existence of simultaneous air quality interventions can lead to inaccurate conclusions. Although reductions in global mean temperature are effective in improving air quality in many locations and sometimes yield compounding benefits, we show that the air quality impact of climate policy depends on air quality precursor emissions stringency. 

Our approach can be extended to include results from higher-resolution modeling, but also to incorporate other interventions towards sustainable development which interact with climate action and have spatially-distributed equity dimensions.

Large mitigation potential of smoke PM2.5 in the US from human-initiated fires

Abstract: Increasing fire activity and the associated degradation in air quality in the United States has been indirectly linked to human activity via climate change. In addition, direct attribution of fires to human activities may provide opportunities for near term smoke mitigation by focusing policy, management, and funding efforts on particular ignition sources.

We analyze how fires associated with human ignitions (agricultural fires and human-initiated wildfires) impact fire particulate matter under 2.5 microns (PM2.5) concentrations in the contiguous United States (CONUS) from 2003 to 2018. We find that these agricultural and human-initiated wildfires dominate fire PM2.5 in both a high fire and human ignition year (2018) and low fire and human ignition year (2003). Smoke from these human levers also makes meaningful contributions to total PM2.5 (~5-10% in 2003 and 2018). Across CONUS, these two human ignition processes account for more than 80% of the population-weighted exposure and premature deaths associated with fire PM2.5.

These findings indicate that a large portion of the smoke exposure and impacts in CONUS are from fires ignited by human activities with large mitigation potential that could be the focus of future management choices and policymaking.

A Large Ensemble Global Dataset for Climate Impact Assessments

Abstract: We present a self-consistent, large ensemble, high-resolution global dataset of long‐term future climate developed by integrating a spatial disaggregation (SD) pattern-scaling technique and a bias-correction (BC) delta method. The delta method adds the anomalies or deltas (future climate trends) onto a historical, detrended climate that is based on the third phase of the Global Soil Wetness Project (GSWP3). The anomalies or deltas are derived by spatially disaggregating zonal climate projections from the MIT Integrated Global System Modeling (IGSM) framework based on regional hydroclimate change patterns from the 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models.

Four emission scenarios are considered to represent the existing energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2°C and Paris 1.5°C. For each emission scenario, a distribution of plausible trajectories is provided by a 50-member ensemble to represent the uncertainty in the Earth system (e.g., the climate sensitivity, rate of heat uptake by the ocean, uncertainty in carbon cycle), allowing for constructing a 900-member ensemble of regional climate outcomes. This global dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity.

Quantitative assessments clearly indicate the ability of the dataset to represent the expected large-scale climate features across various regions of the globe. This large‐ensemble, high-resolution dataset can be used for assessing impacts of climate change from a risk-based perspective across different applications, including hydropower, water resources, wind power resources to name a few.

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