Empirical analysis of factors influencing industrial eco-efficiency in the Yellow River Basin from a social embeddedness perspective.

Measuring industrial eco-efficiency (IEE) is essential to improve environmental quality and industrial restructuring. However, most studies ignore the influence of embeddedness on industrial eco-efficiency and lack analysis of the pathways of influence factors. Therefore, this study assesses industrial eco-efficiency in the Yellow River Basin (YRB) using a super-efficiency model slacks-based measure (Super-SBM) that considers non-desired outputs, outlines the social embeddedness of IEE, and empirically analyzes the driving mechanism of IEE from the perspective of embeddedness by constructing hierarchical linear modeling (HLM) to address the pathways of action of the influencing factors of industrial eco-efficiency. The results showed that 50.79% of the overall differences in IEE in the YRB were caused by social embeddedness. Economic development level, industrial agglomeration, and environmental regulation (ER) are significant direct influencing factors. Increasing cognitive and cultural embeddedness will enhance the positive relationship between economic development level and IEE. Political and relational embeddedness significantly moderates the positive relationship between industrial agglomeration and eco-efficiency. Cultural embeddedness can significantly and directly affect industrial eco-efficiency and weaken the positive relationship between ERs and industrial eco-efficiency. Therefore, improving IEE should consider both fundamental and embedded factors. Our findings are conducive to promoting high-quality development in the YRB and support the government in formulating differentiated policies. In addition, this paper tries to establish an empirical analysis method suitable for social embeddedness theory, and the empirical results help to improve the situation due to the lack of empirical analysis of social embeddedness theory.

Land-Use Carbon Emissions in the Yellow River Basin from 2000 to 2020: Spatio-Temporal Patterns and Driving Mechanisms.

In the context of global climate governance, the study of land-use carbon emissions in the Yellow River Basin is crucial to China's "dual-carbon" goal in addition to ecological conservation and the high-quality developments. This paper computed the land-use carbon emissions of 95 cities in the Yellow River Basin from 2000 to 2020 and examined its characteristics with respect to spatio-temporal evolution and driving mechanisms. The findings are as follows: (1) The overall net land-use carbon emissions in the Yellow River Basin rose sharply from 2000 to 2020. (2) From a spatial perspective, the Yellow River Basin's land-use carbon emissions are high in the middle-east and low in the northwest, which is directly tied to the urban development model and function orientation. (3) A strong spatial link exists in the land-use carbon emissions in the Yellow River Basin. The degree of spatial agglomeration among the comparable cities first rose and then fell. "Low-Low" was largely constant and concentrated in the upper reaches, whereas "High-High" was concentrated in the middle and lower reaches with an east-ward migratory trend. (4) The rates of economic development and technological advancement have a major positive driving effect. Moreover, the other components' driving effects fluctuate with time, and significant geographical variance exists. Thus, this study not only provides a rationale for reducing carbon emissions in the Yellow River Basin but also serves as a guide for other Chinese cities with comparable climates in improving their climate governance.

The Built Environment Assessment of Residential Areas in Wuhan during the Coronavirus Disease (COVID-19) Outbreak.

The COVID-19 epidemic has emerged as one of the biggest challenges, and the world is focused on preventing and controlling COVID-19. Although there is still insufficient understanding of how environmental conditions may impact the COVID-19 pandemic, airborne transmission is regarded as an important environmental factor that influences the spread of COVID-19. The natural ventilation potential (NVP) is critical for airborne infection control in the micro-built environment, where infectious and susceptible people share air spaces. Taking Wuhan as the research area, we evaluated the NVP in residential areas to combat COVID-19 during the outbreak. We determined four fundamental residential area layouts (point layout, parallel layout, center-around layout, and mixed layout) based on the semantic similarity model for point of interest (POI) picking. Our analyses indicated that the center-around and point layout had a higher NVP, while the mixed and parallel layouts had a lower NVP in winter and spring. Further analysis showed that the proportion of the worst NVP has been rising, while the proportion of the poor NVP remains very high in Wuhan. This study suggested the need to efficiently improve the residential area layout in Wuhan for better urban ventilation to combat COVID-19 without losing other benefits.