ESG disclosure topics and reporting frameworks: exploratory research across automotive, construction, and energy industries
ESG開示トピックと報告フレームワーク：自動車、建設、エネルギー産業における探索的研究 (AI 翻訳)
Bence Lukács, Robert C. Rickards, Péter Molnár, Alex Suta, Árpád Tóth
🤖 gxceed AI 要約
日本語
本論文は、中央東欧（CEE）地域の自動車、建設、エネルギー産業におけるESG報告の現状を評価する。60社の2021年サステナビリティ報告書をPythonベースのテキストマイニングで分析し、ESRSに沿ったカスタム辞書を用いて開示内容の差異を明らかにした。気候変動と生物多様性に関する定性的開示にばらつきがあり、透明性と一貫性の向上が必要と結論づけている。
English
This paper assesses ESG reporting practices in the automotive, construction, and energy industries in Central Eastern Europe (CEE). Using Python-based text mining on 60 sustainability reports from 2021, it finds significant variance in qualitative disclosures on climate change and biodiversity, highlighting the need for enhanced transparency and consistent metrics aligned with ESRS.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文はCEE地域に焦点を当てているが、日本企業が欧州進出時にESRS準拠の開示を求められるケースが増えており、開示の実態と課題を理解する上で参考になる。特に自動車・建設・エネルギー業界は日本でもGXの中心であり、テキストマイニング手法の応用可能性も示唆している。
In the global GX context
This study provides empirical evidence on ESG disclosure practices in CEE, a region often underrepresented in global reporting research. It demonstrates the use of automated text analysis to assess alignment with ESRS, offering methodological insights for global disclosure scholarship. The findings on cross-industry variance are relevant for policymakers and standard-setters aiming for harmonization.
👥 読者別の含意
🔬研究者:Provides a replicable text-mining methodology for analyzing ESG disclosures and empirical evidence on CEE reporting practices.
🏢実務担当者:Highlights gaps in qualitative ESG disclosures, especially on climate and biodiversity, that companies can address to improve reporting quality.
🏛政策担当者:Offers insights into current disclosure practices in CEE, supporting efforts to enhance regulatory frameworks and enforcement.
📄 Abstract（原文）
Abstract Environmental, Social, and Governance (ESG) reporting and proper measurement of greenhouse gas emissions are becoming increasingly important for industries with substantial environmental impact. This research aims to assess the current state of ESG reporting practices and highlight areas for improvement across the automotive, construction and energy industries operating in the Central Eastern European (CEE) region. To achieve this aim, a multi-industry sustainability disclosure database was created and analyzed through a Python-based text-mining methodology, using term frequency-inverse document frequency and keyword-in-context analysis. The process involved extracting and preprocessing text from 60 sustainability reports for the year 2021, followed by constructing a custom dictionary of key ESG terms aligned with the European Sustainability Reporting Standards. The findings reveal considerable variance in the focus of qualitative disclosures across industries, particularly regarding climate change and biodiversity. The investigation underscores the need for enhanced transparency, consistent metrics, and rigorous validation in ESG reporting. The study also provides new insights into the technical possibilities of automated text analysis for sustainability reporting in the CEE region, and highlights key areas where improvement appears necessary.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.1007/s43621-025-01533-xfirst seen 2026-05-05 19:08:01
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