Best management practices (BMPs) for reducing agricultural non-point source pollution are widely available. However, agriculture remains a major global contributor to degradation of waters because farmers often do not adopt BMPs. To improve water quality, it is necessary to understand the factors that influence BMP adoption by farmers. We review the findings of BMP adoption studies from both developed and developing countries, published after (or otherwise not included in) two major literature reviews from 2007 and 2008. We summarize the study locations, scales, and BMPs studied; the analytical methods used; the factors evaluated; and the directionality of each factor's influence on BMP adoption. We then present a conceptual framework for BMP adoption decisions that emphasizes the importance of scale, the tailoring or targeting of information and incentives, and the importance of expected farm profits. We suggest that future research directions should focus on study scale, on measuring and modeling of adoption as a continuous process, and on incorporation of social norms and uncertainty into decision-making. More research is needed on uses of social media and market recognition approaches (such as certificate schemes and consumer labeling) to influence BMP adoption.

We present a framework to compare water treatment costs to source water protection costs, an important knowledge gap for drinking water treatment plants (DWTPs). This trade‐off helps to determine what incentives a DWTP has to invest in natural infrastructure or pollution reduction in the watershed rather than pay for treatment on site. To illustrate, we use daily observations from 2007 to 2011 for the Bob McEwen Water Treatment Plant, Clermont County, Ohio, to understand the relationship between treatment costs and water quality and operational variables (e.g., turbidity, total organic carbon [TOC], pool elevation, and production volume). Part of our contribution to understanding drinking water treatment costs is examining both long‐run and short‐run relationships using error correction models (ECMs). Treatment costs per 1000 gallons (per 3.79 m3) were based on chemical, pumping, and granular activated carbon costs. Results from the ECM suggest that a 1% decrease in turbidity decreases treatment costs by 0.02% immediately and an additional 0.1% over future days. Using mean values for the plant, a 1% decrease in turbidity leads to $1123/year decrease in treatment costs. To compare these costs with source water protection costs, we use a polynomial distributed lag model to link total phosphorus loads, a source water quality parameter affected by land use changes, to turbidity at the plant. We find the costs for source water protection to reduce loads much greater than the reduction in treatment costs during these years. Although we find no incentive to protect source water in our case study, this framework can help DWTPs quantify the trade‐offs.

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Watershed protection, and associated water quality improvements, has received considerable attention as a means for mitigating health risks and avoiding expenditures at drinking water treatment plants (DWTPs). This study reviews the literature linking source water quality to DWTP expenditures. For each study, we report information on the modeling approach, data structure, definition of treatment costs and water quality, and statistical methods. We then extract elasticities indicating the percentage change in drinking water treatment costs resulting from a 1% change in water quality. Forty-six elasticities are obtained for various water quality parameters, such as turbidity, total organic carbon (TOC), nitrogen, sediment loading, and phosphorus loading. An additional 29 elasticities are obtained for land use classification (e.g., forest, agricultural, urban), which often proxy source water quality. Findings indicate relatively large ranges in the estimated elasticities of most parameters and land use classifications. However, average elasticities are smaller and ranges typically narrower for studies that incorporated control variables consistent with economic theory in their models. We discuss the implications of these findings for a DWTP's incentive to engage in source water protection and highlight gaps in the literature.

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...

There are a number of established, scientifically supported metrics of sustainability. Many of the metrics are data-intensive and require extensive effort to collect data and compute the metrics. Moreover, individual metrics do not capture all aspects of a system that are relevant to sustainability. A pilot project was initiated to create an approach to measure, monitor, and maintain prosperity and environmental quality of a regional system. The goal was to produce a straightforward, inexpensive methodology that is simple to use and interpret. This requires historical data be readily accessible, metrics must be applicable to the relevant scale, and results must meet the needs of decision-makers. Because sustainability is a multidimensional concept, the research group consisted of a multidisciplinary team that identified the major components of an environmental system. We selected metrics to capture the multidimensionality of sustainability in environmental systems and included: (1) emergy to capture the quality-normalized flow of energy through the system; (2) ecological footprint to capture the impact of humans on the system; (3) green net regional product to estimate human prosperity and well being within the system; and (4) Fisher information to capture the dynamic order of the system. We were able to compute metrics for a test geographic region using existing datasets. Preliminary analysis indicates that each metric reveals a somewhat different trend. These preliminary findings support the idea that characterization of sustainability requires a multidisciplinary approach and demonstrate the need to measure multiple aspects of an environmental system.

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We estimate an individual travel cost model for Great Sand Dunes National Park and Preserve (GSD) in Colorado using on-site, secondary data. The purpose of the on-site survey was to help the National Park Service better understand the visitors of GSD; it was not intended for a travel cost model. Variables such as travel cost and income were estimated based on respondents' Zip Codes. Following approaches found in the literature, a negative binomial model corrected for truncation and endogenous stratification fit the data the best. We estimate a recreational benefit of U.S. $89/visitor/year or U.S. $54/visitor/24-h recreational day (in 2002 U.S. $). Based on the approach presented here, there are other data sets for national parks, preserves, and battlefields where travel cost models could be estimated and used to support National Park Service management decisions.