Asian Angle | In Southeast Asia’s climate battle, AI could prove a double-edged sword
AI has the potential to be a new engine of growth but should not be a panacea for the region’s climate problems

Southeast Asia has emerged as a bright spot in the global artificial intelligence (AI) race, with a market size projected to reach US$8.9 billion and an annual growth rate of more than 27 per cent this year.
Recently, this upward trajectory has been further supported by strong AI-related investments, with more than US$30 billion invested into the region in the first half of 2024. A large proportion of investments pledged have gone towards infrastructure development, along with research and development.
Key investments include Apple’s US$250 million campus expansion in Singapore; Google’s US$2 billion investment to develop Malaysia’s first data centre and Google Cloud region – a physical location equipped with infrastructure that enables faster, more efficient access to high-performance cloud services; Microsoft’s US$1.7 billion deal to enhance Indonesia’s cloud and AI infrastructure; and Nvidia’s announcement to open the Vietnam research and development centre. If implemented properly, AI could be a new engine of growth for the region, raising its GDP by 10 to 18 per cent, or an additional US$1 trillion by 2030.

For Southeast Asia, AI’s potential transcends its economic benefits. As a region vulnerable to the impacts of climate change – with nearly 700 natural disasters recorded by the Asean Disaster Information Network in the first five months of 2025 alone – the predictive power of AI has been touted as a strategic tool against the impact of global warming. In the Philippines, AI-powered weather forecasting is accurately predicting weather patterns at the neighbourhood level, owing to its imaging resolution that is 10 times higher than previous forecasts. In Thailand, the AI Nowcast system is forecasting rainfall in Bangkok three hours ahead of time, enhancing the city’s flood preparedness. AI is also being used to monitor air quality in the Indonesian capital of Jakarta, one of the world’s most polluted cities, and promote a healthier urban environment.
The expansion of Generative AI (Gen AI), large language models and machine learning has strengthened multi-hazard, people-centred early warning systems from being purely hazard-based to becoming impact-based. With its capacity to process large volumes of data and generate real-time simulations, AI can enhance systems to forecast not only weather events, but also their potential impacts and the areas most likely affected. Such a system significantly enhances disaster preparedness and enables the adequate deployment of preventive measures, especially in vulnerable communities.
However, despite AI’s positive potential, the region should not view it as a panacea in this climate-changing world. In short, the same AI that is used to fight the effects of climate change also aggravates the problem.

First, data centres that power AI are energy-intensive, consuming energy not only to store, process and send data, but also for the continuous electricity needed to cool these facilities.