Chiara Colesanti Senni and ETH Zurich​​​​​​​

About the partnership

In 2026, MRC entered into a research partnership with Dr. chiara Colesanti Senni and ETH Zurich to co-develop the Climate Contribution Evidence Engine (CC-EE), an AI-powered platform designed to automate and scale the assessment of corporate climate transition plans. The project combines the development of a cutting-edge technical infrastructure with a research program in sustainable finance, both of which will be made fully available in open access to the financial sector, the academic community, and all other interested stakeholders.
The CC-EE is part of X-TrA, a joint collaboration between ETH Zurich and the University of Zurich on exposure and transition risk assessment for climate and nature.

Background: A verification gap that cannot be ignored

Corporate climate disclosure has grown exponentially in recent years. Approximately 6,000 companies now claim alignment with the 1.5°C climate goal — yet only around 140 fully disclose across all credibility indicators tracked by CDP. Meanwhile, more than 5,000 companies are subject to CSRD-mandated, audited climate-risk reporting. The gap between the volume of commitments and the capacity to verify them is widening.
Manual assessment of transition plans is rigorous but inherently limited in scale. It cannot keep pace with the growth of corporate disclosure, let alone provide the systematic coverage that investors and policymakers increasingly require. A scalable, transparent, and reproducible approach to transition plan analysis is urgently needed.

The CC-EE Platform

The Climate Contribution Evidence Engine is an agentic retrieval-augmented generation (RAG) platform that automates corporate climate analysis across four sequential stages:
  • Data Collection : a distributed web crawler systematically scans annual reports, CSRD filings, earnings calls, news articles, and corporate websites across all EU languages. Content is stored in a vector database optimized for semantic retrieval.
  • Evidence Identification : multilingual semantic queries, combined with rule-based filters and multimodal RAG, retrieve the most relevant excerpts from both textual and visual data sources.
  • Data Scoring : a language model evaluates retrieved excerpts against more than 50 climate indicators, assigning confidence scores and verifying findings against IPCC 1.5°C pathways and the IEA Net-Zero-by-2050 scenario.
  • Verification : a dashboard for human reviewers displays text excerpts, indicator labels, and full citation trails and a feedback loop generates training data for continuous model improvement.

The CC-EE evaluates corporate climate performance across three complementary dimensions:

  • ACT-Core Indicators — Is the transition plan credible? This pillar assesses the quality and credibility of corporate transition plans, examining GHG target-setting, governance structures, low-carbon investment commitments, and actual performance against 1.5°C pathways.
  • Climate Solutions — Is the company delivering green outcomes? This pillar measures companies’ concrete contributions to decarbonization through green products and services, including EU Taxonomy-aligned revenues and avoided emissions.
  • Climate Finance — Is the company financing climate action? This pillar captures climate-related financial flows beyond core operations, including carbon credits, climate-related sponsorship, and investments with secondary climate impacts

Research in sustainable finance

In parallel to the technical platform, the partnership supports a substantive academic research program leveraging CC-EE outputs. Large-scale analysis of corporate climate disclosures will generate new insights into which transition strategies most effectively drive real-world corporate change, and will inform evidence-based investor engagement and capital allocation for decarbonization. The research outputs will contribute to the broader academic literature on corporate climate governance and the effectiveness of climate-related financial regulation.

Open access and impact

Full transparency is a design principle of the CC-EE. All outputs will be open-access and reproducible. Companies will be able to consult the evidence compiled about them, challenge assessments, and submit complementary information. The platform is intended to serve as a shared public infrastructure for sustainable finance — transforming what is currently a labor-intensive and fragmented manual process into a scalable, auditable, and institutionally credible resource.

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