Digitalization has become a key driver of business innovation in recent years. It provides businesses with new opportunities to innovate and create value. Digital technologies, such as cloud computing, big data analytics, and artificial intelligence, have helped businesses boost the development of new products and services, optimize their operations, and improve customer engagement. This study aimed to analyze the impact of digitalization on business performance within business innovation. This study applied an ordinary least square regression model and an intermediary to explore relationship in the chain of digital capability–business model innovation–company performance. The object of investigation was 1663 listed A-share companies Shanghai and Shenzhen in the software and information technology service sectors. The results showed that digital capabilities could be divided into three dimensions according to the hierarchical relationship: (1) basic digital capabilities, (2) digital operation capabilities, and (3) digital integration capabilities, all of which significantly positively affected enterprise performance. Furthermore, while business model innovation significantly positively affected corporate performance, it was also driven by the preceding variables of digital capabilities. Business model innovation enhanced the positive impact of basic digital capabilities, digital operation capabilities, and digital integration capabilities on company’s performance. Considering the empirical results, this study underlines that the government should promote digital skills development, create supportive regulatory environments, promote access to funding for innovations, foster partnerships between businesses and technology providers, and promote collaboration between businesses, which are conducive to extending digitalization within the business innovation model and improving business performance.

China has been undergoing a very rapid but unbalanced urbanization, characterized by under-urbanization of its population and faster urbanization of the land. In such a situation, the urbanization of the population and the land may produce different effects on the natural environment. In addition, due to substantial inter-regional differences, the influence of urbanization on the environment is likely to vary across regions at different stages of economic and social development. This article expands the basic STIRPAT–Stochastic Impacts by Regression on Population, Affluence and Technology, model by adding industrialization level, foreign trade degree, population urbanization level and land urbanization level. Based on panel data from 2006 to 2014 and using this extended STIRPAT model, the article analyses the effects of the driving forces, especially population urbanization and land urbanization, on the environmental impact for the whole of China as well as on its eastern, middle and western regions. The results indicate that for the whole of China, population urbanization produces a significant negative effect on the environmental impact, while land urbanization has a small, but not statistically significant, positive effect. The effects of population urbanization and land urbanization vary across the eastern, middle and western regions, which are at different stages of economic and social development. Population urbanization and land urbanization have no significant influences on environmental impact in the eastern and middle regions, while in the western region population urbanization has a significant negative influence on environmental impact. The main driving factors of environmental impact remain population, affluence and energy intensity. This study also quantitatively calculates the actual contribution rate of each driving force for the 2006–2014 period. It contributes to understanding the characteristics and key driving forces in each region, allowing for appropriate policy recommendations.

Purpose: The purpose of this paper is to identify the drivers of manufacturing transformation and upgrading and examine their influencing paths and effects. The study also dynamically evaluates and compares the achievements of the manufacturing transformation and upgrading of various economies. Design/methodology/approach: This paper establishes the mechanism model of manufacturing transformation and upgrading based on the partial least squares approach to structural equation modeling. With the help of a mechanism model, the study carries out empirical analysis using panel data from the G20 for 2005–2015. Findings: Innovation, measured by innovation input and innovation output, has the largest positive effect on manufacturing transformation and upgrading. Economic development has a negative direct effect, as well as positive indirect effects. Its total effect is positive but small. Economic openness has no significantly direct effect, but has significantly positive indirect effects. Its total effect is significantly positive, but is the smallest among drivers. Developed economies have relatively higher but decreasing achievements, whereas emerging economies have lower but increasing achievements and maintain the catch-up trend toward developed economies. Originality/value: Few macro empirical studies examine the drivers and achievements of manufacturing transformation and upgrading. A need has arisen for evidence-based research and policies to guide governments in their efforts to promote manufacturing transformation and upgrading. This may be the first macro-level study to empirically analyze the drivers of manufacturing transformation and upgrading, and their influencing paths as well as achievements.

This article aims to analyze the interplay between the digital economy (DE) and the real economy (RE), examining how they impact each other in terms of empowerment and supply effects. The study object is China from 2011 to 2021. This study applies the panel vector autoregressive model (PVAR). The study’s findings underscore a delayed empowerment effect within the DE. While DE growth has the potential to substantially enhance the future overall expansion of the tangible economy, it might concurrently dampen the short-term structural balance of the latter. However, the supply effect in the RE mode exhibits a similar delay. The time-lagged factors relating to the tangible economy’s total growth and structural fine-tuning play a pivotal role in fostering the progress of DE. Self-enhancement mechanisms significantly influence the overall growth of the tangible economy. However, this mechanism does not have the same significance in regard to enhancing structural coordination. Although the tangible economy’s expansion can catalyze structural refinement, the inverse relationship—where structural enhancement profoundly fuels tangible economic growth—does not hold true to a substantial extent. By assessing the overall degree of coupling and coordination between the DE and the tangible economy, it becomes apparent that these two domains are not tightly integrated. Instead, they exist in a fundamentally coordinated state, with a year-on-year upwards trend in their alignment, albeit at a modest pace. Furthermore, this coupling coordination degree displays a progressively diminishing trend from the southeastern coastal regions to the western interior, revealing a pronounced spatial imbalance. The contribution of this paper lies in its comprehensive enhancement of the theoretical framework and empirical research in the integration of energy and digital economy, addressing sustainable development, regional economic disparities, and practical policy implications to support future strategies for blending digital advancement with renewable energy utilization.

Currently, coronavirus disease 2019 (COVID-19) is spreading globally, which poses great challenges to the whole world and human beings. The aim of this research is to understand the determinants and residents’ willingness to pay (WTP) for purchasing masks against COVID-19 in China. On the basis of protection motivation theory and contingent value method, this research shows that most residents are willing to purchase masks against COVID-19. COVID-19 knowledge, perceived severity, perceived vulnerability, and response efficacy are positively and significantly associated with residents’ WTP and the WTP value. However, self-efficacy is only significantly associated with residents’ WTP while not with WTP value. Furthermore, compared with other residents, residents in Hubei province have a higher level of COVID-19 knowledge, perceived severity, perceived vulnerability, self-efficacy and response efficacy, and the WTP value is higher. The average value of residents’ WTP value for purchasing masks against COVID-19 in Hubei province is ¥120.92 ($18.73) per month during the epidemic, while it is ¥100.16 ($15.50) for other residents. In addition, the effects of demographic factors such as age, gender, income, etc., on residents’ WTP and WTP value have also been examined.

Drawing upon a quasi-natural experiment, this research investigates the influence of China’s National Digital Economy Innovation Development Pilot Policy on urban energy intensity. By examining a sample of 282 Chinese cities with the difference in differences (DID) approach, the findings provide robust empirical support for the proposition that digital economy pilot policies substantially reduce urban energy intensity. Furthermore, the policy’s effectiveness in lowering urban energy intensity differs across cities with varying administrative levels and population scales. The results suggest that the policy’s impact is more pronounced in ordinary cities (non-provincial capitals/municipalities) and in those with smaller populations. An examination of the underlying mechanisms reveals three principal pathways through which the policy affects energy consumption: (1) digital economic development, which promotes optimal resource allocation and enhanced energy intensity; (2) technological innovation, driving advances in green technologies and supporting sustainable industrial upgrades; and (3) economic agglomeration, which leverages economies of scale and industrial clustering to bolster energy efficiency. The conclusions underscore the necessity of expanding digital economy pilot zones, strengthening investments in digital infrastructure, and fostering greater technological innovation to sustain improvements in energy efficiency and environmental performance.