| Title | The impact of the digital economy on green total factor productivity in Belt and Road countries: the mediating role of energy transition |
|---|---|
| ID_Doc | 62744 |
| Authors | Wang, AQ; Ren, JY |
| Title | The impact of the digital economy on green total factor productivity in Belt and Road countries: the mediating role of energy transition |
| Year | 2023 |
| Published | |
| Abstract | Introduction: The prospective Belt and Road (B & R) Initiative by China must be thoroughly examined by the participating nations in all respects. It is now essential to investigate whether the digital economy of the B & R countries can support green total factor productivity (GTFP). This study examines the connection between green total factor productivity (GTFP) and the digital economy in B & R countries with the aim of providing China with practical recommendations for advancing the initiative.Methods: This study explores 40 B & R countries from 2006 to 2021, calculates the GTFP using the unexpected super-efficient SBM model and the Global Malmquist-Luenberger index method, and constructs the digital economy index using the principal component analysis method. OLS, FMOLS methods, and spatial panel regressions are used to examine the digital economy-GTFP nexus.Results and Discussion: In the selected 40 B & R countries, there is a non-linear relationship between the digital economy and GTFP, and the overall effect of the digital economy on GTFP is negative, implying that the growth of the digital economy will cause a decline in GTFP. Energy transition has mediation effects that can mitigate the negative impact of digital economic growth on GTFP. The spatial spillover effects of the digital economy on the GTFP of neighboring countries are evident. There is also heterogeneity; the digital economy will reduce GTFP in high- and middle-income countries, but the negative effects are not evident in low-income countries. This paper adds to the discussion of the digital economy and green development by drawing different conclusions from previous studies using a variety of regression models, providing a fresh foundation for policy-making. |
| https://www.frontiersin.org/articles/10.3389/fenvs.2023.1213961/pdf |
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