AI’s Climate Paradox in Southeast Asia: Balancing Innovation and Environmental Costs

AI in Southeast Asia: A Double-Edged Sword for Climate and Growth

Southeast Asia’s AI market is booming, expected to hit US$8.9 billion with a growth rate above 27% this year. Investments totaling over US$30 billion in the first half of 2024 have boosted infrastructure and research efforts. Major players like Apple, Google, Microsoft, and Nvidia are expanding their AI data centers and R&D facilities across the region.

If harnessed well, AI could add 10 to 18% to Southeast Asia’s GDP—around US$1 trillion by 2030. But its impact goes beyond economics. The region faces nearly 700 natural disasters recorded in just five months of 2025, making AI’s predictive capabilities critical for climate resilience.

AI’s Role in Climate Preparedness

AI-powered weather forecasting is improving disaster readiness. For instance, the Philippines uses AI to predict weather at a neighborhood level with ten times the resolution of traditional models. Thailand’s AI Nowcast system forecasts rainfall in Bangkok three hours ahead, helping reduce flood damage. In Jakarta, AI monitors air quality in one of the world’s most polluted cities, aiding public health efforts.

Generative AI and machine learning have enhanced early warning systems by shifting from hazard-based to impact-based alerts. These systems analyze huge datasets and simulate real-time outcomes, allowing communities to prepare more effectively for disasters.

The Environmental Cost of AI

Despite its benefits, AI’s environmental footprint is a real concern. Data centers, essential for AI, consume massive amounts of electricity and water. Globally, data centers and networks account for 1-1.5% of energy use and contribute 1% of greenhouse gas emissions. Google reported a 48% rise in emissions since 2019, driven largely by data center growth.

Southeast Asia hosts nearly 500 data centers, with a combined capacity close to 2,000 megawatts. Singapore, Indonesia, and Malaysia lead the pack. These facilities also demand huge volumes of water—up to 4.2 million liters daily for a 100MW data center, including potable water. This adds pressure in a region where over 100 million people already face water scarcity.

E-waste is another challenge. Southeast Asia generated 12.3 million tonnes of e-waste in 2021, and expanding AI hardware use will increase this volume. Short lifespans of AI training components like GPUs and servers could add 5 million tonnes globally by 2030. Improper disposal risks releasing toxins harmful to health and ecosystems.

Balancing AI Growth with Sustainability

Governments and organizations must factor AI’s environmental impact into policies. While AI governance often focuses on data privacy and fairness, sustainability needs stronger emphasis. ASEAN’s current AI guidelines mention environmental concerns but lack concrete measures.

Striking a balance between regulation and innovation is key. Regional standards should promote energy efficiency in data centers and require transparency on AI’s carbon, power, and water footprint. Malaysia’s 2024 Guideline for Sustainable Development of Data Centres is a good example, mandating disclosure of resource use.

The ASEAN Centre for Energy’s white paper on next-generation data centers explores green energy alternatives and public-private partnerships with companies like Amazon and Microsoft. Additionally, circular economy strategies are emerging, with Thailand leading efforts on e-waste recycling and the Right to Repair movement.

ASEAN’s collaboration with Japan on resource circulation tackles critical minerals recovery from e-waste, reducing environmental pollution and supporting sustainability.

Looking Ahead

AI offers powerful tools for economic growth and climate resilience in Southeast Asia. But its environmental downsides can’t be ignored. Effective regulation, transparency, and sustainable practices are essential to ensure AI contributes positively without worsening climate challenges.

For IT and development professionals working with AI in the region, understanding and addressing these trade-offs will be critical. Integrating sustainability into AI projects and infrastructure development isn't just responsible—it’s necessary for long-term success.

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