TASR: A Trustworthy LLM-based Framework for TCFD-Aligned Sustainability Report Analysis

Reliable and transparent assessment of environmental, social, and governance (ESG) disclosures is critical for sustainable finance, regulatory oversight, and risk-aware decision-making. However, existing sustainability reporting evaluations rely on costly manual reviews or third-party ratings, which limit reproducibility. This work proposes a trustworthy large language model (LLM)-based framework for automated sustainability report analysis aligned with the Task Force on Climate-related Financial Disclosures (TCFD). We propose TASR (Trustworthy Analysis for Sustainability Report), a three-stage framework for TCFD-aligned sustainability report analysis that integrates LLM-based scoring, benchmarking against third-party ESG ratings, and downstream predictive modeling. Experiments on 100 sustainability reports from U.S. oil, gas, and mining companies demonstrate strong alignment with Bloomberg Environmental Disclosure scores and high score stability across repeated evaluations. Furthermore, predictive models trained on the LLM-generated TCFD scores achieve meaningful predictive performance in forecasting disclosure benchmarks, highlighting their practical utility for sustainability rating. The results suggest that LLM-based TCFD scoring offers a potentially scalable and transparent alternative for sustainability disclosure assessment.