Regulations to improve air quality, save energy, or reduce climate risks account for the largest share of benefits and costs of the US regulatory program.  We address the economic methods for evaluating this class of regulations in “Benefit-Cost Analysis of Air Pollution, Energy, and Climate Regulations.  This work was recently published as open access in the Cambridge Core Element Series in public economics. The methods considered are relevant for OMB’s ongoing revision of BCA guidance (Circulars A-4 and A-94), and EPA’s proposed changes for measuring the economic benefits of reducing greenhouse gas emissions.  

The work compares and contrasts Regulatory Impact Assessment (RIA) in the U.S. and Europe, and addresses five methodology issues: the estimation of costs, the estimation of benefits, discounting methods, distributional analysis, and the evaluation of less and more fundamental uncertainty. The intended audience is regulators and other constituencies interested in the nexus between scholarship and practice, analysts in government agencies and research organizations, and academic scholars and their graduate students.

The following are some of the key takeaways. 

Comparative RIA Practice in the EU and the US. The integration of BCA into regulatory evaluation has varied among countries, reflecting different proportionality requirements for the use of RIA, and different traditions for using environmental BCA to evaluate regulations and policies. The use of regulatory BCA seems likely to increase with additional policymaking in the air, energy, and climate policy areas, given the significance of the resource tradeoffs. Methods will continue to evolve as the demand for regulatory analysis continues to grow.

Cost Estimation. Methods for regulatory cost estimation differ in the degree to which regulatory characteristics, market behavior, and market distortions are represented. Engineering cost estimation remains the modal method in RIA in Europe and the U.S. Regulations having a significant sectoral or economy-wide impact can justify the resources for constructing partial equilibrium (PE) or general equilibrium (GE) models that explicitly represent the effects of market adjustments. The welfare costs of GE feedbacks with market distortions are well documented in the literature; however, the distortions represented in computational GE models have been somewhat selective. Adding environmental quality non-separably in utility, adding environmental quality to production, and modeling imperfect labor markets, terms-of-trade effects, and externalities in nonregulated sectors, among others, would help fill in the GE picture. Given the challenges of modeling these factors simultaneously, research on the most high-valued modeling improvements would likely to be useful for RIA.

Benefit Estimation The benefit and costs of regulations are separately estimated in RIA practice, and market prices, market price-derived shadow prices, or benefit transfers like the VSL and social cost of greenhouse gas emissions are used to value outcomes. Representing preferences for health or environmental quality non-separably in utility functions in computational GE models, and adding environmental inputs into production functions, would allow the integration of BCA within a conceptually consistent GE framework. Policy-relevant spatial heterogeneities, such as local exposure risks and demographic characteristics, would also need to be represented in this model. Making these adjustments comprehensively enough to be relevant for the evaluation of many kinds of regulations, however, is likely to be difficult for routine practice for the foreseeable future.

Pricing Greenhouse Gas Emissions.  The social cost of greenhouse gases (SC-GGHs) is the method used in the U.S., Canada, and Germany, among others, while the “target consistent” approach is used in the UK and France. These alternatives reflect different views about the role of economic evaluation for supporting climate policy. Using the SC-GHGs implies confidence in the capacity of integrated assessment models to forecast the value of long-run climate damages, the belief that discounting is appropriate for representing intergenerational tradeoffs, and the view that BCA should be used to determine the level of greenhouse gas emissions. Proponents of the target-consistent approach are more likely to doubt the reliability of long-run damage valuations, believe that safe margins, in the form of precautionary emissions targets, are normatively justified, and support a deliberative process for establishing carbon emissions targets to address intergenerational distributional concerns. In this framework, regulatory BCA shows the degree to which a regulation burdens or benefits the emissions target.

Discounting

Discounting perspectives in the academic literature are reflected in the guidance documents of different countries. European governments use consumption discount rates with added risk premia. These rates often fall in the range of one to four percent. Canada and Latin American Countries usually use discount rates based on the social opportunity cost of capital, with rates often varying from six to ten percent.  The pending revision of Circular A-4 proposes switching to a consumption discount rate, paired with a shadow price of capital.

The positive discounting literature is motivated from infrastructure finance, where capital costs are sourced through borrowing. This literature does not represent regulatory cost incidence, where regulations are financed off-budget through economic adjustments throughout the economy. An economy wide perspective is needed to capture the extent to which regulations induce economic actors to trade present for future consumption, reflecting structural details of credit markets (segmentation and credit constraints) that influence consumer discount rates. Doing so would raise consumption discount rates considerably above the standard now used.

The intergenerational discounting literature reflects the view that discounting is an appropriate way to address intergenerational tradeoffs. An alternative is to use transfers to address intergenerational equity, or to impose environmental performance standards the current generation should meet such as greenhouse gas emission targets. In this case, the role of discount rate is to efficiently ration capital for the present generation.

Distributional Analysis. The relative burden of environmental regulations on capital and labor is ambiguous in the literature, as is the impact on sector and economy-wide employment. The empirical characteristics of market structure and the type of regulation affect factors returns and employment levels, and the degree to which producer burdens are passed on to the consumer side of the market.

The literature is also relatively inconclusive about the distributional impact of regulations on consumer expenditures. Income-class burdens reflect the policy instrument (e.g., tax or performance standards), type of fuel, and institutional factors, such as whether transfer payments are indexed to the price level. Heterogeneities also affect household fuel consumption patterns holding income constant. These include household characteristics (e.g., age of residents), local climate conditions, transportation options, and commuting patterns. The price and sources of energy also vary regionally. These factors introduce horizontal distributional differences that are as policy relevant as the vertical distributional impacts of regulations.

The full dimensionality of this distributional pattern is hard to represent in RIA, and distributional assessments tend to be relatively limited. Impacts on key constituencies, such as labor in the regulated sector, or small business, are often profiled. This limited recording is likely to miss significant distributional impacts.

Computational GE model can provide disaggregated surplus measures that embody all welfare channels. These models can also be linked with bottom-up or PE models to represent sectors or regions in greater detail.

Uncertainty Evaluation. Strong assumptions about the validity of forecasts are implicit in the use of many applied economic models. Conventional Monte Carlo simulation and traditional sensitivity analyses may not adequately represent forecast uncertainties. Decision-science methods for modeling more fundamental uncertainty, such as robustness-based methods, are likely to be relevant in many regulatory contexts, especially for regulations addressing climate change. The utility and feasibility of expanding the use of fundamental uncertainty evaluation methods in RIA would be a useful area for future research.