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This book explores the status of water quality trading and recent changes in the industry and is a guide for implementing and using water quality trading for regulatory compliance purposes. Topics such as current legal and regulatory challenges, in depth case studies, and future applications are discussed in detail. This book offers a look at where and how optimizing investments in water quality through trading are unfolding. Municipalities, industries, agencies, and environmental organizations all benefit from this guidance.

Water quality regulations in the United States apply almost exclusively to point sources. In impaired watersheds where both point and nonpoint sources contribute to pollution, the U.S. Environmental Protection Agency (EPA) is encouraging the use of point-nonpoint trading to reduce the cost of point sources to meet their permit requirement, and to encourage nonpoint sources to voluntarily contribute more towards meeting overall water quality goals. The EPA guidance encourages trading programs to set a nonpoint source eligibility baseline that extracts some \"extra\" abatement from nonpoint sources. Research has shown that setting an eligibility baseline that is substantially more stringent than current management could discourage nonpoint source participation and significantly hinder trading. In this paper we examine how choosing the eligibility baseline for agricultural sources affects the efficiency goal of trading (reducing costs to point sources), as well as how it affects the EPA goal of encouraging nonpoint abatement Using data from the Chesapeake Bay Watershed we find that eligibility baselines set to encourage additional nonpoint source abatement reduce the supply of credits in a market; the more stringent the baseline, the fewer the trades and the smaller the overall abatement from nonpoint sources. A subsidy to farmers for reducing the cost of meeting a baseline encourages greater nonpoint source abatement, but may not benefit the trading market.

Markets in pollution permits for managing environmental quality have been advocated by economists since early 1970s as a mechanism that can deliver pollution reduction targets at lower cost to regulated entities than traditional uniform command-and control approaches. This study explores whether a ‘smart market’ cap-and-trade scheme between non-point sources can offer meaningful, robust and policy amenable, advantages over alternative approaches for nitrogen management in a realistic setting: 6504 individual farms in Limfjorden catchment, Denmark. The scheme involves multilateral trading of nitrogen emission rights among farms via changes in agricultural land management practices under a catchment-level cap on total nitrogen load. In this, the first exploration of non-point to non-point smart market nitrogen trading in a real setting, we estimate efficiency gains compared to uniform command-and-control regulation, explore the robustness of these gains in the face of non-participation, and reflect on farmers’ potential acceptance of the trading market in comparison with its command-and-control analog: spatially-targeted regulation, implemented via location-specific limits on nitrogen leaching. Results indicate that the smart market has the potential to substantially reduce the cost of meeting the catchment’s nitrogen reduction target. For a 21.5% reduction from baseline nitrogen load, the market delivers cost savings of 56% (DKK273 million, €36.6 million) compared to uniform regulation, with participating farms realising a mean net benefit of DKK 723/ha (€ 97/ha). Market performance is relatively robust against transaction cost; when delivering a 21.5% reduction in nitrogen load to Limfjorden, approximately 70% of the overall efficiency gain could be retained if only 24% of farms engaged with the market.

The objective of this paper is to provide an efficient framework for effluent trading in river systems. The proposed framework consists of two pessimistic and optimistic decision-making models to increase the executability of river water quality trading programs. The models used for this purpose are (1) stochastic fallback bargaining (SFB) to reach an agreement among wastewater dischargers and (2) stochastic multi-criteria decision-making (SMCDM) to determine the optimal treatment strategy. The Monte-Carlo simulation method is used to incorporate the uncertainty into analysis. This uncertainty arises from stochastic nature and the errors in the calculation of wastewater treatment costs. The results of river water quality simulation model are used as the inputs of models. The proposed models are used in a case study on the Zarjoub River in northern Iran to determine the best solution for the pollution load allocation. The best treatment alternatives selected by each model are imported, as the initial pollution discharge permits, into an optimization model developed for trading of pollution discharge permits among pollutant sources. The results show that the SFB-based water pollution trading approach reduces the costs by US$ 14,834 while providing a relative consensus among pollutant sources. Meanwhile, the SMCDM-based water pollution trading approach reduces the costs by US$ 218,852, but it is less acceptable by pollutant sources. Therefore, it appears that giving due attention to stability, or in other words acceptability of pollution trading programs for all pollutant sources, is an essential element of their success.

The economic concerns of low-income farmers are barriers to nutrient abatement policies for eutrophication control in surface waters. This study brings up a perspective that focuses on integrating multiple-pollutant discharge permit markets with farm management practices. This aims to identify a more economically motivated waste load allocation (WLA) for non-point sources (NPS). For this purpose, we chose the small basin of Zrebar Lake in western Iran and used the soil and water assessment tool (SWAT) for modeling. The export coefficients (ECs), effectiveness of best management practices (BMPs), and crop yields were calculated by using this software. These variables show that low-income farmers can hardly afford to invest in BMPs in a typical WLA. Conversely, a discharge permit market presents a more cost-effective solution. This method saves 64% in total abatement costs and motivates farmers by offering economic benefits. A market analysis revealed that nitrogen permits mostly cover the trades with the optimal price ranging from $6 to $30 per kilogram. However, phosphorous permits are limited for trading, and their price exceeds $60 per kilogram. This approach also emphasizes the establishment of a regional institution for market monitoring, dynamic pricing, fair fund reallocation, giving information to participants, and ensuring their income. By these sets of strategies, a WLA on the brink of failure can turn into a cost-effective and sustainable policy for eutrophication control in small basins.

Water quality trading (WQT) programs aim to efficiently reduce pollution through market-based incentives. However, WQT performance is uneven; while several programs have found frequent use, many experience operational barriers and low trading activity. What factors are associated with WQT existence, prevalence, and operational stage? In this paper, we present and analyze the most complete database of WQT programs in the United States (147 programs/policies), detailing market designs, trading mechanisms, traded pollutants, and segmented geographies in 355 distinct markets. We use hurdle models (joint binary and count regressions) to evaluate markets in concert with demographic, political, and environmental covariates. We find that only one half of markets become operational, new market establishment has declined since 2013, and market existence and prevalence has nuanced relationships with local political ideology, urban infrastructure, waterway and waterbody extents, regulated environmental impacts, and historic waterway impairment. Our findings suggest opportunities for better projecting program need and targeting program funding.

Water-quality trading is an area of active development in environmental markets. Unlike iconic national-scale air-emission trading programs, water-quality trading programs address local or regional water quality and are largely the result of innovations in water-pollution regulation by state or substate authorities rather than by national agencies. This article examines lessons from these innovations about the “real world” meaning of trading and its mechanisms, the economic merits of alternative institutional designs, utilization of economic research in program development, and research needed to improve the success of environmental markets for water quality.

Climate change and population growth serve as fundamental problems in assessing potential impacts on future surface water quality. In addition to uncertainties in climate depicted in various representative concentration pathway (RCP) scenarios, futuristic population growth mimicking historical conditions is subject to uncertainties related to changing development patterns. The combination of climate change and population characteristics exacerbates concerns regarding the future water quality performance of river systems. Previous studies have established linkages among future climate, population impacts and watershed water quality performance. However, these linkages have not been specifically incorporated into water quality trading programs. Rather than temporally-variant adjustment factors, WQT programs use constant margins of safety for pollutant reduction credits resulting in trade ratios that do not explicitly account for futuristic climate and population uncertainties. Hence, this study proposes a conceptual framework for water quality trading establishing adjustment factors as margins of safety on trade ratios for pollutant reduction credits examining climate and population characteristics separately followed by evaluating them combined. This new framework is demonstrated using a programming script that calculates the margins of safety based on simulation results conducted through a water quality model of the Jordan River in Salt Lake City, UT, USA over a 3-year timeframe. With margins of safety over magnitudes of ±2 over the Jordan River simulations, this research introduces the framework as a foundation for developing adjustment factors for addressing climatic and population characteristics upon river systems.