Industrial Upgrading Research Articles

Digital economy and its effect on total factor productivity in services: evidence from China

ABSTRACT The emergence of the digital economy has provided a new development model and driving force for social and productive activities. The quest to accelerate the transformation and upgrading of the service economy through the digital economy has garnered significant attention in society and academia. In light of this context, this study employs data envelopment analysis (DEA) to measure total factor productivity (TFP) in the service sector, utilizing provincial panel data from China for the period 2007–2019. The empirical results indicate that the digital economy significantly contributes to the growth of TFP in the service sector, and specifically, the channels of its impact include alleviating resource mismatch and promoting industrial structure upgrading. Furthermore, the impact of the digital economy on services TFP varies by region and stage of development of the digital economy. Moreover, based on the informational characteristics of the digital economy, the digital economy not only affects the local service TFP, but also generates a spatial spillover effect on the service TFP of neighbouring regions. This study provides a new empirical evidence to examine the productivity improvement of the digital economy in the service sector and provides important policy implications for achieving high-quality development of the service sector.

Servitization in the construction industry: a multiple case study from a value-adding perspective

Purpose The purpose of this paper is to address the urgent need for transformation and upgrading in the construction industry amid the accelerating industrialization process. The focus is on understanding how value-added for construction enterprises can be achieved through servitization. Design/methodology/approach Grounded in the perspective of added value of construction enterprises, this paper uses a multicase analysis method, selecting four internationally renowned construction enterprises as the research subjects. The analysis investigates how these case study enterprises transition toward servitization and explores the trends in servitization in construction. Findings The research results indicate that servitization manifests in two aspects: contractor transition to servitization, and the shift of the construction industry toward integrated delivery. Furthermore, servitization in construction can achieve value-adding through three pathways: increasing product value, creating market demand and providing customized services. Originality/value This study contributes by providing insights into the manifestations of servitization and pathways for value-added. It provides a reference for the overall direction and basic strategy of servitization in construction.

Empirical Study on the Impact of New-Quality Productivity on Regional Economic Development in China

New-quality productivity (NQP) reflects the modernization of productivity, characterized by higher technological levels, better quality, and greater efficiency and sustainability. Against the backdrop of technological innovation, NQP plays an increasingly significant role in economic development. Sustained growth of NQP is key to maintaining stable and rapid economic progress. This paper adopts the new-quality productivity index developed by scholar Lu Jiang, Guo Ziang and Wang Yuping, which comprises three dimensions, including innovation capacity, technological research and development, and production efficiency. By using this index, an OLS regression analysis is conducted on the economic development levels of various regions. Through regression analysis, the paper examines the impact of these indicators on regional economic development and provides relevant policy recommendations. The results show with a higher new-quality productivity index tend to have a higher level of economic development. Theoretically, this result further illustrates that new-quality productivity enhances production efficiency through technological innovation and resource optimization, thereby driving economic growth. New-quality productivity not only transcends the traditional factor-driven growth model but also injects stronger innovative momentum and potential for sustainable development into regional economies. On a practical level, improving the level of new-quality productivity can effectively promote the rational allocation of resources, accelerate industrial structure upgrades, and significantly enhance the affluence and vitality of regional economies.

Exploring socioeconomic and environmental coordination in the Yangtze River Delta Region using a game theory stepwise-cluster factorial coupling coordination model

Economic growth has brought about escalating environmental challenges. The purist of socioeconomic and environmental coordination necessitates a deep understanding of their interplay. Therefore, a game theory stepwise-cluster factorial coupling coordination model (GSFCM) has been developed to investigate the spatial and temporal patterns and driving forces of socioeconomic and environmental coordination in the Yangtze River Delta Region. Results reveal an upward in socioeconomic and environmental status from 2015 to 2020. All 41 cities in the region show progress towards coordination, coupled with spatial inequalities. Hangzhou, Shanghai, and Zhoushan demonstrate the highest coupling coordination degree (CCD), while Bozhou, Suzhou, and Fuyang located in Anhui exhibit the lowest levels. Socioeconomic factors contribute 82.98% to the enhancement of CCD, environmental factors contribute 11.05%, and their interaction accounts for 5.92%. Main drivers influencing CCD include Gross Domestic Product (GDP) per capita, the proportion of the added value of tertiary industry, PM2.5 concentration, and their interactions. Policy related to regional collaboration, industrial upgrades, and investment in environmental infrastructure are recomended to achieve a harmonious balance between economic growth and environmental protection. These insights are expected to support policy decisions in sustainable development of the region.

A Matching Policy to Address ESG and Non-ESG Risks Impacted by a Relocation Policy in China’s Chemical Industry

China’s chemical industry has faced severe environmental, social, and governance (ESG) issues, such as high safety and environmental accidents and risks. To address these issues and promote industrial upgrading, China’s central government has issued a national relocation and improvement policy targeting its chemical industry. However, its countrywide policy implementation may also lead to other ESG risks during the relocation of chemical enterprises, namely industrial transfer. The typical ESG risks that appear to occur in developed eastern region provinces include a one-size-fits-all solution and unemployment, while less developed central and western region provinces may encounter pollution transfer, carbon leakage, environmental injustice, and health disparities. These ESG risks might overlap with other economic and financial (non-ESG) risks, like stranded assets, industry hollowing-out, and debt sustainability issue. These ESG and non-ESG risks could result from potential mismatches between chemical enterprises and chemical parks, categorized as mismatching errors explained by social-ecological systems, behavioral economics, and information economics. To better manage these risks, we propose an ESG matching policy comprising a national standardized ESG scoring and ranking system, a deferred acceptance mechanism, and a score announcement instrument. Such a policy innovation aims at achieving fair and efficient chemical enterprise–chemical park pairs, which would help manage both ESG and non-ESG risks and provide a just transition toolkit for China and other developing countries.

The Effects of Big Data Regulations on Industrial Modernization in Resource-Based Cities: A Semi-Natural Study Using a State-Level Big Data Coordination Zone

As a core component of the digital economy, big data provides resource-based cities with a new impetus for upgrading their industrial structures. This paper employs a double difference method with panel data from 86 resource cities in China from 2009 to 2020 in order to investigate the effects of big data policies on resource cities' upgrading of their industrial structures objectively. Quasi-control groups are the quasi-natural trials of two batches of 19 resource cities in national big data comprehensive experimental zones. The empirical results demonstrate that the nationwide comprehensive experimental zone for big data exerts a considerable influence on the industrial upgrading of resource-based cities. This conclusion remains robust even after putting it through a series of tests, including the consolidation test and the stability test. Ultimately, this study offers a novel approach to catalyzing industrial upgrading in a big data context, shedding light on crucial issues such as economic instability in resource-based cities.

Unveiling the nonlinear drivers of urban land resources on carbon emissions: The mediating role of industrial upgrading and technological innovation

As cities continue to develop, land resources play a pivotal role in the pursuit of sustainable urban development. However, the impact and mechanisms of land resources on reducing emissions remain unclear. Therefore, our study employs an ensemble learning model within a Monte Carlo Simulation framework to explore the nonlinear relationship between land resources and carbon emissions. Additionally, we utilize a fixed effects model to identify the mechanisms of impact. Specifically, industrial and residential land uses are identified as primary drivers of carbon emissions, contributing 17.04 % and 4.75 % of the total variance in emissions, respectively. In contrast, a standard deviation increase in land price leads to a reduction of 4.65 tons in per capita carbon emissions. In addition, industrial upgrading and innovation emerge as critical mediators in promoting carbon reduction. This research contributes to sustainable urban planning and policy-making, offering insights into how strategic land use planning reduces urban carbon footprints.

Do Fiscal Incentives Contribute to Pollution Control? Empirical Evidence from China

Given the growing concerns over environmental degradation and the demand for sustainable development, the Chinese government has implemented several fiscal incentive policies to enhance environmental governance. Taking the phased comprehensive demonstration cities of the Energy Saving and Emission Reduction Fiscal Policy (ESERFP) as an exogenous shock, this study uses a staggered difference-in-differences method to evaluate the impact of the fiscal incentive policy on pollution control using panel data from 268 prefecture-level cities in China from 2003 to 2017. The results indicate that the industrial pollutant emissions in the demonstration cities significantly decreased compared with those in the non-demonstration cities under the influence of the ESERFP. Specifically, industrial wastewater discharges in the demonstration cities decreased by 15.5% while industrial sulfur dioxide emissions decreased by 19.5%. Moreover, promoting industrial structure upgrades and green technology innovations are the main mechanisms of the ESERFP in reducing industrial pollution emissions. Furthermore, the emission-reduction effect of the ESERFP is more significant in areas with more fiscal resources, lower promotion incentives based on local economic performance, greater emphasis on environmental protection, and those with no old industrial bases. Further analysis shows that the positive effect of the ESERFP on pollution control in the demonstration cities remains relatively effective after the demonstration period ends, and the policy does not sacrifice economic dividends. Overall, this study explores the impact of fiscal incentive policies designed to achieve environmental improvements via pollution control, offering valuable fiscal policy insights for China and other developing economies seeking solutions to environmental pollution, including fiscal incentive policy formulation and implementation, fiscal incentives to support regional green transformations, improving the differentiation and precision of fiscal incentives and enhancing environmental performance assessment.

Do public-private partnerships (PPPs) promote the sustainable development of infrastructure (SDI)? Evidence from China

PurposeTo address challenges such as inadequate funding and inefficiency in public infrastructure construction, PPPs have gained significant global traction. This study aims to comprehensively assess the impacts and mechanisms of PPPs on the SDI and to provide rational policy recommendations based on the findings.Design/methodology/approachWe collated a dataset from 30 Chinese provinces covering the years 2005–2020 as our research sample. The study’s hypotheses are tested using a double fixed-effects model, a chained mediated-effects model and a multidimensional heterogeneity analysis.FindingsOur findings indicate that PPPs have a facilitating effect on SDI in general. This boost usually lags behind policy implementation and is cyclical in the time dimension. In the spatial dimension, PPPs contribute significantly to SDI in the eastern and western regions, but not in the central region. From the perspective of the dynamics of economic, social and industrial development, PPPs in economically backward areas are difficult to promote SDI, promote it the most in economically medium regions and are slightly less in economically developed regions than in medium regions. This promotion effect has an inverted U-shaped relationship with social development and diminishes with industrial structure upgrading. Finally, due to the negative relationship between PPPs and social development and between social development and SDI, PPPs are shown to contribute to SDI and are identified as critical paths. However, PPPs suppress SDI by inhibiting economic and industrial development.Originality/valueThis study makes three novel contributions to the existing body of knowledge: (1) we innovatively introduce the United Nations Sustainable Development Goals (SDGs) into the field of infrastructure research, offering fresh perspectives on SDI enhancement; (2) revealing the mechanisms by which PPPs affect SDI through the three dimensions of economic, social and industrial development enabling policymakers to better understand and optimize resource allocation and improve planning, design and management of PPP projects for sustainable infrastructure and (3) we assess the spatiotemporal variances of PPPs’ effects on SDI and the diversity across regions at different social, economic and industrial structures developmental stages, offering critical insights to global decision-makers to devise tailored policy measures.

Teacher competency development model of higher vocational schools in Shandong province, China

Teacher competency is fundamental to successful vocational education reforms and quality enhancement. The rapid economic transformation and industrial upgrading in Shandong Province require highly skilled talents, prompting the implementation of modern vocational education (MVE) reform. However, the current competency of vocational teachers (VTs) often falls short of MVE standards, and there is an urgent need for a development model suitable for regional characteristics as a guide. In this study, we aim to assess the current state of VT competency development in Shandong Province and propose an integrated competency development model tailored to its specific needs. Our research employed a mixed-methods design, incorporating a cross-sectional survey of 280 VTs and 140 administrators and semi-structured interviews with management personnel. We conducted the exploratory factor analysis (EFA) on a comprehensive survey questionnaire and identified six key determinants of VT competency development. Based on these findings and expert interviews, we developed an integrated competency development model, comprising the teacher’s personal qualities and educational philosophy, educational technology competence, digital teaching competence, social policy, professional identity, and school culture and support. This model offers practical implications for designing effective teacher training programs and establishing supportive institutional environments, ultimately aiming to enhance the quality of vocational education through improved teacher competency.

The Impact of National Big Data Pilot Zones on the Persistence of Green Innovation: A Moderating Perspective Based on Green Finance

Enhancing the persistence of corporate green innovation is crucial for promoting sustainable societal development. As big data technologies and related policies advance, digitalization emerges as an effective enabler of green innovation in enterprises. This study, grounded in resource-based theory and corporate ecology theory, examines the impact of national-level big data pilot zone reforms on the persistence of corporate green innovation, utilizing data from China’s A-share-listed companies from 2011 to 2021. It also explores the moderating effect of green finance development. Empirical results indicate that (1) the implementation of national-level big data pilot zones positively influences the persistence of green innovation, with robustness and endogeneity tests confirming these findings. (2) The development of regional green finance positively moderates this relationship. (3) A mediation analysis reveals that national-level big data policies enhance innovation persistence by alleviating financing constraints, promoting talent aggregation, and facilitating industrial upgrading. (4) The heterogeneity analysis shows that the positive impact of these reforms is more pronounced in non-state-owned, non-heavy polluting firms, those receiving high subsidies, and in regions with stringent environmental regulations. This study offers a novel theoretical perspective on and empirical evidence for digital transformation from the angle of national policy, supporting societal green development and contributing to policy enhancement aimed at achieving sustainable growth.

Energy transition policy and rural revitalization of Chinese ethnic regions

This study addresses socio-economic disparities and environmental challenges in China's ethnic regions by examining the impact of energy transition policies on rural revitalization, with a particular focus on the mediating effects of population quality, economic growth, and industrial upgrading. Using data from 46 Chinese ethnic regions spanning 2003 to 2022, the analysis reveals that industrial upgrading significantly enhances the effectiveness of these policies, while educational attainment, as a measure of population quality, further amplifies their benefits. Although economic growth also contributes positively to rural revitalization, its mediating role is less pronounced. These findings highlight the need for a comprehensive strategy that integrates energy transition with socio-economic development to promote sustainable rural progress. The study underscores the critical role of incorporating energy transition into broader development policies, particularly through industrial and educational improvements, to achieve sustainable development in China's ethnic regions, and offers valuable policy recommendations.

The smarter the cleaner: Evaluating the impact of artificial intelligence on haze pollution

This paper innovatively evaluates the level of artificial intelligence (AI) by counting AI firms and employs a two-way fixed-effects model to analyze the impact of AI on haze pollution (HP), using panel data of 264 Chinese cities from 2006 and 2021. It is found that AI is an effective means to reduce urban HP, and that for every 1 % increase in the number of urban AI firms, PM 2.5 is reduced by about 2.22 %. Regarding mechanisms, AI mainly reduces urban HP through two ways: stimulating technology innovation and promoting industrial structure upgrading. Heterogeneity tests show that the haze reduction effect of AI varies due to urban HP level, urban location and urban characteristics, and is more pronounced in cities with high HP, as well as in western cities, northern cities, cities of high administrative levels, and resource-based cities. Our findings provide new insights for policy makers to better utilize AI technology to reduce HP.

Does urban shrinkage impact energy efficiency?: Evidence from Chinese Counties

Some cities in China are facing challenges due to population loss while also attempting to address energy conservation and emissions reduction. Although urban shrinkage can relieve pressure from energy consumption demands, such as water, electricity and gas, does it improve urban energy efficiency? This study attempts to answer this question. Based on Point of Interest (POI) big data and Global Human Settlement Layer (GHSL) raster data, this study identifies urban shrinkage from the coupling perspective of administrative and economic boundaries. It also examines the impact of urban shrinkage on energy efficiency. The results suggest that Chinese counties’ overall energy efficiency is experiencing a four-stage "decline-rise-decline-rise" trend, and the urban shrinkage of Chinese counties exists in three major areas: the Northeast, the Southwest, and the Centre. Compared to non-shrinking cities, urban shrinkage has a significant negative impact on improving energy efficiency. This impact exhibits significant heterogeneity. Specifically, compared with mature resource cities and cities in Western China, regenerative cities, non-resource cities and cities in Central China have less impact on energy efficiency. In addition, urban shrinkage may impede energy efficiency improvement by hindering industrial structure transformation and upgrading, energy-saving technology innovation, and financial development. Clarifying the relationship between urban shrinkage and energy efficiency is helpful for shrinking cities to change their development strategies, which is critical for sustainable development.

Does advanced human capital structure improve carbon emission performance in China? Empirical research from a spatial spillover perspective

Facilitating coordination between economic growth and carbon mitigation is vital for the sustainability of human society. Identifying the driving factors of Carbon emission performance (CEP) is crucial for its improvement. Nevertheless, empirical research on the relationship between advanced human capital structure (AHCS) and CEP remains scarce. This study analyzes the impact of AHCS on CEP utilizing a dataset covering 30 Chinese provinces from 2006 to 2021. First, we construct a global epsilon-based measure (EBM) model with undesired output to accurately measure the multi-period CEP for Chinese provinces. Next, we evaluate the level of AHCS from the perspective of dynamic evolution. Subsequently, a spatial Durbin model (SDM) is employed to examine the spatial spillover effects of AHCS on CEP. The results illustrate that CEP exhibits significant positive spatial spillover effects across provinces. Additionally, AHCS has a direct and significant positive impact on local CEP and can indirectly enhance CEP in neighboring regions through spillover effects, although the impact varies across different regions. The impact mechanism analysis indicates that AHCS enhances CEP via technological advancement, industrial structure upgrading (ISU), and energy efficiency improvement, despite the asymmetric effects observed between local and neighboring provinces. Therefore, shaping a favorable educational environment to optimize the human capital structure is critical for the improvement of CEP. In addition, promoting resource sharing and technology exchange through regional cooperation is essential for China’s low-carbon transition development.

The Influence of Urban New Energy Development Orientation on New Energy Technology Innovation in Firms

Unlike existing energy policies that adopt a macro-level perspective, this paper examines the impact of China’s new energy demonstration cities (NEDC) policy on micro-agents. Using longitudinal data covering Chinese publicly listed firms in new energy industries, the empirical results show that the NEDC policy significantly increases firm’s new energy patents in pilot cities compared to those in non-pilot cities. We examine the potential channels via industrial upgrading effect, relative scale effect, direct incentive effect, and factor aggregation effect. In addition, we find that the NEDC policy performs better in areas with more governmental new energy attention and higher infrastructure levels, and state-owned businesses are more proactive.

The High-Quality Development of the Construction Industry in the Context of the Digital Economy—Based on the Mediating Effect of Entrepreneurship

The digital economy is an important engine in leading the high-quality development of the economy, and entrepreneurship is an endogenous force propelling economic growth. These factors are essential drivers for advancing the transformation and upgrading of the construction industry and improving efficiency. This study integrates entrepreneurship into the analytical framework of industrial development and explores the primary pathways and theoretical mechanisms of the digital economy for the high-quality development of the construction industry from the dimensions of entrepreneurial innovation spirit and entrepreneurial pioneering spirit. On this basis, we empirically test the role mechanism between the digital economy, entrepreneurship, and the high-quality development of the construction industry. The results of this study indicate that (1) the digital economy significantly promotes the high-quality development of the construction industry through an improvement in technical efficiency; (2) the innovation and entrepreneurship of entrepreneurs are important mechanisms for the digital economy to promote the high-quality development of the construction industry, and at this stage, entrepreneurial spirit promotes more than the spirit of innovation; and (3) the development of the digital economy plays a greater role in enhancing low-productivity regions and the central and eastern regions, and the inclusive characteristics of the digital economy are beginning to emerge.

Emerging Technologies and Applications of AI Chips: Integrating Deep Learning Algorithms with Advanced Hardware Architectures

Due to the ongoing promotion of the latest technological revolution and industrial upgrading, as well as the widespread use of artificial intelligence and deep learning, the intelligent Internet of Things terminal has reached a more advanced stage of development. The combination of training data, model algorithms, and processing power is the basis for advancing artificial intelligence. The semiconductor business plays a crucial role in shaping the future of AI algorithms and the computing power industry. This study explores the progression of artificial intelligence, and essential technologies, and examines the categorization and structure of AI chips. The process of deep learning was then analyzed, demonstrating the application of AI in real life. Next, this study showcases distinct AI chip-related models, specifically designed for the automated implementation of Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). The study then proceeds to examine the unique procedure and the significance of the resulting data. The future of AI was examined in the domains of space exploration, healthcare, and autonomous driving, along with future forecasts.

Application of Smart Polymer Materialsarts

With the wide application of polymer materials, the performance of ordinary polymer materials gradually cannot meet the shortcomings of use in special environments, such as limited strength, insufficient wear resistance, poor stability and so on. And can not adjust and repair according to changes in the environment. Therefore, the demand for intelligent polymer materials has gradually expanded, and has been applied to a variety of fields after research. This paper introduces the information and application of three kinds of intelligent polymer materials: targeting, shape memory and self-healing. Targeting is mainly used in cancer or other diseases that require precise drug delivery; The characteristics of shape memory can be applied to plaster and surgical sutures in the medical field, and can also be applied to the performance upgrade of traditional manufacturing industry. Self-healing can be used to improve the service life of composite materials by using different methods according to the requirements of different material use scenarios.

Digital Economy and the Development of Rural Speciality Industries

Integration of digital economic (technological) development is an inevitable choice for the growth of industries in China's urban and rural areas. Industrial prosperity in the countryside requires the development of speciality industries according to the reality of the countryside. The integration of characteristic industries and digital economy helps rural industries to flourish and accelerates the realisation of the goal of rural revitalisation. Due to different natural endowments, villages in the categories of agglomeration and upgrading, suburban integration, characteristic protection and relocation and annexation focus on the development of different characteristic industries. According to the actual situation of each type of villages, the development of characteristic industries and the digital transformation and upgrading of characteristic industries are the basic paths for the optimisation and development of rural industries in China. The development of special industries in different types of villages requires a sound digital foundation, the pooling of financial resources in various aspects, and the support and promotion of the construction of digital talents.