Statetron has unveiled its Andromean Class™, a new industrial architecture designed to integrate artificial intelligence (AI), robotics, autonomous logistics, energy storage and digital intelligence within a single advanced manufacturing ecosystem.
The company is proposing its first Andromean Class facility in Qatar, with an initial development concept spanning approximately 10 hectares and an architecture designed to support expansion toward multi-gigawatt-scale energy storage manufacturing capacity.
Unlike a conventional battery assembly facility, the proposed development is conceived as an intelligent, energy-integrated industrial platform in which manufacturing, logistics, energy, data and maintenance systems operate as a coordinated ecosystem.
Project Scope
The proposed Qatar facility will integrate AI-driven production, robotic assembly, autonomous material movement, digital-twin technology, automated quality control and intelligent energy management.
At the core of the manufacturing platform will be Statetron’s modular 5 MW Power Block architecture, designed for large-scale energy-storage applications serving utility, industrial, renewable-energy and grid-infrastructure markets.
The manufacturing platform is intended to support configurable energy-storage durations and progressively increase production capacity in line with market demand.
The facility’s architecture will also allow production, automation, logistics and energy infrastructure to be expanded progressively, with the long-term objective of reaching multi-gigawatt-scale manufacturing.
Factory Designed as an Energy System
The Andromean Class concept extends beyond battery manufacturing by integrating renewable energy generation, energy storage and intelligent power management into the facility itself.
Under the concept, energy systems will power the manufacturing operation while the factory produces energy-storage infrastructure, with digital intelligence connecting and optimizing the overall system.
Statetron said Qatar provides a compelling environment for the development, citing the country’s focus on advanced manufacturing, AI, robotics, Internet of Things (IoT), data analytics and clean technologies.
The proposed facility is also intended to benefit from Qatar’s strategic industrial and logistics infrastructure, including the Umm Alhoul Free Zone, which is positioned for heavy manufacturing, logistics and emerging technologies and is located adjacent to Hamad Port.
Statetron’s Andromean Class Vision
Statetron said the Andromean Class represents a new approach to industrialisation in which the factory itself becomes an intelligent and energy-integrated system.
The company aims to replace fragmented manufacturing processes with a continuously connected industrial environment where production, logistics, energy, data and maintenance are optimized together.
Lars Carlstrom, CEO of Statetron, said: “The first industrial revolution mechanised production. The digital revolution connected it. The next era will make industry intelligent, autonomous and energy-native.”
He added: “We don’t want to build another battery factory. We want to build the blueprint for how industry itself will operate in the next decade.”
The proposed Qatar development represents Statetron’s first application of the Andromean Class architecture and is intended to establish a scalable platform for the future manufacturing of large-scale energy-storage systems.
Is universities’ AI embrace undermining their net-zero targets?
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Amid all the debates about student employability and academic integrity, the question of how AI mandates square with universities’ commitments to addressing climate change has been largely neglected. Juliette Rowsell reports
For at least a decade, universities around the world have been declaring their determination to get to net-zero emissions. Then, in 2019, a slew of universities aroundtheworld went a stage further, declaringclimate “emergencies” that often came with accelerated targets to eliminate net institutional carbon emissions.
Announcing Cardiff University’s climate emergency in 2019, for instance, its then vice-chancellor Colin Riordan said the university had already fully divested from fossil fuels and must “lead by example and accelerate our plans to reduce our carbon emissions, energy and water use and overhaul our operational activities”.
And in 2021, Universities UK’s Confronting the climate emergency report committed all UK universities to setting targets for reducing the carbon footprint of sources they control directly (known as source 1 emissions) and of the energy they use (known as source 2). It also promised that institutions would “set a target” for reducing scope 3 emissions, caused by the production of products and services they use – or, failing that, “commit to a programme of work to set targets as soon as possible”. They would publish these targets on their websites and “set out how progress against these targets will be reported in a transparent, consistent, and understandable way”.
But then, late the following year, ChatGPT was unleashed, and institutional attention turned towards the adoption of AI. There were concerns about it, of course, but they were centred around its corrosive effect on academic integrity. And notwithstanding those concerns, many universities have committed to making AI tools available to all staff and students and embedding technology into the curriculum – on the grounds that the technology is here to stay and students need to be fluent in its use.
In 2025, for instance, California State University rolled out ChatGPT Edu – OpenAI’s customised education version of its large language model tailored to higher education institutions – to more than 460,000 students and more than 63,000 staff, making it the biggest educational roll-out of ChatGPT in the world. And later that year, the University of Oxford became the first UK institution to roll out ChatGPT Edu to students, while earlier this year the University of Manchesterannounced a “world-first” partnership with Microsoft to provide access to its AI tool, 365 Copilot, for all staff and students.
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Yet amid all the debates about employability, pedagogy and the integrity of student assessment, the potential conflict of mass AI roll-outs with net-zero targets has been rather overlooked.
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A June report by UK IT body Jisc and the Environmental Association for Universities and Colleges (EAUC) warned that pressure for universities to adopt AI has “outpaced clarity about what responsible action looks like in practice”.
The report cites statistics from the International Energy Agency, which show that global data centres’ energy consumption could more than double by 2030 – or even, according to Greenpeace Germany, increase elevenfold. And data centres already account for 6 per cent of all electricity consumption in the US and the UK, according to an industry body – and a much higher proportion in some other countries.
“As institutions with public commitments to net zero, tackling climate change, and broader environmental sustainability goals…it is imperative that we recognise how the adoption of these technologies is contributing to both our individual and collective environmental footprint across multiple dimensions,” the Jisc/EAUC report read.
Water consumption is also a major concern, as large amounts of water are needed to cool data centres. Research published in the journal NPG Clean Water in 2021 found that even a relatively small 1 MW data centre can consume about 25 million litres of water per year, while the UK water company Affinity Water told UK MPs in May that one recent proposal for a data centre had estimated that its daily water need would be equivalent to that of 147,000 people.
And Jonatan Pinkse, research director at the Centre for Sustainable Business at King’s College London, noted that Google’s recently released sustainability report revealed an 18 per cent year-on-year increase in carbon emissions as it expands its AI operations, and an 81 per cent increase in emissions from 2019, despite having a 2030 net-zero target.
“The negative [of AI use] has become so clear so fast that there’s no going around it any more,” he said.
Michael Draper, professor in legal education at the University of Swansea and director of the university’s academic regulations and student cases board, said: “If you actually design your assessment so that AI [must be] used by the student, and some students are saying, ‘Well, we shouldn’t be doing this because of the impact on the environment, and some of the ethical concerns’, there are real concerns around that, and that conversation isn’t really had…It’s all around academic integrity. The point I raise…is that institutions often have a commitment to UN sustainability goals. Well, where does that commitment fit with the widescale adoption of artificial intelligence when it’s using all this energy and water?”
One reason that such conversations rarely occur is that the environmental impact of AI is not universally known. Cal Innes, a digital sustainability specialist at Jisc and co-author of the Jisc/EAUC report, told Times Higher Education that “a lot of people are going into [AI usage] as they do with a lot of aspects of digital behaviours: not realising that there’s an environmental footprint associated with it”.
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But even university managers who are aware of the problem and determined to address it face an uphill struggle since AI companies rarely disclose information regarding individual institutions’ AI use, the Jisc/EAUC report says, making it almost impossible for universities to produce reliable estimates of the environmental cost of their AI use.
THE asked several universities that have announced major AI roll-outs about how they are tracking their emissions data in relation to AI use. However, out of California State, Oxford, Manchester, Arizona State, the Massachusetts Institute of Technology and the universities of Cambridge and Surrey, only Surrey, Manchester and MIT provided responses.
A spokesperson for Surrey, which recently announced that AI is being incorporated into curricula for all subjects, explained that it had “robust monitoring processes” in place as part of its sustainability agenda, and added it is “applying those same standards to how we procure AI tools for our framework”. It estimates its indirect emissions and reports them in both its annual sustainability report and its institutional annual report.
“Scope 3 emissions are harder to pin down than direct emissions, as they depend on suppliers reporting their own carbon data back to us,” the spokesperson conceded. However, “we’ve recently updated our procurement policy to require this, and we’re now starting to collect that data, including from external AI vendors”.
A spokesperson for Manchester, meanwhile, argued that it is important that staff and students have “equitable” access to AI tools, and for them to be equipped with “necessary skills for the workplace”, including learning to use AI “responsibly”. The university is working “closely” with Microsoft to ensure transparency around AI’s environmental impacts, the spokesperson said, adding it has initiated “groundbreaking research in partnership with Microsoft to model the impacts of our Copilot usage and to inform the actions the university will take to manage and minimise these impacts. We are exploring how best to understand and monitor these impacts as the university-wide roll-out progresses.”
Alex de Vries-Gao, founder of Digiconomist, which examines the impact of technology trends on the environment, is concerned by AI’s typical absence from universities’ sustainability statements: he would expect organisations to reference it even if merely to acknowledge “how difficult it may be to obtain the right information”.
“If you’re not capable of getting the numbers, at least talk about it and show that you’re thinking about this because if you’re not discussing it, you’re probably not thinking about it,” he said.
A spokesperson from OpenAI said the company gives considerable thought to the best use of its computing power and said it supports its partners to meet their sustainability and water-consumption goals. And they pointed out that the Jisc/EAUC report did not take into account modern closed-loop water cooling systems, which are more efficient than traditional cooling towers that allow water to evaporate away, adding that it is currently developing data centres in Norway that run entirely on renewable energy.
OpenAI believes AI will be instrumental in tackling climate change by optimising energy systems and accelerating research, the spokesperson added, noting that the company is partnering with leading universities to accelerate these efforts, such as Oxford and the US National Laboratories.
Microsoft declined to comment but its 2026 Environmental Sustainability Report said that in the 2025 financial year it had “replenished more water than we withdrew” and “achieved our milestone to match 100% of our annual electricity consumption with renewable energy”. It added that the company is “scaling clean, reliable energy to meet growing demand from cloud and AI, exploring sources from nuclear to fusion, and investing in the grid infrastructure and technologies needed to get there”.
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Universities are also exploring how AI can help them reduce their own scope 1 emissions. While MIT was unable to provide details on its generative AI (GenAI) use and how it is tracking such data, it outlined that it is currently expanding a pilot launched in 2023 that uses machine learning to optimise its energy systems and increase efficiency in heating and cooling its buildings. It said the programme had reduced the pilot building’s energy use by up to 40 per cent annually and was now being expanded to additional buildings and updated so MIT systems can automatically respond to live data.
But Charlotte Bonner, chief executive of EAUC and co-author of the report with Jisc, noted that traditional machine learning is distinct from GenAI tools. And De Vries-Gao, who is also completing a PhD at the Vrije Universiteit Amsterdam Institute for Environmental Studies, said machine learning tools do not have as significant an impact as GenAI and are more likely to have been developed in-house, which provides institutions with greater transparency over their energy use.
That lack of transparency about AI’s environmental cost is the “ultimate problem”, he said, adding that institutions are also “not capable of really dissecting what is the benefit of each individual application [of AI] either. So you’re missing both sides of the equation.”
Examples of such applications are also few and far between. Bonner and Innes had wanted to include more positive examples of universities using AI to address the environmental impact, but “once we really started to look into the research available, we found that where claims were being made about how AI use was helping drive positive environmental change, it was nearly always as a result of traditional machine learning rather than GenAI,” Bonner said. “We can’t really find at this moment in time any substantiated evidence that generative AI is helping drive positive environmental change.”
And even though universities’ AI use may lead to research breakthroughs that ameliorate climate change, King’s’ Pinkse said this is far from guaranteed.
“We hope that it will of course lead to fantastic breakthroughs,” he said. “But you can never really promise it because…the whole point of research and development [is] that we don’t always know what the outcome is going to be.”
Source: Getty Images montage
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So what are universities to do? None of the experts that THE spoke to said that abandoning AI was the way forward. Pinkse’s view is that universities are in an “almost impossible” dilemma given the ubiquity of AI.
“If a university were to say, ‘We’re becoming a non-AI university’, students would start saying, ‘We’re not too sure about this one’,” Pinkse said. “That puts them in a very vulnerable position because they are competing with other universities and other organisations. There are [also] demands that we deliver students who are AI literate and so forth. So I don’t think it’s that easy to say, ‘We should not be doing this’…It’s unfair as a demand on universities.”
Meelis Kitsing, rector of the Estonia Business School, thinks he has found the right balance by seeking to produce graduates who are not only AI-literate but also able to engage with wider ethical debates regarding technology. To that end, the school has embedded questions on AI throughout its curriculum and strategy, embracing the tech while also remaining critical of it.
The institution is also hosting its seventh “digitalisation and sustainability” summer course bringing together students, academics and policymakers to examine how AI is reshaping business models while simultaneously intensifying energy demands.
“We need to encourage people to be critical thinkers, rather than ideologues who believe that AI will solve all problems – or the opposite: that AI will only create problems for sustainability,” Kitsing said. “I think that truth is more likely to be characterised by different shades of grey, rather than be black and white.”
Ultimately, the “guilt” for AI use’s environmental impact should not fall on academics and students, Innes believes. He referred to a concept known as “greenshifting”, whereby the responsibility for environmental harm is projected by companies on to consumers, which he said “diverts away from institutional responsibility, where we have the greatest leverage”.
But do universities really have any meaningful leverage over BigTech firms?
Bonner suspects that they have more power than they think to demand greater transparency on AI’s water consumption and energy use, provided they act collectively – and, ideally, with other sectors, such as health systems. And she can “foresee a future” in which universities develop shared “sector-specific large language models” and data centres – in a similar way that numerous institutions have access to Isambard-AI, the University of Bristol-based supercomputer purpose-built for AI research.
That, as she and others pointed out, would allow them to better track the technology’s usage and energy consumption. “But I don’t think that we’ll ever get to a situation where we don’t use some of [the AI developed by] the household names,” she conceded.
Universities could also restrict academics and students to “acceptable” uses of AI, Innes suggested, noting that creating just eight seconds of video with AI can produce up to 2,000 times more carbon than a single text prompt. But he was wary of singling out AI firms when other forms of digital technology also have significant environmental impacts. While it is important to hold them to account, “we’re not routinely questioning the footprint of watching YouTube videos, doomscrolling social media, or leaving webcams on in online conferences”, he noted.
It is abundantly clear that AI is only going to become ever more closely integrated into university teaching, research and administration. Jisc itself, for instance, is currently running a trial of AI marking tools. But in the midst of another unprecedently hot summer across Europe and North America, the phrase “climate emergency” has never seemed more apt – and it would seem odd for institutions pledged to combating it to simply dismiss AI’s huge carbon footprint as a problem too difficult and poorly documented for them to engage with.
For her part, Bonner conceded that institutions can often feel “overwhelmed” when it comes to tackling the sustainability consequences of AI. But she urged them not to wait until they have “perfect data” on it before acting.
“We don’t want there to be a paralysis of action because the data quality isn’t there,” she said. Because if universities take that approach, “we’re never going to do anything”.
Professor Fulufhelo Nemavhola, Image: Durban University of Technology
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When the Sustainable Development Goals (SDGs) were adopted in 2015, they gave the world one of the most ambitious frameworks for shared development in modern history. The 17 goals created a common language for poverty, hunger, health, education, gender equality, water, energy, work, infrastructure, inequality, climate action, peace and partnerships.
But the world of 2026 is not the world of 2015.
Artificial intelligence now shapes education, research, public administration, labour markets, health systems and democratic debate. Cloud infrastructure has become as strategically important as physical infrastructure. Data has become a source of economic power. Digital platforms increasingly mediate how societies learn, trade, communicate, govern and form political opinion. Cybersecurity is now a daily concern for universities, hospitals, municipalities and states.
The SDGs remain important. But as the world begins to think beyond 2030, it must confront a difficult question: can any country achieve sustainable development if it lacks the capacity to understand, govern, produce, and benefit from the technologies that increasingly determine its future?
This is why the world needs a serious conversation about the 18th SDG: technological sovereignty and digital justice – in particular in Africa and the Global South.
Technology is a field of power
This is not a call to multiply global slogans. It is a call to recognise that technology has become the operating system of development itself. Education, health, agriculture, energy, climate adaptation, industrialisation, public finance and governance are now mediated by digital systems, artificial intelligence, data infrastructure and advanced technologies. A development framework that treats technology only as an enabler, rather than as a field of power, dependency and justice, is incomplete.
Several proposals for an 18th SDG have already emerged. Some have argued for universal communication. Others have proposed responsible and inclusive artificial intelligence, digital technology, animal welfare, space sustainability, or ethnic-racial equality as possible missing goals.
These proposals are important because they reveal a wider truth: the 2015 SDG framework is now under pressure from realities that have intensified since its adoption. My argument is that the most comprehensive missing goal is technological sovereignty and digital justice, particularly for the Global South.
Technology in the list of 17
Technology is not absent from the existing SDGs. SDG 9 already speaks to industry, innovation and infrastructure. Other goals also refer to science, innovation, data, energy systems, education technologies and partnerships. But technological sovereignty, artificial intelligence governance, data justice, cybersecurity resilience and domestic innovation capability are not treated as development goals in their own right. That is the gap.
For Africa and the Global South, this issue is urgent. Many universities teach through foreign platforms, store research data in external cloud systems, rely on imported laboratory equipment, use artificial intelligence tools designed elsewhere, and depend on digital architectures over which they have limited control.
Many governments procure digital systems without building domestic capability. Many countries generate vast quantities of data without having sufficient sovereign infrastructure, technical expertise or regulatory capacity to govern that data in the long-term public interest.
This creates a new development divide. It is no longer only a divide between those with and without access to technology. It is a divide between those who design technology and those who merely consume it; between those who own data infrastructure and those who supply data; between those who govern artificial intelligence and those who are governed by it.
An 18th SDG should, therefore, be framed as follows: ensure equitable technological sovereignty, responsible artificial intelligence, data justice and inclusive innovation capacity for all nations.
Technological sovereignty
Such a goal would not promote technological isolation. Sovereignty does not mean autarky or withdrawal from global cooperation. It means the ability to participate in global systems with dignity, capability and bargaining power. It means that developing countries should not be permanent consumers of technologies designed elsewhere, but co-creators of the technological future.
Higher education must be central to this agenda. Universities cannot remain observers of technological dependence. They must become engines of technological capability. This means producing, not only graduates and publications, but also prototypes, patents, start-ups, open technologies, local platforms, community innovations, industrial partnerships and public-interest research.
Universities of technology play a particularly important role. Their mandate is applied knowledge, problem-solving, industry engagement and social innovation. They are well placed to translate technological sovereignty into practical development outcomes, including medical devices, clean energy systems, water technologies, agricultural tools, digital public infrastructure, cybersecurity capacity, smart manufacturing, and inclusive entrepreneurship.
But this requires a shift in how governments, funding agencies and universities define impact. Research systems often reward publications more easily than prototypes. They measure journal outputs more consistently than technology transfer.
They celebrate international collaboration but do not always ask whether it builds local capability. They encourage innovation rhetoric but underfund the laboratories, workshops, testing platforms, and commercialisation pathways required to move ideas into products.
An 18th SDG would help correct this imbalance. It would give countries, universities and development agencies a language for measuring technological capability as a development outcome. It would ask whether a country can build, maintain, adapt and govern the technologies on which its development depends.
The proposed SDG 18 should include measurable targets: affordable and secure digital infrastructure; responsible AI governance; national data protection and data sovereignty frameworks; cybersecurity readiness; local language inclusion in digital systems; investment in research and development; technology transfer; local manufacturing of critical technologies; support for university-based innovation; and fair participation of developing countries in global technology governance.
The United Nations has already recognised the importance of digital cooperation through the Global Digital Compact. That is a positive step. But a compact is not the same as a goal. A compact can guide cooperation; a goal shapes measurement, accountability, funding, institutional behaviour and political priority.
Existing goals depend on technology
The SDGs matter because they tell the world what to count. If technological sovereignty is not counted, it will remain peripheral. If digital justice is not measured, it will remain rhetorical. If innovation capacity in the Global South is not treated as a development priority, the world will continue to reproduce dependency while speaking the language of partnership.
The world does not need an 18th SDG as if the existing 17 were already irrelevant. It needs one because the existing goals are increasingly dependent on technology.
Health systems depend on diagnostics, data and medical technologies. Education depends on connectivity and learning platforms. Agriculture depends on sensors, climate intelligence and logistics systems. Climate adaptation depends on modelling, satellites and early-warning tools. Governance depends on secure information systems. Work depends on digital skills and readiness for automation.
Technological sovereignty is, therefore, not a separate development concern. It is becoming the foundation for many existing goals.
The SDGs promised that no one should be left behind. In the 21st century, those most likely to be left behind are countries, communities and institutions that are locked out of the technologies that define the future.
The world needs an 18th SDG. It should be technological sovereignty and digital justice.
Professor Fulufhelo Nemavhola is the deputy vice-chancellor for research, innovation, and engagement at the Durban University of Technology (DUT) in South Africa.
The AI for Good global summit, at Palexpo in Geneva, 7 July 2026. (Keystone/Salvatore Di Nolfi)
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As AI promises to accelerate global development, the race for dominance risks leaving most countries as bystanders with no choice but to accept a future they had no say in. AI expert Martin Wahlisch argues Geneva must change that – at next year’s global AI summit.
The ITU’s AI for Good Global Summit in Geneva has come and gone once again. This time it coincided with the first report by the Independent International Scientific Panel on Artificial Intelligence. One of its conclusions is difficult to dispute: AI will reshape economies, public services, scientific discovery and national security. Like electricity and the internet before it, the countries with the capacity to develop and deploy AI will increasingly dominate markets, set standards and shape geopolitical influence.
As governments begin looking beyond the Sustainable Development Goals, AI is shaping education, healthcare, productivity, public administration and economic growth. In many respects, the post-2030 development agenda will be an AI agenda.
Yet the global conversation remains largely focused on the capabilities of AI models, their risks and the safeguards required. Far less attention is paid to the political economy of AI: who owns the data centres, who builds the AI models, and who profits most.
Division and extraction
Just a handful of companies are developing the world’s most advanced foundation models. Three US firms – Amazon, Microsoft and Google – control roughly two-thirds of the global cloud market, while Taiwan’s TSMC manufactures over 90 per cent of the world’s most advanced semiconductors. Nvidia supplies the vast majority of high-end AI chips that power frontier systems. Most governments will spend the coming decade deploying and regulating technologies developed elsewhere, running on infrastructure they neither own nor influence. The result is not simply an AI divide. it is rather a widening gap in capability, development and power.
AI is also creating a new extractive economy. The raw materials are data, public research, human creativity, energy and computing power. They are sourced globally, processed through infrastructure controlled by relatively few actors and transformed into products whose value largely flows back to the platform owners. Countries risk supplying the inputs of the AI economy while importing the intelligence built from them.
That imbalance is unlikely to correct itself. As AI models become cheaper and more capable, demand for chips, cloud services, data centres, electricity and water will continue to grow. Greater access to AI applications does not necessarily reduce dependence on the infrastructure behind them, as cloud computing has demonstrated. It lowered the cost of digital services while concentrating critical infrastructure in the hands of a few providers. AI is likely to deepen that trend.
A voice for all
This is why we need to shift from AI for good to AI for all. AI for all means more than universal access to increasingly powerful technologies. Countries need the talent, research institutions, computing capacity and public infrastructure to develop AI on their own terms and share in the value it creates.
Global AI governance has an important role to play, but we should also be realistic about its limits. Frontier AI will continue to be driven largely by companies with extraordinary financial resources and access to computing power. The United Nations will not determine their research agendas, nor should it pretend to. Its comparative advantage lies elsewhere. It can build international norms, promote inclusive governance and ensure that countries not developing frontier AI have a meaningful voice in shaping the principles under which these technologies are deployed.
International Geneva’s role
Geneva has a particular responsibility. For decades it has been where governments negotiate the rules for trade, telecommunications, health and humanitarian action, and artificial intelligence deserves the same institutional ambition.
The global AI summit process has evolved remarkably in just four years, since it began in the United Kingdom with the Bletchley Park AI Safety Summit in 2023, where 28 countries and the European Union met to address the risks of frontier AI. In Seoul, the process focused on a smaller coalition committed to advancing governance. Paris broadened participation again, with 58 countries, the EU and the African Union Commission endorsing its declaration. This year, New Delhi marked another important step, with 92 countries, alongside the EU and the International Fund for Agricultural Development, supporting the New Delhi Declaration.
When the summit comes to Geneva in 2027, the ambition should be greater still: to make it a truly global summit, bringing together as many states as possible to shape not only how AI is governed, but also how its benefits are shared. Geneva offers a unique advantage as most UN member states already maintain a diplomatic presence in the city. It lowers the barriers to participation, making it easier for governments from all regions, particularly developing countries, to take part in shaping the future of AI governance.
Switzerland should seize that opportunity by ensuring that the 2027 summit becomes the broadest global forum on AI to date. If artificial intelligence is becoming foundational infrastructure for the global economy, its governance cannot remain the preserve of a handful of governments and technology companies. More countries need the capacity not only to use AI, but to build it, shape it and benefit from it. AI for good inspired an important global conversation. AI for all should become its next ambition.
Martin Wählisch is an associate professor of international relations at the University of Birmingham and a member of its Centre for AI in Government. His research focuses on international affairs, AI governance and multilateral cooperation.
Action to address climate change can drive strong, sustainable growth. The state is central in setting the direction of change and mobilising investment and innovation to achieve it. But time is not on our side. Research to guide policy has never been more urgent; it must proceed alongside action.
The new growth story of the 21st century must be about sustainability and resilience, and with artificial intelligence (AI) at centre stage. It will be very different to the polluting and unsustainable models that so many countries have followed. Private investment will be at the core. But the role of the state will be critical in making it happen. The task of governments and public institutions is to devise comprehensive strategies, policies and institutions that can foster transformation across all economic sectors.
The role of the state must include setting incentives, aligning expectations and spurring innovation and entrepreneurship. It will be in the vanguard of system change, including in our cities, energy, transport, land and water.
This requires a clear strategic vision, tackling market failures and encouraging private investment in new areas. At the same time, it must manage the socio-economic and political challenges arising from dislocation and vested interests. And it must be aware of and avoid the dangers of government failure. In so doing, it must beware both market and institutional fundamentalisms.
These are ambitious tasks that require a renewed and effective state. This article outlines key dimensions of the state action that are needed to enable this growth story. Together, they form both a sense of direction and a research agenda. Because of the urgency, action and learning must go together. There will be mistakes along the way, but this should not be an argument for inaction. Delay is dangerous.
This is a time of crisis – and a new growth story is needed
Much of the global political landscape is currently divided. Across the world, there has been erosion of constitutional democracy. Some of this is related to the sluggish economic growth that the world has been experiencing, eroding living standards for many, undermining communities and leading to anger and discontent.
In this context, climate action has been characterised by many as an issue for elites. Yet it is not sustainability-aligned policy that has led the world to its current state. On the contrary, the lack of it has contributed to the difficulties. Climate inaction creates instability, migration and conflict. Climate action drives growth.
Transforming our economies towards sustainability can provide exactly what is needed to overcome the political and economic difficulties of the present: integrated, well-designed and well-implemented public policy and institutional structures that drive development through private and public investment, innovation and systemic change. Such actions can unleash the forces that will transform our economies, creating strong, sustainable, resilient and inclusive growth.
Climate action will drive the new world growth story. There is no inevitable trade-off between climate action and growth: the former drives the latter. And it is a much more attractive form of growth than the dirty and destructive models of the past.
Developing nations, where most of future growth and investment are set to happen, are at centre stage. In these places, most of the infrastructure remains to be built, the vast majority of the world’s renewable resources are concentrated and the investment needs are the greatest. Their cities can be built in different ways, choosing health and efficiency over pollution and congestion; their agricultural systems can combine local knowledge and AI to become smarter and less toxic; and their economies can leverage their clean endowments, potentially benefiting from a new world economic geography where abundant clean energy or the availability of specific minerals is a key asset.
Strong investments and innovation are necessary across the whole economy – see Figure 1 for five key climate investment areas. Such investment must reach all forms of capital, including physical, natural, human and social.
Clean energy investment drives development; natural capital investment underpins it; adaptation and resilience investment sustains it; and just transition investment makes change equitable, desirable and possible. This investment would strengthen demand, supply and efficiency in the shorter term; stimulate innovation and discovery, creating new investment opportunities in the short and medium term; and avoid the destructive impacts of climate change in the longer term. Carbon-intensive growth self-destructs; it is not a viable option.
Alongside the necessary increase in investment, the growth story comes from a number of key drivers (see Figure 1):
Lower costs and technological advancement, with the clean already being cheaper than the dirty across much of the economy, and innovation proceeding apace across most action areas.
Increasing returns to scale, shown by many new products and activities.
Increased resource efficiency, leading to higher productivity.
Reduced pollution, improving health, reducing mortality and increasing productivity.
Improvements in the key systems of cities, energy, transport, land and water – for example, cities where you can move and breathe are much more productive than those that are heavily polluted and congested.
AI can magnify these drivers, enabling green and intelligent growth (Stern et al, 2025). See The Growth Story of the 21st Century (Stern, 2025) for a further description of the drivers of growth.
Figure 1: Key investment sectors and growth drivers
The role of the state: five key areas for action
The role of the state as indicated above is crucial to the delivery of the growth story. It will chart a path, and thereby expectations, create the conditions for investments, steer action towards shared objectives and help to bring society together. The remainder of this article focuses on five key areas for state action. These are also priority areas for research, which must move quickly and alongside action. Time is not on our side.
National strategy and direction
Structural change at speed requires strong political support. The state must establish a clear direction and craft a compelling national vision and narrative. And it must act so that some of the tangible benefits of change are experienced directly and soon. It is crucial to foster a shared understanding that the transition is a growth story with sustainable and resilient investment at its core. This narrative both resists the separation of climate and development, and tackles misinformation.
There is no doubt that many challenges will arise, but the role of the state is to make practical choices to overcome them. The narrative should be one of hope, presenting climate policy for what it is: action with the objective of advancing development and wellbeing. It is about technological innovation, modernisation, efficiency, creation of job opportunities, industrial dynamism, resilience and more.
It is a narrative and strategy that highlight both the growth story that action enables and the immense curtailment of rights to and opportunities for development that inaction causes. By creating a hostile environment, climate change can erode fundamental human rights, such as access to food and health, and damage economic and human development across the board. The argument should also embrace a discussion of what is ethical and responsible in relation to future generations and others in the current generation (Stern, 2026).
A core task in generating change and building support is the tackling of key market failures (Stern, 2022), thus reframing incentives and helping to create the conditions for the required investment flows. These market failures, which are many and interwoven, must be tackled together in complementary ways.
A narrow understanding of market failures, highlighting only the externality from greenhouse gas emissions, has led many economists to focus overwhelmingly or exclusively on pricing carbon emissions in formulating policy. Such pricing is indeed a critical part of policy, but it must be complemented by action on other key important failures.
These include research and development (R&D) and innovation, which stem from creators’ inability to capture the full value of their ideas due to knowledge spillovers. Without policy, that leads to under-investment in innovation. Research shows that combining carbon pricing and R&D can be much more powerful than carbon pricing in isolation (Acemoglu et al, 2012).
Another critical market failure concerns networks. Without policy, markets do not adequately account for network interdependencies and can ignore systemic risks and opportunities. Understanding and acting to improve ‘feedbacks among interacting elements’ (Catanzaro and Buchanan, 2013) can bolster efficiency and unlock economic value. For example, the quality of urban mobility is linked to urban planning decisions, the value of having an electric car to the availability of chargers, the viability of clean energy generation projects to grid availability, and so on.
Further important market failures concern information – for example, on how products have been made and other co-benefits where health from avoided pollution can be of great importance (see Figure 2). Capital market imperfections can hinder finance for the strong new investments that are required – development banks can play a key role here.
Figure 2: Key market failures related to climate and sustainable development
Institutional strength, governance and the investment environment
Policy predictability, coherence and a clear direction of change are all fundamental in building the confidence needed to foster investment in the green transition. Policies will change as circumstances change and learning takes place.
But for investor confidence, such change should be ‘predictably flexible’. Criteria for flexibility should be transparent – for example, reducing subsidies for renewables as and when costs fall and diffusion occurs. Concerted action across government, to foster policy alignment, and convening stakeholders, to generate cohesive action, can advance investment more quickly than measures that are uncoordinated across ministries.
‘Country platforms’ can provide mechanisms for coordinating the mobilisation of domestic and international sustainable finance in alignment with national development priorities. Standards and regulation can guide investments and signal which forms are consistent with the long-term trajectory of the economy, in alignment with sustainability goals. As far as possible, sharing standards and coordinating strategies across countries can enlarge markets and accelerate progress (Bhattacharya et al, 2025).
An example of consistency in policy-making is in Uruguay. The country has attracted clean investment and transformed its electricity matrix in less than two decades through sustained and clear policy and regulation. It has also built credibility in innovative ways: by holding itself accountable for climate action, having established a sovereign bond with a structure where, if its aims are not met, it will pay a higher interest rate (Godfrid et al, 2025).
Further, creating a positive investment environment requires strong macroeconomic policies, which are fundamental to ensuring the high investment levels needed in sustainable sectors are translated into increased output and not just a crowding out of private investment. Debt management is part of that story. Legal institutions are also of great importance in offering clarity on obligations and for dispute resolution.
Industrial policy for a dynamic and green private sector
Fostering private investment in the new growth story requires tackling market failures, building credibility in strategy and policy, and creating a strong investment environment across the whole economy. It also involves identifying where a country’s clean economic potential and comparative advantage lie. Such was the case with the Danish government’s support for private firms and entrepreneurs in the wind power sector, which was key to creating the country’s renowned innovation ecosystem (Technology Executive Committee, TEC, 2023).
Providing the right support requires that each country crafts tailored measures, as Japan did through its Ministry of International Trade and Industry, which selected and ‘nurtured’ industries through customised measures such as tax breaks and low-interest loans, accelerating their development (Johnson, 1982). Industrial clusters can drive sectoral growth by enabling economies of scale and collaboration, as seen in China (World Economic Forum, WEF, 2026).
Fortunately, the misguided sneering at ‘industrial policy’ typical of the years of market fundamentalism of the 1980s and 1990s has subsided. Building new industries and transforming technologies in a purposive way requires strategy and policies.
While the majority of investment for the transition will be private, there will also be a critical role for public investment, a crucial element of the role of the state. For example, public transport will play a fundamental role. In many countries, the electricity grid will be owned by the public sector. In many cases, this infrastructure facilitates private activity and builds for long-term growth and prosperity.
Finance will play a key role in fostering a green private sector. Priorities include reducing investment barriers, such as high costs of capital. This frequently involves better management and sharing of risk. Given that domestic capital markets in many emerging markets and developing countries are shallow, facilitating access to financial instruments, such as foreign exchange hedging, as Brazil’s finance ministry has done through its Eco Invest programme, can have catalytic effects.
Adequate access to finance is also of importance for households, whose transition requires their own investment. Often poorer households face a higher cost of capital. How households and organisations are supported and costs distributed is a key part of policy for the transition. The national and multilateral development banks can play a central role both in helping to create the conditions for investment and in managing risk and reducing the cost of capital.
Building stability in trade and collaborating across frontiers in innovation and clean energy generation can accelerate the drivers of the growth story, with positive spillovers across countries. State leaders should work through coalitions of the willing to foster market expansion, resource sharing, cohesive policy-making, access to finance and more, contributing both to resilient supply chains and fostering innovation.
Building trust and coordination across countries can generate predictability, while conflict and volatility can be dangerous. Multilateral action is also of importance in tackling the debt, fiscal and financial constraints confronted by emerging markets and developing nations (Stern, 2021).
Public discussion, social buy-in and workforce adaptation
When the transition plans are put into action, there will be deliberate structural change at speed and scale. Some vested interests will push back, in particular fossil-fuel sectors where high rents are at stake. Without pro-active state action, ordinary citizens will face challenges that will understandably create opposition, from dislocation to increases in the cost of living. Careful policy design must manage these risks while pursuing the economic and social gains from change, which will vary across countries with different systems and structures.
Public action will be necessary to help to overcome dislocation, particularly by investing in people and places to create new opportunities, or by providing low-cost capital to help households to manage change. Some vested interests will have to be confronted head-on. Public discussion and building a shared understanding or narrative are crucial to political and social ‘buy-in’. That discussion will often be focused on localised interactions.
It is critical that policy sustains social buy-in and, for this, that the benefits of the transition are shown early. Successful interventions require designing policy packages that account for and reconcile varied interests, articulating and managing negative impacts well in advance.
Still, not all challenges can be foreseen; there will be difficulties and learning that arise along the way. As emphasised, being flexible while maintaining predictability is a critical element of policy-making, both for investment and acceptance. Thus, political systems must remain open and receptive to public input and should be active in initiating civic dialogue.
What Jürgen Habermas called the public sphere – the space in which public opinion is formed – can take many forms, including citizens’ assemblies. Well-managed transitions, rather than generating backlash, should benefit communities and their workers, potentially increasing support for incumbent governments. For example, one study suggests that Spain’s coal phase-out process had some electoral success, with government, industrial and social discussion playing an important role (Bolet et al, 2024).
Research and action
The five areas of action described (see Figure 3) are also five areas for research. In its pace and scale, the necessary transformation is unprecedented in economic history, except in wartime.
The urgency means that this is ‘public policy as if time matters’ (Stern, 2018). It concerns structural and systemic change across the whole economy of a nation. And it will lead to a new economic geography where activity moves towards low-cost clean energy. It will require financing for investment to move much more strongly into emerging markets and developing economies, where the majority of future economic growth will occur.
These are great challenges for both action and research, and they require a new economics of structural transformation. But research and action must move together. Delay is dangerous.
Figure 3: The role of the state – an action agenda
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