1The confluence of AI and climate technology represents a pivotal moment in the global effort to combat environmental degradation. From advanced climate modeling to precision agriculture, AI’s analytical capabilities are reshaping our approach to climate adaptation and mitigation, offering a powerful suite of tools to address what is arguably the most complex challenge of the 21st century. The transformative potential of AI is being demonstrated across a wide range of sectors, providing a compelling case for its central role in future climate strategies.1 AI has become a practical and powerful force, delivering tangible results in the fight against climate change. The technology’s ability to process vast amounts of data and derive actionable insights can improve the efficiency of energy production and consumption, facilitate the introduction of renewable energy sources, and strengthen resilience to climate change.1
2AI has significant potential in addressing climate change mitigation and adaptation in a wide range of domains.2
3The domains where AI can contribute to mitigating climate change include electricity systems, transportation, buildings and cities, industry, agriculture and forestry, and carbon dioxide removal. AI can improve the deployment of clean energy by informing optimal locations for new solar and wind farms and maximizing electricity output from existing plants.3 The technology also enhances grid operations through predictive vegetation management and dynamic line ratings, which help maintain reliability and increase transmission capacity. In manufacturing, AI improves energy efficiency and reduces waste by optimizing production schedules. Beyond these applications, AI is also dramatically accelerating the search for novel, low-cost battery materials, which are crucial for grid-scale energy storage. Fuel-efficient routing, which has enabled emissions reductions equivalent to taking over 630,000 gas-powered cars off the road for a year, and the Solar API, which uses AI and satellite imagery to estimate solar savings for millions of buildings, are prime examples of scaled AI solutions with a direct climate impact.4 Accurate and precise emissions monitoring is fundamental to achieving climate change mitigation. AI provides cross-cutting capabilities for monitoring carbon dioxide and methane emissions globally, enabling the attribution of emissions to specific facilities and locations, and facilitating the assessment of the overall impact of climate policies.3 Beyond monitoring, AI improves the cost and performance of carbon capture technology by enabling the design and selection of materials.
4AI can also play a crucial role in climate change adaptation in various domains, including climate prediction, societal impacts, and solar engineering. Climate change is amplifying the frequency and severity of extreme weather events, making adaptation strategies a critical necessity. AI is enhancing the accuracy and lead time of weather forecasts, enabling countries with limited resources to prepare for extreme events.3 AI provides critical flood forecasts several days in advance to millions of people, optimizes fire management by predicting where wildfires are likely to occur, and facilitates rapid detection of new ignitions. In the marine environment, AI systems can map large Antarctic icebergs in a fraction of a second, providing crucial data on meltwater release rates.5 This predictive capacity enables businesses and governments to better prepare for climate-related disasters. The technology is also used to restore natural ecosystems, with AI-powered computers paired with drones in Brazil to define targets and drop seeds for reforestation efforts.6
5The applications of AI are broad and expanding, with each new tool offering a piece of the solution to the complex climate puzzle. The ability of AI to process vast, disparate datasets and deliver timely, actionable insights makes it a potent tool for addressing climate change. The effectiveness of every AI initiative, ranging from optimizing a wind farm to predicting a flood, is directly tied to the quality of the data that fuels it. The successful and scalable deployment of these applications, therefore, hinges on a robust and well-governed data ecosystem. Responsible AI begins with a foundation of trustworthy data, and a robust data governance framework is the mechanism by which this is achieved.7
6Robust data governance is a powerful enabler for AI, serving not as a regulatory burden but as a strategic asset that unlocks its full potential for climate action. That is achieved by building trust, operationalizing responsible innovation, and fostering a culture of strategic investment.
7A primary benefit of robust data governance is the cultivation of trust among stakeholders, which is a prerequisite for effective climate collaboration. By enforcing clear policies and providing transparent oversight, data governance confirms that AI systems are reliable, transparent, and fair. On a technical level, this translates into tangible benefits, such as improved output precision and reduced hallucinations.
8The creation of trustworthy AI begins with trustworthy data. Proactive data governance builds this trust by confirming that AI models use reliable, properly consented data with appropriate data lineage. Data lineage, the ability to trace data from its source through its transformations to its final use, is a crucial component of this trust. It provides the audit trail that stakeholders need to have confidence in the integrity of the data and, by extension, the reliability of the AI outputs. This confidence, in turn, facilitates a broader consensus among diverse stakeholders. The causal link between data integrity and institutional collaboration is profound; without a shared, trusted foundation of data, coordinated action is nearly impossible. The lack of a unified data system and reliance on inconsistent data sets are recognized barriers that make coordinated climate action difficult.8 Therefore, a well-defined governance framework is not only a technical requirement but also a social and institutional necessity for scaling effective climate solutions.
9Effective data governance provides a structured approach to managing the entire AI lifecycle, which is essential for both responsibility and scalability. By enforcing version control, audit trails, and continuous monitoring, governance helps organizations maintain transparency and accountability for their AI initiatives.9 That is particularly critical for high-risk use cases, where the potential for negative impact is significant. For example, in assessing climate-related financial risks, data governance ensures that the data and models used for decision-making are transparent and auditable.10
10Data governance is a robust framework for streamlining collaboration and improving operational efficiency in managing data. It provides a structured approach that helps eliminate data silos, which are a major impediment to progress and create significant misalignment when multiple teams are involved in AI initiatives.9 By establishing a single source of truth for high-value data assets, data governance allows diverse teams to collaborate more efficiently while maintaining accountability and visibility.7 The ability to provide an organization with secure access to high-quality, relevant data leads to improved operational efficiency and, ultimately, more effective climate action.
11The benefits of data governance extend beyond operational efficiency; they are a direct catalyst for innovation. Organizations that proactively invest in data governance position themselves to lead in responsible innovation, unlocking the full potential of AI at scale. This requires elevating data governance to a board-level priority and embedding its metrics into enterprise key performance indicators (KPIs) and board reporting.7 This strategic focus sends a clear signal that data is a central pillar of the organization’s AI strategy.
12Strategic investment also entails modernizing architecture and tooling. This includes investing in platforms that centralize, cleanse, and govern data for AI readiness, with a focus on interoperability and lineage tracking. AI itself can be a powerful tool for automating many aspects of governance, such as anomaly detection and data quality validation.7 AI-powered data governance tools can learn from data patterns and user interactions, seamlessly adapting to evolving business needs and regulatory requirements.11 This automation is crucial for handling the massive volume and velocity of data in a complex data landscape, which would be infeasible to manage solely through manual processes.
13While the opportunities are vast, the path to a data-driven, AI-enabled, climate-resilient future is fraught with significant challenges. These barriers are not only technical but also extend to ethical, societal, and institutional issues that impede the effective flow and use of data for AI.
14The most immediate challenges are technical and operational. Despite the sheer volume of data being generated through various devices and instruments, the lack of high-quality, easily accessible, and standardized data often hinders the impactful use of AI for climate change applications.5 Critical information is scattered across disconnected systems, institutions, and organizations, leading to a fragmented data landscape.8
15A significant problem is the limited geographic coverage of existing datasets. Much of the available data focuses on the northern hemisphere, covering a limited range of geographies and building types mainly found in industrialized countries.5 This bias is particularly pronounced in datasets for building energy modeling, where benchmark data is scarce and may not be precise. The consequence is that models trained on this limited data may inherit existing biases and uncertainties, making them less applicable to other regions, particularly the Global South, where cultural and behavioral factors differ significantly. The challenge is compounded by technical disconnects, such as the use of proprietary or legacy data formats that pose conversion challenges, and inconsistent data formats across sources that complicate multi-source data integration. These issues are exacerbated by the immense data volumes, which are challenging to transfer and process, as well as a lack of adequate computing power and network capacity, particularly in regions with limited infrastructure.
16The challenges facing data governance for climate AI extend beyond the technical realm to encompass profound ethical and societal issues. A central concern is the potential for AI systems to perpetuate bias, leading to inaccurate or unfair outcomes. Algorithmic bias can be introduced if the data used to train AI models is not properly governed. This can also arise from structural or parametric biases within the models themselves, leading to systematic errors or discrepancies between the model’s output and real-world climate data.12 For example, a model might consistently underestimate rainfall extremes or produce inaccurate temperature projections, leading to ineffective mitigation or adaptation strategies.
17A more complex problem is the intersection of the digital divide and climate injustice. In early 2025, nearly three billion people remain offline, many of whom reside in low- and middle-income countries that are most vulnerable to the impacts of climate change.13 This lack of digital access cuts off communities from crucial AI-driven early warnings and disaster-related information. The connection between a lack of geographically diverse data and the digital divide is critical; AI models are often trained on data from and for the developed world, making their outputs less accurate and potentially irrelevant to the communities most in need of climate-resilient solutions. That perpetuates a cycle where the communities most affected by climate change are unable to access or benefit from the most advanced solutions.
18These considerations highlight the double-edged nature of technology. While AI offers immense benefits, its own carbon footprint is a significant concern.14 Data centers, which are essential for processing the massive volumes of data required by AI, account for around 1.5% of global electricity demand and contribute approximately 0.5% to global carbon dioxide emissions.15 A fundamental ethical question arises: Do the environmental and social benefits of a given AI application truly outweigh its emissions and resource consumption? This ethical calculus is an essential component of a responsible AI strategy. A recent study shows that AI can make a substantial contribution to reducing greenhouse gas emissions in three key sectors, namely, power, food, and mobility, which collectively contribute nearly half of global emissions. It is reported that by 2035, AI’s potential to reduce emissions from the energy sector could be two and a half times as much as the emissions from data centers.15
19The institutional challenge of fragmented governance is a root cause of many of the technical and operational problems. Climate data is scattered across disconnected systems, and competing regulatory requirements across different jurisdictions complicate efforts to create unified data systems.8 This fragmented landscape hampers cross-border data flows and creates uncertainty for both policymakers and businesses.16
20Effective data sharing is a wicked problem, intertwined with social, economic, and political concerns that no single organization can address alone. It requires diverse coalitions and thoughtful facilitation. The lack of a clear governance structure, where it is unclear who owns specific datasets and who is responsible for data quality, can significantly impact stakeholder decision-making and compromise the organization’s ability to function effectively. A lack of public confidence in how private data is handled can make partnerships difficult, highlighting the critical need for a new model of engagement and trust.
21The issue is not solely one of technology or policy, but also of capacity and institutions. Many countries, particularly in the Global South, lack foundational governance structures and expertise to handle complex issues, creating an uneven capacity gap.16 That leads to a disconnect between data availability and the capacity to translate that data into actionable insights for climate-smart decision-making.17 Without a strategic shift away from fragmented approaches toward a more unified and collaborative framework, these barriers will continue to prevent the full-scale deployment of AI for climate action.
22To address these challenges, a variety of pioneering attempts have been initiated. They serve as powerful prototypes for the future of data governance in climate AI.
23A crucial step in addressing the data fragmentation challenge is the creation of open, accessible data platforms. Many data platforms related to climate change are provided by the U.S. Federal Government.18 The Climate Data Records (CDRs) program at the U.S. National Oceanic and Atmospheric Administration (NOAA) provides a model for this approach. CDRs are robust, scientifically sound climate records that are thoroughly checked using standards established by the National Research Council, providing trustworthy information on climate variability and change.19 By making this data publicly available, NOAA ensures a reliable, consistent foundation for climate research and policy development.
24On a broader scale, the Climate Change Knowledge Portal (CCKP) of the World Bank serves as a one-stop shop for climate-related data, providing access to comprehensive global, regional, and country-specific information.20 By centralizing and curating previously scattered data, the CCKP lowers the barrier to entry for decision-makers and researchers. Similarly, the OS-Climate initiative, an open-source project hosted by the Linux Foundation, is building the data and analytics infrastructure needed to integrate climate risk into financial decision-making at scale.21 Its Data Mesh project is a key innovation, enabling the federation of data from public, proprietary, and commercial sources while addressing data providers’ concerns.
25The challenge of institutional trust in data sharing is addressed by new governance models, most notably data trusts. Data trusts are an approach to looking after and making decisions about data, similar to land trusts that steward land on behalf of local communities.22 A prominent example is the Climate Action Data Trust (CAD Trust), a decentralized metadata platform that uses blockchain technology to link, aggregate, and harmonize major carbon credit registry data.23 The purpose of this trust is to create a transparent and immutable record of carbon market activity, thereby preventing double-counting ‒ a persistent issue that can compromise market integrity and confidence.
26The governance model of the CAD Trust is a prototype for addressing the complex, multi-stakeholder nature of climate collaboration. It is an independent entity founded by the World Bank, the International Emissions Trading Association (IETA), and the Government of Singapore, with its leadership structure comprising the Council, Board, Technical Committee, and the User Forum. This multi-stakeholder approach ensures that the platform is guided by a diverse set of perspectives and that it is technically sound, transparent, and responsive to the needs of the market. The CAD Trust demonstrates how a clear governance model, leveraging modern technology, can build the trust necessary for a functional, global system.
27Private sector companies with deep data and AI expertise are also playing a critical role, often in partnership with public entities. Climate initiatives through public-private partnerships demonstrate how companies’ data assets can be leveraged for large-scale, consumer-facing climate solutions. These applications show the potential to drive significant, aggregate emissions reductions by empowering millions of individual users. Initiatives such as the Climate Data Store, Climate Montreal, and the Pacific Climate Impacts Consortium serve as hubs to bring together stakeholders from various sectors, helping to ensure legitimacy and trust in the climate data ecosystem.24
28On the enterprise side, companies like ClimateAi are providing B2B solutions that highlight the commercial value of AI-driven climate intelligence.25 ClimateAi’s services help businesses and investment managers assess and mitigate climate-related risks in their supply chains and portfolios. These partnerships illustrate that AI-driven climate solutions are not only a public good but also a source of competitive advantage and resilience for the private sector.
29The key to unlocking the full potential of AI for climate action is a strategic, holistic, and proactive approach to data governance. The opportunities presented by AI are immense, from optimizing clean energy grids to enhancing disaster resilience and enabling a more transparent carbon market. These opportunities, however, are currently constrained by a fragmented, biased, and poorly governed data ecosystem. The challenges ‒ technical, ethical, and institutional ‒ are significant and deeply interconnected, requiring a coordinated and comprehensive response.
30The path forward requires a new era of collaboration rooted in shared principles of trust, transparency, and equity. It requires modernizing technical infrastructure to ensure interoperability and close critical data gaps, particularly in underrepresented regions. Most importantly, it calls for the development of new, collaborative governance models, for example, climate data trusts, that can bridge the divide between the public and private sectors, build mutual confidence, and ensure that AI serves the needs of all, especially the communities most vulnerable to the impacts of a changing climate. By investing in robust data governance, from boardrooms to international policy, organizations and governments can ensure that AI is a powerful, reliable, and just tool in building a more resilient and sustainable world. The problem is complex, but the strategic solutions are within reach, provided there is the collective will to govern our data responsibly.
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