Effluent Trading Research Articles

Optimizing effluent trading and risk management schemes considering dual risk aversion for an agricultural watershed

Increasing amounts of wastewater are discharged to water bodies, with a risk of exceeding their capacity to cope with such loads. Nutrient discharge forms two types of risk, i.e., economic and excess effluent risks. In this study, a Bayesian dual risk aversion based stochastic programming (BDRSP) is proposed for selecting optimal effluent trading and multi-risk management schemes. The BDRSP framework includes uncertain simulation for nutrient loading, optimization techniques for optimal trading planning, copulas for disclosing spatial correlation of nutrient pollution as well as TOPSIS for selecting optimal risk management schemes. BDRSP is applied to a real case of Daguhe watershed, China for planning of a NH3-N trading system. Trading ratios are estimated based on ratio between environmental damages at the watershed outlet that emission discharges in two sources. Optimal effluent trading scheme is obtained considering random pollutant loading and the associated dual risk. The spatial pattern of nutrient pollution risk is identified based on joint probability distributions and the related joint exceedance probability of different locations with copulas. Optimal dual risk management schemes are generated considering system benefit, unit revenue as well as NH3-N loading and its spatial pattern. Risk management schemes under high economic and excess effluent risk control levels (i.e. 0.85≤ε≤1, 0.4≤ρ≤0.6 and 0.85≤ε≤1, 0.8≤ρ≤1) are recommended.

Optimal design of two-dimensional water trading based on risk aversion for sustainable development of Daguhe watershed, China

Water related problems, including water scarcity and pollution, have become increasingly urgent challenges especially in arid and semiarid regions. Two-dimensional water trading (2DWT) mechanism has been designed to unify the quantity and quality of water for relieving the water crisis. This study aims to develop a risk aversion optimization-two dimensional water trading model (RAO-2DWTM) for planning the regional-scale water resources management system (RWMS). This is the first attempt on planning RWMS through risk aversion optimization within the two-dimensional water trading framework. RAO-2DWTM cannot only support in-depth analysis regarding the effect of decision maker's preferences on system risk in different trading scenarios, but also reflect the interaction between water right trading and effluent trading, as well as disclose the optimal scheme of water resource management under uncertainties. Twenty four scenarios associated with different trading scenarios and robust levels are analyzed. The optimization scheme under the optimal risk control level is determined based on TOPSIS. Results revealed that 2DWT would bring high benefit with reduced risk cost, water deficit and emissions, implying the effectiveness of 2DWT mechanism. The results also disclosed that risk aversion behavior can mitigate water scarcity and pollution, as well as reduce risk cost, but may lead to some losses of system benefit. Consequently, decision makers should make trade-offs between system benefit and risk in identifying desired trading schemes.

Multi-watershed nonpoint source pollution management through coupling Bayesian-based simulation and mechanism-based effluent trading optimization

Multiple rivers flowing into the same bay can be correlated in water quality management and together determine the environmental status of the bay. Nonpoint source pollution management for multi-watershed aiming to alleviate environmental contamination as well as yield considerable economic and environmental benefits can be under additional challenges. In this study, a Bayesian simulation-based multi-watershed effluent trading designing model (BS-METM) is established for multi-watershed nonpoint source pollution management through incorporating techniques of water quality simulation, uncertainty analysis with Bayesian inference, optimal design for effluent trading, as well as mechanism analysis. BS-METM is capable of reflecting parameter uncertainties in nutrient simulation, disclosing the detailed optimal trading schemes under the impact of uncertainties and vital factors, and identifying optimal effluent trading mechanisms through revealing interaction among trading processes of multiple watersheds. BS-METM is applied to a real case of adjacent coastal watersheds (i.e. Daguhe and Moshuihe watersheds), which are identified as major sources of total phosphorus and ammonia nitrogen loadings to Jiaozhou Bay, China. Effluent trading optimization under multiple mechanisms, including intra-watershed trading, cross-watershed trading and non-trading, are conducted. The optimized industry scales and trading processes are obtained. The effects of vital factors on the trading process (i.e. environmental allowance-violation risk level and water availability level) are investigated. The interactions between water availability level and trading mechanism are also analyzed. It is proved that non-trading mechanism would be recommended under low water availability level and cross-watershed trading mechanism would be recommended under medium and high water availability level. The results provide a solid scientific basis for nonpoint source pollution management as well as effective sustainable development for multi-watershed region.Graphical abstract

A robust optimization for agricultural crops area planning and industrial production level in the presence of effluent trading

Suitable cropland use is not only beneficial for satisfying human daily demand, but also for controlling non-point source pollution leaks into adjacent rivers. Optimal industrial production levels are economical, and they also avoid water deterioration. With the promise of being lower than the total allowed emission cap, water use participants have to balance trade-offs between optimal production levels and emission amounts. Effluent trading seems to be a cost-effective method to reduce effluent emission as it allows effluent reallocation among different sectors. Because of changing hydrological information and continuous development of treatment technology, the effluent production ratio is regarded as uncertain and is characterized as polygonal budget sets. This study tries to control the total emission quantity by optimizing cropland use and non-agricultural production levels using effluent trading under uncertain future environments. To illustrate the feasibility of the proposed model, an application is conducted in Indonisian Citarum River. The application finds that the proposed model is able to (1) identify an optimal effluent trading scheme that balances various production plans from multiple water users; (2) balance the trade-off between total emission reduction and total benefit maximization by changing budget levels; and (3) ease the decision makers burden, avoid information losses or distortions, and guide them in adjusting farmland planning, production levels, and effluent trading results under uncertainty. Based on the results, managerial implications are analyzed in terms of (1) the optimal crop area planning in the agricultural sector and the optimal production level in non-agricultural sectors; and (2) the optimal effluent trading pattern that expands economic development without deteriorating water environments. Finally, the comparison analysis with a traditional deterministic model, verify that with the incorporation of robust parameters, flexible solutions are offered to decision makers that have different attitudes toward constraints-violation risks.

Effluent trading planning and its application in water quality management: A factor-interaction perspective

In this study, a Bayesian risk-induced interval stochastic modeling framework (BRISF) is proposed for planning effluent trading program among point and nonpoint sources as well as identifying interactions of important trading factors under system risk. BRISF incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian inference with random walk Metropolis algorithm (RWM), and constraint-violation risk-based two-stage stochastic programming (CRTSP) within a general framework. Bayesian inference is employed for uncertainty analysis of SWAT model parameters and uncertain prediction of nutrient loadings; this process provides the random inputs for optimization process. CRTSP is capable of dealing with multiple uncertainties in modeling effluent trading program as well as system risk of environmental allowance violation. BRISF is applied to a real case of Xiangxihe watershed in China for water quality management. Solutions for optimal trading scheme corresponding to different risk levels are generated. Thousands of scenarios are examined to analyze the individual and interactive effects of trading ratios and treatment rates on trading system. Comparison between cross-industry and intra-industry effluent trading scheme is also conducted. It is proved that cross-industry trading would bring about higher benefit with reduced pollution loading; cross-industry effluent trading scheme would be recommended to achieve optimal water quality management and system benefit.

Policy choice and riverine water quality in developing countries: An integrated hydro-economic modelling approach

Industrialization and urbanization, as a result of rapid economic development, have led to the deterioration of water quality in many rivers in developing countries. The Kelani River in Sri Lanka provides drinking water to Colombo and a range of market and non-market ecosystem services; but these services are threatened by deteriorating water quality. We apply a hydro-economic model that accounts for spatial patterns of water quality and abatement cost variability between firms in the catchment. The hydro-economic model combines a hydrological model of water quality with an economic optimization model to determine a cost-effective policy under alternate policy regimes. These include: the existing policy based on effluent concentration standards, effluent trading and effluent trading with multiple zones and an effluent tax. Tradeable permits with multiple zones are the least cost policy option that accounts for both spatial externalities and abatement costs. However, given current institutional capabilities, an effluent tax would be a more realistic second best policy as a transition from the current policy of effluent concentration standards to a policy based on the quantity of effluents.

Effects of Important Factors on Effluent Trading: A Bayesian Estimation-Based Inexact Two-Stage Stochastic Programming Method

We proposed a Bayesian estimation-based inexact two-stage stochastic programming (BITSP) for identifying factors’ effect on effluent trading. BITSP incorporates nutrient fate modeling with Bayesian estimation and inexact two-stage stochastic programming (ITSP). Based on the water quality protocols, Bayesian estimation is used to analyze parameter uncertainty of nutrient modeling as well as provide the random inputs for the optimization process. ITSP can then be used for dealing with multiple uncertainties associated with randomness and intervals. A case study for water management in the Xiangxihe watershed is conducted. Results reveal that strict environmental limits increase the desire for permit trading program. The results also reveal that treatment rate have an obvious effect on effluent trading through changing the buying and selling behavior of point sources

Effluent trading in river systems through stochastic decision-making process: a case study.

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.

Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach

In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic–possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision alternatives.

Framework Design and Influencing Factor Analysis of a Water Environmental Functional Zone-Based Effluent Trading System.

The efficacy of traditional effluent trading systems is questionable due to their neglect of seasonal hydrological variation and the creation of upstream hot spots within a watershed. Besides, few studies have been conducted to distinguish the impacts of each influencing factor on effluent trading systems outputs. In this study, a water environmental functional zone-based effluent trading systems framework was configured and a comprehensive analysis of its influencing factors was conducted. This proposed water environmental functional zone-based effluent trading systems was then applied for the control of chemical oxygen demand in the Beiyun River watershed, Beijing, China. Optimal trading results highlighted the integration of water quality constraints and different hydrological seasons, especially for downstream dischargers. The optimal trading of each discharger, in terms of pollutant reduction load and abatement cost, is greatly influenced by environmental and political factors such as background water quality, the location of river assessment points, and tradable discharge permits. In addition, the initial permit allowance has little influence on the market as a whole but does impact the individual discharger. These results provide information that is critical to understanding the impact of policy design on the functionality of an effluent trading systems.

Uncertainty analysis for an effluent trading system in a typical nonpoint-sources-polluted watershed.

Conventional effluent trading systems (ETSs) between point sources (PSs) and nonpoint sources (NPSs) are often unreliable because of the uncertain characteristics of NPSs. In this study, a new framework was established for PS-NPS ETSs, and a comprehensive analysis was conducted by quantifying the impacts of the uncertainties associated with the water assimilative capacity (WAC), NPS emissions, and measurement effectiveness. On the basis of these results, the uncertain characteristics of NPSs would result in a less cost-effective PS-NPS ETS during most hydrological periods, and there exists a clear transition occurs from the WAC constraint to the water quality constraint if these stochastic factors are considered. Specifically, the emission uncertainty had a greater impact on PSs, but an increase in the emission or abatement uncertainty caused the abatement efforts to shift from NPSs toward PSs. Moreover, the error transitivity from the WAC to conventional ETS approaches is more obvious than that to the WEFZ-based ETS. When NPSs emissions are relatively high, structural BMPs should be considered for trading, and vice versa. These results are critical to understand the impacts of uncertainty on the functionality of PS-NPS ETSs and to provide a trade-off between the confidence level and abatement efforts.

An inexact simulation-based stochastic optimization method for identifying effluent trading strategies of agricultural nonpoint sources

Agricultural nonpoint sources (NPS) pollution has long been regarded as the leading challenge in maintaining water quality of receiving water bodies. Effluent trading can serve as a cost-effective way to achieve optimal configuration of discharge permits in agricultural NPS pollution control. However, great difficulties exist in practical effluent trading planning, including uncertainties related to randomness and imprecision, system risk of nutrient loadings being unacceptably high, and factors with spatiotemporal heterogeneity within the watershed. In this study, an inexact simulation-based stochastic optimization method (ISSOM) is developed for identifying effluent trading strategies in response to the above challenges. With the aid of technique of interval analysis, uncertain parameters related to nutrient yields can be handled and dynamic variation of NPS contaminant loadings can be reasonably addressed. Besides, ISSOM can tackle uncertainties expressed as fuzzy, stochastic and interval formats and capture the notion of risk under high-variability situation in NPS pollution control. The ISSOM is applied to a real case of agricultural NPS pollution mitigation through effluent trading in Xiangxihe Watershed. Results show that the uncertainties play a major role in successfully launching an effluent trading program, and trading scheme can mitigate agricultural NPS pollution with an increased system benefit. Results also reveal that the agricultural zones of Xiakou and Gufu are the major pollution sources and main purchasers in effluent trading, and Guizhou zone is the main vendor which contributes least to nutrient discharge. These findings can not only facilitate identification of the main pollution sources and optimal effluent trading schemes, but also gain insight into the tradeoff among the agricultural benefit, system risk, and satisfaction degree.

A Recourse‐Based Interval Fuzzy Programming Model for Point‐Nonpoint Source Effluent Trading under Uncertainty

AbstractIn this study, a recourse‐based interval fuzzy programming (RIFP) model is developed for tackling uncertainties expressed as fuzzy, interval, and/or probabilistic forms in an effluent trading program. It can incorporate preregulated water‐pollution control policies directly into its optimization process, such that an effective linkage between environmental regulations and economic implications (i.e., penalties) caused by improper policies due to uncertainty existence can be provided. The RIFP model is applied to point‐nonpoint source effluent trading of the Xiangxi River in China. The efficiency of trading efforts between water quality improvement and net system benefit under different degrees of satisfying discharge limits is analyzed. The results are able to help support (1) formulation of water‐pollution control strategies under various economic objectives and system‐reliability constraints, (2) selection of the desired effluent trading pattern for point and nonpoint sources, and (3) generation of tradeoffs among system benefit, satisfaction degree, and pollutant mitigation under multiple uncertainties. Compared with the traditional regulatory approaches, the results demonstrate that the water‐pollution control program can be performed more cost‐effectively through trading than nontrading.

A robust simulation–optimization modeling system for effluent trading—a case study of nonpoint source pollution control

In this study, a robust simulation-optimization modeling system (RSOMS) is developed for supporting agricultural nonpoint source (NPS) effluent trading planning. The RSOMS can enhance effluent trading through incorporation of a distributed simulation model and an optimization model within its framework. The modeling system not only can handle uncertainties expressed as probability density functions and interval values but also deal with the variability of the second-stage costs that are above the expected level as well as capture the notion of risk under high-variability situations. A case study is conducted for mitigating agricultural NPS pollution with an effluent trading program in Xiangxi watershed. Compared with non-trading policy, trading scheme can successfully mitigate agricultural NPS pollution with an increased system benefit. Through trading scheme, [213.7, 288.8] × 10(3) kg of TN and [11.8, 30.2] × 10(3) kg of TP emissions from cropped area can be cut down during the planning horizon. The results can help identify desired effluent trading schemes for water quality management with the tradeoff between the system benefit and reliability being balanced and risk aversion being considered.

Annual Nutrient Limits and Effluent Trading Programs Change the Planning, Design and Operation Paradigm

Annual Nutrient Limits and Effluent Trading Programs Change the Planning, Design and Operation Paradigm

Point/Nonpoint Effluent Trading with Spatial Heterogeneity

AbstractPotential earnings from permits sales may provide an incentive for farmers to accept water quality regulation. We derive optimal adjustments of point/nonpoint effluent trading ratios for heterogeneity in marginal environmental damage and degradation/retention of the pollutant across locations in a watershed. A simulation based on data from the Kymi River Valley, Finland, indicates that farmers are the greatest suppliers of permits, as expected, but that gains from trading vary substantially. Some farmers may become net buyers of permits and thus net losers from regulation. The benefits of effluent trading are distributed unevenly among point sources as well.

Economic approach for control agricultural nonpoint source pollution in China

This paper mainly discusses the feasibility to establish economic policy systems for control and management of agricultural nonpoint source pollution in China. The current situation of serious agricultural nonpoint source pollution in China is described firstly. Based on the environmental policy and economics theories, the system of economic policies for control and management of agricultural nonpoint source pollution is designed in this paper. This system includes the policy objective, the designing principle and the methods. The key issues include pollution charge, inputs tax for restriction, subsides for induction and incentive, effluent trading for least cost reduction. The emphases are optimized on inputs tax and agricultural chemical tax permit under complete information, as well as sub-optimized inputs tax under incomplete information, subsides for farm due to positive and negative externality. The functions and suitability of the policies are also analyzed in the paper. According to the field experiment results and other relating economic data in watershed of the Chaohe River, Beijing, some economic approaches to reducing agricultural nonpoint source pollution are proposed. The main idea is to encourage and support the farmers to improve their farming way, and to implement the policy of castigating charge simultaneously. The feasibility of the policies are analyzed with consideration of economy, technology and institution. It is concluded that the economic policies are necessary and feasible.

An inexact fuzzy two-stage stochastic model for quantifying the efficiency of nonpoint source effluent trading under uncertainty

Reduction of nonpoint source (NPS) pollution from agricultural lands is a major concern in most countries. One method to reduce NPS pollution is through land retirement programs. This method, however, may result in enormous economic costs especially when large sums of croplands need to be retired. To reduce the cost, effluent trading can be employed to couple with land retirement programs. However, the trading efforts can also become inefficient due to various uncertainties existing in stochastic, interval, and fuzzy formats in agricultural systems. Thus, it is desired to develop improved methods to effectively quantify the efficiency of potential trading efforts by considering those uncertainties. In this respect, this paper presents an inexact fuzzy two-stage stochastic programming model to tackle such problems. The proposed model can facilitate decision-making to implement trading efforts for agricultural NPS pollution reduction through land retirement programs. The applicability of the model is demonstrated through a hypothetical effluent trading program within a subcatchment of the Lake Tai Basin in China. The study results indicate that the efficiency of the trading program is significantly influenced by precipitation amount, agricultural activities, and level of discharge limits of pollutants. The results also show that the trading program will be more effective for low precipitation years and with stricter discharge limits.

Modeling and simulation of point-non-point source effluent trading in Taihu Lake area: perspective of non-point sources control in China

The past decades have witnessed some efforts in point source water pollution controls in China. However, adequate abatement efforts have not been implemented on non-point source control, and non-point source contributions remain and have increased as a share of surface water degradation. It has been noted that conventional command-and-control regulations are ineffective for agricultural non-point source pollution, and watershed abatement trading between point and non-point sources may serve as a cost-effective way to deal with it. In this paper, the feasibility of point-non-point sources effluent trading in China and cost-effectiveness of the trading system on water pollution control are evaluated using a stochastic programming model and a combined probabilistic watershed simulation of a representative agricultural watershed in the Taihu Lake area. The method and model can be used to assess economic and environmental opportunities of trading in similar watersheds in China. The use of explicit emission target and reliability decision rules in the chance-constrained programming model is a practical simplification to convert a stochastic program into a solvable deterministic problem. Based on the simulation, suggestions on development and implementation of point-non-point sources abatement trading scheme in China were discussed.

Lessons about Effluent Trading from a Single Trade

Despite many years of existence, programs that allow transferable discharge permits to control water pollution have had quite limited success. This paper discusses a single trade that recently took place in the Lake Dillon drainage basin between point and nonpoint pollution sources. This trade demonstrates many of the challenges that are faced in effluent trading but also highlights the potential efficiency gains that can be achieved through such programs.