AI Could Unlock US$600 Billion in Annual Climate and Sustainability Value, Says Temasek-BCG Report

SINGAPORE, May 19, 2026 – Artificial intelligence is rapidly emerging as one of the most significant accelerators of climate and sustainability outcomes globally, with a new report by Temasek and Boston Consulting Group estimating that current AI capabilities could unlock approximately US$600 billion in annual value across climate and sustainability sectors by 2028.

Released during Ecosperity Week 2026 in Singapore, the report, titled The Private Capital Opportunity in AI-Enabled Climate and Sustainability Sectors, argues that AI is increasingly becoming a structural enabler for decarbonisation, resource optimisation, climate resilience and inclusive development.

Rather than positioning AI solely as a productivity tool, the report frames it as a mechanism capable of simultaneously generating financial returns and measurable environmental or social benefits.

“Sustainability is, at its core, about resource efficiency,” the report states.

“AI’s fundamental capability is optimizing how resources are used. Where these two domains meet, financial returns and sustainability outcomes are positively correlated. Not by coincidence. By design.”

The report identifies more than 40 investable subsectors where AI applications are already beginning to reshape climate, energy and sustainability outcomes across industrial systems, grids, carbon management, water infrastructure, agriculture, insurance and education.

Industrial Systems and Climate Risk Modelling Lead AI Opportunity

According to the report, industrial equipment and systems efficiency represents the single largest AI-enabled climate opportunity, accounting for an estimated US$300 billion in annual value potential by 2028.

The report argues that industrial sectors are particularly suited to AI-driven optimisation because every reduction in energy waste simultaneously lowers operating costs and carbon emissions.

Deploying current AI capabilities across industrial systems could potentially reduce Scope 1 and Scope 2 emissions by around 0.6 gigatons annually, equivalent to the emissions of Germany, while also reducing material waste and workplace injuries.

Applications highlighted include predictive maintenance, adaptive process optimisation, energy management systems and AI-enabled safety systems.

One example cited is Japan’s Tokuyama Cement, which deployed ABB’s AI-driven optimisation platform to continuously adjust temperature, airflow and fuel rates in real time, resulting in a 3 per cent reduction in thermal energy consumption and a 70 per cent decline in manual operator interventions.

The report also identifies climate risk modelling and AI-enabled insurance analytics as another major growth area, with an estimated US$75 billion annual value opportunity.

AI-based climate hazard intelligence systems are increasingly helping utilities, insurers, logistics operators and governments better predict and respond to climate-related disruptions.

The report notes that insured losses from natural hazards exceeded US$100 billion in both 2023 and 2024, while climate-related disruptions continue affecting aviation, utilities and logistics networks globally.

AI platforms are now combining satellite imagery, atmospheric models, sensor networks and computer vision to create asset-level climate risk scores and predictive systems.

US insurer deployments using AI-driven underwriting platforms reportedly achieved combined ratio improvements of 4.4 per cent within the first year through improved climate risk pricing accuracy.

AI-Driven Grids, Storage and Energy Flexibility Gain Momentum

The report also highlights the growing role of AI in modernising electricity systems as grids face increasing pressure from electrification, renewable energy integration and AI-driven data centre expansion.

AI-enabled grid, storage and flexibility management applications are projected to generate approximately US$32 billion in annual value by 2028.

The report argues that the global energy transition is shifting from a purely infrastructure expansion challenge toward an orchestration challenge, where AI increasingly determines how efficiently existing grid assets are coordinated, monitored and dispatched.

Applications include predictive maintenance for transmission systems, AI-enabled virtual power plants, grid congestion management and real-time battery dispatch optimisation.

The report highlights deployments where AI-driven battery storage optimisation increased revenues from installed assets by 25 to 30 per cent without requiring additional hardware investment.

Meanwhile, AI-enabled virtual power plant systems are increasingly aggregating distributed energy resources such as rooftop solar, residential batteries and electric vehicles into coordinated grid assets.

The report also points to emerging collaborations between data centre operators and energy companies to create “flexible AI factories” capable of shifting computational workloads in response to grid conditions, effectively transforming data centres into dispatchable energy assets.

Sustainability Investing Expands Beyond Traditional Climate Tech

One of the report’s broader conclusions is that AI is fundamentally expanding what qualifies as climate and sustainability investing.

Sectors not traditionally categorised as climate-focused, including industrial process control, catastrophe risk analytics, grid flexibility and adaptive learning systems, are increasingly becoming central to sustainability investment strategies because AI makes their environmental and social performance measurable and improvable.

The report categorises opportunities across three broad domains:
• climate and energy transition
• natural capital and resource management
• social systems and livelihoods

It also distinguishes between “priority”, “attractive” and “opportunistic” investment sectors based on market scale, AI readiness and sustainability impact.

Among the priority sectors identified are:
• industrial equipment and systems efficiency
• climate risk modelling
• grid and flexibility management
• inclusive education
• materials discovery

The report notes that AI applications in inclusive education alone could unlock around US$13 billion annually while helping underserved populations gain improved access to learning systems.

AI Infrastructure Demands Also Raise Sustainability Concerns

Despite the optimistic outlook, the report also acknowledges concerns surrounding AI’s own environmental footprint.

It notes that compute-intensive AI models and hyperscale data centres require substantial electricity, water and land resources, raising concerns over emissions, grid pressure and community impacts.

However, the report argues that across many sectors, AI-enabled optimisation is still likely to reduce aggregate resource consumption overall.

“The relevant question is whether the systems that could be optimized by AI would consume fewer resources in aggregate than they currently do,” the report states.

The study ultimately argues that the climate and sustainability impact of AI should be evaluated sector-by-sector based on measurable outcomes rather than broad assumptions.

As AI infrastructure investment accelerates globally, the report suggests that future competitive advantage may increasingly depend on companies capable of combining proprietary datasets, operational integration and sustainability performance rather than AI capability alone.