Data-Driven Evolution Analysis and Trend Prediction of Hotspots in Global PPP Research
Abstract
:1. Introduction
2. Research Design
2.1. Data Processing and Research Methods
2.1.1. Data Processing
2.1.2. Research Method
- (1)
- Natural breakpoint method. Based on the distribution of numerical statistics and the number of pieces of the literature in different periods, this method can set class boundaries on the data values with large differences. K-means, mean shift, KNN, and other clustering methods mainly explore the center of the class. In contrast, the natural breakpoint method mainly explores the class boundary. Therefore, this method is suitable for solving the turning point of the cumulative difference data, especially the value of the cumulative number of pieces of the literature, so as to realize the phase division of the PPP literature from 1989 to 2022 [12,13,14].
- (2)
- Co-word analysis. This method can reflect the cascading relationships between objects. Among them, betweenness centrality is the key indicator to measure the importance of nodes in the network [15]. It is generally believed that keywords with betweenness centrality greater than 0.1 occupy a core position in the network [16,17]. This paper used CiteSpace 6.1 R3 to analyze the academic cooperation between countries and determine the intensity of cooperation between countries and the mainstream research countries. Moreover, we also used strategic coordinate analysis to mine the centrality and density of theme words, determining the position of each theme word in the strategic coordinate graph and analyzing the evolution trends [18].
- (3)
- The 2-mode network clustering algorithm. Through selecting two research objects, this method applied the unweighted pair group method with arithmetic mean (UPGMA) to determine the correlation coefficient between objects [19], and the 2-mode matrix was calculated [20,21]. Furthermore, we applied the R studio to the coupled biclustering graph between two objects. Among them, the connection between objects can determine the strength of correlation. The color depth represents the frequency, showing the heat degree of any keyword [22]. In summary, it identifies research hotspots in different phases and provides the data basis for the evolution and prediction of theme words.
2.2. Evolution Mechanism and Prediction Model Construction
2.3. Trend Analysis of Literature Characteristics
2.3.1. Division of Literature Phases
2.3.2. Analysis of Time Series Characteristics of Literature Quantity
- (1)
- Exploration period (1989–2008): the attention concerning PPP was lower before 2008, and the amount of PPP literature increased year by year.
- (2)
- Rapid development period (2008–2017): with the increasing demand for investment in infrastructure construction, the PPP model has received extensive attention. However, this is due to the influence of the global economic environment, risk, benefit sharing, etc. With 2010 and 2014 as the time nodes, the fluctuation in the PPP literature volume is quite obvious. Moreover, the Global Infrastructure Hub (GIH), Office of Public–Private Partnership (OPPP), and the Global Infrastructure Facility (GIF) were established from 2014 to 2015. These not only promoted the development of global infrastructure [24] but also promoted the academic follow-up. Hence, the global PPP literature volume has rebounded rapidly after 2015 and has continued growing in a straight upward trend.
- (3)
- Steady development period (2017–2022): The growth of the PPP literature volume tends to be stable. Furthermore, the annual publication volume is approximately 360. Isaac et al. [25] believes that with the impact of the COVID-19 era on the global economy, the entire PPP market and construction industry are in deficit, and the economic growth fell by 2.3% last year. Simultaneously, due to the policy uncertainty, it is not difficult to see that the PPP research progress remains stagnant in this period [26].
2.3.3. Attention Analysis of Literature Quantity
- (1)
- The graph shows an overall growth state. Since 2006, the volume growth trend in global PPP literature is obvious. Conversely, the USA and the UK perform the most prominently. After 2014, the global PPP literature growth gains momentum, and simultaneously, China and Australia show the greatest increases. Since 2007, the growth of global PPP literature tends to flatten out. However, China still maintains rapid growth and is significantly ahead of other countries. After 2019, except for China, the USA, and the UK, other countries all show slow growth or even a downward trend.
- (2)
- On the contrary, the total number of pieces of literature between China and the USA is similar. PPP literature in the USA has always been significantly ahead of China, and shows a steady growth trend from 2002 to 2017. However, the strong development period of PPP research in China is mainly concentrated in the second half of this period (2017–2022). It can be shown as: the China Public Private Partnerships Center (CPPPC) was officially approved in 2017, and the number of PPP-related policies promulgated also exploded in China. Until 2016, the number of joint policy enactments accounted for 35.6% of the total number of policy enactments [28]. Therefore, under the effective promotion of national policies, Chinese PPP practice and academic research received unprecedented attention [29]. Moreover, the number of PPP publications in China began to surpass that of the USA in 2017 and maintained a relatively stable growth rate.
3. Derivative Analysis of Data Characteristics
3.1. Feature Derivative Analysis of Keywords
3.1.1. Exploration Period (1989–2008)
3.1.2. Rapid Development Period (2008–2017)
3.1.3. Steady Development Period (2017–2022)
3.2. Coupling Analysis of Countries and Keywords
3.2.1. Analysis of Geographical Heat and Cooperation
- On the whole, the distribution of global PPP literature is uneven. The top 5 continents in the global PPP literature are the Americas, Europe, Asia, Oceania, and Africa. From a partial perspective, the total number of pieces of literature in the Americas is close to that in Europe, and the total number of publications accounts for approximately 66% of the total global literature. Moreover, the distribution of literature in European countries is also more balanced, and the intensity of academic cooperation is higher. In contrast, the number of pieces of African literature is less than 3%, significantly lower than the average of other continents.
- Country collaboration network analysis can measure the scientific research strength of the country in this field. Among them, the connection color indicates the time of national cooperation, and the node size indicates the number of pieces of literature in each country, as shown in Figure 8b.
3.2.2. Comparison of Betweenness Centrality
- These are the countries where PPP literature first appeared: the USA (1989) and the UK (1992). On the one hand, the Top 5 countries in the total number of pieces of literature are the USA, China, the UK, Australia, and India; On the other hand, the Top 5 countries in terms of betweenness centrality are the USA, the UK, Italy, the Netherlands, and Canada. Additionally, through comparison, it was found that although China and India rank the second and fifth in the world in terms of the number of publications, their betweenness centrality is lower than 0.1. The reason is that China has a unique political background and economic characteristics that restrict the interaction and cooperation with other countries [44]. Overall, there is a large gap between developing and developed countries in academic influence.
- Through the comparative analysis of literature quantity and betweenness centrality, it can be determined that developed countries: the USA and the UK, have a higher academic discourse power in the field of PPP research, and the research direction of these countries is an important evolutionary reference target. In addition, China and India are the most important evolutionary reference targets for the keywords of developing countries. Furthermore, we find that the modernization process of some developing countries (China, India, etc.) are also an important research reference for economically backward countries. In particular, related research in these areas: hydro economy and sustainability [45].
3.2.3. Cluster Analysis of National Coupling
4. Evolution Prediction of Theme Word and Keyword
4.1. Attention Evolution of Theme Word
- Standardization and intelligence may become the innovation in future research. The betweenness centrality of government and law, as well as computer science, is greater than 0.1 until the steady development period. In addition, their frequency also increases significantly. It shows that the government will pay increasing amounts of attention to policy formulation and intelligent application of PPP modes in the future. Cheng [50] pointed out that the combination of PPP mode with network and intelligence has application value, which is conducive to improving the efficiency of resource allocation and the management level for government function management. Moreover, the global cloud computing market is also critical to driving the development of digital infrastructure. Simultaneously, the digital economy is expected to grow from approximately USD 500 billion in 2021 to nearly USD 1 trillion in 2026 [51]. Additionally, the emerging infrastructure, such as blockchain applications [52], smart city [53], and data center [54], etc., will increase the willingness to invest in the private sector.
- Compared with business and economics, the frequency of environmental studies is close behind. In contrast, the centrality trend of environmental studies falls sharply after a slight rise. This suggests that there may be a large divergence in environment-related research. This research content (sustainability and renewable energy source) may distract the academic attention from environmental studies [55].
- The literature on business economics is steadily increasing, but it is not innovative enough. Although the frequency of business and economics increases from 186 to 632, the betweenness centrality gradually decreases. This shows that the academic linkage effect of PPP biased towards the financial economy is weakening. However, these research directions (asset securitization, debt-to-equity swap, etc.) still have a good research heat [56].
- Digital informatization, policy support, and health care jointly drive the development of PPP research. On the one hand, health care sciences and services, computer science, and government and law are the most significant keywords in the growth of betweenness centrality, which have attracted more and more attention from the global academia. On the contrary, technological innovation and application is the research focus needing extra attention, especially in this cross-disciplinary integration of research: information digitization [57], national policy support, and health care services [58,59], etc. Despite the frequency in the literature, these keywords are not among the top, but their academic attention is annually increasing, which will promote the progress of relevant research and has considerable potential for development.
4.2. Strategic Trend Evolution of Theme Word
- I quadrant: this quadrant has a high degree of academic influence, indicating that the theme word has close academic links with other theme words. Simultaneously, it has close internal links. To a certain extent, this shows that such theme words are at the core of the research field. Therefore, this quadrant is the highest influence and the most mature research field.
- II quadrant: the centrality of this quadrant is low, and the density is high. Moreover, it is at the edge of the research field. Although its academic development is relatively mature, the research content in this field does not match the current research hotspots.
- III quadrant: the theme of this quadrant has low centrality and low density. Additionally, the development of this theme is not mature. Although there is a potential development trend, the development is not systematic, and the connection with other themes is relatively loose.
- IV quadrant: the centrality of this quadrant is high and the density is low, indicating that this theme is at the center of the research. Although the research in this quadrant is immature, however, there is a high possibility of emerging topics in this field, with higher novelty index and academic development potential.
4.2.1. Clustering Division of Theme Words
4.2.2. Core Identification of Theme Words
4.3. Keyword Diversion Prediction
4.3.1. Calculation of Novelty Index
4.3.2. Keyword Prediction
- Integration of business economy and sustainability. A combination of computer science, government and law, and health care sciences. Among them, health economics, transition economy, waste management, and clean energy economy, etc., are the most novel keywords, which indicates the economic sustainability and effective value of the PPP model, and they receive increasing amounts of academic attention from countries and scholars.
- Integration of policy support and innovative technology applications. It is a combination of computer science, government and law, and health care sciences.
- On the one hand, with the progress of the times, scholars’ demand for innovative research is increasing, and the application of advanced technology is more reflected in these aspects: big data, data envelopment analysis, machine learning and blockchain application. Second, the emergence of emerging concepts such as: smart city, data center, etc., also promoted interest in private capital for emerging PPP projects financing. However, policy uncertainty risk is a key influencing factor for many PPP projects. Additionally, the lack of available data analysis will lead to the risk of uncertainty in PPP project evaluation, leading to a reduction in the investment enthusiasm for PPI. Therefore, scientific data analysis and machine learning applications can provide an effective policy guidance for government, so as to realize the intelligent decision-making applications [64].
- On the other hand, the NI of public health also reaches 0.13. The COVID-19 recovery plan has received urgent global attention in recent years [65]. Furthermore, this research also receives extensive academic attention: medical facilities construction, medical service policy, data analysis in medical health, public health management, etc.
- Urbanization in developing countries is a combination of public administration, business and economics, government and law, management, and urban studies. Among them, mainstream PPP research will continue to focus on traditional areas of application in developing countries/regions (Sub-Saharan Africa, China, and India), such as: transport project, urban renewal model, and cooperation mechanism, etc. Moreover, infrastructure will always be the research focus in the field of urban studies. Furthermore, it is still the basic research direction for developing countries and even developed countries in the long-term.
5. Conclusions and Future Work
5.1. Conclusions
- The derivative characteristics of the literature data are explained descriptively. First, we divided global PPP research into three phases: the exploration period, the rapid development, and the steady development period, and verified the rationality. Based on this, more findings were as follows: we identified the main keywords of each period, the mainstream countries for PPP research and research trends, the attention evolution and strategic position of theme words. These provide an important data basis and evolution basis for the prediction of future keywords.
- The evolutionary direction of PPP research can be summarized as economic sustainability, diversity of financing mechanisms, innovation in the application of technology, and the applicability in developing countries. The specific manifestations are, primarily, the integration of business economy and sustainability, as well as transition economy, new energy economy, clean energy economy, and debt-to-equity swap, etc.; and additionally, the integration of policy support and innovative technology applications, such as government intelligent decision-making, big data analysis, smart city, etc.; and ultimately, urbanization in developing countries, such as transportation facilities construction, urban renewal strategy, public sector comparison, etc.
- Moreover, global academic attention to developing countries will become increasingly high, especially in China, India, and Sub-Saharan Africa. In academic cooperation, the willingness of developing countries to cooperate is enhancing. The research direction is more manifested in urban infrastructure construction, cooperation mechanism research, public sector comparison, etc.
5.2. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Comparison of Literature Quantity | Comparison of Betweenness Centrality | |||||
---|---|---|---|---|---|---|
Rank | Numbers | Centrality | Year | Country | Centrality | Country |
1 | 902 | 0.55 | 1989 | The USA | 0.55 | The USA |
2 | 815 | 0.05 | 2000 | China | 0.36 | The UK |
3 | 630 | 0.36 | 1992 | The UK | 0.13 | Italy |
4 | 355 | 0.11 | 1998 | Australia | 0.12 | The Netherlands |
5 | 270 | 0.02 | 2003 | India | 0.12 | Canada |
6 | 265 | 0.12 | 1999 | The Netherlands | 0.11 | Australia |
7 | 256 | 0.06 | 2002 | Russia | 0.10 | Germany |
8 | 194 | 0.12 | 2003 | Canada | 0.06 | Russia |
9 | 191 | 0.13 | 1996 | Italy | 0.05 | China |
10 | 144 | 0.10 | 1998 | Germany | 0.02 | India |
Total Count (2017–2022) = 2922 | |||||
---|---|---|---|---|---|
Cluster | Theme Words | Keywords (Top 5) | Frequency | Rate | Rank |
C0 | Environmental studies | Sustainability, Climate Change, Waste Management, Public Sector Comparator, Green Retrofits | 616 | 0.211 | 2 |
C1 | Public administration | Project Management, Concession Period, Public Procurement, Private Sector, Project Procurement | 434 | 0.149 | 3 |
C2 | Business and economics | Financing Model, Risk Allocation, Value For Money, Trust, Real Options | 659 | 0.226 | 1 |
C3 | Computer science | Innovation, Monte Carlo Simulation, System Dynamics, Big Data, Evaluation | 288 | 0.099 | 5 |
C4 | Government and law | China, Privatization, Developing Countries, India, Policy | 185 | 0.063 | 7 |
C5 | Health care sciences | Case Study, Public Health, Telemedicine, COVID-19, Health Economics | 273 | 0.093 | 6 |
C6 | Management | Game Theory, Efficiency, Risk Management, Renegotiation, Cooperation | 304 | 0.104 | 4 |
C7 | Urban studies | Infrastructure, Investment, Toll Road, Transport Project, Redevelopment | 163 | 0.056 | 8 |
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Zhao, L.; Yang, S.; Wang, S. Data-Driven Evolution Analysis and Trend Prediction of Hotspots in Global PPP Research. Buildings 2023, 13, 206. https://doi.org/10.3390/buildings13010206
Zhao L, Yang S, Wang S. Data-Driven Evolution Analysis and Trend Prediction of Hotspots in Global PPP Research. Buildings. 2023; 13(1):206. https://doi.org/10.3390/buildings13010206
Chicago/Turabian StyleZhao, Likun, Shaotang Yang, and Shouqing Wang. 2023. "Data-Driven Evolution Analysis and Trend Prediction of Hotspots in Global PPP Research" Buildings 13, no. 1: 206. https://doi.org/10.3390/buildings13010206
APA StyleZhao, L., Yang, S., & Wang, S. (2023). Data-Driven Evolution Analysis and Trend Prediction of Hotspots in Global PPP Research. Buildings, 13(1), 206. https://doi.org/10.3390/buildings13010206