In recent years, many papers in environmental economics have considered the household’s decision to invest in energy-efficient technologies for their home. The vast majority of these studies have concluded that investment levels in these technologies are sub-optimal for a variety of reasons. In this paper, we synthesize the suggested drivers of these investment wedges and propose a dynamic modeling framework of a housing choice and an energy-efficient-investment choice that includes the proposed channels. We discuss the estimation challenges associated with this model and conclude with suggestions for future research.

Place-based environmental regulations target pollution-intensive sectors in polluted areas. These regulations can improve local quality of life by reducing air pollution, while simultaneously reducing labor demand. I develop a framework to study the heterogeneous effects on worker welfare, considering changes in pollution exposure, sectoral and spatial labor distribution, and unemployment. I focus on the U.S. Environmental Protection Agency’s regulation of ozone and fine particulate air pollution during the 2000’s. First, I develop a triple-difference estimator to measure the employment effects on college-educated and non-college-educated workers. I find that, on average, regulation decreased employment by 7.6% among non-college-educated workers and by 3.6% among college-educated workers. However, these average treatment effects vary substantially depending on the intensity and type of regulation. I use this causal evidence to develop empirical moments that serve to identify key parameters of a new general equilibrium search and matching model with endogenous worker location choice and pollution exposure. I use the model to evaluate the welfare effects of regulation in North Carolina. I find the effects differ by worker skill level and geographic location. Low-skill workers in regulated areas experience notable welfare losses. I show these losses can be mitigated by improving labor mobility across sectors and areas.

The residential solar market has grown significantly in the past decade due partly to falling prices and government subsidies. However, this growth has been driven by high-income households, leading to inequality in the distribution of subsidies. In this paper, we investigate how household income affects demand for residential solar systems and the distributional effects of renewable energy tax credit policies. We estimate a dynamic model of solar adoption using novel household-level data on hourly energy consumption, prices, household income, and solar panel installation for utility company customers in the Phoenix, AZ, metropolitan area from 2013 to 2017. We find that the household’s sensitivity to the system cost decreases as income increases. While low-income households are more sensitive to reductions in the system cost, high-income households are more likely to receive the full benefit of a non-refundable tax credit due to their higher tax liability. Specifically, making the tax credit refundable would increase the take-up rate among low-income households by 16%, with no effect on high-income households. Finally, our counterfactual analysis demonstrates that targeted policies designed to allocate 40% of total benefits to lower-income groups can enhance equity in solar adoption while increasing total solar production by 2% compared to nonrefundable policies. Our findings highlight the importance of designing subsidy programs that effectively balance distributional equity and overall efficiency.

We examine the impact of long-term cumulative exposure to fine particulate air pollution (PM_2.5) on mortality among individuals aged 65 and older. Developing causal evidence on the long-term effects of PM_2.5 exposure is challenging due to residential sorting, latent health, and measurement error in pollution exposure. We address these challenges by developing an instrumental variable analog to the Cox proportional hazards model. The IV leverages quasi-random variation in long-term PM_2.5 exposure caused by the expansion of Clean Air Act regulations. We are currently estimating the model using longitudinal data on millions of senior citizens from the US Centers for Medicare and Medicaid Services.