Sustainable infrastructure technologies: impact and policy

This dissertation presents three studies that examine localized spatial effects of sustainable infrastructure investments in several contexts. The first chapter estimates public local economic benefits and costs of contaminated land cleanup and redevelopment through New York’s Brownfield Cleanup Program. Unlike prior literature, I value site redevelopment separately from cleanup, assess whether green redevelopment adds value, and examine time to capitalization. Using a property level fixed effects model and controlling for time-varying shocks, I find that the program has added 4% to nearby property values, producing a $1.26 billion property tax gain that outweighs its public costs. Most benefits stem from program participation and site cleanup. Furthermore, site entry and site cleanup do not immediately capitalize into nearby property values and are valued by the housing market with a lag of approximately three years. The second chapter estimates the value of contaminated land cleanup through Illinois’ Site Remediation Program in the presence of foreclosures and demonstrates the potential consequences of not controlling for time-varying unobservables in hedonic regressions. Failure to control for incidence of foreclosures, which can act as a time-varying correlated unobservable, can lead to overestimation of the property effects of contaminated site participation in SRP by as much as 46% in absolute terms. Mean differencing of time-varying neighborhood amenities substantially reduces this bias. Given that foreclosures may be correlated with a myriad of neighborhood amenities, these findings provide cautionary evidence suggesting that studies employing hedonic analysis should control for time-varying market conditions. The third chapter studies electricity consumption patterns generated by newly commissioned renewable energy plants in Indonesia to determine whether these facilities provide electricity to the regions in which they are located. This paper uses a novel application of satellite nighttime light data used as a proxy for energy consumption combined with data on geocoded locations of 15 renewable energy plants and Village Potential Statistics survey for Indonesia. I estimate a difference-in-difference model to compare the intensity of nighttime lights in nearby areas around incoming plants to that in geographically matched locations before and after each plant becomes operational. Results show that a 1% increase in capacity of a newly commissioned renewable energy plant leads to a 0.09% increase in nighttime lights relative to the matched control group. This corresponds to a 0.15% increase in residential electricity consumption and implies that local populations benefit, although to a small degree, from the energy these plants produce. Results further show that the immediate increase in nighttime lights associated with plant commissioning grows over time, but at a decreasing rate