A Spatial Empirical Examination of the Relationship Between Agglomeration and Green Total-Factor Productivity in the Context of the Carbon Emission Peak

Abstract

We aim to explore the impact of economic agglomeration on the development of green total-factor productivity (GTFP) from both theoretical and empirical levels. We use the non-radial directional distance function method to formulate the GTFP index and further empirically study the impact of economic agglomeration on GTFP. The results indicate that: 1) there is a “U-shaped” curve relationship between economic agglomeration and GTFP, and the formation mechanism is that the economic agglomeration has a threshold effect on the agglomeration externalities such as infrastructure sharing, knowledge spillover, and labor market upgrading. 2) The mismatch of industrial structure is an important reason that the economic agglomeration in this region has not produced an obvious spatial spillover effect on other regions; relaxing restrictions on the concentration of economic activity to regional centers would contribute to the improvement of GTFP. 3) GTFP has the classic “snowball effect” in the time dimension but has the obvious “warning effect” in the space and time dimension. The conclusions of the research show that it is necessary to conform to the redistribution of economic geography, promote the rational allocation of human resources in the territorial space, and promote the coordination of economic agglomeration and green economic development goals.

Challenges and prospects of decarbonization of the economy in the age of AI

“…The contribution of the paper to the improvement of scientific knowledge in the field of decarbonization and combating climate change is as follows. Unlike Hao et al (2022), Miśkiewicz et al (2021), Reshetnikova and Pugacheva (2022), Wang et al (2022), it has been proved that disruptive technological innovations are not homogeneous, but are characterized by serious differences in terms of energy efficiency. A new classification of these innovations according to the criterion of consequences for decarbonization has been proposed; technologies with high energy intensity (for example, robots) and technologies that support decarbonization (for example, IoT, Big Data and AI) have been identified.…”

“…This article uses a broad interpretation of the AI era, which is not limited to AI alone and covers the whole set of disruptive (subversive) technological innovations of Industry 4.0, in particular, robots, Big Data and data analytics, as well as the Internet of Things (IoT). All technologies of the AI era are characterized by high energy intensity, which was the criterion for their generalization in the works of Hao et al (2022), Miśkiewicz et al (2021), Reshetnikova and Pugacheva (2022), Wang et al (2022).…”

Interaction and spatial effects of green technology innovation and financial agglomeration: Empirical evidence from China under the goal of “double carbon”

“…Not only that, financial development will have varying degrees of impact on the development of green technology due to the continuous strictness of regional environmental policies, which indicates that the impact of regional financial development on green technology innovation is not linear, and will produce a periodic difference depending on the changes of region, time or surrounding conditions. Further, Wen et al (2021) started from the perspective of nonlinear effects and used the panel threshold model and the spatial Durbin model to verify that the financial agglomeration in the Yangtze River Delta region has a nonlinear direct influence on technological innovation and an "inverted U-shaped" spatial spillover effect (Hao et al, 2022). It can be seen that when financial development reaches different stages and is dynamically affected by different factors, the impact on technological innovation will change.…”

Spatial correlations and driving mechanisms of low-carbon agricultural development in china

“…Green/low carbon total factor productivity has become an important basis for judging the sustainability of the economy 1 Hao et al, 2022). While earlier DEA models could measure environmental efficiency with undesired outputs, the weak disposable relationship that exists between undesired and desired outputs was ignored.…”