A team from Chongqing Technology and Business University employs provincial panel data on industrial robot installations (2011–2020) and super-efficiency DEA along with threshold regressions to assess AI’s direct impact on green economic efficiency (GEE) and its modulation by environmental regulations, green technological innovations, and intellectual property frameworks.
Key points
- Proxying AI via log-transformed industrial robot stock weighted by provincial employment
- Measuring GEE with a super-efficiency Slack-Based Measure DEA model incorporating inputs, GDP outputs, and ‘three wastes’ pollutants
- Applying threshold regressions to reveal how environmental regulations, green innovation types, and IP protections modulate AI’s GEE impact
Why it matters: The findings show how aligning AI with governance and innovation policies can advance sustainable economic transitions and low-carbon growth.
Q&A
- What is green economic efficiency?
- Why use industrial robots as a proxy for AI?
- What is the super-efficiency Slack-Based Measure DEA model?
- How do governance mechanisms modulate AI’s impact on GEE?