Shared manufacturing has attracted extensive attention for its role in improving production efficiency and optimizing resource allocation. However, most existing studies inadequately address energy consumption and carbon emissions during production processes. To promote the sustained development of shared manufacturing, this paper investigates resource scheduling and optimization problems from the perspective of energy conservation and emission reduction. A multi-objective production scheduling optimization model is developed with the respective goals of minimizing costs and production time, and minimizing energy consumption and carbon emissions. To solve the model, a hybrid optimization algorithm is proposed by integrating the non-dominated sorting genetic algorithm with particle swarm optimization. Using real-world data for empirical analysis, the results show that: (1) as production utility increases, carbon emissions and energy consumption rise, with an accelerating marginal growth rate; (2) comparative analysis based on different weight settings for production cost and maximum production time indicates that a higher weight assigned to production cost is associated with greater production utility, suggesting that platform operators should place greater emphasis on cost control; (3) comparisons between the proposed hybrid genetic algorithm incorporating particle swarm optimization and other algorithms demonstrate that the hybrid approach achieves faster convergence and yields higher-quality solutions, thereby validating the applicability of the model. The findings provide a basis for advancing energy conservation and emission reduction efforts and for promoting the sustained development of shared manufacturing.

Corporate sustainable innovation is a key driving force for high-quality development and green transformation. Under the guidance of the “dual carbon” goals, the external environment has placed higher requirements on enterprises' environmental, social, and governance (ESG) responsibility fulfillment, and ESG performance has gradually become an important criterion for measuring corporate sustainable development. Using A-share listed companies from 2010 to 2024 as research samples, this paper constructs a multiple regression model to systematically examine the impact of enterprises' ESG responsibility fulfillment on their sustainable innovation capability. By introducing continuous indicators of innovation input and output, this paper characterizes enterprises' sustainable innovation capability from both input and output dimensions. A series of robustness and endogeneity tests are conducted using the instrumental variable method, Heckman two-stage model, propensity score matching (PSM), and other methods. Furthermore, this paper explores the mediating effects of digital transformation and financial flexibility, introduces the degree of industry competition to investigate its moderating effect, and finally conducts heterogeneity analysis based on enterprises' pollution attributes and regional differences. The results show that enterprises' active ESG responsibility fulfillment can significantly improve their sustainable innovation capability, and this conclusion still holds after a series of robustness and endogeneity tests. After distinguishing different dimensions of ESG, it is found that environmental, social, and governance performance all enhance enterprises' sustainable innovation capability, among which social and governance dimensions have more significant promoting effects. Mechanism analysis indicates that ESG responsibility fulfillment can strengthen sustainable innovation capability by promoting corporate digital transformation and improving financial flexibility. The moderating effect test shows that the degree of industry competition positively moderates the relationship between ESG responsibility fulfillment and sustainable innovation. Heterogeneity analysis reveals that the promoting effect of ESG responsibility fulfillment on sustainable innovation capability is more significant in non-heavy polluting enterprises and enterprises located in the eastern and central regions. From the two dimensions of corporate digital transformation and financial flexibility, this paper reveals the internal mechanism of how enterprises' ESG responsibility fulfillment affects their sustainable innovation capability, and points out the heterogeneous effects across industries and regions. It aims to provide theoretical references and managerial implications for enterprises to fulfill ESG responsibilities, enhance their sustainable innovation capability, and promote their high-quality development.

The occurrence of emergencies is contingency. In order to solve the problem of excessive or insufficient reserves when the government reserves emergency materials alone over-or under-stocking of emergency supplies, a cooperation relationship is established between government and enterprises in the form of government-enterprise collaborative stockpiling. Considering the characteristics of material loss, the degree of material loss is reflected in the rotation and renewal cost generated by the loss. A decision-making model is constructed for the government-enterprise collaborative stockpiling with the objective of maximizing the expected profits of enterprises. The optimal form and amount of reserves is derived for enterprises through Stackelberg game theory. The results indicate that the optimal reserve decision of enterprises is influenced by three factors:the government procurement price, the probability of unexpected emergencies, and the cost of enterprise rotation and update. The willingness of enterprises to choose cooperation with the government increases with the probability of unexpected emergencies and government procurement price. Among them, as the cost of rotation updates increases, the physical reserves of the enterprise will correspondingly decrease, and the production capacity reserves will also increase accordingly. Finally, the conclusions obtained are verified through arithmetic numerical examples, providing a scientific basis for enterprise reserve decision-making.

Against the backdrop of profound adjustments in the global economic landscape and the ongoing advancement of the national innovation-driven development strategy, regional innovation ecosystems, as core vehicles for concentrating innovation factors and stimulating innovation vitality, have become a key engine for promoting high-quality regional economic development. China is currently at a critical stage in its transition from factor-driven growth to innovation-driven development, and building sound and well-ordered regional innovation ecosystems has become an important work at both the national and local levels. However, regional innovation ecosystems in China face practical challenges, including ambiguous role definitions of innovation actors, inefficient flows of innovation resources, and highly restrictive innovation environments. Drawing on niche theory, this paper employs the entropy method, niche suitability models, and evolutionary momentum models to conduct a comprehensive evaluation and spatiotemporal analysis of innovation ecosystems across 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) in China from 2020 to 2024. Based on the evaluation results, they can be categorized into 4 types: innovation-leading, steadily catching-up, breakthrough-constrained, and foundation-building. The findings reveal that, the national niche suitability has remained relatively stable from 2020 to 2024. And a pattern has emerged characterized by the eastern regions taking the lead, the central regions providing support, the western regions striving to catch up, and the northern regions undergoing transformation. Further analyses using σ-convergence and β-convergence tests show that while the gap in niche suitability among regions has narrowed to some extent, a widespread catching-up effect has yet to materialize. On this basis, this paper proposes differentiated policy recommendations in three respects:optimizing the structure of innovation actors, promoting the balanced allocation of resources, and improving the environmental support system. These recommendations aim to address the limitations of existing studies that predominantly focus on evaluating partial regions, lack in-depth spatiotemporal analysis, or fail to incorporate timely updates, thereby contributing to the coordinated development and overall upgrading of innovation ecosystems across China.

Promoting the deep integration of the innovation chain, industrial chain, capital chain, and talent chain (“four chains”) is an important path for enhancing national industrial competitiveness and achieving high-quality economic development. Based on the panel data of 31 provincial-level regions in China (excluding Hong Kong, Macao, and Taiwan) from 2014 to 2023, an evaluation index system for the “four chains” in the manufacturing industry is constructed. The Critic-entropy weight method is used to determine the weights, and the coupling coordination degree model is employed to quantify their synergy level. Further, a spatial analysis framework is established, and the kernel density estimation, global Moran′s I index, LISA time path, and Theil index decomposition methods are successively adopted to conduct empirical research from the aspects of distribution dynamics, spatial correlation, agglomeration stability, and sources of differences. The results show that from 2014 to 2023, the coupling coordination degree of the “four chains” presented a spatial pattern of “high in the southeast and low in the northwest”, with the national average shifting from fluctuation to stability. Low-value provincial-level regions accelerated their catch-up, while high-value provincial-level regions saw a slowdown in growth. The kernel density map displayed the characteristics of “wider bandwidth, lower main peak, and rightward shift of the tail”, indicating a slow expansion of regional differences. Spatial correlation was significant, showing an agglomeration pattern of “high-high” and “low-low” adjacency. The Theil index analysis indicated that the overall difference gradually increased to 0.025, with intra-group differences still dominant but the contribution rate declined during the periods of 2015—2016; inter-group differences continued to increase, with the contribution of the North China regions decreasing, while that of the South China and Southwest China regions increasing, becoming the primary source of regional differences. This paper, by integrating the analysis chain of “spatial distribution-correlation stability-sources of differences”, reveals the spatiotemporal evolution mechanism of the “four chains” integration, providing empirical evidence and policy references for optimizing regional industrial layout and resource allocation.

Digital twin technology, as a key technology to break through the core bottleneck of interaction and integration between the physical and information worlds in manufacturing, is an important means to achieve intelligent manufacturing. To deeply explore the influencing factors of digital twin technology adoption in manufacturing enterprises and reveal its internal mechanism and driving logic, a four-dimensional index system of technology, organization, environment, and economy is constructed based on literature analysis and expert interviews. The DEMATEL-ISM method is adopted to systematically analyze the causal relationship and hierarchical structure of the influencing factors, and ultimately 16 main influencing factors are identified. These factors are distributed in the four quadrants of the causal relationship coordinate, forming an 8-level hierarchical explanation structure model. The research results show that government incentive policies, data security and privacy protection, and digital twin maturity exhibit the highest influence degree and are the root factors of the system; industrial chain collaboration and cooperation and application benefits demonstrate the highest centrality, while intelligent infrastructure represents the lowest-level root factor. Factors in the technological and economic dimensions constitute the main influencing paths, while factors in the environmental dimension connect and act on the main paths, ultimately affecting the surface-level organizational dimension factors. Specifically, intelligent infrastructure, data security and privacy protection, and digital twin maturity, three factors in the technological dimension, are the key factors driving manufacturing enterprises to adopt digital twin technology. At the same time, government incentive policies, application benefits, and industrial chain collaboration and cooperation in the environmental, organizational, and economic dimensions play an important role in the decision-making process. Based on this, targeted suggestions are put forward, aiming to provide strong theoretical support and practical guidance for the digital transformation and intelligent upgrading of the manufacturing industry.

The Third Plenary Session of the 20th Central Committee of the Communist Party of China made significant arrangements to create a first-class business environment characterized by market orientation, rule of law, and internationalization. The business environment has thus become a subject of intense academic and industrial focus through both theoretical and practical explorations. What kind of business environment is beneficial to improving regional innovation capacity has also become a pressing research question. Given that the business environment is a complex ecosystem, this paper adopts a complex systems perspective and uses fuzzy-set qualitative comparative analysis to analyze the complex relationship between the business environment and regional innovation capacity from the perspective of configuration. The results show that a single business environment element is not a necessary condition for high regional innovation capacity, but optimizing the innovation environment universally enhances regional innovation capacity. Three types of business environment configurations can foster high regional innovation capacity: the government-led, human resource-based type driven by finance and innovation, the government and market dual-logic type driven by finance and innovation, and the type driven by the market and innovation, presenting multiple paths for improving regional innovation capacity in China. From the perspective of institutional configuration theory, this paper deeply analyzes the impact of the coupling relationship of multiple elements in the business environment on regional innovation capacity, aiming to reveal the construction path of the business environment that can cultivate high regional innovation capacity, enrich the cognitive framework of the relationship between the business environment and regional innovation capacity in theory, and provide practical references for policymakers.

The resilience and security of industrial chain in China's medical equipment sector have garnered significant attention, particularly in addressing various emergencies. From the perspective of New Quality Productive Forces, this paper first summarizes the current status of the resilience and security of industrial chain in China's medical equipment sector, then systematically reviews policies aimed at enhancing such resilience and security, and finally proposes countermeasures to further strengthen the resilience and accelerate domestic substitution. During the 14th Five-Year Plan period, China has seen continuous growth in the market shares of complete equipment and key components of medical equipment. The market shares of complete equipment in multiple sectors have exceeded 50%, and a majority of key components are now domestically produced. However, some critical components or sub-components, especially high-end, high-reliability, and premium-grade components, still rely on foreign suppliers, posing challenges to the independence and controllability of the entire industrial chain. To enhance the resilience and security of industrial chain in the medical equipment sector from the perspective of New Quality Productive Forces, continuous innovation is essential to achieve full domestic substitution. China has introduced a series of policies and measures to support industrial chain in terms of institutional design, financial investment, and industrial foundation, effectively boosting the development, resistance, recovery, control, and innovation capabilities of the sector. Looking ahead, driven by an innovation-oriented ecosystem and robust policy support, the resilience and security of industrial chain in China's medical equipment sector are expected to further improve. It is recommended that the government leverage its control over the downstream of the industrial chain as a policy tool to integrate and regulate midstream and upstream, foster a moderately competitive industrial structure, launch pilot programs for medical big data, establish unified standards and property rights systems, and actively promote the intelligentization of medical equipment.

As a traditional heavy industry base in China, Liaoning Province possesses a solid industrial foundation in both traditional materials and advanced basic materials. However, it also faces challenges in transforming its manufacturing sector toward high-end, intelligent, and green development. Based on the perspective of industrial chain synergy and framed within the theoretical context of New Quality Productive Forces driving industrial upgrading, this paper systematically examines the current status of China's new materials industry, the strengths and weaknesses of Liaoning's new materials sector, and the driving mechanisms and implementation pathways for its upgrading. This paper finds that Liaoning holds significant advantages in the manufacturing of advanced basic materials and their application in equipment manufacturing. Nonetheless, the industry suffers from structural issues such as disrupted industrial chains, regional development imbalances, and delayed green transformation. Empowering the industry with New Quality Productive Forces can help reconstruct the value logic of the industrial chain: digital and intelligent technologies enhance production efficiency and responsiveness through data-driven R＆D, flexible manufacturing, and full life cycle management; industrial chain synergy networks improve industrial resilience through vertical integration and horizontal collaboration; and green transformation promotes sustainable development through low-carbon process innovation, circular economy systems, and green standards. This paper further proposes that Liaoning should leverage its institutional advantages to improve top-level designs that promote the development of New Quality Productive Forces and the new materials industry; utilize the capital and technological strengths of large state-owned enterprises to drive collaborative innovation among small and medium-sized private enterprises within upstream and downstream of the industrial chain; encourage universities, research institutions, and enterprises to establish collaborative innovation platforms for new materials, thereby strengthening the deep integration of industry, academia, research, and application, optimizing regional factor allocation, and promoting talent mobility; and the government should coordinate the division of labor across the industrial chain, establish dedicated funds to support breakthroughs in core technologies, promote the development of pilot testing platforms for new materials, and improve green finance and insurance compensation mechanisms.

With the deep integration of digital technology and products, enterprises are facing problems such as diversified market demands and high demands from consumers for personalized service experiences. Many enterprises pay more attention to the close integration of products and services, hoping to meet the diversified needs of consumer groups and enhance their market competitiveness by improving the level of smart product-service systems. Currently, there is a relative lack of evaluation research on smart product-service systems, making it difficult for enterprises to accurately understand the advantages and disadvantages of their own solutions, thereby limiting their optimization and development. Based on existing literature and theory, this paper clarifies the core characteristics of smart product-service systems, considers factors related to the design of smart product-service systems, and constructs an evaluation index system for smart product-service systems, which includes 10 secondary indicators under four aspects:user experience, economy, environmental impact, and service. It uses the rough number improved best-worst method (BWM) to calculate indicator weights and accurately describes the distribution of fuzzy evaluation information to ensure the quality of decision information. At the same time, it uses the rough number improved TOPSIS method to rank the alternative solutions. By introducing Euclidean Distance and combining the concept of distance in rough set theory for calculation, the uncertain characteristics of the data are reflected. Four new energy intelligent vehicles, namely Tesla Model 3, NIO L7, Xiaopeng P7i, and NIO ET5, which are suitable for the research scenario, are selected as alternative solutions for example calculations. It is found that the smart product-service systems of NIO ET5 perform outstandingly under the evaluation criteria in this article. The evaluation method combining BWM and TOPSIS based on rough number improvement can effectively avoid uncertain factors in the evaluation of smart product-service systems. Rough numbers objectively integrate group opinions, and the combination of BWM and TOPSIS methods effectively simplifies the calculation process, improves the accuracy of judgments, and increases the reliability of weight results. This paper selects new energy vehicles that meet the application scenarios as research examples, fully demonstrating the feasibility and effectiveness of the constructed evaluation model. It provides an effective scientific basis and references for the development of smart product-service systems, promotes the development of smart product-service systems evaluation, and provides theoretical support and practical guidance for enterprises to achieve business model innovation and improve user satisfaction and competitiveness.

China has entered the service economy era, characterized by the “two 50%” phenomenon in its industrial structure:the value added of the service sector accounts for 50% of GDP, and the value added of the producer service sector constitutes 50% of the total value added of the service sector. In contrast, the two proportions in developed countries generally exceed 70%, reflecting the “two 70%” phenomenon. On the new journey of the New Era, the continuous rise of the service sector′s share in China is a prevailing trend. To maintain economic growth efficiency and enhance international competitiveness in conjunction with this rise, it is crucial to avoid premature deindustrialization and preserve the supporting role of the manufacturing sector in building a modern industrial system. The solution lies in increasing the proportion of producer services within the service sector and promoting the integrated development of manufacturing industry and producer services, thereby advancing the path of servitization of manufacturing industry and synergistically promoting the strategy of building up China′s strength in manufacturing and the construction of a powerful country in service industry. This paper further employs China′s input-output data and the complete consumption coefficient to measure the level of servitization of manufacturing industry and the degree of integration of manufacturing industry and producer services, analyzing the overall characteristics of this integration and the specific traits of various sub-industries. The findings reveal clear trends towards the servitization of manufacturing industry and the integration of manufacturing industry and producer services, although the integration level requires further enhancement. Notably, there exists heterogeneity in the levels of integration between different factor-intensive manufacturing sectors and producer services. From the macro, meso, and micro levels, this paper proposes countermeasures and suggestions to promote the integration of China′s manufacturing industry and producer services. At the macro level, it is essential to optimize the integrated development environment by strengthening resource provision. At the meso level, industries should adopt differentiated integration paths tailored to their specific characteristics. At the micro level, enterprises should accelerate digital transformation to empower new models of integration. This study aims to provide policy-making references for facilitating the integration of China′s manufacturing industry and producer services, thereby supporting high-quality economic development and advancing toward a higher level of modernization.

The widespread application of digital technology has intensified the complexity of the socio-economic system. This complexity not only exists at the overall level but also permeates the internal structure of enterprises, causing disruptive effects on classical organizational structures. In order to adapt to this challenge, service-oriented manufacturing networks have emerged as a collaborative organizational structure that integrates manufacturing and service functions, forming a complex network organizational system. The essence of this structure lies in achieving the transformation of enterprise organization through stock optimization and incremental adjustment, forming a linked network based on production factor relationships and spatial layout. Optimizing the organizational structure of service-oriented manufacturing networks is considered an important engine for promoting high-quality economic development and enhancing the modernization level of the industrial chain in China. The proposed method in this paper involves four steps: first, optimize the organizational structure of the service-oriented manufacturing networks using the autonomous service-oriented manufacturing network connection evaluation method, and provide the connection weights of the service-oriented manufacturing networks based on the classical autonomous comprehensive evaluation method; second, in the calculation of modularity in service-oriented manufacturing networks, consider the value coefficient, which means that the evaluated object prioritizes highlighting its own attributes and the advantages of high-density connection nodes in the cooperative ecological network; third, use the concept of modularity and apply the Girvan-Newman algorithm to optimize the organizational structure of service-oriented manufacturing networks; finally, illustrate and discuss the method through numerical examples, expanding the application of modularity containing value coefficients in network organization optimization. This method can effectively respond to complex network environments and collaborative needs among multiple entities and is suitable for application scenarios with multiple interactions among multiple entities and virtual agglomeration production models based on cyberspace. It not only offers new ideas for the research of service-oriented manufacturing networks but also provides references for promoting high-quality economic development and enhancing the modernization level of the industrial chain in China.

With the continuous changes in the market environment, the high-end equipment manufacturing industry must enhance quality and efficiency through collaborative innovation. In collaborative innovation activities, evaluating the cooperation performance of various entities is an indispensable part. To accurately evaluate the cooperation performance of collaborative innovation entities in the high-end equipment manufacturing industry, a performance evaluation model based on a combination weighting-cloud model is proposed. Firstly, a performance evaluation index system for the cooperation performance of collaborative innovation entities in the high-end equipment manufacturing industry is constructed from five dimensions:collaborative innovation investment, collaborative innovation process, collaborative innovation output, government support, and regional (industrial) innovation level. Secondly, the subjective weight is calculated using the order relationship analysis method (G1), and the objective weight is calculated using the improved entropy weight method. The subjective and objective weights are then optimized and combined to obtain the final weight of the indicator. Finally, the overall evaluation level is obtained by combining the cloud model evaluation method, and the results are clearly and vividly presented in the cloud map using Matlab software. Taking the collaborative innovation of a high-end equipment manufacturing enterprise as an example, this paper obtains a good final evaluation result, which is consistent with the result obtained using the fuzzy comprehensive evaluation method and the set pair analysis method. The combination weighting-cloud model exhibits rationality and effectiveness, enriching the theoretical system of collaborative innovation performance evaluation and providing a certain reference value for other evaluation methods. This paper adopts a combination weighting method that combines the advantages of subjective and objective weight determination, reducing subjective arbitrariness while also considering data authenticity. At the same time, it introduces the concept of cloud model to reduce ambiguity and randomness in the evaluation process, ensuring the scientific validity of the evaluation results.

Secondary entrepreneurship is an important way for the sustainable development of manufacturing enterprises, and has already become a hot topic in the current academic and industry circles. Knowledge and commercial resources are the key resources for secondary entrepreneurship in manufacturing enterprises. Based on RBV theory and dual entrepreneurship theory, an empirical analysis is conducted on 223 valid sample enterprises. The results indicate that both knowledge-based and commercial resources have a positive impact on the secondary entrepreneurship of manufacturing enterprises, with knowledge-based resources having a stronger effect on exploratory entrepreneurial behavior, while with commercial resources having a stronger effect on exploitative entrepreneurial behavior; entrepreneurial orientation positively regulates the relationship between knowledge resources and two types of entrepreneurial behavior, while negatively regulates the relationship between commercial resources and exploratory entrepreneurship. The moderating effect on commercial resources and exploitative entrepreneurial behavior is not significant.

In order to explore the impact of recycling scale and remanufacturing extended warranty services on the decisions of recycling channel in closed-loop supply chain, based on the model that manufacturers provide extended warranty services and retailers sell extended warranty services, considering the scale of recycling to promote remanufacturing extended warranty services, the Stackelberg game method is adopted to establish a centralized decision model and two extended warranty service sales decentralized decision models in which manufacturers and retailers are respectively responsible for recycling. The results show that the recycling scale has a certain limiting effect on different recycling channels; the centralized decision model has the highest profit, and the manufacturer and the retailer have the highest profit when the retailer is responsible for recycling in the decentralized decision model. And the impact of recycling scale on different model decisions is analyzed, and it is pointed out that the expansion of recycling scale will have a positive impact when centralized decision-making and the retailer is responsible for recycling work; the expansion of recycling scale can have a negative impact when manufacturers are responsible for recycling work.

It is of great significance for the innovation and development of platform enterprises to explore the impact of disruptive innovation on the platform ecosystem. Based on the research perspective of modularization theory, taking the short video giant Tik-Tok platform as the research object, the mechanism of impact of disruptive innovation on the platform ecosystem is analyzed. The impact mechanism of disruptive innovation on the platform ecosystem is summarized from three aspects of platform module interaction behavior, innovation structure and platform governance mechanism in combination with the actual situation of Tik-Tok platform. It is found that the impacts of disruptive innovation on the platform ecosystem are as follows: Changing the interaction behavior of modules, improving the internal autonomy of each module, and accelerating the value creation of the platform ecosystem; Simplifying the platform governance mechanism, improving the governance efficiency of the platform ecosystem, and contributing to the stable and benign operation of the platform; Optimizing the platform innovation structure, breaking the bottleneck of platform ecosystem innovation, and further promoting the rapid development of the platform.

At present, digital technology has become one of the core driving forces to promote economic and social development of China. The digitalization transformation of manufacturing industry is the focus of the development of the digital economy of China, and digital talents are the main starting point to promote the deep integration of digital technology and real economy. However, at present, problems such as the lack of attraction of talents of manufacturing industry, the structural imbalance between scale and quality of talents, and the mismatch between training in colleges and universities and market demand etc. have become the constraints of digitalization transformation of manufacturing industry. The government, colleges and universities, and institutions should cooperate collaboratively to build a systematic and efficient collaborative training system for talents of manufacturing industry, and strive to promote the digitalization transformation of employees of manufacturing industry.

In recent years, national policies have strongly supported the development of advanced manufacturing industries and promoted high-quality economic development by promoting the agglomeration of advanced manufacturing industries. The agglomeration degree of advanced manufacturing industry in 30 provinces, autonomous regions and municipalities in China (excluding Tibet, Hong Kong, Macao, and Taiwan) are calculated through location entropy, the multiple regression model is established, and the impact of advanced manufacturing industry agglomeration on economic growth are empirically tested. It has been found through research that coastal areas such as Jiangsu, Tianjin, Shandong, and Guangdong etc. have a higher degree of agglomeration, while western regions such as Yunnan, Guizhou, and Qinghai etc. have a lower degree of agglomeration; the agglomeration of advanced manufacturing industries has a significant positive promoting effect on regional economic growth, and its impact on economic growth exhibits significant regional heterogeneity; the agglomeration level of advanced manufacturing industry is the highest in the eastern region, but its promoting effect on the economy of the central region is the most obvious; in regions with better economic strength, the agglomeration of advanced manufacturing industries can better promote economic growth. On this basis, suggestions are proposed to leverage the leading role of advanced manufacturing demonstration zones, and improve the industrial chain of advanced manufacturing industry, aiming to provide recommendations and reference for the development of advanced manufacturing industry agglomeration in China.

With the development and application of big data, Internet of Things and other new generation information technologies, enterprises at each node of the manufacturing supply chain have successively built their own information systems. However, due to the disunity of development standards, most of the nodal enterprises are still isolated islands of information. Hence, the improvement of the level of data interoperability between nodal enterprises has become the key of transformation and upgrading of manufacturing supply chain driven by the new generation of information technology. However, the co-existence of cooperation and competition between supply chain enterprises makes the data interoperability more complicated in supply chain background. In view of it, a moderating effect model with mediation is built, aiming to study the influence mechanism of data interoperability on manufacturing supply chain performance, and the specific paths of relationship quality and supply chain synergy influencing supply chain performance are identified under the moderating effect of data interoperability. The survey data of 395 valid samples are applied to test the model. The results show that the trust and commitment dimensions of relationship quality have significant positive effects on supply chain performance; supply chain synergy partially mediates between trust and supply chain performance, and between commitment and supply chain performance; data interoperability plays a moderating role in the process of trust and commitment affecting supply chain collaboration; the moderating effects of data interoperability on trust and supply chain performance, commitment and supply chain performance are partially mediated by supply chain collaboration.

As high-end transformation being one of the main tasks of the current development of equipment manufacturing industry in China, its transformation process mechanism analysis and identification of main factors are the key of the regulation and development of equipment manufacturing industry. Based on the perspective of collaborative evolution, through the multiple order parameter identification model, the relevant data of the equipment manufacturing industry in 31 provincial regions in the mainland of China are sorted out on the basis of existing indicator system of the high-end transformation process of equipment manufacturing industry, and five key factors are identified in the high-end transformation process of equipment manufacturing industry in China. According to the identified control order parameters and main order parameters, the control strategies for high-end transformation of equipment manufacturing industry in each province are given, aiming to enrich the process mechanism of high-end transformation of equipment manufacturing industry and provide development ideas for the high-end transformation of equipment manufacturing industry.

In order to better respond to the national call for double creation work and improve the economic efficiency of Liaoning Province, it is imperative to vigorously develop the high-end equipment manufacturing industry. The development of high-end equipment manufacturing industry is the only way for industrial upgrading and transformation in Liaoning Province, and it is of great theoretical and practical significance to study the collaborative innovation and development of high-end equipment manufacturing industry. Taking the collaborative innovation development of high-end equipment manufacturing industry in Liaoning Province as the theme, the development status are analyzed based on the characteristics of high-end equipment manufacturing industry, the problems are pointed out that the high-end equipment manufacturing industry in Liaoning Province is lack of key core technology, lack of digital intelligence, and imperfect industrial chain. A collaborative innovation model is established between the high-end equipment manufacturing enterprises and government, universities, societies, scientific research institutes, financial institutions and service institutions, so as to solve problems arising from development.

The implementation of the new round of Northeast Revitalization policy has achieved good results, but there is still a certain distance from the goal of the all-round revitalization of Northeast China. Based on the perspective of industrial revitalization, the academic mechanism of digital technology is analyzed to curb the Danwei(government-enterprise-community hybrid)attenuation trend. The internal fit is discussed between the industrial Internet and the transformation needs of the Northeast manufacturing industry according to the experience and lessons of the transformation from the “Rust Belt” to the “Smart Belt” in the United States. It is pointed out that digital technology can greatly activate the elements of Danwei system to form secondary advantages which create a Chinese road different from the “Rust Belt” revitalization practices in Europe and America, thus the Chinese model and Chinese experience are provided to the industrial revitalization in the era of global digital economy.

Government R&D subsidy is an important means of inspiring innovation, which is complementary to the R&D investment of enterprises. Through the analysis of intermediary utility and moderating utility between government R&D subsidies, independent R&D investment of enterprises and global value chain positions, and by panel regression of different industries, it is found that government R&D subsidies and enterprise R&D investment have a positive effect on the rise of global value chain position of manufacturing industry. As a mediating variable, R&D human resource input plays a significant positive role in improving the human capital structure and promoting the global value chain position of manufacturing industry. There is a positive regulating effect between the government R&D subsidies and enterprise R&D independent investment, which is conducive to the promotion of global value chain position of manufacturing industry. Government R&D subsidy policy should be fully combined with the actual development needs of enterprises to activate their own development motivation.

The Porter Hypothesis states that environmental regulation would promote technological innovation among enterprises, thereby supporting economic growth. However, whether the hypothesis is valid in China remains to be tested. An environmental regulation panel model is constructed to assess the impact of regulation on green technology innovation, examining the data from 27 manufacturing industries in China from 2006 to 2015. Based on extant research results on environmental regulation and technological innovation in China and abroad, the green technology innovation is divided into green product innovation and green process innovation, and they are taken as the explained variables, the environmental regulation is taken as the explaining variable, and the total industry profits and innovative human resources as the control variables. The results show that at the current stage the implementation of strict environmental regulations has inhibited both green product innovation and green process innovation for the 27 industries.

By applying DEA-Malmquist index method, the total factor productivity of the Chinese equipment manufacturing industry is analyzed from industrial and time perspectives, and the differences and influencing factors are analyzed by cluster analysis and stepwise regression. The results show that:from the industrial perspective, the total factor productivity of instrument, culture and office machinery manufacturing industries is the highest; from the time perspective, the total factor productivity and technology progress index of equipment manufacturing industry tend to be at the same level, which shows that the innovation is the key factor of promoting industrial development. The market structure has the most significant influence on the total factor productivity of equipment manufacturing industry. Suggestions are proposed accordingly of improving the innovation and development of the Chinese equipment manufacturing industry.

The development of disruptive innovation has attracted wide attention all over the world. As one of the important technologies, artificial intelligence has attracted great attention in China. As a key area of artificial intelligence development in China, the development level in Liaoning Province has a profound impact on the development of regional economy and technological innovation in the future. On the basis of defining the core concepts and characteristics of disruptive innovation and artificial intelligence and reviewing the existing research results, the development status of artificial intelligence industry in Liaoning Province is considered, the problems in the development of artificial intelligence industry are found in the perspective of subversive innovation. The factors affecting the development of artificial intelligence in Liaoning Province are extracted through analysis. Finally countermeasures are put forward to the development of artificial intelligence industry in Liaoning Province, which provide reference for Liaoning Province to develop into a strong province in artificial intelligence.

Based on property rights theory, decentralized control theory, principal-agent theory and high-level echelon theory, the state-owned manufacturing listed companies from 2016 to 2018 are taken as research objects, and the influence of equity checks on corporate performance is studied, and the role mechanism of top management team characteristics is analyzed on the relationship between equity balance and corporate performance. The research has found that the equity checks are not conducive to the improvement of the corporate performance, the higher the equity checks are, the worse the corporate performance is. The gender heterogeneity, age heterogeneity and educational background heterogeneity of the top management team play a part of intermediary role between equity checks and corporate performance, while the heterogeneity mediating effect of tenure is not significant. The research is to provide the targeted reform basis and management ideas for the mixed ownership reform of state-owned enterprises.

By studying the data of listed private manufacturing companies in China from 2014 to 2018, the relationship is studied between ownership concentration ratio, operating liability ratio and agency cost. It is found that there is a significant positive correlation between the proportion of operational liabilities and the first agency cost, and a significant negative correlation between ownership concentration ratio and agency cost. Namely, the higher the proportion of operational liabilities, the higher the agency cost, and increasing the ownership concentration ratio can effectively restrain the ascending of agency cost. Further research shows that when enterprises cannot achieve the effect of external supervision by introducing liabilities, they can improve the ownership concentration ratio to make up for the lack of overall supervision, so as to effectively restrain the ascending of agency cost of managers to shareholders.

Under the background of increasing demand for urban rapid logistics distribution and more individualized user requirements, there are many problems in urban rapid logistics distribution, such as untimely distribution, low efficiency and so on. In order to improve the distribution efficiency of urban rapid logistics, the pre-storage is taken as the end node of urban rapid logistics distribution network, the warehouse is sunk directly to the end user, and the distance of end distribution is shortened. Considering the scope of pre-storage distribution services, the K-means clustering method with road non-linear coefficient is used to construct clustering model for location selection of pre-storage. An example analysis and calculation of the location of a pre-storage in an urban area are carried out by using IBM SPSS Statistics 20 software.

Manufacturing is an important foundation of the national economy, and intelligent manufacturing is the main trend of the future development of the manufacturing industry. Therefore, it has practical value to research industry policy of the intelligent manufacturing. Taking the 13 typical industry policies of intelligent manufacturing promulgated by Liaoning Province in 2015—2019 as the research object, a two-dimensional analysis framework of policy tools and industrial innovation chains is constructed, and quantitative analysis is carried out about the policy text content. The problems are pointed out that the policy tools are not used in a balanced way, and their distribution structure is unreasonable; and that the supply structure of policy tools needs to be improved in each stage of innovation chain. The corresponding policy suggestions are put forward.