Research Insight: Decarbonization and air quality at the organizational level

Science

Poor air quality remains a major global health threat and leading cause of mortality, disproportionately affecting vulnerable populations. But the air-quality impacts of climate policies are often overlooked. As corporate emissions tracking, renewable energy adoption, and digital infrastructure expansion gain prominence, there is a pressing need for a comprehensive framework to evaluate the sectoral, spatial and temporal air quality impacts of these policies. Our research focuses on air pollution as a key example of these unintended consequences and aims to develop a framework that integrates these overlooked dimensions into climate policy assessments.

This framework is designed to quantify the amount of energy consumed by different organizations—in industry, government, academia and beyond—and how organizational decision-makers can incorporate air quality in their efforts to reduce carbon emissions in alignment with climate goals. Among the options at their disposal are to reduce energy purchases, shift to renewable energy sources, or reduce air travel. Such choices both impact and are impacted by larger societal systems such as transportation systems and the electricity grid, and could shape the future of air quality at regional and global scales. 

An important component of organizational energy consumption stems from digital infrastructure growth; data centers consume up to 10 percent of U.S. electricity and strain energy grids, exacerbating regional pollution disparities. To enable organizations to better align data center operations with climate and air quality goals, we need to know where emissions are being generated and how they are affecting neighboring communities.

Computational Tools

Organizational decisions on electricity or air travel, as well as energy-intense data center operations, result in carbon emissions in different places and at different times—emissions that could be optimally reduced to minimize air pollution. Our findings suggest that for the same unit of carbon emissions reduced, the associated air quality benefits can differ widely, implying potential gains from prioritizing decarbonization efforts in sectors and locations with the highest air quality benefits. Evaluating these dynamics requires a high-resolution approach that captures both physical and behavioral drivers of emissions, their atmospheric interactions, and their effects on air quality and health.

To quantify emissions at a high spatial and temporal resolution, we employ sector-specific models. For the energy sector, we use a dispatch energy grid model, US Electricity Generation Optimization model (US-EGO), to simulate unit-level electricity generation and transmission changes under different scenarios, capturing shifts in energy supply and emissions. For the aviation sector, we use an aviation emissions model that averages high-res emissions over a year. And to track how these sectoral emissions alter pollutant concentrations across regions, we use GEOS-Chem, a 3D atmospheric chemistry model. 

Strategy

To assess organizational air quality impacts, we’re calculating current annual pollution (e.g., from energy purchases and air travel), and how much of that pollution could be reduced by policy changes (e.g., replacing short-haul air travel with train travel, or a percentage of purchased energy with renewables). 

Our research advances sustainability by providing policymakers and researchers with a rigorous framework to assess the air quality impacts of climate and energy policies, ultimately promoting long-term human and environmental well-being. By integrating atmospheric modeling, causal inference, and policy evaluation, it provides policymakers and researchers with a comprehensive approach to assessing the air quality impacts of climate and energy policies. This enables the effective use of newly available high-resolution emissions and pollution exposure data to evaluate how the benefits and burdens of decarbonization strategies are distributed across different communities.

MIT Center for Sustainability Science and Strategy (CS3) PhD student Albert Chen, a doctoral candidate in Social and Engineering Systems at MIT’s Institute for Data, Systems and Society, is developing “a framework to quantify how organizational climate actions and digital infrastructure growth shape air quality and public health across space, time, and sectors—advancing policy-relevant tools to align decarbonization with environmental outcomes.” Chen has been accepted into the 2025-2026 cohorts of the Society of Energy Scholars at MIT and the Martin Family Society of Fellows for Sustainability.