Problem: Academic events, which require considerable amounts of energy, transportation, catering, water, material usage, and waste generation, lack integrated computational methodologies for assessment and optimization of their ecological footprint. Approach: The paper suggests a framework for computational engineering analysis and sustainability optimization of academic events, which involves activity-based modeling of carbon emissions, circular economy principles, IoT-Edge-Cloud sensing and monitoring architecture, and multi-objective optimization. A constrained four-objective problem (minimization of carbon emissions, energy, and waste, maximization of Circularity Index) is solved by MGWO and SPEA2 algorithms, and a Java-based Green Event Planner tool calculates initial, minimized and residual emissions based on activity information and emission factors. 1,000 people scenario is used to compare a conventional setup (Scenario A) with a green event setup (Scenario B). Results: In terms of resource performance, the optimized configuration lowered energy use by 4,200 to 2,750 kWh (34.5%), carbon footprinting by 3,250 to 1,780 kg CO₂e (45.2%; from 3.25 to 1.78 kg CO₂e per participant), water use by 30% and solid waste by 37.1%. There was an increase in the share of renewable energy sources from 15% to 40%, the share of materials with recyclable/reusable potential from 22% to 65%, the Circularity Index from 0.35 to 0.68 (+94.3%) and the Sustainability Index Score from 0.47 to 0.82 (+74.5%). Travel interventions can be credited for 48.97% of reductions estimated by the source-wise planner and 81.6% according to the ablation study; this result is consistent with the maximum elasticity coefficient (+0.82) for the transportation category. Conclusion: The proposed framework offers a data-based approach for supporting resource-efficient, sustainable and low-carbon oriented decisions about organizing academic events.
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