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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.

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.

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 Chinas 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.

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.

This paper proposes a simple two-step estimation method (Climate Adaptive Response Estimation - CARE) to estimate sectoral climate damage functions, which account for long-run adaptation. The paper applies this method in the context of residential electricity and natural gas demand for the world's fifth largest economy — California. The advantage of the proposed method is that it only requires detailed information on intensive margin behavior, yet does not require explicit knowledge of the extensive margin response (e.g., technology adoption). Using almost two billion energy bills, we estimate spatially highly disaggregated intensive margin temperature response functions using daily variation in weather. In a second step, we explain variation in the slopes of the dose response functions across space as a function of summer climate. Using 18 climate models, we simulate future demand by letting households vary consumption along the intensive and extensive margins. We show that failing to account for extensive margin adjustment in electricity demand leads to a significant underestimate of the future impacts on electricity consumption. We further show that reductions in natural gas demand more than offset any climate-driven increases in electricity consumption in this context.

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.

BackgroundPer- 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.MethodsWe 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.ResultsAverage 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.ConclusionsOur findings provide evidence of a causal relationship between filtration of drinking water containing high levels of exposure to PFASs and improved reproductive outcomes.

Using fine-scale panel data and an econometric model, we predict land use change in the Midwestern United States if a new bioenergy crop, Miscanthus × Giganteus (miscanthus), is introduced. To explain farmers' current crop choices, we use a local, limited dependent variable regression based on soil and weather characteristics. To this model, we add miscanthus as a new crop, based on its place dependent BioCro model-predicted yield. We find that the vast majority of land used to grow miscanthus will come from land now used for non-major crops, pasture, woodland, and other uses. This implies that miscanthus can help mitigate climate change by displacing oil usage without causing food conflict.

Farmers may adapt to climate change by substituting away from the crops most severely affected. In this paper we estimate the substitution caused by a moderate change in climate in the US Midwest. We pair a 10-year panel of satellite-based crop coverage with spatially explicit soil data and a fine-scale weather data set. Combining a proportion type model with local regressions, we simultaneously address the econometric issues of proportion dependent variables and spatial correlation of unobserved factors. We find the change in expected crop coverage and then we link those changes to the expected changes from an estimated climate dependent yield equation. Ceteris paribus, we find that climate induced changes in yield are offset by land coverage changes for rice and cotton but they are strongly amplified for corn and soy.

Accumulating evidence indicates that environmental temperature substantially affects economic outcomes and violence, but the reasons for this linkage are not well understood. We systematically evaluate the effect of thermal stress on multiple dimensions of economic decisionmaking, judgment, and destructive behavior with 2,000 participants in Kenya and the US who were randomly assigned to different temperatures in a laboratory. We find that most dimensions of decision-making are unaffected by temperature. However, heat causes individuals to voluntarily destroy other participants' assets, with more pronounced effects during a period of heightened political conflict in Kenya.

Climate scientists have spent billions of dollars and eons of supercomputer time studying how increased concentrations of greenhouse gases and changes in the reflectivity of the earth’s surface affect dimensions of the climate system relevant to human society: surface temperature, precipitation, humidity, and sea levels. Recent incarnations of physical climate models have become sophisticated enough to be able to simulate intensities and frequencies of some extreme events, like tropical storms, under different warming scenarios. In a stark juxtaposition, the efforts involved in and the public resources targeted at understanding how these physical changes translate into economic impacts are disproportionately smaller, with most of the major models being developed and maintained with little to no public funding support. The goal of this paper is first to shed light on how (mostly) economists have gone about calculating the “social cost of carbon” for regulatory purposes and to provide an overview of the past and currently used estimates. In the second part, I will focus on where empirical economists may have the highest value added in this enterprise: specifically, the calibration and estimation of economic damage functions, which map weather patterns transformed by climate change into economic benefits and damages. A broad variety of econometric methods have recently been used to parameterize the dose (climate) response (economic outcome) functions. The paper seeks to provide an accessible and comprehensive overview of how economists think about parameterizing damage functions and quantifying the economic damages of climate change.

Agriculture in both the developing and developed country context is highly sensitive to weather shocks. The intensity of these shocks is likely to increase under climate change, leading to an ongoing debate regarding the ability of farmers to insulate yields and income against accelerating environmental extremes. We study crop diversity as an avenue for increased resilience. Diversity in agricultural systems has been suggested in the agroecology and environmental economics literatures as a powerful means of on-farm insurance, both through physical and market-based channels. However, large-scale empirical evidence of its effectiveness is lacking, and crop diversity is largely absent from the empirical climate impacts literature. We examine the insurance benefits of crop diversity in the context of India at the height of the Green Revolution, a period of rapid change in agricultural diversification due to the increased penetration of a small set of high-yielding variety crops. Building on a basic empirical model from the climate impacts literature, we show that areas with higher crop diversity of planted area display measurably more drought resilience, both in terms of gross and net revenues. We decompose this aggregate result to show that diversification has implications for farmer welfare both through physical (yield) and market (price) channels.

Climate change poses many new risks to people in the developing world. Unlike in standard choice under uncertainty problems, the risks from climate change are both ambiguous and non-stationary. We examine climate projections concerning what scenarios are most likely to unfold. We explore how individual farmers in the developing world are likely to respond to these challenges. This analysis points out data gaps and valuable future research paths. Urbanization offers one possible adaptation channel. We explore the opportunities and challenges posed by climate change induced accelerated Least Developed Country (LDC) urbanization.

A study finds that meeting climate-change mitigation targets will lead to a substantial reduction in economic damages. Here, economists present opposing views on the approach used by studies such as this one.

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.

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.

85% of China's GHG emissions are attributed to urban economic activities, and this share is expected to rise given China's fast urbanization process. This paper provides estimates of city-level industrial CO2 emissions and their growth rates for all 287 Chinese prefecture-level and above cities during the years 1998–2009. We decompose the CO2 emission changes into scale, composition and technique effects. The decomposition results show that these three effects differ significantly across the three tiers of cities in China. The scale effect contributes to rising CO2 emissions, while the technique effect leads to declining CO2 emissions in all cities. The composition effect leads to increasing CO2 emissions in the third-tier cities, while it reduces CO2 emissions in the first and second-tier cities, due to the relocation of energy-intensive industries from the latter to the former type of cities. Based on these decomposition results, we identify the separate channels through which the inflow of FDI and the environmental regulations affect city-level CO2 emissions. The decomposition framework in our paper can help policy makers and scholars to better understand Chinese cities’ trade-offs between economic growth and environmental goals.

Assessing past impacts of observed climate change on natural, human and managed systems requires detailed knowledge about the effects of both climatic and other drivers of change, and their respective interaction. Resulting requirements with regard to system understanding and long-term observational data can be prohibitive for quantitative detection and attribution methods, especially in the case of human systems and in regions with poor monitoring records. To enable a structured examination of past impacts in such cases, we follow the logic of quantitative attribution assessments, however, allowing for qualitative methods and different types of evidence. We demonstrate how multiple lines of evidence can be integrated in support of attribution exercises for human and managed systems. Results show that careful analysis can allow for attribution statements without explicit end-to-end modeling of the whole climate-impact system. However, care must be taken not to overstate or generalize the results and to avoid bias when the analysis is motivated by and limited to observations considered consistent with climate change impacts.

We identify two issues with the reverse regression approach as implemented in several classic reconstructions of past climate fluctuations from dendroclimatologcical data series. First, instead of estimating the causal relationship between the proxy, which is measured with significant error, as function of climate and formally inverting the relationship, most papers estimate the inverted relationship directly. This leads to biased coefficients and reconstructions with artificially low variance. Second, we show that inversion of the relationship is often done incorrectly when the underlying causal relationship is dynamic in nature. We show analytically as well as using Monte Carlo experiments and actual tree ring data, that the reverse regression method results in biased coefficients, reconstructions with artificially low variance and overly smooth reconstructions. We further demonstrate that correct application of the inverse regression method is preferred. However, if the measurement error in the tree ring index is significant, neither method provides reliable reconstructions.

There is growing interest in assessing the role of climate change in observed extreme weather events. Recent work in this area has focused on estimating a measure called attributable risk. A statistical formulation of this problem is described and used to construct a confidence interval for attributable risk. The resulting confidence is shown to be surprisinglywide even in the case where the event of interest is unprecedented in the historical record.

This paper reviews the literature on the relationship between climate and the energy sector. In particular, we primarily discuss empirical papers published in peer-reviewed economics journals focusing on how climate affects energy expenditures and consumption. Climate will affect energy consumption by changing how consumers respond to short run weather shocks (the intensive margin) as well as how people will adapt in the long run (the extensive margin). Along the intensive margin, further research that uses household and firm-level panel data of energy consumption may help identify how energy consumers around the world respond to weather shocks. Research on technology adoption, e.g. air conditioners, will further our understanding of the extensive margin adjustments and their costs. We also note that most of the literature focuses on the residential sector. Similar studies are urgently needed for the industrial and commercial sectors.

Agricultural production is heavily dependent on weather outcomes, and hence climate change has the potential to significantly alter the sector's productivity. Both reduced form studies as well as integrated assessment models have found that the agricultural sector might experience significant impacts. We discuss the advantages of empirical reduced-form studies and their link and potential usefulness to integrated assessment models. We further discuss challenges facing empirical studies and recent research that looks at the longer term changes in climate and attempts to measure adaptation.

The lack of robust water markets makes it difficult to value irrigation water. Because water rights are appurtenant to land, it is possible to infer the value of water from observed differences in the market price of land. We use panel data on repeat farmland sales in California's San Joaquin Valley to estimate a hedonic regression equation with parcel fixed effects. This controls for sources of omitted variables bias and allows us to recover the value of irrigation water to landowners in our sample. We show that a more traditional cross-sectional regression results in an artificially low value of irrigation water. © The Author (2014).

Current forecasts suggest that the vast majority of growth in energy demand will come from the developing world, and that China will play a major part in that growth. This paper presents evidence suggesting that the shape of the income distribution, which is typically omitted from forecasting models, plays a major role in driving household acquisition of energy-using durable goods in rural China. We use province-level data for rural households to show that the share of the population living above the poverty line is an important determinant of household appliance holdings even controlling for average household income.

One of the major adaptation mechanisms to climate change is increased demand for cooling via the air conditioning of indoor environments. China's demand for air conditioners has displayed explosive growth since 1995. This paper provides estimates of the income and short run weather sensitivity of air conditioner adoption across urban areas for 29 Chinese provincial entities. We show that the adoption decision displays significant income and weather sensitivity in the short run, with adoption being higher the year following a hot summer. © 2014 Copyright 2014 by Koninklijke Brill NV, Leiden, The Netherlands.

Heavier vehicles are safer for their own occupants but more hazardous for other vehicles. Simple theory thus suggests that an unregulated vehicle fleet is inefficiently heavy. Using three separate identification strategies we show that, controlling for own-vehicle weight, being hit by a vehicle that is 1000 pounds heavier generates a 40–50% increase in fatality risk. These results imply a total accident-related externality that exceeds the estimated social cost of US carbon emissions and is equivalent to a gas tax of $0.97 per gallon ($136 billion annually). We consider two policies for internalizing this external cost, a weight-varying mileage tax and a gas tax, and find that they are similar for most vehicles. The findings suggest that European gas taxes may be much closer to optimal levels than the US gas tax.

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 (<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. [ABSTRACT FROM AUTHOR]

A recent paper in this journal argues that the choice of statistical model is responsible for the divergence in damage estimates of climate change on US agriculture. We provide five arguments why we believe this assertion is misguided. © 2013 Springer Science+Business Media Dordrecht.

Anthropogenic climate change has triggered impacts on natural and human systems world-wide, yet the formal scientific method of detection and attribution has been only insufficiently described. Detection and attribution of impacts of climate change is a fundamentally cross-disciplinary issue, involving concepts, terms, and standards spanning the varied requirements of the various disciplines. Key problems for current assessments include the limited availability of long-term observations, the limited knowledge on processes and mechanisms involved in changing environmental systems, and the widely different concepts applied in the scientific literature. In order to facilitate current and future assessments, this paper describes the current conceptual framework of the field and outlines a number of conceptual challenges. Based on this, it proposes workable cross-disciplinary definitions, concepts, and standards. The paper is specifically intended to serve as a baseline for continued development of a consistent cross-disciplinary framework that will facilitate integrated assessment of the detection and attribution of climate change impacts. © 2013 The Author(s).

In this article, we first review the history of carbon dioxide (CO2) emissions from the combustion of fossil fuels for the People's Republic of China (China). As Chinese regulators have announced efforts to directly regulate CO2 emissions, we review the history of institutions charged with climate regulation in China. Next we review China's existing market-based approaches to emission control in the form of the world's largest effluent fee system for air and water pollutants as well as seven pilot carbon markets. We conclude with a discussion of the issues and challenges ahead in adopting market-based instruments to regulate CO2 emissions in China.

One of the obvious modes of adaptation to higher temperatures due to climate change is the increased demand for cooling and decreased demand for heating in the built environment. California’s residential sector uses relatively little electricity for heating, and it is therefore expected that the demand for electricity will increase as households operate existing air conditioners more frequently, and in many regions, will install air conditioners where there currently are few. This paper provides reduced form estimates of changes in electricity consumption due to increased use of installed cooling equipment under a hotter climate. This study adds to the literature by incorporating the change in temperature responsiveness due to likely increases in air conditioner penetration under climate change using a two-stage method. It shows that taking into account these capital investments may lead to higher projections of electricity consumption. These increases in projected electricity consumption were mapped to the ZIP codes in the study data. The paper shows suggestive evidence that more Caucasian and wealthy ZIP codes are projected to experience relatively smaller increases in consumption, while ZIP codes with a higher share of Latino population are projected to experience larger increases in consumption

This study simulates the impacts of higher temperatures resulting from anthropogenic climate change on residential electricity consumption for the nine San Francisco Bay Area counties (Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, and Sonoma). Flexible temperature response functions are estimated by climate zone, which allows for differential effects of days with different meant temperatures on households’ electricity consumption. The estimation uses a comprehensive household-level data set of billing data for Pacific Gas and Electric Company). The results suggest that the temperature response varies greatly across climate zones. Simulation results using three downscaled climate models suggest that for constant population the total demand for the households that were considered may increase by between 1 to 4 percent by the end of the century. The study further simulates the impacts of higher electricity prices and different scenarios of population growth.

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This paper examines the effects of U.S. gasoline content regulations on groundlevel ozone pollution. These regulations are costly and have been shown to fragment gasoline markets and raise prices paid by consumers. We provide the first comprehensive empirical estimates of the regulations’ air quality benefits. We exploit the fact that gasoline regulations vary by time and place of introduction, using both difference-in-difference and regression discontinuity designs. We show that federal regulations targeting the emissions of volatile organic compounds (VOCs), one of the two main precursors to ozone, do not substantially improve air quality. This outcome is driven by the response of refiners to the regulation: minimizing the cost of abatement involves removing a type of VOC from gasoline that is not an important determinant of ozone pollution. In California, however, we show that precisely targeted regulations requiring the removal of VOCs particularly prone to forming ozone caused a significant improvement in air quality.

This paper examines the consequences of data error in data series used to construct aggregate indicators. Using the most popular indicator of country level economic development, the Human Development Index (HDI), we identify three separate sources of data error. We propose a simple statistical framework to investigate how data error may bias rank assignments and identify two striking consequences for the HDI. First, using the cutoff values used by the United Nations to assign a country as ‘low’, ‘medium’, or ‘high’ developed, we find that currently up to 45% of developing countries are misclassified. Moreover, by replicating prior development/macroeconomic studies, we find that key estimated parameters such as Gini coefficients and speed of convergence measures vary by up to 100% due to data error.

We examine the effects of the 1990 Clean Air Act Amendments (CAAA) on ambient concentrations of PM10 in the United States between 1990 and 2005. Consistent with prior literature, we find that non-attainment designation has no effect on the average monitor in non-attainment counties, after controlling for weather, socioeconomic characteristics at the county level and lagged concentrations. In sharp contrast, if we allow for heterogeneous treatment by type of monitor and county, we do find that the 1990 CAAA produced substantial effects. Our estimation results suggest that non-attainment counties with single monitors experienced a drop in concentrations of 10.5% relative to attainment counties. In non-attainment counties with multiple monitors, the overall effect of the regulation is an increase of ambient PM10 concentrations by 1.9%. The dirtiest monitors in these counties, however, experienced drops in PM10 of 6.1%, which suggest that regulators focus their attention on the dirtiest monitors.

Our results suggest that the anticipated path of China's Carbon Dioxide (CO2) emissions has dramatically increased over the last five years. The magnitude of the projected increase in Chinese emissions out to 2015 is several times larger than reductions embodied in the Kyoto Protocol. Our estimates are based on a unique provincial level panel data set from the Chinese Environmental Protection Agency. This dataset contains considerably more information relevant to the path of likely Chinese greenhouse gas emissions than national level time series models currently in use. Model selection criteria clearly reject the popular static environmental Kuznets curve specification in favor of a class of dynamic models with spatial dependence.

The key finding of an influential paper that received the International Association for Energy Economists' Best Paper Award (2004) is that utilities have been overstating electricity savings and underestimating costs associated with energy efficiency demand side management (DSM) programs. This claim is based on point estimates of average DSM-related savings and costs implied by an econometric model of residential electricity demand. In this response we first argue that the choice of test statistics, by not weighting estimated savings and costs by utility electricity sales and DSM expenditures respectively, biases results in favor of rejecting the null hypothesis that utility-reported electricity savings reflect true values. We also note that utility estimates of average program savings and costs are rejected based on point estimates alone; no attempt is made to evaluate the uncertainty surrounding these estimates. We use the same data and econometric model to estimate the appropriate test statistics. We then construct nonparametric bootstrap confidence intervals. We fail to reject the average electricity savings and DSM program costs reported by utilities using both the weighted and unweighted test statistics. Our results suggest that the evidence for rejecting utility estimates of DSM savings and costs should be re-interpreted.

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