Digital transformation, industrial structure change, and economic growth motivation: An empirical analysis based on manufacturing industry in Yangtze River Delta

Intelligent transformation, fintech, and green Growth：A general equilibrium analysis based on credit allocation perspective

This paper conducted an in-depth study to elucidate the impact of corporate intelligence transformation and regional financial technology on green economic growth, particularly the role of credit resource allocation. We developed a multi-sector general equilibrium model, integrating the heterogeneity of intelligent transformation in production sectors and accounting for the influence of Fintech on financial institutions. Within this model framework, panel data from 2011 to 2021 at the provincial, municipal, and micro-enterprise levels in China were used to validate the theoretical model through a mixed regression approach. The findings indicate that as intelligent transformation firms receive more credit resources, their potential for green economic growth increases, contributing to reduced regional carbon emissions. Additionally, the excess productivity of intelligent transformation firms has a significant positive impact on regional carbon reduction efforts. Moreover, the advancement of Fintech reduces financial institutional costs, further optimizing credit allocation and lowering overall market interest rates, thereby promoting green development within the region. However, advancements in Fintech may also redirect more credit resources toward low-risk general enterprises, resulting in a credit crowding-out effect for intelligent transformation firms. These findings indicate that, while promoting intelligent transformation, policy measures should also balance the resource allocation effects of Fintech across different types of enterprises.

Evaluating the impact of technological development policy on the high-quality development of the manufacturing industry: Mechanisms and threshold effects

As the world's largest manufacturing country, the rapid growth of China's manufacturing industry has historically relied on factor input. To achieve high-quality development of the manufacturing industry, China must accelerate the transformation from extensive factor input to innovation. The purpose of this study is to further explore the impact mechanisms of technological development policy on the high-quality development of the manufacturing industry. It selected data from 30 provinces and cities in China spanning from 2011 to 2021 for the study. Initially, it established a benchmark regression model to verify the positive impact of technological development policies on the high-quality development of the manufacturing industry. Subsequently, a mediation effect model was used to analyze the role of high-tech industry development in mediating this impact, and a moderation effect model was applied to study the moderating effect of the level of infrastructure informatization. Additionally, taking the industrial structure as a threshold variable, a panel threshold effect model was employed to explore the impact of technological development policy on the high-quality development of the manufacturing industry. It was found that enhancing the level of high-tech industries further facilitates the high-quality development of the manufacturing industry through technological development policy. The level of infrastructure informatization positively moderates the impact of technological development policy on the high-quality development of the manufacturing industry. Moreover, the industrial structure exhibits a threshold effect in this impact.

Are spatial imbalances in industrial structural change widening the common wealth gap?

The evolution of China's industrial structure from 2010 to 2021 is assessed based on the rationalization and sophistication of its industries. The Theil index quantifies spatial variability, while the Quadratic Assignment Procedure (QAP) investigates if changes in industrial structure imbalance will increase wealth disparity. The study's findings indicate a noticeable spatial imbalance in industrial structure change. The overall level of common wealth is low but steadily increasing, following a stepped-down structure of "east-center-west." Additionally, the north and south regions exhibit a pattern of "high in the north and low in the south." There is a pattern of higher values in the north and lower values in the south. In terms of common wealth and its dimensions, there is a ladder-like pattern with high values at the core decreasing towards the west. Between 2010 and 2021, the common wealth development shifted from a lower level to a higher one. Beijing, Jiangsu, and Shanghai constantly ranked in the top echelon, while Guangxi remained in the fifth echelon. The speed and difficulty of transitioning between echelons vary. Moving from the fourth echelon to the third echelon takes longer, while transitioning from the third echelon to higher echelons presents tougher challenges. Spatial imbalances in industrial structure changes widen the gap in common wealth. In particular, the impact of the gap in the advanced industrial structure on the common wealth gap is significantly higher than that of the gap in industrial rationalization. Reducing disparities in advanced industrial structure is more effective in reducing the overall wealth gap.

The impact of digital transformation of infrastructure on carbon emissions: Based on a "local-neighborhood" perspective

In light of the recent worldwide scientific and technological revolution, it is imperative that urban infrastructure undergo a digital transformation in order to lower carbon emissions and support sustainable urban growth. However, to date, there is a lack of empirical research on carbon emissions based on the digital transformation of urban infrastructure. This paper uses data from 178 prefecture-level cities in China from 2005 to 2020 to study the impact of digital transformation of urban infrastructure on carbon emissions based on the "local-neighbourhood" perspective using a spatial difference-in-differences model. The results show that the digital transformation of urban infrastructure reduces the intensity of local carbon emissions while also reducing the carbon emissions of neighbouring cities, with a spatial spillover effect, and the boundary of this spatial spillover is 600 km. Mechanistic analyses suggest that digital transformation of urban infrastructure can reduce carbon emissions locally as well as in nearby areas by promoting green technological innovations. In light of this, this study has important policy implications for maximising the contribution of digital transformation of infrastructure to reducing carbon emissions.

Digital economy and urban economic resilience: The mediating role of technological innovation and entrepreneurial vitality

Drawing on the diffusion of innovation theory, we argue that the development of digital economy has a positive effect on urban economic resilience. Using panel data from 284 cities in China from 2011 to 2018, we empirically examine the relationship between digital economy and urban economic resilience. We find a positive and significant link between them, mediated by technological innovation and entrepreneurial vitality. Moreover, the heterogeneity analysis shows that the impact of digital economy is most pronounced in smaller cities, with its effects diminishing in larger cities and megacities. Our results underscore the importance and the direction of fostering digital economy development.

Green innovation and enterprise digital transformation: Escape from the "dilemma" of development and governance choices

The digital economy is now the expected norm for economic development, warranting strategic importance for enterprise digital transformation. Nonetheless, enterprises have a lengthy journey to embark upon for digital transformation. On the one hand, resource-based demands pose a significant challenge due to the development characteristics of the initiative; on the other hand, excessive emphasis on economic gains may result in severe environmental issues. Therefore, this paper examines whether green innovation, which combines environmental and economic benefits, can effectively address the above dilemma. The study includes all A-share listed companies from 2010 to 2020 as the research sample, and empirically investigates the impact of green innovation on enterprise digital transformation and its mechanism based on resource-based view. The study concluded that (i) green innovation has a significant positive impact on corporate digital transformation performance, exhibiting asymmetric effects. The robustness tests confirmed the validity of the findings. (ii) Enterprises that actively engage in green innovation can effectively reduce their financial constraints, enhance their operational capacity, and enable the efficient allocation of resources, thereby promoting digital transformation within the enterprise. (iii) There is a regional imbalance in the conversion of green innovation performance into economic performance. The aforementioned results offer fresh insights for investigating the connection between green innovation and digital transformation. Additionally, these findings hold significant implications for the discourse on the synergistic advancement of the environment and economy.

Intelligence level evaluation and influencing factors analysis of equipment manufacturing industry in the Yangtze River Delta

The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.

Index construction and application of digital transformation in the insurance industry: Evidence from China

In the context of digitization, the insurance industry's value chain is undergoing significant shifts. However, the existing research on its comprehension and measurement remains relatively limited. This study constructs an index system for digital transformation in the insurance industry (DTII) on three components: digital infrastructure, digital platform, and digital applications. Utilizing data from 31 provinces in China, this study employs the entropy weight method, analytic hierarchy process method and minimum relative entropy method to measure the weights of indicators, empirically applying this index system. The results show that DTII in China experiences rapid advancement with an average annual growth rate of 20.46% from 2014 to 2020 and there exists strong regional convergence. In addition, the spatial agglomeration and spatial effects of DTII are mainly concentrated in the life insurance industry and the eastern region. This study provides an index system and empirical evidence for evaluating the DTII, providing policy insights for exploring the sustainable development path of the insurance industry in the digital era.