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The optimal agricultural structure and population size within typical watersheds needs to be identified based on the water ecological carrying capacity (WECC). However, real-world systems of water ecological management are complicated as multiple uncertainties exist in the system parameters, which need some effective optimization methods to deal with. This research presents an inexact simulation-based fuzzy credibility-constrained mixed-integer programming (ISFCCMIP) model. Through integrating interval linear programming, fuzzy credibility-constrained programming, mixed-integer programming, global nutrient export from watersheds, and the Kirchner–Dillon model within a general framework, the developed ISFCCMIP model can effectively deal with the multiple uncertainties in the simulation and optimization processes of water ecological management systems. The developed ISFCCMIP model is applied to a real-world case study in the Xinfengjiang Reservoir Watershed. Results show that the total population that can be carried by the watershed WECC would decrease from [204885, 412367] to [121235, 271280], when the credibility level increases from 0.55 to 0.95. On the contrary, the total agricultural benefit would increase from [3.72, 5.06] × 108 to [3.75, 5.10] × 108 $. The total population in the base year far exceeds the watershed WECC. Although the total agricultural benefit in the base year is between the upper and lower bounds of the optimized results, the agricultural structure is not reasonable and needs to be adjusted. Concurrently, multiple results on the optimal agricultural structure and population size are obtained under different credibility levels and in different carrying capacity scenarios. Such results can provide a series of decision alternatives for watershed policy makers to consider the tradeoff between socio-economic development and water ecological protection. The results also assist the sustainable development of the Xinfengjiang Reservoir Watershed. The proposed model is effective for the optimal management of agricultural structure and population size within a reservoir watershed based on the WECC under multiple uncertainties. It also provides a reference for other areas with similar concerns. • An inexact simulation-based fuzzy mixed-integer programming model was developed. • The model is used for watershed management based on water ecological carrying capacity. • Water quantity and quality conditions and their joint effect can be considered. • The model can address uncertainties in both simulation and optimization processes. [ABSTRACT FROM AUTHOR]

Conserving and restoring ecosystem services in urban areas is an efficient measure to deal with increasing ecological challenges for cities while enhancing urban ecological resilience. However, rising inequalities threaten to improve critical environmental justice, public health and urban ecological resilience. This study proposed a non-monetary framework for accounting for urban ecosystem services and explored the inequality of ecosystem services using the Gini coefficient. Further, a regression-based decomposition method was used to identify primary socioeconomic factors that contributed to inequality. The results showed that the total ecosystem service in the urban areas presented a trend of first growing rapidly and then fluctuating steadily from 6.78 × 1022 sej in 1985 to 1.14 × 1023 sej in 2020 the percentage of existing services in the total ecosystem services was over 50%. The Gini coefficient of 1985 was more extensive than that of other years, which is 0.461. The runoff regulation service and the temperature mitigation service were the primary contributors for the inequality. Meanwhile, such income per capita, nonfarm rate and industrial production provided over 70% contribution rate to the inequality. This study assessed the inequality of urban ecosystem services and analyzed the driving factors, emphasizing the importance of reducing inequality, and providing a specific reference basis and theoretical support for decision-makers in urban planning and development strategies. • Establishing a framework to calculate urban ecosystem services. • Analyzing the inequality of ecosystem service inequality. • Pointing out the contributions of the related ecosystem services to the inequality. • Identifying certain socioeconomic factors that are affecting the inequality and these contributions. [ABSTRACT FROM AUTHOR]

To improve the capabilities of conventional methodologies in supporting greenhouse gas (GHG) emission mitigation from food production under uncertain conditions, an integrated approach was developed through incorporating copula-based violation risk analysis into a general life cycle analysis (LCA) framework. This approach strengthened the applicability of LCA in terms of uncertainty and risk reflections for food systems in the background of urbanization under multiple GHG emission targets. In detail, such an approach can (a) reflect uncertainties of food production and consumption processes, (b) tackle joint probability of two correlated variables (i.e., GHG emissions and economic benefits) through the employment of Archimedean copula (i.e., Gumbel, Frank, and Clayton copula) and Gaussian copula, and (c) assess violation risk of GHG emissions in food production considering the trend of future urbanization. A case study was proposed to illustrate application of the approach in Dalian City, China. Considering uncertainties of dietary patterns in 2020, two scenarios (scenarios of baseline and increase) were proposed according to dietary structures of urban and rural residents. In detail, the scenario of baseline represented the recent dietary pattern of Dalian in 2015. Scenario of increase indicated 50% rise in beef, mutton, and milk compared with the dietary pattern under the scenario of baseline. The results showed that based on the current agenda upon GHG emission intensity in China, violation risks under the scenario of baseline would be more prominent than the violation risks under the scenario of increase. [ABSTRACT FROM AUTHOR]

Due to the lack of automatic control for urban drainage systems in China, urban stormwater runoff pollution cannot be addressed effectively. To remedy this disadvantage, a theoretical framework of real time control (RTC) system was established. The key indicator identification and pollution load estimation of stormwater runoff pollution were investigated to provide control parameters (such as amount, duration, and intensity of rainfall). Stormwater storage tank (SST) is the main component of the RTC system. In order to enhance the efficiency of SST, a simulator was developed. A back propagation neural network (BPNN) was adopted to predict the turbidity of SST. The developed BPNN contained input, implicit and output layers. A simulation device of SST was proposed to provide the input data for the developed BPNN. Also, several RTC parameters of water quality (e.g., turbidity, ammonia nitrogen, total phosphorus and chemical oxygen demand) of SST were investigated by an experimental simulation. Then turbidity was chosen as the key RTC parameter. The turbidity prediction of the developed BPNN included two types, one was based on a single variable (i.e., flow quantity) at the inlet of SST to predict the turbidity at the inlet of SST. The other was based on multiple variable (i.e., flow quantity, ammonia nitrogen, total phosphorus and chemical oxygen demand) at the inlet of SST to predict the turbidity at the outlet of SST. Based on the training and verification processes of the experimental data, the results indicated that the developed BPNN performed well (R > 0.94) in predicting turbidity of SST. The developed BPNN was incorporated into a RTC system as a simulator, which can thus help efficient decision making for facilitating water storage and pollution reduction of stormwater storage tanks. [ABSTRACT FROM AUTHOR]

Due to frequent and serious waterlog and environmental pollution in cities in recent years, urban stormwater management (USM) has become an essential issue of urban sustainable development in China. Low Impact Development (LID) technologies are effective and popular measures of USM, and can be used to reduce urban waterlog and the associated environmental pollution. In this paper, in order to identify an optimal strategy of LID technologies based on economic efficiencies and environmental performances, the construction costs and environmental benefits of the four LID technologies (including grass swales, bioretention cells, green roofs, and permeable pavement) were analyzed. Then, due to the uncertain feature of rainfall, concentration of pollutants and many social-economic factors, an inexact fuzzy chance-constrained programming (IFCCP) model was developed. Multiple uncertainties that can be expressed as interval parameters, fuzzy sets, and stochastic distribution can be addressed effectively and incorporated directly into the modeling process. Based on the analysis of the precipitation probability distribution and land use data, the developed model was applied to a university campus with relatively high building density and plot ratio in Beijing. Then, optimal solutions of multiple precipitation probabilities ( p m ) under varying USM goals were obtained. The four LID technologies were chosen to meet multiple USM goals based on multiple p m values. When   p m = 0.1 , 0.05 , 0.02 and   0.015 , the total construction investments would be 1.46 to 4.66, 1.46 to 5.25, 2.31 to 8.54 and 3.95 to 12.7 million yuan, respectively. The relationship between construction costs and p m represented the relationship between economic benefit and system risk. The results indicated that USM was influenced by the rainfall capacity, construction costs would increase with the increase of precipitation, and the risks of constraint violation would decrease as the construction costs increase. When considering the four LID technologies during the planning stage of USM, the preferred selection order would be bioretention cells, permeable pavements, grass swales and green roofs. Therefore, the solutions under each p m level could provide the references for desired USM plans. [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]

Nitrogen (N) was one of the primary causes to water environment depravation, having a thorough understanding of N loading to water body is important for reducing the N pollution purposefully. In this research, we took the Miyun reservoir watershed as a typical area, of which a material flow model of the N metabolism was built. Afterwards, the material flow analysis (MFA) was conducted to identify the main pathways that generated N loading. And then the Monte Carlo (MC) method was applied to make uncertainty analysis of the N loading calculated through the MFA. The MFA results showed that the total N loading to surface water of the Miyun reservoir watershed was 5505.42 × 103 kg N, of which about 74.89% was originated from the agriculture subsystem (i.e., crop planting, fruit planting and vegetable planting subsystems). The main pathways of the agriculture subsystem that generated N loading were recognized as maize, chestnut, apple, pear, cabbage, cucumber and tomato planting. The uncertainty analysis results revealed that the N loading calculated from the MFA had some uncertainty, of which the fruit planting and industry subsystem had the biggest uncertainty, while the N loading from the fish farming subsystem had the lowest uncertainty. To make directed actions to protect the water quality of the Miyun reservoir watershed, the main pathways of each subsystem that generated N loading need to be regulated. This study provided a new framework of combing the MFA and MC method to simulate N loading to surface water at watershed scale, and was of vital significance to provide decision support for the watershed N pollution management globally. [ABSTRACT FROM AUTHOR]

In this research, a hybrid nonlinear grey-prediction and quota allocation model (HNGP-QAM) was developed for supporting optimal planning of China's carbon intensity reduction at both departmental and provincial levels in 2020. At such dual levels, HNGP-QAM can not only help forecast carbon intensity and its fluctuations over the concerned period, but also facilitate the identification of China's carbon intensity reduction target in 2020 and the corresponding quotas for minimizing the total abatement cost. Two scenarios were developed based on multiple governmental policies and allocation schemes among provinces and departments. The results showed that the total abatement cost would be 92.07 and 98.93 × 10 9 , as well as 180.57 and 194.19 × 10 9 RMB (It is another shortname for China Yuan) for provincial and departmental allocation schemes under the reduction ratios of 40 and 45%, respectively. Furthermore, the west, the east, and the central China would be allocated the emission assignments that would be accounting for 48.53, 28.26, and 23.21% of the total national emission reduction, respectively. The obtained results were particularly useful for multi-level governments in providing information to identify the carbon intensity reduction target, conducting emission reduction assignments among provinces and departments, as well as supporting relevant policy-making. The results also suggested that the developed HNGP-QAM be applicable to similar engineering and planning problems. [ABSTRACT FROM AUTHOR]

Concerns over water conflicts between human beings and ecosystems are increasing. Also, wastewater discharged by manufacturing industries is causing multiple ecological and environmental impacts in many regions. In this research, to comprehensively assess ecological and environmental impacts of wastewater discharged from large-scale industries, a hybrid life-cycle and fuzzy-set-pair analyses (HLCA-FSPA) approach was proposed. This approach represented an integration of life cycle analysis, set pair analysis, and fuzzy sets theory. The developed method could improve previous studies in systematically reflecting impacts of industrial wastewater in terms of multiple dimensions, and considering uncertain parameters in the evaluation process. It could give a complete and robust assessment of wastewater environmental and ecological impacts based on life cycle inventory/database and uncertainty analyses. The developed HLCA-FSPA method was then applied to a pulp and paper mill in Shandong Province of China. The results indicated that the impact of wastewater at the stage of pulp production was under the limit of China's wastewater discharge standard (i.e., level III) with the connection degree of 0.47. Comparatively, the impact level of the entire life cycle of copying paper production was III with a slightly decreased connection degree (i.e., 0.38). Such a difference reflected possibility variations of the impacts at different stages. The results also indicated that the developed method can be expanded to other areas based on the corresponding LCA database. Thus, the results could provide scientific bases for supporting decision-making in industrial wastewater management to mitigate the associated ecological and environmental impacts. [ABSTRACT FROM AUTHOR]

Cities consume most of the global energy and account for approximately 75% of global CO 2 emissions. Moreover, resource-based cities tend to consume higher amounts of fossil fuel and produce higher carbon emissions than other cities, whereas there are still insufficiencies about energy carbon reduction in resource-based cities in developed urban agglomerations. Therefore, it is necessary to explore the energy planning of such cities to achieve carbon peaking and carbon neutrality, and this is crucial for achieving carbon neutrality in urban agglomerations. This study aims to apply a scenario-based analysis approach to a resource-based heavy industrial city in China, Tangshan, based on the Long-range Energy Alternative Planning model, to explore Tangshan's future energy consumption, generation, and associated carbon emissions from 2010 to 2050. The impacts of different energy policies are analyzed for the Tangshan energy system by considering four scenarios. Under each scenario, energy consumption increased steadily by 2050, but the growth rate is different. The business as usual scenario is the fastest, and the low-carbon scenario is the slowest. In the comprehensive and integrated low-carbon transition scenarios, CO 2 is expected to peak at 298.34 million tons (Mt) in 2028. The main industrial sectors of Tangshan with high output value, high energy consumption, and high carbon emissions include smelting and pressing of ferrous metals, petroleum processing and coking, raw chemical materials and chemical products, etc. Combining Tangshan's coal resource endowment, the development of renewable energy, and introducing advanced technology can effectively reduce carbon emissions and increase the diversity of energy supply. We analyzed the cost-benefit of the energy system and found that the implementation of carbon capture and storage technology could effectively reduce carbon emissions by 219.62 Mt and increased costs by 563.12 billion yuan. The research could provide a scientific basis for the medium and long-term energy sustainable transformation of resource-based cities, especially for resource-based cities in urban agglomerations. [ABSTRACT FROM AUTHOR]

Modern agriculture calls for efficient and environmental-friendly agricultural water and land management practice. Agricultural water and land utilization mode has a great impact on the amount of carbon emissions, and thus the aim of this work is to propose an optimal modeling approach for generating efficient agricultural water and land management alternatives and reducing carbon emission in agricultural water-energy-food nexus system. This study presents a novel approach consisting of carbon footprint lifecycle assessment method and bi-level multi-objective stochastic programming model. The proposed approach has contributions in following aspects: (1) the environmental impact of different resource allocation strategies can be measured in optimization (2) the spatial variability of spatial data (e.g., ET 0 and precipitation); can be fully reflected via remote sensing information; (3) tradeoffs among two decision-making levels and their conflicting objectives under randomness of surface water availability can be addressed. The proposed approach was applied to the middle reaches of the Heihe River basin, northwest China. After solving the proposed model, the optimal water and land use alternatives under different hydrological years can be generated. Decision makers can plan agricultural production strategy according to optimization schemes, and reduce carbon emission through increasing vegetable cultivation and surface water utilization. Furthermore, the performance comparison of different models indicated that fierce conflicts exist between income fairness and economic benefits. The comprehensive evaluation value of bi-level multi-objective stochastic programming model is 0.7036, which is highest among single- and multi-objective models, showing that such model has obvious advantage in dealing with multiple decision-making levels and conflicting objectives. This approach can help decision makers of similar regions manage the agricultural production system in a more efficient and environment-friendly way. [Display omitted] • Lifecycle assessment of carbon footprint was used for measuring environmental impacts. • Spatial data (e.g. ET 0 and precipitation) were collected and processed with the help of remote sensing information. • Vegetable cultivation increasing and groundwater use decreasing can help reduce carbon emission in agricultural production. • BLMOSP model has obvious advantage among single- and multi-objective models for addressing such problems. [ABSTRACT FROM AUTHOR]

The pursuit of the sustainable agriculture often ignores to consider the ecological benefits. To achieve this goal, it is necessary to evaluate the ecological impacts related to the sustainable development. Therefore, to assess the overall ecological impact of crop production in 31 provinces in China, this study proposes to set up a non-monetary framework and indicators based on Emergy theory. Further analysis focuses on sustainable ecosystem under three different planting scenarios to identify the driving factors. The results demonstrated that the ecosystem disservice of crop production processes is greater than the services. The total emergy inputs, fertilizers used and R%, all of these can affect the ecological benefits. Furthermore, simply changing the cultivation pattern cannot achieve a balance between ecology and economy, there is no doubt that other method needed are needed. These findings provide valuable insights for developing appropriate policies for decision makers. • Establishing a framework to calculate the agri-ecosystem services and dis-services. • Analyzing the ecological benefits of crop system in the three different scenarios. • Building three emergy-based ecosystem service indicators to assess the crop system. • Proposing some feasible advices from ecological benefits perspective. [ABSTRACT FROM AUTHOR]

Intensive coastal reclamation activities can lead to ecological degeneration in coastal areas. Therefore, the engineering design concept of an ecosystem-based coastal reclamation has aroused great interest. However, the inconsistency of standards and the unclear understanding of ecological processes are the difficulties in this field. In this paper, we selected eco-seawall as object to determine and optimize the 4 engineering parameters (including L 1 , L 2 , a, and h 1). We set up 20 groups of working conditions under analyzing, and applying a computational framework to optimize eco-seawall designing processes. The computational efficiency would be enhanced by this developed method from the following aspects: (a) establishing surrogate model for eco-seawall to replace the high cost of simulation process; (b) searching engineering control parameters to optimize eco-seawall design; (c) obtaining the Petro curve set of optimal parameters for coastal reclamation management and ecological restoration. The design domain of the related parameters was determined by analyzing simulated and experimental data. As the results, the design domain were obviously reduced through optimization process: L1∈[0.1, 0.3]; L2∈[0.2, 0.8]; a∈(30,60]; h1∈[0.2,0.4]. Additionally, according to pilot-scale experiment, it can be concluded that the overtopping frequency for the low slope angle dam is 66.7%; the overtopping frequency for the high slope angle dam is 44.4%. The optimal design range for the practical engineering design can be provided by the optimal solution set of eco-seawall obtained from the optimization results. The calculation approach used in this research will provide reference for other engineering optimization work. • Using a optimization method to optimize engineering parameters of eco-seawall. • Establishing an surrogate model for eco-seawall. • Searching engineering control parameters to optimize eco-seawall design. [ABSTRACT FROM AUTHOR]

Miyun Reservoir plays a fundamental role in providing drinking water for Beijing Municipality. In this study, agricultural nitrogen (N) flow was analysed for water pollution mitigation in Miyun Reservoir watershed. A partial substance flow analysis (SFA) method was applied to evaluate the historic changes in N flow from 2001 to 2017. Based on this method, N flow, N losses, N accumulation, and nitrogen use efficiency (NUE) over 17 years were identified and quantified. The results showed that N input, N output, and N stock gradually decreased in this period. The decline of N input mostly resulted from the considerable decrease in chemical fertilizer use and livestock excrement application. With decreasing N input, the N stock also decreased over these years. The N losses generally decreased, except for 2009–2012, as a result of the substantial increase in harvested grains which generated even more straw for feeding or burning. In addition, the average NUE was as low as approximately 15% which may due to the huge straw storage and N losses. It is indicated that there was a great potential to decrease N input especially chemical fertilizer for mitigation of water pollution through analysis. Therefore, combined technology innovation with policy impact was proposed in Miyun County. Reducing chemical fertilizer, improving waste cycling, reducing ammonia volatilization, preventing erosion and runoff and improving peasant awareness of environmental protection were proposed as recommendations. The historic analysis of N flow could be helpful to understand the challenges of N pollution. The integrated policy and technology measures will be effective to achieve water quality control. Both will provide a reference for other countries or regions with the similar issues. [ABSTRACT FROM AUTHOR]

The community diversity of phytoplankton is quite high, and hundreds of phytoplankton species can coexist. Aggregating phytoplankton species into different phytoplankton functional groups (PFGs) can better assess and predict the potential structural and functional effects of aquatic ecosystems. The continuous interaction of key PFGs is the key cause of cyanobacterial blooms in drinking water supply systems, especially in shallow border reservoirs, but the reasons have not been understood. This study was carried out in Macau's two shallow border reservoirs, the Main Storage Reservoir (MSR) and Sai Pa Van Reservoir (SPVR). Then, phytoplankton samples from these reservoirs were collected monthly over a four-year period (from January 2011 to December 2014) to reveal key factors driving PFGs succession and their synthetic effects on the dominant species. The trophic state index (TSI) of TN and TP of the two reservoirs ranged from 36.25 to 83.95 and 8.32–56.13, respectively, indicating that the SPVR and MSR were between a eutrophic and hypereutrophic state. The phytoplankton analysis results showed that there were 14 dominant PFGs and 8 dominant PFGs in the MSR and SPVR, respectively. The group S 1 was always predominant in the SPVR during the succession of PFGs, while the succession of PFGs in the MSR were quite different, the PFGs could mainly be summarized as S 1 /S N /X 1 (2011) → X 1 /J/B/T B /P (2012, 2013) →S 1 /X 1 /J (2014). In addition, redundancy analysis (RDA) through stepwise discriminant analysis was used to analyze the dominant PFGs and abiotic factors in both reservoirs. The results indicated that the turbidity value of the reservoir was the key factor influencing groups S 1 , Lo and S N in the SPVR. However, the different PFGs were influenced by the turbidity, NH 3 –N, NO 3 –N and EC values of the reservoir in the MSR. The relationship between PFGs and environmental factors was described by stepwise regression analysis. The investigation of the different ecological responses between the interconnected reservoirs was useful for targeted water source protection. [ABSTRACT FROM AUTHOR]

With the cascade hydropower stations (CHSs) development and climate change in a large dam-reservoir-river (DRR) system, distinguishing the impact of climatic and anthropogenic factors on vegetation dynamics is vital for a sustainable watershed ecosystem management. In this study, a Breaks for Additive Seasonal and Trend (BFAST) algorithm was adopted to detect the breakpoints (BP) and trend change of climatic factors (CFs) and vegetation dynamics at the pixel scale in the DRR system. The partial correlation and time lag analysis methods were chosen to examine the relationship between vegetation dynamics and CFs (i.e., temperature and precipitation). The multiple linear regression analysis method was adopted to separate the impacts of climatic and anthropogenic factors on vegetation dynamics in multiple periods (i.e., from 1999 to 2013, before BP (BBP), and after BP (ABP)). The most significant results indicated that the area fraction of the BP of vegetation dynamics was 19% of the basin, and the trend BP mainly occurred in 2008. The trend of BP mainly was positive BBP and negative ABP in the basin, and its area fraction was 53.01%. For the eight CHSs, the vegetation dynamics around Ludila and Guanyinyan were more affected by their constructions than that around the other hydropower stations. For the CFs, the vegetation dynamics was mainly affected by precipitation. The lag periods of temperature and precipitation on vegetation dynamics were four months and one month, respectively. Most of the vegetation dynamics appeared to have been impacted by anthropogenic factors in the DRR system. For the eight CHSs, the impact degree of anthropogenic factors on vegetation dynamics BBP was more severe than that ABP, especially around Ludila and Guanyinyan hydropower stations. These results are beneficial for sustainable watershed ecosystem management in the DRR system and ecological development planning around the CHSs. [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]

An entropy-based multi-objective interval stochastic programming (EMISP) approach based on interval hierarchical projection, interval information entropy, interval analysis and chance-constrained programming was developed for supporting agricultural water management. EMISP improved upon existing methods through measuring and balancing ecological and economic benefits of irrigated croplands under uncertainty. To demonstrate its applicability, the developed method has been applied to an arid Chinese watershed. The results suggested that grain crops would produce higher ecological benefits than economic crops. A series of benefit- and risk-explicit plans for agricultural water use were generated, which indicated that the relaxation of water availability constraints would generate higher comprehensive benefits, but increase the risks of system infeasibility. The results from the EMISP model were also compared to those from four potential alternative models. These comparisons revealed that EMISP could achieve higher water productivity and comprehensive benefits with controllable water-shortage risks. • An interval hierarchical projection method was proposed for measuring ecological benefits of croplands. • An entropy-based multi-objective interval stochastic model was developed to balance multiple objectives under uncertainty. • The results show that grain crops would produce higher ecological benefits than economic crops. • The developed model could achieve higher water productivity and comprehensive benefits. [ABSTRACT FROM AUTHOR]

Reservoir operation policies affect reservoir nitrogen (N) cycling and budgets through complex influences on physical and biogeochemical processes in water and sediment. However, the underlying influencing mechanisms, especially the impact of operations on sediment processes, remain unclear. This study combines a coupled hydrodynamic-eutrophication-sediment model and a reservoir operation model to clarify reservoir operation impact on N cycling processes in water and sediment as well as impact on stored and discharged water quality. Three typical reservoir operation policies (i.e., standard, hedging, and eco-friendly) were selected to create operation scenarios. The hydrodynamic-eutrophication-sediment model was used to simulate N cycling processes and N concentrations in stored and discharged water under each scenario. Using China's Danjiangkou Reservoir as a case study, results revealed that reservoir operation policies significantly affected spatiotemporal dynamics of N cycling processes and water quality. Specifically, reservoir sediment acted as a N source, contributing to annual net release of 9.5–16.1 Gg N. Higher water levels promoted sediment ammonium release through effects on reservoir submerged area and sediment temperature. Moreover, denitrification processes in water and sediment collectively contributed to annual N removal of 14.0–16.9 Gg N. Annual N removal was greater in the higher water level scenario despite its lower denitrification rate per unit area. Additionally, these processes and hydrodynamic conditions combine to affect N forms and concentrations. The N concentration in the lower water level scenario was generally higher, risking algal bloom outbreaks in the shallow-water zone. This study is intended to help guide reservoir operation policymaking to account for N management while also coordinating water quantity and quality targets. • Spatiotemporal dynamics in N cycling processes and water quality are assessed. • Higher storage levels can promote sediment ammonia release in deep reservoirs. • Denitrification in water and sediment significantly contributes to N removal. • Tradeoffs exist between the quantity and quality of water released to downstream. [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]

Revealing the long-term development trajectory and peak scenarios of the urban residential building sector is crucial for formulating targeted emission reduction strategies and contributing to achieving the "Dual carbon" target. Given that, significant variables affecting carbon emissions from urban residential buildings (URBCE) at both macro and end-use building levels are identified in this study. Furthermore, an innovative dynamic simulation model is developed by integrating the Monte Carlo simulation model with the Kaya identity model, taking into account the uncertainties of the variables. Subsequently, the long-term evolution trajectories, potential emission peaks, and peak time of the URBCE close to 2060 in China's hot summer and cold winter (HSCW) zone are simulated. Results indicate that URBCE is expected to reach its peak of 272.43 Mt CO 2 in 2040 under the baseline scenario. Through the dynamic simulation, the probable peak time of energy consumption in the HSCW zone is 2044 (±1.85), with a peak of 145.42 (±6.91) Mtce, and the possible peak time of carbon emissions is probably 2039 (±1.60), with a peak of 271.93 (±13.40) Mt CO 2. According to dynamic sensitivity analysis, carbon emission factor, per capita residential space, and heating energy intensity can promote the peak size and timing. These findings lay a robust foundation for the formulation of regionally specific carbon reduction targets and initiatives. Dynamic simulation results of peaking time and peak value of URBEC and URBCE. [Display omitted] [ABSTRACT FROM AUTHOR]

Production, consumption, and disposal of plastics are associated with the generation of a large amount of greenhouse gas (GHG). Polyethylene terephthalate (PET) is one of the most widely used plastics, which is mainly produced and consumed by China and causing increasing concerns. The previous studies mainly focused on flows and stocks of PET. Detailed information on GHG emissions for the entire life cycle of PET in China is limited. Particularly, the key paths of emission reduction for life-cycle PET considering carbon neutrality are unknown. In this research, a network analysis system and model of GHG emissions were developed for PET in China and helping explore characteristics of GHG emissions over the three development periods of the PET industry. The results showed that the most potential stage of carbon neutrality for PET was the stage of PET production, accounting for approximately 74.9% over 2000 to 2018. The manufacturing process of PET fibers and bottles would have a major contribution to GHG emissions. At the same time, GHG emissions from the mechanical recovery process should not be ignored. The plastic restriction order for PET and the waste treatment ways of low-carbon would have a significant contribution to emission reduction. According to the results, this study identified the most potential key process of carbon neutrality in the PET life cycle and proposed policies to reduce GHG emissions, which would provide scientific support for the PET industry to achieve the goal of carbon neutrality in China. • A comprehensive model for quantifying GHG emissions in PET life-cycle is developed. • The P stage of PET (74.9%) is the main source of GHG emissions in 2000–2018. • The M process of PET fibers has a major contribution to GHG emissions in M stage. • The plastic limit order in China has a significant effect on reducing GHG emissions. [ABSTRACT FROM AUTHOR]

Water production, utilization and recycle in urban system associated with a large amount of energy consumption and carbon emission that has increasingly international attention under sustainable development goals. In this study, a comprehensive urban water resources system management model is proposed under water-energy-carbon nexus and multiple uncertainties. The water-energy-carbon nexus was reflected in the processes of water extraction, water production and sewage treatment, and two objectives of maximizing the system benefit and minimizing carbon emission is transformed into two linear programming problem through the dual-objective interval fractional programming. Considering the interaction among water supply/demand, pollutants discharge control, energy consumption and self-energy utilization, indirect carbon emission as the main constraints, the developed model is applied to a typical water and energy sources shortage area of Qingdao city in north China. Multiple scenarios related to energy consumption and carbon emission control are designed to search the relationship among energy consumption, carbon emission and water resources allocation schemes. The results indicated that water demand as the main driving factor of energy consumption and carbon emission in urban water system cannot be directly limited through water supply structure adjustment. Through introducing self-energy supply (sludge utilization) and energy consumption control, water resources allocation schemes and water supply structure would be changed to improve energy saving potential with an obvious decrease trend of carbon emission intensity from 0.7921 tCO 2 /MWh to 0.5544 tCO 2 /MWh. The model could effectively provide reasonable urban water system management strategies under different water allocation demands and carbon emission challenges scenarios and deal with multiple uncertainties in decision-making process. [Display omitted] • Urban water resources system brings ecological environment issues. • A comprehensive urban water resources system management model is proposed. • Maximizing the economic benefits of per carbon emission to obtain the scheme. • Sludge utilization and energy consumption control can reduce carbon emission. • The model provides experiences for water-scarce regions around the world. [ABSTRACT FROM AUTHOR]

Freshwater ecosystems face serious degeneration risks from anthropogenic activities, among which land use heterogeneity transformation is one of the main threats on aquatic biodiversity. However, the mechanisms by which land use heterogeneity affects fish community structure in aquatic ecosystems are still difficult to determine. The results of this study showed that we detected a total of 164 fish species during two seasons, with 97 fish species belonging to 15 orders, 31 families and 74 genera in dry season and 138 fish species belonging to 14 orders, 40 families and 98 genera in wet season. The intergroup differences between the upstream, midstream and downstream of the basin in dry season were smaller than those in wet season, and the species richness in wet season were higher than this in dry season. The values of β-diversity between the upstream and midstream regions was greater than downstream in dry season. The value of β-diversity between upstream and downstream regions was larger than in the midstream in wet season. It concluded that the β-diversity values were generally smaller in wet season than in dry season. Land use types on both sides of the Han River basin are mostly cropland, forest land and man-made surfaces. The results of calculating LUI and fish life history characteristics showed a greater correlation between K and M values in the dry water period and a greater correlation between PD50 and longevity in the rich water period. Moreover, we calculated the correlation between the land use index (LUI) and β-diversity NMDS values, respectively, showed a significant positive correlationship (0.712 in dry season; 0.742 in wet season). This research (a) obtained the spatial and temporal distribution of α-diversity and β-diversity data of fish in the whole basin using eDNA technology; (b) analyzed the relationship of land use heterogeneity on fish life history characteristics; and (c) revealed the correlation between land use index and β-diversity communities. Overall, this study can provide data support and theoretical basis for integrated watershed ecosystem management, and provide suggestions for environmental protection and management around the watershed. • obtained the spatial and temporal distribution of fish diversity data based on eDNA technology for the whole basin. • analyzed the relationship of land use heterogeneity on fish life history characteristics. • revealed the correlation between land use index and diversity communities. [ABSTRACT FROM AUTHOR]

Multiple environmental pollution appears along with the development of industry and has become a hot issue that needs to be dealt with and solved. Among various remediation materials, a green renewable resource biochars were rapidly developed and widely used because of their low cost, excellent performance and eco-friendly characteristics. Based on this, this work detailedly discussed the whole process, including precursor collection, and preparation, modification, application and evaluation of biochar combined with the latest developments. Different from the previous summary studies that simply listed the composition and content of biochar, the future development direction and challenges are prospected in detail. Aims to overcome the shortcoming of few comprehensive studies on biochar, and the reaction process and mechanism of water and soil were revealed separately according to different pollution systems and own characteristics. This work reviewed the development of preparation and modification methods of biochar in recent years, the versatility and effects of biochar in water and soil applications, and then summarized the mechanisms between biochar and polluting media (organic and inorganic pollutants) as well as environmental benefits and economic evaluation of biochar. Furthermore, the existing main challenges and future development trends are discussed in detail. This comprehensive review of biochar is expected to provide new insights for achieving carbon neutrality and sustainable development in the environmental field. [Display omitted] [ABSTRACT FROM AUTHOR]

Water, energy and food (WEF) are closely intertwined. The complex interaction can form the nexus risk due to WEF scarcity. The WEF scarcity nexus risk can cause severe economic losses to adjacent regions through regional trade chains. Investigating the spatial transmission process can help understand regional vulnerability to the WEF scarcity nexus risk. Thus, this research proposes a systematic framework of the WEF scarcity nexus risk monetization method, ecological network analysis (ENA), entropy weight model (EWM) and technique for order preference by similarity to an ideal solution (TOPSIS) to analyze the generation, evolution, transmission and evaluation of WEF scarcity nexus risk. The aims are to intuitively show the harm of WEF scarcity nexus risk through risk monetization and quantitatively analyze WEF scarcity-induced risk spatial transmission. The results indicated that the water-energy-food (W-E-F) scarcity nexus risk was a critical factor restricting regional economic development. Resource-scarce regions/sectors transferred WEF scarcity nexus risk to certain neighboring partners through network trade to ensure local demands. Especially in highly resource-dependent regions (e.g., Guangdong-Hong Kong-Macao region), Hong Kong and Macao would transfer much of the local WEF scarcity nexus risk to Guangdong province, making potential economic losses in Guangdong more significant than local losses. Instead of considering Guangdong, Hong Kong and Macao as competitors, the WEF scarcity nexus risk could be reduced if the three regions worked in a coordinated pattern. Such cooperation has implications for optimizing the industrial structure and sustainable development goals for clean water, affordable energy and food security. [ABSTRACT FROM AUTHOR]

High flow events (including both the high flow pulses and floods) exert many critical ecological functions in riverine ecosystems and their elimination will lead to severe ecological degradation. However, common hydropower operations are only required to legally maintain the minimum environmental flows (e-flows). The extra release of high flow events will result in revenue loss of hydropower producers. In this research, we propose an approach to assessing the eco-compensation standard which is necessary to compensate the loss of hydropower producers and promote the release of high flows to downstream rivers. The scenario tree approach is adopted for the generation of possible scenarios of flows and electricity prices to deal with the uncertainties of the two factors in deregulated electricity markets. A stochastic linear programming method is proposed to determine the maximum mean annual revenues under two e-flow provision scenarios, i.e., only sustaining the minimum e-flows (scenario 1) and sustaining both the minimum e-flows and a specified number of high flow events (scenario 2). The revenue difference under the two e-flow scenarios is set as the eco-compensation standard. A case study in the Wangkuai reservoir, China, demonstrates that the eco-compensation standard is obviously influenced by the installed hydropower generation capacity (IHGC) and the number of high flow events planned to be released. Moreover, the greatest standards may be not corresponding to a very large IHGC. [ABSTRACT FROM AUTHOR]

The problem of fresh water scarcity is one of the major constraints for regional development. Rapid urbanization and rising economic prosperity has further exacerbated problems with not only water quantity, but also water quality. Hence, sustainable utilization of water resources should be a priority, especially in water stressed areas. However, most current studies on optimal water allocation models are based on physical water, which cannot fully reveal the water flows embedded in imported and exported raw materials and products throughout all production processes. Thus, they fail to achieve sustainability goals. Therefore, the objective of this study was to set up an optimal water allocation model for industrial sectors based on water footprint accounting. In this paper, first the internal and external blue and gray water footprints were evaluated. The results indicated that the external water footprint was the major contributor to Dalian's water footprint, which shared around 72.58% of the total. Among all the industrial sectors, the chemical industry and chemical production sector had the highest total WF. Second, analyses of water productivity intensity of the WF and physical water for the industrial sectors were conducted. The comparison of the results revealed that physical water productivity intensity failed to explain the embedded water inflows in the production processes among the regions and sectors. Third, an optimization model based on the water footprint accounting results was thus set up. The optimal consequences indicated that a water allocation plan could meet both the water requirements for the blue and gray water footprints of the industrial sectors, and their target output goals as well. Therefore, this optimal allocation model is both rational and applicable for sustainable water utilization in future water management strategy formulations. [ABSTRACT FROM AUTHOR]

Accurately grasping the dynamic change information of the landscape is essential for promoting sustainable land management aiming at ensuring the ecosystem health of shallow lakes. We proposed a new Tendency-Pattern-Service (TPS) conceptual framework for an integrated investigation of spatio-temporal landscape variations process and their ecological effects. It consisted of three components, namely, tendency variation analysis, pattern dynamics analysis, and service function analysis. Taking Baiyangdian Lake, a typical shallow lake in north China, between 1991 and 2015 as a study case, we used the proposed TPS framework to analyze spatio-temporal dynamics in landscape variations under different water levels due to water loss and recovery. The results were as follows. (1) Each landscape type varied in different degrees in the study period. When the water depth recovered to a normal level (7.86 m), the shallow water zone increased by 136.63%, while the deep water zone declined by 67.12% compared with that in a high water level year. (2) The landscape pattern changed toward a highly heterogeneous and fragmented state, and landscape categories gradually became equally. (3) Habitat quality evaluation showed the area of the suitable landscape was reduced. Thus, even when the water level recovered, the habitat quality was not improved and suitable habitat landscapes were gradually replaced by moderately suitable habitat landscapes. Considering the relationship between habitat quality and landscape changes in response to different water levels, it is important to take landscape patterns and ecosystem services into account comprehensively in lake restoration and management of shallow lakes. [ABSTRACT FROM AUTHOR]

Approximately half of the global population resides in cities and accounts for over 80% of global carbon emissions. To improve efficiency in carbon emission reduction, and enhance adaptability of cities to climate change, it is essential to undertake accounting of urban carbon cycle. This plays a prominent role in global carbon cycle. An integrated accounting framework for urban carbon cycle was established herein to support uniform accounting and place-specific comparisons, covering natural and artificial carbon cycles, vertical and horizontal carbon flows, carbon storage and fluxes, and carbon inputs and outputs. Guangyuan, a mountainous city in China that was seriously stricken by Wenchuan earthquake in 2008, was chosen as the case to demonstrate the framework and investigate the effects of post-earthquake reconstruction on carbon cycle. The results show: 1) the proposed framework could effectively assess dynamics of carbon cycle under external disturbances, 2) there were abundant vegetation and correspondingly a large natural carbon tank and vertical carbon input, and low industrialization degree and correspondingly small horizontal and vertical carbon outputs in Guangyuan, 3) the post-earthquake reconstruction led to a series of impacts on carbon cycle, from both aspects of amount and structure, and 4) such impacts of post-earthquake construction on artificial carbon cycle were distinct with a tendency toward low-carbon development in Guangyuan. The results were helpful for investigating the effects of natural disturbance and human interference on urban carbon cycle, which in turn could improve the adaptation to climate change. [ABSTRACT FROM AUTHOR]

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. • An integrated optimization modeling framework is developed to achieve optimal labor productivity restoration in a warming climate. • Various uncertainties are addressed through ensemble climate projections and interval programming models. • Intensification of heat stress leads to a large decrease in labor capacity over China except for the Tibetan Plateau. • More losses in working hours are projected for moderate labor activities compared to light and heavy labor activities. • Less developed regions tend to have more working hours recovered by working overtime than air conditioning. [ABSTRACT FROM AUTHOR]

As the basis of urban social-economic development, water and energy resources are indivisible and interrelated in the production and consumption activities of cities. The frequent intercity trade of energy-related goods aggravates water withdrawal in neighboring cities and causes the spatial transfer of resources. The spatial correlation of water withdrawal and energy consumption is rarely considered in the city-level water-energy nexus. As one of China's most serious water shortage regions, Hebei Province is the third-largest energy consumer in China and is a major supplier of mineral resources, industrial raw materials, water resources, electricity, and agricultural products to Beijing and Tianjin. The spatial Durbin model with the two-way fixed effects is applied to explore spatial effects of water-energy nexus in cities of Heibei province. Seven influencing factors, including energy consumption, energy structure, population, GDP, water withdrawal intensity, urbanization, and agriculture structure were considered. Results showed that all factors had positive impacts on water withdrawal except energy structure. Water withdrawal intensity made the most considerable contribution to water withdrawal, followed by urbanization, energy consumption, GDP, industrial structure, and population. The urbanization level of neighboring cities exerted the most significant positive spatial spillover impact on local water withdrawal, whereas the energy consumption showed a negative impact. The exploration of city-level spatial correlation regarding water withdrawal and energy consumption provides a reference for regional cooperation to improve resource utilization efficiency, establishing sustainable energy and water systems, and promoting sustainable development. [ABSTRACT FROM AUTHOR]

Online monitoring data of atmospheric environmental quality often deviate or are missing, causing a great impact on regional atmospheric quality analysis. In this study, a deep learning method to repair atmospheric environmental quality data based on Gaussian diffusion and gate recurrent unit (GD-GRU) was developed to improve repair accuracy. A multi-source Gaussian diffusion model was developed to estimate PM 2.5 based on the pollutant diffusion law and the data of 61 stations in Guilin. The root mean square error (RMSE) of the estimated and observed value was extracted as the error sequence. The error value was regarded as output of gate recurrent unit (GRU) with the inputs of weather and pollutant parameters. Missing data were calculated by Gaussian diffusion estimated value and the error predicted by GRU. The established GD-GRU model was applied to repair the long-sequence missing data. The analytical results indicated that the GD-GRU model had higher prediction accuracy of extreme values than Gaussian diffusion model and GRU model, because GD-GRU based on Gaussian diffusion can calculate the extreme value by simulating the diffusion and transmission mechanism. The established model predicted PM 2.5 concentration in the next hours with an RMSE of 12.561, which was approximately 21.02% better, on average, than methods like autoregressive integrated moving average model (ARIMA), support vector regression (SVR), recurrent neural network (RNN), long short-term memory model (LSTM), and GRU. The established GD-GRU model demonstrated good performance on extreme values prediction and air quality data repair, thus providing a new method for air quality long-sequence missing data repair. [ABSTRACT FROM AUTHOR]

The environmental impacts of the fashion industry have been aroused wide concerns. The globalization and fragmentation of the textile and fashion system have led to the uneven distribution of environmental consequences. As denim is the fabric of jeans that is representative of fashion, this study assessed virtual carbon and water flows embodied in the global denim-product trade, and footprints of denim production were quantified by life-cycle assessment and water footprint assessment. Results indicated that virtual carbon embodied in the global denim trade increased obviously from 14.8 Mt CO 2 e in 2001 to 16.0 Mt CO 2 e in 2018, and the virtual water consumption dropped from 5.6 billion m3 to 4.7 billion m3 from 2001 to 2018. The denim fabric production and cotton fibre production respectively contribute the most of the carbon emissions and water consumption. Polyester blended denim has 5% larger carbon footprint and 72% lower water footprint than cotton denim, and contributes to increasing embodied carbon emissions (from 4% in 2001 to 43% in 2018). Increasing the utilization of polyester blended denim would save water but face more pressures on carbon emission reduction. In the past two decades, virtual carbon and water flows embodied in the global denim trade are relocating, main jean consumers (i.e., the USA, EU-15, and Japan) withdraw the denim manufacturing supply chain and developing countries (i.e., China, India, and Pakistan) with higher carbon and water footprint undertake main global denim production, facing increasing climate-related risks and water crisis. The South-South cooperation helps share successful experiences, save production cost, and lessen resource consumption and environmental emissions. The production and consumption of denim should be shifted to circular and sustainable ways and new business models are required. The analysis framework can provide the basis for exploring environmental flows of product-level trade, and results can offer a basis for environmental policies and control strategies of the fashion industry, and as well as the sustainable production and consumption of garment. [ABSTRACT FROM AUTHOR]

Inflow forecast is an important input for reservoir operation. Due to limited forecast techniques, the inflow forecast always has uncertainties (usually in the form of forecast errors). Handling inflow forecast uncertainties effectively is important for optimizing reservoir operation. The magnitude of this uncertainty depends on both the forecast lead times and the hydrological periods (i.e., wet season vs. dry season). Two-stage Bayesian stochastic dynamic programming (TBSDP) is an effective way to address the forecast uncertainties. However, current TBSDP methods pay more attention to the uncertainty differences caused by forecast lead times, neglecting the uncertainty differences in wet and dry seasons. Starting with a TBSDP model, we proposed a new two-stage and two-period version of the model (TTBSDP), which can not only consider the forecast uncertainty differences caused by forecast lead times, but also separately accounts for forecast uncertainty in wet and dry seasons. Because dealing separately with the uncertainties in the two periods increases the computational complexity, we further explored the conditions under which degree of forecast uncertainties this separation is necessary. We compared our new model's results (in terms of cumulative annual power generation) with the previous Bayesian stochastic dynamic programming models. With increasing forecast uncertainty in the first stage of the overall forecast horizon (in the wet season), the TTBSDP model produced superior results at higher uncertainty, with the most stable performance (the smallest variation of cumulative annual power generation when the forecast uncertainty coefficient increases). The proposed new model is benefit for hydropower operations when the magnitude of forecast uncertainty in the wet seasons is large. • A two-stage and two-period Bayesian stochastic dynamic programming model was developed. • The new model dealt separately with forecast uncertainties not only in two stages but also in two hydrological periods. • The TTBSDP model produced superior results at higher uncertainty, with the smallest variation of power generation. [ABSTRACT FROM AUTHOR]

This paper develops an interval multi-stage robust programming model for regional low-carbon electric power system planning with energy-water nexus. The model integrates interval programming and multi-stage stochastic programming to handle uncertainties with different probability distribution information. In addition, under the framework of robust programming, the optimal solutions could provide tradeoff information between system cost and decision maker's risk preference. The hybrid programming method is tailored for a case study of Shandong Province, China to validate its applicability and provide useful managerial insights, where energy-water interactions are gain great concerns. The model helps to make robust strategies associated with risk control for capacity expansion, carbon capture and storage (CCS) technology retrofitting, power generation, imported electricity, and water allocation for electricity generation, with the goal of minimizing total cost. Besides, considering the variation of water resource conditions and environmental policies under climate change, the impacts of water resource availability and carbon emission caps on regional electric power system planning are explored and discussed. It is found that stricter carbon emission cap policy would facilitate the investment on CCS devices but have little impact on the installed capacity structure, meanwhile it would increase water consumption per power generation. However, scarcer water resource situation would promote the development of renewable energy generation, and limit the investment on CCS technology. • Low-carbon power system management optimization with energy-water nexus under uncertainties. • Impact analysis of various water resource availability and carbon emission cap on optimal strategies. • Useful managerial insights on energy system planning from energy-water nexus perspective. • Robust solutions with risk-cost tradeoff information to reflect different risk preferences. [ABSTRACT FROM AUTHOR]