- freely available
Inexact Mathematical Modeling for the Identification of Water Trading Policy under Uncertainty
AbstractIn this study, a two-stage inexact credibility-constrained programming (TICP) method is developed for identifying the efficiency of water trading under multiple uncertainties. TICP can tackle uncertainties expressed as probabilistic distributions, discrete intervals and fuzzy sets. It can also provide an effective linkage between the benefits to the system and the associated economic penalties attributed to the violation of the predefined policies for water resource allocation. The developed TICP method is applied to a real case of water resource allocation management and planning in the Kaidu-kongque River Basin, which is a typical arid region in Northwest China. Different water resource allocation policies based on changes to the water permit and trading ratio levels are examined. The results indicate that the efficiencies of water trading are sensitive to the degrees of satisfaction (i.e., interval credibility levels), which correspond to different water resource management policies. Furthermore, the comparison of benefits and shortages between trading and non-trading schemes implies that trading is more optimal and effective than non-trading. The results are helpful for making decisions about water allocation in an efficient way and for gaining insight into the tradeoffs between water trading and economic objectives.
Share & Cite This Article
Zeng, X.; Li, Y.; Huang, G.; Yu, L. Inexact Mathematical Modeling for the Identification of Water Trading Policy under Uncertainty. Water 2014, 6, 229-252.View more citation formats
Zeng X, Li Y, Huang G, Yu L. Inexact Mathematical Modeling for the Identification of Water Trading Policy under Uncertainty. Water. 2014; 6(2):229-252.Chicago/Turabian Style
Zeng, Xueting; Li, Yongping; Huang, Guohe; Yu, Liyang. 2014. "Inexact Mathematical Modeling for the Identification of Water Trading Policy under Uncertainty." Water 6, no. 2: 229-252.
Notes: Multiple requests from the same IP address are counted as one view.