AI-Mediated Green Advertising Platform: A Circular Economy Framework for Sustainable Urban Visual Pollution Mitigation in India

This repository contains the complete research data, analytical resources, and computational materials supporting the manuscript entitled "AI-Mediated Green Advertising Platform: A Circular Economy Framework for Sustainable Urban Visual Pollution Mitigation in India." The dataset includes the primary analytical outputs used in the study, including: Main manuscript tables (Table 1–Table 2) presenting sector-wise advertising waste composition and PLS-SEM results. Supplementary Tables (ST1–ST16) containing detailed life cycle assessment (LCA) data, tree damage assessment, Urban Visual Ecological Load (UVEL) scores, Multi-Criteria Sustainability Model (MCSM) results, cost–benefit projections, Monte Carlo simulation statistics, film industry environmental analysis, international regulatory benchmarking, differential evolution optimization, policy scenario comparisons, hypothesis validation, stakeholder analysis, circular economy metrics, equity assessment, India 2030 sustainability projections, and sensitivity analyses. Publication-quality Figures (Figures 1–10) in SVG format corresponding to the final manuscript. Python analytical workflow (analytics_suite.py) used to generate the computational analyses, figures, and supplementary datasets. The study integrates life cycle assessment (ISO 14040/14044 principles), Partial Least Squares Structural Equation Modelling (PLS-SEM), differential evolution optimization, Monte Carlo simulation, stakeholder analysis, and circular economy assessment to evaluate the environmental, economic, and policy impacts of transitioning from conventional physical advertising to an AI-mediated Green Advertising Platform (AI-GAP). These materials are provided to promote research transparency, reproducibility, and future reuse. All files correspond to the final version of the manuscript and should be cited together with the associated publication.