The digital economy has significant economic structural transformation effects and income distribution effects. This article analyzed the impacts and mechanisms of digital economy development on the efficiency of dual-economic factor allocation and the urban–rural income gap from a theoretical perspective, empirically tested by using China’s provincial panel data from 2008 to 2017 and a bidirectional fixed effects model. It was found that the development of the digital economy has significantly improved the efficiency of factor allocation in the dual-economy, which has a significant improvement effect on the allocation efficiency of capital and labor. The development of the digital economy alleviates the problem of surplus labor factors and insufficient capital input in the agricultural sector by promoting nonagricultural employment and the flow of capital factors to the agricultural sector. The development of the digital economy can significantly reduce the urban–rural income gap by improving the efficiency of factor allocation in the dual-economy. The main contribution of this article is verifying that the flow of production factors triggered by the digital economy has a configuration efficiency improvement effect and further extending the economic structure effect of the digital economy to the field of distribution, examining the feasible path of optimizing the income distribution pattern of the digital economy.

This article investigated the “National Information City for Public Service” policy as the representative policy of China’s digital transformation of urban governance to empirically analyze its impact on urban environmental pollution using the DID method. The results indicated that: ① The “National Information City for Public Service” policy has significantly reduced the level of urban environmental pollution by 1.65–2.11% on average. After conducting the robustness test of the PSM-DID method and excluding the effect of exogenous interference of the smart city pilot policy in China, the evaluation showed no significant difference from the conclusion above. ② The mechanism test results showed that the “National Information City for Public Service” policy could reduce urban environmental pollution through the technological innovation effect, industrial structure upgrading effect, resource allocation optimization effect, and urban informatization level improvement effect. ③ The heterogeneity analysis of the city scale presented a positive relationship between the city scale and the level of environmental pollution improvement effect under the “National Information City for Public Service” framework. Meanwhile, the heterogeneity analysis of city characteristics showed that cities with better human capital qualities, stronger local government financial strength, and more advanced financial development levels would obtain greater benefit from the environmental improving effect of this policy. Notably, the environmental improving effect of digital transformation of urban governance would be further amplified in cities with the dual superposition of the Innovative City Pilot Policy and the policy of “National Information City for Public Service”. This paper contributed significant referential insights into promoting urban digital transformation and improving urban ecological environment.

Based on the panel data of prefecture-level cities in China from 2006 to 2019, this paper uses the PSM-DID method to empirically test the internal impact mechanism among high-speed railway opening, inter-regional factor allocation efficiency, and urban environmental governance. The research results show that: (1) There is a serious factor-misallocation problem among prefecture-level cities in China. From 2006 to 2019, the factor misallocation between prefecture-level cities led to an average annual loss of total factor productivity in China’s economy of 52.5%, an average labor misallocation of 23.16%, and an average capital misallocation of 18.69%. Since 2013, capital misallocation has exceeded labor misallocation as the main reason for factor misallocation among prefecture-level cities in China. (2) The opening of high-speed railways can promote the efficiency of urban factor allocation through the technological innovation effect, the foreign investment attraction effect, and the population agglomeration effect. The improvement of urban factor allocation efficiency can promote the improvement of urban environmental quality through the effects of industrial structure optimization, income enhancement, and human capital agglomeration. Therefore, the opening of a high-speed railway can improve urban environmental quality through the intermediary effect of improving the efficiency of urban factor allocation; that is, the opening of a high-speed railway has a dual positive effect of economic efficiency and environmental quality improvement. (3) The optimization effect of factor allocation and the environmental governance effect of the opening of high-speed railways have strong urban scale heterogeneity, urban characteristic heterogeneity, and regional heterogeneity. The research content of this paper has important guiding significance for the construction of China’s new development paradigm, accelerating the construction of “a unified national market,” and green and low-carbon development.

The resource allocation efficiency of the energy industry in the Yangtze River economic belt is related to the green and high-quality development of the region. This study constructed a quantitative model which modified from the traditional HK model to evaluate the efficiency of energy industry resource allocation. This paper makes a quantitative evaluation of the efficiency of inter-industrial and inter-regional resource allocation of the energy industry in the Yangtze River economic belt from 2000 to 2019. The results show that: the average annual loss of total factor productivity of the energy industry in the Yangtze River economic belt is as high as 25.11% due to the misallocation of resources among the energy industries, capital misallocation, labor misallocation, and intermediate input misallocation were 12.32%, 7.08%, and 5.08%, respectively. The misallocation of resources among the energy industries of the provinces in upper, middle, and lower reaches is gradually increasing in turn. The average annual loss of total factor productivity (TFP) in the energy industry of the Yangtze River economic belt is 6.5% due to the misallocation of resources between regions. Capital misallocation, labor misallocation, and intermediate input misallocation were 2.48%, 3.40%, and 0.63%, respectively. In the upper, middle, and lower reaches of the Yangtze River, the labor force, capital, and intermediate input of each province’s energy industry were misallocated to a different extent. According to the trend of resource input in the energy industry of various provinces and cities, the middle and lower reaches of the Yangtze River also show different characteristics.

Digitization and green low-carbon are the main directions of China’s economic development in the future. This paper aims to explore the relationship between improvements in the digital level of manufacturing industry segments and carbon emissions. It is found that the digitization level of China’s manufacturing industry segments is still at a low level, which needs to be further improved, and the digitization level of technology-intensive industries is higher than that of capital-intensive and labor-intensive industries. There is a serious misallocation of production factors and R&D factors among manufacturing industries, which is mainly caused by capital factors. Improvement in the digital level of manufacturing industry segmentation can significantly improve the double-layer factor allocation efficiency of the manufacturing industry, and can synchronously realize carbon emissions reduction through improvements in the double-layer factor allocation efficiency of the manufacturing industry; in other words, the improvement in the digital level of China’s manufacturing industry has the dual effects of improving factor allocation efficiency and carbon emissions reduction. Further analysis shows that this effect has significant heterogeneity of ownership. Therefore, China should focus on accelerating the digital transformation of the manufacturing industry, improve the allocation efficiency of traditional and R&D factors in the manufacturing industry through this digital transformation, and accelerate the realization of green and low-carbon development.