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Modern agriculture calls for efficient and environmental-friendly agricultural water and land management practice. Agricultural water and land utilization mode has a great impact on the amount of carbon emissions, and thus the aim of this work is to propose an optimal modeling approach for generating efficient agricultural water and land management alternatives and reducing carbon emission in agricultural water-energy-food nexus system. This study presents a novel approach consisting of carbon footprint lifecycle assessment method and bi-level multi-objective stochastic programming model. The proposed approach has contributions in following aspects: (1) the environmental impact of different resource allocation strategies can be measured in optimization (2) the spatial variability of spatial data (e.g., ET 0 and precipitation); can be fully reflected via remote sensing information; (3) tradeoffs among two decision-making levels and their conflicting objectives under randomness of surface water availability can be addressed. The proposed approach was applied to the middle reaches of the Heihe River basin, northwest China. After solving the proposed model, the optimal water and land use alternatives under different hydrological years can be generated. Decision makers can plan agricultural production strategy according to optimization schemes, and reduce carbon emission through increasing vegetable cultivation and surface water utilization. Furthermore, the performance comparison of different models indicated that fierce conflicts exist between income fairness and economic benefits. The comprehensive evaluation value of bi-level multi-objective stochastic programming model is 0.7036, which is highest among single- and multi-objective models, showing that such model has obvious advantage in dealing with multiple decision-making levels and conflicting objectives. This approach can help decision makers of similar regions manage the agricultural production system in a more efficient and environment-friendly way. [Display omitted] • Lifecycle assessment of carbon footprint was used for measuring environmental impacts. • Spatial data (e.g. ET 0 and precipitation) were collected and processed with the help of remote sensing information. • Vegetable cultivation increasing and groundwater use decreasing can help reduce carbon emission in agricultural production. • BLMOSP model has obvious advantage among single- and multi-objective models for addressing such problems. [ABSTRACT FROM AUTHOR]

High flow events (including both the high flow pulses and floods) exert many critical ecological functions in riverine ecosystems and their elimination will lead to severe ecological degradation. However, common hydropower operations are only required to legally maintain the minimum environmental flows (e-flows). The extra release of high flow events will result in revenue loss of hydropower producers. In this research, we propose an approach to assessing the eco-compensation standard which is necessary to compensate the loss of hydropower producers and promote the release of high flows to downstream rivers. The scenario tree approach is adopted for the generation of possible scenarios of flows and electricity prices to deal with the uncertainties of the two factors in deregulated electricity markets. A stochastic linear programming method is proposed to determine the maximum mean annual revenues under two e-flow provision scenarios, i.e., only sustaining the minimum e-flows (scenario 1) and sustaining both the minimum e-flows and a specified number of high flow events (scenario 2). The revenue difference under the two e-flow scenarios is set as the eco-compensation standard. A case study in the Wangkuai reservoir, China, demonstrates that the eco-compensation standard is obviously influenced by the installed hydropower generation capacity (IHGC) and the number of high flow events planned to be released. Moreover, the greatest standards may be not corresponding to a very large IHGC. [ABSTRACT FROM AUTHOR]

This paper develops an interval multi-stage robust programming model for regional low-carbon electric power system planning with energy-water nexus. The model integrates interval programming and multi-stage stochastic programming to handle uncertainties with different probability distribution information. In addition, under the framework of robust programming, the optimal solutions could provide tradeoff information between system cost and decision maker's risk preference. The hybrid programming method is tailored for a case study of Shandong Province, China to validate its applicability and provide useful managerial insights, where energy-water interactions are gain great concerns. The model helps to make robust strategies associated with risk control for capacity expansion, carbon capture and storage (CCS) technology retrofitting, power generation, imported electricity, and water allocation for electricity generation, with the goal of minimizing total cost. Besides, considering the variation of water resource conditions and environmental policies under climate change, the impacts of water resource availability and carbon emission caps on regional electric power system planning are explored and discussed. It is found that stricter carbon emission cap policy would facilitate the investment on CCS devices but have little impact on the installed capacity structure, meanwhile it would increase water consumption per power generation. However, scarcer water resource situation would promote the development of renewable energy generation, and limit the investment on CCS technology. • Low-carbon power system management optimization with energy-water nexus under uncertainties. • Impact analysis of various water resource availability and carbon emission cap on optimal strategies. • Useful managerial insights on energy system planning from energy-water nexus perspective. • Robust solutions with risk-cost tradeoff information to reflect different risk preferences. [ABSTRACT FROM AUTHOR]