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