Advanced Decision-Support Model for Streamlining Environmental Compliance in Multi-Stakeholder Projects

Ensuring timely and effective environmental compliance in multi-stakeholder projects remains a persistent challenge due to complex regulatory landscapes, competing interests, and dynamic operational environments. This study introduces an Advanced Decision-Support Model (ADSM) designed to streamline environmental compliance processes by integrating regulatory intelligence, risk analytics, real-time monitoring, and participatory decision-making tools into a unified framework. The model addresses key bottlenecks in traditional compliance workflows, including fragmented communication, inconsistent interpretation of regulations, delays in reporting, and limited visibility into emerging risks. ADSM leverages multi-criteria decision analysis, machine learning–based predictive assessments, and rule-based compliance engines to support proactive planning, early issue detection, and coordinated action among regulatory agencies, project managers, environmental consultants, and community stakeholders. The framework incorporates modular components that enable automated compliance checklists, dynamic risk ranking, scenario simulations, and stakeholder alignment mapping. By synthesizing diverse data streams including environmental quality metrics, site activity logs, regulatory updates, and community feedback the model enhances transparency and supports evidence-driven decision-making. ADSM also embeds conflict-resolution logic to reconcile divergent stakeholder priorities, ensuring balanced outcomes that satisfy regulatory requirements while maintaining project continuity. The system’s participatory dashboard enables real-time collaboration, allowing users to track compliance status, assign responsibilities, generate audit-ready documentation, and forecast the implications of potential non-compliance events. Pilot implementation in a complex environmental infrastructure project demonstrated significant improvements in compliance accuracy, communication efficiency, and regulatory reporting timelines. The model reduced ambiguity in decision pathways, facilitated rapid identification of non-compliance triggers, and enhanced stakeholder engagement through structured information-sharing protocols. Results indicate that ADSM can substantially mitigate compliance-related delays and enforcement risks by promoting coordinated, transparent, and anticipatory management practices Overall, the Advanced Decision-Support Model represents a transformative approach to environmental compliance in multi-stakeholder contexts. By combining data-driven analytics with collaborative governance mechanisms, it offers a scalable and adaptable solution capable of enhancing regulatory alignment, operational efficiency, and stakeholder confidence across diverse project environments. The study underscores the model’s potential to inform policy development, support sustainable project delivery, and strengthen environmental governance frameworks globally.