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This study establishes an economic financial support model for the improvement of environmental quality through the development rural revitalization based on the extended Cobb-Douglas production function. Using statistical data from Shaanxi Province, China, from 2004 to 2019, a vector autoregressive (VAR) model is used to empirically analyze the development effect of financial support for rural revitalization and to give the focus points and optimization paths for financial support for environmental quality, rural revitalization and sustainable development. The research results show that financial support plays an active and long-term role in improving environmental quality and promoting rural revitalization and sustainable development. Specifically, the effect of financial instruments in supporting rural revitalization and sustainable development is continuous. In the insurance system, increasing the scale of agricultural insurance support and expanding the coverage of agricultural insurance are key to improving environmental quality and promoting rural revitalization and sustainable development. Therefore, financial policy makers should improve the targeting of financial instruments to provide the right guidance for improving the quality of rural environment and enhancing rural economy, so as to ultimately realize rural revitalization in China., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

With the acceleration of urbanization, domestic waste has become one of the most inevitable factors threatening the environment and human health. Waste classification is of great significance and value for improving urban environmental quality and promoting human well-being. Based on the theory of planned behavior, we added external and socio-economic factors to systematically examine how they affect residents' waste classification behavior (WCB). We collected 661 valid data through a questionnaire survey conducted in Jinan, a pilot city for waste classification in China. Key driving factors were identified by combining binary logistic regression and the principal component analysis. The results showed that the elderly, women, and people with higher education are more likely to participate in waste classification. Attitude, collaborative governance, and institutional pressure positively affect WCB, while subjective norm and infrastructure have a negative effect. Knowledge mastery and degree of publicity are positively and significantly related to WCB, but other perceived behavioral control sub-variables negatively affect WCB. Based on the results and status of waste classification in Jinan, we propose the multi-agent linkage governance pattern from various dimensions to explore a powerful guiding incentive that can enhance WCB and provide a reference for waste management policymakers., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In China, exploring the relationship between human capital and green economy in the context of economic transformation can promote the construction of an ecological civilization and high-quality economic development. This paper is dedicated to assessing the impact of human capital stock, human capital structure, and the difference between the two on China's green economy. Using the super-efficiency slack-based measure (SBM) model, this paper evaluated the green total factor productivity (GTFP) of 30 Chinese provinces to assess the greenness of its economy and analyzed its spatiotemporal evolution from 2000 to 2017. A spatial error model (SEM) was established to explore the impact of human capital on green economy. The results show that (1) the GTFP of China increased continuously from 0.219 to 0.457, showing a regional spatial differentiation of "eastern region > central region > western region." The spatial gaps of the green economy in the east-west and north-south directions are narrowing. (2) Moran's I index and Moran's I scatter plot indicate a significant spatial correlation between human capital and China's green economy. The local spatial correlation between human capital and green economy is mainly characterized by "high-high" and "low-low" types of agglomeration. (3) The effects of human capital stock and human capital structure on green economy were both positive, with coefficients of 0.0005 and 0.1601, respectively, but the effects of human capital structure were not significant. (4) The results of regional regression show that the difference between the human capital impact coefficients on green economy in the eastern and midwestern regions is small. The impact of human capital stock and human capital structure on green economy is consistent with the national level results. It can be preliminarily concluded that the development of China's green economy relied more on improving the human capital stock than on the improvement of the human capital structure. This study further enriches the literature on the green economy and provides information that can support government policy-making., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)