{"id":86834,"date":"2026-06-18T11:27:29","date_gmt":"2026-06-18T10:27:29","guid":{"rendered":"https:\/\/www.lse.ac.uk\/granthaminstitute\/?post_type=news&p=86834"},"modified":"2026-06-18T11:27:30","modified_gmt":"2026-06-18T10:27:30","slug":"will-carbon-removal-ever-be-as-cheap-as-solar","status":"publish","type":"news","link":"https:\/\/www.lse.ac.uk\/granthaminstitute\/news\/will-carbon-removal-ever-be-as-cheap-as-solar\/","title":{"rendered":"Will carbon removal ever be as cheap as solar?"},"content":{"rendered":"\n

Is the solar power–carbon removal cost analogy useful or realistic and what would need to be true for it to hold? Drawing on new research and recent conversations at the 4th International Conference on Carbon Dioxide Removal, Leonie Meissner and Josh Burke provide some reflections.<\/p>\n\n\n\n

Carbon dioxide removal (CDR) is essential to meet net zero emission targets. Yet the cost of removing carbon using CDR remains prohibitively high – particularly via methods that store carbon in the geosphere or have long-run scalability such as ocean alkalinity enhancement. A small number of companies (such as Microsoft, Google and Stripe) have provided catalytic funding under the assumption that costs will fall as deployment expands, eventually enabling regulatory demand to take over. This is the same logic that underpinned public and private support for wind and solar power: and broadly speaking, it worked for those technologies.<\/p>\n\n\n\n

But will CDR become as cheap as solar? This was a recurring question at the 4th International Conference on Carbon Dioxide Removal (formerly known as the International Conference on Negative Emissions), held in Milan from 10–12 June. The solar analogy proved so ubiquitous that a delegate poll on conference irritants ranked it near the top! Yet what belies the faux rancour is a serious question because the answer matters for how governments design public policy.<\/p>\n\n\n\n

Learning rates and their limits<\/h2>\n\n\n\n

One way to assess future cost trajectories for technologies is through learning rates: the percentage by which costs fall each time cumulative production of a technology doubles. A learning rate of 20% means costs drop by a fifth with every doubling of output, reflecting the economies of scale familiar across many industries. That figure, incidentally, is also the historical learning rate for solar power between 1976 and 2019<\/path><\/svg><\/a>.<\/p>\n\n\n\n

Existing estimates of CDR learning rates typically draw on empirical analysis of technology characteristics that determine the cost reduction potential using either experience-based learning-by-doing models<\/path><\/svg><\/a> or component-based learning curve models<\/path><\/svg><\/a>. The field remains nascent, with most published work focused on bioenergy with carbon capture and storage (BECCS) or direct air capture and storage (DACCS).<\/p>\n\n\n\n

The table below summarises the learning rates currently suggested by the literature across the main ‘durable CDR’ methods (those that store carbon over longer timeframes). But these ranges mask significant uncertainty and, as our new research shows, may not accurately reflect what is actually happening in the market.<\/p>\n\n\n\n

CDR technology<\/strong><\/td>Learning rate range according to the literature<\/strong><\/td><\/tr>
Mineralisation<\/td>0.1%1<\/sup><\/td><\/tr>
Alkalinity enhancement (AE)<\/td>10%2<\/sup><\/td><\/tr>
Bioenergy carbon capture and storage (BECCS)<\/td>3–12%3<\/sup><\/td><\/tr>
Direct air capture and storage (DACCS)<\/td>5–20%4<\/sup><\/td><\/tr>
Biomass carbon removal and storage (BiCRS)<\/td>5–12%5<\/sup><\/td><\/tr>
Biochar<\/td>10–20%6<\/sup><\/td><\/tr>
Enhanced weathering (EW)<\/td>6%7<\/sup><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Notes and sources<\/em>: 1. Carbon Finance Lab<\/path><\/svg><\/a>. 2. Carbon Finance Lab<\/path><\/svg><\/a>. 3. Modelled rates: 5–15% based on bioenergy<\/path><\/svg><\/a> generation and CCS<\/path><\/svg><\/a>. 4. Component-based estimates: 4.87–11.02%<\/path><\/svg><\/a> and up to 20%<\/path><\/svg><\/a>. 5. Very limited literature. Overlaps with BECCS and biochar literature. 6. Junginger et al.’s<\/path><\/svg><\/a> bioenergy learning curve data is the closest proxy; pyrolysis equipment is modular and manufactured, suggesting it may follow a faster learning rate. 7. Carbon Finance Lab<\/path><\/svg><\/a>.<\/p>\n\n\n\n

What the data reveal: a discouraging picture beyond BECCS?<\/strong><\/h2>\n\n\n\n

In a report we published<\/a> this month, we take a different approach. Rather than modelling from technology characteristics, we calculate one-factor<\/path><\/svg><\/a> learning rates using a novel dataset covering 43 million tonnes of carbon dioxide (MtCO₂) of contracted removal across all continents and all major durable CDR methods. These include alkalinity enhancement, BECCS, BiCRS, biochar, DACCS and mineralisation – see graph below.<\/p>\n\n\n\n

Comparison between CDR learning rates and relevant technology benchmarks<\/h5>\n\n\n\n
\"\"<\/figure>\n\n\n\n

Notes<\/em>: The chart is centred on 0%. Left of centre equals costs rising (negative learning). Right of centre equals costs declining (positive learning). NGCC + CCS = natural gas combined cycle + carbon capture and storage.<\/p>\n\n\n\n

Source<\/em>: Figure 4.1 in Meissner et al. (2026)<\/a>. CDR learning rates are authors’ own calculations based on data from CDR.fyi, AlliedOffsets, Sylvera and Frontier. Learning rates for analogous technologies are from: Roser<\/path><\/svg><\/a> (2023); Louwen et al.<\/path><\/svg><\/a> (2018); Hahn Menacho et al.<\/path><\/svg><\/a> (2022); Rubin et al.<\/path><\/svg><\/a> (2015); Haas et al<\/path><\/svg><\/a>. (2023); and Eash-Gates<\/path><\/svg><\/a> et al. (2020)<\/p>\n\n\n\n

Of all the methods we examine and of those that dominate scenarios and discourses from the Intergovernmental Panel on Climate Change, only BECCS is broadly consistent with the peer-reviewed literature. It demonstrates a learning rate of around 8% – comparable to offshore wind power – reflecting the fact that most BECCS plants are retrofits of existing bioenergy infrastructure, meaning firms can capture economies of scale relatively quickly without the barriers of entirely novel deployment.<\/p>\n\n\n\n

Beyond BECCS, the picture is less encouraging. Several CDR pathways cluster in the low single digits; others, including BiCRS, DACCS, biochar and enhanced weathering, exhibit near-zero or even negative learning rates, meaning costs are rising rather than falling with deployment. The overall learning rate for durable CDR across our dataset is just 1%.<\/p>\n\n\n\n

While analysing first sales may underestimate learning-rates as CDR firms sell below cost, these negative learning rates reflect commercial realities. For DACCS, they show the struggles to achieve scale<\/path><\/svg><\/a>: the recent difficulties at Climeworks are illustrative of an industry caught in a ‘valley of death’, where the cost reductions that greater scale should deliver cannot materialise without a market large enough to drive that scale in the first place. For biochar, rising prices reflect a different dynamic – a combination of growing demand for biochar credits, tightening feedstock supply<\/path><\/svg><\/a>, and the likelihood that early prices simply undervalued the true cost of production. Here, negative learning is perhaps not a failure of technology: it is the market course correcting after initial agreements.<\/p>\n\n\n\n

The two least mature methods – alkalinity enhancement and enhanced weathering – sit at opposite ends of the spectrum. Alkalinity enhancement shows a learning rate of around 8%. Enhanced weathering, by contrast, exhibits strongly negative learning rates, comparable in structure to the troubled trajectory of nuclear power (though not entirely unexpected because of diseconomies of scale and rising input costs<\/path><\/svg><\/a>).<\/p>\n\n\n\n

So where does CDR sit in comparison to solar’s journey?<\/h2>\n\n\n\n

The solar story is not as linear as the analogy implies. The headline of ‘four decades of steep cost decline’ masks variation. Between around 2001 and 2005, solar costs actually rose with deployment. This was due to several factors: feed-in tariff programmes in Japan and Germany drove demand that temporarily outpaced manufacturing capacity, material prices (particularly of refined silicon) surged, and labour and installation costs remained stubbornly high. For this short period, solar experienced something that resembles much of the CDR landscape today. However, when making comparisons, clearly CDR should not be considered as a homogenous technology.<\/p>\n\n\n\n

So, what brought solar out of the period of rising costs? The reversal from around 2006 can be attributed to a combination of factors.<\/p>\n\n\n\n

First, silicon producers responded by rapidly expanding their capacity, flooding the market and collapsing input costs. Second, manufacturers reduced silicon consumption per watt by making thinner wafers and improving yields. Indeed, research by Kavlak<\/path><\/svg><\/a> and colleagues suggests that improved module efficiency was the leading driver of cost reduction between 1980 and 2012, accounting for roughly 25% of the total decrease. Government-funded R&D was the most important mechanism over that period overall, with economies of scale growing in relative importance after 2001. The lesson is not simply that deployment drives down costs: it is that a targeted and sequenced<\/path><\/svg><\/a> technology push – with R&D investment, tax credits and manufacturing incentives – combined with sustained demand pull created the conditions for learning to accelerate.<\/p>\n\n\n\n

Third, China’s investment has contributed to decreasing costs. Since 2011, China has invested up to US$50 billion in solar PV, according to the International Energy Agency<\/path><\/svg><\/a>. This has led China to dominate 80% of solar manufacturing, which has further decreased the overall cost of production worldwide. <\/p>\n\n\n\n

Of the CDR methods we examine, most methods find themselves closer to the reality of solar in the 1980s–90s and arguably only the current biochar dynamics closely resemble solar’s 2001–05 moment: demand is rising, supply is constrained and feedstock costs are climbing<\/path><\/svg><\/a>. Whether that is followed by a subsequent period of rapid cost reduction, as occurred in solar, remains to be seen.<\/p>\n\n\n\n

So how valid is the comparison between solar and CDR and what does this mean for policy?<\/h2>\n\n\n\n

A rapid reduction of costs similar to what happened with solar would be ideal. However, solar as well as other renewable energy technologies demonstrate considerable differences<\/path><\/svg><\/a> that must be recognised when drawing parallels.<\/p>\n\n\n\n

Solar produces electricity from which a direct private benefit can be reaped by investors, while CDR is less tangible, instead benefiting society at large over time. Additionally, solar expansion could benefit from existing electricity infrastructure, while CDR transport and storage infrastructure needs to be built from scratch. Lastly, and perhaps most importantly, solar is generally viewed favourably by the public, making large-scale government incentivisation schemes possible. CDR, in contrast, may be perceived more negatively due to potential leakage risks and fears that CDR will displace a focus on the mitigation of greenhouse gas emissions. These perceptions may minimise political willingness to provide the necessary policy support for CDR.<\/p>\n\n\n\n

However, learning rates are indicative, not predictive: this is a point worth emphasising given how often they are used in government planning. For policymakers, our findings carry several practical implications.<\/p>\n\n\n\n

Overly optimistic assumptions about learning rates risk distorting subsidy mechanisms: governments may underpay early movers in expectation of falls that do not materialise, or overprice contracts if learning proves faster than expected in specific methods. For emissions trading system integration, our data suggest that durable CDR costs are unlikely to converge with prevailing carbon prices in the near term – and for some methods may diverge further before they narrow.<\/p>\n\n\n\n

It is too early to declare that CDR is on the cusp of a sustained cost decline  and thus entering a post 2001–05 solar moment, as most methods are too early on in their development for that. Whether the reduction in costs will occur depends on factors that are uncertain: capital costs, carbon and energy prices, the cost of monitoring, reporting and verification, and the terms on which project finance can be secured. Most importantly, it will depend on the policy infrastructure.<\/p>\n\n\n\n

Cost reductions at the scale required will not happen through market forces alone. The combination that worked for solar is no less necessary for CDR and arguably even more so, since the benefits do not directly accrue to consumers but to society as a whole. Recognising the difference between solar and CDR is important to develop a successful policy framework for CDR. The question is whether policymakers are prepared to underwrite demand so that the technologies we need successfully cross the valley of death.<\/p>\n\n\n\n

Carbon dioxide removal prices and the challenge of emissions trading system integration<\/em> by Leonie Meissner, Josh Burke and Luca Taschini is available here<\/a>.<\/p>\n\n","protected":false},"excerpt":{"rendered":"\n

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