AI for Climate Change: Can Artificial Intelligence Fight the Problem It Also Worsens?

 

         The Pakistan Times  | Islamabad Times 

The AI Climate Challenge

Few questions in technology today are as genuinely two-sided as this one: does AI for climate change represent a powerful solution, or does the enormous energy cost of running AI itself make the problem worse? The Pakistan Times Live examines both sides of this debate  the real climate benefits AI is delivering in 2026, and the mounting environmental cost of the technology powering them.

The Climate Solutions AI Is Already Delivering

AI for climate change has moved well beyond theory into measurable, deployed applications. Weather forecasting that used to take roughly 12 hours to compute can now be completed in about one minute using AI models, giving communities and emergency planners dramatically more lead time ahead of extreme weather events. In materials science, AI has helped identify millions of new candidate battery materials that would have been effectively impossible to discover through traditional laboratory methods alone, accelerating the development of better energy storage for renewable power.

In the energy sector specifically, the International Energy Agency notes that AI applications are being used across a wide range of optimisations that lead to emissions reductions  from cutting methane emissions in oil and gas operations to improving the efficiency of renewable energy systems by optimising grid management. According to research published in npj Climate Action, AI-driven improvements to solar and wind power systems alone could reduce global emissions by roughly 1.8 billion tonnes of CO2 equivalent per year by 2035, with AI's broader climate applications potentially cutting business-as-usual emissions by 3.2 to 5.4 billion tonnes annually by the same year. AI is also proving valuable in precision agriculture, reducing one of the most diffuse and historically hard-to-measure sources of emissions in the global economy, and in climate finance, where AI-enhanced risk assessment is helping make capital more accessible for sustainable development projects in emerging economies.

The Growing Cost of AI Itself

At the same time, the environmental footprint of AI for climate change applications comes bundled with a genuinely large and rapidly growing cost from the technology's own infrastructure. According to industry analysis, AI and data centres now generate somewhere between 2.5 and 3.7 percent of global greenhouse gas emissions  a figure that has officially surpassed aviation's roughly 2 percent share, while growing at around 15 percent annually compared with aviation's 2 to 4 percent growth rate. The International Energy Agency separately estimates that dedicated AI data centre electricity demand will more than quadruple by 2030, with AI-driven data centre growth on track to consume nearly as much energy by the end of the decade as the entire country of Japan currently does  and only around half of that demand is expected to be met by renewable sources.

Major tech companies present a mixed picture on this front. Amazon, the world's largest corporate purchaser of renewable energy, still saw its emissions rise by roughly 6 percent in 2024 compared with the year before, driven largely by AI-related data centre expansion, while Google's emissions in 2023 were about 48 percent higher than in 2019, primarily due to data centre energy demand tied to AI growth. Emerging research — not yet peer-reviewed — has also pointed to a newer concern: AI data centres may be creating localised "heat islands," warming surrounding land by as much as 16 degrees Fahrenheit in some cases, an effect that could compound conditions for the more than 340 million people already dealing with extreme heat linked to a warming planet.

Analysis: The Real Question Isn't Whether AI Helps or Hurts

What emerges clearly from the current data is that framing AI for climate change as either purely beneficial or purely harmful misses the more useful question entirely. Both realities are simultaneously true: AI is delivering genuine, measurable climate benefits through faster forecasting, smarter grids, and accelerated materials discovery, while its own infrastructure is consuming energy and emitting carbon at a rate that has already overtaken a major polluting industry like aviation. The more precise question industry researchers are increasingly asking is not whether AI helps or hurts the climate, but under what specific conditions its climate benefits exceed its climate costs  and that answer depends heavily on choices that are still being made right now, particularly around what kind of electricity powers the data centres running these systems.

This is why the energy mix question has become so central to the debate: AI running on clean, renewable power carries a fraction of the carbon footprint of the same AI workload running on fossil-fuel-generated electricity. Given that grid interconnection queues for new renewable projects can stretch three to five years, and that 2026 has been described by industry analysts as the year AI infrastructure shifts from "scale at all costs" toward "responsible scale," the net climate impact of AI over the next several years will likely be decided less by the technology's raw capability and more by how quickly clean energy infrastructure can be built to power it.

Conclusion

AI for climate change is neither the clean climate solution its most optimistic advocates describe nor simply an environmental liability, as its harshest critics suggest  it is genuinely both, simultaneously, and the balance between the two is still actively being written through today's energy and infrastructure decisions. The Pakistan Times Live will continue tracking how this balance shifts as AI's role in climate policy and its environmental footprint both continue to grow.

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