This study investigates the effects of unverifiable green signals—vague environmental claims, trust-substitute cues, and function-stacking—on consumer purchasing behaviors in e-commerce settings. Using detailed product-level data collected from two major Chinese online platforms, Taobao and Pinduoduo, during the peak shopping period in November 2023, we analyze the impact of these signals on product sales using ordinary least squares (OLS), instrumental variable (IV), and propensity score matching (PSM) methods. Results indicate that vague environmental language and function-stacking significantly boost sales across platforms, highlighting consumers’ preference for easily interpretable and seemingly comprehensive products. However, trust-substitute signals exhibit mixed effects, with them being beneficial on platforms with stronger credibility frameworks (Taobao) and less effective or even detrimental on platforms characterized by price competition and weaker governance (Pinduoduo). This study contributes to the literature on consumer trust and digital greenwashing by identifying platform-specific responses to unverifiable eco-claims and underscoring the importance of heuristic processing theories and trust formation mechanisms in digital marketing contexts. These findings underscore the complex dynamics of greenwashing strategies and stress the necessity for enhanced regulation and clearer communication standards to protect consumers and genuinely support sustainable consumption.

While the emergence of shared autonomous vehicles can be an effective solution to improve transport issues and achieve sustainable development, the benefits associated with shared autonomous vehicles can only be realized when the public intends to use them. Therefore, it is necessary to conduct an in-depth study on the public’s intention to use shared autonomous vehicles and identify the key influencing factors. This study mined social media data to obtain real public perceptions. A qualitative exploratory analysis was used to identify thematic variables regarding social media data on shared autonomous vehicles, from which a research model of the public’s intention to use SAVs was proposed. Then, a questionnaire survey was conducted, and the structural equation model and Bayesian network were used to analyze the questionnaire data quantitatively. The findings reveal how perceived risk, social information, trust, perceived usefulness, and personality traits affect the public’s intention to use shared autonomous vehicles, and how to enhance the public’s intention to use them. This study will enrich the research on traveler psychology in the context of intelligent travel and provide theoretical basis and decision support for future policies to promote shared autonomous vehicles.

The rapid growth of road freight has increased urban carbon emissions, but decarbonization in this sector remains slow compared to other areas. This study examines city-level road freight decarbonization, focusing on extreme values, with the goal of establishing a quantitative reference indicator for tailored policies. Using data from 342 Chinese cities, we applied K-means clustering and Extreme Value Theory (EVT) to estimate the extreme levels of freight vehicles decarbonization (FVDEL) under various economic scenarios. Results show notable differences among city types. High-Tech and Light Industry Cities (Type I) display a more substantial decarbonization potential, with a key threshold around 1.27%. Surpassing this level indicates higher readiness for zero-emission road freight, while Heavy Industry-Manufacturing Cities (Type II) tend to behave more predictably during economic ups and downs because of their close ties between industry and freight activities. The study also finds that purchase subsidies tend to have diminishing returns, whereas operational incentives like electricity price discounts and road access advantages are more effective in maintaining adoption. By proposing EVT-based thresholds as practical benchmarks, this research connects statistical modeling with policy implementation. The proposed reference indicator offers useful guidance for assessing urban decarbonization capacity and developing customized strategies to promote zero-emission freight systems.