Assessing the digital economy and its effect on carbon performance: the case of China

Abstract

With China’s carbon neutrality target and the rapid growth of the digital economy, it is critical to understand how the digital economy can decouple economic growth from carbon emissions. This paper innovatively calculates the digital economy index in China from 2004 to 2019 and explores how the digital economy affects total factor carbon productivity (TFCP) and its spatial spillover effect. The empirical results indicate that (1) the development level of digital economy in eastern provinces is significantly higher than that in other provinces. (2) The digital economy positively promotes TFCP. Interestingly, digital industrialization has a more substantial effect on improving TFCP, while industry digitization has a weaker effect. (3) The digital economy not only helps improve the local TFCP but also has spatial spillovers to the surrounding areas and has the most prominent effect on the TFCP of the northern. (4) The digital economy affects TFCP through four channels industrial digital upgrading, human capital, market integration, and resource allocation. (5) The effects of the digital economy on TFCP exhibit significant heterogeneity in terms of time, region, and pollution degree. The findings of this study have important policy implications for promoting the transition to the digital economy and low-carbon development.

Nonlinear spatial impacts of the digital economy on urban ecological welfare performance: evidence from China

“…However, owing to the differences in the level of digital infrastructure among cities, the DE may generate new digital inequality and the "digital divide" phenomenon in the early stage of development, and lead to the "siphon effect" because of the relative lack of new digital infrastructure such as mobile base stations, Internet of Things, big data centers, and cloud platforms. In peripheral cities, given the relative backwardness of the soft environment such as the institutional environment and policy preferences, the development of DE may accelerate the flow of all kinds of factors from the peripheral cities to the central city, thus generating the "siphon effect" phenomenon, leading to the "Matthew effect" (i.e., "the weaker is weaker, the stronger is stronger"), further distorting the allocation of factors, and impeding the enhancement of the performance of urban EWP (Cui et al, 2023). However, with the rising level of DE development in cities, DE development can effectively reduce the cost of searching, trading, matching, and copying by alleviating information asymmetry (Xue and Li, 2022), thus reducing the transaction barriers, breaking the market boundary, and expanding the scope of the market, which is conducive to the flow of factors in a larger space, and optimizing the distortion of factor allocation (Qu et al, 2023).…”

“…Urban EWP (EPW): The current common methods for EWP measurement are as follows: (1) the ratio method, such as using the ratio of social welfare or happy life index to the ecological footprint; Wang and Li 10.3389/fevo.2024.1361741 Frontiers in Ecology and Evolution frontiersin.org and (2) the non-parametric method, such as data envelopment analysis. Since it is difficult for a single indicator to measure both factors at the same time, and the evaluation system constructed through tends to duplicate information (Cui et al, 2023), with reference to Tang and Zhao (2023) and Xue and Li (2022), in this paper, the Super SBM-DEA method is used to measure urban EWP using efficiency values of inputs and outputs. EWP refers to the efficiency of natural ecological factor inputs into human welfare, in which natural ecological factor inputs reflect the consumption of natural resources and EE disturbance by human activities.…”

Spatio-Temporal Characteristics of Industrial Carbon Emission Efficiency and Their Impacts from Digital Economy at Chinese Prefecture-Level Cities

“…The main algorithms are data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Compared with the latter, DEA does not need to set specific function forms and is more suitable for multi-input and multi-output decision-making units (DMU) efficiency measurement [26]. Therefore, DEA models are more widely used.…”

“…This is because the development of a digital economy breaks the constraints of geographic distance on eco-nomic activities, strengthens regional connectivity, promotes the synergistic development of technological innovation and the common transformation of industrial structure in various regions, and influences the cooperation and innovation of neighboring regions through spatial spillover effects [25]. Previous studies on carbon emission efficiency have mostly utilized econometric models and spatial econometric models to emphasize the spatial effect of the digital economy on carbon emission efficiency [9,10,26], but traditional econometric models simplify the linear relationship between the variables and the dependent variable. Another study found a potential non-linear relationship between the digital economy and industrial carbon emission efficiency.…”

Digital Technology, Factor Allocation and Environmental Efficiency of Dairy Farms in China: Based on Carbon Emission Constraint Perspective

“…This can be achieved by reducing resource waste, enhancing output efficiency, and mitigating non-essential carbon source pollution [19]. Consequently, the adoption of digital technology in factor allocation can contribute to the improvement of environmental efficiency [20]. In 2022, the State Council released "The 14th Five-Year Plan for the Advancement of the Digital Economy", emphasizing the need for extensive and profound integration of digital technologies across economic, social, and industrial sectors.…”