[Air-L] CFS - Call for Submissions: AI in environmental governance – possibilities and risks

[Tracey P. Lauriault]

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 at miun.se
*Yanto Chandra*, Professor, Department of Public and International Affairs,
City University of Hong Kong, Hong Kong SAR, ychandra at cityu.edu.hk
<ychandra at 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 at uni-konstanz.de
<ines.mergel at uni-konstanz.de>
*Barbara Zyzak*, Associate Professor in Public Policy and Administration,
Norwegian University of Science and Technology (NTNU),
barbara.k.zyzak at ntnu.no  <barbara.k.zyzak at 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 at 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
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-- 
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