The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Energy technological progress, energy consumption, and CO2 emissions: Empirical evidence from China
Abstract
Abstract Technical progress is usually considered to be an important way to effectively reduce carbon emissions. However, the advancement of energy technology may cause rebound effect which may lessen the emission reduction effect of technical progress. This suggests that the real impact of energy technological progress is worthy of further study. By taking the rebound effect into consideration, this study estimates the real effect of energy technological progress and energy consumption on carbon emissions in China, based on an interaction model, and using data from China's 30 provinces for the period 2005–2016. Key results include the following: (1) An inverted U-shaped relationship between energy technological progress and carbon emissions is detected. (2) Across technical progress, energy consumption has an inverted U-shaped effect on carbon emissions. (3) Turning points are found in both the direct effect and the technical effect of China's energy technological progress. This indicates that they initially increase carbon emissions, and then reduce them, although the rebound effect continues to have a positive impact on the increase of carbon emissions. (4) The largest differences between regions with different energy technology levels appear in the direct effect and the technical effect of energy technological progress on CO2 emissions, and there is almost no difference in rebound effect. Some relevant policy recommendations are proposed, based on the above findings.
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Cites
Arellano, M., & Bond, S. (1991) · The Review of Economic Studies · doi:10.2307/2297968 · Cited by 33,272
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
2006 IPCC Guidelines for National Greenhouse Gas Inventories
Paustian, K. H., Ravindranath, N. H., & van Amstel, A. R. (2006) · Socio-Environmental Systems Modeling · Cited by 6,706
Understanding Interaction Models: Improving Empirical Analyses
Brambor, T., Clark, W. R., & Golder, M. (2006) · Political Analysis · doi:10.1093/pan/mpi014 · Cited by 6,223
A Note on the Theme of Too Many Instruments*
Roodman, D. (2009) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.2008.00542.x · Cited by 5,084
Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries
Färe, R. G., Grosskopf, S., Norris, M., & Zhang, Z. (2016) · American Economic Review · Cited by 4,262
Interaction Effects in Multiple Regression.
Richardson, S. C., Jacard, J., Turrisi, R., & Wan, C. K. (1991) · The Statistician · doi:10.2307/2348747 · Cited by 3,309
(2006) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 1,982
Interaction Effects in Multiple Regression
Jaccard, J., & Turrisi, R. (2003) · doi:10.4135/9781412984522 · Cited by 1,797
Patents and innovation counts as measures of regional production of new knowledge
Acs, Z. J., Anselin, L., & Varga, A. (2002) · Research Policy · doi:10.1016/s0048-7333(01)00184-6 · Cited by 1,745
Cited by
Wang, X., Wang, X., Ren, X., & Wen, F. (2022) · Energy Economics · doi:10.1016/j.eneco.2022.105966 · Cited by 329
Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression
Huang, J., & Kaewunruen, S. (2023) · Energies · doi:10.3390/en16020966 · Cited by 38
Metadata
- Authors
- Gu, W., Zhao, X., Yan, X., Wang, C., & Li, Q.
- Venue
- Journal of Cleaner Production
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 236
- Pages
- 117666
- ISSN
- 0959-6526 (ISSN-L)
- OpenAlex
- W2964316850
- Cited by
- 268
- References
- 68
- FWCI
- 31.79
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gu, W., Zhao, X., Yan, X., Wang, C., & Li, Q. (2019). Energy technological progress, energy consumption, and CO2 emissions: Empirical evidence from China. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.117666
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