Reversing the Property Rights: Practice-Based Approaches for Controlling Agricultural Nonpoint-Source Water Pollution When Emissions Aggregate Nonlinearly

Sergey Rabotyagov, Adriana Valcu-Lisman, Catherine L. Kling
September 2012  [12-WP 533]

Nonpoint-source pollution remains a troubling source of water quality problems despite decades of economics research on the matter. Among the chief difficulties for addressing the issue are the property rights assignments implicit in the current policy environment that favor agricultural nonpoint-source pollution, the unobservability of field-level emissions, and complex fate and transport relationships linking them to ambient water quality. Theoretical and practical considerations lead to the focus on observable abatement actions (conservation practices). Biophysical models are increasingly more capable of linking abatement actions to policy-relevant water quality outcomes. If costs of abatement actions are known, finding the least-cost mix of abatement actions is possible, while incorporating the nonlinearity of the pollution process. When costs are not known or information is incomplete, regulators can rely on flexible incentive-based programs, but the design of such programs is complicated by the complexities of emission aggregation. In this work, we focus on the regulator capable of focusing on nonpoint-source emitters. We address the design and performance of three practice-based approaches, ranging from the command-and-control approach mandating practices, to the more flexible performance standard approach where farmers are free to select the optimal mix of on-farm conservation practices, to a fully flexible approach where credits for conservation practices are freely tradable. We do so by utilizing the representation of the nonlinear emission aggregation (fate and transport) process (the Soil and Water Assessment Tool model), and consider cases ranging from the regulator having perfect information on the costs of conservation practices to no information at all. We show how workable programs utilizing the biophysical models and simulation-optimization approaches can be designed, and assess their performance relative to the efficient case. We find that flexible programs perform well both in terms of cost and water quality goals attainment. In particular, a trading program designed around an approximation of the nonlinear pollution process performs well, relative to first-best under no information on the cost of conservation practices.

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