The study focused on three of the most prominent engineered carbon removal methods now moving from laboratory to real-world deployment. Direct air carbon capture and storage, or DACCS, strips carbon dioxide from ambient air using chemical sorbents and locks it underground. Bioenergy carbon capture and storage, known as BECCS, grows biomass that absorbs carbon, burns it for energy and captures the resulting emissions. Enhanced rock weathering, or ERW, spreads crushed silicate minerals on land, accelerating natural chemical reactions that pull carbon dioxide from the atmosphere and eventually lock it away in oceans and soils. Each carries a distinct risk and cost profile, from land-use conflicts and energy demands to mining and transport burdens, yet the researchers discovered something remarkable: the technology itself mattered far less than the way it was implemented.

To isolate what actually drives public acceptance, the team ran a multifactorial vignette experiment embedded in surveys conducted in Brazil, Malaysia, Saudi Arabia, Italy, Norway and the United Kingdom. Roughly 1,800 respondents per country were assigned to one of the three technologies and then evaluated five hypothetical projects out of 36 possible variants. Each project description differed along four attributes grounded in a multidimensional conception of justice: who implemented the project, whether the decision-making process involved consultation, how profits were distributed, and how much carbon the project could remove. Respondents rated each project on a ten-point scale from strong opposition to strong support, generating more than 54,000 individual evaluations for statistical analysis.

The headline result is unambiguous. Across every country and every technology, procedural fairness exerted the strongest effect on support. Compared with projects approved by authorities without any consultation, opening the decision-making process to local communities or scientific experts increased support by roughly 1.0 to 1.7 points on the ten-point scale. The effect was largest in Brazil, where consulting the affected population raised support by 1.68 points, and smallest but still substantial in Saudi Arabia, at just over 1.0 point. Notably, consulting local communities and consulting scientific experts produced nearly identical gains, suggesting that what matters to the public is the principle of being heard, not the specific identity of who is asked.

Distributive fairness came a close second. When developers retained all profits, projects fared worst. Redistributing revenues to affected local communities increased support by between roughly 0.5 and 1.25 scale points, with the largest effect again in Brazil, followed by the United Kingdom and Italy. Not-for-profit arrangements, in which carbon removal operates as a public service without generating profit, also raised support everywhere, though less dramatically in Saudi Arabia. By contrast, whether a company or a regional government carried out the project made little difference in most countries, with only a modest penalty for corporate implementation in the United Kingdom, Italy, Norway and Malaysia. The lesson for the emerging carbon removal industry is uncomfortable: the public appears far more interested in how benefits flow than in who owns the machinery.

Perhaps the most striking finding concerns trade-offs. The researchers tested whether a project promising high removal capacity could compensate for unfair governance, and the answer was a clear no. Across nearly all countries, the effect of increasing carbon removal was remarkably similar regardless of whether projects consulted anyone or shared profits. Respondents rewarded projects that were both effective and fairly governed, but these two considerations operated independently. A high-capacity project run without consultation and with privatized profits still lost the support that fairness would have generated. The reverse also held: progressive governance could not rescue a project with meager removal performance. From the perspective of public legitimacy, neither technical excellence nor ethical design alone is sufficient.

Support levels themselves varied across countries in ways the authors interpret cautiously. Brazil and Saudi Arabia showed the highest average support, with means between roughly 7.1 and 7.3, while European respondents were more moderate, with Norway recording the lowest scores, averaging around 5.1 to 5.4 depending on the technology. The researchers warn that cross-cultural differences in survey response styles, such as tendencies toward acquiescence or avoiding extreme answers, may partly explain these gaps, and they emphasize the experimentally varied attributes over absolute levels. Within Europe, DACCS consistently received lower support than the other two methods, while in Malaysia no significant differences emerged among the technologies at all.

When asked directly what level of public involvement would be fair, respondents revealed a strong appetite for engagement. Allocating decision-making authority to local communities was judged the fairest option for permitting projects by around 44 percent of respondents in the United Kingdom and Italy, 43 percent in Norway, and between 34 and 39 percent in Brazil, Saudi Arabia and Malaysia. Only a tiny fraction, between 2 and 9 percent, thought local communities should have no involvement at all. Strikingly, political orientation played almost no role in shaping support, a sharp contrast with the well-documented polarization of climate attitudes more broadly. Carbon removal, the data suggest, has not yet been absorbed into partisan identity, and public judgments rest instead on risk perceptions, trust and effectiveness.

Even self-described opponents of hosting carbon removal near their homes responded to fairness cues. Dividing respondents into opponents, moderates and supporters of local projects, the researchers found similar preference structures across all three groups, with opponents reacting as strongly or more strongly to governance attributes than anyone else. Opponents, in other words, do not categorically reject carbon removal; they remain open to projects developed through consultation and benefit sharing. This conditional openness offers developers a genuine path forward, but also a narrow one, since perceived injustice can trigger the protests, litigation and delays that have stalled energy infrastructure around the world.

The authors caution that today’s moderate support and weak polarization reflect the novelty of these technologies and low public familiarity, not a stable consensus. As real projects take shape and costs, trade-offs and distributional questions become tangible, controversies are likely to intensify, and spillovers from adjacent debates over biofuels, carbon storage, fracking or nuclear waste could reshape perceptions rapidly. Their prescriptions are correspondingly structural: open decision processes to public and expert scrutiny, treat carbon removal as a public good rather than a profit engine, and experiment with regional, upstream engagement at the planning stage rather than leaving participation to developer-led consultations after key choices are made. With national carbon removal governance still nascent in most of the world, the window for embedding justice into deployment pathways remains open, but the study’s message is that it will not stay that way for long.

Subject of Research: Public support for novel carbon dioxide removal technologies and the role of procedural and distributive fairness in their governance

Article Title: Public support for novel carbon removal hinges on procedural and distributive fairness

Article References: Fritz, L., Losi, L., Merk, C., Boldrini, M., Bosetti, V., Baum, C. M., & Sovacool, B. K. (2026). Public support for novel carbon removal hinges on procedural and distributive fairness. Nature Climate Change, 16(10), 1152-1161. https://doi.org/10.1038/s41558-026-02741-7

Image Credits: AI Generated

DOI: 10.1038/s41558-026-02741-7

Keywords: carbon dioxide removal, public perception, procedural fairness, distributive fairness, direct air capture, BECCS, enhanced rock weathering, climate governance, survey experiment, social acceptance, climate policy, benefit sharing

The study rests on a deceptively simple premise drawn from democratic theories of policy implementation: laws and regulations succeed more often when the administrators who carry them out understand the distinctive characteristics and demands of the governed. Applied to artificial intelligence, this means the EU’s ambitious AI agenda — including its landmark regulatory framework — depends on knowing which citizens view the technology with suspicion and why. If large segments of the public distrust AI, or distrust the institutions deploying and regulating it, compliance becomes harder, public resistance grows, and the promised benefits of first-mover adoption may never materialize.

To answer the question, the researchers turned to Eurobarometer 95.2, a survey conducted in 2021 that measured European citizens’ knowledge of and attitudes toward science and technology during the COVID-19 pandemic. Eurobarometer surveys are the European Commission’s standard instrument for gauging public opinion across member states, and the 95.2 wave included detailed questions about how respondents perceived the impacts of AI on society and the economy. The dataset and all question wording are publicly available, allowing the authors to test associations between demographic characteristics, institutional attitudes, science engagement, and AI perceptions across the Union.

The central findings are striking in their clarity. Individuals who hold positive views of EU-level governments tend to hold more favorable views of artificial intelligence. This association matters enormously in the European context, because it is precisely the EU institutions — the Commission, the Parliament, and the member-state regulators acting under EU law — that serve as the primary AI regulators. A citizen who is skeptical of Brussels is, in effect, skeptical of the referee assigned to police the technology. The study thus links a well-documented phenomenon in European politics, the persistence of Euroscepticism, directly to the emerging politics of algorithmic governance.

Equally significant is the role of social class. The researchers found that people in higher social classes tend to have more positive perceptions of AI’s impacts. This class gradient echoes a long tradition in social science research showing that policy attitudes are stratified by socioeconomic position, and it raises uncomfortable questions about who stands to benefit from the AI transition. If working-class and lower-income Europeans view artificial intelligence with greater suspicion — perhaps because they perceive greater risks to their jobs, their communities, and their autonomy — then a top-down regulatory strategy crafted largely by and for educated elites risks deepening existing divides rather than bridging them.

The third pillar of the study concerns engagement with science and technology. Respondents who attended science museums and public meetings about science were associated with more favorable views of AI, as were those who expressed a preference for involving non-experts in scientific decision-making. This latter finding is particularly intriguing: it suggests that people who believe ordinary citizens should have a voice in science policy are not reflexively anti-technology. Instead, participation itself appears to breed confidence. Exposure to scientific institutions and ideas, whether through a museum visit or a town-hall discussion, seems to demystify technologies like AI and soften public apprehension.

Taken together, these findings expose a dual trust deficit that threatens the implementation of existing EU AI policies. On one side stands a class divide, in which the populations most likely to be affected by automation and algorithmic decision-making express the least enthusiasm for the technology. On the other side stands an institutional divide, in which citizens who distrust the EU and national governments extend that distrust to the AI systems those governments are charged with overseeing. The authors argue that both deficits pose genuine problems for policy implementation, because democratic legitimacy is not merely a matter of passing well-designed legislation — it requires that the governed accept and support what is being done in their name.

The remedy the study proposes is procedural rather than technological. By transparently engaging citizens in meetings on the topic of AI, and by including non-experts in the development of AI use cases, governments may be able to improve public views of the technology. This recommendation aligns with a growing body of research on transparency and trust, which has found that explaining how automated decisions are made can increase the perceived trustworthiness of algorithmic systems, and that transparency in government generally strengthens citizens’ confidence in public institutions. It also resonates with the citizen-science movement, which has demonstrated that involving laypeople in genuine research activities builds both scientific literacy and goodwill toward scientific institutions.

The stakes of getting this right extend well beyond Europe’s borders. The EU has positioned itself as a global standard-setter in AI governance, and its regulatory approach is already influencing lawmakers in other jurisdictions. But regulatory leadership is fragile if domestic publics are not on board. Previous technology policy debacles — from troubled government website rollouts to public backlash against automated decision systems in welfare administration — illustrate how quickly implementation can falter when citizens perceive systems as opaque, imposed, or unaccountable. The study’s findings suggest that the EU’s first-mover advantage depends as much on sustained public engagement as on legislative craftsmanship.

For the scientists and policymakers watching this space, the message is clear: artificial intelligence governance is not only a technical or legal challenge but a social one. The people least trusting of AI in Europe are those with less confidence in government and lower social standing, and the most promising path to winning them over runs through transparency, participation, and genuine inclusion of non-expert voices in shaping how AI is used. If the EU can close these trust deficits, it may yet convert its regulatory ambition into democratic legitimacy — and if it cannot, the first-mover advantage it seeks may prove elusive, no matter how sophisticated its rulebook becomes.

Subject of Research: Public trust in artificial intelligence and its implications for EU AI policy implementation

Article Title: Governing AI in the EU: public perception, policy dilemmas, and the path forward

Article References: Jensen, C., & Chen, C. (2025). Governing AI in the EU: public perception, policy dilemmas, and the path forward. Global Public Policy and Governance, 5(2), 162-185. https://doi.org/10.1007/s43508-025-00114-y

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00114-y

Keywords: artificial intelligence, European Union, public trust, AI regulation, Eurobarometer, social class, Euroscepticism, science engagement, policy implementation, citizen participation, algorithmic governance, public perception