ARTIFICIAL INTELLIGENCE–ENABLED ENVIRONMENTAL GOVERNANCE IN CHINA–ASEAN COOPERATION: THE ROLE OF INTERNATIONAL ORGANIZATIONS AND DIGITAL TECHNOLOGIES

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1. Verfasser: Yijuan Jiao1*
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Sprache:Englisch
Veröffentlicht: Zenodo 2026
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author Yijuan Jiao1*
author_facet Yijuan Jiao1*
contents <p><strong><em><span>This study investigates how artificial intelligence (AI) can be embedded into environmental governance to improve the effectiveness of China–ASEAN environmental cooperation. It focuses on two enabling conditions that are frequently mentioned in policy but insufficiently integrated in empirical models: (i) the facilitative role of international organizations in building shared capacity, standards, and trust, and (ii) the enabling role of digital technologies (interoperable platforms, data governance, and digital transparency) in translating AI capability into cooperation outcomes. Method: The manuscript specifies a mixed-method design. First, a cross-sectional questionnaire is developed for practitioners involved in China–ASEAN environmental cooperation (government agencies, international organizations, NGOs, research institutes, and technology partners). The survey measures international organization facilitation, AI-enabled environmental governance capability, digital technology integration, and cooperation effectiveness using a five-point Likert scale. Second, semi-structured interviews are designed to triangulate causal mechanisms and contextual barriers such as data sovereignty, interoperability, and responsible AI safeguards. Consistent with contemporary PLS-SEM reporting guidance, the model is tested using partial least squares structural equation modeling with mediation and moderation. In this manuscript, the quantitative results are illustrative and are generated from a reproducible synthetic dataset to demonstrate the PLS-SEM workflow. Field data collection using the specified questionnaire and interviews is planned. Findings: The illustrative analysis indicates that international organization facilitation positively influences AI-enabled environmental governance capability (β = 0.44, p < .001). AI-enabled environmental governance capability positively predicts China–ASEAN cooperation effectiveness (β = 0.40, p < .001). International organization facilitation also has a direct positive effect on cooperation effectiveness (β = 0.28, p < .001), and AI capability partially mediates this relationship (indirect β = 0.17, p < .001). Digital technology integration strengthens the AI capability → cooperation effectiveness link, suggesting that interoperable infrastructures and data governance are boundary conditions for effective AI-enabled cooperation. Originality/Implications: The study contributes an integrated, practice-oriented framework linking international organizations, AI-enabled governance capability, and digital infrastructure conditions to cooperation effectiveness. It extends emerging scholarship on algorithmic governance and digital environmental governance by theorizing the institutional and infrastructural mechanisms through which IOs can facilitate cross-border AI-enabled governance. Practically, the findings recommend a cooperation sequence: co-design interoperable environmental data standards, build responsible AI assurance and auditability, and leverage international organizations as neutral conveners to sustain trust, financing, and technical capacity while also addressing AI’s environmental footprint.</span></em></strong></p>
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spellingShingle ARTIFICIAL INTELLIGENCE–ENABLED ENVIRONMENTAL GOVERNANCE IN CHINA–ASEAN COOPERATION: THE ROLE OF INTERNATIONAL ORGANIZATIONS AND DIGITAL TECHNOLOGIES
Yijuan Jiao1*
Artificial Intelligence; Digital Environmental Governance; China–ASEAN Cooperation; International Organizations; Data Governance; PLS-SEM.
<p><strong><em><span>This study investigates how artificial intelligence (AI) can be embedded into environmental governance to improve the effectiveness of China–ASEAN environmental cooperation. It focuses on two enabling conditions that are frequently mentioned in policy but insufficiently integrated in empirical models: (i) the facilitative role of international organizations in building shared capacity, standards, and trust, and (ii) the enabling role of digital technologies (interoperable platforms, data governance, and digital transparency) in translating AI capability into cooperation outcomes. Method: The manuscript specifies a mixed-method design. First, a cross-sectional questionnaire is developed for practitioners involved in China–ASEAN environmental cooperation (government agencies, international organizations, NGOs, research institutes, and technology partners). The survey measures international organization facilitation, AI-enabled environmental governance capability, digital technology integration, and cooperation effectiveness using a five-point Likert scale. Second, semi-structured interviews are designed to triangulate causal mechanisms and contextual barriers such as data sovereignty, interoperability, and responsible AI safeguards. Consistent with contemporary PLS-SEM reporting guidance, the model is tested using partial least squares structural equation modeling with mediation and moderation. In this manuscript, the quantitative results are illustrative and are generated from a reproducible synthetic dataset to demonstrate the PLS-SEM workflow. Field data collection using the specified questionnaire and interviews is planned. Findings: The illustrative analysis indicates that international organization facilitation positively influences AI-enabled environmental governance capability (β = 0.44, p < .001). AI-enabled environmental governance capability positively predicts China–ASEAN cooperation effectiveness (β = 0.40, p < .001). International organization facilitation also has a direct positive effect on cooperation effectiveness (β = 0.28, p < .001), and AI capability partially mediates this relationship (indirect β = 0.17, p < .001). Digital technology integration strengthens the AI capability → cooperation effectiveness link, suggesting that interoperable infrastructures and data governance are boundary conditions for effective AI-enabled cooperation. Originality/Implications: The study contributes an integrated, practice-oriented framework linking international organizations, AI-enabled governance capability, and digital infrastructure conditions to cooperation effectiveness. It extends emerging scholarship on algorithmic governance and digital environmental governance by theorizing the institutional and infrastructural mechanisms through which IOs can facilitate cross-border AI-enabled governance. Practically, the findings recommend a cooperation sequence: co-design interoperable environmental data standards, build responsible AI assurance and auditability, and leverage international organizations as neutral conveners to sustain trust, financing, and technical capacity while also addressing AI’s environmental footprint.</span></em></strong></p>
title ARTIFICIAL INTELLIGENCE–ENABLED ENVIRONMENTAL GOVERNANCE IN CHINA–ASEAN COOPERATION: THE ROLE OF INTERNATIONAL ORGANIZATIONS AND DIGITAL TECHNOLOGIES
topic Artificial Intelligence; Digital Environmental Governance; China–ASEAN Cooperation; International Organizations; Data Governance; PLS-SEM.
url https://doi.org/10.5281/zenodo.18916811