Sustainable development Archives | Digital Watch Observatory

China links AI data centre to direct green electricity supply

China has launched what state media described as the country’s first AI data centre powered entirely through a direct green electricity connection, linking AI infrastructure more closely with renewable energy supply.

The facility has started operations in Zhongwei, in the Ningxia Hui Autonomous Region, a western region that has become central to China’s computing and clean-energy strategy.

Operated by China Telecom Ningxia Branch, the data centre is built to a wind-powered liquid-cooling standard. According to the company, the facility achieves a Power Usage Effectiveness rating of 1.15, supporting high-performance AI computing while reducing energy use compared with conventional data centres.

The project is part of China’s wider effort to connect computing capacity with renewable energy resources. Ningxia has already hosted large-scale projects that directly supply green electricity to data centre clusters, including a 500 MW solar facility in Zhongwei linked to China’s computing-electricity coordination model.

Zhongwei is also a key node in China’s ‘Eastern Data, Western Computing’ initiative, which aims to shift data-intensive workloads from eastern economic centres to western regions with more land and renewable-energy resources.

The new facility is expected to support AI computing, data processing and industrial digital transformation. It could also increase demand for servers, AI chips, liquid-cooling equipment and other parts of China’s domestic technology supply chain.

The project highlights how energy availability and efficiency are becoming central to AI infrastructure policy, as countries and companies face rising power demand from data centres and advanced AI systems.

Why does it matter?

AI infrastructure is becoming an energy-policy issue. China’s green-powered data centre model shows how governments may try to match growing AI compute demand with renewable-energy deployment, regional data-centre planning and industrial supply-chain development. For China, the project also supports a broader strategy of moving compute workloads westward, reducing pressure on eastern cities and using renewable resources in regions such as Ningxia. The challenge will be proving that such facilities can deliver reliable AI computing at scale while genuinely reducing emissions across the full power and data-centre system.

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UN secretary-general calls for greater transparency on AI’s climate impact

UN Secretary-General António Guterres has called on AI companies to publicly disclose the environmental impact of their operations, including carbon emissions, water consumption, and land use. Speaking at London Climate Action Week, Guterres proposed an AI Environmental Transparency Initiative, arguing that communities are often left without clear information about the environmental impact of nearby data centre developments.

Citing a UN study, Guterres said data centres consumed more electricity in 2025 than all but ten countries, accounting for around 1.5% of global electricity demand. That share could approach 3% by 2030, while AI-related water consumption and pollution are also projected to rise significantly. By 2030, that figure is projected to nearly double to close to 3 per cent, while the water use and pollution associated with AI are also expected to double within four years.

Guterres noted that coal still provides around 30% of the electricity used by data centres globally, while renewables account for approximately 27%. He called on AI companies to power their facilities entirely with renewable energy by 2030. Guterres called on AI firms to commit to powering their facilities entirely from renewable sources such as wind and solar by 2030, though existing clean energy commitments from major tech companies have already been complicated by the rapid pace of AI deployment.

Guterres linked the proposal to broader concerns about climate change and energy security, arguing that both are rooted in continued dependence on fossil fuels. He noted that the planet has just endured its eleven hottest years on record, and that last year marked the first time the three-year global temperature average broke through the 1.5 degrees Celsius threshold set by the 2015 Paris Agreement.

He also noted that renewable energy surpassed one-third of global electricity generation in 2025 for the first time, while coal’s share fell below one-third, although he cautioned that rising AI-related electricity demand could complicate progress.

Coal’s share of global generation also fell below one-third for the first time, though significant challenges remain, particularly given policy reversals in the US under President Donald Trump, who has embraced fossil fuels and cut support for renewables.

Guterres, whose term ends in December 2026, will convene world leaders again at the annual COP climate summit later this year. He reiterated calls for every major emitter to accelerate action, reduce methane emissions, and move away from coal, oil, and gas, with the speech delivered during a heatwave affecting much of London and Europe.

Why does it matter?

The rapid expansion of AI infrastructure is bringing its environmental footprint under increasing scrutiny. As data centres consume growing amounts of electricity and water, policymakers are beginning to ask whether AI companies should be subject to the same transparency expectations applied to other carbon-intensive industries. Standardised reporting could provide governments, investors and local communities with a clearer understanding of AI’s environmental impact.

The proposal also highlights the growing intersection between AI governance and climate policy. As countries seek to expand AI capabilities while meeting emissions targets, the availability of clean energy, sustainable infrastructure and transparent environmental reporting is likely to become an increasingly important part of discussions on responsible AI development.

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MIT researchers develop cooling system to cut data centre energy and water use

A startup founded by researchers from MIT has developed a nuclear-inspired cooling system designed to improve data centre energy efficiency while reducing water consumption. The technology targets one of the fastest-growing sources of electricity demand, as the rapid expansion of AI infrastructure drives increased computing requirements.

Ferveret’s system uses a specialised liquid to immerse servers, replacing traditional air-based cooling methods that account for a significant share of data centre energy consumption. Its Adaptive Phase Cooling approach improves heat transfer through controlled bubble formation, increasing efficiency while reducing reliance on water-intensive cooling systems.

The company reports computational efficiency gains of up to 15% compared with existing liquid cooling technologies, alongside improved overall performance when combined with power optimisation software. Ferveret is already testing its system with several data centre operators and AI hardware companies as it moves towards wider commercial deployment.

The startup says its modular design enables easier integration into existing facilities while allowing data centres to operate more effectively in regions with limited water resources. By reducing energy waste and improving heat management, the technology aims to support the growing demand for AI computing without further increasing environmental strain.

Why does it matter?

The rapid growth of AI is driving unprecedented demand for computing power, placing increasing pressure on electricity grids, water supplies and data centre infrastructure. Cooling systems are a major contributor to both energy consumption and operating costs, making efficiency improvements a growing priority for the technology sector.

Innovations such as liquid immersion cooling could help reduce the environmental footprint of AI infrastructure while supporting continued growth in computing capacity. As governments and companies seek to balance AI expansion with sustainability goals, advances in cooling, power management and resource efficiency are becoming an increasingly important part of the broader AI ecosystem.

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Researchers develop AI governance tools for public health across the Global Majority

A research team led by Professor Jude Kong from the University of Toronto is developing new tools to monitor, assess, and govern the use of AI in public health across the Global Majority, with a particular focus on Africa.

The team, which includes Jake Effoduh, Jim Hinton, Abbas Yazdinejad, and Maral Niaz, has begun mapping how AI is being integrated into healthcare systems and infrastructure. The work focuses on identifying key actors, technologies and use cases, providing a clearer picture of how AI is becoming embedded in public health systems.

The next phase involves developing a dynamic dashboard designed to track AI systems and support evidence-based decision-making. Rather than relying solely on top-down governance frameworks, the team aims to co-develop tools that policymakers, civil society organisations, educators and practitioners can use in their own contexts.

In practice, this means creating tools that are not only technically robust but also socially legitimate and locally relevant. While strengthening AI literacy and governance capacity across the Global Majority, the initiative aims to empower policymakers with evidence-based insights, support civil society in understanding AI systems, and enable more informed and inclusive decision-making processes.

By bringing together expertise in technology, law, public policy and social impact, the project reflects the multidisciplinary nature of AI governance. The team will present its findings at the AI for Good Global Summit in Geneva, during ITU’s Kaleidoscope sessions on Thursday, 9 July 2026, from 15:30 to 16:30.

Why does this matter in AI world?

AI for the Global Majority (AI4GM) is a joint initiative of the Geneva Graduate Institute, Microsoft and the International Telecommunication Union. The initiative supports research on how AI can benefit majority populations in areas including governance, education, health, finance, and digital innovation.

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China brings AI into advanced ocean forecasting systems

China has unveiled LangYa 2.0, an upgraded AI-powered ocean forecasting system designed to predict complex marine phenomena with greater precision and detail. The model was unveiled at the Fourth China Digital Earth Conference in Qingdao and represents a step forward from earlier ocean monitoring tools.

Developed by the Institute of Oceanology at the Chinese Academy of Sciences, the system goes beyond monitoring variables such as temperature and salinity to forecast high-impact events, including typhoons, storm surges, extreme rainfall, internal waves, mesoscale eddies, and sea ice.

The platform combines specialised AI sub-models trained on diverse datasets and informed by physical ocean processes.

LangYa 2.0 is designed to provide decision-support information for applications including disaster preparedness, maritime safety, polar navigation and climate adaptation. The system can simulate rapid typhoon intensification and sudden track shifts, while also forecasting hidden ocean dynamics that may impact offshore infrastructure.

According to researchers, the model ranked first in a 2025 international Arctic sea ice forecasting evaluation, highlighting its potential for polar forecasting applications. Researchers are exploring ways to expand the system into broader climate and ecological modelling, with the aim of supporting future marine intelligence and environmental monitoring platforms.

Why does it matter?

Accurate ocean forecasting plays a critical role in disaster preparedness, maritime safety, climate adaptation and the protection of coastal infrastructure. AI-based systems can process large volumes of environmental data more quickly and identify complex patterns that may be difficult to capture using traditional forecasting methods alone.

LangYa 2.0 also reflects a broader trend towards using AI in Earth system science. As climate-related risks become more frequent and complex, governments and researchers are increasingly investing in AI-driven tools to improve environmental monitoring, risk assessment and decision-making.

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Cambridge researchers test AI-designed vaccine in human trial

Researchers at the University of Cambridge have developed an experimental vaccine using AI, marking what they describe as the first human test of a vaccine component designed entirely by AI. The experimental approach aims to provide broad protection against entire families of viruses, including coronaviruses with pandemic potential.

The AI system analysed genetic data from multiple coronaviruses and designed a ‘super-antigen’ intended to help the immune system recognise and respond to a broad range of viral variants, including those that may emerge through future mutations. An initial trial involving 39 volunteers focused primarily on safety, while a larger follow-up study is planned to evaluate immune responses and effectiveness in greater detail.

Researchers say the approach could help vaccine development keep pace with rapidly evolving threats, including influenza, emerging COVID-19 variants and viruses with the potential to spread from animals to humans. The team is also exploring similar AI-designed vaccines for influenza, bird flu, and Ebola-like viral haemorrhagic fevers, where current protection options remain limited.

Researchers describe the findings as an early but significant step towards using AI to accelerate vaccine design and strengthen preparedness for future disease outbreaks. The study highlights growing expectations that AI may become a central tool in global pandemic prevention strategies.

Why does it matter?

Traditional vaccine development often focuses on responding to specific pathogens after they emerge. By contrast, AI-assisted design could help researchers develop vaccines that provide protection against entire families of viruses before outbreaks occur.

If successful, the approach could shorten development timelines, improve preparedness for future pandemics and support efforts to address rapidly evolving infectious diseases. The research also highlights the growing role of AI in scientific discovery and biomedical innovation.

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UN warns of AI’s growing environmental footprint

As AI continues to reshape economies, industries and daily life, a new report from the United Nations University (UNU) highlights the environmental challenges associated with its rapid adoption. While discussions often focus on greenhouse gas emissions linked to AI systems, researchers argue that the technology’s impact on water resources, land use and electronic waste deserves equal attention.

According to the report, data centres supporting AI applications could consume up to 945 terawatt-hours of electricity annually by 2030. Beyond electricity demand, AI-related water consumption could reach levels equivalent to the annual household needs of 1.3 billion people, while the land footprint associated with AI infrastructure may exceed 14,500 square kilometres.

Researchers note that environmental pressures vary significantly depending on the technologies and energy sources used to power AI systems.

The UN report also finds that routine AI use, rather than model training alone, accounts for a significant share of resource consumption. Everyday activities such as generating images, videos and text require substantial computing power, with image generation demanding significantly more energy than basic text-based tasks. Growing adoption may further increase total resource consumption despite improvements in efficiency.

Researchers note that the environmental costs of AI infrastructure are often concentrated in specific regions, while the benefits of AI are distributed more broadly across the global economy. Expanding data centres, rising electricity demand, increasing water consumption and growing volumes of electronic waste could place additional pressure on communities and countries already facing resource constraints.

The report calls for responsible AI development supported by greater transparency, sustainable infrastructure planning, international cooperation and governance measures aimed at keeping technological progress within environmental limits.

Why does it matter?

Debates about AI sustainability often focus on carbon emissions, but the report argues that water consumption, land use and electronic waste are becoming equally important considerations as AI infrastructure expands. These impacts could become increasingly significant as governments and companies invest in larger data centres and more powerful AI systems.

The findings also highlight the need for environmental considerations to be integrated into AI governance and infrastructure planning. As AI adoption accelerates worldwide, policymakers face growing pressure to balance technological innovation with sustainability and resource management goals.

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Canada launches AI for All national strategy to accelerate adoption and digital sovereignty

Canada has launched AI for All, a new national AI strategy aimed at accelerating AI adoption, strengthening digital sovereignty, and positioning the country as a leading AI economy.

Announced by Prime Minister Mark Carney, the strategy combines proposed legislation, investments, and programmes intended to ensure AI is adopted responsibly and benefits businesses, workers, students, and communities across Canada.

The strategy targets an additional C$200 billion in economic growth, 250,000 new AI-related jobs over the next five years, and an increase in AI adoption from just over 12% today to 60% by 2034. The government also plans to provide up to 90,000 AI-related jobs and work placement opportunities for young Canadians.

The strategy is built around three principles: building trust, creating opportunities, and reinforcing Canadian sovereignty. To build trust, the government plans to modernise digital legislation, strengthen protections for personal information, address harms such as deepfakes and surveillance pricing, introduce an online safety regime, and expand the capabilities of the Canadian AI Safety Institute.

To create opportunities, the government will establish a National AI Literacy Initiative, provide access to trusted AI agents for post-secondary students, help small and medium-sized businesses adopt AI, support worker training, and launch an AI Missions Program with a flagship health mission focused on diagnostics, patient care, and system efficiency.

To reinforce sovereignty, Canada plans to build domestic AI foundations, including compute, cloud, connectivity, data, and talent. Measures include a world-leading public AI supercomputer, investments in sovereign compute and cloud infrastructure, better access to growth capital for Canadian AI companies, strategic public procurement, and expanded support for AI talent.

The government said the strategy is intended to ensure more AI value is created in Canada while strengthening privacy, data protection, public services, productivity, and economic security.

Why does it matter?

Canada’s AI for All strategy links AI adoption directly to economic growth, workforce development, public trust, and technological sovereignty. The strategy reflects a wider shift among governments: AI policy is no longer focused only on research excellence, but also on compute infrastructure, cloud sovereignty, data governance, safety institutions, business adoption, public procurement, and skills. Its success will depend on whether Canada can turn ambitious targets into measurable adoption across businesses, public services, and workers.

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European Commission unveils roadmap for AI and digitalisation in energy

The European Commission has published a Strategic Roadmap for Digitalisation and AI in the Energy Sector, outlining how digital technologies could support a more resilient, competitive and secure European energy system.

The roadmap outlines how digital tools and AI could help consumers and businesses reduce energy costs through greater efficiency, smarter energy consumption and improved management of electricity demand. It also highlights the role of digital technologies in supporting the integration of renewable energy into electricity grids.

The Commission has structured the roadmap around three main priorities. These priorities include integrating data centres into energy systems in a sustainable manner, accelerating the deployment of digital and AI-enabled technologies such as smart meters and intelligent grid solutions, and establishing a framework for secure cross-border energy data sharing.

The Commission said the plan will also focus on cybersecurity, AI trust, digital skills and international cooperation. As part of the next phase, the Commission plans to support industry cooperation initiatives and launch the AI.grids community, which will focus on developing AI models for energy network management across the EU.

Why does it matter?

The energy sector is becoming increasingly dependent on digital technologies to manage growing electricity demand, integrate renewable energy sources and maintain grid stability. AI and advanced data analytics could help improve efficiency, reduce costs and support more flexible energy systems.

At the same time, greater digitalisation introduces new challenges related to cybersecurity, data governance and infrastructure resilience. The roadmap signals the EU’s intention to ensure that digital transformation in the energy sector supports both sustainability goals and long-term energy security.

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MIT develops ChartNet dataset to improve AI chart understanding

MIT researchers have developed a new dataset, ChartNet, to improve how vision-language models interpret charts and other graphical data.

The dataset is designed to help AI systems better combine visual, numerical, and linguistic information, a task that remains difficult even for advanced models. MIT said chart understanding is important for applications such as business trend analysis, financial reporting, and scientific figure interpretation.

ChartNet contains more than one million synthetic chart images, each paired with supporting code, numerical tables, textual descriptions, and question-and-answer pairs. The dataset was created through an automated pipeline that generates and augments chart examples, supported by quality checks to ensure that the code is executable and the resulting charts are accurate and clean.

The researchers developed ChartNet to address a key limitation in current AI systems: the lack of large, high-quality training data for robust chart interpretation. Many existing datasets rely on limited chart images collected from the internet and lack the supporting information needed for models to understand the underlying data.

MIT researchers used ChartNet to train several open-source vision-language models, including IBM’s Granite Vision series. The dataset improved model accuracy across chart reconstruction, chart data extraction, chart summarisation, and chart question answering.

In MIT’s testing, smaller open-source models trained with ChartNet consistently outperformed much larger commercial models on several chart-interpretation tasks. The researchers said the dataset could help smaller organisations use AI for analytical work without relying only on large proprietary systems.

Why does it matter?

ChartNet shows how better training data can improve AI performance in specialised analytical tasks. If smaller open-source models can interpret charts more accurately after training on high-quality datasets, organisations with limited budgets may gain access to stronger AI tools for business analytics, research, financial reporting, and scientific communication. The work also highlights a broader point in AI development: model capability depends not only on size, but also on the quality and structure of training data.

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