| Title |
Are environment-related technologies key to unlock the path towards sustainable development: An econometric analysis |
| ID_Doc |
30867 |
| Authors |
Rao, A; Kumar, S |
| Title |
Are environment-related technologies key to unlock the path towards sustainable development: An econometric analysis |
| Year |
2024 |
| Published |
Geoscience Frontiers, 15, 4 |
| DOI |
10.1016/j.gsf.2023.101702 |
| Abstract |
The objective of this study is to investigate the correlation between energy intensity and environmentrelated technology in industrialized countries. By utilizing panel data from 23 countries over a span of 32 years (1990-2021), this research aims to contribute to the comprehension of the role of green innovation in sustainable development. The study employs the Stochastic Impacts by Regression on Population, Affluence, and Technology model while controlling for variables including population growth, gross domestic product in purchasing power parity, Information and Communication Technology capital deepening, renewable energy consumption, and green innovation represented by research and development expenditure on environment-related technology. The results of the analysis, employing panel unit root tests, cross-sectional dependence tests, and a Method of Moments quantile regression, reveal that green innovation has a positive influence on diminishing energy intensity, with a more substantial impact at higher quantiles. Moreover, ICT capital deepening is determined to have a positive and noteworthy effect on reducing energy intensity. The findings of this study offer valuable insights for policymakers in their endeavours to accomplish sustainable development goals. (c) 2023 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Author Keywords |
Technology; Sustainable development; Energy intensity; Quantile regression |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001295364700001 |
| WoS Category |
Geosciences, Multidisciplinary |
| Research Area |
Geology |
| PDF |
https://doi.org/10.1016/j.gsf.2023.101702
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