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Electricity consumption and anaerobic reactions cause direct and indirect greenhouse gas (GHG) emissions within domestic sewage treatment systems (DSTSs). GHG emissions in DSTSs were influenced by the sewage quantity and the efficacy of treatment technologies. To address combined effects of these variables, this study presented an approach for identifying pathways for GHG mitigation within the DSTSs of cities under climate change and socio-economic development, through combining life cycle analysis (LCA) and the Hierarchical Archimedean copula (HAC) methods. The approach was innovative in the following aspects: 1) quantifying the GHG emissions of the DSTSs; 2) identifying the correlations among temperature changes, socioeconomic development, and domestic sewage quantity, and 3) predicting the future fluctuations in GHG emissions from the DSTSs. The effectiveness of the proposed approach was validated through its application to an urban agglomeration in the Pearl River Delta (PRD), China. To identify the potentials of GHG mitigation in the DSTSs, two pathways (i.e., general and optimized) were proposed according to the different technical choices for establishing facilities from 2021 to 2030. The results indicated that GHG emissions from the DSTS in the PRD were [3.01, 4.96] Mt CO 2 eq in 2021, with substantial contributions from Shenzhen and Guangzhou. Moreover, GHG emissions from the sewage treatment facilities based on Anaerobic-Anoxic-Axic (AAO) technology were higher than those based on other technologies. Under the optimized pathway, GHG emissions, contributed by the technologies of Continuous Cycle Aeration System (CASS) and Oxidation Ditch (OD), were the lowest. Through the results of correlation analysis, the impact of socioeconomic development on domestic sewage quantities was more significant than that of climate change. Domestic sewage quantities in the cities of the PRD would increase by 4.10%–28.38%, 17.14%–26.01%, and 18.15%–26.50% from 2022 to 2030 under three Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. These findings demonstrated that the capacities of domestic sewage treatment facilities in most cities of the PRD should be substantially improved from 0.12 to 2.99 times between 2022 and 2030. Under the optimized pathway, the future GHG emissions of the CASS method would be the lowest, followed by the OD method. • The correlations between domestic sewage quantity, socioeconomic development, and climate change were focused on. • A hybrid approach based on LCA and HAC was proposed for GHG mitigation in domestic sewage treatment systems. • A typical region of China was adopted for illustrating applicability of the developed approach. • Two pathways were proposed according to the different choices for technical facilities from 2022 to 2030. [ABSTRACT FROM AUTHOR]

• The method of compiling the multi-regional input output is optimized. • Carbon emissions of urban agglomerations (UAs) are studied from multiple perspectives. • Carbon flows within the UA driven by final demand and primary inputs are identified. • Carbon emissions of the UA are predicted when considering uncertainty. Urban agglomerations (UAs) play a momentous role in carbon reduction. The prerequisites for achieving carbon reduction goals in UAs are accounting and predicting their carbon emissions. When considering carbon reduction goals in China, it is crucial to pay attention to the joint influence of carbon emissions and economic benefits. Hence, in this study, an improved multi-regional input–output (MRIO) approach was established to quantify and predict carbon emissions for the UA, incorporating a biproportional scaling method (RAS) and Latin hypercube sampling (LHS). Specifically, a) the carbon emissions of UAs were quantified using the MRIO model from the perspectives of production, consumption and income; b) the carbon flows between cities in UAs were identified based on final demand and primary inputs, and c) the features of UAs' carbon emissions in the future were predicted using RAS and LHS. To verify the effectiveness of the approach, a case study of a typical UA region in China [i.e., the Pearl River Delta (PRD)] was proposed. The results showed that the contribution of sectors to carbon emissions could be identified from multiple perspectives, and carbon flows can help regions coordinate emissions reductions. The majority of future carbon emissions would be generated from the areas of population and economic agglomeration (i.e., Guangzhou and Shenzhen), although the growth trend of carbon emissions of those would keep lower. The policy of carbon reduction should be urgently carried out in locations with high carbon emissions growth rates (e.g., Zhaoqing and Zhuhai). To improve the ability for carbon reduction in the PRD, cooperation in multiple cities to promote energy efficiency is advocated. The government should also increase technical support for carbon reduction and consider the balanced development of the economy, population, and resources in the PRD. [ABSTRACT FROM AUTHOR]

Abstract A stable and reliable urban energy supply is essential for supporting sustainable regional and even national development. Further, a general definition and method is necessary to comprehensively evaluate urban energy supply security for different energy resources, identify key problems, and conduct regulations. Therefore, ecological network analysis (ENA) is introduced as a general systematic analysis tool to simulate and assess urban energy supply security, based on the idea that various components related to energy supply exist and interact in a common network. The proposed ENA model was used to evaluate and compare the security of coal, crude oil, and natural gas supplies for Beijing during 1997–2012. The evaluation included overall sustainability assessment, system property analysis, and structure analysis of energy supply systems. The results showed that the coal supply had the highest level of supply security, followed by the crude oil supply and then the natural gas supply. In addition, the crude oil supply system showed the most competitiveness in terms of the energy supply systems' properties. Scenario analysis yielded suggestions for improving urban energy supply security by maintaining energy production, increasing energy consumption within a certain range, strengthening and diversifying supply sources, and increasing strategic energy reserves. The ENA-based general model provides a useful tool for systematic evaluation of urban energy security from the supply perspective to quantitatively support urban energy management. Highlights • Ecological network analysis is applied to evaluate urban energy supply security. • A general network model of urban energy supply systems is established. • The supply security of coal, crude oil, and natural gas in Beijing are evaluated. • Different energy supply systems are compared via structure and property analysis. • Suggestions based on scenario analysis are given to improve energy supply security. [ABSTRACT FROM AUTHOR]

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

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]

• A systematic framework was proposed to assess urban flood resilience (UFR). • Each resilience phase (e.g., resistance, adaptation, & recovery) were considered. • The system framework incorporated hybrid flood model and system performance curve. Urban flooding can seriously threaten urban ecological security and human life, and therefore urban flood resilience (UFR) is important for urban safety and stability. To comprehensively evaluate urban system performance during the entire process of rainfall, runoff, flooding, and drainage, we developed a systematic framework for UFR assessment covering runoff simulation, flood estimation, and resilience assessment, which broadly corresponded to the phases of resistance, adaptation, and recovery. The UFR in the phases of resistance, adaptation, and recovery was simulated and assessed using a system performance curve (SPC) and technically combining with the hybrid flood model while mainly considering the total simulation time and inundated urban proportion in SPC. Because the extent of urban flooding can be influenced by climate change and the rate of urbanization, we chose the corresponding representative factors of precipitation and infiltration rate and considered 21 simulation scenarios (seven rainfall return periods and three infiltration rates) for which UFR was quantified according to urban system performance. The effectiveness of this framework was demonstrated in application to a typical highly urbanized area (i.e., Dongguan, China). The following results were derived: (1) The inundated area under the pessimistic scenario (i.e., S19) would be nearly four times greater than that under the optimistic scenario (i.e., S3); (2) The values of UFR in Dongguan were 0.9494–0.9863, locating at the high and very high level; (3) The lowest UFR value was 0.6552 in the Shuixiang New City district; and (4) The rainfall return period was the main factor influencing UFR under relatively short rainfall return periods (i.e., S1–S9), while infiltration rate was the principal influencing factor under relatively long rainfall return periods (i.e., S10–S21). The proposed systematic framework could be applied in other cities and large-scale regions like urban agglomerations and provinces. [ABSTRACT FROM AUTHOR]

In this study, a hybrid approach was established to identify future heterogeneity of urban ecosystem health (UEH) transferred by variations of land-use, socio-economic development, and ecological function in lower scales of cities (i.e., districts), incorporating uncertainty analysis into a multi-criteria decision assessment framework. In consideration of multiple driving factors of UEH scores, the contribution of the main indicators to UEH scores was identified by geographical detector model. To verify effectiveness of the approach, a case study was applied in Dongguan City of China. The results indicated that UEH scores of Dongguan didn't greatly change during 2010–2020. Conversely, the scores would increase from 2023 to 2030, influenced by socio-economic development and ecological function under the district scale. Concurrently, the heterogeneity of urban ecosystem health ranks (UEHRs) in 32 districts occurred during 2010–2020, further influencing UEH scores of Dongguan. Owing to inconsistent of land-use, ecological function, and socio-economic development of districts, the heterogeneity of UEHRs was intensified in 2025–2030. From 2010 to 2020, the spatial variations of UEH scores in Dongguan City depended on the synergy of land-use intensity and economic growth. The joint influence from economic development, energy consumption and land-use patterns would contribute greatly to the variations of UEH scores in Dongguan City from 2023 to 2030. Thus, heterogeneity of UEHRs under district scale as well as promotion for sustainable economic development should be paid attention to by decision-makers, to identify internal characteristics in urban ecosystems. [ABSTRACT FROM AUTHOR]

In highly urbanized areas, domestic water consumption and wastewater discharge are affected by several dynamic factors, such as economy, population, and environment, which further influence the greenhouse gas (GHG) mitigation potential of urban domestic water systems (UDWSs). To analyze GHG emission features of the UDWSs and their interactions with urban economic development, a hybrid approach of correlation analysis and a programming model was incorporated in a life-cycle framework to provide decision-making support for GHG mitigation strategies in the UDWSs. To verify the applicability of the method, Shenzhen city in Guangdong Province was chosen to analyze the potential of the UDWS for mitigating GHG emissions. The GHG emissions from the UDWS in Shenzhen was 747.82 kt CO 2 eq in 2020. Compared with 2020, domestic water consumption would increase by 11.35 % and 18.67 % in 2025 and 2030, and wastewater discharge would increase by 12.88 % and 19.73 % in 2025 and 2030, respectively. In the trend of socio-economic development, the amount of water demand and wastewater discharge of urban residents would increase by 11.49 % to 18.75 % and 12.88 % to 19.75 % from 2020 to 2030, respectively. Optimum strategies of technologies for wastewater treatment and energy mix for electricity generation were obtained, with consideration of GHG mitigation goals in the future. As energy consumption contributed more than 25 % of GHG emissions in the USDWs of Shenzhen, the contributions of new energy sources for electricity production (i.e., offshore wind, solar photovoltaic, biomass, and nuclear) would increase by 0.58–1.02 times and 0.68–1.20 times from 2020 to 2025 and 2025 to 2030, respectively. The capacity of wastewater treatment would increase by 53.33 % to 80.00 % and 27.44 % to 29.82 % of the SBR and AAO technologies, respectively. Thus, it is suggested that decision makers should pay close attention to optimizing energy mix of electricity production. • Urban domestic water systems (UDWSs) were focused on. • Domestic water consumption and wastewater discharge were affected by socio-economic factors. • A hybrid approach was proposed, incorporating LCA, copula functions, and a programming model. • Potential of GHG mitigating in the UDWS was analyzed. [ABSTRACT FROM AUTHOR]

Agriculture plays a central role in maintaining food security and achieving sustainable development for human society. It is a challenge for the agricultural sector to mitigate greenhouse gas (GHG) emissions and maintain agricultural production. However, dual-level uncertainties exist in the processes of agricultural GHG accounting and emission reduction management. In this research, an integrated approach for identifying adaptation strategies in agricultural GHG emission reduction management was developed through incorporating life cycle analysis (LCA) of agricultural production into a general mathematical programming model. This approach strengthened the applicability of LCA and the comprehensiveness of programming models in generating agricultural adaptive actions under different GHG emission restriction targets. Also, dual-level uncertainties of LCA and adaptation management can be effectively addressed. A case study was proposed to illustrate application of the approach in Dalian City, China. The results indicated that farming patterns in eight districts would change significantly. The total area of maize fields would account for the primary proportion (i.e., 40-45 %) in its agricultural sector. Rice, peanut and cabbage fields would be the minor contributors in terms of GHG emissions. In addition to effective rainfall (i.e., [156, 259] mm/ha), a certain amount of water would be supplied for agricultural irrigation to maximize the city's agricultural yields. Compared with other agricultural crops, rice fields would need the largest amount of irrigation water (i.e., [153.72, 277.98] Mt) to meet the requirements of local government plans. [ABSTRACT FROM AUTHOR]

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

Understanding the complex ways in which urbanization influences energy-environmental efficiency (EEE) is essential for achieving energy savings and emission reductions. The multidimensional impacts of urbanization on EEE were evaluated using a hybrid approach that combined a super slack-based measure (SBM) model with a stochastic impacts by regression on population, affluence, and technology (STIRPAT) model. This approach was applied to evaluate the influences of multi-dimension urbanization on EEE, taking account of undesirable outputs from environmental pollution. The appropriateness of the approach was verified using panel data of 21 cities in Guangdong Province from 2005 to 2016. The results indicated there existed obvious regional differences in the indexes of urbanization and EEE in Guangdong Province during this period. Further analysis of the multidimensional impacts of urbanization revealed that two factors (i.e., proportion of the urban resident population and urban compactness) had significant positive impacts on EEE. Conversely, the impacts of the following three factors on EEE were significant negative: (1) per capita GDP, (2) contribution of the secondary industry, and (3) proportion of land used for urban construction. Some factors (e.g., population size and urban built-up area) did not have significant effects on EEE in Guangdong Province. These findings indicated that decision makers in Guangdong Province could consider the positive influences of the population urbanization and the negative influences of the land urbanization on EEE. • The impacts of multidimensional urbanization on energy–environmental efficiency (EEE) were studied. • Imbalances in EEE within Guangdong Province were reflected. • A hybrid approach of super-SBM and extended STIRPAT model was proposed. • There were positive and negative influences from population and land urbanization on EEE respectively. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is an essential element for food production. Under the influences of correlated variables of food demand and socio-economic development, a hybrid approach was proposed to identify reactive nitrogen (Nr) loss from livestock-based food and biofuel production systems. The approach included the following components: (1) crop simulation model, DeNitrification-DeComposition, taking into account several field situations and climate change scenarios, and (2) correlation analysis of five types of livestock-based food demand and economic benefits based on copula functions and Markov Chain Monte Carlo simulation. The approach was applied to the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) in China. Production of fodder crops (maize, wheat and soybeans) was examined under two climate change scenarios, Representative Concentration Pathways 4.5 and 8.5. Influence of international trade of soybeans, wheat, and maize on nitrogen fertilization was examined under two additional scenarios. Results showed that compost production from livestock manure and food loss in the production of 1000 kg of livestock-based food would reduce demand for 10.45 kg of urea in crop production, accounting for 9.2%–14.66% of total nitrogen fertilizer demand. Among all livestock-based food, fertilizer demand between 2020 and 2030 is projected to be the highest in fodder crop production to support pork production. Total Nr loss of livestock-based food production in GBA was estimated to be 1.10 to 1.58 Mt under scenario 1, and 1.09 to 1.57 Mt under scenario 2. • Focused on reactive nitrogen loss from livestock and biofuel production system. • A hybrid fodder production and food demand/economic development approach was used. • Used copula–MCMC simulation analysis of joint probabilities of correlated variables. • Reactive nitrogen loss per tonne of pork production would be larger than other livestock-based food in 2020 and 2030. [ABSTRACT FROM AUTHOR]

Urban non-point source (NPS) pollution management is receiving increased attention because of the environmental and ecological consequences of urbanization. We developed a Storm Water Management Model (SWMM)-based interval double-sided chance-constrained programming (SWMM–IDCCP) model for the optimal design of low impact development (LID) during urban NPS pollution management at a community scale. Pollution control efficiency and economic costs related to LID practices were considered in the SWMM–IDCCP model, simultaneously addressing uncertainties in pollutant output and decision-making processes. Specifically, by combining SWMM, Monte Carlo simulation, interval linear programming, and double-sided chance-constrained programming, the SWMM–IDCCP model can evaluate export loads of urban NPS pollutants and pollution control efficiencies of LID practices and analyze the corresponding uncertainties. Concurrently, the proposed model can provide reliable and optimal implementation schemes for LID practices under different scenarios, dealing with uncertainties such as discrete intervals and double-sided random parameters. The proposed model was applied to the Dongguan University of Technology in Dongguan City, South China. The random distribution characteristics of pollution load were identified. The ranges of pollution load for total suspended solids, chemical oxygen demand, total nitrogen, and total phosphorus were 76,430–165,698, 44,366–74,155, 2118–3,830, and 97–177 kg in 2021, respectively. Probability distribution characteristics of pollution control efficiencies of three LID practices (i.e., bio-retention cell, green roof, and permeable pavement) for the four urban NPS pollutants were obtained. Using the SWMM-IDCCP model, LID configuration schemes were obtained under diverse pollution control targets and confidence levels, taking into account variations in the decision preference of different administrators. This research indicates that the SWMM-IDCCP model can be used to manage urban NPS pollution at a community scale under uncertainty. [ABSTRACT FROM AUTHOR]

Highlights • A new evaluation framework of urban ecosystem health (UEH) is proposed. • UEH is evaluated from dimensions of structure, function, process and development. • The fuzzy matter-element model is established to evaluate the UEH states. • The health states of typical Chinese cities along the 'B & R' are assessed. • Suggestions are proposed to improve health states of different types of cities. Abstract As key social nodes and development engines, the status of various cities is important for the construction of "the Silk Road Economic Belt" and "21st Century Maritime Silk Road" (hereafter called the "B&R"). To comprehensively evaluate the status of cities along the B&R, the concept of urban ecosystem health was applied in this paper, and an innovative assessment framework of "structure-function-process-development" was proposed based on the understanding of the concept of urban ecosystem health. Then, four-layer assessment indicators were established to reflect the health status of urban ecosystems. Subsequently, a fuzzy matter-element model was constructed to measure the urban ecosystem health status, grade the health levels, and identify the limiting factors. A case study was conducted on 14 typical Chinese cities along the B&R. The temporal dynamics indicated that the health states of most of these cities gradually improved during the study period. With respect to spatial differences, city types could be ranked from high to low health in the order of coastal innovative and open cities, inland open cities, and open-window cities. Based on further analysis of limiting factors, suggestions to improve the ecosystem health levels of cities along the "B&R" were proposed for the different types of cities. [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]

• Interactions of water resources in adjacent regions was examined. • Variations in water supply and demand were analyzed through autoregressive models. • Interaction relationship between multi-regional nexus water shortage was solved. • Water resources allocation strategies were obtained through the optimization model. • The models were applied in Beijing-Tianjin-Hebei urban agglomeration of China. Growing agricultural and economic activities have brought tremendous pressure to regional water resources management. Considering the interactions of water resources allocation among adjacent regions with connected water systems, it is necessary to pay attention to the unification of socio-economic development and environmental protection between the regions. This is of significance to many agglomeration areas that are composed of multiple large cities such as the Beijing-Tianjin-Hebei urban agglomeration (BTH) in China. In this research, an ensemble approach was proposed to tackle water resources allocation problems under the constraints of water resources to reflect interdependence of the composed three areas, i.e., Beijing, Tianjin and Hebei. The approach included ARIMA (Autoregressive Integrated Moving Average) method, ANN (Artificial Neural Network) modelling, copula functions, and multi-objective chance-constrained programming model. It can 1) reflect the variations characteristic of regional water supply and demands, 2) solve the interactions between multi-regional water resources, and 3) obtain desired water resources allocation strategies under multi- scenario and scale conditions. Then, the approach was applied in typical regions of China, i.e., Beijing-Tianjin-Hebei urban agglomeration. Considering 27 scenarios of the nexus water shortage risk, multi-objective joint opportunity constraint programming model aimed at maximizing economic benefits and ecological benefits, in combination with economic, agricultural, water resources and environmental constraints. As the optimal strategies indicated, the maximum GDP in BTH would remain at 8514 billion yuan. The minimum amount of water allocation and wastewater discharge would be 24–26 billion m3 and 2100–2101 million m3 respectively. [ABSTRACT FROM AUTHOR]

• A novel uncertainty analysis method of agricultural grey water footprint was proposed. • Impacts of production conditions on agricultural grey water footprint were reflected. • Interval characteristics of agricultural grey water footprint can be obtained. • The proposed method can help identify water pollution risk more accurately. Agricultural grey water footprint (GWF) is an important index for quantitatively describing the impact of agricultural production on water environment, which can help evaluate regional water pollution risk (WPR). However, changes in agricultural production conditions lead to strong uncertainties in agricultural GWF. Therefore, advanced methods to deal with these uncertainties for improving the accuracy of regional WPR assessment are required. By combining min–max/max-max programming models with a GWF assessment framework, an integrated uncertainty analysis method for agricultural GWF affected by production conditions was proposed in this study. Based on the method, extreme scenarios of GWF evaluation were established to obtain lower and upper bounds of agricultural GWF. The proposed method was applied in the Guangdong Province in South China, and the following results were obtained. Agricultural GWFs in the Guangdong Province were [124.67, 237.37], [110.63, 204.90], and [104.04, 178.01] Gm3 in 2005, 2010, and 2015, respectively. Different contributing factors of agricultural GWF were shown in the minimum and maximum GWF scenarios. For crop planting, the contribution of dryland and paddy fields to GWF was larger than that of garden land in the minimum GWF scenario, whereas the contribution of garden land was greatest in the maximum GWF scenario. For livestock and poultry breeding, the contribution of livestock raised under dry cleaning condition to the GWF was larger than that under flushing condition in the minimum GWF scenario, whereas the opposite situation was observed in the maximum GWF scenario. Based on the interval values of agricultural GWF, the WPR of the cities of Zhanjiang, Maoming, Yunfu, Chaozhou, Foshan, and Dongguan were identified as unacceptable. By identifying the uncertainties within agricultural GWF evaluation induced by the variability of production conditions, the proposed method can provide helpful information for agricultural planning in the context of sustainable agricultural development. [ABSTRACT FROM AUTHOR]

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]

A simulation-based interval stochastic bi-level multi-objective programming (SISBLMOP) model was proposed in this research, through integrating the global nutrient export from watersheds model, interval parameter programming and stochastic chance-constrained programming into a general bi-level multi-objective programming framework. The SISBLMOP model can handle multiple uncertainties expressed as discrete intervals and probability density functions in both the simulation and optimization processes. System complexities, including the hierarchy structure of upper- and lower-level decision makers, can also be addressed in the model. The proposed model is applied to a real-world case study of the Xinfengjiang Reservoir Watershed in South China to identify the satisfactory implementation levels of multiple best management practices (BMPs). The model results show that multiple BMP schemes for water quality management can be obtained under different upper- and lower-level decision-making and risk-violation scenarios, reflecting the cooperation and gaming results of the two-level decision makers. Consequently, the corresponding BMP implementation costs are acceptable to both the upper- and lower-level decision makers. The model is widely applicable and can be effectively used for water quality management under multiple uncertainties and complexities. • A simulation-based interval stochastic bi-level programming model was developed. • System hierarchical structure and multi-objective characteristics were considered. • The stochastic characteristics of nutrient export can be reflected. • The model can reflect uncertainties in both simulation and optimization processes. • The model can support watershed water quality management. [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]

In this research, an export coefficient based inexact fuzzy bi-level multi-objective programming (EC-IFBLMOP) model was developed through integrating export coefficient model (ECM), interval parameter programming (IPP) and fuzzy parameter programming (FPP) within a bi-level multi-objective programming framework. The proposed EC-IFBLMOP model can effectively deal with the multiple uncertainties expressed as discrete intervals and fuzzy membership functions. Also, the complexities in agricultural systems, such as the cooperation and gaming relationship between the decision makers at different levels, can be fully considered in the model. The developed model was then applied to identify the optimal land use patterns and BMP implementing levels for agricultural nonpoint source (NPS) pollution management in a subcatchment in the upper stream watershed of the Miyun Reservoir in north China. The results of the model showed that the desired optimal land use patterns and implementing levels of best management of practices (BMPs) would be obtained. It is the gaming result between the upper- and lower-level decision makers, when the allowable discharge amounts of NPS pollutants were limited. Moreover, results corresponding to different decision scenarios could provide a set of decision alternatives for the upper- and lower-level decision makers to identify the most appropriate management strategy. The model has a good applicability and can be effectively utilized for agricultural NPS pollution management. [ABSTRACT FROM AUTHOR]

In this research, an export coefficient based dual inexact two-stage stochastic credibility constrained programming (ECDITSCCP) model was developed through integrating an improved export coefficient model (ECM), interval linear programming (ILP), fuzzy credibility constrained programming (FCCP) and a fuzzy expected value equation within a general two stage programming (TSP) framework. The proposed ECDITSCCP model can effectively address multiple uncertainties expressed as random variables, fuzzy numbers, pure and dual intervals. Also, the model can provide a direct linkage between pre-regulated management policies and the associated economic implications. Moreover, the solutions under multiple credibility levels can be obtained for providing potential decision alternatives for decision makers. The proposed model was then applied to identify optimal land use structures for agricultural NPS pollution mitigation in a representative upstream subcatchment of the Miyun Reservoir watershed in north China. Optimal solutions of the model were successfully obtained, indicating desired land use patterns and nutrient discharge schemes to get a maximum agricultural system benefits under a limited discharge permit. Also, numerous results under multiple credibility levels could provide policy makers with several options, which could help get an appropriate balance between system benefits and pollution mitigation. The developed ECDITSCCP model can be effectively applied to addressing the uncertain information in agricultural systems and shows great applicability to the land use adjustment for agricultural NPS pollution mitigation. [ABSTRACT FROM AUTHOR]

The urbanization process has seen an accelerated increase in recent decades, leading to urban runoff pollution becoming more prominent. However, uncertainty of the pollution output and complexity of management systems have made controlling urban runoff pollution challenging. Therefore, it is necessary to propose advanced modeling methods for these challenges. This research presents an integrated urban runoff pollution management (IURPM) model for optimal configuration of low impact development (LID) practices under multiple uncertainties. The IURPM model combines the hybrid land-use prediction and improved pollution estimation models with interval parameter, stochastic parameter, and multi-objective programming. The proposed IURPM model can not only predict the output characteristics, but also provide optimal configuration schemes for the LID practices in the management of urban runoff pollution under multiple scenarios. In addition, uncertainties expressed as discrete intervals and probability density function in the management systems can be effectively addressed. A case study of the IURPM model was conducted in Dongguan City, South China. Results show that considerable amounts of urban runoff pollutants would export from Dongguan City by 2025. The export loads and pollution output flux per unit area would have significant spatial heterogeneity. The results further indicate that population size, gross domestic product, and regional area size are expected to play important roles in the pollution export, while impervious surface coverage and population density would likely have great influences on the output flux of urban runoff pollution. Based on the model findings, multiple LID practices should be adopted in Dongguan City to reduce the urban runoff pollution loads. Using the IURPM model, multiple LID implementation schemes can be obtained under different pollution reduction scenarios and significance levels, that can provide decision-making support for urban water environmental management, considering variations in the policymaker's decision-making preferences. This study demonstrates that the IURPM model can be applied to the optimal configuration of LID practices for the management of urban runoff pollution under uncertainty. • An integrated urban runoff pollution management model was proposed. • The model can predict the loads of urban runoff pollution. • The model can provide optimal configuration schemes of low impact development practices. • Interval and stochastic uncertainties in urban runoff pollution systems can be handled. [ABSTRACT FROM AUTHOR]

In this study, an environmental-friendly modeling system was developed and applied to an agriculture nonpoint source (AGNPS) management in Ulansuhai Nur watershed. In this system, water environmental capacity, credibility-based chance-constrained programming (CCCP), and AGNPS optimization models were integrated into a general modeling framework. It could be used to calculate water environmental capacity of total nitrogen and total phosphorus in Ulansuhai Nur watershed, which could consequentially provide input data for the developed AGNPS optimization model. Also, the inherent uncertainties in estimating water environmental capacities that can be expressed as possibilistic distributions were reflected and addressed based on computational results of three widely used methods. Such uncertainties were consequentially transferred to the proposed CCCP model based on the adoption of multiple credibility satisfactory levels, significantly facilitating objectivity reflection of decision alternatives. The developed modeling system was then applied to Ulansuhai Nur watershed of Inner Mongolia, a semi-arid river basin in northwestern China. Optimal strategies for AGNPS management in Ulansuhai Nur watershed were generated with consideration of the maximum total agricultural income under multiple policy scenarios. The results showed that the total agricultural income would increase with point source pollution being cut down, and would decrease with rising credibility levels, representing decreasing system violation risks. It was indicated that the higher of total nitrogen/phosphorus discharge being less than water environmental capacity of Ulansuhai Nur, the lower the total agriculture incomes. The proposed methods could help decision makers establish various production patterns with cost-effective agriculture nonpoint source management schemes in the basin of Ulansuhai Nur, and gain in-depth insights into the trade-offs between total agricultural incomes and system reliabilities. [ABSTRACT FROM AUTHOR]

With the studies of ecological stoichiometry changing from global to regional and smaller scales, it is of great significance to know how, for a single species in a typical ecosystem, the carbon (C), nitrogen (N) and phosphorus (P) stoichiometry varies with the seasons and responds to environmental stresses. In this study, taking T. chinensis ( Tamarix chinensis Lour.) in the Laizhou Bay coastal wetland as a test object, seasonal variations in leaf C, N and P stoichiometry and their correlations with soil salt were analyzed. The results showed that leaf C, N and P contents and the ratios of C:N, C:P and N:P of T. chinensis exhibited large variations. The leaf C, N and P contents were 388.56–472.07, 18.39–32.86 and 1.02–3.16 g kg −1 , respectively. Also, the ratios of C:N, C:P and N:P were 11.83–24.78, 125.98–425.98 and 8.62–23.66, respectively. The seasonal variation of the leaf C content presented an increasing trend while the leaf N and P contents indicated a trend of first increasing and later decreasing. However, a trend contrary to the leaf N and P was observed for the ratios of leaf C:N, C:P and N:P. There was a significant negative correlation between leaf P content and soil salt. Conversely, a positive correlation was found between the ratios of leaf C:P, N:P and soil salt. Generally, T. chinensis in the Laizhou Bay wetland took a defensive life-history strategy during the growth process. The growth of T. chinensis was limited by N at the beginning of the growing season, and by P in the middle and final stages of the growing season. [ABSTRACT FROM AUTHOR]

Socioeconomic development, leading to significant changes in land-use patterns, has further influenced the output of regional nonpoint-source (NPS) pollution. Multiple uncertainties exist in the processes of land-use changes and NPS pollution export. These uncertainties can deeply affect the management of regional land-use patterns and control of NPS pollution. In this research, an integrated land-use prediction and optimization (ILUPO) model based on system dynamics, export coefficient, interval linear programming, and fuzzy parameter programming models was proposed. The ILUPO model can provide future land-use patterns and NPS pollution loads, and also help optimize the patterns under multiple pollution reduction scenarios. Interval and fuzzy uncertainties in the processes of land-use changes and NPS pollution output can be effectively addressed. The developed model was applied to a water source area in the central part of northern Guangdong Province in South China. For the prediction period 2020–2030 under the high-speed development scenario, results show that cropland area would decrease, while grassland and waterbody areas would increase. In contrast, these three types of land-use would show opposite variation trends under the low-speed development scenario. Construction land area would decrease, while forestland area would increase under both the low-speed and high-speed development scenarios. Variation of the predicted land-use patterns would lead to an increase of total nitrogen loads under each of the scenario, while the total phosphorus loads would show relatively complex variation trends. Regional land-use patterns should be further optimized to mitigate NPS pollution. However, the pollution loads in the study area cannot be reduced by >5% through land-use adjustment. Because cropland would still be the critical source of NPS pollution after optimization, strictly controlling the areas of cropland would be important for the management of such pollution in the research area. In addition, certain areas of grassland and waterbody would need to be converted into cropland and construction land to balance the economic benefit of the system and NPS pollution control. Multiple results obtained from the model under different scenarios of pollution reduction targets and α-cut levels can provide decision-making supports for the local policy makers. The developed ILUPO model can yield insights useful for the planning and adjustment of regional land-use patterns while considering NPS pollution control under conditions of uncertainty. • An integrated model is proposed for land-use prediction and optimization. • System dynamics of land-use patterns affected by multiple factors can be reflected. • Uncertainties in land-use change and NPS pollution export processes can be addressed. • Optimal land-use patterns can be obtained to support land-use decision-making. [ABSTRACT FROM AUTHOR]

[Display omitted] • Patch complexity and urban compactness indexes were developed to assess urban form. • The impact of urban form on PM 2.5 was explored in GBA by spatial panel models. • The complexity of urban patches had a negative effect on PM 2.5 concentrations. • The impact of urban compactness on PM 2.5 concentrations was positive. • A more complex and less compact urban form was adoptable in terms of reducing PM 2.5. Given the serious threat of PM 2.5 pollution to the environment and public health in China, reducing PM 2.5 concentrations through urban planning practices has become a common interest of urban planners and policymakers. In this regard, identifying the relationship between urban form and PM 2.5 concentrations has been a matter of much concern. This paper seeks to investigate how urban form influences PM 2.5 concentrations by taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as a case study. First, the urban form was assessed by two indicators, i.e., the patch complexity index (PCI) and urban compactness index (UCI), which were developed based on multiple landscape metrics and the principal component analysis. Then, the relationship between PM 2.5 concentrations and the two urban form indicators was revealed by spatial panel models. Our results mainly showed that PCI and UCI were negatively and positively associated with PM 2.5 concentrations, respectively, indicating that less compact urban forms with high patch complexity had advantages in reducing the PM 2.5 concentrations. According to these empirical results, the mixed-use urban development with high green coverage, which is conducive to constructing a more complex and less compact urban form, would be adoptable in terms of mitigating PM 2.5 concentrations in the GBA urban agglomeration. [ABSTRACT FROM AUTHOR]

• An integrated modeling approach is developed. • Multiple modeling methods were combined. • The approach is applied in a City Cluster of China. • Coordination for WEF and economy is analyzed under multiple scenarios. In this research, an integrated approach is developed through the incorporation of systems dynamics (SD), catastrophe progression (CP), and coupled coordination degree (CCD) analysis within a general modelling framework. The developed SD-CP-CCD is then employed to simulate and evaluate relationships among the water-energy-food (WEF) and the economic systems. It will improve traditional methods for WEF nexus analysis through a) incorporating urban WEF connections and changes into the cross-system coupling analysis framework, b) evaluating its coordination with economic growth and a multi-scale and regional disparity, and c) promoting policy recommendations to ensure the guarantee of urban WEF under economic growth. The approach is then verified through WEF nexus system of Urban Agglomerations of Pearl River Delta City Cluster (PRDCC) in China. Temporal and spatial characteristics of CCD in the region were analyzed. The results revealed their performance in cities with multiple economic backgrounds. Also, the fragile factors of each city were reflected by multi-scale exploration. Regional inequality and spatial agglomeration effects were considered. The results showed that CCD has noticeable temporal and spatial differences. Geographically, CCD varied from uncoordinated to high-level coordination. The eastern cities were much better coordinated than the western cities. Due to the increasing pressure of population and economic growth in the future, the upward trend of CCD would occur in most cities. The vulnerability factors of each city were explored. Simultaneously, apparent spatial inequality and agglomeration patterns were observed, with a downward trend over time. The temporal and spatial patterns of CCD revealed in this study indicated that PRDCC policymakers should formulate policies that suit the characteristics of cities and reduce the targeted regional imbalances. [ABSTRACT FROM AUTHOR]

Management of nonpoint source (NPS) pollution is highly important in watershed water environmental and ecological security. However, the many complexities and uncertainties that exist in the processes of export and management of NPS pollution exert substantial influences on the reliability of multiple management practices. This study developed an inexact multiobjective possibilistic mean-variance mixed-integer programming (IMPMMP) model for NPS pollution management through optimization of watershed land use pattern and livestock production structure. By coupling interval parameter programming, mixed-integer programming, multiobjective programming, and an export coefficient model within a general possibilistic mean-variance model framework, the IMPMMP model deals effectively with system uncertainties and complexities. Moreover, the risk of exceeding criteria (REC) in NPS pollution management systems can be considered. The proposed IMPMMP model was applied to a real-world case study in the Xinfengjiang Reservoir watershed in South China. Results showed that the preference of decision makers regarding land use adjustment plays a decisive role in determining model feasibility. The area provided for each land use type that could be adjusted has to reach a certain threshold to achieve the goals of reduced pollution load and REC control. The NPS pollution loads after optimization would be exported primarily from different land uses and the human population. Compared with NPS nitrogen pollution management, it is more difficult to reduce the NPS phosphorus load and to manage the corresponding REC through adjustment of the land use pattern and livestock production structure. Moreover, it is difficult to simultaneously reduce the NPS nitrogen and phosphorus pollution loads and REC in each subbasin. The model, which can provide policy makers with a series of schemes for optimization of land use pattern and livestock production structure, has satisfactory applicability and could be used for watershed NPS pollution management. Unlabelled Image • An integrated model was proposed for watershed nonpoint source pollution management. • Uncertainties in pollution output and management systems can be handled. • Multiobjective characteristics and mixed-integer parameters can be reflected. • Risk of exceeding criteria under uncertainty can be considered. [ABSTRACT FROM AUTHOR]