Sustainability accounting of neighborhood metabolism and its applications for urban renewal based on emergy analysis and SBM-DEA.

Rapid urbanization exacerbates urban metabolic activities associated with irreversible environmental degradation. Evaluating urban metabolic performance is an effective method to meet targets for sustainable development in contemporary urban areas. Neighborhoods, which are regarded as the basic parts of cities, can detail the metabolic structure and interactions from a bottom-up perspective. In consequence, this study proposed an eco-efficiency model which combined emergy synthesis and slack-based measure data envelopment analysis. A case study with questionnaire and statistical data for eight communities in Chongqing, China was used to shed a light on the properties of emergy metabolic flows and eco-efficiency. The results showed that the study communities heavily depended on external resources. Intense communal activities within relatively small geographic areas caused immediate surroundings to suffer from enhanced environmental pressures. Eco-efficiency performances were mostly invalid, and a significant heterogeneity existed among communities. Enhancing the performance of renewable and non-renewable resources whilst also reducing the production of wastes was essential to improve the overall eco-efficiency of local communities. To assist with this, a neighbor-level sustainable renewal framework was proposed which took account of metabolic flows and communal renewal operations. The findings of this study can provide a paradigmatic example for urban renewal projects elsewhere that embed metabolic performance in neighborhood redevelopment.

Causal complexity of environmental pollution in China: a province-level fuzzy-set qualitative comparative analysis.

Environmental problems are endowed with the causal complexity of multiple factors. Traditional quantitative research on the influencing mechanism of environmental pollution has tended to focus on the marginal effects of specific influencing factors but generally neglected the multiple interaction effects between factors (especially three or more). Based on the panel data of 30 Chinese provinces between 2011 and 2020, this study employs fuzzy set qualitative comparative analysis (fsQCA) - which can provide a fine-grained insight into the causal complexity of environmental issues - to shed light on the influencing mechanism of environmental pollution. The results show that there are several different configurations of pollution drivers which lead to high pollution or low pollution in provinces, confirming the multiple causality, causal asymmetry, and equifinality of environmental pollution. Furthermore, the combination effect of advanced industrial structure, small population size, and technological advance is significant in achieving a state of green environment compared to environmental regulation factors. In addition, spatiotemporal analysis of the configurations indicates that strong path dependencies and spatial agglomeration exist in current local environmental governance patterns. Finally, according to our findings, targeted policy recommendations are provided.