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The impact of digital industrialization on regional pollution control and carbon reduction in China is an area that remains largely unexplored despite being a new driving force in promoting high-quality economic development. This study constructs a combined system synergy model to measure the synergistic governance effect of regional pollution and carbon reduction in China from 2011 to 2020 and then estimates the direct impact and spatial spillover effect using a spatial dual-weight model. Our findings indicate that digital industrialization has a greater impact on regional pollution reduction and carbon reduction as geographical distance decreases, with the spillover effect with close geographical relationships being higher than that of adjacent. Furthermore, the heterogeneity analysis reveals that the added value of digital technology and services has a significantly positive effect, while the spatial spillover effect of the added value of digital infrastructure is significantly negative. Finally, our mechanism judgements prove digital industrialization can impact the level of regional co-governance of pollution and carbon reduction through source prevention, process control, and end-treatment. Our study provides a factual basis for further promoting China’s environmental pollution control and carbon reduction behavior and offers a method to use different spatial weights in depth.

Combined with the research of new quality productivity, this paper analyzes the connotation of computing resources and its role in digital industrialization and industrial digitization, and demonstrates the relationship between computing resources and various elements of digital economy through the combination of qualitative analysis and quantitative model, and evaluates its economic effect. On the basis of the qualitative discussion of computing power resources as the new quality productivity, the gray correlation method and the improved Cobb-Douglas production function are used to quantitatively evaluate the correlation between computing power resources and the development of digital economy. Data analysis shows that there is a significant positive correlation between computing power resources and digital economy, two important aspects of digital industrialization and industrial digitalization, and computing power is the key factor to promote the development of digital economy. This paper puts forward the elements of computing force resources and its core role in the digital economy, empirical analysis calculates the computing force resources to the development of digital economy, studied the computing force resources in the process of digital industrialization and digital mechanism, and put forward to strengthen the construction of computing force resources, promote the development of green computing force, support technology innovation and talent training and other specific Suggestions, promote the development of digital economy in high quality track.

As a development trend of cross-border integration of industries in the digital economy, service-oriented manufacturing has achieved certain research results in the construction of wide value chain. However, in the transition development stage, the collaborative efficiency problem of service-oriented manufacturing value co-creation is increasingly prominent due to the unbalanced development of digitalization level of heterogeneous subjects. Therefore, based on the perspective of various subjects in the value chain of service-oriented manufacturing, this paper analyzes the synergistic mechanism of value co-creation of core manufacturing, service enterprises and customers under the influence of digitalization level difference. Then the paper constructs a three-party digital cooperative evolution game model and uses Netlogo software to simulation analyse. The results reveal the collaborative path of service-oriented manufacturing digital transformation. According to the comparison of the paths of actual enterprises, it is found that the paths has universality, which provide strategic reference for service-oriented manufacturing digital transformation.

The global economy is moving into a new era characterized by digital and green development. To examine the impact of digital industrialization development on the energy supply chain, in relation to the sustainable development of China’s energy security, we discuss the nonlinear impact and transmission mechanism of digital industrialization on the supply chain of the energy industry using a panel threshold regression model based on sample data on the development of the provincial natural gas industry in China from 2006 to 2020. We found that there are multiple threshold effects of digital industrialization level development on energy supply chain length, and the results are statistically significant, i.e., digital industrialization development positively contributes to natural gas supply chain length after digital industrialization is raised to or crosses the critical threshold. Meanwhile, the heterogeneity analysis results show that there are differences in the impact of digital industrialization on the energy supply chain from sub-sectors, regional development differences, and different development periods. Therefore, we provide some factual support and experience for achieving the construction goal of “Digital China” and accelerating the digital reform of the energy supply chain as well as transforming and upgrading the economic structure.

The coordinated development of industrial digitalization and digital industrialization has propelled the establishment of a modern industrial system and the advancement of new industrialization. Industrial digitalization enhances the fundamental capabilities of traditional industries through the application of digital technology, thereby fostering the emergence of a new technology-economy paradigm within these industries. This process expedites the integration of the digital economy with traditional industries, enabling the augmentation of basic industrial capabilities and the modernization of the industrial chain in the context of new industrialization. Digital industrialization considers data as a novel factor in production. Through innovation, industrialization, and commercialization of digital technologies, it gives rise to emerging industries, formats, and models. Ultimately, this leads to the formation of the digital industrial chain and industrial clusters. To drive new industrialization, it is important to prioritize the real economy and promote the coordinated development of industrial digitalization and digital industrialization through mutually beneficial interaction, integration, and balanced models.

The wave of digitalization is driving the restructuring of the global value chain, providing an excellent opportunity for China to leapfrog into the digital era. The convergence between digital industrialization and industrial digitalization (hereinafter referred to as CDIID) is an indicator to measure the sustainability of the digital economy. The main objective of this paper is to measure the level of CDIID in China and verify the impact of CDIID on export technology complexity and its mechanism. The nonparametric stochastic frontier method is used to measure the level of CDIID of each province in China from 2013 to 2019, and the fixed-effect model is used to investigate the impact effect and mechanism of CDIID on export technology complexity. Empirical research finds that the level of CDIID plays a positive role in promoting the export technology complexity, and in the short term, more attention should be paid to the development of industrial digitalization to enhance export technology complexity. The mechanism test results show that CDIID enhances export technology complexity through the channels of industrial structure upgrading and innovation ability improvement. In terms of industrial digitalization driven by digital industrialization, the channel of innovation ability improvement has a significant impact. In terms of the path of industrial digitalization to promote digital industrialization, it has an inhibitory effect on both channels in the short run. This paper provides empirical evidence and a decision-making basis for China to promote the sustainable development of the digital economy and build new advantages in international competition.

Business credit supply entails a firm providing credit to its customers as a means to gain a competitive edge. The advent of the digital economy has brought about profound changes in business practices. In this context, it becomes crucial to examine how the digital economy impacts the business credit supply of enterprises. This study employs a theoretical framework to derive insights and carries out an empirical analysis using the City Digital Economy Development Index spanning from 2008 to 2021, along with data from A-share listed companies in Shanghai and Shenzhen. The objective is to explore the influence of the digital economy on corporate business credit supply and its underlying mechanisms. The findings reveal that the digital economy can enhance corporate business credit supply by reducing the incidence of bad debt, thus enabling companies to extend more credit to their customers. This research contributes empirical evidence for understanding the microeconomic impact of the digital economy, while also providing theoretical insights to advance the development of the digital economy and optimize the allocation of financial resources, thereby alleviating corporate financing constraints.

Mitigating carbon emissions in the construction sector is crucial for attaining the objectives of “carbon peak” and “carbon neutrality”. This research utilizes panel data from 30 Chinese provinces spanning 2011 to 2020, employing a two-way fixed-effect model and a mediation-effect model to quantitatively assess the impact of digital industrialization (DI) on carbon emissions within this industry. The findings indicate that DI notably decreases carbon emissions; however, its effectiveness is structurally imbalanced. Digital services (DI-S) have a more pronounced effect on reducing emissions than digital technologies (DI-T). Notably, DI significantly lowers indirect carbon emissions but has a minimal positive impact on direct emissions in construction. Mediation analysis shows that DI greatly enhances green innovation capabilities (GIC) and green financial services (GFS), indirectly contributing to emission reductions, evidencing a full mediation effect. This process underscores the role of DI in promoting green technological innovation and financial development, thereby supporting the sector’s low-carbon transformation.

Achieving environmental sustainability has become an urgent priority in the era of rapid digital economic expansion, which presents both opportunities and challenges for environmental sustainable development. This study investigates the impact of digital economy (DIE) on environmental sustainability (ENS) through the dual dimensions of digital industrialization (DII) and industrial digitalization (IND), employing the wavelet-based quantile-on-quantile regression method to capture both quantile dependencies and temporal variations. The results reveal that DIE positively impacts ENS in the long term, while its short-term effects are mixed, with positive effects at lower and higher quantiles but negative impacts at mid-range quantiles of [0.35–0.45] and [0.65–0.7]. Specifically, DII exerts a predominantly negative short-term effect on ENS due to the environmental costs of digital infrastructure expansion, but turns positive in the long term as digital industrialization matures, especially in [0.85–0.95]. IND, conversely, exerts a consistently positive impact on ENS in both short- and long-term scenarios, highlighting its role in enhancing industrial efficiency and reducing emissions. These results emphasize the need for targeted policies, including prioritizing industrial digitalization, developing green infrastructure, and adopting phased digital development strategies to maximize environmental benefits.

In the era of Industry 4.0, integrating digital technologies into industrial processes has become imperative for sustaining growth and fostering innovation. This research paper explores the profound impact of AI-driven intelligent data analytics and predictive analysis on economic efficiency and managerial practices within Industry 4.0. With a focus on knowledge, innovation, technology, and society, this study delves into the transformative potential of these advanced technologies. Intelligent data analytics, powered by artificial intelligence (AI), has revolutionized the way industries harness vast datasets. Uncovering real-time patterns, correlations, and opportunities empowers decision-makers with accurate and timely insights. Predictive analysis, rooted in statistics and machine learning, aids in forecasting trends and managing risks, offering economic stability across sectors. Using a mixed-methods approach, the study combines qualitative interviews with 19 Chinese operations managers and quantitative data from an online survey of 286 managers. The study ranks various Industry 4.0 technologies through ordinal regression based on their impact on environmental sustainability and economic management. Results show that smart sensors, radio-frequency identification, AI, and analytics are the most influential technologies for enhancing economic and environmental outcomes. Conversely, technologies like additive manufacturing and automated robots yield less favorable results. The study also identifies a noticeable gap in professionals’ understanding and adoption of AI and augmented reality. Environmental concerns around the disposal of electronic waste generated by these technologies are also highlighted. The research thus offers significant insights for companies seeking to adopt intelligent data analytics to enhance economic performance and environmental sustainability. On the managerial front, the fusion of these technologies enables agile and responsive frameworks, promoting dynamic strategies in response to changing market dynamics. This culture of continual improvement fosters excellence and foresight in managerial processes. However, challenges exist, including the underutilization of data, data complexity, historical biases, and the need for tailored AI solutions across industries. Ethical considerations, data privacy, and security also pose concerns. Collaborative innovation among stakeholders is crucial to addressing these challenges and seizing opportunities. Governments, academia, and industry players must collaborate to develop technologically advanced, economically viable, and socially responsible solutions. As industries transition to Industry 4.0, this paper advocates a critical approach that embraces technology’s potential while mitigating risks. The future lies in a technologically advanced, economically resilient, and socially inclusive industrial landscape driven by AI-powered knowledge and innovation.