Effect of technology innovation and spillovers on the carbon intensity of human well-being

Testing the EKC hypothesis for ecological and carbon intensity of well-being: The role of forest extent

The G-20 countries represent a considerable percentage of the global economy and are crucial in matters to do with support for environmental sustainability. The uniqueness of this study lies in determining the effects of forests on human well-being and environmental degradation with respect to G20, offering a unique perspective regarding the efforts to battle climate change. The study analyzed the impact of income, forest extent and education on ecological and carbon intensity of well-being following the Environmental Kuznets Curve (EKC) hypothesis. Based on annual data from 1990 to 2022 and employing the Method of Moments Quantile Regression, the results validate the presence of an inverted U-shaped relationship between GDP and environmental well-being which refers to the existence of EKC. Our results connect improved ecological and carbon intensity of well-being with expanding forest extent and improving education levels. Forest management combined with educational management work as an effective mechanism reducing environmental degradation while also positively contributing to human well-being. In addition, through these informed and rational decisions, policy makers can promote the environmental stability of forests.

Impact of industrial structure upgrading on green innovation: evidence from Chinese cities

Industrial structure upgrading has a spatial spillover effect, which is an important way to accelerate urban green innovation and realize urban sustainable development. Therefore, it is very important to study the impact of industrial structure upgrading on urban green innovation. Using panel data of 260 prefecture-level and above cities in China from 2005 to 2019, this paper studies the spatial effect of industrial structure supererogation on green innovation by using the spatial Durbin model (SDM). The results show that both industrial structure upgrading and green innovation show a trend of regional agglomeration and fluctuation. The direct effect of an industrial structure supererogation on green innovation is significantly positive, while the indirect effect and total effect are significantly negative. The direct effect, indirect effect, and total effect of industrial structure rationalization on green innovation are significantly positive. This study can provide a useful reference for the coordinated development of industrial structure upgrading and green innovation.

Impact of urban innovation on urban green development in China's Yangtze River Economic Belt: perspectives of scale and network

Understanding whether and how urban innovation offers a sound solution to the dilemma of urban green development is a crucial response to mitigate the detrimental effect on natural resources and environment for transitioning to sustainable urban development. To address the critical issue, we propose urban green development evaluation index system, and then examine how the urban innovation affects urban green development from the perspectives of government-scale, enterprise-scale, and spatial correlation network, all of which are originally applied in the 108 cities of Yangtze River Economic Belt of China (YREB) during period 2006-2018. The evaluation results show that urban innovation promotes urban green development, and both government-scale and enterprise-scale contribute to the effects. The constructed spatial correlation network of urban innovation illustrates the network structural form and reveals the network property, and further results tell that increasing network density and centrality would promote green development obviously. More specifically, the network density of urban innovation has been tied to the enhancement of urban green development, which is more significant in middle reaches than in lower and upper reaches of YREB. Similarly, optimizing the network's degree centrality and closeness centrality can help facilitate urban green development in whole YREB. Thus, the research findings would provide new insights into the essence and driving forces from various scale and hidden network when exploring and seeking urban green development path.

Driving Factors for R&D Intensity: Evidence from Global and Income-Level Panels

Research and development (R&D) has long been recognized as an important component of sustainable development, with a key role in the combatting of climate change. Moreover, R&D activity is increasingly acknowledged as an important contributing factor to global post-pandemic economic recovery. However, little is known about the determinants of R&D intensity (the share of R&D expenditure in GDP) and countries have repeatedly missed their set targets for this indicator. This article tackles this issue for a global panel consisting of 62 countries over the period 2007–2015 by using a dynamic system-generalized method of moments (SYS-GMM) panel model to uncover driving factors for R&D intensity. We also perform investigations on two homogenous subpanels constructed based on the income level of sample countries (High-income, and Middle- and Low-income subpanels), which contributes to assuring the robustness of results, along with formal model diagnostics and employment of alternative explanatory variables. We mainly find that the number of researchers is the most important driving factor for R&D intensity. High-technology exports have a statistically significant effect on R&D intensity only in middle and low-income countries. Patents are conducive to R&D intensity only in the high-income panel. Trade-openness is a significant mitigating factor for R&D investments throughout the panels and model specifications. Policy implications of results are also discussed.

Research on the influence of technological innovation on carbon productivity and countermeasures in China

Increasing carbon productivity is an important measure taken by China to deal with global climate change, and technological innovation is the fundamental way to promote industrial carbon productivity. To explore the low-carbon effects of technological innovation, based on the panel data of 30 provinces in China from 2009 to 2017, this paper established a spatial panel measurement model and a panel threshold regression model to explore the spatial spillover effects and threshold characteristics of technological innovation on industrial carbon productivity. The research shows the following: on the one hand, technological innovation and industrial carbon productivity each has obvious spatial correlation, and technological innovation has a significant spatial spillover effect on the improvement of industrial carbon productivity, and the indirect spillover between regions is greater than the direct spillover effect within the area. On the other hand, the impact of technological innovation on industrial carbon productivity has a double threshold effect. With the continuous improvement of technological innovation capabilities, the promotion of industrial carbon productivity has become increasingly more influential. Through the division of threshold values, the technological innovation capabilities of various regions in China are significantly heterogeneous, and the overall level is low. Although technological innovation capabilities have improved in recent years, there is still much room for improvement. Finally, this article puts forward relevant suggestions from the construction of regional technological innovation system, economical green circular development, and the establishment of a green technological innovation system.