The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Digital economy, energy efficiency, and carbon emissions: Evidence from provincial panel data in China
Abstract
Improving energy efficiency and lowering carbon emissions are of great importance to realize the "dual carbon" goal of carbon peak and carbon neutrality. Digital economy is a new engine of economic development, but whether or how it affects energy efficiency and carbon emissions are unclear. Utilizing panel data of China's 30 provinces from 2012 to 2019, this study empirically explores the relationships among digital economy, energy efficiency, and carbon emissions. Meanwhile, from the perspective of energy efficiency, applying mediation models and panel threshold model, it analyzes the direct, indirect, and nonlinear influencing mechanisms of digital economy on carbon emissions. The results reflect that the development of digital economy in China intensifies carbon emissions. Energy efficiency serves as a vital partial mediator between the two. The enhancement of energy efficiency can lower carbon emissions. However, the development of digital economy is not conducive to improving energy efficiency, thereby, indirectly increasing carbon emissions. The mediating effect of energy efficiency accounts for 30.58 % of the total effect of digital economy on carbon emissions. Meanwhile, taking energy efficiency into account, the impact of digital economy on carbon emissions has a significant double-threshold effect and presents an N-shaped trend. [0.824, 0.912] is the optimal range of energy efficiency, within which the growth of the digital economy can empower carbon emission abatement to some extent. In addition, the expansion of population size, the coal-based energy consumption structure, and the industrial structure significantly increase carbon emissions. The improvements in living standards and environmental regulations can help to decrease carbon emissions, but the emission abatement effects are not significant. Those conclusions reveal the importance of optimizing the level and quality of digital economy and adopting differentiated digital economy development policies based on energy efficiency to achieve carbon emission reduction.
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1 retracted
Zhang, S., Ma, X., & Cui, Q. (2021) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.798922 · Cited by 72
RetractedCohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30505-6 · Cited by 6,583
Threshold effects in non-dynamic panels: Estimation, testing, and inference
Hansen, B. E. (1999) · Journal of Econometrics · doi:10.1016/s0304-4076(99)00025-1 · Cited by 5,260
Ehrlich, P. R., & Holdren, J. P. (1971) · Science · doi:10.1126/science.171.3977.1212 · Cited by 3,584
Additive manufacturing and sustainability: an exploratory study of the advantages and challenges
Ford, S., & Despeisse, M. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.04.150 · Cited by 1,889
Effects of population and affluence on CO2 emissions
Dietz, T., & Rosa, E. A. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.1.175 · Cited by 1,879
Total-factor energy efficiency of regions in China
Hu, J.-L., & Wang, S.-C. (2006) · Energy Policy · doi:10.1016/j.enpol.2005.06.015 · Cited by 1,487
Bekun, F. V., Alola, A. A., & Sarkodie, S. A. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.12.104 · Cited by 1,385
de Sousa Jabbour, A. B. L., Jabbour, C. J. C., Foropon, C., & Godinho Filho, M. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.01.017 · Cited by 1,043
Li, Z., & Wang, J. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131570 · Cited by 701
Cited by
Wang, J., Liu, Y., Wang, W., & Wu, H. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.136954 · Cited by 303
Metadata
- Authors
- Zhang, L., Mu, R., Zhan, Y., Yu, J., Liu, L., Yu, Y., & Zhang, J.
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 852
- Pages
- 158403
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W4293832396
- Cited by
- 473
- References
- 62
- FWCI
- 80.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, L., Mu, R., Zhan, Y., Yu, J., Liu, L., Yu, Y., & Zhang, J. (2022). Digital economy, energy efficiency, and carbon emissions: Evidence from provincial panel data in China. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2022.158403
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