Environmental Economics Archives - Page 71 of 73 - An Economic View of the Environment

Does economic analysis shortchange the future?

Decisions made today usually have impacts both now and in the future. In the environmental realm, many of the future impacts are benefits, and such future benefits — as well as costs — are typically discounted by economists in their analyses. Why do economists do this, and does it give insufficient weight to future benefits and thus to the well-being of future generations?

This is a question my colleague, Lawrence Goulder, a professor of economics at Stanford University, and I addressed in an article in Nature. We noted that as economists, we often encounter skepticism about discounting, especially from non-economists. Some of the skepticism seems quite valid, yet some reflects misconceptions about the nature and purposes of discounting. In this post, I hope to clarify the concept and the practice.

It helps to begin with the use of discounting in private investments, where the rationale stems from the fact that capital is productive – money earns interest. Consider a company trying to decide whether to invest $1 million in the purchase of a copper mine, and suppose that the most profitable strategy involves extracting the available copper 3 years from now, yielding revenues (net of extraction costs) of $1,150,000. Would investing in this mine make sense? Assume the company has the alternative of putting the $1 million in the bank at 5 per cent annual interest. Then, on a purely financial basis, the company would do better by putting the money in the bank, as it will have $1,000,000 x (1.05)³, or $1,157,625, that is, $7,625 more than it would earn from the copper mine investment.

I compared the alternatives by compounding to the future the up-front cost of the project. It is mathematically equivalent to compare the options by discounting to the present the future revenues or benefits from the copper mine. The discounted revenue is $1,150,000 divided by (1.05)³, or $993,413, which is less than the cost of the investment ($1 million). So the project would not earn as much as the alternative of putting the money in the bank.

Discounting translates future dollars into equivalent current dollars; it undoes the effects of compound interest. It is not aimed at accounting for inflation, as even if there were no inflation, it would still be necessary to discount future revenues to account for the fact that a dollar today translates (via compound interest) into more dollars in the future.

Can this same kind of thinking be applied to investments made by the public sector? Since my purpose is to clarify a few key issues in the starkest terms, I will use a highly stylized example that abstracts from many of the subtleties. Suppose that a policy, if introduced today and maintained, would avoid significant damage to the environment and human welfare 100 years from now. The ‘return on investment’ is avoided future damages to the environment and people’s well-being. Suppose that this policy costs $4 billion to implement, and that this cost is completely borne today. It is anticipated that the benefits – avoided damages to the environment – will be worth $800 billion to people alive 100 years from now. Should the policy be implemented?

If we adopt the economic efficiency criterion I have described in previous posts, the question becomes whether the future benefits are large enough so that the winners could potentially compensate the losers and still be no worse off? Here discounting is helpful. If, over the next 100 years, the average rate of interest on ordinary investments is 5 per cent, the gains of $800 billion to people 100 years from now are equivalent to $6.08 billion today. Equivalently, $6.08 billion today, compounded at an annual interest rate of 5 per cent, will become $800 billion in 100 years. The project satisfies the principle of efficiency if it costs current generations less than $6.08 billion, otherwise not.

Since the $4 billion of up-front costs are less than $6.08 billion, the benefits to future generations are more than enough to offset the costs to current generations. Discounting serves the purpose of converting costs and benefits from various periods into equivalent dollars of some given period. Applying a discount rate is not giving less weight to future generations’ welfare. Rather, it is simply converting the (full) impacts that occur at different points of time into common units.

Much skepticism about discounting and, more broadly, the use of benefit-cost analysis, is connected to uncertainties in estimating future impacts. Consider the difficulties of ascertaining, for example, the benefits that future generations would enjoy from a regulation that protects certain endangered species. Some of the gain to future generations might come in the form of pharmaceutical products derived from the protected species. Such benefits are impossible to predict. Benefits also depend on the values future generations would attach to the protected species – the enjoyment of observing them in the wild or just knowing of their existence. But how can we predict future generations’ values? Economists and other social scientists try to infer them through surveys and by inferring preferences from individuals’ behavior. But these approaches are far from perfect, and at best they indicate only the values or tastes of people alive today.

The uncertainties are substantial and unavoidable, but they do not invalidate the use of discounting (or benefit-cost analysis). They do oblige analysts, however, to assess and acknowledge those uncertainties in their policy assessments, a topic I discussed in my last post (“What Baseball Can Teach Policymakers”), and a topic to which I will return in the future.

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.

Moving Beyond Vintage-Differentiated Regulation

A common feature of many environmental policies in the United States is vintage-differentiated regulation (VDR), under which standards for regulated units are fixed in terms of the units’ respective dates of entry, with later vintages facing more stringent regulation. In the most common application, often referred to as “grandfathering,” units produced prior to a specific date are exempted from a new regulation or face less stringent requirements.

As I explain in this post, an economic perspective suggests that VDRs are likely to retard turnover in the capital stock, and thereby to reduce the cost-effectiveness of regulation in the long-term, compared with equivalent undifferentiated regulations. Further, under some conditions the result can be higher levels of pollutant emissions than would occur in the absence of regulation. Thus, economists have long argued that age-discriminatory environmental regulations retard investment, drive up the cost of environmental protection, and may even retard pollution abatement.

Why have VDRs been such a common feature of U.S. regulatory policy, despite these problems? Among the reasons frequently given are claims that VDRs are efficient and equitable. These are not unreasonable claims. In the short-term, it is frequently cheaper to control a given amount of pollution by adopting some technology at a new plant than by retrofitting that same or some other technology at an older, existing plant. Hence, VDRs appear to be cost-effective, at least in the short term. But this short-term view ignores the perverse incentive structure that such a time-differentiated standard puts in place. By driving up the cost of abatement with new vintages of plant or technology relative to older vintages, investments (in plants and/or technologies) are discouraged.

In terms of equity, it may indeed appear to be fair or equitable to avoid changing the rules for facilities that have already been built or products that have already been manufactured, and to focus instead only on new facilities and products. But, on the other hand, the distinct “lack of a level playing field” – an essential feature of any VDR – hardly appears equitable from the perspective of those facing the more stringent component of an age-differentiated regulation.

An additional and considerably broader explanation for the prevalence of VDRs is fundamentally political. Existing firms seek to erect entry barriers to restrict competition, and VDRs drive up the costs for firms to construct new facilities. And environmentalists may support strict standards for new sources because they represent environmental progress, at least symbolically. Most important, more stringent standards for new sources allow legislators to protect existing constituents and interests by placing the bulk of the pollution control burden on unbuilt factories.

Surely the most prominent example of VDRs in the environmental realm is New Source Review (NSR), a set of requirements under the Clean Air Act that date back to the 1970s. The lawyers and engineers who wrote the law thought they could secure faster environmental progress by imposing tougher emissions standards on new power plants (and certain other emission sources) than on existing ones. The theory was that emissions would fall as old plants were retired and replaced by new ones. But experience over the past 25 years has shown that this approach has been both excessively costly and environmentally counterproductive.

The reason is that it has motivated companies to keep old (and dirty) plants operating, and to hold back investments in new (and cleaner) power generation technologies. Not only has New Source Review deterred investment in newer, cleaner technologies; it has also discouraged companies from keeping power plants maintained. Plant owners contemplating maintenance activities have had to weigh the possible loss of considerable regulatory advantage if the work crosses a murky line between upkeep and new investment. Protracted legal wrangling has been inevitable over whether maintenance activities have crossed a threshold sufficient to justify forcing an old plant to meet new plant standards. Such deferral of maintenance has compromised the reliability of electricity generation plants, and thereby increased the risk of outages.

Research has demonstrated that the New Source Review process has driven up costs tremendously (not just for the electricity companies, but for their customers and shareholders, that is, for all of us) and has resulted in worse environmental quality than would have occurred if firms had not faced this disincentive to invest in new, cleaner technologies. In an article that appeared in 2006 in the Stanford Environmental Law Journal, I summarized and sought to synthesize much of the existing, relevant economic research.

The solution is a level playing field, where all electricity generators would have the same environmental requirements, whether plants are old or new. A sound and simple approach would be to cap total pollution, and use an emissions trading system to assure that any emissions increases at one plant are balanced by offsetting reductions at another. No matter how emissions were initially allocated across plants, the owners of existing plants and those who wished to build new ones would then face the correct incentives with respect to retirement decisions, investment decisions, and decisions regarding the use of alternative fuels and technologies to reduce pollution.

In this way, statutory environmental targets can be met in a truly cost-effective manner, that is, without introducing perverse incentives that discourage investment, drive up costs in the long run, and have counter-productive effects on environmental protection.

It is not only possible, but eminently reasonable to be both a strong advocate for environmental protection and an advocate for the elimination of vintage differentiated regulations, such as New Source Review. That is where an economic perspective and the available evidence leads.