The council had originally approved planning permits for the Merrifield data centre precinct, but rescinded support for one facility after residents and campaigners raised concerns about energy and water use, local infrastructure, and consultation with Traditional Owners. The backlash may be a sign that policymakers are starting to consider AI\u2019s ecological footprint.<\/p>\n
As AI is rolled out in increasingly sensitive areas of public life, including healthcare, policing, and welfare, governments have focused on the need for ethical, safe, and responsible deployment. There are also fierce debates over copyright and AI\u2019s impact on jobs.<\/p>\n
As important as these discussions are, environmental consequences have rarely been part of the equation to date.<\/p>\n
Missing piece of the \u2018responsibility\u2019 puzzle<\/p>\n
Governments and stakeholders have been busy discussing how AI might help lift Australia\u2019s sagging productivity; Treasurer Dr Jim Chalmers wrote earlier this month<\/a> that he expects AI to \u201ccompletely transform our economy\u201d, and that he is optimistic AI \u201cwill be a force for good\u201d. <\/p>\n However, the Treasurer and others promoting AI are less vocal about the technology\u2019s negative externalities.<\/p>\n The Productivity Commission\u2019s interim report,\u00a0Harnessing data and digital technology<\/a>, pointed to AI\u2019s potential to \u201cimprove service delivery, lift productivity and help solve complex social and environmental challenges\u201d. But it largely overlooked AI\u2019s environmental impacts, saying \u201cfew of AI\u2019s risks are wholly new issues\u201d and that higher energy demand is par for the course with \u201cthe information technology revolution\u201d.<\/p>\n Likewise, a Therapeutic Goods Administration (TGA) consultation paper on the\u00a0regulation of AI in medical device software<\/a>\u00a0omitted any discussion of environmental impact, despite recommending more transparency and accountability in the way AI tools are assessed and monitored.<\/p>\n The absence matters. As AI becomes embedded in essential services such as healthcare, its environmental footprint becomes not just a technical issue, but a public health and equity concern, particularly for communities already facing water insecurity or climate risk.<\/p>\n In a sector that is trying to decarbonise and reduce its impact, uncritical adoption of AI could prove counterproductive.<\/p>\n First Nations perspectives<\/p>\n For some First Nations communities, water scarcity is not theoretical, it is a daily reality.<\/p>\n In remote and regional Australia, many First Nations peoples face ongoing systemic barriers to safe, reliable, and culturally appropriate water access. These are demands that extend far beyond infrastructure and include deeply held cultural and spiritual connections with water.<\/p>\n Research\u00a0conducted by CSIRO<\/a>\u00a0between 2008 and 2010 in the Daly (NT) and Fitzroy (WA) river catchments was the first of its kind to document Indigenous social and economic values tied to aquatic ecosystems, linking river flows directly to Indigenous livelihoods, resource use, and planning processes.<\/p>\n The\u00a0Northern Australia Water Resource Assessment<\/a>\u00a0reinforces these insights, framing rivers as vessels of sustenance, heritage, and governance, and asserting Traditional Owners as inherently central to water and development planning.<\/p>\n Yet Australia\u2019s AI reform dialogue has mostly omitted these cultural linkages, even when it does consider AI\u2019s consumption of resources. In the context of AI-powered healthcare, this omission is especially troubling.<\/p>\n Innovations celebrated for improving diagnostics or service delivery often rely on energy- and water-intensive data systems, whose environmental toll is seldom disclosed or evaluated through an equity lens. When AI is embedded in healthcare services for Indigenous populations, with no accounting for its resource footprint, those least consulted risk bearing the heaviest cost.<\/p>\n This raises serious equity questions when governments invest in digital transformation strategies without accounting for water-intensive technologies such as AI.<\/p>\n As the United Nations Environment Programme\u00a0has noted<\/a>, policymakers must ensure the social, ethical, and environmental aspects of AI use are considered, not just the economic benefits.<\/p>\n \u201cWe need to make sure the net effect of AI on the planet is positive before we deploy the technology at scale,\u201d said Golestan (Sally) Radwan, Chief Digital Officer of the United Nations Environment Programme.<\/p>\n Just how thirsty is AI?<\/p>\n Data centres remain one of the fastest-growing consumers of global electricity, using approximately\u00a0460 terawatt\u2011hours<\/a>\u00a0in 2022, and projected to more than double by 2026 with increasing AI and cryptocurrency activity.<\/p>\n These facilities often depend on water-intensive cooling systems, such as evaporative methods, which can consume large volumes of potable water and exacerbate stress on local supply. With AI workloads driving higher server densities and increased heat output, water\u00a0demand for cooling is rising sharply<\/a>, especially for hyperscale data centres, making water scarcity a growing operational risk.<\/p>\n For context, a study from the University of California, Riverside calculated that training GPT\u20113 in Microsoft\u2019s advanced US data centres evaporated about\u00a0700,000 litres of clean freshwater<\/a>, a sobering figure for a single model\u2019s development phase.<\/p>\n AI is being promoted as a climate solution, through better modelling, emissions tracking, and even water management optimisation. But the industry\u2019s own resource use can directly undermine those goals.<\/p>\n As the\u00a0OECD notes<\/a>: \u201cAI-enabled products and services are creating significant efficiency gains, helping to manage energy systems and achieve the deep cuts in greenhouse gas (GHG) emissions needed to meet net-zero targets. However, training and deploying AI systems can require massive amounts of computational resources with their own environmental impacts.\u201d<\/p>\n According to\u00a0one report<\/a>, data centres in the US consumed about 4.4 percent of the country\u2019s electricity in 2023, which could nearly triple to 12\u202fpercent by 2028. Meanwhile, Google\u2019s US data centres went from using 12.7 billion litres of cooling water in 2021 to over 30 billion litres just three years later, and UC Riverside estimates that running just 20 to 50 ChatGPT queries uses\u00a0roughly half a litre of fresh water<\/a>.<\/p>\n In response, the global tech sector has invested heavily in green branding. Microsoft, for example, has publicly committed to being \u201ccarbon negative and water positive by 2030<\/a>\u201d.<\/p>\n Notable absence<\/p>\n Australia\u2019s healthcare system is rapidly adopting AI across clinical, administrative, and operational domains.<\/p>\n From diagnostic imaging to digital scribes, clinical decision support, and personalised treatment plans, AI is being held up as a core enabler of future-ready care. Federal reports, such as\u00a0Our Gen AI Transition<\/a>, point to AI\u2019s potential to improve efficiency and free up clinicians for more patient-centred work.<\/p>\n But that optimism comes with a caveat: the integration of AI into healthcare is unfolding with limited consideration of its environmental toll. The healthcare sector is already one of the most resource-intensive in Australia, responsible for around\u00a0seven percent of national greenhouse gas emissions<\/a>. AI risks adding a new layer of resource demand.<\/p>\n While regulatory bodies are beginning to grapple with questions of safety, accountability, and clinical transparency, environmental impacts remain conspicuously absent from most discussions.<\/p>\n The TGA, for instance, has flagged a need for clearer regulation of AI in medical software, noting that some tools, such as generative AI scribes, may already be operating outside existing rules if they suggest diagnoses or treatments without approval. Yet neither the TGA\u2019s consultation paper nor its updated guidance documents meaningfully address the carbon or water costs of these tools.<\/p>\n According to Consumer Health Forum CEO Dr Elizabeth Deveny, the TGA\u2019s review surfaced critical issues around trust, transparency, and consent, from hidden AI tools embedded in routine software, to confusion about who is responsible when things go wrong.<\/p>\n She notes: \u201cTrust is the real product being managed.\u201d Yet environmental transparency is foundational to that trust, particularly when AI is deployed into hospitals and clinics already experiencing the impacts of climate and infrastructure strain.<\/p>\n Equally important is the broader policy context.\u00a0A Deeble Institute Perspectives Brief<\/a>\u00a0cautions that AI\u2019s success in healthcare hinges on transparent and nationally consistent implementation frameworks, shaped through co-design with clinicians and consumers.<\/p>\n But such frameworks must also consider the material cost of AI, not just its clinical or administrative promise. Otherwise, we risk solving one set of problems, such as workforce strain or wait times, while silently compounding others, including water insecurity, emissions, and energy grid pressure.<\/p>\n Global pressure is building<\/p>\n In Europe, data centre water use is already triggering regulatory scrutiny. The EU is finalising a Water Resilience Strategy that will impose usage limits on tech companies, with a focus on AI-related growth.<\/p>\n \u201cThe IT sector is suddenly coming to Brussels and saying we need a lot of high-quality water,\u201d\u00a0said Sergiz Moroz<\/a>\u00a0of the European Environment Bureau. \u201cFarmers are coming and saying, look, we cannot grow the food without water.\u201d<\/p>\n Even the\u00a0United Nations<\/a>\u00a0has weighed in, stating bluntly: \u201cAI has an environmental problem\u201d.<\/p>\n The UNEP emphasises that AI strategies must be deeply integrated with sustainability goals, moving beyond simple transparency and they should \u201cintegrate sustainability goals into their digitalization and AI strategies\u201d.<\/p>\n The new\u00a0Coalition for Environmentally Sustainable AI<\/a>, co\u2011led by the UNEP, brings together governments, academia, industry and civil society to ensure that \u201cthe net effect of AI on the planet is positive\u201d.<\/p>\n Communities\u2019 concerns<\/p>\n The Hume Council\u2019s data centre decision is not an isolated objection, and as Australia rapidly expands its digital infrastructure to support AI and other emerging technologies, the communities asked to host this infrastructure will increasingly demand to be heard.<\/p>\n Data centres have a real and often disruptive presence: high heat output, constant noise from cooling systems, diesel backup generators, as well as their heavy water and energy use. Once operational, they offer few jobs and limited direct benefit to the communities that surround them.<\/p>\n Late last year, the\u00a0Senate Select Committee on Adopting Artificial Intelligence<\/a>\u00a0observed that stakeholders provided extensive evidence on AI\u2019s environmental footprint, from energy use, greenhouse gas emissions, to water consumption, and also recognised AI\u2019s potential to help mitigate these same challenges.<\/p>\n Yet, despite this, only one of the report\u2019s 13 recommendations addressed the environment, and even that was disappointingly vague:<\/p>\n \u201cThat the Australian Government take a coordinated, holistic approach to managing the growth of AI infrastructure in Australia to ensure that growth is sustainable, delivers value for Australians and is in the national interest.\u201d<\/p>\n As Dr Bronwyn Cumbo, a transdisciplinary social researcher at the University of Technology Sydney, writes in\u00a0The Conversation<\/a>, Australia has a unique opportunity to embed genuine community participation in the design and planning of its digital infrastructure.<\/p>\n \u201cTo avoid amplifying the social inequities and environmental challenges of data centres,\u201d she argues, \u201cthe tech industry and governments across Australia need to include the communities who will live alongside these crucial pieces of digital infrastructure.\u201d<\/p>\n Public trust in AI cannot be divorced from the physical and environmental contexts in which it operates. If the benefits of AI are to be shared, then the burdens, from emissions and water use to noise and land occupation, must be acknowledged and addressed. This is especially true in healthcare, where ethical use and public confidence are paramount.<\/p>\n The Hume Council vote is a reminder that local communities are paying attention. Whether policymakers and those with an interest in promoting wider uptake of AI are listening is another matter. Likewise, there are questions about whether the health sector is doing enough to investigate and highlight the potential environmental impacts.<\/p>\n Jason Staines is a communications consultant with a background spanning journalism, government, and strategic advisory roles. He has reported for outlets including AAP, Dow Jones, The Sydney Morning Herald and The Age, and later worked in government as a Senior Research Officer at the Australian Treasury\u2019s post in New Delhi and as an analyst in Canberra. He holds a Master of International Relations from the University of Sydney and a Bachelor of Arts (Communication) from the University of Technology Sydney.<\/p>\n The article was written in his capacity as a Croakey editor and journalist.<\/p>\n This article was originally published by Croakey<\/a>.<\/p>\n![\"Artificial](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2025\/09\/shutterstock_1501165988-1024x556.jpg\")
There are serious equity questions when governments invest in digital transformation strategies without accounting for the cultural impacts of water-intensive technologies such as AI (Jack Kinny \/ Shutterstock).<\/p>\n