1. Introduction
Globalisation has become an irreversible historical trend, particularly since the 1990s, and its velocity has accelerated [1]. Economic globalisation is a worldwide reorganisation of industries spearheaded by developed nations and propelled by multinational firms [2]. This industrial adjustment not only reflects the general shift of some industries but, more crucially, the transfer of specific production links within the same sector [3]. The high-end segment of the manufacturing industry returns to developed nations, while the low-end segment travels to a country with little human capital, such as China or Southeast Asia, transforming it into a “world factory” [4]. However, due to their respective roles as a “world’s factory”, developing nations have been at the bottom of the global value chain for a long time, and the high-input and high-consumption economic growth mode has led to an excessive reliance on environmental resources and a severe overdraft [5].
Industrial upgrading is not merely an adjustment within the industry or a conversion between industries. Its true purpose is to improve and realise enterprise leapfrogging growth via industrial upgrading, as well as to enhance value acquisition in the international labour division [6]. It can be divided into emerging industries, leading industries, pillar industries, and declining industries based on the stage of industrial development [7]. Sustainable economic development cannot be achieved by focusing solely on the development of industries that account for a large portion of the national economy and ignoring the development of emerging and leading industries [8]. In actual production and life, the development of the industry should aim to break through from the bottom of the “smile curve” to both ends, i.e., to invest in and transform product research and development or product marketing in order to obtain higher added value in the industrial chain and subsequently higher profit [9]. Due to the realistic context of the importance of industrial upgrading, industrial upgrading has become one of the central topics of economic research in the past decade, and scholars have conducted a great deal of research on the subject [10].
What are the main advances in existing research, what are the commonalities, and what are the future directions? In order to obtain “objective” answers to these questions, this paper uses CiteSpace technology to systematically search, sort, and analyse the Web of Science (WOS) literature, detecting important research directions, major contributing literature, and research hotspots and summarising the latest progress and future research directions. Through the co-occurrence analysis and co-citation analysis of the retrieved data, this paper makes up for the lack of quantitative and dynamic analysis of industrial upgrading in the literature as a whole, provides an overall picture of industrial upgrading in the literature, clarifies the key directions of research in the field, identifies key authors and important journals, and explores future hotspots. By doing so, it can offer guidance and insights that are tailored to the needs of the Chinese industrial sector, which can help shape and support the continued development of industries in China.
2. Literature Review on Industrial Upgrading
Industrial upgrading is defined as “the process by which economic actors, nations, firms, and workers move from low-value to relatively high-value activities in global production networks” [11]. Industrial economics is an applied economics field that examines the relationship structure between industries, the development law of firm organisational structure within the industry, and the law of interaction, with “industry” as its topic of study [12]. Industrial economics arose as a result of the extension of microeconomic analysis to the macro level and the deepening of macroeconomic analysis to the micro level [13]. Individual quantitative analysis results in aggregate analysis, which, in turn, results in industrial analysis. In reality, the expansion of the national economy is not an aggregate process but rather the development of individual enterprises within a certain framework [14]. Indeed, at some point, the national competitive advantage is derived from an industrial competitive advantage [15]. Therefore, despite the little history of industrial economics, it has become the economic theoretical foundation for the nation’s economic development strategies and industrial policies [16]. It can be seen that the emergence of industrial upgrading is the inevitable result of the in-depth development of economic analysis and the continuous improvement of people’s need to understand economic laws under the conditions of economic development and that these factors all contribute to the advancement of industrial analysis [17].
The technical level improves as a result of the macro-level modernisation of the industrial structure. The significance of tangible or hard factors such as labour, land, and capital in the input of industrial factors has decreased, whereas the significance of intangible factors such as knowledge, technology, information, and management has increased [18]. The importance of soft factors has grown; the industrial structure has become high-tech, soft, and knowledge- and information-based. On the medium and micro levels, according to the “Scientific and technological innovation evaluation index system in China”, the industrial structure upgrade can be evaluated on four dimensions: the industrial structure upgrade’s main body, foundation, input, and output [19].
From the perspective of a subject, industrial upgrading is reflected in the upgrading of the primary body, be it a company, a nation, or a region, in order to improve its own capital and technology structures and gain greater economic returns [20]. Even with limited resources, the government can support R&D. The allocation of government funds for basic scientific research determines the development of new enterprises and technologies. Such selective allocation is likewise an example of industrial policy [21]. Innovation in R&D is essential for liberalising import trade and technologically enhancing exports of manufactured goods. In addition, industry-level studies demonstrate that international trade has a substantial impact on the technological complexity of manufacturing as a whole, mostly via resource reallocation [22].
From the fundamental perspective, GDP serves as a comprehensive measure of the macroeconomy, encompassing all industries and types of economic activity within a nation [23]. The evolution of the three industrial structures was unavoidable following the development of socialised production and the improvement of people’s living conditions to some degree [24].
From the perspective of input, foreign direct investment (FDI) is an important mechanism for the transfer of technology, contributing more to economic growth than local investment. However, FDI only has better productivity if the host nation possesses a minimum threshold level of human capital [25]. The investing country can concentrate on industries with comparative advantages. As a result, the industrial structure of importing and exporting nations with direct investment is more rational, thus boosting the expansion of international trade [26].
From the perspective of output, the theory of new structural economics asserts that the government must determine the economic and social returns of technical innovation and industrial upgrading and prioritise its limited resources accordingly [27]. This will help the economy develop efficiently and rapidly. It is widely considered that the concept of “gains from trade” and the advantages of specialisation are key drivers of modern economic growth. If technology markets are vital to the economy, they deserve more attention [28].
Upgrading industrial structures refers to the transition of businesses from labour-intensive, low-tech economic activities to capital-intensive, high-tech economic activities [29]. The leap of firms in the value chain links indicates the upgrading of industrial structure, the process of rising from low value-added links to high value-added links, with consumer-driven and producer-driven forces driving the climb [30]. In conclusion, despite the fact that the existing literature reviews and framework studies are sufficiently comprehensive, we believe that theoretical contributions are necessary as the global economy continues to evolve. In this article, we utilise the “Scientific and Technological Innovation Evaluation Index System in China” to conduct a visual analysis. By doing so, we aim to present a research framework that is more accessible and provides insights into future research trends.
3. Methodology and Data
3.1. Bibliometric Analysis
Bibliometrics is the utilisation of statistical techniques to analyse various forms of publications, books, and articles, with a specific emphasis on their scientific content. These methods find widespread application within the field of library and information science [31]. When exploring a research topic, it is common for people to approach it from various research perspectives or based on different theories. This diverse discussion fosters the gradual development of a comprehensive knowledge system within related research fields. When exploring a research topic, individuals frequently engage in discussions from diverse research perspectives or grounded in various theories [32]. This multifaceted approach contributes to the progressive development of a comprehensive knowledge system within related research domains [33]. In particular, literature review is a “study of research” category that achieves research results [34]. The significance of literature review in educational research and its integral role in scientific research activities are evident [35].
This study undertakes a bibliometric visual analysis of industrial upgrading. The citation counts in this study are the feature used in performance analysis. The number of citations per article was also determined and displayed in tables. Exploring a focal study area’s structure and evolution is conducted via science mapping. By examining the co-citation patterns, we can gain insights into the significance attributed to a cited article by researchers. Consequently, publications that are frequently referenced indicate their prominence in shaping the development of the focal area.
3.2. Data
The Web of Science (WOS) is an outstanding online database that evolved into a tool for finding and evaluating articles [36]. In order to comprehend industrial upgrading, we gathered publication data from the WOS. However, since the research object of this study is academic journals, we excluded news reports, conference notices, solicitations, book reviews, and other documents.
This study used CiteSpace 6.1.R 5 visualisation tools and collected 1535 articles from the Web of Science database for 30 years. All the references came from Web of Science core collection; document types are “Articles” or “Reviews” and only focus on English language. Usually, we think that this kind of article has more research value and is representative of the literature. The “Time Slicing” setting is based on the publication years of retrieved literature to select “1”, meaning that the retrieved literature will be divided into units of one year each.
After filtering by the fields, this study, therefore, used “industrial upgrading” or “R&D/R&D HR” or “Market” or “GDP” or “FDI” as search terms. This study finally generated a unique database with 1535 results. Table 1 shows the summary of data sources and selection.
4. Result
The changing trend of the number of publications related to the topic can reflect the development speed and research heat of the topic in a certain period of time. The changing trend in the number of papers related to the topic can reflect the development speed and research enthusiasm of the topic in a certain period of time. Through the statistics of the annual number of papers, we can grasp the development stage and changing trend as a whole.
According to the number of publications for each year in Figure 1, 1535 publications were retrieved and downloaded in December 2022. We found that these publications were retrieved more than 30 years ago, and more and more scholars realised the significance of measuring industrial upgrading. From the perspective of data year, the first study directly discussing industrial upgrading appeared in 1991. Since then, scholars have carried out continuous studies on industrial upgrading. In terms of the number of published articles, the number of publications is on the rise, maintaining a steady growth of more than 30 articles in 20 years from 1991 to 2011. In 2008, the financial crisis occurred, which had a certain impact on the development of the industry. The research on this phenomenon triggered a small increase in the number of publications. Since 2012, the annual growth rate has been increasing by 10% to 15%. In particular, in 2022, the number of published papers has reached the highest of 241. The increase in the number of published papers in the past 10 years fully demonstrates that this field has received great attention from a large number of journals and scholars.
4.1. Mapping and Analysis of Author and Cited Authors
An author with a high number of publications in the WOS database represents that this scholar has conducted a relatively in-depth study in this field. Figure 2 shows that CiteSpace draws the distribution of the top 10 authors by frequency.
The scholars Ren and Siyu of Nankai University, who published the most articles in this research field (eight articles), mainly focused on the relationship between energy efficiency and industrial structure upgrading. The research shows that green and sustainable development has an obvious driving effect on the industry [37]. Other authors have published five articles in the field in WOS source journals. China University of Mining and Technology scholar Dong, F. thinks industrial convergence affects the energy efficiency of manufacturing in newly industrialised countries [38]. Xiamen University scholar Lin, Boqiang focus on green technology, CO2 emissions, Urban innovation, environment [39]. Beijing Institute of Technology scholars Wu, Haitao and Hao Yu and Xinjiang University scholars Ran Qiying and Yang, Xiaodong focus on the promotion effect of green economic growth on industrial structures [40]. Nanjing University scholar Li, Xing and Nanchang University scholar Chien-Chiang Lee both research the relationship between industrial upgrading and the global value chain [41]. National Research University Higher School of Economics scholar Lee Keun in view of the fourth industrial revolution discusses changing global value chains and industrial upgrading in emerging economies [42]. The articles of the top ten scholars are representative of understanding the research scope and context of scholars in the field of “industrial upgrading.”
Analysing the authors cited in a study can assist us in identifying the most influential authors and tracing the evolution of research focus among core authors over time. Table 2 shows the top 5 cited authors from this research area, centrality shows the degree of one node linking with other nodes. GEREFFI is the most important cited author with a frequency of 132 and a centrality of 0.10.
In 1994, GEREFFI combined the value chain theory with industrial organization and global economy for the first time and proposed the global commodity chain theory, but this theory did not emphasise the importance of value creation of enterprises on the chain. In 1999, GEREFFI took South Korea and Singapore as the objects to empirically study the principle of comparative advantage in developing countries and proposed that industrial upgrading could be achieved through the path of assembly, manufacturing, and research and development, which became a classic case in this field. In 2001, GEREFFI and some researchers in this field established the concept of global value chain and its analysis framework in “DS Bulletin”. As one of the founders of the theoretical analysis method of the global value chain, he made great contributions to the formation of the global value chain. Humphery, a member of the Innovation Research Group at Sussex University in the UK, also played an important role in the development of GVC theory. In 2000, he summarised four forms of industrial upgrading from the perspective of the global value chain: process upgrading, product upgrading, function upgrading, and cross-industry upgrading. In 2002, he and Schmitz divided the governance of global value chains into three models: network, quasi-hierarchical, and hierarchical. On the basis of this research, in 2003, Humphery, Gereffi, and Sturgeon divided the governance model of the global value chain into five forms, namely market type, module type, relationship type, leadership type, and hierarchy type. It can be seen that Humphery and Gereffi are the leaders of the global value chain theory, and their research objects and conclusions are more applicable to developed economies [43]. Michael Porter’s Diamond Model is a diamond-shaped framework that focuses on explaining the reasons why certain industries within a particular nation are competitive internationally and that the capability of certain companies in certain countries could yield consistent innovation [44]. Porter argues that any company’s ability to compete in the international arena is based mainly on an interrelated set of location advantages that certain industries in different nations possess as follows: firm strategy, structure, and rivalry; factor conditions; demand conditions; and related and supporting industries. If these conditions are favourable, it forces domestic companies to continuously innovate and upgrade. Acemoglu Daron’s research covers a range of topics, including political economy, economic development, economic growth, technological change, inequality, labour economics, and the economics of networks. He connects the dots between artificial intelligence, productivity, wages, and inequality and explains how to counterbalance the impacts of automation. Also, in Table 2, only Lin, Boqiang is still highly cited in the industrial upgrading area, and he is more focused on green industrial development.
4.2. Mapping and Analysis of Countries
Figure 3 presents the top 10 productive countries, and Table 3 shows that among the total 1535 papers, 1009 publications are from China, which is more than half of the sample size.
Meanwhile, this result is consistent with the authors’ research, which emphasises China’s role in the industrial upgrading field. China is affected by domestic environmental factors, China’s economic growth rate has been declining since 2011, stepping into the economic “new normal”, and China is faced with a series of new problems and challenges. First, China is facing the dual pressure of developed countries and emerging economies in the international environment. Its scientific and technological innovation capability lags behind, and its industrial competitiveness is insufficient. Second, investment has brought about many problems, such as overcapacity and excessive growth. China needs to make new adjustments to the direction and pattern of investment. Third, the cost of domestic production factors is rising, the export direction of labour-intensive industries is in urgent need of change, and economic growth lacks new impetus. Fourth, land supply, environmental pollution, and other problems seriously hinder China’s economic development. China’s economic development model in the “old normal” is difficult to sustain, and various problems and challenges force China to focus on promoting the transformation and upgrading of industrial structure and promoting high-quality economic development. The United States is an industrial power in the world, and its industry has always played a decisive role in its economic development. The continuous optimisation and upgrading of industrial structure and the constant change in pillar industries undergo the whole process of American industrialisation. Germany, Italy, Spain, Australia, Japan, South Korea, England, and Canada are developed countries. They attach great importance to the guiding role of government policies as well as the role of the market. The government supports the development of small and medium-sized enterprises via various measures, such as the establishment of investment funds by the state, the provision of preferential interest rates, and the active support of fiscal and trade policies, which effectively promotes the technological transformation and innovation of small and medium-sized enterprises, and also establishes a number of large enterprise groups, enhancing and consolidating the global competitiveness of the country’s leading industries.
4.3. Mapping and Analysis of Keywords
The keywords of this study are often a high generalisation of the core content of the study, among which the word frequency of the keywords is a high degree of condensed research content in this field. The statistics and analysis of high-frequency keywords in a field can quickly sort out the research hot spots and research topics in this field. CiteSpace 6.1.R 5 information visualisation software is used to analyse keywords, the word frequency of every keyword can also be used for visual mapping, and the connection between every keyword is displayed through the connection. The CiteSpace software was used to set the time span as 1 year, and the keywords were visualised. In order to describe the keywords more accurately, we first deleted some general and irrelevant words, such as “empirical research” and “dynamics”, then combined words with the same meaning, such as “carbon” and “CO2”, in the following figures.
4.3.1. Keywords on Industrial Upgrading
As shown in Figure 4, we obtained 841 nodes and connections among 2421 nodes. This means that 841 keywords are obtained according to the operation, and there are 2421 co-occurrence relations between the keywords. In this network diagram, every node is a centre, representing a keyword. Every keyword plays a pivotal role in connecting each article. The larger the node is, the stronger the pivotal role and centrality are, and the more important the keyword is in this visual network. Similarly, the size of the keyword font is proportional to its frequency of occurrence. The thickness of the line between each node represents the closeness of the connection between each keyword. The thicker the line, the more times the two keywords appear in the same article and the stronger the co-occurrence. The colour on the top corresponds to the year. From left to right, they go from 1991 to 2022. The colour on the bottom corresponds to the time when the keyword first appeared and the frequency of occurrence in the following years. Figure 4 shows that the two node icons of “economic growth” and “impact” are the largest and most core keywords. It is quite consistent with the study of “industrial structure has an impact on the economy” in this paper. The next big icons are carbon emission, industrial structure, productivity, innovation, etc. We can see that these keywords are also the research focus in this field. Moreover, it can be seen from the colour that the research development in this field has been the most prosperous in recent years, and the co-occurrence of many keywords also appeared in the last decade. In order to further analyse these 841 keywords, the Log Likelihood Ratio (LLR) algorithm is used to cluster keywords via CiteSpace information visualization software. The clustering index of Modularity Q (network modularity index) is 0.527 > 0.3 and the Mean silhouette (network homogeneity index) is 0.7699 > 0.7, which proves that the obtained clustering results are good. In order to focus on the analysis, the obtained keyword clustering results were screened, and the top 10 most important keywords clustering were selected. The papers contained in these 10 tags account for 80% of the total papers, indicating a high degree of concentration on research topics.
From the obtained keyword clustering network graph Figure 5, overlapping parts of each cluster are small, which indicates that there is sufficient space for development in their respective development fields. Figure 5 is obtained via further statistics of keyword clustering labels. The network homogeneity index of each label is observed to have a good clustering effect, indicating that the topic studied in this article in this clustering is highly similar.
Among all clustering labels in Table 4, the label “#0 global value chain”, which contains the most articles, has 340 articles, which is the largest cluster in this cluster. This cluster erupted in 1995. The keyword is “economic growth”. It indicates that extending the domestic links of the global value chain, cultivating the connection and connection between the global value chain and the domestic value chain, and constructing the chain-to-chain competition may provide a solid division of labour foundation for the industrial upgrading of the country and the coordinated development of regional economy. The earliest label “#1 carbon emissions” appeared in 1992. The keyword “impact” indicates that promoting carbon emission reduction and industrial upgrading have a mutual influence on each other. At the same time, labels “#3 energy efficiency, #4 biogas upgrading, #10 wastewater treatment” and keywords “industrial structure, productivity, energy consumption” help analyse industrial upgrading from the perspective of energy. Industrial upgrading can reduce carbon emissions, while industrial structure upgrading and economic development model transformation are also some of the most effective ways to promote carbon emission reduction. The keywords of tags “#5 green finance, #7 industrial structure rationalization, #8 structural change” are “innovation, trade, policy, and environmental regulation”, which discusses industrial upgrading from the perspective of economic growth. In the capitalist market economy, economic growth is mainly manifested in the opening of the market, and industrial upgrading can only be produced by pursuing the maximization of the added value of products. The keywords “China, regulation” of the labels “#6 environmental regulation, #9 evolution”, although industrial upgrading is an economic discipline, are also political concepts, which need corresponding supporting policies. From the cluster analysis of keywords, it can be clearly seen that scholars’ research hotspots in the field of “industrial upgrading”, and through further analysis of the hot words with the highest frequency under the keyword cluster label, the main key points of scholars’ research in this field, and the evolution path of research hotspots are sorted out.
4.3.2. Keywords on “Scientific and Technological Innovation Evaluation Index System in China”
We used the “Scientific and technological innovation evaluation index system in China” and classified the keywords into four dimensions. This index system is suitable for illustrating current foci and depicting a comprehensive research framework in industrial upgrading. These four figures (Figure 6, Figure 7, Figure 8 and Figure 9) describe the four dimensions that would help researchers understand these areas more logically and guide other scholars to extend their research to related sites. Overall, the earliest of the four dimensions to emerge are studies on the subject of research and market output in relation to industrial upgrading.
Among these keywords, “economic growth” appears the most times with the keyword “industrial upgrading”. On the one hand, this reflects that “industrial upgrading” is closely related to “economic growth”. On the other hand, this means that “economic growth” is the fundamental purpose of industrial upgrading. In addition to “economic growth”, we also find that researchers pay more attention to keywords such as “carbon emission”, “innovation”, “urbanization”, and “policy”. “Carbon emission” is also a keyword closely related to industrial upgrading, and it is one of the driving forces of industrial upgrading. At the same time as rapid economic growth, huge economic benefits have also resulted in huge “energy consumption”. There are more and more contradictions among economic growth, resource consumption, and environmental protection, which have attracted extensive attention and research from scholars. Scientific and technological innovation and industrial upgrading have always been the developmental direction of the future industry. To speed up the strategic layout of new industries and the intensity of industrial structure adjustment, in the fields of renewable energy and resources, network information, green finance, advanced materials and manufacturing, and urbanisation, a scientific and technological revolution will occur to realise the sustainable development of energy and the region. Scientific and technological innovation and industrial upgrading have always been the developmental direction of the future industry. To speed up the strategic layout of new industries and the intensity of industrial structure adjustment, in the fields of “renewable energy” and resources, network information, “green finance”, advanced materials and manufacturing, and “urbanization”, a scientific and technological revolution will occur to realise the sustainable development of energy and the region. From the micro point of view, industrial structure upgrading refers to the “firm” via the improvement of the management modes, “innovation”, “creativity technology”, “efficiency”, “productivity”, and other means to elevate the enterprise to a new level, forming a more advanced industrial structure. From a macro perspective, industrial structure upgrading refers to the transformation of a country’s economic growth mode. If “China”, as a manufacturing power, wants to cross the middle-income trap, the per capita GDP exceeding a certain number is only a superficial data threshold. The real standard is to see whether industrial upgrading has been completed and whether the industry, after upgrading, has successfully absorbed the vast social labour force into the industry. These changes in growth mode are due to the comprehensive upgrading of the internal factors and structure of the social production mode, so these changes in growth mode are also called the upgrading of industrial structure.
From the perspective of the global value chain and international division of industry, industrial upgrading means that enterprises in the value chain of (product) division in the global industry or those not embedded in the value chain obtain market connections and technological progress by embedding in the value chain to enter the enterprises with a higher added value.
To be specific, regarding the combination of independent research and development and the introduction of technology, in the introduction of foreign advanced technology to broaden the industrial horizon while upgrading the existing domestic science and technology, the government needs to increase investment and improve the importance of independent research and development of science and technology. We will vigorously train scientific research personnel, including the active training of domestic and foreign high-tech researchers, and accelerate the upgrading process of industrial structure through various efforts. The research on “policy” is beneficial to the construction of a new scientific industrial policy system. The process of industrial upgrading is also the process of the birth, development, and death of enterprises, that is, the process of the existence and disappearance of countless enterprises within the life cycle. Among them, “governance” and “policy” constitute the external environment of an enterprise, usually called the policy environment. Although the policy environment affecting industrial upgrading varies greatly, and different countries, regions, and the same country present different policy environments at different times, it is not difficult to explore the universal characteristics and the rules that the selection subject jointly follows. Four types of policies have appeared in different countries, regions or at different times within the same country: dominant comprehensive intervention, selective industry intervention, competitive environmental intervention, and crisis business intervention. The main reasons are the development stage, institutional mechanism, and cultural differences. At the same stage of development or even with the same social system, different cultures will also bring about policy differences.
4.4. Analysis of Research Trend
The emergent word algorithm is aimed at the calculation of the sudden increase of scholars’ interest in a certain aspect of research in a specific field. It extracts “emergent word” from a large number of texts by investigating the change characteristics and time distribution of the word frequency of specific words in the contained text content rather than simply analysing its word frequency [45]. Compared with the analysis of common high-frequency words, the analysis of emergent words is more helpful in understanding the development trend and research frontier of a certain field. In this study, the CiteSpace information visualisation software is used to continue the operation on the basis of the previous clustering, and each cluster is automatically identified to extract the outburst words. Supplemented with the timeline graph, Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14 are obtained. Blue colors present the emergence start and end years, and red colors present emergence intensity of major emergent words.
As shown in Figure 10, regarding “industrial upgrading”, according to the time range of emergence of the top 20 prominent words from 1991 to 2020, we can see that “developing country” and “globalization” have the longest outburst time of 17 years. The term “developing country” first appeared in 1996. It was the keyword that attracted early attention. Since then, however, a similar phenomenon for the two words “developing country” and “system” has occurred. It showed that this research field has been in a state of change and development, and more research directions have been derived from the industry.
From Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14, emergent words can be roughly divided into the following categories: “global value chain”, “innovation”, “energy”, and “policy”.
Overall, “global value chain” is the word with the highest intensity, and it is the turning point for research on “industrial upgrading”. Since Humphery and Gereffi established the concept of the global value chain and its analytical framework in the late 20th and early 21st centuries, scholars have quickly followed up this research, exploring the dynamic mechanism, governance structure, and industrial upgrading in this theory. In terms of industrial upgrading, global value chains should focus on industrial upgrading, including technological process upgrading, product upgrading, industrial function upgrading, and chain upgrading, among which the chain upgrading is reversible, regardless of the way it is integrated. The global value chain provides an important theoretical basis and analytical framework for understanding the capture and low-end lock-in of industries and enterprises in developing countries and exploring how to escape capture and achieve successful industrial upgrading. The innovative practice of industrial upgrading of Chinese enterprises and industries has provided fresh research materials and even emerged the China paradox, which has become a typical fact that overturns existing theories. “Technological innovation” has attracted much attention in recent years. When reviewing the typical cases of successful enterprises in industrial upgrading, researchers will find that independent innovation of enterprises is the key support on the way to the success of industrial upgrading. Based on this, some researchers analyse the issue of industrial upgrading from the perspective of independent innovation. Technological progress contributed more to the change in industrial structure and high-tech industry should be the driving force, and the modern service industry and modern manufacturing industry should be the two wheels of development to promote the upgrading of the overall industrial structure [46]. Also, economic growth affects the industrial structure, which, in turn, is affected by the effect of technology selection. Therefore, appropriate technologies should be selected according to different economic conditions, technological development levels and the accumulation degree of capacity reserves of the technology system to be selected within the province [47]. However, independent innovation promoted industrial upgrading. Similarly, industrial upgrading promoted independent innovation via the micro-level demand-pulling effect, meso-level regional synergy effect, and macro-level international trade effect. By summarising the literature, it can be found that, on the one hand, independent innovation provides key support for the high-end development of enterprises or industries, but more in-depth research is needed on its mechanism and implementation approach, as well as how to break through the constraints of independent innovation itself [48]. On the other hand, existing studies have found that industrial upgrading and independent innovation have a two-way relationship, so how to build a virtuous cycle of independent innovation and industrial upgrading is worth further research.
“Energy” and “policy” are important parts of studying industrial upgrading. Industrial development in developing countries often begins with labour-intensive and environment-dependent industries. With the increasing environmental externalities of economic development, environmental regulation has become an important external force forcing industrial restructuring and upgrading. The study results show that two main factors were affecting a country’s comparative advantage, namely environmental regulation policy and factor endowment. Based on the data of China’s manufacturing industry, we empirically studied the effect of environmental regulation, the factor endowment effect, and the mechanism of industrial international competitiveness. Obviously, China was not the pollution sanctuary of developed countries, and the effect of environmental regulation on comparative advantage showed a U-shaped relationship [49]. But, the barrier effect, transfer effect, innovation compensation effect, and substitution effect of environmental regulations could promote the upgrading of industrial structure. The environmental regulation had an obvious promoting effect on the upgrading of the industrial structure, but it would be restricted by the economic development stage of the city [50]. However, the interaction between independent innovation and environmental regulation could significantly promote the upgrading of manufacturing structure, forcing the mechanism to be linked with regional innovation capacity and promoting the coordinated development of environmental regulation policies and innovation-driven policies [51]. The analysis shows that “industrial upgrading” will still be the research frontier in this field at present and in the future.
Using the CiteSpace software for analysis comes with certain considerations and limitations. Firstly, parameter configuration is crucial, as incorrect choices can lead to misleading results. Secondly, the analysis is constrained by the selection of data sources, as different databases may contain varying content, potentially limiting the comprehensiveness of the analysis. Due to the constraints of the CiteSpace software, our research focused exclusively on retrieving and analysing studies published in English in the Web of Science (WoS) database, which may not provide a fully comprehensive dataset. For instance, China is one of the key countries in the study of industrial upgrading, and many articles are published in Chinese. Future research should explore articles in other languages and databases to broaden the scope of analysis.
5. Conclusions
From the methodological perspective, we use CiteSpace to analyse the relevant literature on industrial upgrading in the Web of Science database from 1991 to 2022 via the SCI and SSCI databases. After drawing knowledge maps and visual analysis, we can clearly see the key areas, development context, and research trends of scholars in this field. By using different keywords, this methodology can be used in other areas within different fields to demonstrate the visualised bibliometric analysis. In terms of research focus and hot spots, the keyword analysis finds that “global value chains, technological innovation perspectives, energy development, environmental regulation perspectives, and foreign direct investment perspectives” are the main directions of industrial upgrading research, representing the research focus in the field of industrial upgrading. After further analysis of keyword clustering, we obtained the 11 most important research topic tags in this field. Then, it extracted the salient words and analysed the future research trend in this field, which has certain reference and reference values for future scholars’ research. Additionally, this can serve as guidance for researchers on how to use this technique to appropriate analytic components of published articles in the literature on industrial upgrading systems.
Currently, the global industry exhibits distinct characteristics and developmental trends compared to historical industrial revolutions. On the one hand, developing countries’ manufacturing sectors are experiencing a notable “upsurge in high-end” production, closely tied to a new wave of global industrial transformation and relocation. However, concurrently, developed countries are witnessing a “reverse flow” trend in their manufacturing sectors, influenced, to some extent, by factors such as the COVID-19 pandemic and conflicts.
This new trend presents numerous novel features, foreshadowing a more intricate future for global industrial development involving a multitude of factors and competitive dynamics. This poses formidable challenges to the global industry as a whole. Developed countries are trending towards a “hollowing out” of their manufacturing sectors, while developing nations are striving to catch up. In this context, some countries, like the United States and European nations, are increasingly focusing on securing their dominance in international industrial competition. They are actively promoting the “reverse flow” of manufacturing, implementing a series of policies aimed at encouraging the return of manufacturing to their respective homelands, and have put forth strategies for reindustrialization. In contrast, developing countries, especially emerging economies, are gradually shifting their industrial focus from the low-value segments of the “smile curve” towards high-value-added segments, driving continuous advancements in high-end manufacturing. This transformation has led to alterations in the global value chain landscape. Within this emerging trend, the tug-of-war between globalisation and deglobalisation persists, intensifying competition and mutual impacts among industries across nations.
At the same time, the “industries of the future” may become the primary battleground for major nations’ competition. Countries around the world, especially developed nations and emerging economies, are increasingly engaged in fierce competition in the field of future industries. Concepts, technologies, and sectors such as neuroscience, artificial intelligence, uncrewed technology, metamaterials, aerospace, and ocean exploration are continuously emerging. Nations are formulating proactive industry support policies and increasing investment and support in these areas. Future industries have become the main arena for competition and rivalry between government and corporate sectors, with countries vying for dominance in key areas such as artificial intelligence, quantum information science, advanced manufacturing, biotechnology, and advanced communication networks.
Furthermore, the scale of green industries will continue to expand. With the implementation of global carbon neutrality initiatives and ongoing government investments in green and low-carbon technologies, the global green industry is poised for further growth. New energy sources, including solar, biomass, wind, tidal, and hydrogen energy, will continue to replace traditional fossil fuels. The Ukraine crisis has added pressure to the transformation of Europe’s energy structure, accelerating the transition to clean energy in Europe and the United States. In the future, the overall scale of the green industry may surpass that of the fossil fuel industry, leading to a structural transformation in the international energy sector. Countries that possess expertise in green technologies and dominate the discourse in green industries will establish new industrial advantages.
In the future, a country looking to achieve industrial upgrading can consider several key aspects.
Firstly, medium and long-term industrial planning that aligns with market demands should be developed to chart a path for industrial development, shifting from scale-based operations to specialisation and refinement. Mature industries within the global value chain should be elevated, and their role as industry standard-setters should be promoted.
Secondly, we should consider breakthrough critical industrial technologies to enhance the impact of foundational research, foster a culture of independent innovation, boost research and development with a focus on securing intellectual property rights, advance the development of indigenous brands, and strengthen the core competitiveness of businesses.
Thirdly, core industry policies in the digital economy should be reinforced, including tax incentives, research and development funding, technological support, talent cultivation, and market support, particularly in areas like artificial intelligence as part of national development strategies and long-term investments, while improving the quality of higher education.
Fourthly, green technology innovation is another crucial avenue to develop strategies for green technology innovation, channel social capital into green innovation and industries, enhance international competitiveness, and establish regional clusters for green industries.
In summary, these measures will contribute to driving industrial development, fostering technological innovation, and enhancing global competitiveness within industries.
Conceptualization, X.C.; writing—original draft preparation, X.C.; methodology, F.F.; software, F.F.; validation, F.F.; writing—review and editing, R.R.; supervision, R.R.; project administration, R.R. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
No new data were created or analysed in this study. Data sharing is not applicable to this article.
Thanks for the support of “Party School of Chongqing Wanzhou District of C.P.C: Chongqing, Wanzhou, CN”.
The authors declare no conflict of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Footnotes
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Summary of search details.
| Data Source | Web of Science |
|---|---|
| Citation indexes | SSCI; SCI-EXPANDED; |
| Years | January 1991 to December 2022 |
| Searching keywords | “industrial upgrading” or “R&D/R&D HR” or “Market” or “GDP” or “FDI” |
| Document types | “Articles” or “Reviews” |
| Language | English |
| Sample size | 1535 |
Top 5 most cited authors.
| Author | Frequency | Centrality | Year |
|---|---|---|---|
| Gereffi Gary | 162 | 0.10 | 1999 |
| Lin, Boqiang | 132 | 0.06 | 2017 |
| Michael E. Porter | 121 | 0.02 | 2007 |
| Humphery John | 105 | 0.09 | 2003 |
| Acemoglu Daron | 88 | 0.06 | 2011 |
Map of countries.
| Author | Frequency | Centrality | Year |
|---|---|---|---|
| Peoples R China | 1009 | 0.29 | 2002 |
| USA | 201 | 0.38 | 1993 |
| England | 106 | 0.16 | 1998 |
| Italy | 73 | 0.18 | 2002 |
| Canada | 58 | 0.06 | 1994 |
| Germany | 51 | 0.08 | 1998 |
| Australia | 47 | 0.03 | 1997 |
| Japan | 40 | 0.09 | 2004 |
| Spain | 39 | 0.14 | 2000 |
| South Korea | 37 | 0.06 | 2010 |
Top 10 keyword clustering.
| Cluster Label | Count | Centrality | Year | Keywords |
|---|---|---|---|---|
| #0 global value chain | 340 | 0.09 | 1995 | economic growth |
| #1 carbon emissions | 300 | 0.12 | 1992 | impact |
| #2 life cycle assessment | 191 | 0.03 | 2013 | carbon emission |
| #3 energy efficiency | 156 | 0.04 | 1999 | industrial structure |
| #4 biogas upgrading | 156 | 0.05 | 2000 | productivity |
| #5 green finance | 148 | 0.06 | 2004 | innovation |
| #6 environmental regulation | 126 | 0.02 | 2006 | China |
| #7 industrial structure rationalization | 91 | 0.02 | 2006 | trade |
| #8 structural change | 89 | 0.05 | 1994 | policy |
| #9 evolution | 86 | 0.02 | 2019 | regulation |
| #10 wastewater treatment | 82 | 0.00 | 2016 | energy consumption |
References
1. Borio, C.E.; Filardo, A.J. Globalisation and Inflation: New Cross-Country Evidence on the Global Determinants of Domestic Inflation. 2007; Available online: https://www.bis.org/publ/work227.htm (accessed on 7 July 2023).
2. Esping-Andersen, G.; Billari, F.C. Re-theorizing family demographics. Popul. Dev. Rev.; 2015; 41, pp. 1-31. [DOI: https://dx.doi.org/10.1111/j.1728-4457.2015.00024.x]
3. Neffke, F.; Henning, M.; Boschma, R. How Do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions. Econ. Geogr.; 2011; 87, pp. 237-265. [DOI: https://dx.doi.org/10.1111/j.1944-8287.2011.01121.x]
4. Chen, Y.-C. The Upgrading of Multinational Regional Innovation Networks in China. Asia Pac. Bus. Rev.; 2007; 13, pp. 373-403. [DOI: https://dx.doi.org/10.1080/13602380701291941]
5. Jinchen, T. One Belt and One Road’: Connecting China and the World; Global Infrastructure Initiative Website: Amsterdam, The Netherlands, 2016.
6. Rüßmann, M.; Lorenz, M.; Gerbert, P.; Waldner, M.; Justus, J.; Engel, P.; Harnisch, M. Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consult. Group; 2015; 9, pp. 54-89.
7. Li, Q.; Liu, S.; Yang, M.; Xu, F. The effects of China’s sustainable development policy for resource-based cities on local industrial transformation. Resour. Policy; 2021; 71, 101940. [DOI: https://dx.doi.org/10.1016/j.resourpol.2020.101940]
8. Stimson, R.J.; Stough, R.R.; Roberts, B.H. Regional Economic Development: Analysis and Planning Strategy; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2006.
9. Qin, Y.; Qin, K.; Xu, C. The Viscous Mechanism of Transformation and Upgrading of China’s Manufacturing and ITS Path-from the Perspective of the Causes of “Smiling Curve”. Proceedings of the 2013 Sixth International Conference on Business Intelligence and Financial Engineering; Hangzhou, China, 14–16 November 2013; pp. 525-529.
10. Chesbrough, H.; Bogers, M. Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. New Frontiers in Open Innovation; Oxford University Press: Oxford, UK, 2014; pp. 3-28.
11. Barrientos, S.; Gereffi, G.; Rossi, A. Economic and social upgrading in global production networks: Developing a framework for analysis. Int. Labor Rev.; 2010; 150, pp. 319-340. [DOI: https://dx.doi.org/10.1111/j.1564-913X.2011.00119.x]
12. Waldman, D.E.; Jensen, E.J. Industrial Organization: Theory and Practice; Routledge: Abingdon, UK, 2016.
13. Kose, M.A.; Prasad, E.; Rogoff, K.; Wei, S.-J. Financial Globalization: A Reappraisal. IMF Staff. Pap.; 2009; 56, pp. 8-62. [DOI: https://dx.doi.org/10.1057/imfsp.2008.36]
14. Roberts, R. Managing innovation: The pursuit of competitive advantage and the design of innovation intense environments. Res. Policy; 1998; 27, pp. 159-175. [DOI: https://dx.doi.org/10.1016/S0048-7333(98)00034-1]
15. Murmann, J.P. Knowledge and Competitive Advantage: The Coevolution of Firms, Technology, and National Institutions; Cambridge University Press: Cambridge, UK, 2003.
16. Bulow, J.I.; Summers, L.H. A Theory of Dual Labor Markets with Application to Industrial Policy, Discrimination, and Keynesian Unemployment. J. Labor Econ.; 1986; 4, pp. 376-414. [DOI: https://dx.doi.org/10.1086/298116]
17. Wang, Z.; Yin, F.; Zhang, Y.; Zhang, X. An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China. Appl. Energy; 2012; 100, pp. 277-284. [DOI: https://dx.doi.org/10.1016/j.apenergy.2012.05.038]
18. Zang, J.; Wan, L.; Li, Z.; Wang, C.; Wang, S. Does emission trading scheme have spillover effect on industrial structure upgrading? Evidence from the EU based on a PSM-DID approach. Environ. Sci. Pollut. Res.; 2020; 27, pp. 12345-12357. [DOI: https://dx.doi.org/10.1007/s11356-020-07818-0]
19. Wang, W.; Zhang, C. Evaluation of relative technological innovation capability: Model and case study for China’s coal mine. Resour. Policy; 2018; 58, pp. 144-149. [DOI: https://dx.doi.org/10.1016/j.resourpol.2018.04.008]
20. Fernandez-Stark, K.; Gereffi, G. Global Value Chain Analysis: A Primer; 2nd ed. Cambridge University Press: Cambridge, UK, 2019; pp. 54-76. [DOI: https://dx.doi.org/10.4337/9781788113779.00008]
21. Mazzucato, M. Financing innovation: Creative destruction vs. destructive creation. Ind. Corp. Chang.; 2013; 22, pp. 851-867. [DOI: https://dx.doi.org/10.1093/icc/dtt025]
22. Sheng, B.; Chen, S. Global value chains, firm heterogeneity and firms’ cost addition. Ind. Econ. Res.; 2017; 4, pp. 1-16.
23. Singh, T. Does International Trade Cause Economic Growth? A Survey. World Econ.; 2010; 33, pp. 1517-1564. [DOI: https://dx.doi.org/10.1111/j.1467-9701.2010.01243.x]
24. Utterback, J.M.; Suárez, F.F. Innovation, competition, and industry structure. Res. Policy; 1993; 22, pp. 1-21. [DOI: https://dx.doi.org/10.1016/0048-7333(93)90030-L]
25. Borensztein, E.; De Gregorio, J.; Lee, J.-W. How does foreign direct investment affect economic growth?. J. Int. Econ.; 1998; 45, pp. 115-135. [DOI: https://dx.doi.org/10.1016/S0022-1996(97)00033-0]
26. Büthe, T.; Milner, H.V. The Politics of Foreign Direct Investment into Developing Countries: Increasing FDI through International Trade Agreements?. Am. J. Polit. Sci.; 2008; 52, pp. 741-762. [DOI: https://dx.doi.org/10.1111/j.1540-5907.2008.00340.x]
27. Lin, Y. The New Structural Economics—Reframing Development Economics. Economics; 2010; 1, pp. 1-32.
28. Arora, A.; Gambardella, A. The market for technology. Handb. Econ. Innov.; 2010; 1, pp. 641-678.
29. Rodrik, D. Coordination failures and government policy: A model with applications to East Asia and Eastern Europe. J. Int. Econ.; 1996; 40, pp. 1-22. [DOI: https://dx.doi.org/10.1016/0022-1996(95)01386-5]
30. Gereffi, G. The reorganization of production on a world scale: States, markets and networks in the apparel and electronics commodity chains. Regionalization and Labour Market Interdependence in East and Southeast Asia; Palgrave Macmillan: London, UK, 1997; pp. 43-91.
31. Golding, C. Must we gather data? A place for the philosophical study of higher education. High. Educ. Res. Dev.; 2013; 32, pp. 152-155. [DOI: https://dx.doi.org/10.1080/07294360.2012.744712]
32. Schneider, B.; White, S.S. Service Quality: Research Perspectives; SAGE: Los Angeles, CA, USA, 2004.
33. Heaton, J. Reworking Qualitative Data; SAGE Publications: Thousand Oaks, CA, USA, 2004; ISBN 9780761971436
34. Wiles, R. What Are Qualitative Research Ethics?; Bloomsbury Academic: London, UK, 2012; 128.
35. Denney, A.S.; Tewksbury, R. How to write a literature review. J. Crim. Justice Educ.; 2013; 24, pp. 218-234. [DOI: https://dx.doi.org/10.1080/10511253.2012.730617]
36. Adriaanse, L.S.; Rensleigh, C. Comparing Web of Science, Scopus and Google Scholar from an Environmental Sciences perspective. South Afr. J. Libr. Inf. Sci.; 2011; 77, pp. 169-178. [DOI: https://dx.doi.org/10.7553/77-2-58]
37. Ren, S.; Hao, Y.; Wu, H. The role of outward foreign direct investment (OFDI) on green total factor energy efficiency: Does institutional quality matters? Evidence from China. Resour. Policy; 2022; 76, 102587. [DOI: https://dx.doi.org/10.1016/j.resourpol.2022.102587]
38. Dong, F.; Li, Y.; Zhang, X.; Zhu, J.; Zheng, L. How does industrial convergence affect the energy efficiency of manufacturing in newly industrialized countries? Fresh evidence from China. J. Clean. Prod.; 2021; 316, 128316. [DOI: https://dx.doi.org/10.1016/j.jclepro.2021.128316]
39. Lin, B.; Ma, R. Green technology innovations, urban innovation environment and CO2 emission reduction in China: Fresh evidence from a partially linear functional-coefficient panel model. Technol. Forecast. Soc. Chang.; 2021; 176, 121434. [DOI: https://dx.doi.org/10.1016/j.techfore.2021.121434]
40. Ren, S.; Hao, Y.; Wu, H. How Does Green Investment Affect Environmental Pollution? Evidence from China. Environ. Resour. Econ.; 2021; 81, pp. 25-51. [DOI: https://dx.doi.org/10.1007/s10640-021-00615-4]
41. Li, X.; Zhou, W.; Hou, J. Research on the impact of OFDI on the home country’s global value chain upgrading. Int. Rev. Financ. Anal.; 2021; 77, 101862. [DOI: https://dx.doi.org/10.1016/j.irfa.2021.101862]
42. Lee, K.; Malerba, F.; Primi, A. The fourth industrial revolution, changing global value chains and industrial upgrading in emerging economies. J. Econ. Policy Reform; 2020; 23, pp. 359-370. [DOI: https://dx.doi.org/10.1080/17487870.2020.1735386]
43. Gereffi, G. International trade and industrial upgrading in the apparel commodity chain. J. Int. Econ.; 1999; 48, pp. 37-70. [DOI: https://dx.doi.org/10.1016/S0022-1996(98)00075-0]
44. Porter, M.E. Competitive Advantage of Nations: Creating and Sustaining Superior Performance; Simon and Schuster: New York, NY, USA, 2011.
45. Light, R. From words to networks and back: Digital text, computational social science, and the case of presidential inaugural addresses. Soc. Curr.; 2014; 1, pp. 111-129. [DOI: https://dx.doi.org/10.1177/2329496514524543]
46. He, D.X.; Yao, Z.Q. Developing modern service industry to promote industrial structure optimization and upgrading. China Econ. Trade Guide; 2008; 1, pp. 20-22.
47. Huang, M.; Li, J. Technology selection, industrial structure upgrading and economic growth. J. Econ. Res.; 2009; 7, pp. 143-151.
48. Wu, F.; Liu, R. Industrial upgrading and independent innovation capacity building: An empirical study based on China’s provincial panel data. Chin. Ind. Econ.; 2013; 5, pp. 57-69.
49. Fu, J.; Li, L. Environmental regulation, factor endowment and industrial International competitiveness: An empirical study based on Panel data of China’s manufacturing Industry. Manag. World; 2010; 10, pp. 87-98.
50. Cheng, Z.; Li, L.; Liu, J. Environmental regulation and industrial structure upgrading: An analysis based on the dynamic spatial panel model of Chinese cities. China Sci. Technol. Forum; 2017; 2, pp. 66-72.
51. Wei, P.; Yu, Y. Impact of environmental regulation on industrial structure upgrading of manufacturing industry: A systematic GMM analysis based on provincial dynamic panel data. Explor. Econ. Probl.; 2017; 9, pp. 144-152.
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; Rajah Rasiah