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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]

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]

• 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]

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]