Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based Model
Environmental, social, and governance (ESG) evaluation has become increasingly critical for company sustainability assessments, especially for enterprises in the construction industry with a high environmental burden. However, existing methods face limitations in subjective evaluation, inconsistent...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/15/2710 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849407477519482880 |
|---|---|
| author | Binqing Cai Zhukai Ye Shiwei Chen |
| author_facet | Binqing Cai Zhukai Ye Shiwei Chen |
| author_sort | Binqing Cai |
| collection | DOAJ |
| description | Environmental, social, and governance (ESG) evaluation has become increasingly critical for company sustainability assessments, especially for enterprises in the construction industry with a high environmental burden. However, existing methods face limitations in subjective evaluation, inconsistent ratings across agencies, and a lack of industry-specificity. To address these limitations, this study proposes a large language model (LLM)-based intelligent ESG evaluation model specifically designed for the construction enterprises in China. The model integrates three modules: (1) an ESG report information extraction module utilizing natural language processing and Chinese pre-trained language models to identify and classify ESG-relevant statements; (2) an ESG rating prediction module employing XGBoost regression with SHAP analysis to predict company ratings and quantify individual statement contributions; and (3) an ESG intelligent evaluation module combining knowledge graph construction with fine-tuned Qwen2.5 language models using Chain-of-Thought (CoT). Empirical validation demonstrates that the model achieves 93.33% accuracy in the ESG rating classification and an R<sup>2</sup> score of 0.5312. SHAP analysis reveals that environmental factors contribute most significantly to rating predictions (38.7%), followed by governance (32.0%) and social dimensions (29.3%). The fine-tuned LLM integrated with knowledge graph shows improved evaluation consistency, achieving 65% accuracy compared to 53.33% for standalone LLM approaches, constituting a relative improvement of 21.88%. This study contributes to the ESG evaluation methodology by providing an objective, industry-specific, and interpretable framework that enhances rating consistency and provides actionable insights for enterprise sustainability improvement. This research provides guidance for automated and intelligent ESG evaluations for construction enterprises while addressing critical gaps in current ESG practices. |
| format | Article |
| id | doaj-art-c96165144eec4f32b6bf11c2d59e333b |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-c96165144eec4f32b6bf11c2d59e333b2025-08-20T03:36:03ZengMDPI AGBuildings2075-53092025-07-011515271010.3390/buildings15152710Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based ModelBinqing Cai0Zhukai Ye1Shiwei Chen2School of Management, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Management, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Management, Fujian University of Technology, Fuzhou 350118, ChinaEnvironmental, social, and governance (ESG) evaluation has become increasingly critical for company sustainability assessments, especially for enterprises in the construction industry with a high environmental burden. However, existing methods face limitations in subjective evaluation, inconsistent ratings across agencies, and a lack of industry-specificity. To address these limitations, this study proposes a large language model (LLM)-based intelligent ESG evaluation model specifically designed for the construction enterprises in China. The model integrates three modules: (1) an ESG report information extraction module utilizing natural language processing and Chinese pre-trained language models to identify and classify ESG-relevant statements; (2) an ESG rating prediction module employing XGBoost regression with SHAP analysis to predict company ratings and quantify individual statement contributions; and (3) an ESG intelligent evaluation module combining knowledge graph construction with fine-tuned Qwen2.5 language models using Chain-of-Thought (CoT). Empirical validation demonstrates that the model achieves 93.33% accuracy in the ESG rating classification and an R<sup>2</sup> score of 0.5312. SHAP analysis reveals that environmental factors contribute most significantly to rating predictions (38.7%), followed by governance (32.0%) and social dimensions (29.3%). The fine-tuned LLM integrated with knowledge graph shows improved evaluation consistency, achieving 65% accuracy compared to 53.33% for standalone LLM approaches, constituting a relative improvement of 21.88%. This study contributes to the ESG evaluation methodology by providing an objective, industry-specific, and interpretable framework that enhances rating consistency and provides actionable insights for enterprise sustainability improvement. This research provides guidance for automated and intelligent ESG evaluations for construction enterprises while addressing critical gaps in current ESG practices.https://www.mdpi.com/2075-5309/15/15/2710construction industryESG ratingLLMIntelligent Evaluation |
| spellingShingle | Binqing Cai Zhukai Ye Shiwei Chen Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based Model Buildings construction industry ESG rating LLM Intelligent Evaluation |
| title | Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based Model |
| title_full | Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based Model |
| title_fullStr | Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based Model |
| title_full_unstemmed | Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based Model |
| title_short | Intelligent ESG Evaluation for Construction Enterprises in China: An LLM-Based Model |
| title_sort | intelligent esg evaluation for construction enterprises in china an llm based model |
| topic | construction industry ESG rating LLM Intelligent Evaluation |
| url | https://www.mdpi.com/2075-5309/15/15/2710 |
| work_keys_str_mv | AT binqingcai intelligentesgevaluationforconstructionenterprisesinchinaanllmbasedmodel AT zhukaiye intelligentesgevaluationforconstructionenterprisesinchinaanllmbasedmodel AT shiweichen intelligentesgevaluationforconstructionenterprisesinchinaanllmbasedmodel |