Focusing on the construction of the National Big Data Comprehensive Pilot Zone (NBDCPZ), we adopted the difference-in-differences model to examine the impact of big data on green total factor productivity (GTFP), using data from 276 cities in China for the period of 2012 to 2019. We also conducted mediating effects and moderating effects tests to explore whether a factor exists through which, or relying on it, big data influences the GTFP. The result of the baseline regression showed that the construction of the NBDCPZ significantly increased GTFP. After a series of robustness tests, this conclusion remains valid. In addition, we examined the mediating effects of industrial structure and green innovation and found that the construction of the NBDCPZ increased the level of GTFP, mainly by promoting industrial structure upgrading and green innovation activities. We identified the moderating effects of different types of environmental regulation on the relationship between urban digital transformation and GTFP and found that market incentive environmental regulation (MIER) has a significant positive moderating effect on big data increasing GTFP. Conversely, the moderating effects of command and control environmental regulation (CCER) and social participation environmental regulation (SPER) were found to be insignificant. These findings suggest that urban digital transformation, through its contribution to increasing GTFP, is an important pathway to high-quality development.

Arable land resources are the basic livelihood security for people in poverty-stricken areas, and poor people are prone to uncontrolled expansion of arable land and exogenous ecological damage to secure their livelihoods. To avoid this vicious cycle, China’s poverty eradication policy requires greater management and restoration of arable land in poverty-stricken areas, but it is unknown what impacts it may bring. Therefore, this study examines the impact of policy implementation on arable land by the Difference-in-Differences (DID) model and uses the mediating and moderating models to test the policy’s mechanism on arable land. The results reveal that the policy significantly reduces the arable land, and the results remain robust, controlling for potential endogeneity variables and robustness tests. What’s more, the results of the mediating and moderating effect models indicate that the reduction of arable land is mainly due to the increase of unit yield, guiding the local people to abandon degraded land and to carry out ecological restoration, thus reducing the arable land area and achieving sustainable development. Finally, recommendations are proposed from the perspective of human-land coordination.

The paper distinguishes the political capital of private enterprises into three types, in terms of the political identity of entrepreneurs, partial state ownership, and government background of entrepreneurship. The first two are classified as explicit political capital, while the third is called implicit political capital. We investigated how political capital, the institutional environment, and access to protected industries intersect within private sector enterprises (PSEs). The data sample from 2011 to 2018 of listed private companies in China is employed. Firstly, the findings indicate political capital significantly enhances access to protected industries for PSEs. Second, implicit political capital shows a strong impact, while the effects of partial state ownership and entrepreneurs’ political identity are weak. Third, explicit political capital weakens the effect on access to protected industries, while implicit political capital still holds significant influence under the improvement of institutional environments. It reveals the situation under the imperfect environment of the market economic system and legal system in China. To be explicit, the concept of ruling by man or power is deeply rooted in the traditional Chinese social governance and social structure model. Therefore, there is enormous room for implicit political capital to function.