Agent-based modeling of participants' behaviors in an inter-sectoral groundwater market.

While being affected by economic and hydrological conditions, the behaviors of water market participants can also be caused by their psychological characteristics and social stimuli of the environment. This paper employs agent-based modeling (ABM) approach to simulate a local groundwater market in central Iran. The proposed ABM framework couples social, economic, and hydrological sub-models. The social sub-model benefits from the theory of planned behavior under field studies to design psychology-based behaviors of trading agents. Moreover, in continuous interaction with the FlowLogo hydrological sub-model, the economic sub-model simulates the inter-sectoral water trading under a double-auction mechanism. The inter-sectoral trading includes selling the farms' irrigation water to the industry sector. The calibration and validation results for an eight-year simulation period (2010-2018) confirm the acceptable performance of the proposed ABM framework. Water trading patterns experience relatively extreme variations in the first years. However, with the adaption of the agents' bids to the market conditions, they gradually emerge in a more stable form in the last years. Furthermore, updating the psychological factors increases the agents' intention of participating in the market, and thus, the competition level over time. Finally, the hydro-economic analysis reveals that implementing the dynamic cap-and-trade policy increases the total net benefits of market participants by an average of 27% per year while reducing the region's groundwater drawdown by 56 cm. Such inter-sectoral water markets can help with the sustainable exploitation of groundwater resources.

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.

A decision-making framework for river water quality management under uncertainty: Application of social choice rules.

An important issue in river water quality management is taking into account the role played by wastewater dischargers in the decision-making process and in the implementation of any proposed waste load allocation program in a given region. In this study, a new decision-making methodology, called 'stochastic social choice rules' (SSCR), was developed for modeling the bargaining process among different wastewater dischargers into shared environments. For this purpose, the costs associated with each treatment strategy were initially calculated as the sum of treatment cost and the fines incurred due to violation of water quality standards. The qualitative simulation model (QUAL2Kw) was then used to determine the penalty function. The uncertainty associated with the implementation of strategies under the economic costs (i.e., the sum of treatment and penalty costs) was dealt with by a Monte-Carlo selection method. This method was coupled with different social choice methods to identify the best solution for the waste load allocation problem. Finally, using the extended trading-ratio system (ETRS), the most preferred treatment strategy was exchanged among dischargers as the initial set of discharge permits aimed at reducing the costs and encouraging dischargers to participate in the river water quality protection scheme. The proposed model was finally applied to the Zarjoub River in Gilan Province, northern Iran, as a case study. Results showed the efficiency of the proposed model in developing waste load allocation strategies for rivers.