The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector. (October 2022)
- Record Type:
- Journal Article
- Title:
- The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector. (October 2022)
- Main Title:
- The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector
- Authors:
- Liu, Jun
Liu, Liang
Qian, Yu
Song, Shunfeng - Abstract:
- Abstract: Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics. Highlights: We investigate the effect of artificial intelligence on carbon intensity. AI significantly reduces carbon intensity. There areAbstract: Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics. Highlights: We investigate the effect of artificial intelligence on carbon intensity. AI significantly reduces carbon intensity. There are both the heterogeneities of developmental stages and industrial in the effects of AI on carbon intensity. … (more)
- Is Part Of:
- Socio-economic planning sciences. Number 83(2022)
- Journal:
- Socio-economic planning sciences
- Issue:
- Number 83(2022)
- Issue Display:
- Volume 83, Issue 83 (2022)
- Year:
- 2022
- Volume:
- 83
- Issue:
- 83
- Issue Sort Value:
- 2022-0083-0083-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-10
- Subjects:
- Artificial intelligence -- Carbon dioxide emissions -- Carbon intensity -- China's industrial sector
Planning -- Periodicals
Economic policy -- Periodicals
Social policy -- Periodicals
Planification -- Périodiques
Politique économique -- Périodiques
Politique sociale -- Périodiques
ECONOMIC PLANNING
SOCIAL PLANNING
DECISION-MAKING
361 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00380121 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.seps.2020.101002 ↗
- Languages:
- English
- ISSNs:
- 0038-0121
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8319.576000
British Library DSC - BLDSS-3PM
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- 22867.xml