【公开学术报告-第143期】Dynamic Impact and Strategic Optimization of Sustainability Certification on E-Commerce Platforms

Dynamic Impact and Strategic Optimization of Sustainability Certification on E-Commerce Platforms

Speaker: Xiaohang (Flora) FENG (Ph.D. Candidate in Marketing, Carnegie Mellon University)

Date & Time: Mon. 20, October 2025, from 10:30 to 12:00 (Beijing Time)

Place: Tongji Building A2101 

ABSTRACT

Sustainability badges are increasingly central to e-commerce, yet their long-term effects on platform outcomes remain unexplored because A/B tests are typically short-term. Although badges attract green consumers, price premiums and dissatisfaction gaps can ultimately reduce overall demand. This study evaluates Amazon’s Climate Pledge Friendly (CPF) badge using a dynamic structural model integrated with a multimodal generative AI framework. We ask: (1) What badge threshold best supports long-term

platform goals? (2) Should the badging strategy change as the proportion of green products varies? (3) How can generative AI address the challenges of unstructured data in policy simulations? Using data on more than 1,200 products tracked for six months in Amazon.com, the structural model captures forward-looking seller behavior, where daily certification and pricing decisions account for future returns. To simulate multimodal content adaptation, we introduce the Green-Flora-GPT framework, which transforms non-green product listings into green-aligned images and descriptions. The model uses a novel utility-aware contrastive loss to maintain semantic coherence, brand consistency, and authentic green signaling. We find that full badge coverage is not optimal: the median revenue of badged products peaks when 80% of products are certified, whereas consumer welfare is highest at a much lower badge rate (20%). As more products become green, the optimal strategy for platform-wide total revenue becomes increasingly selective, although overall badge visibility remains relatively stable (10–30%). This study offers practical insights for managing sustainability signaling in evolving digital marketplaces. 

Keywords: sustainability, eco-labeling, e-commerce, seller competition, market equilibrium, structural model, generative AI, multimodal representation learning

GUEST BIO:

Xiaohang (Flora) FENG is a Ph.D. candidate in Marketing (Quantitative Track) at Carnegie Mellon University’s Tepper School of Business (2020–2026 expected), advised by Kannan Srinivasan. She holds an M.S. in Machine Learning (2022–2024, GPA 3.93/4.33) and an M.S. in Industrial Administration with a focus on Marketing (2020–2022, GPA 3.83/4.33), both from Carnegie Mellon. She received a B.A. in Foreign Languages with a minor in Economics from Peking University (2016–2020, GPA 3.80/4.00) and was an exchange student at the University of Pennsylvania (2019, GPA 4.00/4.00). Her research uses methods such as causal inference, generative AI, and computer vision to explore topics including artificial intelligence, sustainability, and e-commerce.