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The rapid development of urbanization in China inevitably leads to a sharp increase in building energy consumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China. • The NCA algorithm is suitable for multivariate nonlinear sensitivity analysis. • NCA-GA-RBF has stronger and more stable generalization ability and accuracy. • In this paper, climate is the main factor affecting thermal parameters. • Global costs have a stronger economic interpretation. [ABSTRACT FROM AUTHOR]

Residential central heating (RCH) is an important part of residents' well-being policy. However, it is accompanied by extensive energy consumption and carbon emissions. To identify and design efficient future clean heating strategies, we quantified the carbon emissions from RCH in China from 2000 to 2017. Notably, our study is the first to analyze the driving factors of RCH carbon emissions using decomposition analysis and introduce the climate parameter into the Kaya identity. The following results were obtained. 1) The RCH carbon emissions reached the flection point in 2016 (321.75 MtCO 2). 2) Area-related energy intensity is the key driving factor to mitigate carbon emissions of RCH; the area-related carbon intensity in 2017 was one-third less than that in 2000. Conversely, per capita residential heating area and urban population increased carbon emissions from RCH. 3) At the provincial level, the recommended emission mitigation strategies were different. For example, Heilongjiang and Jilin should focus on decreasing the emission factors, whereas Shandong and Xinjiang need to focus on decreasing the area-related energy intensity. The results of this study can help governments to formulate more rational and feasible policies for the emission road map, and the targeted advice also offers a reference for future studies. [ABSTRACT FROM AUTHOR]