Working Papers
Pollution Taxes as a Second-Best: Accounting for Multidimensional Firm Heterogeneity in Environmental Regulations (with Fan Xia and Bing Zhang)
[Abstract]
This paper documents the first-order welfare effects of firm heterogeneity under a homogeneous emission tax regime. Local and firm-level variations in market power, abatement costs, and abatement benefits can create a gap between optimal and realized emission reduction. We examine this question in the context of China’s highly concentrated cement industry, which was subjected to multiple emission tax changes across time and location between 2011 and 2018. Using a comprehensive firm-level data set that allows us to estimate firm-level responses to regulation, we find substantial heterogeneity in the compliance behavior of firms --- through adjustments in output levels, price, and emission intensity. We then use the structurally estimated firm-level marginal abatement costs to quantify the deviation of local marginal pollution abatement costs from its marginal benefits. The model shows that the gap between observed abatement and production firm responses and the socially optimal responses is explained by two factors: the firm’s market power and the correlation between local abatement costs and benefits. By using variation in market power generated by two data-driven approaches and local abatement costs and benefits, we can empirically assess the importance of each of these two drivers of the suboptimal response to emission taxes. A counterfactual analysis shows that output-based rebates coupled with a homogeneous emission tax can help mitigate the distortion from market power.
Coal Phase-out in a Second-Best Setting: Evidence from China's Winter Heating Ban (with Antonio M. Bento and Bing Zhang)
[Abstract]
This paper assesses how unexpected rising natural gas price, induced by a coal ban in China’s heating sector in targeted cities in 2017 winter, affects pollution and disturbs firms’ production both within and outside the targeted region, under pre-existing distortion in the coal market. Employing a panel approach and an event study design, we find that monitor-level NO2 pollution increases significantly in the targeted region, by 6.4 percent, and even more in the non-targeted region, by 11.6 percent. Using detailed firm-level emission and energy consumption data across sectors, we estimate the cross-price elasticity between coal consumption and natural gas prices by sector, employing the Common Correlated Effects Estimator. We document that the extent of the pollution increase can be explained by production reshuffling, where products from gas-intensive plants are substituted with those from coal-intensive plants within the same industry, resulting in an increase in coal consumption. Pre-existing distortion in the coal market, the coal price cap, further accelerates the increase in coal consumption, as the coal market fails to mitigate the production reshuffling through price effect. Through back-of-the-envelope calculations, we estimate that the increase in coal consumption could have been around 40 percent lower if the coal market follows the same cross-price elasticity between natural gas and coal as in the contemporary European coal market.
[Draft Coming Soon]
Electric Vehicle Sharing: Crowding Adoption Out or In? (with Jonathan A. Libgober)
[Abstract]
We estimate the impact of an Electric Vehicle (EV) sharing program — specifically BlueLA — on EV adoption. This program provides EVs in heavily trafficked areas in several low- to middle-income neighborhoods in Los Angeles. Using data on EV purchases from a subsidy program and a difference-in-differences strategy, we estimate that BlueLA caused at least a 32% increase in new EV adoptions within the zip codes it entered. Although car sharing provides a substitute for car ownership, our findings are consistent with the hypothesis that increasing familiarity with EVs could facilitate adoption.
Does Market Power in India's Agricultural Markets Hinder Farmer Climate Change Adaptation? (with Rajat Kochhar)
[Abstract]
What role do government policies which distort market competition play in impeding farmers' climate change adaptation? We study this question in the context of India, where longer-run adaptation to climate change has been inadequate --- posing a considerable risk to its ~250 million agricultural workers. We exploit spatial discontinuities in intermediary market power, created by state-level laws that restrict farmer-intermediary transactions to the same state, to determine how spatial competition affects farmers' adaptation. We find that a farmer selling in the 75th percentile of the competition index compared to one that faces the 25th percentile of the competition index achieves a 4.9 percent higher output for each additional day of extreme heat. This effect is driven by increased input usage by farmers in anticipation of higher prices after climate shocks, an effect limited only to high competition areas. We then propose and estimate a quantitative spatial trade model with intermediary market power to examine the welfare implications of higher competition for adaptation. Our structural estimates suggest that the farmer's economic loss (i.e. their climate damage function) due to extreme weather could be mitigated by 13.8 percent if government regulation distorting market competition is dismantled. These results highlight the importance of understanding the political economy of reforming these competition-distorting laws to accelerate climate change adaptation.
Work in Progress
Using Big Data to Estimate the Environmental Benefits of Congestion Pricing: Evidence from California (with Antonio M. Bento, Rajat Kochhar, and Andrew R. Waxman)
[Abstract]
This paper examines the distributional effect of congestion pricing on local air quality. To first estimate the effect of traffic congestion on local air quality, we combine two unique sources of big data: real-time high-frequency pollution readings obtained from Google Street View cars from the firm Aclima , and real-time data on the speed and flow of vehicles in California freeways from the California Performance Measurement System (PeMS). We find a consistent non-linear localized relationship between vehicle congestion on freeways and localized air pollution, depending on vehicle speeds and weather conditions. We also utilize instrumental variables and the LASSO approach to account for traffic endogeneity using a rich dataset on vehicle accidents. Lastly, we simulate the effect of congestion pricing on traffic redistribution and calculate the induced pollution redistribution using the estimates that link the relationship between traffic and pollution.
Non-Academic Writings
Good for the Environment, Good for the Economy: The Potential of Transportation Electrification Policies to Foster Economic Growth in Greater Los Angeles (with Sam Boysel, Dan Ibarrola, Monica Morlacco, and Kate Weber), Public Exchange at University of Southern California and Los Angeles Cleantech Incubator, May 2022