Abstract. Anthropogenic stressors such as climate change, increased fire frequency, and pollution drive shifts in ecosystem function and resilience. Scientists generally rely on biological indicators of these stressors to signal that ecosystem conditions have been altered. However, these biological indicators are not always capable of being directly related to ecosystem components that provide benefits to humans and/or can be used to evaluate the cost-benefit of a change in health of the component (ecosystem services). Therefore, we developed the STEPS (Stressor-Ecological Production function-final ecosystem Services) Framework to link changes in a biological indicator of a stressor to final ecosystem services. The STEPS Framework produces "chains" of ecological components that explore the breadth of impacts resulting from the change in a stressor. Chains are comprised of the biological indicator, the ecological production function (EPF, which uses ecological components to link the biological indicator to a final ecosystem service), and the user group who directly uses, appreciates, or values the component. The framework uses a qualitative score (high, medium, low) to describe the strength of science (SOS) for the relationship between each component in the EPF. We tested the STEPS Framework within a workshop setting using the exceedance of critical loads of air pollution as a model stressor and the Final Ecosystem Goods and Services Classification System (FEGS-CS) to describe final ecosystem services. We identified chains for four modes of ecological response to deposition: aquatic acidification, aquatic eutrophication, terrestrial acidification, and terrestrial eutrophication. The workshop participants identified 183 unique EPFs linking a change in a biological indicator to a FEGS; when accounting for the multiple beneficiaries, we ended with 1104 chains. The SOS scores were effective in identifying chains with the highest confidence ranking as well as those where more research is needed. The STEPS Framework could be adapted to any system in which a stressor is modifying a biological component. The results of the analysis can be used by the social science community to apply valuation measures to multiple or selected chains, providing a comprehensive analysis of the effects of anthropogenic stressors on measures of human well-being.
and internal review of a final rule by the EPA; OMB review of the final rule; publication of the final rule in the Federal Register; and, finally, implementation of the rule. If the rule is expected to have an impact on the U.S. economy of $100 million per year or more, then it is deemed "economically significant" 1 and must be accompanied by a formal BCA at both the proposal and final stages (Fraas, 1991). This process can be time-consuming. To give an example, the EPA's regulations concerning discharges from Concentrated Animal Feeding Operations (CAFOs) were formally proposed two years after they were initially conceived and the rule was finalized three years after proposal (USEPA, 2009). In another illustrative case, the EPA's Steam Electric effluent guidelines were proposed four years after their conception, and finalized two years after the proposal (USEPA, 2015a). Within such timelines an iterative sequence of data collection, analysis, review, and revision must be conducted in compliance with a series of internally and externally imposed intermediate deadlines. The process begins with collecting large amounts of data. (In the case of the Steam Electric rule, for example, a nearly-400-page questionnaire was distributed to each manufacturing facility that might be subject to the new regulation.) The collected data are then used to develop policy options. Environmental engineers then estimate changes in pollution emissions, and water quality scientists produce estimates of changes in ambient water quality levels associated with each option. Economists use these predictions, as well as other information, to estimate the benefits and costs of each option considered for the proposed rule. After the rule is formally proposed, the process pauses for a public comment period-usually lasting between 60 and 120 daysduring which interested parties submit comments on the proposal to the EPA. Often a large portion of the public comments are submitted by the regulated industry, and these may include new data and analyses. The EPA then must respond to all submitted public comments and modify the rule options and analyses accordingly. Before a rule can be proposed or finalized, it also must pass through several rounds of internal review, plus external review by other federal agencies and OMB. While the overall time from conception to proposal of a rule, and then from proposal to finalization may stretch into years, the time to conduct a BCA may be more constrained. At each stage of review, EPA staff may be required to analyze new options for the rule on relatively short turnaround times. Furthermore, the policy options as originally configured might be partially or wholly obsolete before a rule-making is completed, and EPA analysts must be prepared to make rapid adjustments to the analysis in response to evolving requests from managers as the rulemaking proceeds. 2 These factors create a demand for flexible and timely benefit analysis approaches. In addition to the time pressure benefit-cost analysts may find themselves un...
Analysts often extrapolate estimates of the value of environmental improvements reported in prior studies to evaluate new policy proposals, a practice sometimes referred to as "benefit transfer." Benefit transfer functions are frequently specified based on statistical considerations alone. However, such a purely statistical approach can lead to willingness-to-pay functions that fail to satisfy some aspects of theoretical consistency that may be especially important for policy evaluations. In this paper, we examine several previous meta-analyses of nonmarket valuation studies in light of the adding-up condition, which is one important aspect of theoretical validity. We then use meta-regression to estimate a new willingness-to-pay function for surface water quality improvements intended to be used for benefit transfers. We estimate the meta-regression model using summary results from 51 previously published stated preference studies. An important feature of our approach is that we develop the meta-regression estimating equation to ensure that the resulting benefit transfer function will necessarily comply with the adding-up condition. This is achieved by first specifying a marginal willingness-to-pay function and then deriving an expression for total willingness-to-pay. This leads to a non-linear estimating equation, so we estimate the parameters of the model using non-linear least squares. We discuss the advantages and disadvantages of our approach relative to other structural approaches, and we compare our empirical results to a more traditional nonstructural meta-regression model. Finally, we examine the quantitative importance of imposing the adding-up condition in our case study by performing some illustrative calculations of willingness-to-pay for hypothetical water quality improvements using both structural and non-structural models. The findings, conclusions, and views expressed in this paper are those of the authors and do not necessarily represent those of the U.S. EPA. No Agency endorsement should be inferred.
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