AI Governance at a Climate Crossroads: Aligning Global Rules with Green Goals

AI and Climate Action: Twin Challenges Converging

Global leaders increasingly view artificial intelligence and climate change as intertwined existential challenges. United Nations Secretary-General António Guterres warned that today’s uncertainty is “compounded by two existential threats – the climate crisis and the rapid advance of technology, in particular, artificial intelligence” (Ref). Calls are growing to treat AI with the same urgency and coordinated global action as climate change. Unlike climate governance—anchored by frameworks like the Paris Agreement and annual COP summits—AI lacks a comparable international regime (Ref) (Ref). This gap is closing. In 2025, new governance efforts are coming online, notably the European Union’s AI Act and UN-led initiatives, aiming to ensure AI advances support climate goals rather than undermine them. These efforts recognize that AI is a double-edged sword: it offers powerful tools for climate action, yet unchecked AI development can carry heavy environmental costs and widen global inequalities.

At the Summit of the Future and other forums, leaders emphasized that global AI governance must evolve in tandem with sustainable development (Ref) (Ref). AI is increasingly used to accelerate climate solutions—from improving climate model predictions to monitoring greenhouse gas emissions in real time. But to truly “supercharge climate action” responsibly (Ref), AI systems themselves must be developed and deployed under frameworks that ensure they are transparent, accountable, energy-efficient, and accessible to all. The sections below examine how emerging regulations intersect with climate action, focusing on three critical areas: curbing AI’s environmental footprint, fostering transparency and accountability in climate AI, and promoting global equity in AI for climate resilience.

Regulating AI’s Environmental Footprint: Green AI or Greenwashed?

As the AI industry expands, so does its energy and resource appetite. Training and running large AI models can consume enormous electricity and water, and require mining of minerals for data centers (Ref). The International Energy Agency projects that by 2026 the AI sector will use at least ten times more energy than in 2023 (Ref). In Europe alone, data center electricity demand is on track to rise ~30% from 2023 levels by 2026, largely due to AI workloads (Ref). This surge directly conflicts with climate objectives unless AI development becomes far more sustainable. Policymakers are starting to respond. The EU’s new Artificial Intelligence Act — which entered into force in mid-2024 and begins phased enforcement in 2025 — explicitly aims to ensure AI respects “environmental protection, while boosting innovation” (Ref). It is the first major AI law to embed energy and resource considerations.

Under the EU AI Act, developers of large “general-purpose AI” models (such as big generative models used across many tasks) must document and disclose the energy consumed in training those systems (Ref). Regulators can demand this information at any time, reflecting a push for transparency around AI’s carbon footprint. Notably, if a model’s energy consumption is extraordinarily high, it may even be deemed a “systemic risk” under the Act, triggering extra oversight (Ref). This creates a direct incentive for AI providers to optimize efficiency and keep emissions in check to avoid onerous compliance (Ref). The law also mandates the development of technical standards for energy efficiency in AI. EU standards bodies, in collaboration with international organizations like ISO/IEC, are now working to codify best practices for improving AI systems’ resource performance across their lifecycle (Ref) (Ref). Although these standards will take time (the first progress report is due in 2028) (Ref), the intent is clear: make AI leaner and greener through measurable benchmarks. The Act further envisions voluntary codes of conduct for sustainable AI, encouraging industry to adopt “energy-efficient programming” and design techniques that minimize resource use (Ref). There is even discussion of an AI-specific energy label (akin to appliance efficiency labels) to inform users about a system’s carbon footprint (Ref).

Despite these steps, critics argue that current proposals still fall short of fully addressing AI’s environmental costs. The EU AI Act, at its core, leans on the hope that AI will be a net positive for sustainability, rather than imposing hard limits on AI’s carbon emissions (Ref) (Ref). Recital text in the law touts AI’s potential benefits for climate, agriculture, and biodiversity, and even allows regulators to waive certain requirements for AI systems deployed “for exceptional reasons” of environmental protection (Ref) (Ref). This reflects a belief in “AI for sustainability” more than a commitment to the sustainability of AI itself (Ref) (Ref). In practice, the Act’s binding provisions on energy use are limited. Requiring documentation of energy use and considering it in risk assessments are important first moves, but they do not cap or reduce AI’s carbon output directly. Much relies on future standards and voluntary measures (Ref) (Ref), which may take years to materialize. Meanwhile, evidence mounts that AI’s footprint is significant and growing. Generative AI models, for example, are “orders of magnitude more resource-intensive” than traditional algorithms (Ref). Training a single state-of-the-art model can emit hundreds of tons of CO₂, and running complex AI services continuously consumes enormous amounts of water and electricity (Ref). These impacts raise the pressing question: are the societal and environmental costs of ever-larger AI models acceptable (Ref)?

Encouragingly, AI is also part of the solution to its own problem. Researchers are applying AI to improve energy efficiency in many domains, including the climate science field itself. For instance, machine learning is being used to speed up climate modeling and reduce its energy costs without sacrificing accuracy (Ref). AI-optimized climate models can run simulations faster and with less computation, potentially saving significant emissions for the scientific community. Similarly, AI helps data centers optimize cooling and workloads in real time to cut power usage. Governance frameworks will need to catalyze these positive applications while restraining AI’s unsustainable extremes. Moving forward, we may see stronger measures – for example, requirements to use renewable energy in AI training, or carbon-footprint disclosures for AI services – to ensure the AI revolution aligns with climate targets. In 2025, the groundwork is being laid: the EU has put environmental language into law, international standards are under development, and awareness of “Green AI” is growing. The challenge will be translating this early momentum into enforceable, impactful policy that holds AI builders accountable for minimizing emissions. Anything less risks AI becoming an unchecked driver of climate change, undermining the very goals it is touted to advance.

Transparency and Accountability in AI-Powered Climate Solutions

Beyond energy use, another cornerstone of trustworthy AI in sustainability is transparency – both in how AI systems work and in the data they produce – coupled with robust accountability for their impacts. As governments and organizations deploy AI for climate modeling, emissions tracking, and resource optimization, they are confronting questions of trust: How do we know an AI’s predictions or recommendations are reliable? If an AI system makes a critical error – say, underestimating flood risk or miscalculating a factory’s emissions – who is answerable, and how can we correct it? Emerging AI governance frameworks seek to bring light into AI’s “black boxes” and ensure human accountability remains front and center, especially for high-stakes climate applications.

The EU AI Act squarely addresses this through strict requirements on “high-risk” AI systems. AI that affects critical infrastructure (such as energy grids or water supply) is classified as high-risk under the Act (Ref). The same goes for AI used in areas like environmental monitoring if tied to safety or public services. For any high-risk AI, the law mandates an extensive suite of transparency and oversight measures (Ref). Providers must implement risk management processes, use high-quality (bias-checked) data, and maintain detailed technical documentation explaining the system’s design, purpose, and performance (Ref). They are required to keep logs of the AI’s operations and provide clear instructions to users so that outputs can be understood and appropriately acted upon (Ref). Crucially, high-risk AI systems cannot be fully autonomous in sensitive decisions: the Act requires human oversight mechanisms to monitor the AI and intervene or override when necessary (Ref). These obligations—covering accuracy, robustness, cybersecurity, and more—aim to make AI tools deployed in critical climate-related contexts transparent in their function and accountable in their use (Ref) (Ref). For example, an AI system managing electric grid distribution would need to log its actions and be auditable, and operators would need the ability to revert to manual control if the AI behaves anomalously. Any serious incidents or malfunctions (including those causing environmental harm) must be reported to regulators (Ref), creating external accountability for failures. Together, these measures start to form an accountability web around high-impact AI: one that documents what the AI does, informs stakeholders, keeps humans in the loop, and flags problems for public authorities.

Transparency is equally vital in the use of AI for climate data and analytics. Consider AI-driven climate models or emission measurement tools that inform policy decisions. If a government bases its climate strategy on AI projections, it must trust the system’s integrity. This has led to calls (in academia and civil society) for “algorithmic transparency” in climate AI – meaning the assumptions, data sources, and uncertainties of such models should be openly communicated. The ethos of the scientific community, where climate models are extensively peer-reviewed and documented, provides a template. AI-enhanced models should be held to similar standards of scrutiny. Governance efforts are starting to reflect this: for instance, the UNESCO Recommendation on the Ethics of AI (a global framework adopted by UN member states) highlights transparency as a key principle, urging that AI systems be explainable and their datasets traceable. Although not legally binding, this ethical guideline reinforces the norm that climate-related AI algorithms should not operate as inscrutable black boxes when public safety and well-being are at stake.

In practice, some climate AI initiatives are already embracing radical transparency. The nonprofit coalition Climate TRACE is a notable example. It employs AI and satellite imagery to track greenhouse gas emissions worldwide, and pointedly makes its data open and accessible to everyone. Climate TRACE is described as an “independent, transparent resource” providing governments, companies, and citizens free, open data on exactly where emissions are coming from (Ref). The methodologies underlying its AI models are published and peer-reviewed, enabling experts to audit and validate the results (Ref). By democratizing emissions data, this approach not only holds polluters accountable but also builds public confidence in the AI’s findings. It shows how transparency in AI design and output can bolster climate accountability: leaders can pinpoint sources of emissions and track progress on reductions in near-real-time (Ref). As co-founder Al Gore put it, harnessing AI in this transparent way gives us “a picture of the world like we’ve never seen before” and enables climate action “in a way some never believed possible” (Ref). Going forward, governance frameworks can draw lessons from such projects. Requiring open data practices or independent audits for AI models used in emissions accounting, for instance, could become a norm under international climate agreements or national laws.

However, transparency alone is not a panacea; it must tie into accountability mechanisms. Even with better documentation, AI errors will occur. A climate-focused AI might wrongly predict a mild storm that turns out severe, or a company might misuse an AI analysis to under-report its emissions. In these cases, accountability means there are ways to challenge, appeal, or rectify the AI-driven decisions. One emerging concept is the idea of an “AI accountability charter” for climate tools, where organizations using AI in critical climate applications commit to independent oversight boards or community monitoring. Another is integrating AI governance into existing accountability structures: for example, incorporating AI audits into environmental regulations and corporate climate disclosures. The EU AI Act’s approach again provides a template at the regulatory level: by forcing human oversight and risk management, it implicitly ensures a human (or company) is accountable if things go awry, not an “autonomous” AI. The Act also enables public authorities to step in: the creation of an EU AI Office means there will be a regulator empowered to obtain information and enforce rules (Ref). On a global scale, the UN is discussing something analogous – proposals for an international AI observatory or panel that could, among other tasks, evaluate major AI incidents and advise on responses (Ref) (Ref). Over time, we may see climate-oriented AI tools being certified or overseen by such bodies to ensure they meet transparency and safety benchmarks.

In summary, 2025 marks a turning point where transparency and accountability are being woven into the governance of AI, much as monitoring and verification have long been pillars of climate agreements. AI systems that guide climate action are increasingly expected to show their work and justify their results. The EU’s rules for high-risk AI, combined with global ethical guidelines and pioneering open-data projects, are carving a path toward AI that is visible, understandable, and answerable. For sustainability professionals, this trend is encouraging: it means AI can be integrated into climate strategies with greater confidence and oversight. Yet implementation will be key. The coming years will test whether these governance measures are robust enough to prevent AI-related mishaps – from trivializing climate risks to exacerbating them – and to ensure that the deployment of AI in climate action remains worthy of public trust.

Global Equity: Ensuring AI Benefits All in Climate Adaptation

A critical question remains: will the advances in AI for climate resilience be shared equitably across the globe, or will they deepen the divide between AI “haves” and “have-nots”? Without intentional governance, there is a real risk of a global AI divide mirroring the existing climate divide. Wealthy nations and corporations are investing heavily in AI capabilities – from cutting-edge climate prediction tools to sophisticated carbon market algorithms – while many developing countries struggle with limited data, lack of technical expertise, and insufficient infrastructure to leverage these technologies. Global governance efforts in 2025 are increasingly grappling with this issue of inclusive access, recognizing that climate action is a common challenge that demands broad participation in AI solutions.

At the 2024 UN General Assembly, leaders from the Global South voiced concerns that the current AI landscape is reinforcing inequality. Brazilian President Luiz Inácio Lula da Silva warned of a “true oligopoly of knowledge” in which a handful of companies in the Global North concentrate AI power, creating “unprecedented concentration” of technology that excludes developing nations (Ref). He called for an “emancipatory AI” – AI development that actively includes and addresses the needs of poorer countries (Ref). Similar sentiments were echoed by others, noting that AI’s benefits are not reaching many parts of the world, which could leave vulnerable communities further behind in adapting to climate change (Ref). In climate terms, this inequity could mean, for example, that only affluent regions have AI-enhanced early warning systems for extreme weather, or only big agritech firms have AI to optimize crop yields, while smallholder farmers and disaster-prone small island states miss out. Such an outcome would undermine the global solidarity needed to combat climate change.

Fortunately, the importance of AI access and capacity-building is being recognized in emerging governance frameworks. The UN Secretary-General’s High-Level Advisory Body on AI released a landmark report in late 2024, which puts equitable distribution of AI’s benefits at the forefront (Ref) (Ref). Among its key recommendations are the creation of an international AI capacity development network and a global fund for AI (Ref). The capacity development network would link centers of expertise around the world and provide resources – from technical training to compute power and datasets – specifically to support developing countries in harnessing AI (Ref). The proposed global fund aims to bridge the “AI divide” by financing access to AI tools and infrastructure where resources are lacking (Ref). In essence, these measures seek to ensure that countries with fewer means are not left behind as AI becomes integral to climate adaptation and mitigation efforts. Importantly, the advisory body also highlighted representation gaps in current AI governance and called for including diverse voices, particularly from the Global South, in shaping the rules of the road (Ref). This is a recognition that global AI policies (whether on data, standards, or ethics) must not be decided solely by tech-rich nations if they are to be fair and effective.

Concrete initiatives are starting to put these principles into action. Under the UN’s climate convention process, a dedicated Initiative on AI for Climate Action was launched to help developing countries leverage AI for their climate needs (Ref). In fact, at COP28 the Parties requested that this initiative give special attention to Least Developed Countries (LDCs) and Small Island Developing States, focusing on their capacity needs (Ref). In 2024, the UNFCCC Technology Executive Committee (which guides climate tech support) began developing a technical paper on AI for climate and even ran a global AI innovation challenge to identify open-source, AI-powered climate solutions from and for developing countries (Ref). Such efforts acknowledge that local context matters: AI solutions need to be tailored to on-the-ground realities and languages, and local innovators should be empowered to develop tools for their own communities. The emphasis on open-source is particularly noteworthy, as it lowers barriers to entry – an open AI tool for, say, drought prediction can be adopted and improved by anyone, rather than being locked behind proprietary systems. We are also seeing collaborations between universities and international organizations to provide open data platforms and tools. For example, climate data portals and AI-ready satellite imagery archives are being made freely available, which researchers in any country can use to build climate risk models. These are early steps toward leveling the playing field.

Yet significant challenges remain to achieve global equity in AI-for-climate. One issue is the digital infrastructure gap: many vulnerable regions lack reliable internet, let alone advanced computing clusters to run AI models. Governance frameworks and funding mechanisms will need to invest in digital infrastructure as a foundation. Another issue is data inequality. AI needs data, but climate-relevant data (like detailed weather records or emissions inventories) may be sparse in some developing nations. International support is needed to improve data collection and sharing. There are also concerns about brain drain – if talented AI experts from developing countries are drawn to tech hubs in Silicon Valley or Europe, their home countries may struggle to build local AI capacity. Policies that encourage knowledge transfer and retain talent (such as through remote collaboration networks or incentives to establish AI research centers in the Global South) are crucial.

Current governance frameworks are only beginning to address these nuances. The EU AI Act, for instance, applies mainly within the EU (though it has extraterritorial effects for companies abroad selling into Europe (Ref)), and it does not directly tackle global equity concerns. However, Europe’s insistence on fundamental rights and ethical AI may indirectly benefit other regions by setting a high standard that global companies follow. The more significant moves on equity are coming from the multilateral arena. The UN’s Global Digital Compact, expected to be discussed in 2024–2025, is anticipated to include commitments on universal and equitable access to digital technologies, potentially including AI for sustainable development (Ref). Meanwhile, UNESCO’s ethical AI framework urges member states to enact policies ensuring inclusive access and to share best practices in AI for the SDGs.

In evaluating whether current governance ensures global equity, one must conclude that we’re not there yet, but awareness is rising. The frameworks in play – EU, UN, etc. – have started to articulate the problem and propose remedies. Implementation is lagging: the UN advisory recommendations, for example, are not yet operational but they provide a roadmap that could be taken up by the international community. What’s crucial is that as AI governance regimes solidify (be it through treaties, standards, or national laws), equity is treated as a core design principle, not an afterthought. Otherwise, AI could become another facet of climate injustice, where those who contributed least to global emissions also benefit least from advanced technologies to cope with them. The encouraging sign is that both in climate circles and AI policy discussions, the lexicon now includes “AI divide,” “capacity building,” and “inclusivity” as prominent themes. Sustaining this focus will require continued advocacy. As Guterres aptly noted, “a world of AI haves and have-nots would be a world of perpetual instability”, and we must never allow AI to stand for “advancing inequality” (Ref). Ensuring equitable access to AI for climate action is not just a moral imperative but pragmatic: climate change respects no borders, and solutions anywhere can strengthen resilience everywhere.

Conclusion: Toward Synergistic Governance of AI and Climate

In 2025, the worlds of AI governance and climate action are converging as never before. The EU’s AI Act represents a bold first attempt to regulate artificial intelligence with public interest guardrails, including provisions aligned with sustainability and safety. Simultaneously, the UN and international community are actively debating how to oversee AI’s rapid evolution on a global scale, so that it serves humanity’s collective goals – paramount among them, combating climate change. This article’s exploration of environmental costs, transparency measures, and equity considerations reveals both promising advances and critical gaps in the current governance landscape.

On the one hand, there is clear progress. Policymakers are no longer ignoring AI’s climate impact: energy efficiency and resource use have entered the regulatory vocabulary, and nascent steps (like energy transparency requirements (Ref) and planned standards for green AI (Ref)) lay the groundwork for more sustainable AI development. Likewise, transparency and accountability are being taken seriously, with legal mandates ensuring high-risk AI systems can be audited and controlled by humans (Ref). This directly benefits climate-related AI applications, which will be safer and more reliable under such oversight. And critically, the conversation around global equity in AI has begun to yield concrete proposals—from UN-backed capacity networks (Ref) to competitions uncovering homegrown AI solutions in climate-vulnerable nations (Ref)—signaling a recognition that inclusivity must be a pillar of AI governance.

On the other hand, much work remains to align AI governance fully with climate action imperatives. Current proposals do not yet adequately rein in AI’s carbon footprint: they rely on voluntary action and future reviews, while the growth of energy-intensive AI continues unchecked in the short term (Ref). There is a risk of policy lag—that regulations will always be a step behind AI’s explosive expansion, much as climate policy has often trailed the escalating climate crisis. To avoid this, governments might need to consider more assertive measures (for example, incentives or requirements for using renewable energy in AI development, or integrating AI emissions into national climate targets). Similarly, the push for transparency and accountability must keep pace with AI’s complexity. As AI systems (like advanced climate-economic simulators or geoengineering decision aids) become more sophisticated, oversight frameworks will need continuous updating and expert involvement to ensure humans remain in control and moral responsibility isn’t blurred.

Perhaps the most delicate balance to strike is between regulating risks and enabling innovation. Sustainability experts know that AI, if properly guided, can be a powerful ally in achieving climate goals—whether by optimizing energy systems, revealing emission hotspots, or aiding climate research. Heavy-handed or poorly tailored regulations could inadvertently stifle these positive uses. For instance, overly onerous compliance costs might deter small startups working on climate AI for developing regions. The key is a risk-based approach (embodied in the EU Act) that places the strongest safeguards on the most consequential uses, while still encouraging experimentation in low-risk, socially beneficial AI. Regulatory sandboxes – such as those the EU Act allows for climate-related AI in the public interest (Ref) – are a smart way to provide flexibility for innovation while maintaining oversight. Going forward, iterative governance (where policies are continuously reviewed and adjusted) will be essential, given both AI and climate science are evolving fields. We are likely to see deeper collaboration between technologists and policymakers, perhaps via the proposed international scientific panel on AI (Ref) akin to the IPCC for climate, to inform evidence-based regulation that advances in step with AI capabilities.

In conclusion, aligning AI governance with climate action is an ongoing journey, but one that is gaining momentum and urgency. The year 2025 finds us at a crossroads: decisions made now about how we govern AI will profoundly influence our ability to meet climate targets in the years ahead. The intersection of these domains is fertile ground for innovation in policy – from crafting incentives for “carbon-neutral AI” to forging international agreements that ensure AI’s benefits reach everyone fighting on the frontlines of climate change. For sustainability professionals and policymakers alike, the task is to maintain a critical lens: celebrate the tools and solutions AI offers, yet rigorously question their impacts and distribution. As we refine AI governance, we should measure success not just by averting AI’s risks, but by the extent to which we harness AI in service of a just and livable planet. That means an AI governance regime where efficiency, transparency, and equity are not just buzzwords, but enforceable realities that guide AI development in harmony with our climate goals. Achieving this synergy will require continued global cooperation and adaptive governance – truly a collective intelligence applied to managing artificial intelligence. The foundation is being laid in 2025; what happens next will determine whether AI becomes a boon for climate action or another obstacle. The imperative for all stakeholders is to steer the outcome toward the former, ensuring that our intelligent machines become trusted partners in humanity’s most urgent mission.

References:

1. Guterres, A. (2023). Opening remarks at UN General Debate – Two existential threats: climate and AI. United Nations (Ref)

2. White & Case LLP. (2024). Energy efficiency requirements under the EU AI Act. – Insight (Ref) (Ref)

3. Warso, Z., & Shrishak, K. (2024). Hope: The AI Act’s Approach to Address the Environmental Impact of AI. TechPolicy Press (Ref) (Ref)

4. Wong, C. (2024). How AI is improving climate forecasts. Nature News Feature (26 March 2024) (Ref)

5. Nicholas Institute, Duke Univ. (2023). Climate TRACE – Open emissions tracking for transparency. (Ref)

6. Just Security. (2023). AI at UNGA79: Recapping Key Themes. (Analysis of UN General Assembly debates) (Ref) (Ref)

7. techUK. (2024). Governing AI for Humanity: UN Report Proposes Global Framework for AI Oversight. (Summary of UN Advisory Body recommendations) (Ref) (Ref)

8. United Nations University – EHS. (2024). Bonn AI & Climate 2024 – Expert Meeting Report. (UNFCCC Technology Mechanism initiative) (Ref) (Ref)

9. Baker Donelson. (2024). EU AI Act Tightens Grip on High-Risk AI Systems. (Legal briefing) (Ref) (Ref)

10. Climate TRACE Coalition. (2022). Climate TRACE Unveils Open Emissions Database of 352 Million Sources. (Press release) (Ref)