The volume of sustainability-related data generated by companies, regulators, and markets is growing at an unprecedented pace. Corporate climate disclosures, transition plans, ESG filings, and impact reports now represent a vast and rapidly expanding body of information — yet the tools available to analyze, verify, and compare this data have not kept pace. The result is a growing credibility gap: commitments multiply, but their robustness remains difficult to assess at scale.
Artificial intelligence and data science offer powerful means to close this gap. By automating the analysis of large volumes of unstructured information, enabling more systematic and reproductible assessments, and surfacing patterns that manual review cannot detect, these technologies have the potential to fundamentally transform how investors, researchers, and regulators evaluate corporate sustainability strategies.
At the same time, the methodological foundations of sustainable finance itself continue to evolve. Measuring a company’s actual contribution to climate goals — accounting not only for its own emissions trajectory but for the climate solutions it develops and the external climate action it finances — requires frameworks that go beyond existing standards. Concepts such as avoided emissions, which capture the climate value of green products and services, remain insufficiently standardized and poorly integrated into investment practice.
Mirova Research Center is actively engaged in building the tools, methods, and open knowledge infrastructure needed to address these challenges. Through partnerships with leading academic institutions and collaborative industry initiatives, MRC contributes to the development of AI-powered analytical platforms, methodological frameworks for corporate climate assessment, and standardized approaches to measuring avoided emissions.
In partnership with Chiara Colesanti Senni and ETH Zurich, MRC is co-developing the Climate Contribution Evidence Engine (CC-EE), a next-generation AI platform for the automated analysis of corporate climate transition plans. Combining natural language processing and machine learning, CC-EE will scan thousands of corporate disclosures across multiple languages and formats, scoring them against more than 50 climate indicators to assess the credibility of companies’ climate commitments. The platform will be made fully open-access, providing a scalable, transparent, and auditable infrastructure for investors, researchers, and policymakers working on corporate decarbonization
The Avoided Emissions Platform (AEP) is an international initiative co-founded by MRC in 2023, bringing together eleven partners — investors, companies, and climate experts — to standardize the measurement of avoided emissions, often referred to as Scope 4. MRC contributes to developing the AEP’s open-access reference framework, which includes a comprehensive methodological guide, a sectoral database of more than 60 validated avoided emission factors, and an online assessment tool. By clarifying definitions and methodologies, the AEP enables companies to credibly quantify their climate contribution through green products and services.
The Climate Contribution Framework (CCF) is a methodological framework developed by MRC, in partnership with Sweep, to measure companies’ actual contribution to global net-zero objectives. Launched in 2025, the CCF evaluates corporate climate action across three pillars — reducing a company’s own footprint, developing low-carbon solutions, and financing external climate projects — and synthesizes them into a balanced, sector-adjusted score. Designed as a complement to existing standards such as SBTi and TCFD, the CCF offers investors and companies a transparent and actionable tool for identifying genuine climate leaders and encouraging more ambitious transition strategies