Search Results (12)

The strategic goal of high-quality national development depends on intelligent manufacturing, where introducing and cultivating high-end technical talent is crucial. Although prior research has linked talent policies to technological innovation, few studies have examined how targeted talent policies promote intelligent transformation in enterprises. Methods: Focusing on industry fit, this study uses new-energy-vehicle companies to represent advanced manufacturing. Drawing on targeted talent policies issued by major Chinese cities from 2016 to 2022, we employ a multi-period difference-in-differences model to assess how these policies attract high-skilled talent related to the new-energy automotive sector and drive intelligent investment and technological upgrading. Results: Our findings indicate that targeted talent policies significantly boost intelligent investment, which holds for robustness tests. Mechanism analyses reveal that these policies optimize firms’ human capital by increasing the share of highly educated and technical employees, thereby enhancing technological innovation, patent output, production quality, and efficiency. Conclusions: This research extends the capital–skill complementarity theory by highlighting the importance of specialized talent for intelligent transformation. The results offer data-driven insights for refining talent policies to support the intelligent development of the new-energy-automobile industry. Full article

This study investigates the impact of industrial agglomeration on the upgrading of China’s automobile industry (UCAI) using panel data from 28 Chinese provinces spanning 2000 to 2020. The automobile industry is vital to China’s manufacturing and service sectors, with its upgrading capable of driving national economic growth and contributing to sustainable development goals. We employ the Malmquist productivity index based on the Data Envelopment Analysis (DEA) method, implemented through DEAP 2.1 software, to assess the UCAI. System Generalized Method of Moments (GMM) analysis, conducted using Stata 17 software, was used to examine the impact of industrial agglomeration on this process, while also exploring the threshold effect of human capital and the moderating effect of government. The results indicate that industrial agglomeration significantly enhances the upgrading of the automobile industry; however, human capital acts as a critical threshold. Below this threshold, agglomeration does not have a significant impact on the upgrading of the automobile industry, while exceeding it allows for significant positive effects. Additionally, government has a moderating effect in facilitating this process by implementing policies that support innovation and sustainable practices. Based on these findings, this paper presents several policy implications aimed at further promoting the UCAI and advancing sustainable development in the sector. Full article

The dual-credit policy promotes green transition in automobile companies. This paper investigates the dual-credit policy framework in the Chinese automotive industry, with a focus on the phenomenon of free riding. This occurs when traditional vehicle manufacturers within an alliance benefit from the excess credits generated by a transitioning vehicle company without fully committing to their own green transitioning. The focus of this study lies on an alliance constituted by a transitioning vehicle company in partnership with two traditional vehicle manufacturers, all interconnected via equity ties. Utilizing an agent-based system dynamics model, this study explores the strategic behaviors emerging from such credit collaborations and their consequent effects on operational efficiency and financial performance. The findings reveal that 1. free riding negatively impacts the transitioning company’s revenue but benefits the alliance by easing transition pressures and boosting collective performance; 2. stricter policies increase intra-alliance credit transfers and performance, while lower credit prices reduce transfer value and harm the transitioning company’s earnings. This study implies that transitioning vehicle companies with equity-linked partners can benefit from a nuanced understanding of how policy mechanisms interact with alliance dynamics under free riding. By adjusting credit transfer strategies in line with market conditions and policy trends, they can better navigate the dual-credit policy landscape, balancing individual profitability with the needs of the broader alliance and long-term sustainability goals. Full article

Dual credit policy (DCP) is a market-based mechanism introduced by the Chinese government to promote the new energy vehicle (NEV) industry and improve energy savings in China. To offer sufficient impetus for the NEV industry while providing sufficient transitional buffer time for automobile manufacturers (AMs), the government needs to scientifically design how to gradually increase its dual credit requirement for AMs year by year. To assist the multi-year DCP design, this paper proposes a generalized Nash equilibrium model to predict AMs’ short-term decisions (i.e., vehicle production and credit trading) and long-term decisions (i.e., investment in production capacity expansion and research and development) under any DCP, considering the interactions among AMs’ decisions, vehicle prices, and credit price. Based on the equilibrium model, we then develop a bi-level programming problem to optimize the multi-year DCP. With numerical experiments, we show that implementing the optimal DCP can effectively enhance the market share of NEVs. In the context of the optimal multi-year DCP, the credit requirements set by the government should maintain a relatively low threshold during the initial years, but rise rapidly after that. Such optimal DCP offers AMs sufficient transition time while compelling a quick shift in their developmental strategies. Full article

It is particularly important to measure the growth prospects of new energy vehicles, especially electric vehicles, as they can effectively reduce the negative effects of the greenhouse effect. The population dynamics analysis model provides a method to comprehensively evaluate the growth mechanism, mode, and development prospects of new energy vehicles. In this research, the sales data of 20 automobile manufacturing enterprises were counted from the website database of the China Automobile Industry Association, and their development mechanism, development mode, and development trend were analyzed in order to help researchers understand the development prospects of China’s new energy vehicle enterprises. The conclusion is that the analysis results of the single population logistic model show that the intrinsic growth rate of Chinese new energy vehicle enterprises is generally relatively low. The intrinsic growth rate of China’s new energy automobile enterprises is lower than that of other mature traditional automobile manufacturing enterprises in China. The level of intrinsic growth rate of new energy vehicle enterprises is similar to that of declining enterprises with significantly declining sales. The Lotka–Volterra model provides the analysis results of the growth mechanism driven by market demand of automobile manufacturing sample enterprises. The market driven mode of China’s new energy vehicle enterprises is not obvious. It is difficult for the current development mechanism of China’s new energy vehicle enterprises to achieve the sustainability of growth. The optimization results of the MCGP model show that China’s new energy vehicle enterprises should transform to a market-driven development model. Full article

The purpose of this study is to analyze the economic ripple effects caused by the supply-side reforms on China’s steel industry. To this end, using the 2012 and the 2017 China Input–Output Tables, this study analyzes the economic ripple effect of the Chinese steel industry caused by its supply-side reform. In this study, the influence coefficients (rear-linked effect) and the sensitivity coefficients (forward-linked effect), conceptualized by Leontief, are used as research tools to analyze the ripple effects of the Chinese steel industry. The analysis results are as follows. First, the fact that 2012 ranked high in professional equipment and meter manufacturing shows that the Chinese government’s supply-side reforms are effective and creating the required shift from traditional manufacturing to qualitative growth. Second, in terms of the sensitivity coefficient, in 2012, most of the top industries contributed significantly to the development of the Chinese economy. The originality of this study is as follows. The input production analysis used in this paper is a methodology mainly used in the steel, coal, automobile, and petrochemical industries, which clearly distinguishes the front and rear industries. Additionally, this study is a novel attempt at comparative research on the Chinese steel industry between 2012 and 2017. Full article

New energy vehicles have a significant advantage in energy saving and environmental pollution reduction in the transportation industry; however, they are still at a disadvantage in the market competition. The Chinese government has introduced lots of policy measures to promote the mass adoption of new energy vehicles (NEVs), specifically the dual credit policy. Moreover, consumer’s preferences are vital factors in their purchase decision making. This study focuses on the production decisions of automobile manufacturers under the decentralized and centralized supply chain, considering the factors of both consumer preferences and dual credit policy. First, under the centralized decision mode, higher demand drives the manufacturer to expand production; however, retailers’ profits are harmed. With the increase in consumers’ environmental preference and cognition of endurance ability, market pricing and demand increase under the decentralized decision mode. The cross effects of preferences bring more profits for manufacturers and retailers. Second, the difference in prices and profits widens, under the two decision modes, as increases in consumer preferences’ value. When consumers have higher environmental preferences, manufacturers and retailers should increase the new energy vehicle pricing. Otherwise, they should decrease pricing to increase the market penetration ratio. In addition, the impacts of one preference on the profit difference are related to the other preference. Full article

The rapid increasing number of automobile products has brought great convenience to people’s living, but it has also caused serious environmental issues, waste of resources and energy shortage during its whole lifecycle. Corporate Environmental Responsibility (CER) refers to the company’s responsibility to avoid damage to the natural environment derived from its corporate social responsibility (CSR), and it plays an important role in solving resource and environmental problems. However, due to various internal and external reasons, it is difficult for the automobile manufacturing industry to find the key drivers for the implementation of CER. This research proposes a model framework that uses the fuzzy decision-making test and evaluation laboratory (fuzzy DEMATEL) method to analyze the drivers of CER from the perspective of the triple bottom line (TBL) of economy, environment and society. Firstly, the common drivers of CER are collected using literature review and questionnaire survey methods. Secondly, the key drivers are analyzed by using the fuzzy DEMATEL. Finally, the proposed approach was verified through a case study. The research results show that some effective measures to implement CER can be provided for the government, the automobile manufacturing industry and the public to promote sustainable development of Chinese Auto Manufacturing Industry (CAMI). Full article

Within the context of the large-scale application of industrial robots, methods of analyzing the life-cycle cost (LCC) of industrial robot production have shown considerable developments, but there remains a lack of methods that allow for the examination of robot substitution. Taking inspiration from the symmetry philosophy in manufacturing systems engineering, this article further establishes a comparative LCC analysis model to compare the LCC of the industrial robot production with traditional production at the same time. This model introduces intangible costs (covering idle loss, efficiency loss and defect loss) to supplement the actual costs and comprehensively uses various methods for cost allocation and variable estimation to conduct total cost and the cost efficiency analysis, together with hierarchical decomposition and dynamic comparison. To demonstrate the model, an investigation of a Chinese automobile manufacturer is provided to compare the LCC of welding robot production with that of manual welding production; methods of case analysis and simulation are combined, and a thorough comparison is done with related existing works to show the validity of this framework. In accordance with this study, a simple template is developed to support the decision-making analysis of the application and cost management of industrial robots. In addition, the case analysis and simulations can provide references for enterprises in emerging markets in relation to robot substitution. Full article

The rapid development of automobile industry in China did improve people’s quality of life. However, it has also damaged the ecological environment. The emission of a large amount of automobiles is one of the serious air pollution sources. In recent years, the shortage of petrochemical energy, the rapid rise of harmful particles in the air (e.g., PM2.5 and PM10), and the increasing worse atmospheric environment are becoming obstacles to China’s sustainable development. Battery electric vehicles (BEVs) are recognized as an ideal alternative to conventional cars. This study aims to explore the factors that can promote consumers’ adoption of BEVs and to construct domains of these factors. Firstly, an open web questionnaire and semi-structured interviews were conducted to widely collect factors that promote consumers’ purchase of BEVs. Then, questionnaire survey and exploratory factor analysis were used to construct domains of promoting consumers’ purchasing willingness. A total of six factors that promote consumers’ adoption of BEVs were obtained. Finally, the research results can provide references for the Chinese government and the BEV manufacturers in the development and promotion of EVs. Full article

Although the rapid progress of the global economy and technology has advanced human civilization, it has also caused tremendous damage to the global ecological environment. Therefore, humans are thinking seriously about the environment and its sustainable development. One of the solutions to environmental problems is new energy vehicles. Since the promulgation of the “Energy Saving and New Energy Vehicle Industry Development Plan (2012–2020)” by the General Office of the State Council, the Chinese government has determined a strategy of pure electric driving technology. The electric vehicle market in China has expanded rapidly, making China the largest electric vehicle market in the world. Hence, research on the situation of electric vehicles in China is highly necessary and of reference value for other countries to develop electric vehicles. As a result, it is a critical issue to develop low-carbon, energy-saving, and intelligent electric vehicles to reduce the environmental impact. This paper establishes a theoretical framework based on the theory of planned behavior (TPB), technology acceptance model (TAM) and innovation diffusion theory (IDT), and explores the key factors influencing consumers’ purchase of electric vehicles. The results show that: The application of the key factor model constructed in this study to consumers’ behavioral intention regarding electric vehicle purchase is acceptable. According to the structural equation modeling (SEM) analysis results, (1) In terms of behavioral intention: Consumers’ control over the resources required to purchase electric vehicles has the highest influence on their behavioral intention, while consultation opinions from consumers’ surroundings also significantly affect their behavioral intention to purchase electric vehicles. In addition, consumers’ environmental awareness and acceptance of technology products will also influence their behavioral intention. (2) In terms of attitude toward behavior: When consumers believe that electric vehicles are more beneficial at the individual, environment or national level, or they believe that the usage of electric vehicles is simpler and more convenient, they will show a more positive attitude towards the purchase of electric vehicles. Consumers consider electric vehicles as forward-looking technology products with similar driving operation and usage cost compared to traditional vehicles. (3) In terms of regulations: The opinions of consumers’ family members, friends, colleagues or supervisors do not significantly affect the attitude or behavior of consumers regarding electric vehicle purchase. The key factors influencing consumers’ purchase of electric vehicles are not only applicable to the design and development of electric vehicles that better suit consumer demands, but also serve as a theoretical basis for the popularization of electric vehicles, and provide a reference for consumers’ choice and purchase. Therefore, the government and relevant manufacturers need to consider increasing the publicity of electric vehicles and launch more attractive battery and charging schemes to attract consumers and promote the sustainable development of the automobile industry. Full article

The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field. Full article