In embracing AI, Southeast Asia must consider sobering climate costs

Landmark UN AI resolution linked to sustainable development goals should be a wake-up call for Southeast Asia to consider the true costs of adopting AI at breakneck speed

Elina Noor

Published: 9:30am, 2 Apr 2024

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The UN General Assembly recently adopted a landmark resolution to promote “safe, secure and trustworthy” artificial intelligence systems for sustainable development. Introduced by the US and supported by more than 120 countries, the AI resolution is the first of its kind for the assembly – an acknowledgement of the extensive but unequal reach of AI and the frenetic efforts to leverage and regulate the technology.

Despite the notions of safety, security and trustworthiness resonating differently in tech capitals from Silicon Valley to Shenzhen, in the policy metropoles of Brussels and Washington, and in the rising economies of the global majority, the UN resolution manages to cut across the disparate geographies and their demographics by attaching itself to UN Sustainable Development Goals.

But it is the focus on AI systems throughout their life cycle – pre-design, design, development, evaluation, testing, deployment, use, sale, procurement, operation and decommissioning – that makes this resolution notable, particularly in light of AI’s outsize climate footprint.

By coincidence, two days before the UN resolution was adopted, the World Meteorological Organization released its annual State of the Climate report. It confirmed last year as the warmest on record and was an indictment of just how much damage human activity continues to wreak on the environment, with records broken again for greenhouse gas emissions, surface temperatures and sea level rise.

For Southeast Asia, a region both bullish on technology and extremely vulnerable to climate change, AI is more often seen as a solution to the environmental crisis than a contributor. Agricultural technology, for example, is being used to manage the food insecurity aggravated by rising sea levels, droughts and floods. AI is also being used to forecast floods, anticipate air pollution and accelerate the region’s green energy transition.

But deploying AI across a range of sectors is not without cost to the climate. Major AI applications and their supporting infrastructure, designed for redundancy and continual updates to meet client expectations, are notoriously resource-intensive.

The groundbreaking ceremony in Singapore for Google’s first data centre in Southeast Asia, on December 15, 2011. Google added a second in Singapore in 2015 and a third in 2022, altogether representing a long-term investment in Singapore of US$850 million. Photo: EPA

Worldwide, data centre electricity usage, often powered by fossil fuels, accounts for up to nearly 4 per cent of greenhouse gas emissions, exceeding even those of the aviation industry. In 2020, data centres alone made up nearly 7 per cent of Singapore’s energy consumption, with a projected increase to 12 per cent by 2030. Cooling a typical Southeast Asian data centre requires more energy because of the region’s heat and humidity.

Where water rather than air is used to cool data centres, several million litres per year can be channelled to a single data centre building. Google, which has three data centres in Singapore, reported water consumption of about 450,000 gallons per day for an average data centre in 2021.

This is to say nothing of the voracious computational power required for generative AI. One study estimates that training a single large language model (LLM) with 175 billion parameters could result in over 500 tonnes of carbon emitted, equivalent to burning over 551,038lbs of coal.

Indonesia’s emissions surge as Asia seeks more energy-intensive data centres

Put another way, it would take more than 8,200 tree seedlings grown for 10 years to sequester the amount of carbon produced by just one such LLM. Already, in 2018, OpenAI noted that since 2012, the amount of “compute” – processing power, memory and hardware resources – used in the largest AI training runs had been increasing exponentially, doubling every 3.4 months.

But resource consumption and carbon emissions are only part of the story. Other types of environmental impact, such as the toxicity of pollutants or effluents and biodiversity loss, are rarely, if ever, declared.

Additionally, it is worth noting that increased efficiency through AI may not have a net positive effect on the environment. It could even exacerbate harm in what some call the “rebound effect”. This is when savings of costs, resources and time through optimisation are transformed into increased consumption and its knock-on effects on the climate.

When OpenAI launched ChatGPT in November 2022, it was based on GPT-3, an LLM with 175 billion parameters. OpenAI is expected to debut GPT-5 this summer. Photo: AFP

The UN resolution’s call for a life-cycle approach to AI is an important step towards an honest accounting of these systems’ environmental impact.

To borrow from internationally recognised standards (ISO 14040 and ISO 14044 for environmental management) and the UN International Telecommunication Union’s information and communication technology-specific life cycle assessment methodology, this could require assessing the entire spectrum of activities – from minerals extraction to system disassembly – and the generation of their own ecological and labour footprint across all 11 stages of the UN’s taxonomy for AI’s life cycle.

While corporations have embarked on “circular” initiatives, such as Microsoft’s plan to reuse 90 per cent of its cloud computing hardware assets by 2025, not all companies are able to do this. There are also reputational risks to weigh if there is to be a “full-stack supply chain” accounting of AI’s true ecological impact, including what conventional economics calls “externalities” – essentially third-party effects.

Yet, with 20 new data centres expected across Southeast Asia by 2027 in addition to the 242 in existence, a regional data centre market seen to grow by a compound annual rate of over 6.5 per cent between 2022 and 2028, and an emissions gap of 2.6-3.2 gigatonnes set against 2030 targets, a business-as-usual approach to a bigger-is-better AI would be surreally dissonant with the region’s climate exigencies.

After all, if OpenAI chief executive Sam Altman is right in predicting that AI will “change the world much less than we all think”, is it worth building fast and breaking things at the potential cost of an irretrievably scarred planet and humanity left off-kilter?

Elina Noor is a senior fellow in the Asia Programme at Carnegie Endowment for International Peace