Data-Driven Policy Iteration: The Path of Optimizing Green Consumption Policy by Digital Marketing

Abstract

Against the backdrop of rising consumer demand for green brands and the improvement of green consumption policies, our research explores the possibility of dynamic policy optimization based on enterprise digital market data. This study proposes a Public-Private Data Synergy Governance (PPD-SG) architecture to prevent market changes in real time and mitigate lagging policy cycles, utilizing enterprise data (consumption trends, User-generated content (UGC), carbon accounts) as core evidence. With an anchor case study on Ant Forest and an understanding of a range of policy instruments, our research demonstrates that data-driven mechanisms can facilitate the instant identification of new demand, policy gaps, and coverage gaps. This research identifies significant institutional barriers and mandates a three-party implementation plan (government mandates, enterprise data briefs, third-party oversight) for agile, evidence-based green governance. This research forges policy iteration theory and identifies implementable directions for practical cooperation on data. The study significantly enriches policy iteration theory by introducing real-time data streams, and its results provide a scalable and viable solution for breaking data fragmentation and overcoming institutional inertia in green governance. The proposed PPD-SG model is highly praised for revealing the defining strengths of digital-age policies, including increased responsiveness, accuracy, and effectiveness in promoting sustainability.