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In the process of urban agglomeration, water-food security can be deemed as a key to support urban development and human living, but which can be challenged by expanded population growth, accelerated industrialization, unbalance regional economic development and diversity of weather (due to climate changes). In this study, a water resources allocation and food production (WF) optimization is developed for regional sustainability under multiple uncertainties. A hybrid two-stage fuzzy programming with Laplace criterion (TSFL) is proposed into a WF optimization to handle hybrid indeterminacies, which can increase the robustness of decision-making. The WF optimization with proposed TSFL method can be applied to a practical case of Beijing-Tianjin-Hebei (BTH) region. The obtained results associated with water deficits, optimal water allocations, inadequate capacities of food production, rational irrigation schedules, sound livestock scales, optimized agricultural possessing layouts and system benefits under various population-economy regulation scenarios can be obtained. The results can reflect the tradeoff between economic development and water-food safety; meanwhile, they can display risk violation of WF plan under various credibility levels and Laplace criterions (based on TSFL method). All above results can facilitate to produce an optimized water-food plan to support the synergetic development of BTH region in a robust manner.

Energy, water, and environment are inextricably interwoven in the complex social and economic networks. This study proposes an optimization model for planning the energy−water−environment nexus system (EWENS) through incorporating the linear autoregressive integrated moving average model prediction model (ARIMA), Monte Carlo simulation, chance-constrained programming (CCP), and type-2 fuzzy programming (T2FP) into one general framework. This method effectively tackles type-2 fuzzy set and stochastic uncertainties. The proposed model can quantitatively explore the interconnections between water, energy, and environment systems and generate an optimized solution for EWENS. The proposed model was applied to a coal-dominated region of China, i.e., Inner Mongolia. Several findings and policy implications were obtained. First, the total water supply for energy-generating activities will range from 1368.10 × 106 m3 to 1370.62 × 106 m3, at the end of planning periods. Second, the electricity for water supply will range from 2164.07 × 106 kWh to 2167.65 × 106 kWh at the end of the planning periods, with a growth rate of 46.06−48.72%. Thirdly, lifecycle carbon dioxide emission (LCDE) is projected to range from 931.85 × 106 tons to 947.00 × 106 tons at the end of the planning periods. Wastewater and SO2, NOx, and particulate matter (PM) emissions are projected to be 42.72 × 103−43.45 × 103 tons, 183.07 × 103−186.23 × 103 tons, 712.38 × 103−724.73 × 103 tons, and 38.14 × 103−38.80 × 103 tons at the end of the planning periods. Fourthly, as the largest electricity-exporting city of China, Inner Mongolia’s electricity outflows will export 1435.78 × 106 m3 of virtual water to other regions, implying that Inner Mongolia is pumping its important water resource to support other regions’ electricity demands. Finally, high carbon mitigation levels can effectively optimize the electricity power mix, reduce consumption amounts of water and coal, and mitigate air pollutants, wastewater, and LCDE. The obtained results provide useful information for managers to develop a sustainability plan for the EWENS.

This study developed a hybrid stochastic-fuzzy model with Green-Z scenario (HFSG) for coordinating regional irrigated production and forest protection (IPFP) under stresses of water and land resources with multiple informations. The novelty of proposed HFSG is that the improvement of handling capacity for various uncertainties can facilitate to generate a practical landscape scale planning and water resources management (land-water plan) with a more robust manner. It not only has advantage of reflecting the meteorological impact (e.g., precipitation) presented as probability distribution, but also identifies natural or artificial vagueness in the process of decision-making. Moreover, Green-Z scenario analysis associated with the risk-averse attitude, threshold of acceptability and coefficient of robustness can discern the tradeoff between expected targets and outcomes under different risk preferences of decision makers. Through the application of proposed HFSG model in a practical land-water plan issue, numbers of results associated with optimized planting scales (including cultivation withdraw and forest recovery), stresses of water and land resources, pollutant mitigations, ecological effects and system benefits under varied Green-Z scenarios can be obtained. The results can support policymaker to produce cleaner strategies for alleviating conflict between irrigated activities and environmental protection, achieving socio-economic and eco-environmental sustainability.

Contradictions of increasing carbon mitigation pressure and electricity demand have been aggravated significantly. A heavy emphasis is placed on analyzing the carbon mitigation potential of electric energy systems via tradable green certificates (TGC). This study proposes a tradable green certificate (TGC)–fractional fuzzy stochastic robust optimization (FFSRO) model through integrating fuzzy possibilistic, two-stage stochastic and stochastic robust programming techniques into a linear fractional programming framework. The framework can address uncertainties expressed as stochastic and fuzzy sets, and effectively deal with issues of multi-objective tradeoffs between the economy and environment. The proposed model is applied to the major economic center of China, the Beijing-Tianjin-Hebei region. The generated results of proposed model indicate that a TGC mechanism is a cost-effective pathway to cope with carbon reduction and support the sustainable development pathway of electric energy systems. In detail, it can: (i) effectively promote renewable power development and reduce fossil fuel use; (ii) lead to higher CO2 mitigation potential than non-TGC mechanism; and (iii) greatly alleviate financial pressure on the government to provide renewable energy subsidies. The TGC-FFSRO model can provide a scientific basis for making related management decisions of electric energy systems.

This study develops a risk-aversion optimization model for an urban electric power system (RAOM-UEPS), taking into account stochastic uncertainties. The RAOM-UEPS can manage stochastic uncertainties and capture associated risks from the stochastic information. This enables managers to analyze the trade-off between system cost and system risk in detail. Then, as a case study, the RAOM-UEPS is applied to the planning of an urban electric power system in Tianjin. Here, three scenarios are considered, each with different proportions of new energy resources and clean production levels (i.e., energy conversion efficiencies). This study aims to develop an urban electric power system (UEPS) optimization model that support the city's transformation from a coal-fired dominated to a low-carbon electric power mix, as well as to promote the sustainable development of society as a whole. The proposed model can facilitate a sophisticated system analysis of energy supply, electric power conversion, capacity expansion, and environment management over multiple periods. The results suggest that coal is dominant in Tianjin's electric power system, which was the primary air-pollutants and CO2 contributor in electric power system. Improving clean production levels and the proportion of new energy resources could effectively save energy resources and mitigate air pollutants and CO2 emissions. These findings can provide a scientific basis for the sustainable development of regional electric power systems, as well as for transformation from coal-dominated to low-carbon electric power cities.

In this study, a multi-reservoir based water-hydroenergy management (MWH) model is developed for planning water allocation and hydroenergy generation (WAHG) under uncertainties. A mixed fuzzy-stochastic risk analysis method (MFSR) is introduced to handle objective and subjective uncertainties in MWH model, which can couple fuzzy credibility programming and risk management within a general two-stage context, with aim to reflect the infeasibility risks between expected targets and random second-stage recourse costs. The developed MWH model (embedded by MFSR method) can be applied to a practical study of WAHG issue in Jing River Basin (China), which encounters conflicts between human activity and resource/energy crisis. The construction of water-energy nexus (WEN) is built to reflect integrity of economic development and resource/energy conservation, as well as confronting natural and artificial damages such as water deficit, electricity insufficient, floodwater, high sedimentation deposition contemporarily. Meanwhile, the obtained results with various credibility levels and target-violated risk levels can support generating a robust plan associated with risk control for identification of the optimized water-allocation and hydroenergy-generation alternatives, as well as flood controls. Moreover, results can be beneficial for policymakers to discern the optimal water/sediment release routes, reservoirs’ storage variations (impacted by sediment deposition), electricity supply schedules and system benefit plans with an effective/sustainable manner.

In this study, a hybrid production-emission nexus based stochastic-fuzzy production planning and air pollution management model (PE-SF-PP) can be proposed for coordinating industrial activities and environmental protection under multiple uncertainties. It can not only handle objective uncertainties expressed as probability and possibility distributions, but also quantify subjective imprecision presented as fuzzy sets with risk-preference attitudes in a production planning and air pollution management issue (PPM). The proposed PE-SF-PP model is applied to a practical PPM in the Beijing city for identifying current urban industry-environment policies. Results of production reduction, industrial layout pattern, production mode, pollutant control measure selection, retreatment efficiency, industrial productivity, and system benefit under various environmental regulation scenarios are analyzed. The obtained results can support policymakers adjusting current strategies with sustainable and robust manner, which can be beneficial to construct a cleaner production mode and optimized industry-environment policy for alleviating urban production-emission conflict.

In this study, a scenario-based interval-stochastic fraticle optimization with Laplace criterion (SISFL) method is developed for sustainable water resources allocation and water quality management (WAQM) under multiple uncertainties. SISFL can tackle uncertainties presented as interval parameters and probability distributions; meanwhile, it can also quantify artificial fuzziness such as risk-averse attitude in a decision-making issue. Besides, it can reflect random scenario occurrence under the supposition of no data available. The developed method is applied to a real case of water resources allocation and water quality management in the Kaidu-kongque River Basin, where encounter serve water deficit and water quality degradation simultaneously in Northwest China. Results of water allocation pattern, pollution mitigation scheme, and system benefit under various scenarios are analyzed. The tradeoff between economic activity and water-environment protection with interval necessity levels and Laplace criterions can support policymakers generating an effective and robust manner associated with risk control for WAQM under multiple uncertainties. These discoveries avail local policymakers gain insight into the capacity planning of water-environment to satisfy the basin’s integrity of socio-economic development and eco-environmental sustainability.

In this study, a developed fuzzy-stochastic method with Hurwicz criterion (FSH) is proposed for planning land utilization in a wetland ecosystem under uncertainty. FSH can not only deal with natural stochastic uncertainties represented as random variables (such as water flow), but also handle subjective impressions expressed as fuzzy sets (such as economic data), even it can reflect the compromise of decision makers’ risk preferences in decision-making processes based on Hurwicz criterion. The proposed method can be applied to a practical land utilization management. Rational land utilization plan (including withdrawing reclamation and wetland recovering (WRWR)) is advocated to relive such contradictions artificially, where WRWR can improve ecological function, leading more intangible ecological benefits to human activities. Meanwhile, trading-oriented mechanism is introduced to increase the economic productivity of land plan. Based on various conjunctive goals of socio-economic development and eco-environmental sustainability, results of ecological effects of wetland ecosystem, land utilization patterns, pollution-mitigation schemes, trading mechanisms and system benefits under varied scenarios are obtained and analyzed. The results indicate that land-indicator-based optimization with trading-oriented mechanism can improve the efficiency of land utilization plan in the mass to satisfy regional water resource/environmental load. Meanwhile, tradeoffs between economic benefit and system-failure risk under optimistic/pessimistic option would support generating a robust plan associated with risk control for land utilization in wetland ecosystem under uncertainties. These detections can avail local policymakers to adjust existing land utilization plan with more efficient and sustainable manners, with aim to harmonize economic development and environmental protection in wetland ecosystem.

In this study, an initial water-rights allocation (IWRA) model is proposed for adjusting the traditional initial water-rights empowerment model based on previous water intake permits, with the aim of improving the productivity of water resources under population growth and economic development. A stochastic scenario with Laplace criterion mixed fuzzy programming (SSLF) is developed into an IWRA model to deal with multiple uncertainties and complexities, which includes dynamic water demand, changing water policy, adjusted tradable water rights, the precise risk attitude of policymakers, development of the economy, and their interactions. SSLF not only deals with fuzziness in probability distributions with high satisfaction degrees, but also reflects the risk attitudes of policymakers with the Laplace criterion, which can handle the probability of scenario occurrence under the supposition of no data available. The developed IWRA model with the SSLF method is applied to a practical case in an alpine region of China. The results of adjusted initial water rights, optimal water-right allocation, changed industrial structure, and system benefits under various scenarios associated with risk attitudes and water productivity improvement were obtained and analyzed. It was found that the current initial water-rights allocation scheme based on previous intake water permits is not efficient, and this can be modified by the IWRA model. Based on the strategies of drinking safety and ecological security, the main tradeoff between agricultural and industrial water rights can facilitate optimization of the current initial water-rights allocation. This can assist policymakers in producing an effective plan to promote water productivity and water resource management in a robust and reliable manner.

Nowadays, rural power supply in China plays an important role in restricting the economic development and improvement of residential living standards. In this study, an interval full-infinite programming rural energy model (IFIP-REM) was developed for supporting distributed energy system (DES) optimal design under uncertainties in rural areas. By affecting the upper and lower bounds of the interval by complex and variable external conditions, IFIP-REM could simulate the influence of external systems. To validate the model, a real case study of DES optimal design in Guanzhong, a rural area of China, was tested and aimed to minimize system cost and constraints of resources, energy supply reliability, and carbon emission mitigation. The data revealed generation of reasonable optimization schemes to obtain interval solutions of IFIP-REM. Compared to centralized energy system (CES), DES reduced electricity purchasing of the municipal grid by 47.5% and extended carbon emission of both upper and lower bounds to [17.13, 44.51] % and [12.42, 36.02] %, respectively. Overall, the proposed model could help managers make decisions of DES optimal design by coordinating conflicts among economic cost, system efficiency, and carbon emission mitigation.

In this study, a wetland reallocation (WR) plan based on water-indicator is designed to confront various ecological crisis and flood risks on Yonding River Floodplain, China, where converting farmland to wetland (CFW) and water diversion (WD) can be considered to balance the tradeoff between manual land utilization and wetland conservation. A risk-simulation based two-stage stochastic fuzzy (RTF) method is developed for handling multiple uncertainties in a WR issue, which cannot only map various risks (including water shortage, soil loss, flood and pollution) through Geographic Information System (GIS), but also deal with objective / subjective uncertainties in a two-stage context. Through risk-simulation, numbers of risk maps have been obtained based on various WD scenarios, which displayed that ecological risk would be the lowest when water diversion reached 200 x 106m3, while flooding risk would increase. Meanwhile, based on WR optimization, natural-artificial wetland reallocations would decrease ecological loss about US$ 0.92 x 106 at highest. Moreover, the optimized land utilizations under various CFW and WD scenarios have been obtained. This study is useful for policymakers of floodplain to adjust current land utilization strategies with consideration of artificial regulations in a sustainable manner.

Climate change may alter energy demand as well as energy supply, thus posing a threat to energy security. This study investigates the long-term energy security responses to climate change for Ontario from a planning perspective. A regional climate model (RCM) is employed to assess the climate-driven changes in energy sectors at a 25 km × 25 km resolution. Reliable projections of changes in climatic variables are provided to assess their impacts on cooling degree days, heating degree days, and energy availability. Quantified sensitivities of residential and commercial energy consumptions to degree days are incorporated with future projections to estimate energy demand changes. We then estimate the impact of climate change on the primary power sources, including nuclear power, hydropower, gas, wind energy, and solar energy from a capacity planning perspective. Results indicate that winter warms more rapidly than summer in Ontario. This leads to heating degree days decreasing 2 times faster than cooling degree days increasing. Changes in degree days result in an increase in summer electricity demand and a reduction in winter gas consumption. We also find that efficiencies of hydropower and wind energy could be reduced in different scales because of decreased resource availability. The efficiency of nuclear power is sensitive to the temperature rise, but relatively less reduced compared to other energy sources. Solar energy production can benefit from climate change for the perspective of a decrease in rainy and cloudy days. With the increased electricity demand and decreased availability of water and wind resources, more green energy capacities are expected to build to ensure the long-term energy security for Ontario.

This study developed a fuzzy-stochastic programming with Green Z-score criterion (FSGZ) method for water resources allocation and water quality management with a trading-mechanism (WAQT) under uncertainties. FSGZ can handle uncertainties expressed as probability distributions, and it can also quantify objective/subjective fuzziness in the decision-making process. Risk-averse attitudes and robustness coefficient are joined to express the relationship between the expected target and outcome under various risk preferences of decision makers and systemic robustness. The developed method is applied to a real-world case of WAQT in the Kaidu-Kongque River Basin in northwest China, where an effective mechanism (e.g., market trading) to simultaneously confront severely diminished water availability and degraded water quality is required. Results of water transaction amounts, water allocation patterns, pollution mitigation schemes, and system benefits under various scenarios are analyzed, which indicate that a trading-mechanism is a more sustainable method to manage water-environment crisis in the study region. Additionally, consideration of anthropogenic (e.g., a risk-averse attitude) and systemic factors (e.g., the robustness coefficient) can support the generation of a robust plan associated with risk control for WAQT when uncertainty is present. These findings assist local policy and decision makers to gain insights into water-environment capacity planning to balance the basin's social and economic growth with protecting the region's ecosystems.

In this study, a simulation-based water-environment management model with Laplace scenario analysis (SWML) is developed for planning regional sustainability in compound wetland ecosystem. SWML can not only deal with spatial and temporal variations of hydrologic elements within the watershed, but also handle multiple uncertainties presented in terms of probability, possibility distributions, and fuzzy sets. Meanwhile, policy scenario analysis with Laplace's criterion (PSL) is introduced to handle probability of each scenario occurrence under insufficient data availability. The developed SWML is applied to water-environment management in Xixian wetland (located in north of China), where a compound wetland ecosystem (CWE) would be embedded into the concept of eco-county to remit contradiction between human activity and environmental issue through ecological mechanism. Results of ecological effect, water allocation pattern, pollution mitigation scheme and system benefit under various scenarios are obtained, which can support policymakers adjusting current plan with a cost-effective and sustainable manner. Meanwhile, satisfaction degrees for constraints using necessity levels and Laplace's criterions can support generating a robust plan associated with risk control for regional sustainability in CWE under uncertainties. The findings can support building a new construction of integrated human being–ecological environment system, which is beneficial to achieve integrity of socioeconomic development and eco-environmental sustainability.

In this study, a multi-level interval fuzzy credibility-constrained programming (MIFCP) method is developed for planning the regional-scale water-energy-food nexus (WEFN) system. MIFCP can not only deal with uncertainties expressed as interval parameters and fuzzy sets, but also handle conflicts and hierarchical relationships among multiple decision departments. The MIFCP approach is then applied to planning the WEFN system of Henan Province, China. Solutions of three different decision targets in various hierarchy levels, five scenarios with different decision makers’ objectives and five credibility levels toward different necessity degrees are examined. Several findings in association with various planting structures, water resources demand, energy consumption, fertilizer and pesticide utilizations and system benefits are achieved. Results reveal that the future total irrigation water can decrease by 1.49% from years 2020–2025. Results also disclose that the total cultivated area can change by 1.91% owing to the variation of fertilizer and pesticide change. Compared to single level programming (SLP) and bi-level programming (BP) approaches, the MIFCP-WEFN model can help decision-makers identify the optimal agricultural water resources management schemes by means of the leadership of water resources managers as well as the feedback of two diverse followers (i.e. energy managers and agricultural managers).

In this study, an agroforestry ecosystem project (AEP) is developed for confronting the conflict between agricultural development and forest protection. A fuzzy stochastic programming with Laplace scenario analysis (FSL) is proposed for planning water resources in an AEP issue under uncertainties. FSL can not only deal with spatial and temporal variations of hydrologic elements and meteorological conditions; but also handle uncertainties that are expressed in terms of probability, possibility distributions and fuzzy sets; meanwhile, policy scenario analysis with Laplace’s criterion (PSL) is introduced to handle probability of each scenario occurrence under the supposition of no data available. The developed FSL can be applied to an AEP issue in Xixian county, located in north of China. The result of ecological effects, water allocation patterns, pollution mitigation schemes and system benefits under various scenarios are obtained, which can support policymakers adjusting current strategy to improve regional ecological function with cost-effective and sustainable manners. Meanwhile, it can support generating a robust water plan for regional sustainability in an AEP issue under uncertainties.

In this study, a mix inexact-quadratic fuzzy water resources management model of floodplain (IQT-WMMF) has developed, through incorporating techniques of credibility-constrained programming (CP), two-stage programming (TP), interval-parameter programming (IPP) and quadratic programming (QP) within a general framework for limited data availability. The IQT-WMMF can provide an effective linkage between system benefit and the associated economic penalty attributed to the violation of the pre-regulated water target under limited data availabilities expressed probabilistic distributions and interval values, meanwhile, imprecise and no-linear economic data would be resolved. The developed method is applied to a real case of planning water resources in the Dahuangbaowa floodplain, China, with the aim to develop a sustainable water resources management in the study region. A number of scenarios with wet land expansion strategies under various credibility levels are analyzed, implying that different policies can lead to varied water-allocation patterns, system benefits, and system-failure risks. The results discover that water deficits and flood damages have brought negative effects on economic development synchronously, which need to effective plans to reduce losses of shortages and floods for achieving higher system benefits. Tradeoffs between economic benefit and system-failure risk can support generating an increased robustness in risk control for water resources allocation under uncertainties, which is beneficial to adjust the current water-allocation sustainably.

In this study, a two-stage inexact credibility-constrained programming (TICP) method is developed for identifying the efficiency of water trading under multiple uncertainties. TICP can tackle uncertainties expressed as probabilistic distributions, discrete intervals and fuzzy sets. It can also provide an effective linkage between the benefits to the system and the associated economic penalties attributed to the violation of the predefined policies for water resource allocation. The developed TICP method is applied to a real case of water resource allocation management and planning in the Kaidu-kongque River Basin, which is a typical arid region in Northwest China. Different water resource allocation policies based on changes to the water permit and trading ratio levels are examined. The results indicate that the efficiencies of water trading are sensitive to the degrees of satisfaction (i.e., interval credibility levels), which correspond to different water resource management policies. Furthermore, the comparison of benefits and shortages between trading and non-trading schemes implies that trading is more optimal and effective than non-trading. The results are helpful for making decisions about water allocation in an efficient way and for gaining insight into the tradeoffs between water trading and economic objectives.