Artificial Intelligence and Sustainable Development in China
Evidence From a Dynamic Autoregressive Distributed Lag Approach
Bibliographic Data
| ID | 12307201 |
|---|---|
| Authors | Zeeshan Khan (0000-0003-1374-0836, School of Public Policy and Management Tsinghua University Beijing China), Walid Chatti (0000-0002-8214-5929, Department of Economics, Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia), Xufeng Zhu (0000-0002-8446-1183, School of Public Policy and Management Tsinghua University Beijing China, corresponding author) |
| Year | 2026 |
| Publication date | 2026-01-26 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Development (JOURNAL) |
| Journal identifiers | ISSN: 0968-0802 • E-ISSN: 1099-1719 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/sd.70676 |
| OpenAlex | W7125782550 |
| Language | EN |
| References cited | 74 |
The impact of AI on sustainable development is an important area of research, but there has been no empirical assessment on the relationships between AI and its various dimensions, including AI patents. To fill this gap in the literature, this study explores how AI can help China's Sustainable Development Goals (SDGs) targets using a DARDL approach, analyzing quarterly data from Q1 2010 to Q4 2020. The results show that AI has a positive relationship with China's level of sustainable development over an extended period of time. It is also found that the amount spent on R&D, the level of human capital, and the percentage of renewable energy in use positively influence sustainability, and that these relationships develop over time (as they provide an enhanced environment for the development of new ideas and technologies). Furthermore, AI has a synergistic effect in increasing renewable energy use and therefore promoting greater levels of sustainable development for both AI and renewable energy users. For policymakers, the implication is to create better incentives and regulations to promote the use of AI; while for managers, it implies strategically implementing AI within their organization as well as upskilling their staff and adopting cleaner energy sources while creating a culture of innovation. This evidence is specific to China, and is not fully generalizable, but it offers indicative guidance for similar economies
Control (management · Distributed lag · Empirical evidence · Energy (signal processing · Incentive · Lag · Renewable energy · Sustainable development · Digital Transformation in Industry · Energy, Environment, Economic Growth · Ethics and Social Impacts of AI
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| Citation velocity | historical |
|---|---|
| Highly cited | No |