(153b) Socially Responsible Process Safety: Game Theory-Informed Social Life Cycle Assessment for Equitable Decision-Making

Process safety incidents in the chemical industry pose complex risks that extend beyond immediate environmental damage and financial losses, often leading to long-term social impacts. Conventional safety assessments such as Hazard Identification and Risk Management (HIRA), and Hazard and Operability (HAZOP) risk analysis primarily focus on technical and economic dimensions, often overlooking the social consequences that may emerge from accidents or inadequate safety protocols [1]. This research introduces a novel, integrative framework that combines Social Life Cycle Assessment (S-LCA), game theory, and hybrid mechanistic data-driven modeling using the Inherently Safer Design Tool (i-SDT) to holistically address the social and safety impacts of process safety incidents, bridging a critical gap in traditional risk assessments. Our study addresses key research questions, including: (i) How can S-LCA be effectively integrated into process safety frameworks to quantify the social costs of incidents, including community disruption, public trust erosion, and impacts on employee well-being? (ii) What strategic insights can game theory provide into the interactions and decision-making processes among key stakeholders (e.g., companies, workers, regulators, communities) in managing process safety risks? (iii) How do different hydrogen production technologies (e.g., steam methane reforming, electrolysis, biomass gasification) vary in their social risk profiles, and what are the optimal mitigation strategies?

Our methodology involves a three-stage approach. First, we utilize in-house developed i-SDT, a probabilistic, data-driven, property-based tool, to perform inherently safer design evaluations across hydrogen production technologies [2, 3]. This hybrid mechanistic data-driven approach leverages both physical properties and probabilistic data to identify safer process configurations, enabling a proactive safety assessment. Next, an S-LCA is conducted through newly developed unified life cycle sustainability assessment (LCSA) framework on the selected technologies [4], focusing on social risk factors such as worker safety, community health, and employment impacts. This assessment quantifies social risks using both primary and secondary data to provide a comprehensive social impact profile for each technology. Finally, we integrate game theory to model the strategic interactions among stakeholders, analyzing equilibrium strategies for decisions related to safety investments, transparency in risk communication, and community engagement [4]. This game-theoretic approach incorporates parameters such as regulatory compliance, reputational concerns, and public perception, allowing for an equilibrium-based analysis that identifies optimal strategies for each stakeholder group. By combining i-SDT’s hybrid modeling with S-LCA and game theory, our framework captures both the probabilistic and social dimensions of process safety in a holistic manner.

The proposed framework represents a significant advancement in process safety analysis by embedding social dimensions and data-driven mechanistic modeling directly into risk assessments. Unlike traditional methods, our approach captures interdependent decision-making processes among stakeholders, enabling dynamic analysis of how one group’s actions influence others. Preliminary results demonstrate notable differences in social risk levels across hydrogen production technologies, with variations in worker safety, health impacts, and local employment. The game-theoretic analysis reveals equilibrium strategies that suggest optimal investments in safety technologies, enhanced transparency with the public, and targeted community engagement. These insights underscore the value of a socially-informed, data-driven approach to process safety, indicating that aligning stakeholder interests can enhance social resilience, public trust, and process safety. Ultimately, this framework provides the chemical industry with a robust decision-making tool, paving the way toward more socially sustainable and risk-resilient process design and operation.

References
1. Khan F, Rathnayaka S, Ahmed S. Methods and models in process safety and risk management: Past, present and future. Process Safety and Environmental Protection. 2015;98:116-47. doi:https://doi.org/10.1016/j.psep.2015.07.005.

2. Eljack F, Kazi M-K, Kazantzi V. Inherently safer design tool (i-SDT): A property-based risk quantification metric for inherently safer design during the early stage of process synthesis. Journal of Loss Prevention in the Process Industries. 2019;57:280-90. doi:https://doi.org/10.1016/j.jlp.2018.12.004.
3. Kazi M-K, Eljack F, Al-Sobhi SA, Kazantzis N, Kazantzi V. Application of i-SDT for safer flare management operation. Process Safety and Environmental Protection. 2019;132:249-64. doi:https://doi.org/10.1016/j.psep.2019.10.023.
4. Mannan M, Al-Ghamdi S, Kazi M-K. Game theory-informed blockchain framework for social life cycle assessment: Enhancing sustainability in Germanium extraction from PV scrap. 2024 AIChE Annual Meeting, San Diego, CA, USA, 2024. (https://aiche.confex.com/aiche/2024/meetingapp.cgi/Paper/691855).