To maintain sustainable development, the government’s macro control is very important, but the system construction of the market itself can’t be ignored. From the perspective of aggregate supply, OFDI (outward foreign direct investment) in developing countries can sustainably promote domestic employment through different kind of channels such as human capital, a reverse spillover of the international technology, and marketization processes. Based on 30 provincial panel data from 2005 to 2017, stating from the C–D production function, this article empirically estimates the total effect and regional differences in the effect of OFDI on domestic employment in China by using the main approach of Sys-GMM. The results show that the promotion effect of OFDI on domestic employment has an obvious lag; the faster the marketization process, the more significant the current period’s substitution effect and the lagged period’s promotion effect of OFDI on domestic employment. This means that it is a lagged-period process for OFDI to enhance total factor productivity and realize the effective allocation of labor resources through various channels. Furthermore, whether the domestic promotion effect of OFDI can be effectively exerted is closely related to the process of domestic marketization. A good market system environment can effectively improve the efficiency of labor resources allocation, thus promoting the sustainable development of domestic employment. China’s market-oriented transformation has not yet been completed. The sustainable growth of China’s employment depends on further promoting a market-oriented economy. Therefore, it is suggested to accelerate the improvement of market mechanisms and related system construction, strengthen the role of the government in public service, and promote the coordinated development of OFDI and IFDI (inward foreign direct investment) in various regions to promote the sustainable development of employment.

Rural revitalization is an important strategy to promote sustainable development of rural areas in China. Rural revitalization listed companies play an important role in implementing the rural revitalization strategy and developing the agricultural industry. However, the financing problem has always been a bottleneck problem with Chinese listed companies. This study used a two-stage DEA (data envelopment analysis) method to evaluate the funds raising efficiency, funds using efficiency, and overall financing efficiency of 34 rural revitalization listed companies in 2018. The results show that the financing efficiencies of 34 sample companies were low, only six companies have overall efficient financing, and there was much room for improvement. Financing efficiency varied greatly depending on the nature of the company, the industry, and the listing board. The efficiency of funds using of state-owned enterprises was much lower than that of private companies. The average efficiency value of agricultural company funds raising was lower than that of manufacturing. The efficiency of small and middle-size enterprises (SMEs) was lower than that of main board companies, but the growth enterprise market (GEM) companies achieved higher efficiency in the funds using. Further, by using the grey relational analysis (GRA) method, we found that the key factors affecting the financing efficiency of sample companies included capital structure, debt-paying ability, governance structure, company age, and operating ability. To improve financing efficiency, the companies should not only optimize their capital structure and governance structure but also improve their management and innovation capabilities. At the same time, the state also needs to give different policies support according to the characteristics of the companies.

Technological innovation and the efficiency of resource allocation in Chinese new energy vehicle enterprises represent critical factors influencing the sustainable development of the industry. By applying a two-stage dynamic network DEA model to analyze the comprehensive and stage-specific technological innovation efficiency of 13 A-share-listed new energy vehicle enterprises between 2017 and 2024, this study reveals that both overall and phase-specific innovation efficiencies remain below optimal levels. Moreover, the average technological R&D efficiency across these firms is found to be lower than their average achievement transformation efficiency, highlighting the urgent need to improve innovation performance in this sector. Grey relational analysis of influencing factors identifies six key determinants of technological innovation efficiency: the shareholding ratio of the largest shareholder, R&D investment intensity, the proportion of employees holding bachelor’s degrees or higher, management capability, return on equity, and total asset turnover. In comparison, government subsidies and total assets exhibit relatively limited influence on technological innovation efficiency.