Lei Nie

A scientific understanding of the relationship between artificial intelligence (AI) and agricultural carbon sinks (ACS) is essential for promoting the low-carbon transformation of agriculture in the Yellow River Basin, accelerating the achievement of the “dual carbon” goals, and enhancing the high-quality development of the regional economy. Using panel data from 97 cities in the Yellow River Basin in China from 2001 to 2023, this study measures the levels of ACS and agricultural net carbon sinks (ANCS), and further examines the mechanisms, regional heterogeneity, and moderating effects associated with the impact of AI on both indicators. The results indicate that: (1) AI significantly improves ACS and ANCS in cities within the Yellow River Basin. Specifically, for every 1% increase in AI development, ACS and ANCS increase by approximately 0.0111 million tons and 0.0138 million tons, respectively. This effect is more pronounced in the upper and lower reaches of the basin, while remaining insignificant in the middle reaches; (2) AI promotes improvement in ACS and ANCS by increasing the level of agricultural mechanization and the intensity of chemical fertilizer application; (3) Grain yield per unit area and agricultural planting structure positively moderate the relationship between AI and both ACS and ANCS. Overall, the findings suggest that AI plays a significant positive role in enhancing ACS and ANCS. Therefore, greater emphasis should be placed on the cultivation, diffusion, and application of AI technologies to achieve the sustained improvement of ACS and ANCS capacity in the Yellow River Basin.

Achieving high-level self-reliance in science and technology requires a science and technology finance ecosystem that is aligned with the needs of technological innovation. To overcome bottlenecks in core technologies, firms must accelerate R&D, strengthen their core competitiveness, and pursue innovation-led, quality-oriented development. Using provincial-level data for 2013–2023, this paper constructs an index system for China’s science and technology finance ecosystem from four dimensions: science and technology financial services, science and technology capital markets, science and technology financial organizations, and government guidance for science and technology. We then measure the development level of this ecosystem and employ a panel data model to examine its impact on innovation in Specialized and Sophisticated SMEs. The results show that a more developed science and technology finance ecosystem significantly promotes innovation in these firms, with a stronger effect on substantive innovation than on strategic innovation. These findings remain robust across a series of robustness checks. Further analysis reveals significant heterogeneity across regions and levels of government intervention: the positive effect is stronger in eastern China and in regions with weaker government intervention. Mechanism tests indicate that the science and technology finance ecosystem promotes innovation by facilitating the accumulation of R&D capital and the agglomeration of scientific and technological talent. This study enriches the literature on science and technology finance ecosystems and SME innovation, and provides policy-relevant evidence for ecosystem development and the cultivation of Specialized and Sophisticated SMEs.

The coordinated development of informal environmental regulation (IER) and the efficiency of government environmental protection expenditure (EGEPE) is key to enhancing the effectiveness of environmental governance. This study focuses on 30 provinces in China over the period from 2008 to 2020, developing a multi‐indicator assessment framework that encompasses both IER and EGEPE. We employ a suite of analytical methods, including the SBM (slack‐based measure) model with undesirable outputs, the entropy technique, a coupling coordination model and a two‐way fixed‐effects model, to dissect the regional disparities, spatiotemporal dynamics and determinant factors influencing the coupling coordination between IER and EGEPE. The results show: ① The coupling coordination degree of IER and EGEPE in the three major regions of China has been on an upward trend, but presents pronounced regional disparities, characterised by East > West > Central regions; ② The number of areas with high‐level coordination has gradually increased, mainly concentrated in the eastern region, while the central and western regions show a trend of ‘low‐level equilibrium trap’ and the trend surface analysis presents a spatial pattern of ‘low in north and high in south—high in east and low in central and western’; ③ Technological innovation plays an important role in promoting the coupling coordination of IER and EGEPE, reflecting the key position of technological development and efficiency improvement in environmental governance.

The adoption of robust fiscal policies is a critical pathway for China to control pollution, promote green development, and advance ecological civilization. This study examines the “Comprehensive Demonstration City of Energy Conservation and Emission Reduction Fiscal Policy” pilot project as a quasi-natural experiment. Employing the Slack-Based Measure Directional Distance Function (SBM-DDF) model and a multi-period Difference-in-Difference (DID) approach, we assess the impact of the energy conservation and emission reduction fiscal policy (ECERFP) on urban green total factor productivity (UGTFP). The results indicate that ECERFP significantly enhances UGTFP. This finding remains robust across multiple tests, including parallel trends, placebo tests, and the Goodman–Bacon decomposition. Mechanism analysis indicates that ECERFP enhances UGTFP mainly through technological innovation and improved energy efficiency. However, its effectiveness varies by geographical location, resource endowment, and city size. While ECERFP can promote urban energy conservation and end-of-pipe pollution control, it also carries the potential risk of inducing a “pollution haven” effect. To maximize the fiscal policy’s leverage and the resource allocation effects, a comprehensive strategy is required—one that advances energy efficiency, stimulates technological innovation, tailors energy conservation measures to local conditions, and nurtures the development of new productive forces to support sustainable urban growth.

Productivity has long been the fundamental driver of human social progress and national prosperity. Against the backdrop of technological advancement and social development, New Quality Productive Forces (NQPFs) have emerged as a new form of productivity, serving as a key focus for corporate transformation and upgrading as well as sustainable national development. Based on the panel data of 28,107 listed companies in China from 2011 to 2022, this study employs a three-way fixed-effects model to investigate the impact of green finance (GF) on corporate NQPFs. The main findings are as follows: First, GF exhibits a significant positive correlation with the enhancement of corporate NQPFs. Second, financing constraints and corporate social responsibility strengthen the empowering effect of GF on corporate NQPFs, while environmental law enforcement weakens this effect, reflecting a “synergistic dilemma” between government intervention and market mechanisms in promoting corporate NQPFs. Third, the effect of GF on corporate NQPFs shows significant heterogeneity depending on environmental and social risks, the nature of property rights, public attention, and firm size. These findings provide important insights for optimizing green finance policies and enhancing corporate productivity.

This study focuses on the Yellow River Basin, a key economic region spanning nine provinces in China, and explores the complex interactions within the water–food–energy systems. Based on the theoretical framework of the coupled coordination of the water–food–energy system, an indicator system is developed to assess the coordination of these systems. Using ArcGIS, the study identifies the spatiotemporal characteristics of the coupling coordination of the water–food–energy systems in the Yellow River Basin. Additionally, a panel data model is employed to analyze the driving mechanisms and optimization pathways for enhancing system coordination in the region. The results reveal that (1) The degree of coupling coordination between the water–food–energy systems in the Yellow River Basin varies significantly across space. (2) Overall, the coupling coordination in the region is relatively low and exhibits a clustered pattern. (3) Research and development (R&D) intensity is a significant factor influencing the coupling coordination of these systems in the region.

As the severity of climate change escalates, agriculture, being one of the primary contributors to global carbon emissions, has progressively come under scrutiny. Thus, fostering a low-carbon agriculture system is paramount in achieving the ambitious “dual carbon” goals of reaching peak carbon and attaining carbon neutrality. This study engages urban panel data from the Yellow River Basin spanning 2001-2020 to compute the agricultural carbon emissions therein. The research harnesses a spatial Durbin model to probe the influencing mechanisms and spatial effects while examining the implications of agricultural mechanization on such emissions. The findings reveal: (1) From a spatiotemporal perspective, total agricultural carbon emissions within the Yellow River Basin exhibited an oscillating “M”-shaped pattern. Upon analyzing spatial patterns, the carbon emissions were highest downstream, moderate midstream, and least upstream, signifying pronounced regional disparities. (2) Concerning the causal elements, agricultural mechanization, from a direct effects standpoint, tends to somewhat diminish local agricultural carbon emissions. Regarding spillover effects, agricultural mechanization similarly represses carbon emissions in adjacent locales. (3) Heterogeneity analysis suggests that in the midstream cities, agricultural mechanization results in a significant decrease in agricultural carbon emissions. Contrarily, upstream and downstream cities witness a stimulating effect. At present, with China's agricultural economy navigating intense environmental pressure, these insights lend invaluable support to practices aimed at curbing agricultural carbon emissions. By shedding light on the interaction between agricultural mechanization and carbon emissions, they offer a novel perspective and empirical data. In turn, these can contribute to formulating policies that seek to reignite rural areas while concurrently striving to meet the strategic objectives of peak carbon and carbon neutrality.

China’s digital economy has been expanding rapidly in the past decade. This expansion is having a profound impact on the country’s economy. Using panel data on 97 prefecture-level cities in the Yellow River Basin from 2011 to 2020, this study investigates the multifaceted relationship between the digital economy and total-factor carbon emission efficiency. The research yields three key findings: (1) The digital economy positively enhances overall carbon emission efficiency. This conclusion is drawn with robustness tests. (2) Green technology innovation serves as a partial mediator between the digital economy and total-factor carbon emission efficiency, and this mediation role is influenced by government intervention, which negatively moderates the relationship between the digital economy and green technology innovation but positively impacts the mediation role of green technology innovation between the digital economy and total-factor carbon emission efficiency. (3) The positive impact of the digital economy on total-factor carbon emission efficiency is more significant in the upper reaches, lower reaches, and resource-based cities of the Yellow River Basin. These findings provide new perspectives and empirical evidence for better understanding the relationship between digital economy development and total-factor carbon emission efficiency. They also provide policy recommendations for achieving strategic objectives, including digital economy development, carbon emission reduction, carbon peaking, and carbon neutrality.

Elucidating the impacts of service industry’s agglomeration on the optimization of industrial structures holds paramount significance in advancing urban economic growth and fostering the coordinated and sustainable development of city economies. This study leverages panel data encompassing 251 prefecture-level cities spanning from 2003 to 2019, employing a spatial Dubin model to scrutinize the influence of distinct types of service industry agglomeration on industrial structure optimization. The results show that specialized agglomeration within the service sector significantly inhibits the rationalization of industrial structures and their underlying fundamentals. Conversely, heightened levels of agglomeration in diversified service industries facilitate the rationalization of industrial structure, predominantly driven by regional spatial spillover effects. Further analysis reveals heterogeneity in service industry agglomeration across cities of varying sizes concerning industrial structure optimization, notably accentuating underutilized spatial spillover effects in smaller cities. In light of these insights, this paper advocates for cities to capitalize on the agglomeration and spillover effects between the service industry and other sectors, strategically selecting optimal service industry agglomeration modes to propel industrial structure optimization.

Although the literature has studied the direction and extent of the effect of green finance on industrial-structure optimization, quantitative analysis of the coupling coordination and spatial–temporal differences between green finance and industrial structure is relatively scarce. Therefore, in this paper, we built the theoretical framework of the coupling coordination relationship between green finance and industrial-structure optimization, and then we used the coupling coordination degree and geographic detector model to investigate the spatial–temporal evolution characteristics and influencing factors of the coupling coordination between the two based on the panel data of 31 provinces from 2012 to 2019. The results show that China’s green finance and industrial-structure optimization have basically reached the primary coupling, and the coupling coordination degree is from 0.40 to 0.43, which shows a “W”-type fluctuation trend of recovery. The regional gap of the coupling coordination degree firstly decreased and then increased, showing a change law of “agglomeration, equilibrium and agglomeration”. In the spatial dimension, the high-level coordination region showed an increasing trend of “fragmentation” fluctuation, while the low-level coordination region concentrated in the central and western regions with a tendency of “low value locking”. The trend surface showed a spatial characteristic of “high in the north and low in the south–high in the east and west and low in the middle”. We also found that the dependence of foreign trade and technological innovation are the main factors affecting the coupling coordination degree, and the interaction between government support and human capital synergistic is the crucial channel for the coevolution of green finance and industrial structure to promote green and low-carbon development.