Recently, social media platforms were flooded with images in the distinctive hand-drawn style of Studio Ghibli. However, rather than being hand-drawn, the images inspired by the animation studio were generated using ChatGPT, a generative artificial intelligence (AI) tool created by OpenAI.
ChatGPT’s Environmental Impact
The energy required to generate text is typically less than that required to generate images. A study by the University of California, Riverside in the US found that a 100-word email generated by an AI chatbot using ChatGPT’s GPT-4 model requires 0.14 kWh. That is still enough electricity to power an LED light bulb for 14 hours.
The same email generated with GPT-4 uses 519 milliliters of water – slightly more than a bottle of water. Mr Sharad Somani, partner at professional services firm KPMG in Singapore, said that generative AI systems require a large amount of computing power to train and operate.
“This power demand translates to significant electricity use and unless it is drawn from renewable sources, it contributes to high carbon emissions,” he added.
The Environmental Toll of Generative AI
Traditional search engines and other traditional AI technologies require computing power as well, but the immense amounts needed to train and operate generative AI models have an environmental impact far outweighing that of the former, experts told CNA TODAY.
So why does generative AI need all this energy? Mr Laurence Liew, director of AI innovation at AI Singapore, explained that every component in the AI supply chain has its own environmental cost.
“Training a single large language model can consume more electricity than hundreds of households use annually,” he said. “The training phase alone for models like GPT-4 or Claude requires thousands of specialised GPUs (graphics processing units) running at full capacity for weeks or months.”
Data Centres and the Environmental Impact
Mega facilities providing the computing infrastructure that IT systems require such as servers, data storage drives and equipment that includes GPUs are known as data centres. However, in providing the computing power, these systems generate a significant amount of heat.
“Cooling systems in data centres typically account for a large percentage of total energy consumption, as many operators maintain their equipment at temperatures of 22°C and below,” Mr Liew explained.
As of May 2024, the more than 70 data centres in Singapore provided 1.4 gigawatts of computing capacity. They contributed 82 per cent of greenhouse gas emissions produced by Singapore’s information and communication technology (ICT) industry last year.
The Environmental Cost of Hardware
The hardware needed to support generative AI also has an environmental impact. Mr Vivek Kumar, CEO of the World Wide Fund for Nature, Singapore (WWF-Singapore), said that these include the lithium-ion batteries used in GPUs.
“The extraction, transportation and supply chains for these materials contribute to carbon emissions, while the reliance on rare earth minerals sourced through mining operations leads to ecosystem destruction, resource depletion and pollution,” he added.
Minimising the Environmental Impact of Generative AI
Experts noted that generative AI’s operation requires a large amount of water and rare earth materials, the extraction of which carries significant environmental costs.
“A single ChatGPT query submitted by a user requires 10 times the amount of electricity as a Google search – 2.9 watt-hours (Wh) compared with 0.3 Wh,” said Mr Liew.
“Generating 1,000 images using several generative AI tools requires about 2.907 kilowatt-hours (kWh) on average,” he added.
Reducing AI’s Climate Impact
Several businesses and organisations in Singapore told CNA TODAY that they have taken proactive measures to mitigate the environmental impact of generative AI.
Singapore’s biggest bank DBS said that it has a four-lever approach to address environmental challenges such as reducing consumption of resources and generating renewable energy.
The Role of Governments
The European Union, for instance, introduced the first-ever legal framework on AI last year, which includes provisions for energy consumption reporting, as well as principles for developing and using AI systems in a “sustainable and environmentally friendly manner”.
Making Generative AI Greener
Experts noted that generative AI models can be made more sustainable by designing and using energy-efficient hardware.
“Developing cutting-edge AI chips that are more sustainable would require substantial investments in research and development,” said Mr Somani of KPMG.
“Getting access to renewable energy can pose a challenge due to limited availability and high costs,” he added.
A Way to Unlock the Full Potential of Generative AI
Mr Oostveen from data storage firm Pure Storage said that companies developing AI should look at improving the efficiency of their models and thereby reducing the computing power needed.
“DeepSeek, an AI model from China that is on par with advanced models from OpenAI and Meta in the United States, but developed at a fraction of their costs, is an example of this approach,” he said.
Reducing the Environmental Impact of Generative AI
“Achieving model efficiency ensures that supporting hardware can meet computing power needs and energy consumption,” Mr Oostveen said.
“Apart from hardware, prioritising smarter data management can help reduce AI’s environmental impact,” he added.
The Future of Generative AI
“The environmental challenges of AI are substantial, but so is our capacity for innovation,” said Mr Liew of AI Singapore.
“With deliberate focus and collaborative effort across industry, government and academia, we can ensure that AI’s benefits don’t come at the expense of our planet’s future,” he said.
References
1.
- OpenAI’s chief executive officer Sam Altman
- Similarweb
- CNA TODAY
2.
- Hayao Miyazaki
- Studio Ghibli
- Never-Ending Man: Hayao Miyazaki
3.
- International Energy Agency
- Carnegie Mellon University
- Hugging Face
4.
- University of California, Riverside
- KPMG
