Enabling efficiency-driven and low-impact water management from robust decision making: A risk- and robustness-based multi-objective decision support model

Journal of Cleaner Production(2023)

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摘要
Increasing water-use efficiency across all sectors has been set as a key target for sustainable water management within the framework of the United Nations Sustainable Development Goals. Agriculture stands out as a key sector where opportunities for sustainable transformation arise, due to its dominant position in water use. The aim of this study is to propose a risk- and robustness-based multi-objective decision support model (RARB-MODSM) tool to help explore water-efficient agricultural development schemes and to enable inclusion of their associated environmental impacts into a stringent assessment of the risks. This tool allows for integration of economic growth and water use objectives with stringent pollution control requirements to help gain insights into the trade-offs between resource efficiency and environmental impact. This paper provides a case study of the application of this decision support tool in a prefectural-level city in China as an example of demonstrating how the tool could help reduce the water usage from the agricultural sector while enabling a higher economic productivity. The results show that expanding agricultural production in two regions of the city may help promote efficiency-oriented water management and improve the entire agricultural system's economic productivity. The results also indicate that this agricultural system is more sensitive to the control over phosphorus discharge and imposing more stringent total phosphorus (TP) control requirements within local agricultural system may help achieve better results on the system-wide pollution control. Overall, this tool demonstrates the applicability of using the systems analysis approach to help navigate the agricultural transition toward a water-efficient and low-impact growth path.
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关键词
Water management,Sustainable development,Decision-support models,Robust optimization,Linear-fractional programming,Decision making under uncertainty
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