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Graphical abstract Highlights • Structures of industrial sectors have significant impacts on energy intensity. • The energy intensity may be affected by uncertainties in industrial activities. • The randomness of energy intensity may lead to unexpected violation events. • Copula-based violation risk analysis can support decision-making in energy-intensity mitigation. Abstract An integrated approach was developed through incorporating a copula-based violation risk analysis into a programming model for optimizing industrial structures of urban clusters in uncertain conditions. Also, this approach can be used to support decision making about promoting advanced manufacturing sectors (AMSs) and mitigating industrial energy intensity. The advantages and the improvements of this approach lie in (a) reflecting the relationships between energy consumption and economic benefits in industrial sectors, and (b) incorporating the violation risk of the targets in energy-intensity mitigation into an optimizing model. A case study was conducted to illustrate the application of this approach in the Pearl River Delta of China, a highly urbanized area that includes the cities of Guangzhou, Shenzhen, Zhuhai, Dongguan, Huizhou, Zhaoqing, Foshan, and Jiangmen. The results indicated that under the desired industrial structures, violation risk of energy-intensity mitigation in the urban cluster of the Pearl River Delta would be indistinctive in the single-city perspective. Also, except Dongguan, Jiangmen and Zhaoqing, the cities of the urban cluster would achieve the goal for developing AMSs, based on the Industrial Plans for the 13th Five Year. [ABSTRACT FROM AUTHOR]

An enhanced radial interval programming approach was developed for supporting the identification of sound agricultural production plans. It was designed to tackle dual uncertainties in both the economic and environmental subsystems as well as reflect differential aspiration levels for 'immunity' against such uncertainty. By adding protection functions to both the objective functions and constraints susceptible to dual uncertainties without any distributions, it improved upon previous methods through simultaneously measuring and manipulating expected return risk and resource-environmental compliance risk. Moreover, this study made novel attempts to explore system-wide optimal levels of robustness based on proposed performance evaluation criteria and investigations over the interactive effects of varied robustness aspirations for different model components. Based on analyzed tradeoffs among system optimality, robustness and uncertainty degree of solutions pertaining to the study case, a unified protection level of 13 (or 10) was recommended for risk-averse (or risk-neutral) decision making. The model outputs were compared to those from three alternatives, revealing that it could achieve a high level of system reliability at a minor price of economic return. This approach could be generalized for wide-scale applications. [ABSTRACT FROM AUTHOR]

Multiple correlated risks caused by uncertain information pose great challenges to the coordinated regulation of water-energy-food nexus (WEFN). In this study, a Copula-based radial interval chance-constrained programming (CRICP) was developed. CRICP can not only quantify composite risk induced by multiple risks with nonlinear correlations, but also tackle dual uncertainty expressed as redial intervals with adjustable protection levels. CRICP was applied to an irrigated agricultural management problem constrained by water, energy, and land resource in Bayan Nur City in northern China. Results indicate that system benefit would increase with the incline of composite risk or the decline of protection level. Monte Carlo simulation verified that the different definitions of composite risk could lead to significant differences in the combination of subsystem risks. The composite risk induced by the simultaneous shortage of multiple resources would have greater impacts on system benefits compared against composite risk caused by shortage of at least one resources, with an increase of [24.65%, 25.95%] in the minimum value of system benefits. Comparisons against two alternative models further verified that CRICP outweighs previous methods by modeling the correlation structure of multiple risks and tackling dual uncertainties without distribution information. • The complex interactions and inherent uncertainty affect the security of Water-Energy-Food Nexus. • A Copula-based radial interval chance-constrained programming method was developed. • It can tackle composite risk induced by random variables with unknown correlations. • It can also address dual uncertainties without detailed distributions. • The bias in decision making process triggered different composite risks were clarified. [ABSTRACT FROM AUTHOR]

Effective water allocation among multiple jurisdictions is a key instrument to improve water use efficiency within the basin scale. To achieve equitable and reasonable water allocation, natural, socio-economic, and ecological conditions within a specific basin need to be systematically considered. Based on the main principles of equitable and reasonable water allocation that were defined by UN Watercourses Convention, an integrated multi-criteria decision making (MCDM) with bankruptcy rules (IMCDM-BR) under multiple hydrological constraints were proposed to allocate trans-jurisdiction water resources in Guanting reservoir basin (GRB), a shared basin between Zhangjiakou and Beijing in China. Projection pursuit (PP), as one of an effective MCDM approach, was employed to synthesize values of the related principles, which were ranked as weights for the corresponding water claims by relevant agents. Then, the weighted bankruptcy rules (BR) with multiple hydrological constraints were applied to allocate water among the related agents of the studying basin. The results of ordinary bankruptcy rules, bankruptcy rules with regular and hydrological constraints, and the proposed methods were compared and discussed. Among them, the proposed IMCDM-BR was recommended as an effective tool to support practical water allocation. Moreover, factors of equitable and reasonable water allocation were comprehensively considered. The results can thus be used for facilitating negotiation in trans-jurisdiction water allocation among agents within basins. [ABSTRACT FROM AUTHOR]

Due to insufficiency and ineffectiveness of monitoring facilities, variations in natural conditions and changes of human behaviors, watershed planning is interwoven with imprecise data, resulting in a multitude of complexities. A robust interval fuzzy programming approach with superiority–inferiority and risk-aversion analyses (RIFP-SIRA) was developed for identifying sustainable agricultural and industrial production strategies at the watershed scale in a highly uncertain environment. RIFP-SIRA represented a novel attempt to tackle synergies of uncertain information in the forms of intervals with non-statistical bounds and conventional intervals. The developed method was applied to a Chinese watershed. A RIFP-SIRA model was formulated and analyzed under three scenarios to investigate the effects of different policies and standards on watershed development plans. Sensitivity of the model's solutions to varied risk-aversion and input-fluctuation levels were analyzed, verifying the superiority of RIFP-SIRA over the existing inexact approaches. This research revealed that RIFP-SIRA could provide local authorities with robust, yet flexible decision plans. It could enable a quantitative analysis over the trade-offs between the economic benefits and environmental risks in the absence of probabilistic information. [ABSTRACT FROM AUTHOR]

In this study, a feasibility-analysis-based inexact fuzzy multistage model (FA-IFMSM) was developed for supporting the planning of energy and environmental systems under multiform uncertainties. In FA-IFMSM, methods of feasibility-analysis-based fuzzy linear programming (FA-FLP) and inexact multistage stochastic programming (IMSP) with recourse were integrated into a general mixed-integer linear programming framework, such that the developed model could tackle uncertainties described in terms of probability distributions, interval values, fuzzy sets, and their hybrid and mixture. Moreover, it could reflect dynamic features of facility-capacity expansions and energy supplies over a multistage context. It could also quantify constraint violation degrees through the adoption of various acceptable degree levels. Then the proposed model was applied to a regional energy and environmental system to illustrate its applicability, where conventional and renewable energy resources and their economic and environmental implications were considered. The results indicated that reasonable solutions were generated. They were helpful for supporting (1) adjustment or justification of allocation patterns of regional energy resources and services; (2) formulation of local policies regarding energy consumption, economic development, and environmental protection; and (3) in-depth analysis of trade-offs among system cost and satisfaction degree, and environmental requirements under multiple uncertainties. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a feasibility based inexact fuzzy programming approach (FBIFP) was developed and applied to the planning of a regional electric power generation system (REPGS). As an extension of existing interval linear programming and fuzzy linear programming, FBIFP could tackle uncertainties expressed as intervals, fuzzy sets as well as their combination in both of the objective function and constraints. Through introducing the index of feasibility degree, FBIFP could be used to facilitate the efficient reflection of constraint violation, and thus allow decision makers to have a complete view of the relationships between uncertain inputs and the related solutions. A hypothetic regional electric power generation system was provided for demonstrating applicability of the developed model. Useful solutions for the planning of REPGS were generated. Interval solutions under different feasibility degrees were obtained. They could be used for helping decision makers identify desired alternatives under various reliability risks of the system. The solutions could also help identify optimal patterns for energy resources allocation, electricity generation and facility capacity expansion with a minimized system cost. Tradeoffs between system costs and system security could be successfully addressed through the analysis of the feasibility degrees of constraints, i.e., a higher feasibility degree would correspond to a higher system cost, while a lower system cost would run into a higher risk of potential instability of the study system. [Copyright &y& Elsevier]