@inproceedings{calamai-etal-2026-disclosure,
title = "From Disclosure to Evaluability: an {NLP}-Based Analysis of Corporate Transition Plans",
author = "Calamai, Tom and
Balalau, Oana and
Guenedal, Theo Le and
Suchanek, Fabian M.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.766/",
pages = "17757--17785",
ISBN = "979-8-89176-459-0",
abstract = "Corporate climate transition plans detail how companies intend to reduce their carbon footprint, but their unstructured and often incomplete nature limits assessments of whether disclosed actions are sufficiently concrete, quantified, and consistent with stated emission-reduction objectives. We analyze transition plans from CDP by decomposing them into the key information needed for such assessments: disclosed mitigation measures, their implementation status, quantitative attributes, action type, specificity, and organizational perimeter. Large language models achieve moderate to strong performance across these tasks (65-91{\%} F1), enabling large-scale diagnostic analysis of transition-plan disclosures. Our results show that the main bottleneck lies in the disclosures themselves. Specifically, 80{\%} of transition plans contain at most two quantified planned measures, and nearly 47{\%} of proposed actions are both generic and lack a specified organizational perimeter. Firms also tend to favor high-level targets, preparatory actions, and energy-related measures over more structural business transformations. Overall, our findings show that current transition-plan reporting often lacks the granularity needed to assess whether disclosed actions are consistent with stated emission-reduction targets."
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<abstract>Corporate climate transition plans detail how companies intend to reduce their carbon footprint, but their unstructured and often incomplete nature limits assessments of whether disclosed actions are sufficiently concrete, quantified, and consistent with stated emission-reduction objectives. We analyze transition plans from CDP by decomposing them into the key information needed for such assessments: disclosed mitigation measures, their implementation status, quantitative attributes, action type, specificity, and organizational perimeter. Large language models achieve moderate to strong performance across these tasks (65-91% F1), enabling large-scale diagnostic analysis of transition-plan disclosures. Our results show that the main bottleneck lies in the disclosures themselves. Specifically, 80% of transition plans contain at most two quantified planned measures, and nearly 47% of proposed actions are both generic and lack a specified organizational perimeter. Firms also tend to favor high-level targets, preparatory actions, and energy-related measures over more structural business transformations. Overall, our findings show that current transition-plan reporting often lacks the granularity needed to assess whether disclosed actions are consistent with stated emission-reduction targets.</abstract>
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%0 Conference Proceedings
%T From Disclosure to Evaluability: an NLP-Based Analysis of Corporate Transition Plans
%A Calamai, Tom
%A Balalau, Oana
%A Guenedal, Theo Le
%A Suchanek, Fabian M.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F calamai-etal-2026-disclosure
%X Corporate climate transition plans detail how companies intend to reduce their carbon footprint, but their unstructured and often incomplete nature limits assessments of whether disclosed actions are sufficiently concrete, quantified, and consistent with stated emission-reduction objectives. We analyze transition plans from CDP by decomposing them into the key information needed for such assessments: disclosed mitigation measures, their implementation status, quantitative attributes, action type, specificity, and organizational perimeter. Large language models achieve moderate to strong performance across these tasks (65-91% F1), enabling large-scale diagnostic analysis of transition-plan disclosures. Our results show that the main bottleneck lies in the disclosures themselves. Specifically, 80% of transition plans contain at most two quantified planned measures, and nearly 47% of proposed actions are both generic and lack a specified organizational perimeter. Firms also tend to favor high-level targets, preparatory actions, and energy-related measures over more structural business transformations. Overall, our findings show that current transition-plan reporting often lacks the granularity needed to assess whether disclosed actions are consistent with stated emission-reduction targets.
%U https://aclanthology.org/2026.emnlp-main.766/
%P 17757-17785
Markdown (Informal)
[From Disclosure to Evaluability: an NLP-Based Analysis of Corporate Transition Plans](https://aclanthology.org/2026.emnlp-main.766/) (Calamai et al., EMNLP 2026)
ACL