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Cited by 7Business Process Modeling and AnalysisBIM and Construction IntegrationImpact of AI and Big Data on Business and Society

AI-Driven Process Mining for ESG Risk Assessment in Sustainable Management

Open access
Censi, Riccardo, Campana, Paola, Bellini, Francesco, Schettino, Fulvio, De Pucchio, Chiara·2025·Buildings
DOI 10.3390/buildings15234260
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Abstract

The construction sector faces growing challenges in integrating sustainability, risk management, and regulatory compliance, in line with initiatives such as the European Green Deal, the Corporate Sustainability Reporting Directive, and international building standards. However, the systematic adoption of ESG metrics in decision-making remains limited due to fragmented data, the lack of predictive tools, and reliance on static reporting. This study proposes and illustrates a digital framework, based on simulated data, that combines Artificial Intelligence, Process Mining, and Robotic Process Automation to enhance ESG risk assessment in sustainable construction management. The model, formalized through Business Process Model and Notation, integrates Machine Learning for risk weighting and classification, and leverages Web Scraping and Business Intelligence for dynamic data acquisition. A simulated case study involving 100 synthetic construction projects is used to demonstrate the internal logic and quantitative feasibility of the framework, showing how automated data integration and predictive modeling can improve the consistency of ESG risk identification and classification. While the results are illustrative rather than empirical, they confirm the analytical coherence and reproducibility of the proposed workflow. From a scientific perspective, it contributes an integrated methodology that bridges predictive analytics and process management for ESG evaluation. From a practical standpoint, it offers a structured and reproducible workflow to anticipate, classify, and mitigate ESG risks, supporting the construction sector’s transition toward data-driven and sustainability-first management practices.

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  • A BPMN-based AI framework combining process mining, machine learning, robotic process automation, and web scraping improves consistency and traceability of ESG risk identification compared to static reporting approaches.

    “Censi et al. [42] develop a BPMN-based AI framework combining process mining, machine learning, robotic process automation, and web scraping to assess ESG risks in construction management. A simulated case study of 100 construction projects shows that AI-driven risk classification improves consistency and traceability of ESG risk identification compared to static reporting approaches.”

    From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [42]

    Sections 3.1–3.3 describe the BPMN-based framework, including process mining, machine learning, RPA, and web scraping, and a simulation of 100 construction projects. Section 4.1 reports consistent risk identification, while the Conclusions report traceability in the simulated event log. The study presents the simulation as a proof of concept; it does not show a direct comparison of risk-identification consistency and traceability against static reporting approaches. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • AI-based multi-criteria decision-making and process mining improve transparency, consistency, and traceability of ESG decisions.

    “Decision-support and prioritization tools such as those proposed by Aljohani [41] and Censi et al. [42] show that AI-based multi-criteria decision-making and process mining improve transparency, consistency, and traceability of ESG decisions, particularly in complex and high-risk environments like manufacturing and construction.”

    From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [42]

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Authors
Censi, R., Campana, P., Bellini, F., Schettino, F., & De Pucchio, C.
Affiliations
  • Sapienza University of Rome (IT)
  • Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Via Ariosto, 00185 Rome, Italy
Corresponding author
Censi, Riccardo
ORCID
Venue
Buildings
Publisher
MDPI AG
Published
2025-11-25
Volume
15
Issue
23
Pages
4260
ISSN
2075-5309 (ISSN-L)
DOI
10.3390/buildings15234260
OpenAlex
W4416771916
Cited by
7
References
55
Citations by year
2026: 7
FWCI
9.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Buildings (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
  • Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
ESG risk assessment, sustainable construction management, process mining, robotic process automation, web scraping, business process modeling, corporate sustainability reporting, regulatory compliance, risk mitigation

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