Search

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]

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]