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发布时间：2021-03-30

论文类型：期刊论文

发表时间：2021-01-10

发表刊物：JOURNAL OF CLEANER PRODUCTION

卷号：272

ISSN号：0959-6526

关键字：Decarbonisation pathways; INDC; Multi-objective optimisation; Provincial; Residential building; Uncertainty

摘要：As national efforts to reduce CO2 emissions towards achieving Intended Nationally Determined Contributions (INDCs), policymakers require regional information for specific sectors to reduce uncertainties within national mitigation target. To understand the implementation barriers of the residential building sector towards achieving INDC, this study proposes an analytical framework for supporting robust decision making at the subnational level, in which a multi-objective optimisation model under uncertainty (MOOU) is developed for establishing decarbonisation pathways of the residential building sector. The proposed model has three key features: (1) systematic and robust decision-making with multi-objective optimisation of economic goals in the presence of uncertainties; (2) synergistic mitigation policies with gradual ramping-up adoption; and (3) quantitative evaluation of the finance provision and implementation rates of mitigation policies. The proposed model is then applied to provide in-depth insights into implementation barriers of 13 types of mitigation policies towards achieving INDCs targets in nine provinces in China, with parallel urban and rural analyses. Results show that the CO2 emissions of residential buildings in Heilongjiang, Liaoning and Beijing will have peaked by 2030. In particular, the CO2 emissions from cooling energy consumption of total building stocks will continually increase. Further-more, improvements of building thermal efficiency and energy mix dominate the mitigation potential in northern China, while the southern China largely depends on the improvement of the efficiency of home appliances. The differences in measures' mitigation effectiveness cause that southern China provinces have higher risks for achieving the subnational INDCs targets compared to northern China. (C) 2020 Elsevier Ltd. All rights reserved