Since 2024, we have been partnering with UN & APEC affiliates to conduct joint research on climate technology under the series of ClimateTech In Focus as our annual flagship report. The 2025 edition was released at Abu Dhabi Sustainability Week 2026.
We are carrying out a vast, frightening experiment of changing every ecological condition, all at once, at a pace that far outstrips Nature’s ability to cope. As we work towards a zero-carbon future, we must work equally towards being Nature-positive.
This report analyzes how AI is transforming climate action in practice, and what is required to translate technical capability into sustainability.
With contributions from more than 120 experts across 28 countries and regions, including 26 heads of state and ministerial-level officials, this report examines how artificial intelligence (AI) is increasingly embedded across energy systems, infrastructure, manufacturing, logistics, finance, certification, education and public governance. The report analyzes how AI is transforming climate mitigation and adaptation in practice, and what is required to translate technical capability into scalable sustainability outcomes. Rather than treating AI as a standalone technological breakthrough, the report emphasizes its role in reducing uncertainty, improving decision quality, and aligning complex systems under real-world constraints.
The report highlights that while AI capabilities are advancing rapidly, their deployment in sustainability contexts often encounters institutional, operational and governance bottlenecks. Drawing on interdisciplinary perspectives, the report explores the conditions necessary to move from pilots and demonstrations to large-scale impact, including data governance, regulatory clarity, operational capacity, talent development and trust between innovators, regulators and adopters. Particular attention is given to cross-border cooperation, public-sector use cases and inclusive innovation models that enable AI to support sustainable development in emerging and resource-constrained contexts.
ClimateTech In Focus 2025: Artificial Intelligence for Sustainability is the second edition of the ClimateTech In Focus series. It is published by United Nations University (UNU), the Pacific Economic Cooperation Council (PECC) and Shanghai Climate Week, presented by the Sino-International Entrepreneurs Federation (SIEF), with WeCarbon as knowledge partner, and draws on contributions from more than 120 experts across 28 countries and regions, including 26 heads of state and ministerial-level officials. The AI for Sustainability report was launched at Abu Dhabi Sustainability Week (ADSW) 2026 on 15 January 2026. This summary follows the report's own executive summary.
Artificial intelligence is no longer a peripheral tool in sustainability efforts. Across energy systems, manufacturing, logistics, finance, certification, education and public governance, AI is increasingly embedded as operational infrastructure, shaping how societies anticipate risk, allocate resources, enforce rules and coordinate action at scale. The report examines how AI is already transforming climate mitigation and adaptation in practice, why progress remains uneven, and what institutional conditions are needed to translate technical capability into durable public value.
In the report's assessment, AI's main contribution to sustainability lies not in breakthrough algorithms but in its ability to reduce uncertainty, compress decision cycles and align complex systems under real-world constraints.
The report finds that technical readiness consistently outpaces institutional readiness. Many AI systems reach the pilot or MVP stage quickly but struggle to scale to large deployments. The binding constraints are rarely funding or model performance; they come from fragmented data ownership, legacy infrastructure, unclear regulatory pathways, limited operational capacity and weak trust between innovators, regulators and adopters. As a result, AI innovation in sustainability often stalls precisely at the point where real impact should begin.
To close this gap, the report argues for a shift in how AI innovation is incubated and governed. Effective incubation goes beyond providing capital and emphasizes deployment readiness: shared operational services, access to secure compute and data environments, domain-specific mentorship, predefined use cases with real buyers, and regulatory sandboxes that allow supervised learning before full-scale approval. Cross-border and networked incubation models are emerging as particularly effective, letting talent, technology and market feedback circulate across regions instead of remaining siloed within national ecosystems.
Education and talent development are equally decisive. With AI already embedded in everyday learning, the question is no longer whether students will use AI but how education systems guide its use. The report highlights a shift from rote knowledge reproduction toward critical thinking, interdisciplinary problem-solving and project-based learning grounded in real sustainability challenges. Successful systems treat AI as a learning assistant rather than an answer machine, and build institutional pathways that let young people move from education into public service, entrepreneurship and policy influence. Countries that compete effectively for AI talent combine flexible visas and incentives with meaningful roles, practical testbeds and long-term integration into national innovation systems.
Equity and openness emerge as defining challenges of the AI era. Open data and shared models can accelerate innovation, but poorly governed openness risks deepening data inequality, particularly for the Global South. The report argues that openness must be conditional and governed, with clear usage rights, traceable provenance and mechanisms that ensure local institutions retain control over locally generated data. Inclusive AI deployment is already visible in resource-constrained settings, where lightweight models, community data systems and public-sector use cases deliver tangible benefits in mobility, disaster preparedness, public health and agriculture. These experiences show that AI can support leapfrog development when paired with appropriate governance and infrastructure.
AI governance is not a brake on climate innovation but its enabling condition. Effective governance requires risk-proportionate oversight that builds trust, transparency and accountability: distinguishing decision-support tools from systems exercising authority, and ensuring explainability, human oversight and public legitimacy for high-impact uses. Treated as a learning system and supported by capacity building and cross-border cooperation, governance enables AI to scale responsibly for climate action rather than constraining it.
The report runs in five parts. AI Evolution at Crossroads looks at the path toward artificial general intelligence, the AI and sustainability paradox, hyperscale sustainable AI data centers, and AI in climate mitigation and adaptation. The AI for Sustainability Landscape maps roles and use cases by automation level. The Focus Sector Deep-dive covers energy and manufacturing, shipping and logistics, finance and investment, and certification and global trade. Powering the Progress turns to incubation, education, open and equitable AI, and the governance of AI risks. The conclusion, What's Next?, sets out recommendations for five groups: governments, regulators and international organizations; financial institutions, investors and trade and compliance ecosystems; enterprises, platforms and infrastructure operators; education institutions, NPOs and talent ecosystems; and SMEs, startups and individuals.
How can hyperscale AI data centres become strategic national assets, with siting, power supply, decarbonization and financing planned together with the grid?
How can AI optimize variable renewable energy and reduce curtailment, and what role will electricity markets play?
As AI moves from information technology (IT) to operational technology (OT) and starts to control equipment directly, how can it be used safely and reliably?
Beyond visas and financing, what institutional and ecosystem conditions does a country need to retain AI talent over the long term?
How can AI development accelerate while responding to people's concerns, and how can global AI governance favour cooperation over fragmentation?
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