The Clean Energy Transition: Priced on the Wrong Data

Offshore wind has rapidly evolved from an emerging technology to a critical component of the UK’s clean energy transition. Already the country’s leading source of renewable energy, the sector’s continued growth is reflected in the record-breaking outcome of Allocation Round 7 (AR7). With £22 billion of private investment secured, AR7 is the largest procurement of offshore wind capacity in British and European history. This momentum is reflected by developments such as Dogger Bank, which is set to become the world’s largest offshore wind farm.

 

Despite this remarkable progress, the path to successful offshore wind deployment remains riddled with uncertainty. Beneath the billions of pounds of investment lies fundamental flaws in how these assets, representing over £100 billion in insured value, are being assessed, priced and financed. Climate change is compounding this problem, rendering existing risk models calibrated to historical climate data unreliable.

 

Developers, insurers, reinsurers and financial backers are all feeling the consequences of this uncertainty, ultimately threatening the economic viability of new developments and making it more challenging for the UK government to meet clean energy targets.

 

When Historical Models no Longer Suffice

The reliability of any risk model depends on the quality of the data underpinning it, and for offshore wind, two factors are increasingly undermining that foundation: the climate and technology.

 

Climate change is challenging the assumptions historical weather patterns have long been based upon. Look no further than the heatwave of 2026 to see this in action, with the UK having experienced its hottest summer on record as 37 consecutive heatwave days hit the country. This is symptomatic of a wider shift in the climate, as storm behaviour, wind patterns, and the interaction between wind and waves all diverge from their historical baselines and extreme weather events increase in frequency and intensity. As a result, weather models built solely on historical datasets are becoming increasingly unreliable at predicting future patterns.

 

At the same time, offshore wind technology is evolving at an unprecedented pace. The UK’s oldest surviving commercial offshore wind farm, North Hoyle, has been operating for over two decades and in that time, the technology deployed has changed beyond recognition. Today’s turbines can reach over 230 metres in height, taller than ‘The Cheesegrater’ in London and dwarfing the original installations which stood at around 70 metres. How these new turbines behave when faced with extreme weather conditions remains a mystery, because they have simply not been in use long enough to know.

 

The Offshore Wind Risk Blind Spot

Existing approaches to offshore wind risk modelling either avoid it entirely or rely on proxy modelling from onshore deployments. The problem with these methods is that they overlook a fundamental source of offshore losses: the combined impact of interactions from both wind and waves.

 

In one North Sea case study, the use of onshore proxies resulted in inaccuracies in physical damage estimates by up to 90 per cent, while business interruption estimates were off by up to 100 per cent, with errors running in both directions depending on the proxies used.

 

Operational realities add another layer of uncertainty. Offshore wind assets are expensive both to build and maintain, relying on a limited pool of highly specialised vessels for construction and repair. While costs are absorbed during large-scale projects, they become disproportionately significant when a single component fails and a £22,000 blade repair can quickly escalate into a £380,000+ claim once vessel mobilisation and weather delays are taken into account.

 

The Cost of Uncertainty

Mispriced risk spreads across every layer of the offshore wind financing ecosystem. Insurers and reinsurers absorb the initial impact, often responding by raising premiums and reducing capacity. Those costs are then passed through to developers as higher project expenses and to lenders as greater financing complexity.

 

Crucially, it impacts the numbers investors care about most: the cost of capital, asset valuations, and the confidence with which a project can be underwritten. A project financed on the basis of one risk profile but ultimately exposed to another is fundamentally different from the asset investors believed they were backing.

 

This challenge is amplified by the long-term nature of claims, which often emerge years after policies are underwritten, meaning today’s pricing decisions may not be tested until an asset is well into operation.

 

From Assumptions to Accuracy

Successfully delivering on net zero targets means moving away from proxy modelling to purpose-built risk assessments grounded in accurate climate data and asset-level vulnerability analysis. Only then can the industry’s true exposure, and the economics of repairing and operating offshore assets, be properly understood.

 

If the UK is to maintain its position as a global leader in offshore wind, assumptions must be replaced with evidence. Asset-level modelling gives insurers, lenders and investors a clearer view of where risks lie and how losses may accumulate across portfolios. By reducing uncertainty, capital can be deployed more confidently, unlocking capacity that supports future offshore wind development. 

 

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Professor Subhamoy (Suby) Bhattacharya is Co-Founder and Chief Scientific Officer at Renew Risk

 

Main image courtesy of iStockPhoto.com and da-kuk