The E-Waste Column no. 205

Jul 8
3 min read

Following from last week’s column on the environmental impacts of artificial intelligence (AI), we are looking at the UN’s recommendations to build a more “responsible AI ecosystem” today.

🌱 Why does our use of AI need to become more responsible?

The United Nations University Institute for Water, Environment and Health (UNU-INWEH) published a report titled “Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints” on 3 June 2026. Based on the environmental impacts of AI that the researchers outlined in their report, they are calling for more responsible AI use. While the “report is not a case against artificial intelligence”, the researchers say we need to urgently address the “unintended impacts [of AI] proactively to make it sustainable and equitable”. According to the researchers, we only “have a narrow window to ensure that the backbone of the technological revolution of our era develops within planetary limits” and that “innovation advances without shifting environmental costs onto vulnerable communities”.

🌱 What role does governance play for the future of AI?

The UN report says “that the future of AI will depend not only on technological innovation but also on governance choices made today”. The researchers fundamentally say that “AI’s environmental footprint [is] a governance and justice challenge, not only a technical problem”. They specifically say that the fact that “[t]he benefits of AI often flow across borders and sectors, while the environmental burdens of data center siting, electricity demand, water withdrawals, land use, mineral extraction, and e-waste can be concentrated in specific communities and regions” needs to be factored into the governance choices being made. In line with this, the UN report sets out a six-principle governance framework to address AI’s risks throughout the full value chain. This framework focuses on “transparency, efficiency by design, equity and environmental justice, lifecycle responsibility, global cooperation, and sustainable use”. On this basis, the researchers say that “with measurement, transparency, and shared responsibility across the ecosystem”, it is possible to grow AI stewardship and AI capability simultaneously.

🌱 What role do governments, communities, and civil society play?

The researchers say governments need “to integrate AI infrastructure into energy, water and land-use planning” and to “require standardized environmental footprint reporting” for AI use. They say that “[i]nternational institutions should support harmonized measurement standards, reduce incentives for cross-border burden shifting, and build compute capacity in excluded regions”. The researchers are also calling for the early involvement of communities and civil society in the siting decisions for data centers, and they say there need to be “enforceable transparency and grievance mechanisms” in place for this.

🌱 What role do investors, companies, and private individuals play?

The researchers say that investors and financial institutions “should treat electricity, carbon, water, and land footprints as material risks in AI infrastructure portfolios”. They also say companies in general need “to design systems that minimise resource consumption”. More specifically, the researchers say AI developers and the AI industry “should treat model selection, default outputs, and routing decisions as footprint determinants” and that they need to take active steps to “improve efficiency by design”. Similarly, the researchers say that data center operators and utilities should be treating “siting and energy procurement as environmental footprint decisions” and that they need to be applying a “cumulative impact assessment” to assess the true impact of their data centers. When it comes to organizations deploying AI and anyone else using AI (including private individuals or consumers), the researchers say these actors should be adopting “fit-for-purpose use”, which effectively means “choosing lower-impact applications where possible” and consistently using “the lightest model and lowest-energy format that meets the task”.

🌱 What needs to change when it comes to raw materials and e-waste?

In their report, the researchers highlight that “[c]ritical-mineral extraction at the upstream end and electronic waste at the downstream end are integral to AI’s footprint and currently fall on communities that capture little of the benefit”. The researchers therefore stress that, going forward, “the communities who provide the critical minerals for advancing AI and the ones that host its infrastructure and e-waste [also need to be] among those who benefit from it”.

💡 In next week’s column, we will be looking at the UN’s AI Environmental Transparency Initiative – so stay tuned.