Call for Submissions: AI in environmental governance – possibilities and risksSpecial Issue Guest Editors (https://journals.sagepub.com/page/ipo/cfp-ai-environmental-governance) *Tove Engvall*, Assistant Professor in Archives and Information Science at Mid Sweden University, tove.engvall@miun.se *Yanto Chandra*, Professor, Department of Public and International Affairs, City University of Hong Kong, Hong Kong SAR, ychandra@cityu.edu.hk <ychandra@cityu.edu.hk> *Ines Mergel*, Professor of Public Administration - Department of Politics and Public Administration, University of Konstanz; University of Vaasa, School of Management, ines.mergel@uni-konstanz.de <ines.mergel@uni-konstanz.de> *Barbara Zyzak*, Associate Professor in Public Policy and Administration, Norwegian University of Science and Technology (NTNU), barbara.k.zyzak@ntnu.no <barbara.k.zyzak@ntnu.no> Summary The objective of this Special Issue is to explore the opportunities and risks of AI in environmental governance, expand our understanding of the drivers, challenges, impacts, and governance mechanisms of AI in this domain, and examine what trustworthy AI and AI literacy mean in the context of environmental governance. This special issue seeks contributions that explore both the conceptual and practical dimensions of trustworthy AI in environmental governance. It welcomes empirical studies showcasing implementation cases across different levels of governance, alongside analyses of frameworks and policies that shape AI’s role in this domain. Particular attention is given to how AI operates within open, global governance contexts characterized by multi-stakeholder and multi-level interactions, and how it can support transitions toward environmentally mission-driven economies. Submissions may also address AI literacy, as well as models and methods for evaluating the direct, indirect, and systemic risks and effects of AI in environmental governance and data governance in this domain. Together, these perspectives aim to deepen understanding of AI’s opportunities and challenges in advancing environmental governance. Scope and theoretical background of the Special Issue Artificial Intelligence (AI) has emerged as a transformative force in environmental governance, offering novel/innovative approaches to complex environmental challenges across domains. Its application spans across countries from real-time environmental monitoring and disaster prediction to advanced resource optimization, enhanced public engagement, and strengthened global collaboration. By reshaping institutional frameworks and informing evidence-based policymaking, AI is increasingly positioned as a catalyst for more resilient and sustainable governance structures. Yet, the successful adoption of AI tools requires public administrations to establish responsible and ethical governance frameworks that mitigate risks and capitalize on opportunities (Tironi & Lisboa, 2023). Critical environmental challenges, such as climate change, biodiversity loss, and ocean acidification (Sakschewski et al., 2025), demand rapid and large-scale societal transformations (IPCC, 2021). This requires not only effective, but also new forms of governance mechanisms that can drive systemic change. This brings us to the question of how digital governance and, particularly, AI can be leveraged to meet these demands, while also managing their risks. A vast majority of the digital governance literature acknowledges the potential of digital technologies to enable transformations of structures, processes, and values (Engvall & Flak, 2022), as well as the achievement of sustainable development goals (Estevez & Janowski, 2013; Medaglia & Misuraca, 2024; Medaglia et al., 2021). In particular, AI is playing a growing role in digital governance contexts (Rizk & Lindgren, 2024) with governments’ investments in AI laboratories to examine the promise of AI-assisted and automated decision-making (Mergel et al., 2024). This raises fundamental questions about how AI can support environmental governance, specifically whether, why, how, and to what extent AI will affect the environment, and what governance mechanisms and policies are needed to ensure that AI will not only cause no harm to the environment but also becomes part of a solution to the challenges arising from its development, management and use (e.g., huge data centres to support AI, with high demands on electricity and water consumption, or using AI to combat climate change and its effects, such as draught, flooding, etc). In the context of environmental governance, AI presents new opportunities to enhance governance, for instance, by improving policy design, evaluating the impact of policies (Clutton-Brock et al., 2021), and enhancing compliance by providing timely and accurate information on environmental regulatory risks (Scott et al., 2025). Furthermore, AI can support decision-making and improve understanding of the complex interactions between the variables contributing to environmental challenges. It can also be used to forecast disasters and accelerate the innovation and transformation of crisis resilience. However, it is crucial to ensure that AI itself does not exacerbate existing inequalities (e.g., gaps in access to AI that lead to economic inequality) or other negative environmental impacts (Galaz et al., 2025). Environmental governance is an information-intensive policy domain, characterized by systems for monitoring natural systems and supranational agreements with transparency requirements, such as the Paris Agreement (2015). Unfortunately, the objectives of transparency to stimulate climate action (Harrould-Kolieb et al., 2023), strengthen accountability, and drive transformation continue to fall short, and transparency remains a contentious issue among signatories (Gupta & van Asselt, 2019). Digital technologies, particularly AI, can leverage information to steer the climate transition, stimulate innovation, and enable new forms of data-driven, cross-sectoral, and multilayered collaboration (Engvall, 2024). Open governance ecosystems typically promote the use of technologies to facilitate networked interactions, connected intelligence, citizen-centric approaches, and crowdsourced deliberations (Meijer, 2024). Although a significant portion of research has examined how AI will revolutionize the public sector operations and has the potential to transform governments and governance, further research is needed to understand how to deploy AI effectively and manage its associated environmental risks (Tan & Chandra, 2025), including direct, indirect, and systemic effects (Bashir et al., 2024; Horner et al., 2016). To that end, we need an improved better understanding of the drivers, challenges, and impacts of AI on environmental governance (Campion et al., 2022), including contextual conditions, outcomes, and the mechanisms that generate these outcomes, as well as AI governance and policy (Chandra & Feng, 2025). In combination with the scholarship on digital governance, AI for environmental governance can facilitate structural societal and institutional transformations, as well as the emergence of new forms of governance, values, and power relations that support sustainable development (Meijer, 2024). The question is what role AI may have in open digital governance ecosystems, including the possibilities and risks, and what governance frameworks and response strategies are required to foster trust and mitigate the risks of increased polarization and value destruction (Edelmann, 2022). It is also crucial to address adverse effects (Meijer, 2024) and gain a deeper understanding of the skills required to design, implement, and manage digital initiatives that achieve sustainability goals (Cordella et al., 2024). A core challenge remains the trustworthiness of AI, particularly within an open, global environmental governance context, where multiple actors with conflicting interests can develop innovations and disseminate information worldwide. The EU has developed guidelines for Trustworthy AI (TAI) (European Commission High-Level Expert Group on Artificial Intelligence (HLEG), 2019), and both the UN General Assembly Declaration (United Nations General Assembly, 2024) and OECD Recommendation on Artificial Intelligence (OECD, 2025) emphasize the importance of Trustworthy AI. One of the requirements in the EU Trustworthy AI framework is to consider societal and environmental well-being (HLEG, 2019). However, there is a need to further explore what Trustworthy AI means in the context of environmental governance, to develop guidelines that can effectively support governance while managing its risks and drawbacks. There is a further need to articulate what AI literacy means in the context of environmental governance, beyond merely acquiring skills to use AI technologies, but to achieve the intended sustainability objectives. Topics and focus areas of the Special Issue This special issue welcomes empirical and conceptual contributions that are methodologically rigorous, along with opinion papers that highlight critical issues in exploring the risks and/or opportunities of AI in environmental governance. We invite submissions from both scholars and practitioners across all sectors of environmental governance [public, private, and civil society], drawing on disciplinary perspectives from digital governance, public administration and management, political science, information systems, information science, technology, and social science. The special issue may cover a broad spectrum of topics related to AI in environmental governance, including, but not limited to: - Conceptual and philosophical foundations of trustworthy AI in environmental governance - Empirical evidence of implementation cases of AI in environmental governance at different levels of government. - Analysis of frameworks and policies for governing AI in the context of environmental governance - Data governance, interoperability, transparency and accountability for trustworthy AI in environmental governance - Analysis of the role of AI in an open global environmental governance context, typically characterised by a networked, multi-stakeholder and multi-level governance context, and implications on public trust and legitimacy - Empirical qualitative, quantitative, or mixed-methods evidence of the role of AI to support the transition to a sustainability mission-driven economy - Conceptual and empirical evidence on AI literacy in environmental governance - Models and methods for analysing and evaluating direct, indirect, and systemic risks and effects of AI in environmental governance - Critical studies of risks and adverse effects of AI in environmental governance Important dates Deadline for abstract submission: 15 February 2026 Notification for invitation to submit a full manuscript: 15 March 2026 Deadline for submission of the full manuscript: 30 July 2026 Review process: August 2026 - January 2027 Final decision on manuscripts: 1 February 2027 Anticipated publication: April 2027 Abstracts should initially be sent to tove.engvall@miun.se by February 15, 2026. Abstracts should be up to 700 words and include the names of all authors and their institutional affiliations. Abstracts will be reviewed by the Guest Editors of the Special Issue. This review will focus on the fit with the special issue theme, feasibility, and potential contribution to the state of the literature. The authors of accepted abstracts will be invited to submit full manuscripts. Full manuscripts will be double-blind peer reviewed. Please note that initial acceptance of an abstract does not guarantee acceptance and publication of the final manuscript. Given the niche topic, the participating authors are expected to review up to three manuscripts. Final manuscripts must be submitted directly through IP’s submission system and need to adhere to the journal's submission guidelines: /author-instructions/IPO References Bashir, N., Donti, P., Cuff, J., Sroka, S., Ilic, M., Sze, V., Delimitrou, C., & Olivetti, E. (2024). 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Perspectives on the twin transition: Instrumental and institutional linkages between the digital and sustainability transitions. Information Polity, 29(1), 35-51. https://doi.org/10.3233/IP-230015 Mergel, I., Dickinson, H., Stenvall, J., & Gasco, M. (2024). Implementing AI in the public sector. Public Management Review, 1-14. Recommendation of the Council on Artificial Intelligence (2025). https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449 Paris Agreement to the United Nations Framework Convention on Climate Change, Dec. 12, 2015. www.unfccc.int Rizk, A., & Lindgren, I. (2024). Automated decision-making in the public sector: A multidisciplinary literature review. International Conference on Electronic Government, Sakschewski, B., et al. (2025). Planetary Boundaries Science (PBScience), 2025: Planetary Health Check 2025. Scott, N., Wong, E. W., & Wu, J. J. (2025). Regulating the AI-Climate Nexus: Current trends, emerging issues, and ways forward. Cambridge Open Engage. doi:10.33774/coe-2025-jh25b This content is a preprint and has not been peer-reviewed. Tan, J., & Chandra, Y. (2025). Debate: AI as a framework for public service innovation. Public Money & Management, 1-3. Tironi, M., & Lisboa, D. I. R. (2023). Artificial intelligence in the new forms of environmental governance in the Chilean State: Towards an eco-algorithmic governance. Technology in Society, 74, 102264. United Nations General Assembly. (2024). Resolution 78/265. Seizing the opportunities of safe, secure, and trustworthy artificial intelligence systems for sustainable development. https://digitallibrary.un.org/record/4043244?v=pdf: United Nations -- Tracey P. Lauriault Associate Professor, Critical Media and Big Data Communication and Media Studies School of Journalism and Communication https://orcid.org/0000-0003-1847-2738 -- Tracey P. Lauriault Associate Professor, Critical Media and Big Data Communication and Media Studies School of Journalism and Communication https://orcid.org/0000-0003-1847-2738