Regional agricultural water resources management with respect to fuzzy return and energy constraint under uncertainty: An integrated optimization approach

Driving forces of China's provincial bilateral carbon emissions and the redefinition of corresponding responsibilities

The carbon transfers caused by inter-provincial commodity flows account for about 35 % of the total carbon emissions in China. There are great differences between the production-side and consumption-side carbon emissions for each province. Therefore, under the constraints of carbon peak and carbon neutralization, bilateral carbon emissions management is crucial to mitigate carbon emissions and the driving forces of bilateral carbon emissions must first be identified. Based on China's inter-provincial input-output data and carbon emissions data released by China Emissions Accounts and Datasets (CEADs), this paper uses a multi-regional input-output model (MRIO) to calculate the bilateral carbon emissions in 30 China's provinces from 2007 to 2017 and then apply structural decomposition analysis (SDA) to measure the influencing factors of these emissions. We also use counterfactual analysis to investigate the adjustment of provincial responsibilities for carbon emissions. The results show that the provinces in central and northern China undertake major net carbon inflows from other provinces in the eastern and southern coastal region. According to the results of SDA, the technological effect is an important factor in curbing the bilateral carbon emissions and the demand effect promotes the bilateral carbon emissions, but their contribution rates show a downward trend. By contrast, the variation in structural effect has significantly restrictive effects on the bilateral carbon emissions. Based on the provincial contribution to emissions mitigation, the adjusted consumption-side carbon emission embodies the principle of "more emission reduction, more compensation". We suggest implementing differentiated bilateral carbon emission management, taking the adjusted consumption-side carbon emission as the evaluation standard, and promoting inter-provincial carbon compensation.

Comparison of multi-objective evolutionary algorithms applied to watershed management problem

Simulation-based optimization (S-O) frameworks are effective in developing cost-effective watershed management strategies, where optimization algorithms have substantial effect on the quality of strategies. Despite the development and improvement of multi-objective evolutionary algorithms (MOEAs) provide more robust alternatives for optimization, they typically have limited applications in real-world decision contexts. In this study, three advanced MOEAs, including NSGA-II, MOEA/D and NSGA-III, were introduced into the S-O framework and applied to a real-world watershed management problem, and their performance and characteristics were quantified through performance metrics. Results show that a higher crossover or mutation probability do not necessarily promote convergence and diversity of solutions, while a larger generation and population size is helpful for MOEAs to find high-quality solutions. Compared to the other two MOEAs, NSGA-II consistently exhibits robust performance in finding solutions with good convergence and high diversity, and provides more options at the same computational cost, while the degenerate Pareto front of the proposed watershed management problem may account for the poor performance of MOEA/D and NSGA-III in terms of diversity. For a 10% TN or TP reduction target, the average cost of the NSGA-II optimized strategies is 32.22% or 47.83% of the commonly used strategies. In addition, this study also discussed the development of resilient watershed management to buffer the impacts of climate change on aquatic system, the incorporation of fuzzy programming into the S-O framework to develop robust watershed management strategies under uncertainty, and the application of machine learning-based surrogate models to reduce computational cost of the S-O framework. These results can contribute to the understanding of MOEAs and provide useful guidance to decision makers.

Dual risk-aversion programming for regional industrial structure adjustment with water-energy nexus: A case study of Tianjin, China

The water-energy nexus (WEN) system is a large-scale complex system that comes with diverse forms of risks owing to many challenges in the process of maintaining economic-resource-environmental sustainability. First, the rapidly increasing demand for water and energy subjects many regions to the high risk of water and energy shortages. Second, decision makers face difficulties in weighing system benefits and loss risks under a series of stricter water-energy policies. To handle the aforementioned dual risks of WEN, in this study we propose copula-based stochastic downside risk-aversion programming (CSDP) for regional water-energy management. CSDP integrates the superiority of the copula analysis method and downside risk-aversion programming into a framework, which can not only reveal the risk interactions between water resources and energy demand by using copula functions under different probability distributions, even previously unknown correlations, but also control economic risk, tackle systemic uncertainties, and provide an effective linkage between system stability and conflicting economic benefits. The proposed model was applied to a water-energy system case study in Tianjin City, China. Optimal solutions for various water resources and energy demand copulas associated with different scenarios and hierarchical risk levels were examined in the CSDP model. The results showed that water resources have a greater influence than energy on industrial structure adjustment in Tianjin, with consequent effects on system benefits, optimal output value schemes, and environmental protection strategies. In addition, the tertiary industry provides a new opportunity for economic growth based on a large amount of water-energy consumption, and its potential resources and water-air pollution risks also deserve extensive attention.

A Copula-based interval linear programming model for water resources allocation under uncertainty

Water scarcity tends to be aggravated by increase in water demand with the trend of socio-economic development. Thus, non-stationary characteristics of water demand should be identified in water resources allocation (WRA) to alleviate the potential influences from water shortages. In this study, a Copula-based interval linear programming model was established for regional WRA. Through combining correlation analysis and an interval linear programming model, this model can: 1) identify interactions between water demand and socio-economic development levels based on Copula functions, 2) explore variations in water shortage with consideration of multiple risk tolerance levels of decision-makers based on Copula sampling, and 3) obtain desired strategies for WRA through an interval linear programming model. Also, Dalian City in China was selected as a case study area to verify the effectiveness of the model for WRA to five water users (i.e., agricultural sector, industrial sector, public service sector, domestic residents, and ecological environment). Considering multiple tolerance levels of decision-makers to water shortage risk, three scenarios (i.e., S1 to S3), indicating 20%, 40%, and 60% of their low, medium, and high tolerance levels, were proposed. The results showed that the correlation between the amount of water demand and indicators of socio-economic development can be described by Clayton and Gaussian Copula functions. The total water supply of Dalian in 2030 would increase by 2.06%-2.65%, compared with the one in 2025. The allocation of water resources across districts was influenced by varied water demand, energy consumption, and risk tolerance levels. Compared with the amount of water allocation in 2025, the contribution of transferred water sources would increase by 6.71% and 7.04% under S1 and S2 in 2030, respectively, and decrease by 14.31% under S3. With the increase of risk tolerance levels of decision-makers, the amount of water supply in Dalian City would gradually decrease.

Optimal Allocation Model for Water Resources Coupled with Ecological Value Factors—A Case Study of Dalian, China

The surface water ecosystem has important ecological value and plays an important supporting and guarantee role in the sustainable development of human society. In this study, an inexact two-stage stochastic programming (ITSP) model was developed for supporting water resource allocation for the four main water sectors (industry, municipal, agriculture, and ecological environment). Several scenarios corresponding to different flow patterns, which reflect different probabilities of water resource availability and environmental carrying capacity, were examined. On the basis of traditional water resource allocation, this model adds consideration of ecological value factors, which is conducive to the synergistic efficiency of socio-economic and ecological water consumption. Results revealed that the water resource carrying capacity, ecological value factors, and water environmental capacity are the main factors affecting the optimal allocation of water resources. Furthermore, the optimal allocation scheme for water resources coupled with ecological value factors were determined to realize the coordinated development of social economic benefits and ecological benefits. The current study findings are of great significance for establishing a rational water resource management system for water resource exploitation and utilization. This model can be used to guide various departments in Dalian to formulate an optimal water resources allocation scheme by considering ecological value factors, and provide a basis for realizing the coordinated development of Dalian’s socio-economic development goals, water resource utilization, and environmental quality improvement.