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Eyes on the Prize: Federal Climate Policy Should Preempt State and Regional Initiatives

In just a few days, Senators John Kerry, Lindsey Graham, and Joe Lieberman will release their much-anticipated proposal for comprehensive climate and energy legislation – the best remaining shot at forging a bipartisan consensus on this issue in 2010. Their proposal has many strengths, but there’s an issue brewing that could undermine its effectiveness and drive up its costs. I wrote about this in a Boston Globe op-ed on Earth Day, April 22^nd (the original version of which can be downloaded here).

Government officials from California, New England, New York, and other northeastern states are vociferously lobbying in Washington to retain their existing state and regional systems for reducing greenhouse gas emissions, even after a new federal system comes into force. That would be a mistake – and a potentially expensive one for residents of those states, who could wind up subsidizing the rest of the country. The Senate should do as the House did in its climate legislation: preempt state and regional climate policies. There’s no risk, because if Federal legislation is not enacted, preemption will not take effect.

The regional systems – including the Regional Greenhouse Gas Initiative (RGGI) in the Northeast and Assembly Bill 32 in California – seek to limit carbon dioxide emissions from power plants and other sources, mainly by making emissions more costly for firms and individuals. These systems were explicitly developed because the federal government was not moving fast enough.

But times have changed. Like the House climate legislation passed last June, the new Senate bill will feature at its heart an economy-wide carbon-pricing scheme to reduce carbon dioxide emissions, including a cap-and-trade system (under a different name) for the electricity and industrial sectors. (In a departure from the House version, it may have a carbon fee for transportation fuels.)

Though the Congress has a history of allowing states to act more aggressively on environmental protection, this tradition makes no sense when it comes to climate change policy. For other, localized environmental problems, California or Massachusetts may wish to incur the costs of achieving cleaner air or water within their borders than required by a national threshold. But with climate change, it is impossible for regions, states, or localities to achieve greater protection for their jurisdictions through more ambitious actions.

This is because of the nature of the climate change problem. Greenhouse gases, including carbon dioxide, uniformly mix in the atmosphere – a unit of carbon dioxide emitted in California contributes just as much to the problem as carbon dioxide emitted in Tennessee. The overall magnitude of damages – and their location – are completely unaffected by the location of emissions. This means that for any individual jurisdiction, the benefits of action will inevitably be less than the costs. (This is the same reason why U.S. federal action on climate change should occur at the same time as other countries take actions to reduce their emissions).

If federal climate policy comes into force, the more stringent California policy will accomplish no additional reductions in greenhouse gases, but simply increase the state’s costs and subsidize other parts of the country. This is because under a nationwide cap-and-trade system, any additional emission reductions achieved in California will be offset by fewer reductions in other states.

A national cap-and-trade system – which is needed to address emissions meaningfully and cost-effectively – will undo the effects of a more stringent cap within any state or group of states. RGGI, which covers only electricity generation and which will be less stringent than the Federal policy, will be irrelevant once the federal system comes into force.

In principle, a new federal policy could allow states to opt out if they implement a program at least as stringent. But why should states want to opt out? High-cost states will be better off joining the national system to lower their costs. And states that can reduce emissions more cheaply will be net sellers of Federal allowances.

Is there any possible role for state and local policies? Yes. Price signals provided by a national cap-and-trade system are necessary to meaningfully address climate change at sensible cost, but such price signals are not sufficient. Other market failures call for supplementary policies. Take, for example, the principal-agent problem through which despite higher energy prices, both landlords and tenants lack incentives to make economically-efficient energy-conservation investments, such as installing thermal insulation. This problem can be handled by state and local authorities through regionally-differentiated building codes and zoning.

But for the core of climate policy – which is carbon pricing – the simplest, cleanest, and best way to avoid unnecessary costs and unnecessary actions is for existing state systems to become part of the federal system. Political leaders from across the country – including the Northeast and California – would do well to follow the progressive lead of Massachusetts Governor Deval Patrick and Secretary of Energy and Environmental Affairs Ian Bowles, who have played key roles in the design and implementation of RGGI, and yet have also publicly supported its preemption by a meaningful national program.

California’s leaders and those in the Northeast may take great pride in their state and regional climate policies, but if they accomplish their frequently-stated goal – helping to bring about the enactment of a meaningful national climate policy – they will better serve their states and the country by declaring victory and getting out of the way.

Is Benefit-Cost Analysis Helpful for Environmental Regulation?

With the locus of action on Federal climate policy moving this week from the House of Representatives to the Senate, this is a convenient moment to step back from the political fray and reflect on some fundamental questions about U.S. environmental policy.

One such question is whether economic analysis – in particular, the comparison of the benefits and costs of proposed policies – plays a truly useful role in Washington, or is it little more than a distraction of attention from more important perspectives on public policy, or – worst of all – is it counter-productive, even antithetical, to the development, assessment, and implementation of sound policy in the environmental, resource, and energy realms. With an exceptionally talented group of thinkers – including scientists, lawyers, and economists – now in key environmental and energy policy positions at the White House, the Environmental Protection Agency, the Department of Energy, and the Department of the Treasury, this question about the usefulness of benefit-cost analysis is of particular importance.

For many years, there have been calls from some quarters for greater reliance on the use of economic analysis in the development and evaluation of environmental regulations. As I have noted in previous posts on this blog, most economists would argue that economic efficiency — measured as the difference between benefits and costs — ought to be one of the key criteria for evaluating proposed regulations. (See: “The Myths of Market Prices and Efficiency,” March 3, 2009; “What Baseball Can Teach Policymakers,” April 20, 2009; “Does Economic Analysis Shortchange the Future?” April 27, 2009) Because society has limited resources to spend on regulation, such analysis can help illuminate the trade-offs involved in making different kinds of social investments. In this sense, it would seem irresponsible not to conduct such analyses, since they can inform decisions about how scarce resources can be put to the greatest social good.

In principle, benefit-cost analysis can also help answer questions of how much regulation is enough. From an efficiency standpoint, the answer to this question is simple — regulate until the incremental benefits from regulation are just offset by the incremental costs. In practice, however, the problem is much more difficult, in large part because of inherent problems in measuring marginal benefits and costs. In addition, concerns about fairness and process may be very important economic and non-economic factors. Regulatory policies inevitably involve winners and losers, even when aggregate benefits exceed aggregate costs.

Over the years, policy makers have sent mixed signals regarding the use of benefit-cost analysis in policy evaluation. Congress has passed several statutes to protect health, safety, and the environment that effectively preclude the consideration of benefits and costs in the development of certain regulations, even though other statutes actually require the use of benefit-cost analysis. At the same time, Presidents Carter, Reagan, Bush, Clinton, and Bush all put in place formal processes for reviewing economic implications of major environmental, health, and safety regulations. Apparently the Executive Branch, charged with designing and implementing regulations, has seen a greater need than the Congress to develop a yardstick against which regulatory proposals can be assessed. Benefit-cost analysis has been the yardstick of choice

It was in this context that ten years ago a group of economists from across the political spectrum jointly authored an article in Science magazine, asking whether there is role for benefit-cost analysis in environmental, health, and safety regulation. That diverse group consisted of Kenneth Arrow, Maureen Cropper, George Eads, Robert Hahn, Lester Lave, Roger Noll, Paul Portney, Milton Russell, Richard Schmalensee, Kerry Smith, and myself. That article and its findings are particularly timely, with President Obama considering putting in place a new Executive Order on Regulatory Review.

In the article, we suggested that benefit-cost analysis has a potentially important role to play in helping inform regulatory decision making, though it should not be the sole basis for such decision making. We offered eight principles.

First, benefit-cost analysis can be useful for comparing the favorable and unfavorable effects of policies, because it can help decision makers better understand the implications of decisions by identifying and, where appropriate, quantifying the favorable and unfavorable consequences of a proposed policy change. But, in some cases, there is too much uncertainty to use benefit-cost analysis to conclude that the benefits of a decision will exceed or fall short of its costs.

Second, decision makers should not be precluded from considering the economic costs and benefits of different policies in the development of regulations. Removing statutory prohibitions on the balancing of benefits and costs can help promote more efficient and effective regulation.

Third, benefit-cost analysis should be required for all major regulatory decisions. The scale of a benefit-cost analysis should depend on both the stakes involved and the likelihood that the resulting information will affect the ultimate decision.

Fourth, although agencies should be required to conduct benefit-cost analyses for major decisions, and to explain why they have selected actions for which reliable evidence indicates that expected benefits are significantly less than expected costs, those agencies should not be bound by strict benefit-cost tests. Factors other than aggregate economic benefits and costs may be important.

Fifth, benefits and costs of proposed policies should be quantified wherever possible. But not all impacts can be quantified, let alone monetized. Therefore, care should be taken to assure that quantitative factors do not dominate important qualitative factors in decision making. If an agency wishes to introduce a “margin of safety” into a decision, it should do so explicitly.

Sixth, the more external review that regulatory analyses receive, the better they are likely to be. Retrospective assessments should be carried out periodically.

Seventh, a consistent set of economic assumptions should be used in calculating benefits and costs. Key variables include the social discount rate, the value of reducing risks of premature death and accidents, and the values associated with other improvements in health.

Eighth, while benefit-cost analysis focuses primarily on the overall relationship between benefits and costs, a good analysis will also identify important distributional consequences for important subgroups of the population.

From these eight principles, we concluded that benefit-cost analysis can play an important role in legislative and regulatory policy debates on protecting and improving the natural environment, health, and safety. Although formal benefit-cost analysis should not be viewed as either necessary or sufficient for designing sensible public policy, it can provide an exceptionally useful framework for consistently organizing disparate information, and in this way, it can greatly improve the process and hence the outcome of policy analysis.

If properly done, benefit-cost analysis can be of great help to agencies participating in the development of environmental regulations, and it can likewise be useful in evaluating agency decision making and in shaping new laws (which brings us full-circle to the climate legislation that will be developed in the U.S. Senate over the weeks and months ahead, and which I hope to discuss in future posts).

What Baseball Can Teach Policymakers

With the Major League Baseball season having just begun, I’m reminded of the truism that the best teams win their divisions in the regular season, but the hot teams win in the post-season playoffs. Why the difference? The regular season is 162 games long, but the post-season consists of just a few brief 5-game and 7-game series. And because of the huge random element that pervades the sport, in a single game (or a short series), the best teams often lose, and the worst teams often win.

The numbers are striking, and bear repeating. In a typical year, the best teams lose 40 percent of their games, and the worst teams win 40 percent of theirs. In the extreme, one of the best Major League Baseball teams ever - the 1927 New York Yankees – lost 29 percent of their games; and one of the worst teams in history – the 1962 New York Mets – won 25 percent of theirs. On any given day, anything can happen. Uncertainty is a fundamental part of the game, and any analysis that fails to recognize this is not only incomplete, but fundamentally flawed.

The same is true of analyses of environmental policies. Uncertainty is an absolutely fundamental aspect of environmental problems and the policies that are employed to address those problems. Any analysis that fails to recognize this runs the risk not only of being incomplete, but misleading as well. Judson Jaffe, formerly at Analysis Group, and I documented this in a study published in Regulation and Governance.

To estimate proposed regulations’ benefits and costs, analysts frequently rely on inputs that are uncertain — sometimes substantially so. Such uncertainties in underlying inputs are propagated through analyses, leading to uncertainty in ultimate benefit and cost estimates, which constitute the core of a Regulatory Impact Analysis (RIA), required by Presidential Executive Order for all “economically significant” proposed Federal regulations.

Despite this uncertainty, the most prominently displayed results in RIAs are typically single, apparently precise point estimates of benefits, costs, and net benefits (benefits minus costs), masking uncertainties inherent in their calculation and possibly obscuring tradeoffs among competing policy options. Historically, efforts to address uncertainty in RIAs have been very limited, but guidance set forth in the U.S. Office of Management and Budget’s (OMB) Circular A‑4 on Regulatory Analysis has the potential to enhance the information provided in RIAs regarding uncertainty in benefit and cost estimates. Circular A‑4 requires the development of a formal quantitative assessment of uncertainty regarding a regulation’s economic impact if either annual benefits or costs are expected to reach $1 billion.

Over the years, formal quantitative uncertainty assessments — known as Monte Carlo analyses — have become common in a variety of fields, including engineering, finance, and a number of scientific disciplines, as well as in “sabermetrics” (quantitative, especially statistical analysis of professional baseball), but rarely have such methods been employed in RIAs.

The first step in a Monte Carlo analysis involves the development of probability distributions of uncertain inputs to an analysis. These probability distributions reflect the implications of uncertainty regarding an input for the range of its possible values and the likelihood that each value is the true value. Once probability distributions of inputs to a benefit‑cost analysis are established, a Monte Carlo analysis is used to simulate the probability distribution of the regulation’s net benefits by carrying out the calculation of benefits and costs thousands, or even millions, of times. With each iteration of the calculations, new values are randomly drawn from each input’s probability distribution and used in the benefit and/or cost calculations. Over the course of these iterations, the frequency with which any given value is drawn for a particular input is governed by that input’s probability distribution. Importantly, any correlations among individual items in the benefit and cost calculations are taken into account. The resulting set of net benefit estimates characterizes the complete probability distribution of net benefits.

Uncertainty is inevitable in estimates of environmental regulations’ economic impacts, and assessments of the extent and nature of such uncertainty provides important information for policymakers evaluating proposed regulations. Such information offers a context for interpreting benefit and cost estimates, and can lead to point estimates of regulations= benefits and costs that differ from what would be produced by purely deterministic analyses (that ignore uncertainty). In addition, these assessments can help establish priorities for research.

Due to the complexity of interactions among uncertainties in inputs to RIAs, an accurate assessment of uncertainty can be gained only through the use of formal quantitative methods, such as Monte Carlo analysis. Although these methods can offer significant insights, they require only limited additional effort relative to that already expended on RIAs. Much of the data required for these analyses are already obtained by EPA in their preparation of RIAs; and widely available software allows the execution of Monte Carlo analysis in common spreadsheet programs on a desktop computer. In a specific application in the Regulation and Governance study, Jaffe and I demonstrate the use and advantages of employing formal quantitative analysis of uncertainty in a review of EPA’s 2004 RIA for its Nonroad Diesel Rule.

Formal quantitative assessments of uncertainty can mark a truly significant step forward in enhancing regulatory analysis under Presidential Executive Orders. They have the potential to improve substantially our understanding of the impact of environmental regulations, and thereby to lead to more informed policymaking.