Income and Consumption-Based Environmental Footprinting
United States Income-Based Emissions Inequality Paper (PLOS Climate 2023)
United States Consumption-based Emissions Inequality Paper (Ecological Economics 2023)
Press Coverage: My work on emissions inequality has been extensively covered in a variety of media outlets, including The Washington Post, Fortune, CNN, Salon, The Guardian, Forbes, The Hill, PBS Newshour, Anthropocene Magazine, MSNBC, and others.
Overview: Broadly, my research analyzes how economic inequality – particularly at the very top of the income distribution – shapes the distribution of environmental benefits and harms through the global economy. Specifically, I use big data to trace the flow of CO2 emissions through approximately 9,000 global industries and 10,000 global commodities and link this to annual United States household-level environmental benefits (income received and goods and services purchased), over a 30 year period. Novel to this research, I move beyond national averages and decile-level analysis to reveal in unprecedented detail the CO2 footprint of economic elites: the top 1% and 0.1% of U.S. households.
This analysis reveals the significant inequality in the scale and distribution of environmental benefits, shows sub-decile trends from 1990-2019, and explores the socio-economic and policy factors that shape inequality of harm. In doing so, it provides policy guidance on which categories of goods and services or income could be targeted for CO2 reduction efforts and which households would be affected by such policies. Finally, by monitoring this data over time, it can post-hoc reveal if certain policy choices are having the intended effects.
To do this work, I draw on approximately a half billion data points per year and combine multiple sources and methods, including: EORA MRIO environmentally extended input-output database, direct emissions data, BLS Consumer Expenditure Surveys, and Census Bureau Current Population Survey data.
Consumption and Municipal Recycling
Previously I have analyzed the recycling rates of all 351 Massachusetts cities and towns over a 12 year period to identify key economic, social, public policy, and recycling program design factors associated with high recycling rates. This used multiple linear regression econometric techniques. My work was the very first panel study to observe the impact of single stream (comingling of paper, metal, plastic, and glass in one bin) recycling programs on recycling rates. Interestingly, we found this popular program design had little effect on recycling success. The market-driven mechanism Pay-As-You-Throw, where households are charged per bag of trash, had the greatest effect on improving recycling rates. These insights provide policy guidance to municipal governments and recycling companies on how to effectively boost recycling rates.
Citizen Science – Testing Remote vs In-Person training methods
In another research line, I developed invasive species identification videos to test the effectiveness of remotely training citizen scientists, via smartphones. While citizen science, the crowdsourcing of data from volunteers, has been around for over a century, the power of smartphone technology provides novel opportunities to collect high quality data from millions of volunteers. Here, we compared the effectiveness of training citizen scientists with app-embedded videos, app-based text, and in person training. Excitingly, we found volunteers trained with the videos had the highest success at invasive species identification. These findings inform efforts on remotely providing highly effective citizen scientists training, at low cost.