Inclusive finance is an important financial development strategy in the world. The promoting effect of the human capital on economic growth has also gained theoretical and empirical support. This paper attempts to deeply examine the interaction among inclusive finance, human capital and regional economic growth as well as their mutual influence mechanism. To the end, data of 31 provinces and cities from 2005 to 2015 were chosen to build a corresponding panel data template. Meanwhile, the fixed effect model was adopted for an empirical test. Results suggest the following. (1) Inclusive finance and human capital can exert a significantly positive promoting effect on regional economic growth. The influence of inclusive finance on regional economic growth is more obvious, while the nonlinear influence of human capital is more significant; (2) Inclusive finance is observed to have a significantly negative influence on economic growth of China’s central region (mainly referring to eight provinces, including Shanxi). The positive promoting effect of inclusive finance on economic growth of China’s west region (mainly referring to 12 provinces and cities, including Chongqing) is found to be the most significant. Differently, human capital exerts the most significantly promoting effect on China’s central region. The economic development level differs in different regions of China. As an increasing number of talent resources gather in the central and western region of China and the financial system is built in the two regions, resource optimization has become a necessity, which is the linchpin to China’s sustainable economic development.

With the advance of data technologies, gig platforms have applied data and algorithms to their management and put more stringent requirements on gig workers through algorithmic management. Gig workers might perform destructive deviant behavior when coping with algorithmic management. It is meaningful to examine how the algorithmic management applied to gig platforms could lead to gig workers’ destructive deviant behavior. Based on the challenge–hindrance framework, we developed a research model and validated it with survey data collected from 423 food delivery riders. We employed multi-level linear regression analysis in data analysis and found that perceived algorithmic management was appraised as both a hindrance and a challenge. As a hindrance, it elicits working/family deviant behavior; as a challenge, it helps reduce working/family deviant behavior. Regulatory focus (a prevention focus vs. a promotion focus) moderates the effect of perceived algorithmic management on stress appraisals (hindrance appraisals vs. challenge appraisals). This study explains algorithmic management’s impact on gig workers’ destructive deviant behavior through the appraisal of algorithmic management as both a challenge and a hindrance. It also provides practical advice to gig platforms, gig workers and policymakers on how to balance the challenge and hindrance roles of algorithmic management in gig work.

The severe air pollution in China has imperiled public health and resulted in substantial economic loss. To tackle the unprecedented pollution challenges, China has launched a campaign-based environmental inspection over all regions to impel local governments’ actual pollution abatement. At the same time, with the public’s awakening awareness about environmental protection, the public has also played a particularly vital role in this inspection. Under this circumstance, the study tries to reveal the impact of Environmental Inspection led by the Central Government (EICG) on air quality improvement, and to examine the role of public engagement in their relationship. Specifically, utilizing daily data covering 249 prefecture-level cities in China from 1 June 2015 to 31 May 2018, this study employed multiple regression models and then found that due to the implementation of EICG, the concentrations of PM_2.5, PM₁₀, SO₂ and NO₂ decline by 2.642 μg/m³, 6.088 μg/m³, 1.357 μg/m³ and 1.443 μg/m³, respectively, and the air quality index decreases by 2.4 in total, which implies that EICG can improve the air quality to a great extent. However, the coefficients for major variables change from negative to positive, suggesting that an attenuation effect of EICG on air quality improvement exists in Chinese institutional background. Meanwhile, public engagement is shown to enhance the positive association between EICG and air quality improvement. Additionally, further analysis demonstrates that EICG promotes the improvement in air quality up to three months after the inspection in cities during the heating period, while the positive effect has existed during one month before the inspection in cities during the non-heating period. Additionally, in contrast to the instant effect in cities not specially monitored, there is a lagged effect of EICG in controlling the air pollution in cities specially monitored.

As a special financial institution, the financial company has a significant impact on the financial market and real economy. Therefore, further study of the efficiency of financial companies is of great practical significance. China’s financial company is equivalent to the “internal bank” of its affiliated group. Therefore, this paper refers to the quantitative analysis and evaluation method of the banking industry, combined with the data characteristics of the financial company’s industry. This paper carries out quantitative analysis and evaluation of the efficiency of 79 Chinese enterprise group finance companies from 2011 to 2016 through the DEA model and the Malmquist index model. The results are as follows: From the static point of view (based on the DEA model), the overall efficiency of Chinese financial companies is low and the efficiency is less affected by scale efficiency than that of pure technical efficiency; from the industry classification, the efficiency of oil processing, steel, and nonferrous metal financial companies is better than other industries. From the dynamic point of view (based on the Malquist index model), the overall efficiency of financial companies from 2011 to 2016 has been slightly improved and the efficiency is easily influenced by the change of scale efficiency; from the industry category, the changes of the efficiency, scale efficiency and pure technical efficiency of financial companies of military industry are fastest, and the technical progress of the auto industry financial companies is optimal.