12 December 2025

COP30: Technology’s Promise and Paradox - What It Means for Business

Written by:Nichola DonovanAvi Marcus, Venessa Parekh, Matthew Billet

The COP30 summit in Belém, Brazil marked a moment of reckoning for international climate action. Governments arrived under mounting pressure to move from ambition to deliver on ever-increasing commitments. At COP28, countries committed to tripling global renewable energy capacity and doubling energy-efficiency improvements by 2030. Progress since has been uneven, and COP30 largely centred on implementation challenges, with technology and AI highlighted as potential accelerators.

AI is positioned as central to strategies for renewable energy optimisation, emissions tracking and climate finance transparency. Yet, as billions pour into AI-driven sustainability initiatives, the focus is widening beyond technical innovation. Developments at and around COP30 underscored the need for robust governance and legal frameworks to ensure these technologies deliver equitable and sustainable outcomes."

1. Investment and Opportunity

COP30 showcased significant commitments to digital climate infrastructure driven by private actors, financial institutions and civil-society coalitions convened around the summit.

The AI Climate Institute is a global initiative to help developing countries build capacity for AI-driven climate action, offering training for policymakers and technical experts, and an open digital learning repository.
Brazil, the UAE and the Gates Foundation pledged $2.8 billion for the world’s first open-source AI model for agriculture, designed to empower 100 million farmers by 2028 with real-time data and insights on crop health, soil conditions and water use.
A repository of over 20 open-source tools launched in Brazil, supporting disaster response, energy and water management across at least 30 countries.

These initiatives signal growing momentum for public–private collaboration and investment in AI-enabled climate solutions.

2. The Data Centre Paradox

Optimism about technology’s role was tempered by its environmental footprint. Data centres, the backbone of AI infrastructure, consume vast amounts of energy and water and compete for critical minerals such as lithium and cobalt that are also essential for renewable technologies.

Global data-centre spending is projected to reach $1.1 trillion by 2029, and by 2030 these facilities could account for 3% of global electricity demand. In the US alone, data centres may consume 8.6% of total electricity by 2035, more than double today’s share. This surge is forcing technology firms to secure clean energy at scale, with major players signing long-term renewable contracts and investing in wind, solar and even early-stage nuclear projects.

While these investments can accelerate the commercialisation of clean technologies, they also expose structural tensions:

There is a question about self-supply vs system benefit. Much of the new capacity is being built to power data centres, not to strengthen wider grids or support electrification of transport and heavy industry.
Access is not equitable. As large tech companies agree renewable deals, smaller players and communities risk being priced out which would create inequities in energy access.
Utilities in some regions are delaying fossil plant retirements and even adding gas capacity to meet surging demand, which ultimately undermines decarbonisation goals.

Businesses should assess the broader implications. Regulators and investors are likely to demand clear, verifiable reporting on energy sourcing, carbon intensity and other key sustainability metrics. Increasingly, organisations will need to demonstrate that technology-related energy investments strengthen overall grid resilience and support wider decarbonisation goals, not simply secure their own operational reliability.

3. Embedding Technology into National Climate Strategies

Beyond individual projects, COP30 discussions signalled an emerging structural shift. Governments are starting to embed digital technologies into national climate plans. This includes plans to integrate technology-driven forecasting into energy transition roadmaps, expanding digital platforms for climate finance transparency, and adopting early-warning systems for disaster resilience. The challenge for Governments is ensuring these systems deliver benefits broadly across whole economies, rather than creating fragmented solutions or deepening the digital divide.

4. Implications for Business

COP30’s emphasis on technology and AI introduces both opportunities and obligations for businesses. While digital solutions can accelerate decarbonisation, they also create new compliance, governance, and risk considerations.

Regulatory and disclosure requirements: COP30 amplified interest in digital climate infrastructure, including the potential use of national platforms for emissions tracking and disclosure. While regulatory timelines remain uncertain, businesses should prepare for a tightening compliance environment, including:

Movement towards real-time emissions reporting integrated with national systems; and
Emerging scrutiny and new verification standards for AI-driven sustainability claims.

Misrepresentation of AI’s climate benefits could trigger greenwashing litigation or enforcement under consumer protection and securities laws.

Energy Procurement and Competition: Long-term renewable energy purchase agreements (PPAs) are becoming essential for tech-heavy operations. Regulators may scrutinise whether large tech firms’ dominance in renewable procurement creates market distortions or inequitable access.

Data Governance and Cybersecurity: AI-driven climate platforms will involve sensitive operational and environmental data. Data protection compliance and liability for errors in emissions tracking or disaster forecasting will require robust contractual safeguards, clear accountability frameworks, and proactive risk management to avoid regulatory penalties and reputational harm.

AI Ethics and Accountability: As AI becomes embedded in climate strategies, businesses face growing scrutiny over transparency and fairness. Regulators and investors will expect:

Explainability of AI models used for emissions tracking and resource optimisation; and
Bias mitigation to ensure equitable outcomes, particularly in agriculture and energy access.

Failure to address these issues could lead to regulatory intervention and fines, reputational damage, and litigation under emerging AI governance frameworks.

Supply Chain and Critical Minerals: Businesses reliant on AI infrastructure or renewable technologies must anticipate scrutiny over sourcing of lithium, cobalt, and rare earths. Emerging due diligence laws will require transparent reporting and risk mitigation across global supply chains

5. Key Takeaway

Discussions convened at and around COP30 highlighted that while technology and AI are essential for speeding up climate action, it isn't a silver bullet. AI innovations can also increase demands on energy, resources, and fairness. Moving forward, it will take not only greater investment but also strong governance, transparency, and collaboration to make sure digital solutions boost entire energy systems and provide widespread benefits, leading to better outcomes for all stakeholders.

For businesses, this means moving beyond compliance to proactive engagement; embedding digital sustainability into strategy, strengthening governance, and collaborating across sectors to ensure technology accelerates, rather than complicates, the path to net zero.

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