Toward Human‐ AI Collaboration for Sustainable Development
Unveiling the Drives of Artificial Intelligence Adoption in Environmental Governance
Bibliographic Data
| ID | 19235482 |
|---|---|
| Authors | Cheng Zhou (0000-0002-5474-2160, Department of Public Administration Huaiyin Normal University Huaian China, corresponding author) |
| Year | 2026 |
| Publication date | 2026-06-01 |
| 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.71277 |
| OpenAlex | W7163058055 |
| Language | EN |
| Citations received | 1 |
| References cited | 53 |
The rapid advancement of artificial intelligence (AI) presents transformative potential for environmental governance, yet the drivers of AI adoption within bureaucracy remain underexplored. This study pioneers a configurational analysis of AI adoption in environmental bureaucracy. Using a mixed‐methods approach combining Grounded Theory and fuzzy‐set Qualitative Comparative Analysis (fsQCA), it examines 62 interviews with officials and 148 internal documents from China's eastern, central, and western provinces. Four core drivers behind AI adoption are identified: cognitive shifts, institutional antecedents, technological enablers, and social‐structural dynamics. These drivers form three configurations: (1) Institutional primacy with technological support, where hierarchical mandates and basic digital infrastructure enable AI adoption even without strong cognitive engagement; (2) Institutional‐cognitive‐technological triad, where balanced institutional pressures, capacity building, and technological readiness foster deeper AI internalization; and (3) Core technological enablement, where advanced infrastructure drives AI adoption with complementary institutional and social supports. The research advances theory by challenging linear models of technology adoption and refining the understanding of institutional isomorphism, revealing how distinct configurational pathways explain AI adoption in environmental governance. It also provides actionable guidance for policymakers to design context‐sensitive implementation strategies and robust governance safeguards that align AI adoption with Sustainable Development Goals
Bureaucracy · Core competency · Corporate governance · Environmental governance · Institutional theory · Sustainability · Sustainable development · Transformative learning · Cognitive Science and Mapping · Qualitative Comparative Analysis Research · Sustainability and Climate Change Governance
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| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |