In an effort to reduce environmental pollution and energy consumption, the Chinese government strongly promotes the usage of electric vehicles. However, studies focusing on assessing consumers’ willingness to pay (WTP) for electric vehicles (EVs) are limited in the country. This research aims to address this research gap by examining influencing factors of consumers’ WTP for EVs in the Chinese perspective. Combined with the existing consumers’ intention factors, the current study further contributed by augmenting the theoretical framework of the Theory of Planned Behavior by including three new dimensions (performance expectancy, information loaded, and perceived risk) to comprehensively analyze the willingness of Chinese consumers. Analysis is performed on survey data from 498 consumers using EVs in Beijing, China. To evaluate formulated hypotheses, structural equation modeling approach is employed. Empirical findings reveal that environmental knowledge and performance expectancy positively and significantly influence behavioral intention. In contrast, overloaded information has a negative impact on behavioral intention. Moreover, subjective norms are significantly and positively related to behavioral intention. The research outcomes further disclose that perceived risk is positively and significantly related to behavioral intention. Finally, behavioral intention has a significant and positive association with WTP for EVs. The study contributes to the literature on sustainable consumption behavior and provides academics and practitioners with essential future directions.

This study used a 2 × 2 experimental design to explore the effects of message type (non-narrative vs. narrative information) and social media metrics (high vs. low numbers of plays) of low-carbon-themed social media short videos on people’s willingness to protect the environment. Subjects completed questionnaires after viewing short videos that contained different message types and social media metrics, and a final sample of 295 cases was included in the data analysis. The study found that, while the type of information (i.e., non-narrative or narrative) of the low-carbon-themed social media short videos had no direct effect on people’s willingness to protect the environment, its indirect effects were significant. These indirect effects were achieved through immersion experience and social influence. Subjects who watched narrative videos had a higher level of immersion experience, which in turn was significantly and positively correlated with environmental intention; meanwhile, those who watched non-narrative videos experienced a higher level of social influence, which in turn was significantly and positively correlated with environmental intention. In addition, subjects who viewed high-volume videos experienced a more positive effect on their willingness to protect the environment. This study explored how message design could promote subjects’ perceptions and positive attitudes towards environmental protection, with important managerial implications.

Green innovation is a significant component of high-value growth closely linked to China’s 14th five-year plan. This research investigates the influence of green innovation on the market performance of small- and medium-sized enterprises (SMEs). The results are based on the primary data gathered via an online questionnaire survey from 453 respondents working for SMEs in China. The structural equation modeling approach is used for data analysis purposes. The research findings highlight that green innovation positively impacts marketing and products. In turn, marketing innovation positively influences product innovation and market performance, and product innovation also significantly boosts market performance. The study’s findings lead us to suggest that organizations in developing countries should focus on SMEs’ green innovation, which will support them in achieving an effective market performance. The study’s limitations are noted so the findings can be interpreted with caution, and directions for future research are outlined for all stakeholders.

Environmental reformation of old-fashioned sectors and the establishment of new pro-ecological businesses via green investment are the main driving forces behind the revolution in the Chinese industrial sector. Green investment aids in the growth of environmentally friendly industries. Hence, the primary objective of the analysis is to investigate the impact of green investment and technological progress on green industrial development. The results of the unit root tests encourage us to apply the ARDL model. The short and long-run estimates attached to R&D expenditures are positively significant, confirming that increasing R&D expenditures help improve the industrial structure. Similarly, the short and long-run estimates attached to green finance investment are positively significant, signifying that green investment benefits the industrial structure. Empirical findings show that technology significantly aggravates industrial structure development in only the long run. Thus, for green industrial development in China, there is a need to increase green investment and technological development up to top-level design.

Due to the depletion of fossil fuels, empirics began looking at the factors that might encourage investment in renewable energy. Socioeconomic productivity can encourage renewable energy development by encouraging authorities, businesses, and families to rely more on renewable energy sources. Therefore, this analysis is the first-ever effort to detect the impact of socioeconomic productivity on renewable energy development. We have used the panel ARDL and QARDL to examine the estimates. The results of the panel ARDL model predict that national income, financial development, productive capacity index, human capital, ICT, institutional quality, and structural changes are beneficial for renewable energy development in the long run. In the short run, only financial development, productive capacity index, human capital, and ICT promote renewable energy development. Likewise, the panel QARDL model estimates that the national income, financial development, and productive capacity index promote renewable energy development in the long run. However, in the short run, only the productive capacity index and financial development promote renewable energy development. Therefore, by integrating productive assets, entrepreneurial skills, and industrial connections, policymakers must work to boost the productive socioeconomic potential.