We are an independent research group of economists, data scientists, and policy experts.
Our research sits at the intersection of climate technology, corporate decarbonisation, and carbon markets.
Development and diffusion of climate technologies by entrepreneurs, corporations, and governments.
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Corporate net-zero commitments and whether company strategies lead to real emission reductions.
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Integrity and impact of compliance and voluntary carbon markets, and their role in corporate and government climate strategies.
Learn more →We combine unconventional data sources with cutting-edge analytical techniques.
We collect novel data sources such as millions of social media posts and web-scraped corporate reports that go beyond traditional datasets.
We use large language models to aggregate, synthesise, and label data at scale, revealing new patterns and preprocessing data for downstream analysis.
We apply rigorous causal inference methods such as difference-in-differences and instrumental variables to identify the real-world impact of technologies and policies.
New preprints on corporate climate innovation, CEO environmental incentives, and global value capture in carbon crediting.
The Net Zero Lab has released three new working papers. Niklas Stolz, Volker Hoffmann, and Benedict Probst use large language models to map the dynamics of corporate climate innovation strategies across 500 companies, finding that radical innovation stagnated after 2022 while incremental initiatives continued to rise.
Fernando Loaiza and Benedict Probst examine whether tying CEO compensation to environmental performance reduces greenhouse gas emissions, finding that current ESG-linked compensation schemes function more as symbolic commitments than as effective drivers of decarbonisation.
In a third study, Niklas Stolz and Benedict Probst use large language models to identify and analyse a global network of organisations involved in 600 carbon crediting projects worldwide, capturing 2,706 unique organisations, 4,350 network ties, and 1,527 interactions with local value chains. They find that large, well-connected organisations controlling carbon rights and services capture the most value, with significant regional variation in local value capture.
Corporate climate innovation → CEO green incentives → Value capture in carbon markets →Injy Johnstone and Benedict Probst served as lead authors on the landmark CDR report.
The third edition of the State of Carbon Dioxide Removal report has been published. Senior Research Fellow Injy Johnstone and Lab Head Benedict Probst served as lead authors on the comprehensive global assessment, which tracks progress in CDR deployment, policy, and research across all major removal methods.
View publication →An analysis of where Article 6 stands after the Bonn Climate Change Conference in June 2026.
Following the Bonn Climate Change Conference, Article 6 has moved from rule-making into implementation. This blog post examines the state of play across Article 6.2 cooperative approaches, Article 6.8 non-market approaches, the Paris Agreement Crediting Mechanism under Article 6.4, and implications for the CDM transition and CORSIA.
Read blog post →A new study argues that scaling permanent carbon removal requires a fundamentally new financing model.
Benedict Probst and Florian Egli published a new paper in PNAS Nexus arguing that current carbon-crediting mechanisms reward short-term, low-capital-intensity projects and fail to support the scale-up of permanent carbon dioxide removal technologies. The authors propose a tiered auction framework where governments set permanent removal targets and run reverse auctions to build markets for novel CDR technologies, stabilising revenues and reducing financing costs.
View publication →Senior Researcher Malte Toetzke presents a demo of the Climate-Tech Monitor, currently under development.
A demo of the Climate-Tech Monitor, developed by Senior Researcher Malte Toetzke, is now available. The tool builds on research analysing the dynamics of innovation networks in climate technologies using large language models.
Watch demo → View publication →