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Purpose: In recent years, green building has become a social hotspot and raised much concern of academic. However, traditional researches of green building mostly focused on technology, while research on social and humanity demand on green residential districts is still scarce. To complement the gap of research and reality, this paper is intended to establish a quantitative evaluation method for social humanity needs of green residential districts based on social life cycle assessment (SLCA).Methods: Based on the SLCA method, an evaluation indicator system for social and humanity demand of green residential districts was proposed, considering different stakeholders (real estate developers, construction enterprises, community residents, and decision makers). Additionally, the adopted evaluation indicator system was applied to a practical community in China as a case study by questionnaire surveys and the method of analytic hierarchy process.Results and discussion: Case study results show that the residents prefer to pay more for a better living environment, and the real estate developers are willing to increase the investment moderately on the projects. Meanwhile, the local government likes to support the development of green residential districts, as well.Conclusions: Analysis results are in line with the social demand for high-quality living environment of Chinese residents and the public concern about buildings’ comprehensive benefits. [ABSTRACT FROM AUTHOR]

The Changchun-Jilin-Tumen economic area is the center of the Old Northeastern Industrial Base with substantial mineral resource in Northeast China. Historical mining exploitation has caused extensive impact on environment. The objective of this study was to quantitatively evaluate the impact induced by mining activities in 106 mining sites and then find out the most significant factors contributing to the environmental damage. The affected environment components were divided into fifteen impacting factors, and environmental assessment was performed by using developed Folchi methods which was combined with adaptive Takagi-Sugeno fuzzy neural network (TSFNN). Correspondence analysis was adopted to explore the effects of the impacting factor on mining environment. Through the comparison with Improved Analytic Hierarchy Process, 96.23 % of evaluation results are the same. Our results showed that TSFNN model can effectively assess impacts of mining exploitation in three categories: slight, moderate, and heavy. The assessment result shows that 64.15 % mining sites are moderately damaged, while 7.5 % are heavily damaged. For all factors studied, environmental geological problem is the dominant factor in Environmental Impact Assessment, accounting for 56.67 % of the weight-wise including geological disasters, environmental pollution, and resources destruction. The anthropic factor exerts a more considerable influence on environment than natural factor. With mining exploitation, geological disaster is the main threat to the mountains in southeast, but environmental pollution and resources destruction are the main threats to the central plains. The assessment and analysis results can provide useful insights in the decision-making process for national authorities in the prioritization of environmental remediation and protection. [ABSTRACT FROM AUTHOR]