In recent years, China has placed significant emphasis on sustainable economic and social development, actively implementing the concept of green development. In 2023, General Secretary Xi Jinping proposed that all regions should actively develop new-quality productivity, signifying a deepening of green and sustainable development principles. As an internationally recognized indicator for measuring corporate sustainability, Environmental, Social, and Governance (ESG) criteria may influence the enhancement of new-quality productivity across regions, particularly in relation to green new quality productivity. This paper investigates the effects of corporate ESG performance on the levels of green and new-quality productivity using data from China’s A-share listed companies from 2013 to 2022. The findings reveal the following: (1) corporate ESG performance significantly enhances the level of green new quality productivity; (2) mechanism tests indicate that corporate ESG performance enhances green new quality productivity by promoting firms’ green innovation; and (3) further analysis shows that the effect of ESG performance on green new quality productivity is more pronounced in firms with low levels of financing constraints, high media attention, and elevated green awareness among executives. These findings provide empirical evidence for strengthening corporate ESG performance, promoting green innovation, elevating regional levels of green and new-quality productivity, and advancing sustainable development, thereby offering valuable insights for developing countries.

An in-depth understanding of the impact of green innovation on the urban–rural income gap is essential for developing countries seeking to address urban–rural imbalances and promote sustainable economic development. This study focuses on China, utilizing provincial panel data from 2007 to 2022, and employs the two-way fixed effects model, the mediating effects model, and the moderating effects model. The study’s key findings are: (1) Green innovation positively reduces the urban–rural income gap. Specifically, the regression results indicate that a one-unit increase in green innovation corresponds to a 0.017-unit reduction in the urban–rural income gap. (2) The heterogeneity analysis reveals that the effect of green innovation on the urban–rural income gap is more pronounced in regions with higher levels of economic development, in non-food-producing areas, and when green utility patents are adopted. Additionally, green innovation narrows the urban–rural income gap predominantly in high-skill regions. (3) Examining the influencing mechanism confirms that green innovation reduces the urban–rural income gap by promoting population urbanization, eco-urbanization, labor force restructuring, and mitigating wage income inequality. (4) The moderating effects analysis indicates that environmental pollution exacerbates the impact of green innovation on the urban–rural income gap; specifically, higher levels of environmental pollution amplify the effect of green innovation in reducing the gap. These findings offer valuable insights for addressing urban–rural income inequality and fostering sustainable socio-economic development in developing countries.

Big data is a pivotal factor in propelling the digital economy forward and emerges as a novel driver in realizing the goals of carbon peaking and carbon neutrality. This study focuses on a quasi-natural experiment, namely national big data comprehensive pilot zones (NBD-CPZs), and employs a multi-period difference-in-differences (DID) model to identify the influence of big data on carbon emissions. The findings of this study are as follows. Overall, big data significantly reduces carbon emissions within the pilot zones. Mechanism analysis shows that big data reduces urban carbon emissions by promoting green innovation, optimizing energy structure, mitigating capital mismatch and improving public awareness of environmental protection. Heterogeneity analysis shows that the carbon reduction effect of big data are more pronounced in cities with high levels of digital economy, non-resource-based cities, cities with strong intellectual property rights protection and the Guizhou Province. Spatial effect analysis indicates that within a radius of 400–500 km, the NBD-CPZ increases urban carbon emissions, signifying a significant siphoning effect; within a radius of 500–900 km, the NBD-CPZ reduces urban carbon emissions, signifying a significant spillover effect, and beyond a distance of 900 km, the spatial effect of the NBD-CPZ is not significant. Based on the above conclusions, this study puts forward several policy recommendations to effectively exert the carbon emission reduction effect of big data.

In recent years, China’s transportation infrastructure has undergone significant changes. High-speed rail, as a new and favored mode of transportation, offers travelers convenience, efficiency, and punctuality, replacing many high-pollution transportation methods. Based on the characteristics of high-speed rail, this paper selects data from 30 provinces spanning from 1999 to 2019. It utilizes the double-difference method to evaluate the impact of high-speed rail opening on economic sustainability. It has been found that the introduction of high-speed rail contributes to economic sustainability during the examination period. The mechanism test also reveals that the opening of high-speed rail promotes green and sustainable development through the industrial agglomeration effect. In addition, the opening of high-speed rail (HSR) has a significant time lag effect and a spatial spillover effect on green sustainable development. The conclusion complements the theoretical framework regarding the impact of high-speed rail on the environment and regional economic sustainability. It also provides guidance for the efficient utilization of high-speed rail, which holds both theoretical and practical importance.

Economic development often results in significant greenhouse gas emissions, contributing to global climate change, which demands immediate attention. Despite implementing various low-carbon policies to promote sustainable economic and environmental progress, current evaluations reveal limitations and deficiencies. Therefore, this study utilizes a dataset detailing policy intensity at a prefecture-level city in China to investigate the impacts of these policies on carbon emission reduction from 2007 to 2022 in 334 prefecture-level cities, employing a fixed-effects model. Additionally, it assesses the policies’ efficacy. The findings indicate a significant negative correlation between China’s low-carbon policies and carbon emissions, supported robustly by multiple tests. Specifically, a one-unit increase in China’s policy intensity correlates with a 0.53-unit reduction in carbon emissions. Furthermore, the heterogeneity analysis shows that variations in urban agglomerations, environmental resource endowments, pollution levels, and low-carbon policy intensities influence the effectiveness of these policies in reducing carbon emissions. This analysis underscores that policy intensity achieves emission reductions through technological innovation, industrial transformation, welfare crowding out, and pollution transfer, with varying impacts across different environmental contexts, pollution levels, and policy intensities. Based on this analysis, we recommend several policies: formulating low-carbon strategies tailored to local conditions, enhancing regional low-carbon policies, establishing cross-regional coordination mechanisms, and so on. These recommendations not only offer valuable policy insights for China but also serve as useful references for the green and sustainable development of other developing countries.