New-energy vehicles (NEV), particularly electric vehicles, are globally popular with political and financial support from governments, which aim at reducing energy consumption and environmental pollution in the transportation sector. This paper designs a matrix model which identifies the factors influencing the decision-making process on purchasing NEVs, and applies the model on a Chinese case to investigate the impact of influencing factors on Chinese consumers’ purchase behavior to NEVs. The influencing factors are divided into five groups: economic, political, social, technological and environmental. Through a detailed questionnaire survey of 526 consumers in China, this paper conducts a correlation and difference analysis between these consumers’ status and the influencing factors on the purchase decision-making of NEVs through SPSS software. The results indicate that economic, technological and political factors have a strong influence on purchase behavior, while social and environmental factors have a weaker influence. Additional principal component analysis on influencing factors’ preference reveals that consumers’ preferences have two tendencies: practicability or appearance, with the former is attracting far more attention. Rather than practicability or appearance, consumers care more about economic and political factors. According to these results, this paper suggests several policy implications on enhancing the consumers’ willingness to purchase NEVs.

Examining the circular economy model is crucial to enable the scaling up of industry and anthropogenic circularity practice. Electrical and electronic waste plastic (e-plastic) has become the focus of urban mining and circular economy due to its rapid growth, valuable resource and potential risks. This article focuses on the recycling companies’ experience in China from 2012 to 2017. The average recycling rate was 33.3% and the recycling amount in 2017 was 558 kt. A two-dimensional coupling model of economic development and renewable resources is firstly constructed. Eventually, four typical resource-based regional models are summarized, namely for demonstration regional model, commissioned regional model, traditional model and potential regional model. It also puts forward differentiated suggestions in terms of maintaining demonstration, strengthening policies, promoting transformation, and tapping potential. At the same time, it is recommended to explore the construction of large-region resource-based recycling centers and big data centers in resource-based demonstration areas.