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Understanding the distribution of regulatory costs is key to evaluating whether a rulemaking exacerbates or ameliorates preexisting economic disparities and is of stated interest to many stakeholders and policy makers. Previous studies on the incidence of command-and-control environmental regulations have predominantly focused on the distribution of costs through final goods prices (the use side). However, the impact of regulations on household income (the source side) can be of first-order importance in determining the overall incidence. Using a detailed computable general equilibrium model of the U.S. economy we study the incidence of single-sector technology mandates across a broad set of industrial sectors. We find the use-side incidence is notably regressive but the source-side effects are progressive on average and tend to dominate the overall incidence of costs. This occurs as a significant share of regulatory costs is passed on through lower returns to capital and natural resources, which predominantly affects upper-income households, while indexed transfer payments partially shields the purchasing power of low-income households from increases in output prices. However, when the regulated sector predominantly produces final goods with inelastic demand and low trade exposure (e.g., utility services) we find that the use-side incidence can dominate leading to regressive distribution of regulatory costs. Finally, we find that the common practice of vintage differentiation, whereby only new sources of pollution are covered, can cause a significantly more regressive distribution of costs, all else equal.

SAGE is an applied general equilibrium model of the United States economy developed for the analysis of environmental regulations and policies. It is an intertemporal model with perfect foresight, resolved at the sub-national level. Each of the nine regions in the model, representing the nine census divisions, has five households based on income quintiles. A single government agent levies taxes on labor earnings, capital earnings, production, and consumption. As with many applied general equilibrium models used for the analysis of U.S. environmental and energy policies, the baseline is calibrated to the Energy Information Administration’s Annual Energy Outlook. The model is solved as mixed complementarity problem (MCP) using the General Algebraic Modeling System (GAMS).

Economy-wide feedbacks can affect the efficiency and incidence of policy interventions, and have long been of interest to stake-holders and researchers. When concerns are raised about the general equilibrium (GE) effects of environmental regulations it is most often with respect to the costs of compliance. However, both regulators and researchers have recently shown interest in the potential economy-wide effects of health improvements, including mortality risk reductions. In a study of the cumulative effects of the Clean Air Act, the U.S. Environmental Protection Agency found the benefits of air quality improvements were more than an order of magnitude smaller when estimated with a GE approach compared with a traditional partial equilibrium (PE) approach. It has been suggested that these results are evidence that the PE benefits of environmental regulations are implausibly large. However, previous GE analyses of environmental policies have characterized the expected health improvements using highly simplified approximations whose validity have yet to be closely examined. We present the first explicit characterization of mortality risk changes in a GE model applied to environmental regulations. We find that reductions in mortality risks can have significant GE feedbacks and that these effects are important for estimating the benefits, economic impacts, and potentially the costs of environmental policies. However, our results also suggest that critical methodological limitations led to biased results in previous studies and that the PE estimates of mortality risk reductions are not necessarily implausibly large.

Concern regarding the economic impacts of environmental regulations has been part of the public dialogue since the beginning of the U.S. EPA. Even as large improvements in environmental quality occurred, government and academia began to examine the potential consequences of regulation for economic growth and productivity. In general, early studies found measurable but not severe effects on the overall national economy. While price increases due to regulatory requirements outweighed the stimulative effect of investments in pollution abatement, they nearly offset one another. However, these studies also highlighted potentially substantial effects on local labor markets due to the regional and industry concentration of plant closures. More recently, a substantial body of work examined industry-specific effects of environmental regulation on the productivity of pollution-intensive firms most likely to face pollution control costs, as well as on plant location and employment decisions within firms. Most econometric-based studies found relatively small or no effect on sector-specific productivity and employment, though firms were less likely to open plants in locations subject to more stringent regulation compared to other U.S. locations. In contrast, studies that used economy-wide models to explicitly account for sectoral linkages and intertemporal effects found substantial sector-specific effects due to environmental regulation, including in sectors that were not directly regulated. It is also possible to think about the overall impacts of environmental regulation on the economy through the lens of benefit-cost analysis. While this type of approach does not speak to how the costs of regulation are distributed across sectors, it has the advantage of explicitly weighing the benefits of environmental improvements against their costs. If benefits are greater than costs, then overall social welfare is improved. When conducting such exercises, it is important to anticipate the ways in which improvements in environmental quality may either directly improve the productivity of economic factors – such as through the increased productivity of outdoor workers – or change the composition of the economy as firms and households change their behavior. If individuals are healthier, for example, they may choose to reallocate their time between work and leisure. While introducing a role for pollution in production and household behavior can be challenging, studies that have partially accounted for this interconnection have found substantial impacts of improvements in environmental quality on the overall economy.

Benefit–cost analysis can serve as an informative input into the policy-making process, but only to the degree it characterizes the major impacts of the regulation under consideration. Recently, the US, amongst other nations, has begun to use estimates of the social cost of CO2 (SC-CO2) to develop analyses that more fully capture the climate change impacts of GHG abatement. The SC-CO2 represents the aggregate willingness to pay to avoid the damages associated with an additional tonne of CO2 emissions. In comparison, the social costs of non-CO2 GHGs have received little attention from researchers and policy analysts, despite their non-negligible climate impact. This article addresses this issue by developing a set of social cost estimates for two highly prevalent non-CO2 GHGs, methane and nitrous oxide. By extending existing integrated assessment models, it is possible to develop a set of social cost estimates for these gases that are consistent with the SC-CO2 estimates currently in use by the US federal government.Policy relevanceWithin the benefit–cost analyses that inform the design of major regulations, all Federal agencies within the US Government (USG) use a set of agreed upon SC-CO2 estimates to value the impact of CO2 emissions changes. However, the value of changes in non-CO2 GHG emissions has not been included in USG policy analysis to date. This article addresses that omission by developing a set of social cost estimates for two highly prevalent non-CO2 GHGs, methane and nitrous oxide. These new estimates are designed to be compatible with the USG SC-CO2 estimates currently in use and may therefore be directly applied to value emissions changes for these non-CO2 gases within the benefit–cost analyses used to evaluate future policies.

It has often been stated that current studies aimed at understanding the magnitude of optimal climate policy fail to adequately capture the potential for “catastrophic” impacts of climate change. While economic modeling exercises to date do provide evidence that potential climate catastrophes might significantly influence the optimal path of abatement, there is a need to move beyond experiments which are abstracted from important details of the climate problem in order to substantively inform the policy debate. This paper provides a foundation for improving the economic modeling of potential large scale impacts of climate change in order to understand their influence on estimates of socially efficient climate policy. We begin by considering how the term “catastrophic impacts” has been used in the scientific literature to describe changes in the climate system and carefully review the characteristics of the events that have been discussed in this context. We contrast those findings with a review of the way in which the economic literature has modeled the potential economic and human welfare impacts of events of this nature. We find that the uniform way in which the economic literature has typically modeled such impacts along with the failure to understand differences in the end points and timescales examined by the natural science literature has resulted in the modeling of events that do not resemble those of concern. Based on this finding and our review of the scientific literature we provide a path forward for better incorporating these events into integrated assessment modeling, identifying areas where modeling could be improved even within current modeling frameworks and others where additional work is needed.

Many estimates of the social cost of CO2 emissions (SCCO2) can be found in the climate economics literature. However, to date far fewer estimates of the social costs of other greenhouse gases have been published, and many of those that are available are not directly comparable to current estimates of the SCCO2. In this paper we use a simplified integrated assessment model that combines MAGICC and (elements of) DICE to estimate the social costs of the three most important greenhouse gases—CO2, CH4, and N2O—for the years 2010 through 2050. Insofar as possible, we base our model runs on the assumptions and input parameters of the recent U.S. government inter-agency SCC working group. We compare our estimates of the social costs of CH4 and N2O emissions to those that would be produced by using the SCCO2 to value the "CO2-equivalents" of each of these gases, as calculated using their global warming potentials (GWPs). We examine the estimation error induced by valuing non-CO2 greenhouse gas emission reductions using GWPs and the SCCO2 for single- and multi-gas abatement policies. In both cases the error can be large, so estimates of the social costs of these gases, rather than proxies based on GWPs, should be used whenever possible. However, if estimates of the social cost are not available the value of non-CO2 GHG reductions estimated using GWPs and the SCCO2 will typically have lower absolute errors than default estimates of zero.

Integrated Assessment Models (IAMs) couple representations of the natural climate system with models of the global economy to capture interactions that are important for the evaluation of potential climate and energy policies. The U.S. federal government currently uses such models to derive the benefits of carbon mitigation policies through estimates of the social cost of carbon (SCC). To remain tractable these models often utilize highly simplified representations of complex natural, social, and economic systems. This makes IAMs susceptible to oversimplification by failing to capture key features of the underlying system that are important for policy analysis. In this paper we focus on one area in which these models appear to have fallen into such a trap. We consider three prominent IAMs, DICE, FUND, and PAGE, and examine the way in which these models represent the transient temperature response to increases in radiative forcing. We compare the highly simplified temperature response models in these IAMs to two upwelling diffusion energy balance models that better reflect the progressive uptake of heat by the deep ocean. We find that all three IAMs are unable to fully capture important characteristics in the temporal dynamics of temperature response, especially in the case of high equilibrium climate sensitivity. This has serious implications given that these models are often run with distributions for the equilibrium climate sensitivity that contain a positive probability for such states of the world. We find that all else equal the temperature response function utilized in the FUND model results in estimates of the expected SCC that are up to 25% lower than those derived with the more realistic climate models, while the functions used in DICE and PAGE lead to expected SCC estimates up to 40% and 50% higher, respectively.

Over the past five decades, the federal government has enacted laws and developed regulations to manage actual and threatened hazardous releases. This paper describes a relatively understudied component of the nation’s response capability – the Superfund Emergency Response and Removal (ERR) Program. Drawing on a new dataset of 113 recent removal actions on 88 sites in the Mid-Atlantic region, we find a great deal of diversity across sites, from the discovery and cause of contamination to the types of risks and the cleanup strategy. The program addresses traditionally studied media such as soil, water, and air contamination, as well as risks from not-yet-released contained contaminants and potential fire or explosion. One of the program’s major strengths is its ability to address this wide range of threats, even though this very heterogeneity complicates research efforts to assess its net benefits. We describe the involvement of potentially responsible parties and EPA expenditures on removal actions. Finally, we consider future challenges for research into the net benefits of the program.

The management of non-native invasive species is a complex but crucial task given the potential for economic and environmental damages. For many invasions the development of socially optimal control strategies requires more than is offered by the single-species, single-control models that have dominated this area of research. We develop a general stochastic optimal control framework that accommodates multiple interacting species while accounting for uncertainty in the temporal population dynamics. This extension to the current line of bioeconomic control models allows for the design of optimal management strategies that utilize both chemical and biological controls in an environment of uncertainty and irreversibility. We demonstrate the benefits of combining chemical and biological controls in long term management strategies through a case study of the hemlock woolly adelgid infestation in the eastern United States. In this application we find that the introduction of natural predators is usually sufficient to manage the infestation, though the availability of chemical controls is important when the detection of an invasion or the subsequent response is delayed.