A growing number of countries worldwide have committed to achieving net zero emissions targets by around mid-century since the Paris Agreement. As the world’s greatest carbon emitter and the largest developing economy, China has also set clear targets for carbon peaking by 2030 and carbon neutrality by 2060. Carbon-reduction AI applications promote the green economy. However, there is no comprehensive explanation of how AI affects carbon emissions. Based on panel data for 270 Chinese cities from 2011 to 2017, this study uses the Bartik method to quantify data on manufacturing firms and robots in China and demonstrates the effect of AI on carbon emissions. The results of the study indicate that (1) artificial intelligence has a significant inhibitory effect on carbon emission intensity; (2) the carbon emission reduction effect of AI is more significant in super- and megacities, large cities, and cities with better infrastructure and advanced technology, whereas it is not significant in small and medium cities, and cities with poor infrastructure and low technology level; (3) artificial intelligence reduces carbon emissions through optimizing industrial structure, enhancing information infrastructure, and improving green technology innovation. In order to achieve carbon peaking and carbon neutrality as quickly as possible during economic development, China should make greater efforts to apply AI in production and life, infrastructure construction, energy conservation, and emission reduction, particularly in developed cities.

Human activities have increased greenhouse gas emissions since the Industrial Revolution, and “emission peaking” and “carbon neutrality” have become serious concerns at this point. The role of information infrastructure in reducing carbon emissions is a critical issue that has received little attention and needs to be addressed. Using panel data from 289 cities in China between 2011 and 2017, this research empirically explores the impact of information infrastructure on urban carbon emission intensity and the mechanism behind this effect. We discover that the construction of information infrastructure significantly reduces urban carbon emissions, and this finding holds true after a series of robustness tests. The mechanism is optimization of industrial structure, agglomeration of producer service industries, and innovation of green technologies. According to the heterogeneity test, the carbon emission reduction is greater in mega cities with higher technological levels and larger urban scales, as well as large cities with better traditional infrastructure. The present work’s findings give empirical support for promoting green and low-carbon development and mitigating global warming.

Whether Fintech enabled by big data technology can improve the efficiency of credit allocation and how it would do has always been the focus in the capital market, especially the intermediary mechanism, which has not yet been convincingly explained. This paper empirically tests the logical relationship and micro mechanism between Fintech and the corporate financing constraint dilemma by using the data of China’s A-share non-financial listed companies from 2011 to 2018. The research found that Fintech has a significant mitigation effect on corporate financing constraints, and the coverage capability of Fintech has a stronger mitigation effect compared to the depth of use. Mechanism research shows that the “technology enabling” role of Fintech can alleviate the financing constraints of enterprises by reducing the degree of information asymmetry between capital supply and demand sides and reducing financing costs. Heterogeneity research shows that the mitigation effect of Fintech on corporate financing constraints is more significant in enterprises with private property, non-main board listing, senior executives with high financial literacy, and enterprises with strong competitive positions in the industry. Further research shows that, in order to identify the impact of Fintech on corporate financing types under an environment without internal control defects, Fintech enables enterprises facing financing constraints to obtain more commercial credit and bank loans; at a time when it is difficult to obtain bank loans, commercial credit has become an alternative financing method of bank loans, promoting the transfer of credit resources from traditional mortgage guarantees to enterprise commercial credit. This study provides a perspective for the research on how Fintech alleviates corporate financing constraints, and it reveals the characteristics of digital empowerment in the development of China’s capital market, providing a theoretical basis and evidence supporting the formulation of relevant policies.

The Green Economy Initiative aims to achieve economic development while minimizing carbon emissions by implementing a low-carbon economy across all sectors. It is noteworthy that ships play a significant role in global commodity transportation, accounting for approximately 80–90 percent. However, this also leads to a surge in fossil fuel consumption and alarming pollution levels. The data utilized in this article spans from 2010 to 2022 and specifically focuses on the shipping industry, drawing from information collected in 20 different provinces of China. Multiple panel regression models were constructed to analyze the influence of fiscal policies on facilitating the transition toward a low-carbon economy. In addition, a vector autoregression model was employed to examine the interconnected dynamics between low-carbon transition and budgetary guidelines. The findings indicate that tax-based policies demonstrate an inverted U-shaped relationship with low-carbon transition, whereas transfer payment policies exhibit an N-shaped pattern. The shipping sector is actively embracing low-carbon practices, largely due to incorporating digital technologies that mitigate the adverse impacts of fiscal regulations.

Industrial optimization is needed as China’s economy moves toward high-quality growth. The construction of new infrastructure, driven by new development concepts and patterns, facilitates industrial optimization. This study aims to explore the impact and mechanisms of new infrastructure construction on industrial structure optimization. The index of industrial structure upgrading and the pace of industrial transformation were calculated using panel data from 266 prefecture-level cities, which spanned from 2011 to 2018. This study uses two-way fixed effects and mediation effect methodologies to experimentally investigate the impact of new infrastructure construction on the optimization of industrial structure, while also considering possible endogeneity concerns. We found that new infrastructure building promotes industrial transformation and urban industrial structure upgrading. These results pass robustness and endogeneity testing. However, the impact of new infrastructure construction on industrial structure upgrading varies across cities. There is a significant driving effect in economically larger cities with better traditional infrastructure, and those in the eastern region. Smaller cities and those with inferior infrastructure have less impact. New infrastructure construction optimizes urban industrial structures through technological innovation and professional agglomeration, according to mediation effect study. Diversified agglomeration does not significantly upgrade industrial structures. The limitations of our model include the fact that the data does not describe industrial structural dynamics and it does not apply on other geographic scales. We illuminate the intricate connection between new infrastructure and industry upgrading by including city heterogeneity and the mediating impacts of technical innovation and professional clustering.