It also anticipates the use of AI will follow an economic principle known as the \u201cJevons paradox\u201d, which predicts that when technological improvements increase the efficiency of a resource, it leads to a rise, rather than a fall, in the total consumption of that resource.<\/p>\n
The paradox is named after economist William Stanley Jevons who observed this effect with the use of coal in 19th-century England. Efficiency gains did not reduce overall consumption. Instead, the lower costs resulted in expanded use and higher overall demand.<\/p>\n
As AI models become cheaper and more attractive, the report expects this to encourage new uses and higher volumes of use, eroding and possibly erasing any savings from efficiency advances.<\/p>\n
To avoid falling into this trap, it lays out a roadmap for responsible AI use based on guiding principles of transparency, efficiency by design, equity and justice, lifecycle responsibility, global cooperation and sustainable use.<\/p>\n
The scale of the problem<\/p>\n
![\"The](\"https:\/\/www.newsbeep.com\/nz\/wp-content\/uploads\/2026\/06\/the-col4-ai-ready-data-centre-on-a-seven-acre-campus-columbu-DQJE6KUDRBAVJK7OOEXDL274UA.jpg\")
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The COL4 AI-ready data centre, on a seven-acre campus, Columbus, Ohio. COL4 spans 256,000 square feet with 50 MW of power across three data halls. (Source: Getty)<\/p>\n
Last year, data centres already consumed as much electricity as Saudi Arabia, which ranks as the world\u2019s 11th largest electricity consumer.<\/p>\n
If electricity use doubles as projected by 2030, the associated carbon footprint would require 6.7 billion trees grown over ten years to offset this demand.<\/p>\n
Data centres would also require 9.3 trillion litres of water and land nearly ten times the size of Mexico City.<\/p>\n
Beyond resource use, the report also underscores the structural inequity at the heart of the AI boom, with only 32 nations hosting AI-specific cloud infrastructure and 90% of that capacity located in the US and China.<\/p>\n
It warns of a widening digital divide between nations that build and control AI systems and those that consume them, with the latter often bearing a disproportionate environmental burden caused by mineral extraction and e-waste.<\/p>\n
Responsible AI use<\/p>\n
Two main forces shape AI\u2019s operational footprint: how much we use it and how we use it.<\/p>\n
This involves all tasks AI models perform, from text and code generation to image and video. Each of these tasks requires different levels of computational effort.<\/p>\n
The model choice also matters as each AI system performs these task with distinct energy and environmental costs.<\/p>\n
The report argues responsible AI requires full value-chain governance, from mineral sourcing to recycling and safe disposal.<\/p>\n
It calls for a twinning of capability and environmental stewardship \u2013 thinking about both what AI can do for us and the protection of the natural environment.<\/p>\n
This would mean making environmental disclosures a routine part of AI development, at both the model and task level, and incorporating projected AI demand in climate and energy planning.<\/p>\n
Responsible AI is crucial as countries are promoting and adopting AI across government and the public sector.<\/p>\n
In Aotearoa New Zealand, the government has launched a national AI strategy and a public service AI framework.<\/p>\n
While the framework was informed by the OECD\u2019s values-based AI principles, including inclusive and sustainable development, there is no requirement for environmental disclosures and no regulator compiling energy use or emissions.<\/p>\n
Likewise in Australia, improving public services is part of the national AI plan. For example, the National Film and Sound Archive of Australia has created Bowerbird, a machine learning-enabled mass audio and video transcription engine, to document material. The Department of Veteran\u2019s Affairs has developed a proof-of-concept tool to see whether AI can help speed up the processing of claims.<\/p>\n
Both countries take a deliberate \u201clight touch\u201d and principles-based regulatory approach to AI. But this approach risks overlooking the growing environmental cost of AI that can\u2019t be solved by improving it.<\/p>\n
The natural environment is foundational to the economy, culture and wellbeing. It should be at the centre of our thinking. It\u2019s time to rethink the AI innovation playbook and shift focus toward a sustainable tech future.<\/p>\n
Author: Amanda Turnbull-McRae is a senior lecturer in Law at the University of Waikato.<\/p>\n