Tech and Net Zero Conference organised by techUK brought together representatives from technology, policy, investment and sustainability to examine the role of the tech sector in the UK’s net-zero transition. Discussions ranged from climate policy, adaptation and resilience to climate-tech investment, transition planning and grid decarbonisation, before turning to one of the most contested issues of the day: the growing environmental and social impact of AI and data-centre infrastructure.
We already know many of the problems. So what is stopping us from acting at the scale and speed required? That question surfaced repeatedly throughout the day, whether the discussion was about scaling climate-tech businesses, embedding sustainability into organisational decisions or securing public support for new digital infrastructure.
Some of the discussions during the conference connected with the Sustainable Finance Forum we recently hosted at the University of Birmingham. Our event approached the challenge from a different direction, bringing together finance, technology, sustainability and research communities to explore the interdependencies between AI, water, energy, infrastructure, investment and resilience. Yet both events ultimately arrived at a similar conclusion: the challenge has moved from simply identifying what needs to change to creating the incentives, evidence, finance, governance, and trust that enable change.
Climate action is becoming a resilience and economic question
One noticeable shift throughout the techUK conference was the language used to talk about climate action. Net zero was frequently connected with affordability, competitiveness, energy security, jobs, industrial growth and resilience. Climate adaptation no longer felt like a peripheral sustainability issue. Discussions focused on very practical questions: can supply chains continue operating during droughts and extreme heat? Will infrastructure designed for historical climate conditions still function reliably in twenty years? What happens when climate risks affect employees, energy systems or critical services? This matters because sustainability becomes much harder to treat as a separate ESG exercise once those impacts become operational and financial risks. Participants repeatedly returned to the importance of translating sustainability into the language organisations already use to make decisions, like risk, resilience, cost, business continuity and value. That translation may be one of the most important steps in moving sustainability from ambition into everyday decision-making.
We need to invest in impact, not simply financial return
One line that particularly captured the challenge was “we need to start investing in impact, rather than financial returns.” That does not mean financial return suddenly becomes irrelevant. The harder question is how we create investment models where environmental and societal value can coexist with a commercially viable return.
The climate-tech investment discussion exposed one of the biggest barriers. The UK has strong research capability, start-ups and early-stage innovation. But moving from a promising technology to a commercially scaled business is much harder. As one speaker put it, “what is exciting in a start-up is not necessarily what is needed in a scale-up”. Early-stage businesses can attract capital around a novel technology or idea. Scaling often requires something much less glamorous: manufacturing capacity, infrastructure, operational discipline, regulatory approval, customers, and significant capital. For capital-intensive climate technologies, this creates a particularly difficult “valley of death”. A company may successfully demonstrate a technology but then require tens or hundreds of millions of pounds to reach commercial scale. Traditional venture investors may see too much risk, while banks may not yet see an established infrastructure asset against which they can lend. The conference discussion identified consistent long-term policy, clearer commercial pathways and blended finance, combining public capital with private investment to reduce risk, as potential ways to close that gap.
The problem is therefore not simply that “there is not enough money”. It is that the type of finance available does not always match the stage of innovation we are trying to fund. That feels particularly relevant for universities. We are good at generating research and early-stage innovation. But if promising solutions cannot cross the gap between demonstration and deployment, research impact stops before the point where it can make the biggest difference.
Long-term policy matters because uncertainty has a cost
A related message was the importance of policy certainty. Businesses can adapt to demanding regulation. Investors can price risk. What is much harder is committing significant capital where the direction of policy may change before the investment has paid back. The climate-tech discussion therefore came back repeatedly to the need for a long-term policy view, alongside blended finance and clearer pathways for technologies to move from testing to commercial deployment. The mismatch between the time horizon of sustainability challenges and the much shorter cycles within which organisations often make decisions. Climate resilience, infrastructure and environmental outcomes may need to be considered over decades, while budgets, investment performance, political cycles and corporate incentives operate over much shorter periods. The question is therefore is what should we invest in, and how do we create enough confidence for organisations to make decisions today whose benefits may only become fully visible many years from now?
AI and data centres: the social licence problem
Data centres are becoming essential infrastructure for AI, cloud services and the wider digital economy, but their expansion also creates visible pressures on electricity, water, land and grid capacity. One speaker argued that this is contributing to a growing social-licence problem. Unlike traditional industrial investment, which was often associated with large numbers of local jobs, communities hosting data centres may see the resource demands and physical development more clearly than the value that remains locally.
That makes trust particularly important. The final session questioned whether organisations and communities receive enough transparent information about the environmental impact of digital services. One participant argued that requests for detailed environmental data can sometimes be met with broad sustainability messaging rather than the empirical evidence needed to assess impacts properly. Quite often data centre proposals include renewable energy, heat reuse for thousands of homes, local employment and a net-zero site, but several of those commitments are later significantly reduced or do not materialise as originally presented.
This raises a wider question: if a community provides land, grid capacity and water, what should it receive in return? Jobs, investment, improved infrastructure, heat reuse and wider digital capability may all form part of that answer, but the balance needs to be visible and credible.Apart from measure of the environmental footprint, how planning, investment and infrastructure decisions can demonstrate genuine local value. The wider lesson is that social licence cannot be created through promises alone. It depends on transparent evidence, credible delivery and communities being able to see a fair balance between the resources they provide and the benefits they receive.
So what should happen next?
Discussions over the two events point towards several practical priorities.
- Connect the disciplines. AI infrastructure cannot be treated as only a technology issue. It also affects energy, water, planning, finance, resilience and local communities. Research and decision-making need to bring these areas together.
- Create clearer routes from research to real-world use. More demonstrators, testbeds, industry-led projects and challenge-based programmes are needed to help promising ideas move beyond research and prove their value in practice.
- Improve finance for scale-up. Many climate-tech businesses can secure early-stage funding but struggle when they need much larger amounts of capital to scale. Blended finance, public de-risking and patient capital could help close this gap.
- Provide greater long-term policy certainty. Businesses and investors need confidence that the policy direction will remain stable long enough for major investments to pay off. Constant uncertainty makes scaling harder.
- Be more transparent about the trade-offs of digital growth. AI and digital infrastructure can support productivity, energy optimisation and climate solutions, but they also consume energy, water and land. The benefits and costs need to be measured and communicated more openly.
- Show clear local benefit. Where communities host major digital infrastructure, they need to see what they gain in return — whether through jobs, investment, infrastructure, heat reuse or wider economic value.
The sector will need to demonstrate what technology can do and who benefits, who carries the cost and whether the promises made at the beginning are still visible at the end. The next stage of the net-zero transition will not be won simply by setting more ambitious targets or developing more impressive technology. It will depend on whether we can build the finance, evidence, infrastructure, policy certainty and public trust needed to turn good ideas into solutions that can genuinely scale.