This paper investigates the relationship between technological progress in the energy sector and carbon emissions based on the Environment Kuznets Curve (EKC) and data from China during the period of 1995–2012. Our study confirms that the situation in China conforms to the EKC hypothesis and presents the inverted U-curve relationship between per capita income and carbon emissions. Furthermore, the inflection point will be reached in at least five years. Then, we use research and development (R & D) investment in the energy industry as the quantitative indicator of its technological progress to test its impact on carbon emissions. Our results show that technological progress in the energy sector contributes to a reduction in carbon emissions with hysteresis. Furthermore, our results show that energy efficiency improvements are also helpful in reducing carbon emissions. However, climate policy and change in industrial structure increase carbon emissions to some extent. Our conclusion demonstrates that currently, China is not achieving economic growth and pollution reduction simultaneously. To further achieve the goal of carbon reduction, the government should increase investment in the energy industry research and improve energy efficiency.

This paper investigates the relationship between energy consumption and technological innovation using a dynamic panel data model and regional-level data from China for the period 1995–2012. In contrast to previous studies, it examines the short and long-run bilateral relationship between technological innovation and energy consumption. The results show that in the short run, technological innovation leads to an increase in energy consumption, while energy consumption has no significant effect on technological innovation. In the long run, however, energy consumption is positively and bilaterally related to technological innovation. These findings suggest that although technological innovation does not directly lead to a reduction in energy consumption as mentioned in the extant literature, it could help achieve sustainability through improving energy efficiency and developing energy structure for developing countries.

The increasing unethical behaviors in the application market make platform governance increasingly important. To our knowledge, in this article, the effectiveness of punishments for unethical behaviors and the responses of app developers and users to the implementation of punishments have remained unclear. So, the purpose of this article is to examine the spillover effect and the mechanism of platform governance of Apple app store. Based on the weekly observation of 2500 apps, we examine the spillover effect of punishments of different severity on previously unpunished developers’ adaptive reaction (e.g., weekly updates and weekly new release) and users’ evaluation. We verify that punishment has effects on the developer's adaptive behaviors in the long term but does not have effects in the short term. However, severe punishments cause developers to conduct adaptive behaviors that are not conducive to the app store. Furthermore, we accentuate that the effect of different punishments on user evaluation depends on the type of apps (i.e., utilitarian versus hedonic). We also discuss the implications of our results as they directly relate to the governance of the app platform and the benefits of users and developers. More broadly, the findings contribute to building a healthy app store ecosystem composed of platforms, developers, and users.

Engineering and technology intensive organizations increasingly adopt AI-enabled recruitment tools to improve selection efficiency, but challenges related to algorithmic fairness and trust influence applicant responses. Despite the use of objective and fair algorithms, applicants may still perceive unfairness, but empirical research on this phenomenon remains limited. Based on expectation disconfirmation theory, this study defines the gap between applicants' self-assessed qualifications and AI-generated person-job fit scores as person-job fit expectation disconfirmation and estimates how it affects job pursuit intention through perceptions of procedural justice. To ensure research rigor, we employed a two-stage online scenario-based experiment, implemented attention checks, and conducted exploratory and confirmatory factor analyses to validate construct reliability and validity. Multiple procedural and statistical remedies were also applied to address potential common method bias. Empirical results show that expectation disconfirmation significantly reduces job pursuit intention. Besides, procedural justice partially mediates this relationship, suggesting that weakened fairness perceptions serve as a critical mechanism underlying the negative effect of expectancy disconfirmation. Furthermore, trust in AI exacerbates the negative impact of expectation disconfirmation in some cases. These insights deepen the understanding of how AI-generated evaluations shape individuals' behaviors and perceptions. This study extends the applicability of expectation disconfirmation theory to algorithmic recruitment contexts and offers novel theoretical and practical implications.