Artificial intelligence is increasingly being adopted across the Asia-Pacific oil and gas industry to improve exploration, drilling, production and operational efficiency. But new research suggests that these productivity gains could have an unintended consequence: instead of reducing emissions, AI could make fossil-fuel production cheaper and more efficient, encouraging companies to extract and sell more oil and gas. The concern is part of a broader debate over AI's indirect environmental footprint, which goes beyond the electricity consumed by data centers.
The underlying concept is sometimes called “enabled emissions.” If AI helps an oil producer identify new reserves, optimize drilling or increase the amount recovered from an existing field, the resulting emissions are not produced by the AI system itself—but AI has helped make the additional fossil-fuel production possible. A recent study estimated that AI-driven productivity improvements in fossil fuels could increase global emissions by roughly 0.47 to 1.8 billion tonnes of CO₂ per year, potentially outweighing emissions reductions achieved through AI applications in renewable energy.
This creates a particularly important dilemma for APAC economies, where oil and gas remain significant parts of the energy system and where companies are actively deploying AI to improve industrial productivity. AI can simultaneously help optimize renewable generation, improve energy forecasting and reduce operational waste while making conventional fossil-fuel operations more competitive. The research suggests that simply making an energy technology more efficient does not guarantee lower overall emissions: if efficiency lowers costs and increases production, total consumption can rise instead.
The broader lesson is that evaluating AI's environmental impact requires looking at what the technology enables, not just how much electricity its computers consume. Policymakers and companies may need to account for AI's downstream effects across energy production, including the possibility that AI accelerates fossil-fuel extraction faster than it accelerates the transition to clean energy. The central question is therefore not whether AI is inherently “green” or “dirty,” but where AI's productivity gains are being directed—and whether those gains ultimately accelerate decarbonization or prolong dependence on fossil fuels.
