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Multiple rivers flowing into the same bay can be correlated in water quality management and together determine the environmental status of the bay. Nonpoint source pollution management for multi-watershed aiming to alleviate environmental contamination as well as yield considerable economic and environmental benefits can be under additional challenges. In this study, a Bayesian simulation-based multi-watershed effluent trading designing model (BS-METM) is established for multi-watershed nonpoint source pollution management through incorporating techniques of water quality simulation, uncertainty analysis with Bayesian inference, optimal design for effluent trading, as well as mechanism analysis. BS-METM is capable of reflecting parameter uncertainties in nutrient simulation, disclosing the detailed optimal trading schemes under the impact of uncertainties and vital factors, and identifying optimal effluent trading mechanisms through revealing interaction among trading processes of multiple watersheds. BS-METM is applied to a real case of adjacent coastal watersheds (i.e. Daguhe and Moshuihe watersheds), which are identified as major sources of total phosphorus and ammonia nitrogen loadings to Jiaozhou Bay, China. Effluent trading optimization under multiple mechanisms, including intra-watershed trading, cross-watershed trading and non-trading, are conducted. The optimized industry scales and trading processes are obtained. The effects of vital factors on the trading process (i.e. environmental allowance-violation risk level and water availability level) are investigated. The interactions between water availability level and trading mechanism are also analyzed. It is proved that non-trading mechanism would be recommended under low water availability level and cross-watershed trading mechanism would be recommended under medium and high water availability level. The results provide a solid scientific basis for nonpoint source pollution management as well as effective sustainable development for multi-watershed region.

Anthropogenically induced global warming intensifies the water cycle around the world. As a critical sector of the water cycle, snow depth and its related extremes greatly impact agriculture, animal husbandry, and food security, yet lack investigation. In this study, five high-resolution climate models are selected to simulate and project snow depth and its extremes over China. The simulation capabilities of models in reproducing the basic climate variables in winter are gauged in terms of spatial and temporal patterns over nine subregions. It is found that the driving global climate model (GCM) can contribute to similar patterns, while the different regional climate model (RCM) schemes lead to large variations in the snowfall accumulating on the land surface. The warming magnitude is larger under a higher representative concentration pathway (RCP) scenario (2.5 °C greater under RCP8.5 than RCP4.5). The distribution of ensemble mean winter precipitation changes is more fragmented because of the relatively low skill in reproducing water-related content in the climate system. The projected precipitation change is larger under RCP8.5 than under RCP4.5 due to the amplification of the hydrological cycle by temperature warming. The projected changes in the ensemble mean snow depth mainly occur over the Tibetan Plateau with a decreasing trend. Only several grids over the Himalayas Mountains and the upper stream of the Yarlung Zangbo River are projected with a slight increase in snow depth. Both the intensity and frequency of extreme snow events are projected to increase in Northeast China and Inner Mongolia, which are important agricultural and animal husbandry production areas in China. The reason behind this projection can be explained by the fact that the hydrological cycle intensified by temperature warming leads to excessive snowfall stacking up during winter. The changes in extreme snowfall events in the future will have a significant impact on China’s agricultural and animal husbandry production and threaten food security.

In this study, an adaptive water resource management framework with combined policies (AWFP) is developed for mitigating adverse effects on water resource in a floodplain area due to population-industry transformation in context of coordinative development of urban agglomeration. A location-entropy based PVRA model (LE-PCRA) and coupla-risk analysis (CRA) can be introduced to reflect the adverse effects of industrial information and driven population on water resources; meanwhile risks (including water shortage, soil loss and flood control) and corresponding correlations have been shown in the risk maps. Moreover, an adaptive scenario analysis based stochastic-fuzzy method (ASSF) can be embedded into an AWFP to deal with multiple uncertainties and their interactions due to subjective and artificial factors. The proposed AWFP is applied to a practical case study of Yongding river floodplain region for confronting adverse effects on water resources due to population-industry transformation in the context of coordinative development of Beijing-Tianjin-Hebei urban agglomeration, China. The results were obtained to reflect the negative effects of population-industry transformation and corresponding water allocation patterns in floodplain, which is effective to confront natural and artificial damages (such as water deficit, water and soil loss, and flood damage), risks and function degradation of floodplain contemporarily. Meanwhile, various policy scenarios (such as farmland returning to wetland, improvement of water resource utilization efficiency, water diversion and flood control) can be analyzed to support adjusting current population-economy strategies and water management patterns to accommodate source function of floodplain with a risk-averse and sustainable manner.

Water related problems, including water scarcity and pollution, have become increasingly urgent challenges especially in arid and semiarid regions. Two-dimensional water trading (2DWT) mechanism has been designed to unify the quantity and quality of water for relieving the water crisis. This study aims to develop a risk aversion optimization-two dimensional water trading model (RAO-2DWTM) for planning the regional-scale water resources management system (RWMS). This is the first attempt on planning RWMS through risk aversion optimization within the two-dimensional water trading framework. RAO-2DWTM cannot only support in-depth analysis regarding the effect of decision maker's preferences on system risk in different trading scenarios, but also reflect the interaction between water right trading and effluent trading, as well as disclose the optimal scheme of water resource management under uncertainties. Twenty four scenarios associated with different trading scenarios and robust levels are analyzed. The optimization scheme under the optimal risk control level is determined based on TOPSIS. Results revealed that 2DWT would bring high benefit with reduced risk cost, water deficit and emissions, implying the effectiveness of 2DWT mechanism. The results also disclosed that risk aversion behavior can mitigate water scarcity and pollution, as well as reduce risk cost, but may lead to some losses of system benefit. Consequently, decision makers should make trade-offs between system benefit and risk in identifying desired trading schemes.

Multiple rivers flowing into the same bay can be correlated in water quality management and together determine the environmental status of the bay. Nonpoint source pollution management for multi-watershed aiming to alleviate environmental contamination can be under additional challenges and yield considerable economic and environmental benefits. In this study, a Bayesian simulation-based multi-watershed effluent trading designing model (BS-METM) is established for multi-watershed nonpoint source pollution management through incorporating techniques of water quality simulation, uncertainty analysis with Bayesian inference, optimal design for effluent trading, as well as mechanism analysis. BS-METM is capable of reflecting parameter uncertainties in nutrient simulation, disclosing the detailed optimal trading schemes under the impact of uncertainties and vital factors, and identifying optimal effluent trading mechanisms through revealing interaction among trading processes of multiple watersheds. BS-METM is applied to a real case of adjacent coastal watersheds (i.e. Daguhe and Moshuihe watersheds), which are identified as major sources of total phosphorus and ammonia nitrogen loadings to Jiaozhou Bay, China. Effluent trading optimization under multiple mechanisms, including intra-watershed trading, cross-watershed trading and non-trading, are conducted. The optimized industry scales and trading processes are obtained. The effects of vital factors on the trading process (i.e. environmental allowance-violation risk level and water availability level) are investigated. The interactions between water availability level and trading mechanism are also analyzed. It is proved that non-trading mechanism would be recommended under low water availability level and cross-watershed trading mechanism would be recommended under medium and high water availability level. The results provide a solid scientific basis for nonpoint source pollution management as well as effective sustainable development for multi-watershed region.

In this study, a sustainable water resources management (WRM) model with scenario-based mixed fuzzy-stochastic method with Green Z-score criterion method (SFGZ) is developed for coordinatin...

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.

In this study, a Bayesian risk-induced interval stochastic modeling framework (BRISF) is proposed for planning effluent trading program among point and nonpoint sources as well as identifying interactions of important trading factors under system risk. BRISF incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian inference with random walk Metropolis algorithm (RWM), and constraint-violation risk-based two-stage stochastic programming (CRTSP) within a general framework. Bayesian inference is employed for uncertainty analysis of SWAT model parameters and uncertain prediction of nutrient loadings; this process provides the random inputs for optimization process. CRTSP is capable of dealing with multiple uncertainties in modeling effluent trading program as well as system risk of environmental allowance violation. BRISF is applied to a real case of Xiangxihe watershed in China for water quality management. Solutions for optimal trading scheme corresponding to different risk levels are generated. Thousands of scenarios are examined to analyze the individual and interactive effects of trading ratios and treatment rates on trading system. Comparison between cross-industry and intra-industry effluent trading scheme is also conducted. It is proved that cross-industry trading would bring about higher benefit with reduced pollution loading; cross-industry effluent trading scheme would be recommended to achieve optimal water quality management and system benefit.

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.

The intensification of heat stress in a changing climate poses great threats to both human health and labor productivity. It is of great practical importance to assess the impacts of climate-induced heat stress on labor productivity and to develop effective adaptation strategies. In this paper, an integrated optimization-based productivity restoration modeling framework is proposed for the first time to develop the optimal policies for adaptation to climate change. To address underlying uncertainties associated with climate and labor management systems, we take into account ensemble projections from five global climate models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5) and inexact system costs. The system costs, including direct and indirect costs such as management costs, energy costs, and labor costs, are presented as interval numbers due to inherent uncertainty caused by population growth, technology development, and other social-economic factors. Uncertain information can be effectively communicated into the optimization processes in this study to generate optimal and reliable decision alternatives. We find that the increased Wet-Bulb Globe Temperature (WBGT) will lead to a large reduction in labor capacities over China except for the Tibetan Plateau under both RCPs by the end of the 21st century. The less developed regions tend to achieve the minimum system cost by having labor productivity recovered through working overtime due to the relatively low cost of overtime. This could result in more heat-related work injuries in the less developed regions. Since the less developed regions are not heat-prone areas in China, the changing climate would be a more dangerous threat and cause more damages to these regions where the residents are less acclimatized to heat stress. Moreover, we obtain a range of minimum system costs from 1.86 to 8.97 billion dollars under RCP2.6 and from 9.42 to 32.31 billion dollars under RCP8.5 (about 0.2% of China's GDP in 2019, 0.01% of China's GDP projected in 2100 under a sustainable socio-economic development scenario) for the restoration of labor productivity in a warming climate. We argue that urgent actions are needed to mitigate global warming impacts on labor productivity.

In this study, a stochastic rough-approximation water management model (SRAWM) associated with optimistic and pessimistic options is proposed for supporting regional sustainability in an irrigation system (IS) of an arid region with uncertain information. SRAWM can not only handle conventional stochastic variations in objective functions or constraints, but also tackle objective and subjective (i.e., risk performance of the decision maker) fuzziness through rough-approximation model based on measure Me. The developed model would be applied to a real case study of an irrigation district (ID) in Kaidu-kongque River Basin, China, which is encountering challenges in economic development and a serious environmental crisis (e.g., drought, water deficit, land deterioration, stalinization, soil erosion and water pollution) synchronously. Simulation technical (i.e., support vector regression) is put into SRAWM framework to reflect dynamic prediction of water demand in the future. Results of optimized irrigation area, water allocation, water deficit, pollution reduction, water and soil erosion and system benefit under various water-environmental policies (corresponding to various ecological effects) are obtained. Tradeoffs between ecological and irrigative water usages can facilitate the local decision makers rectifying the current irrigation patterns and ecological protection polices. Moreover, compromises between systemic benefit and failure risk can help policymakers to generate a robust risk-control plan under uncertainties. These detections are beneficial to achieve conjunctive goals of socio-economic development and eco-environmental sustainability in such an arid IS.

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.

Water resources systems are associated with a variety of complexities and uncertainties due to socio-economic and hydro-environmental impacts. Such complexities and uncertainties lead to challenges in evaluating the water resources management alternatives and the associated risks. In this study, the factorial analysis and fuzzy random value-at-risk are incorporated into a two-stage stochastic programming framework, leading to a factorial-based two-stage programming with fuzzy random value-at-risk (FTSPF). The proposed FTSPF approach aims to reveal the impacts of uncertainty parameters on water resources management strategies and the corresponding risks. In detail, fuzzy random value-at-risk is to reflect the potential risk about financial cost under dual uncertainties, while a multi-level factorial design approach is used to reveal the interaction between feasibility degrees and risk levels, as well as the relationships (including curvilinear relationship) between these factors and the responses. The application of water resources system planning makes it possible to balance the satisfaction of system benefit, the risk levels of penalty and the feasibility degrees of constraints. The results indicate that decision makers would pay more attention to the tradeoffs between the system benefit and feasibility degree, and the water allocation for agricultural section contributes most to control the financial loss of water. Moreover, FTSPF can generate a higher system benefit and more alternatives under various risk levels. Therefore, FTSPF could provide more useful information for enabling water managers to identify desired policies with maximized system benefit under different system-feasibility degrees and risk levels.

Extensive uncertainties exist in many resources and environmental management problems, which can be interrelated and thus amplify the complexity and nonlinearity of study systems. The interactions from dependent random variables pose significant impacts on the potential management strategies. In this study, an inexact copula-based stochastic programming (ICSP) method was developed to deal with interactive uncertainties with interval and stochastic characteristics as well as to address nonlinear dependence among multiple random variables. Specifically, the impacts of their interactions among random variables were revealed based on the concept of copula. ICSP can also reflect the risk of violating system constraints with linear and nonlinear dependences. The developed ICSP method was then applied to planning water resources management problems; results (i.e., system benefit, economic penalty, water allocation, and flood diversion) under a variety of risk levels have been generated. Results are useful for generating desired strategies for water allocation and flood diversion under various individual and joint probabilities. Compared with the conventional joint-probabilistic chance-constrained programming (JCCP) approach, ICSP can better reveal multiple uncertainties and their interrelationships under nonlinear conditions and generate more robust solutions. Training Programme Foundation, Natural Key Research and Development Plan, National Sciences Foundation, Technological Major Projects of Beijing Polytechnic, Natural Science and Engineering Research Council of Canada

This study developed a framework incorporating compatibility comparison, uncertain optimization model and scenario analysis (COS) for generating a comprehensive strategy associated with adjusting population employment structure based on industrial transformation (APEI), resource-environmental regulation and technique improvement from the respective of policymaker / government, to confront the hybrid challenge from economic development, resource crisis and environment problem in urban agglomerations (UA). In this COS framework, a Gini coefficient method is used for evaluating the compatibility of resource-environment consumption per unit employed population, which can reflect the current equal or compatible levels between socioeconomic development and resource-environment capacity in various regions in BTHU. Meanwhile, a scenario-based fuzzy-stochastic method with Laplace criterion (SFSL) is embedded into optimization model to handle multiple uncertainties. The developed COS framework has been applied to a real study in Beijing-Tianjin-Hebei urban agglomeration (BTHU), China, which is facilitate for the generation of various results (such as population employment structure based on industrial transformation, resource-deficit, excessive emission and economic viability) under scenarios. The obtained results can support a comprehensive strategy for improving the harmony of population, economy, resource and environment in an urban agglomeration.

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.

The severe shortage of water resources, increasing energy demand, and depleting fossil resources pose a great challenge for energy and water systems management. This study proposes a dual risk aversion optimization method – an energy water nexus system model, which can generate robust optimization solutions for water resource consumption, fossil energy resource allocation, electricity expansion and generation, electricity import, and analyze the effects of different policies and measures on energy and water nexus systems, as well as explore cost-effective plans for energy and water resources conservation and CO2 emissions reduction. The developed model can tackle the uncertain parameters shown as fuzzy sets and stochastic, control the feasibility robustness of the generated solution by capturing the risk of violating the objective and the constraints caused by stochastic and fuzzy uncertain parameters, as well as tackle issues of multi-objective tradeoffs of energy-water nexus systems. The Beijing-Tianjin-Hebei region was used to verify the proposed model, and the generated results indicate that: (i) the water resource consumption for electricity power generation is expected to increase by 10.29%–11.41% over the study horizon; (ii) coal-based power will be the major source of CO2 emissions, accounting for 95.55%–90.59%; (iii) promoting carbon tax and optimizing the electric power mix are effective measures for saving water and coal resources and mitigating CO2 emissions, but lead to higher system costs. The obtained results provide useful information for managers to balance the conflict among electricity demand, economic objective, and water resource shortage.

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 recent years, increscent emissions in the city of Beijing due to expanded population, accelerated industrialization and inter-regional pollutant transportation have led to hazardous atmospheric pollution issues. Although a number of anthropogenic control measures have been put into use, frequent/severe haze events have still challenged regional governments. In this study, a hybrid population-production-pollution nexus model (PPP) is proposed for air pollution management and air quality planning (AMP) with the aim to coordinate human activities and environmental protection. A fuzzy-stochastic mixed quadratic programming method (FSQ) is developed and introduced into a PPP for tackling atmospheric pollution issues with uncertainties. Based on the contribution of an index of population-production-pollution, a hybrid PPP-based AMP model that considers employment structure, industrial layout pattern, production mode, pollutant purification efficiency and a pollution mitigation scheme have been applied in Beijing. Results of the adjustment of employment structure, pollution mitigation scheme, and green gross domestic product under various environmental regulation scenarios are obtained and analyzed. This study can facilitate the identification of optimized policies for alleviating population-production-emission conflict in the study region, as well as ameliorating the hazardous air pollution crisis at an urban level.

This paper presents some decoding methods based on Tanner graph for the analysis of the repeat accumulate code which has been proved to be linear time encoding and linear time decoding. We study the structure of the repeat accumulate code and make a detail derivation of the procedure of these iterative algorithms. The extensive simulation results show that these efficient decoding methods based on the message passing algorithm are suitable for the repeat accumulate code both in performance and complexity.