Environmental justice consequences of California’s cap-and-trade program

About

In 2013, California implemented one of the world’s most comprehensive and ambitious cap-and-trade climate change policies. The economic benefit of cap-and-trade is well understood: by leveraging market forces, this intervention allows the state to meet its statewide greenhouse gas target at the lowest possible cost. But because cap-and-trade does not require specific emission reductions from any particular location, the same market forces that make the program so cost-effective may also result in undesirable equity consequences. There is growing concern that market-induced spatial reallocation of pollution could widen existing pollution concentration gaps between disadvantaged and other communities. 

In this project, we sought to understand whether the introduction of California’s cap-and-trade policy in 2013 increased local levels of pollution exposure for disadvantaged communities across the state. 

Approach

This project brought together causal inference methods with pollution transport models to analyze the impacts of cap-and-trade on the environmental justice gap between disadvantaged communities and other communities. We first examined the impact of cap and trade on facilities' greenhouse gas emissions and local pollutant emissions from 2008 to 2017. Then, we obtained the cap-and-trade driven pollution exposure from regulated stationary sources and model communities’ exposure by using an atmospheric transport model that accounts for wind, meteorological, and atmospheric conditions. Finally, we compared the pollution exposure between disadvantaged and other communities and analyzed the difference in the environmental justice gap between disadvantaged and other communities before and after the cap and trade was implemented.

Key Findings

We find the implementation of California’s cap-and-trade program reduced GHG and local air pollution emissions. Applying an atmospheric dispersal model to determine resulting pollution concentration changes, we detect the EJ gap, which was widening before 2013, has since fallen across criteria pollutants. Our approach also highlights the importance of implementing atmospheric transport models for analyzing the distributional impacts of environmental policies.