The Mechanism of Textile Recycling Intention and Behavior Transformation: The Moderating Effect Based on Community Response

As an important part of the circular economy, recycling old garments not only lessens resource waste, but also offers significant social benefits and environmental conservation. Taking Hefei City, Anhui Province, China, as a case, this study adopted the innovative Planned Behavior Theory (TPB) model and introduced innovative variable community promotion as the moderating variable to analyze the influencing factors of residents’ used clothing recycling behavior. It was found that residents’ attitudes, perceived behavioral control, and subjective norms were key factors influencing their intention to recycle used clothes. Community promotion activities play a positive role in improving residents’ perceived behavior control. However, there is also an interaction between community promotion and perceived behavior control, indicating that the effect of community promotion is affected by residents’ perceived behavior control level. This shows that the publicity and promotion of the community will improve residents’ enthusiasm for recycling old clothes, but if the publicity or promotion is too strong, it may lead to a decline in residents’ enthusiasm. The results show that improving residents’ environmental awareness, simplifying the recycling process, utilizing social influence, rationally planning community promotion activities, policy support and incentive measures, and establishing multi-party cooperation mechanisms are effective ways to promote the recycling of used clothing and resources. Through these measures, we can better promote the recycling of used clothing, realize the rational development, utilization, and protection of resources, and contribute to the realization of green and high-quality development. However, this study is limited to the research and investigation in Hefei, Anhui Province, and most of the respondents have a certain educational background, so the universal applicability of the data may not be significant.