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Chitchat with AI: Understand the supply chain carbon disclosure of companies worldwide through Large Language Model

arXiv 2511.00024 ↗First posted 26 Oct 2025cs.CY cs.AI cs.CLcited by 0

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Abstract (arXiv)

In the context of global sustainability mandates, corporate carbon disclosure has emerged as a critical mechanism for aligning business strategy with environmental responsibility. The Carbon Disclosure Project (CDP) hosts the world's largest longitudinal dataset of climate-related survey responses, combining structured indicators with open-ended narratives, but the heterogeneity and free-form nature of these disclosures present significant analytical challenges for benchmarking, compliance monitoring, and investment screening. This paper proposes a novel decision-support framework that leverages large language models (LLMs) to assess corporate climate disclosure quality at scale. It develops a master rubric that harmonizes narrative scoring across 11 years of CDP data (2010-2020), enabling cross-sector and cross-country benchmarking. By integrating rubric-guided scoring with percentile-based normalization, our method identifies temporal trends, strategic alignment patterns, and inconsistencies in disclosure across industries and regions. Results reveal that sectors such as technology and countries like Germany consistently demonstrate higher rubric alignment, while others exhibit volatility or superficial engagement, offering insights that inform key decision-making processes for investors, regulators, and corporate environmental, social, and governance (ESG) strategists. The proposed LLM-based approach transforms unstructured disclosures into quantifiable, interpretable, comparable, and actionable intelligence, advancing the capabilities of AI-enabled decision support systems (DSSs) in the domain of climate governance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 59 references that are in the map. First posted on arXiv 26 Oct 2025.

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29 of 59 references.

References · 29

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2022Training language models to follow instructions with human feedbackcited by 15,971
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  6. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  7. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  8. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  9. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  10. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  11. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  12. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
17 more
  1. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,653
  2. 2024A Survey on LLM-as-a-Judgecited by 1,423
  3. 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 575
  4. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
  5. 2024Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Modelscited by 446
  6. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
  7. 2025TabICL: A Tabular Foundation Model for In-Context Learning on Large Datacited by 289
  8. 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 277
  9. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  10. 2023Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarizationcited by 70
  11. 2023Applying Large Language Models and Chain-of-Thought for Automatic Scoringcited by 69
  12. 2024Text Clustering with Large Language Model Embeddingscited by 27
  13. 2023From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Modelscited by 24
  14. 2024Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankerscited by 17
  15. 2024BASS: Batched Attention-optimized Speculative Samplingcited by 11
  16. 2025CarbonChat: Large Language Model-Based Corporate Carbon Emission Analysis and Climate Knowledge Q&A Systemcited by 2
  17. 2025Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Modelscited by 1

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