Sustainable Growth Drivers: Unveiling the Role Played by Carbon Productivity

Spatiotemporal dynamics and influencing factors of carbon productivity in counties of Shandong Province, China

In the context of the increasing global greenhouse effect, the Chinese government has proposed a "dual carbon" target. As a major carbon-emitting province in China, Shandong Province needs to improve its carbon productivity to coordinate carbon emission reductions and sustainable economic growth. This study analyzes the spatial and temporal evolution of carbon productivity at the county scale and the factors influencing it in Shandong Province from 2000 to 2017. The study uses the Dagum Gini coefficient, kernel density analysis, spatial autocorrelation model, and geographically and temporally weighted regression model. The results indicate that the carbon productivity in Shandong Province nearly doubled during the study period, revealing a spatial distribution characteristic of "high in the east and low in the west," together with a significant positive spatial autocorrelation. Intra-regional differences, the most important source of development differences among the three economic circles, rose to 32.11% during the study period, whereas inter-regional differences declined to 26.6%. Gross domestic product per capita and population density play a significant positive role in the development of carbon productivity. The balance of deposits in financial institutions at the end of the year has a weak positive effect, and the local average public finance expenditure and secondary industry structure on carbon productivity are negative in general. Shandong Province should identify specific regions with weak carbon productivity levels and understand the key factors to improve carbon productivity to promote the achievement of the "dual carbon" goal.

Spatial differences, dynamic evolution, and convergence of carbon productivity in China

China is currently developing a green economy, and improving carbon productivity (CAP) is an important part of this process. The current study applied a minimum distance to strong efficient frontier (MinDS) model to measure China's CAP. The Dagum Gini coefficient and kernel density estimation methods were further used to reveal its spatial differences and dynamic evolution, while the coefficient of variation and spatial convergence models were employed to examine its convergence characteristics. The results showed significant spatial differences in China's CAP, with primarily high and low spatial distribution characteristics in the east and west, respectively. Between-regional differences were the main sources of the overall differences. Moreover, the differences between overall, eastern, central, and western regions of China all exhibited a widening trend. Although none showed σ convergence, all had significant absolute β spatial convergence and conditional β spatial convergence characteristics. Collectively, the findings of this study objectively reflect the real level, distribution characteristics, and spatial convergence characteristics of CAP in China as a whole and in each region, while also providing a reference basis for achieving peak carbon neutrality.

Spatial Convergence of Carbon Productivity: Theoretical Analysis and Chinese Experience

Based on the neoclassical framework, we propose the convergence hypothesis of carbon productivity under sustainable growth and prove the different effects of knowledge spillover and technology diffusion on convergence. The convergence hypothesis is tested using China’s provincial spatial dynamic panel data from 1995 to 2019. The results show that China’s provincial carbon productivity has conditional convergence and club convergence characteristics. The convergence speed of dynamic panel regression estimation is greater than that of cross-sectional regression. The convergence rate of dynamic spatial panel regression estimation is faster depending on the spatial spillover difference between the two technologies. In the early stage, the provincial spatial dependence of China’s carbon productivity is mainly knowledge spillover, and the convergence rate is lower than that of the closed economy. Over the past decade, the spatial spillover, dominated by low-carbon technology diffusion, has become the dominant force. The convergence rate is significantly faster than that of a non-spatial-dependent economy. In addition, the mechanism test found that the development of energy efficiency dominates the spatial transfer of technology, so the overall convergence of carbon productivity in China mainly comes from the apparent convergence of energy efficiency in provinces and cities. Our conclusion provides a new reference for the emission reduction actions of countries worldwide because the spatial knowledge spillover carried by capital flows is not conducive to the pursuit of carbon productivity in less developed regions. On the contrary, the dissemination and diffusion of low-carbon technologies can significantly reduce carbon equivalent input in the production process, accelerating the pursuit of developing countries or regions.