Could AI Increase Fossil Fuel Emissions in APAC Oil and Gas?

AI could add to fossil fuel emissions in a less obvious way than power-hungry data centers: by making oil and gas production cheaper and more productive. New research suggests those gains could outweigh some of AI’s benefits for cleaner energy.

The peer-reviewed research in npj Climate Action modeled AI-driven productivity gains across fossil fuels and renewable energy. Across 64 scenarios, researchers estimated a net annual increase of 0.47 billion to 1.8 billion metric tons of carbon dioxide when AI improved both sectors. The results represent modeled economic effects, not measured emissions or forecasts for individual companies or countries.

The study examines what its authors call “enabled emissions”: additional emissions that can result when AI lowers costs or raises productivity in fossil fuel extraction, processing, and energy production. APAC energy companies are already expanding AI across upstream operations.

Malaysia’s PETRONAS Carigali said July 8 that it was expanding its TriCipta AI initiative with IBM and Tridiagonal.AI. The work targets surface-equipment optimization and production and maintenance decisions, while earlier tools have supported geoscience and exploration analysis.

AI efficiency could drive more fossil fuel production

Lower operating costs do not automatically mean lower emissions. AI that reduces exploration costs, improves recovery rates, or makes existing assets cheaper to operate could make additional fossil fuel production economically viable.

Australia’s Woodside provides another example of upstream AI adoption. Its Maint Intel system analyzes maintenance records and equipment performance to recommend maintenance intervals at the North West Shelf project. Woodside said testing on the offshore Angel platform cut model-processing time from five days to under two hours.

Neither deployment shows that AI has increased emissions at PETRONAS or Woodside. Both demonstrate the kinds of operational productivity gains examined by the global research. AI can also support methane detection, equipment reliability, and other emissions-reduction efforts.

AI’s electricity use creates a separate emissions footprint. The International Energy Agency expects global data-center electricity consumption to roughly double from 485 TWh in 2025 to 950 TWh in 2030. TechRepublic has separately covered grid pressure in Australia and changing power and cooling requirements for AI infrastructure. A planned 360 MW Nvidia-powered AI data center in Indonesia shows how quickly regional capacity is growing.

APAC methane cuts lag technical potential

The IEA estimates fossil fuel operations in South and Southeast Asia emitted about 13 million metric tons of methane in 2025. More than 60% came from coal, with the remainder from oil and gas. India and Indonesia were the region’s largest fossil fuel methane emitters.

Existing technology could cut methane emissions across South and Southeast Asia by more than 50%, with 60% of those reductions achievable at no net cost to producers. Under stated policies, emissions are projected to fall only 10% by 2030 and almost 20% by 2035.

China faces a similar gap. More than 90% of available methane reductions in its oil and gas sector could be achieved at no net cost, according to the IEA.

Greater operating efficiency does not necessarily reduce absolute emissions. Operators procuring AI tools should track production KPIs and emissions KPIs separately so efficiency gains are not treated as evidence of a climate benefit without a measured reduction in emissions.

Read more: Australia’s AI boom is also reshaping capital spending, with data centers accounting for a growing share of private investment as demand for compute infrastructure accelerates.