Essays on Environmental and Housing Regulations
Open AccessThis dissertation consists of three independent essays on environmental and housing affordability regulations.Chapter 1 provides the first empirical evaluation of the effects of tradable performance standard (TPS) on emissions from thermal power generation facilities in a developing country. TPS is an important policy instrument for mitigating carbon dioxide (CO2) emissions in developing countries, who play an essential role in achieving drastic global carbon emissions reduction. However, whether a TPS system effectively reduces firm-level emissions in a developing country context remains unknown. This essay takes the first step in answering this question based on a policy experiment in China. Since 2013, China has introduced carbon emissions trading systems based on TPS in eight regions (ETS pilots). This essay provides the first ex-post evaluation of these ETS pilots’ effects on emissions of sulfur dioxide (SO2), a co-pollutant of CO2, from coal-fired thermal power generation facilities using staggered and dynamic difference-in-differences models. This essay uses novel data from NASA’s Aura satellite to measure SO2 emissions at the facility level. Contrary to common belief, results show that although SO2 emissions of all facilities declined steadily from 2010 to 2019, SO2 emissions of facilities covered by the ETS pilots (ETS facilities) increased by about 5-7% relative to those of non-ETS facilities. Moreover, the relative in- crease in SO2 emissions of ETS facilities grew over time. A model is developed to show that the implicit output subsidy from the TPS design could increase the output of cleaner facilities, leading to more SO2 emissions.Chapter 2, co-authored with Hancheng Jiang and Luis Quintero, examines the value of rent stabilization policy and its implications for racial inequality in New York City (NYC). Rent stabilization policies are gaining new legislative momentum in the U.S. amid the so-called housing affordability crisis in major metropolitan areas. Despite their resurgent popularity, important questions about their value and cost remain unanswered. Assessing rent discounts implied by rent regulation is challenging because the counterfactual rents of regulated units in the unregulated market are not observed. We estimate these counterfactual rents and predict the quality-adjusted rent discount for each rent-stabilized unit in NYC using novel data from 2002 to 2017. We find robust average rent discounts of $410 per month (34% of contract rents of stabilized units). The aggregate size of these discounts in NYC is between 4 to 5.4 billion USD per year, roughly 10-14% of the federal budget on means-tested housing programs. We document that discounts: (1) increase linearly with housing tenure; (2) are not progressively distributed; (3) are larger in Manhattan and increasing in gentrifying neighborhoods; and (4) are three times larger for households correctly aware of being beneficiaries. We find that rent stabilization has disproportionately benefited White tenants. Not only are they more likely to occupy rent-stabilized units conditional on observables, but they also receive higher discounts. On average, Black stabilized tenants get$150, Hispanics $135, and AAPI $43 less in monthly rent discounts than White stabilized tenants. This racial gap, which has shrunk over time, is mainly explained by the uneven sorting of households of different races across locations.Chapter 3 studies the relationship between learning and the adoption of renewable energy technologies. With the rapid deployment of onshore wind technologies worldwide, they are emerging as a cost-competitive renewable energy source for developing countries. This study analyzes the role of learning based on the accumulation of experience and knowledge in promoting the rapid deployment of onshore wind technologies in China. Previous studies on learning in China’s wind energy sector have focused primarily on the growth episode before 2012 and have found moderate or negative learning rates. Using a novel project-level dataset obtained from the China Certified Emission Reduction (CCER) program, this essay provides an update on the learning rates of onshore wind energy technologies in the post-CDM era. To overcome potential simultaneity between installation experience and cost reductions, I adopt a novel instrumental approach, which exploits energy producers’ differential exposures to the input and output prices due to the share, location, and vintage of their coal-fired power generation facilities. Results indicate large LBD rates in capital costs of 12%-14% and in levelized cost of electricity (LCOE) of 4%-6% at the industry level, on par with the international experience. Firm-level learning is driven by industrial spillover. Furthermore, patent citation analyses show that although wind energy patents granted to Chinese firms are less likely to be cited compared to those of major foreign wind turbine manufacturers, this citation gap has been closing in recent years.
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