The visual effect of wind turbines on property values is small and diminishing in space and time.

Renewable power generation is the key to decarbonizing the electricity system. Wind power is the fastest-growing renewable source of electricity in the United States. However, expanding wind capacity often faces local opposition, partly due to a perceived visual disamenity from large wind turbines. Here, we provide a US-wide assessment of the externality costs of wind power generation through the visibility impact on property values. To this end, we create a database on wind turbine visibility, combining information on the site and height of each utility-scale turbine having fed power into the U.S. grid, with a high-resolution elevation map to account for the underlying topography of the landscape. Building on hedonic valuation theory, we statistically estimate the impact of wind turbine visibility on home values, informed by data from the majority of home sales in the United States since 1997. We find that on average, wind turbine visibility negatively affects home values in an economically and statistically significant way in close proximity ([Formula: see text]5 miles/8 km). However, the effect diminishes over time and in distance and is indistinguishable from zero for larger distances and toward the end of our sample.

The spatiotemporal pattern of surface ozone and its impact on agricultural productivity in China.

The slowing of agricultural productivity growth globally over the past two decades has brought a new urgency to detect its drivers and potential solutions. We show that air pollution, particularly surface ozone (O3), is strongly associated with declining agricultural total factor productivity (TFP) in China. We employ machine learning algorithms to generate estimates of high-resolution surface O3 concentrations from 2002 to 2019. Results indicate that China's O3 pollution has intensified over this 18-year period. We coupled these O3 estimates with a statistical model to show that rising O3 pollution during nonwinter seasons has reduced agricultural TFP by 18% over the 2002-2015 period. Agricultural TFP is projected to increase by 60% if surface O3 concentrations were reduced to meet the WHO air quality standards. This productivity gain has the potential to counter expected productivity losses from 2°C warming.

Corridor-Level Impacts of Battery-Electric Heavy-Duty Trucks and the Effects of Policy in the United States.

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

Private and External Costs and Benefits of Replacing High-Emitting Peaker Plants with Batteries.

Falling costs of lithium-ion (Li-ion) batteries have made them attractive for grid-scale energy storage applications. Energy storage will become increasingly important as intermittent renewable generation and more frequent extreme weather events put stress on the electricity grid. Environmental groups across the United States are advocating for the replacement of the highest-emitting power plants, which run only at times of peak demand, with Li-ion battery systems. We analyze the life-cycle cost, climate, and human health impacts of replacing the 19 highest-emitting peaker plants in California with Li-ion battery energy storage systems (BESS). Our results show that designing Li-ion BESS to replace peaker plants puts them at an economic disadvantage, even if facilities are only sized to meet 95% of the original plants' load events and are free to engage in arbitrage. However, five of 19 potential replacements do achieve a positive net present value after including monetized climate and human health impacts. These BESS cycle far less than typical front-of-the-meter batteries and rely on the frequency regulation market for most of their revenue. All projects offer net air pollution benefits but increase net greenhouse gas emissions due to electricity demand during charging and upstream emissions from battery manufacturing.

Health and Climate Impacts from Long-Haul Truck Electrification.

Long-haul truck electrification has attracted nascent policy support, but the potential health and climate impacts remain uncertain. Here, we developed an integrated assessment approach with high spatial-temporal (km and hourly) resolution to characterize the causal chain from truck operation to charging loads, electricity grid response, changes in emissions and atmospheric concentrations, and the resulting health and climate impacts across the United States. Compared to future diesel trucks, electrified trucking's net health benefits are concentrated only along the West Coast with a business-as-usual electricity grid. However, with an 80%-renewable electricity grid, most regions would experience net health benefits, and the economic value of avoided climate and health damages exceeds $5 billion annually, an 80% reduction relative to future diesel trucks. Electric trucks with larger batteries may increase health and climate impacts due to additional trips needed to compensate for the payload penalty, but a 2× improvement in the battery specific energy (to ∼320 Wh/kg) could eliminate the additional trips.

Reducing exposure to high levels of perfluorinated compounds in drinking water improves reproductive outcomes: evidence from an intervention in Minnesota.

BACKGROUND: Per- and polyfluoroalkyl substances (PFASs) have been detected in drinking water supplies around the world and are the subject of intense regulatory debate. While they have been associated with several illnesses, their effects on reproductive outcomes remains uncertain. METHODS: We analyzed birth outcomes in the east Minneapolis-St. Paul metropolitan area from 2002 to 2011, where a portion of the population faced elevated exposure to PFASs due to long-term contamination of drinking water supplies from industrial waste disposal. Installation of a water filtration facility in the highly contaminated city of Oakdale, MN at the end of 2006 resulted in a sharp decrease in exposure to PFASs, creating a "natural experiment". Using a difference-in-differences approach, we compare the changes in birth outcomes before and after water filtration in Oakdale to the changes over the same period in neighboring communities where the treatment of municipal water remained constant. RESULTS: Average birth weight and average gestational age were statistically significantly lower in the highly exposed population than in the control area prior to filtration of municipal water supply. The highly exposed population faced increased odds of low birth weight (adjusted odds ratio 1.36, 95% CI 1.25-1.48) and pre-term birth (adjusted odds ratio 1.14, 95% CI 1.09-1.19) relative to the control before filtration, and these differences moderated after filtration. The general fertility rate was also significantly lower in the exposed population (incidence rate ratio 0.73, 95% CI 0.69-0.77) prior to filtration and appeared to be rebounding post-2006. CONCLUSIONS: Our findings provide evidence of a causal relationship between filtration of drinking water containing high levels of exposure to PFASs and improved reproductive outcomes.

North-south polarization of European electricity consumption under future warming.

There is growing empirical evidence that anthropogenic climate change will substantially affect the electric sector. Impacts will stem both from the supply side-through the mitigation of greenhouse gases-and from the demand side-through adaptive responses to a changing environment. Here we provide evidence of a polarization of both peak load and overall electricity consumption under future warming for the world's third-largest electricity market-the 35 countries of Europe. We statistically estimate country-level dose-response functions between daily peak/total electricity load and ambient temperature for the period 2006-2012. After removing the impact of nontemperature confounders and normalizing the residual load data for each country, we estimate a common dose-response function, which we use to compute national electricity loads for temperatures that lie outside each country's currently observed temperature range. To this end, we impose end-of-century climate on today's European economies following three different greenhouse-gas concentration trajectories, ranging from ambitious climate-change mitigation-in line with the Paris agreement-to unabated climate change. We find significant increases in average daily peak load and overall electricity consumption in southern and western Europe (∼3 to ∼7% for Portugal and Spain) and significant decreases in northern Europe (∼-6 to ∼-2% for Sweden and Norway). While the projected effect on European total consumption is nearly zero, the significant polarization and seasonal shifts in peak demand and consumption have important ramifications for the location of costly peak-generating capacity, transmission infrastructure, and the design of energy-efficiency policy and storage capacity.

Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States.

It has been suggested that climate change impacts on the electric sector will account for the majority of global economic damages by the end of the current century and beyond [Rose S, et al. (2014) Understanding the Social Cost of Carbon: A Technical Assessment]. The empirical literature has shown significant increases in climate-driven impacts on overall consumption, yet has not focused on the cost implications of the increased intensity and frequency of extreme events driving peak demand, which is the highest load observed in a period. We use comprehensive, high-frequency data at the level of load balancing authorities to parameterize the relationship between average or peak electricity demand and temperature for a major economy. Using statistical models, we analyze multiyear data from 166 load balancing authorities in the United States. We couple the estimated temperature response functions for total daily consumption and daily peak load with 18 downscaled global climate models (GCMs) to simulate climate change-driven impacts on both outcomes. We show moderate and heterogeneous changes in consumption, with an average increase of 2.8% by end of century. The results of our peak load simulations, however, suggest significant increases in the intensity and frequency of peak events throughout the United States, assuming today's technology and electricity market fundamentals. As the electricity grid is built to endure maximum load, our findings have significant implications for the construction of costly peak generating capacity, suggesting additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual.

Unobserved time effects confound the identification of climate change impacts.

A recent study by Feng et al. [Feng S, Krueger A, Oppenheimer M (2010) Proc Natl Acad Sci USA 107:14257-14262] in PNAS reported statistical evidence of a weather-driven causal effect of crop yields on human migration from Mexico to the United States. We show that this conclusion is based on a different statistical model than the one stated in the paper. When we correct for this mistake, there is no evidence of a causal link.

Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures.

Data from farmer-managed fields have not been used previously to disentangle the impacts of daily minimum and maximum temperatures and solar radiation on rice yields in tropical/subtropical Asia. We used a multiple regression model to analyze data from 227 intensively managed irrigated rice farms in six important rice-producing countries. The farm-level detail, observed over multiple growing seasons, enabled us to construct farm-specific weather variables, control for unobserved factors that either were unique to each farm but did not vary over time or were common to all farms at a given site but varied by season and year, and obtain more precise estimates by including farm- and site-specific economic variables. Temperature and radiation had statistically significant impacts during both the vegetative and ripening phases of the rice plant. Higher minimum temperature reduced yield, whereas higher maximum temperature raised it; radiation impact varied by growth phase. Combined, these effects imply that yield at most sites would have grown more rapidly during the high-yielding season but less rapidly during the low-yielding season if observed temperature and radiation trends at the end of the 20th century had not occurred, with temperature trends being more influential. Looking ahead, they imply a net negative impact on yield from moderate warming in coming decades. Beyond that, the impact would likely become more negative, because prior research indicates that the impact of maximum temperature becomes negative at higher levels. Diurnal temperature variation must be considered when investigating the impacts of climate change on irrigated rice in Asia.

Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India.

Previous studies have found that atmospheric brown clouds partially offset the warming effects of greenhouse gases. This finding suggests a tradeoff between the impacts of reducing emissions of aerosols and greenhouse gases. Results from a statistical model of historical rice harvests in India, coupled with regional climate scenarios from a parallel climate model, indicate that joint reductions in brown clouds and greenhouse gases would in fact have complementary, positive impacts on harvests. The results also imply that adverse climate changes due to brown clouds and greenhouse gases contributed to the slowdown in harvest growth that occurred during the past two decades.