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Many countries have passed environmental laws aiming at preserving natural ecosystems, such as the Endangered Species Act of 1973 in the United States. Although those regulations seem to have improved preservation, they may have had unintended consequences in energy production. Here we show that while environmental constraints on hydropower may have preserved the wilderness and wildlife by restricting the development of hydroelectric projects, they led to more greenhouse gas emissions. Environmental regulations gave rise to a replacement of hydropower, which is a renewable, relatively low-emitting source of energy, with conventional fossil-fuel power, which is highly polluting. Our estimates indicate that, on average, each megawatt of fossil fuel power-generating capacity added to the grid because of environmental constraints on hydropower development led to an increase in annual carbon dioxide emissions of about 1,400 tons. Environmental regulations focusing only on the preservation of ecosystems appear to have encouraged electric utilities to substitute dirtier fuels for hydropower in electricity generation.

The Fukushima nuclear accident in March 2011 generated deep public anxiety and uncertainty about the future of nuclear energy. However, differently to fossil fuel plants, nuclear plants produce virtually no greenhouse gas emissions or air pollutants during power generation. Here we show the effect on air pollution and infant health in the context of the temporary closure of nuclear plants by the Tennessee Valley Authority (TVA) in the 1980s. After the Three Mile Island accident in 1979, the US Nuclear Regulatory Commission intensified inspections throughout the nation, leading to the shutdown of two large nuclear power plants in the TVA area. In response to that shutdown, electricity generation shifted one to one to coal-fired power plants within TVA, increasing particle pollution in counties where they were located. Consequently, infant health may have deteriorated in the most affected places, indicating deleterious effects to public health. Nuclear accidents generate vast echoes in public opinion, and often determine policy decisions to suspend nuclear programs. This study shows the unintended implications of nuclear plant shutdown in Tennessee Valley between 1983 and 1986, demonstrating deleterious consequences for public health.

We measure the effects of firm policies on racial pay differences in Brazil. Non-Whites are less likely to be hired by high-wage firms, explaining about 20 percent of the racial wage gap for both genders. Firm-specific pay premiums for non-Whites are also compressed relative to Whites, contributing another 5 percent for that gap. A counterfactual analysis reveals that about two-thirds of the underrepresentation of non-Whites at higher-wage firms is explained by race-neutral skill-based sorting. Non-skill-based sorting and differential wage setting are largest for college-educated workers, suggesting that the allocative costs of discriminatory hiring and pay policies may be relatively large in Brazil.

Lyme disease (LD) is the most common tick-borne disease in North America. It is caused by Borrelia burgdorferi and transmitted to humans by blacklegged ticks, Ixodes scapularis. The life cycle of the LD vector, I. scapularis, usually takes two to three years to complete and goes through three stages, all of which are dependent on environmental factors. Increases in daily average temperatures, a manifestation of climate change, might have contributed to an increase in tick abundance via higher rates of tick survival. Additionally, these environmental changes might have contributed to better host availability, which is necessary for tick feeding and life cycle completion. In fact, it has been shown that both tick activity and survival depend on temperature and humidity. In this study, we have examined the relationship between those climatic variables and the reported incidence of LD in 15 states that contribute to more than 95% of reported cases within the Unites States. Using fixed effects analysis for a panel of 468 U.S. counties from those high-incidence states with annual data available for the period 2000–2016, we have found sizable impacts of temperature on the incidence of LD. Those impacts can be described approximately by an inverted U-shaped relationship, consistent with patterns of tick survival and host-seeking behavior. Assuming a 2°C increase in annual average temperature—in line with mid-century (2036–2065) projections from the latest U.S. National Climate Assessment (NCA4)—we have predicted that the number of LD cases in the United States will increase by over 20 percent in the coming decades. These findings may help improving preparedness and response by clinicians, public health professionals, and policy makers, as well as raising public awareness of the importance of being cautious when engaging in outdoor activities.

This paper documents the changing trends in US carbon emissions and discusses the main factors that contributed to the historical carbon emissions rollercoaster. We divide the discussion into four periods: up to 1920, 1920–1960, 1960–2005 and after 2005. For each period, we discuss the main drivers of national carbon emissions. We then discuss trends in carbon emissions in the electricity sector. Electricity sector emissions were initially very small, but would become the largest source of US carbon emissions over the period 1980–2015, and the largest contributor to decarbonization since 2007. In the last section, we offer some lessons for what developing economies might learn from the US experience.

Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption).

Climate change threatens all parts of the US electric power system, from electricity generation to distribution. An important dimension of this issue is the impact on electricity demand. While many studies have looked at these impacts, few have tried to represent this effect at higher temporal resolutions (such as daily or sub-daily) or to analyze its seasonal aspects. Our study expands on previous work to improve our understanding of how climate change can affect patterns of hourly electricity demand, the differences in these effects over different seasons, and how this in turn could affect the operations of the power system. For this analysis, we combine a linear regression model, a simplified economic dispatch model, and projections from twenty different climate models to analyze how climate change may affect seasonal demand patterns and, consequently, power plants dispatch. We use this method to analyze a case study of the Tennessee Valley Authority (TVA). The results suggest that climate change can result in an average increase in annual electricity consumption in the TVA region of 6% by the end of the century and an increase in the frequency of peak demand values (the maximum quantity of electricity demanded during an hour). However, this increase is not uniformly distributed throughout the year. During summer, total electricity consumption can increase on average by 20% while during winter, it may decrease on average by 6% by the end of the century. Such changes in demand could result in changes in the typical dispatch patterns of TVA’s power plants. Estimated summer time capacity factors would increase (8 to 37% for natural gas and 71 to 84% for coal) and winter time capacity factor decrease (3% to virtually zero for natural gas and 67 to 60% for coal). Such results could affect the decision-making process of planning agents in the power sector.

When a non-climate institution, policy, or regulation corrects a pre-existing market failure that would be exacerbated by climate change, it may also incidentally induce climate adaptation. This regulation-induced adaptation can have large positive welfare effects. We develop a tractable analytical framework of a corrective regulation where the market failure interacts with climate, highlighting the mechanism of regulation-induced adaptation: reductions in the climate-exacerbated effects of pre-existing market failures. We demonstrate this empirically for the US from 1980 to 2013, showing that ambient ozone concentrations increase with rising temperatures, but that such increase is attenuated in counties that are out of attainment with the Clean Air Act’s ozone standards. Adaptation in nonattainment counties reduced the impact of a 1 °C increase in climate normal temperature on ozone concentration by 0.64 parts per billion, or about one-third of the total impact. Over half of that effect was induced by the standard, implying a regulation-induced welfare benefit of $412–471 million per year by mid-century under current warming projections.

In single-zone multi-node systems (SZMNSs), temperature controls rely on a single probe near the thermostat, resulting in temperature discrepancies that cause thermal discomfort and energy waste. Augmenting smart thermostats (STs) with per-room sensors has gained acceptance by major ST manufacturers. This paper leverages additional sensory information to empirically characterize the services provided by buildings, including thermal comfort, energy efficiency, and demand response (DR). Utilizing room-level time-series data from 1000 houses, metadata from 110,000 houses across the United States, and data from two real-world testbeds, we examine the limitations of SZMNSs and explore the potential of remote sensors. We discover that comfortable DR durations (CDRDs) for rooms are typically 70% longer or 40% shorter than for the room with the thermostat. When averaging, rooms at the control temperature’s bounds are typically deviated around −3 °F to 2.5 °F from the average. Moreover, in 95% of houses, we identified rooms experiencing notably higher solar gains compared to the rest of the rooms, while 85% and 70% of houses demonstrated lower heat input and poor insulation, respectively. Lastly, it became evident that the consumption of cooling energy escalates with the increase in the number of sensors, whereas heating usage experiences fluctuations ranging from −19% to +25%. This study serves as a benchmark for assessing the thermal comfort and DR services in the existing housing stock, while also highlighting the energy efficiency impacts of sensing technologies. Our approach sets the stage for more granular, precise control strategies of SZMNSs.

“Placebo tests” are normally used to support evidence of pollution impacts on health outcomes. In this study, we argue that one should be cautious to proceed with falsification tests. We examine how a large metropolitan area in Brazil copes with increased health-care demand due to high air pollution under hospital capacity constraints. Using wind as an instrument, we find that the pollution exposure increases pediatric hospitalization for respiratory diseases while the number of planned procedures decreases in public hospitals. On average, for every four additional pollution-related admissions, one elective care procedure is displaced. Urgent procedures are not displaced.