NLP Applications for Corporate Sustainability Data Analysis

🆕 NEW PAPER PUBLISHED: Using AI to assess corporate climate transition disclosures 💚 I'm happy to announce that our paper was published in #Environmental #Research #Communications. The paper presents a blueprint for employing #Large #Language #Models (#LLMs) to analyze corporate climate disclosures in detail and with experts in the loop. We proceed in three steps: 1️⃣ #Framework for #Transition #Plan #Assessments: We build the common ground of 28 transition plan frameworks to create 64 key indicators for evaluating companies' efforts to transition towards net zero. 2️⃣ #Expert #Validation of the corresponding #LLM #Tool: We build an LLM-based tool that can automatically and efficiently analyze disclosures (such as sustainability reports). We validate and improve the tool with domain experts from 26 different institutions, including financial regulators, investors, and NGOs. 3️⃣ #Analysis of the #Most #Carbon #Intensive #Companies: Analysing the highest-emitting companies in the world, we find a gap between "talk" (target setting) and "walk" (strategy implementation). Companies with more disclosures tend to have lower emissions. Similar to commitments towards science-based targets, larger emitters remain more intransparent. 🔗 The paper is here: https://lnkd.in/dJkCjFHV 🌟 The best thing? It is all #open-#source and publicly available. You can use the tool yourself, work with it, and improve it. I even wrote #tutorials for newcomers to the field. So really nothing is stopping you from trying it out. 🔗 Check it out here: https://lnkd.in/dgFPqydB This paper is only possible through a great interdisciplinary collaboration with Chiara Colesanti Senni, Julia Bingler, Jingwei Ni, and Markus Leippold! Feel free to use and provide feedback! University of Zurich | ETH Zürich | University of Oxford #climate #change #NLP

We introduce a massive dataset on #ESG transparency and performance and new machine learning framework for extraction in a recently released paper "Assessing Corporate Sustainability with Large Language Models: Evidence from Europe" with Kerstin Forster, Lucas Keil, Victor Wagner, Thorsten Sellhorn, and Stefan Feuerriegel. We apply a large-scale machine learning framework to extract 2.9 million quantitative ESG indicators from the annual and sustainability reports of the 600 largest listed European firms over 2014–2023. Our open-source framework enables systematic, indicator-level tracking of both ESG transparency and performance in line with ESRS standards. 🔍 Key insights: – Firms with top ESG ratings disclose 22% more indicators than bottom rated peers, but this gap is narrowing – Scope 1 & 2 emissions dropped sharply, while scope 3 increased 5.6x, likely due to improved transparency around value chain emissions – Gender equality indicators show progress; other social indicators stagnate – Our open-source dataset and ML pipeline democratize access to ESG data 👉 Read the paper: https://lnkd.in/ezF6Y-dn 📊 Explore the data: https://lnkd.in/eXxcwznT 💾 Download the data: https://osf.io/q2jpv/ 🛠 Code & framework: https://lnkd.in/eEi67_Dg We hope this helps researchers, policymakers, and financial actors monitor and drive progress toward sustainability goals. A key insight from our work is that transparency and performance must be analyzed together as increasing transparency can reveal previously hidden poor ESG performance. While we validate our framework and find high agreement overall, accuracy varies across indicators. The method is likely better suited for analyzing trends across larger samples, not necessarily for high-stakes decisions at the individual firm level, where manual validation or tagged data remain important to ensure precision. Sustainability Reporting Navigator TRR 266 Accounting for Transparency #ESG #AI #SustainabilityReporting #MachineLearning #CSRD #Transparency #OpenScience #LLM

Responsible Banking in the AI Era: ESG Compliance Reimagined Can AI Expose Greenwashing — or Is It Just More Tech Hype? One truth stands out in the race toward sustainable finance: greenwashing is no longer a PR risk—it’s a compliance crisis. Financial institutions are under scrutiny, and ESG regulations are tightening. Stakeholders want transparency, but outdated compliance models can’t keep up. That’s where AI steps in—not just as an efficiency booster, but as a game-changer. AI is turning ESG compliance into a strategic asset: 1. Machine learning models flag ESG risks before they escalate. 2. NLP engines decode global regulations in real time. 3. AI bots audit supply chains and sniff out misleading sustainability claims. An institution I worked with used AI-driven ESG scorecards to overhaul its lending strategy. Outcome? Cleaner portfolios, better risk control, and increased investor trust. We're not talking theory—we’re seeing AI-driven ESG frameworks reduce compliance costs by 40% and improve reporting accuracy by 50%. That’s not a future vision. That’s today’s edge. 3 Actionable Takeaways for Tech & Finance Leaders: a. Build AI-powered ESG analytics into your compliance stack—predict risks, don’t just report them. b. Automate greenwashing detection with NLP and sentiment analysis. c. Use blockchain + AI for tamper-proof ESG tracking and reporting. Are you ready to shift ESG from obligation to advantage? Let’s discuss how AI can help your institution lead with integrity. Please share your thoughts in the comments. I’d love to hear how your teams use tech to achieve better sustainability outcomes. #AIinBanking #ESGCompliance #DigitalTransformation #Fintech #SustainableFinance #AIforGood #AIinESG #BankingInnovation #ResponsibleBanking #RegTech #FutureofFinance

🚀 Recording Milestone! Lecture #9 - Decoding ESG Ratings ESG Rating Analytics for Investment Strategy in my specialized series on #ESGAnalytics, #AI for #NaturalDisasters & #GreenFinance 🎓 Delivered in collaboration with Indian Institute of Technology, Kanpur via the prestigious #Swayamprabha #DTH Educational Channel, this series is equipping thousands of learners across #India with future-ready #ESG & #Climate #Finance insights. 📊 Deep Dive: Decoding ESG Ratings – Analytics for Smarter Investment Strategy 🔍 What was discussed: Why ESG Ratings Matter: $40+ trillion in global assets now integrate ESG (GSIA, 2023) — shaping capital flows, borrowing costs & valuations. Ratings 101: Breaking down Environmental (carbon, biodiversity), Social (labor rights, supply chains), Governance (board structures, corruption risk). The Method Behind the Scores: From score aggregation & risk-weighted models to controversy penalties. From Ratings to Strategy: Best-in-class screening, ESG-tilted portfolios, thematic investing (climate leaders, circular economy), and case studies like Enel’s sustainability-linked bond. Challenges & Future Innovations: Data gaps, greenwashing, regional biases — and emerging solutions with AI-driven NLP, blockchain for verification, and real-time IoT/satellite data. ⚡ Tech in Action: #AI: #Naturallanguageprocessing for #ESG disclosures, real-time controversy tracking. #Blockchain: Immutable ESG reporting and automated verification. Advanced #Analytics: Dynamic ratings that evolve with new data rather than lag behind it. 🌍 Takeaway: ESG ratings are decision tools, not absolute truths. The future lies in transparent, dynamic, and tech-enabled ESG assessments that truly align finance with sustainability. Stay tuned as we continue to decode the future of sustainable finance and investment strategy in this lecture series! #ESGAnalytics #SustainableFinance #GreenFinance #AI #Blockchain #ClimateRisk #InvestmentStrategy #IITKanpur #Swayamprabha #KnowledgeSharing