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The nonlinear effects of environmental innovation on energy sector-based carbon dioxide emissions in OECD countries.
- Source :
- Technological Forecasting & Social Change; Sep2022, Vol. 182, pN.PAG-N.PAG, 1p
- Publication Year :
- 2022
-
Abstract
- This paper empirically investigates the impact of environmental innovation on energy sector-based CO 2 emissions using a large dataset for 32 OECD countries covering the period 1997–2018. To detect the nonlinear relationship between variables, this paper adopts a panel smooth transition regression (PSTR) model, which can estimate both the threshold level endogenously and the smoothness of the transition from one regime to another. The findings indicate that environmental innovation has a reducing effect on CO 2 emissions from the energy sector up to a certain level of innovation is insignificant (1st regime), then it has a reducing effect (2nd regime), and above this level environmental innovation has an increasing effect on carbon emissions (3rd regime), suggesting the existence of a rebound effect. These findings point out that environmental innovations alone are not a solution to struggle environmental problems and should be supported by environmental policies to reveal their environmental reflections. This paper not only makes an important contribution to the empirical literature, but also reveals important policy implications, particularly to achieve climate change targets. • Environmental innovation – energy sector based CO 2 emissions nexus is investigated in 32 OECD countries. • The panel smooth transition regression model is adopted. • The threshold effects of environmental innovation on energy sector-based CO 2 emissions are found. • The results point out that the rebound effects of environmental innovation should be considered in the policy making process. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00401625
- Volume :
- 182
- Database :
- Supplemental Index
- Journal :
- Technological Forecasting & Social Change
- Publication Type :
- Academic Journal
- Accession number :
- 158310675
- Full Text :
- https://doi.org/10.1016/j.techfore.2022.121800