Planning a Water–Food–Energy–Ecology Nexus System toward Sustainability: A Copula Bi-level Fractional Programming Method

Abstract

A copula bi-level fractional programming (CBFP) method is developed for planning the water–food–energy–ecology (WFEE) nexus system. CBFP has advantages in (i) dealing with ratio-objective problems, (ii) balancing the conflicts between hierarchical decision levels, and (iii) reflecting joint risks of correlated uncertain variables. Then, a CBFP–WFEE model is formulated to the Ili-Balkhash basin in Central Asia, in which 108 scenarios associated with different irrigation efficiencies, ecological-flow demands (EBW), and water–land resources have been examined. Solutions of water allocation, hydropower generation, and land-use pattern are obtained. Our major findings are as follows: (i) from 2021 to 2050, water allocation to livestock in East Kazakhstan would remarkably increase (by 40.9%) when water allocation to food is satisfied; (ii) hydropower generation would increase with the rising ecological flows and arable land resources; (iii) EBW is the key factor influencing the water inflow to the Balkhash Lake; (iv) the share of ecosystem water allocation would exceed 17.5% by 2050, and grassland area would rank in the first place of the ecosystem. Compared with conventional bi-level stochastic programming and single-level fractional programming models, the CBFP–WFEE model can achieve a higher ecosystem service value and higher efficiency of water–land resources, which can provide more feasible and sustainable alternatives for the WFEE nexus system. The obtained results can help balance the contradictions among water shortage, economic development, and ecology protection, as well as provide synergic management strategies for regional sustainability.

The interlinkage between land resources, food, water, income, and sustainable environment: Evidence from China's economy with COP27 perspective

“…Therefore, the study narrows it down to some important empirical studies to clarify the growth emissions nexus. Salman et al (2019) studied the affiliation between GDP and CO 2 e. The conclusions demonstrated that GDP negatively impacts emissions in East Asian countries (Zhang, Li, et al, 2021) observed an optimistic association between the growth emission in China. The outcomes emphasized that economic expansion is the stimulating factor in increasing emissions.…”

“…The principal objective of the current investigation is to enhance understanding of the complex interrelationships between food production, food exports, water productivity, and CO 2 e in China. This study aims to contribute to understanding China's food production and export operations' environmental impact by examining empirical data and incorporating preceding research directed by Smith et al (2016) and Zhang, Li, et al (2021). The findings of this research endeavor might potentially provide valuable insights that can be used to shape policies and practices to reduce the environmental footprint associated with these activities.…”

Assessing Distributed Solar Power Generation Potential under Multi-GCMs: A Factorial-Analysis-Based Random Forest Method

“…Climate change can influence the availability of solar energy resources, and climate variables such as sunshine duration, solar radiation intensity (SRI), temperature, and humidity can further affect the feasibility of the solar energy system. , Changes in climate variables are spatially heterogeneous, which can cause the spatial and temporal variations of solar power generation across regions . Global climate models (GCMs) are useful for future climate projections and relevant impact evaluations, such as atmospheric condition prediction, energy system planning, and hydrological modeling. , Due to the course spatial resolution, GCMs’ outputs cannot be used directly as inputs to local scale. , Random forest (RF) is desired for bridging the empirical relationship between large-scale atmospheric variables and local climate observations. , Previously, the RF method was widely used for cleaner energy generation, solar radiation and wind speed forecasting, water resources management, and vegetation classification, especially showing advantages in climate analysis and renewable energy sources prediction. , Srivastava et al used the RF model to forecast the solar radiation, and results indicated that the RF model has a good performance.…”

“…12 Changes in climate variables are spatially heterogeneous, which can cause the spatial and temporal variations of solar power generation across regions. 14 Global climate models (GCMs) are useful for future climate projections and relevant impact evaluations, such as atmospheric condition prediction, energy system planning, and hydrological modeling. 15,16 Due to the course spatial resolution, GCMs' outputs cannot be used directly as inputs to local scale.…”