The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Green technology progress and total factor productivity of resource-based enterprises: A perspective of technical compensation of environmental regulation
Abstract
Abstract The implementation of Industry 4.0 has had an important impact on the value creation of the manufacturing industry, but there are few studies on its driving mechanisms and effect on total factor productivity. In this study, green technology progress is selected as an important representation of Industry 4.0, and samples of China's A-share resource-based enterprises from 2004 to 2018 are used to examine the relationship between environmental regulation, green technology progress, and total factor productivity to explore the role of social, environmental, and technological factors in manufacturing development. The findings show that green technology progress significantly promotes the total factor productivity of enterprises through the improvement of unit labor productivity and that environmental regulation “forces” the green technology progress of enterprises through external pressure. These conclusions remain valid after replacing the explained variables and measuring methods. The results of heterogeneity analysis show that when an enterprise has strong financial and human resource bases, the “forcing” effect of environmental regulation on green technology progress is more significant.
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Cited by
Does carbon emissions trading promote green technology innovation in China?
Zhang, W., Li, G., & Guo, F. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119012 · Cited by 419
Abbas, J., Wang, L., Ben Belgacem, S., Pawar, P. S., Najam, H., & Abbas, J. (2023) · Energy · doi:10.1016/j.energy.2023.126683 · Cited by 347
Zhao, X., Mahendru, M., Ma, X., Rao, A., & Shang, Y. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.01.076 · Cited by 346
Self-citation
Metadata
- Authors
- Song, M., Peng, L., Shang, Y., & Zhao, X.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 174
- Pages
- 121276
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W3206097524
- Cited by
- 435
- References
- 55
- FWCI
- 63.86
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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