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• 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]

In the era of climate change, life cycle analysis (LCA) has been acknowledged as a useful method for examining carbon footprints of products or services., Once captured and stored by trees and other plants, biogenic CO 2 would re-enter the atmosphere sooner or later after the use phase of the product. And many LCA studies did not calculate the temporary carbon storage in biogenic carbon. Thus, this paper proposed a hybrid LCA approach to provide a structured methodology for evaluating carbon footprint of copying paper in consideration of temporary carbon storage. The developed method was then applied to a paper mill of China. It is indicated that the hybrid LCA method could provide a comprehensive methodology for accounting carbon footprints as well as assessing effects of delaying GHG emissions. The results shows that the carbon footprint of 1000 kg copying paper was 647.89 kg CO 2 under scenario 1 and -5094 kg CO 2 under scenario 2. Concurrently, the effect of delaying the emission of the temporarily stored carbon in copying paper was 7.67%, 15.52%, or 23.58% for a certain period of time (i.e., 10, 25, or 30 years). [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]

As an effective measure to promote socio-economic development, industrial transformation indirectly changes the characteristics of urban underlying surfaces, leading to the complexity of controlling nonpoint source (NPS) pollution in cities. However, the relationship between industrial transformation and urban NPS pollution, as well as the critical socioeconomic factors influencing the characteristics of urban NPS pollution during the period of industrial transformation, is still unclear, seriously affecting urban water environment management. In this research, indicator systems for the estimation of industrial transformation and urban NPS pollution were first established. Then, the characteristics of industrial transformation and urban NPS pollution were analyzed through an entropy weight method and a volume–concentration model. In addition, critical socioeconomic factors affecting urban NPS pollution during periods of industrial transformation were identified using Pearson correlation analysis and a grey correlation model. Using Dongguan City in South China as the study area, the results showed that the level of industrial transformation in this city shows an upward trend, and the towns with relatively high level of industrial transformation gradually gathered around Songshan Lake. The four urban NPS pollution index (i.e., chemical oxygen demand, suspended solids, total nitrogen, and total phosphorus) significantly increased during the slow transformation period (2000–2010), then slightly decreased during the rapid transformation period (2010–2020). The pollution loads exhibited significant spatiotemporal heterogeneity. A significantly negative correlation (p < 0.05) was observed between industrial transformation and urban NPS pollution levels during the transformation period. It was found that fertilizer, electricity, and water consumptions, as well as permanent population were the critical socioeconomic factors affecting urban NPS pollution. Control of the application amounts of fertilizers and improvement in the efficiencies of urban water and electricity use are the effective methods for preventing and controlling urban NPS pollution during the period of industrial transformation. In addition, industrial enterprises should increase their investment in research and development activities to accelerate industrial transformation and reduce the urban NPS pollution load. This research contributes to urban NPS pollution control in the area undergoing industrial transformation. • A comprehensive method was proposed for evaluating urban NPS pollution during the period of industrial transformation. • The relationship between industrial transformation and urban NPS pollution was analyzed for the first time. • A significant negative correlation was observed between the levels of industrial transformation and NPS pollution. • Critical socioeconomic factors affecting urban NPS pollution during the industrial transformation period were identified. [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]

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]

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 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 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]

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]

[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]