GU Tianshi, ZHANG Peng, ZHANG Xujia, 2021. Spatio-temporal Evolution Characteristics and Driving Mechanism of the New Infrastructure Construction Development Potential in China. Chinese Geographical Science, 31(4): 646−658 doi:  10.1007/s11769-021-1214-8
Citation: GU Tianshi, ZHANG Peng, ZHANG Xujia, 2021. Spatio-temporal Evolution Characteristics and Driving Mechanism of the New Infrastructure Construction Development Potential in China. Chinese Geographical Science, 31(4): 646−658 doi:  10.1007/s11769-021-1214-8

Spatio-temporal Evolution Characteristics and Driving Mechanism of the New Infrastructure Construction Development Potential in China

doi: 10.1007/s11769-021-1214-8
Funds:  under the auspices of the National Natural Science Foundation of China (No. 41601553), the Heilongjiang Province General Undergraduate High School Youth Innovation Talent Training Program Project (No. UN-PYSCT-2017193, UN-PYSCT-2017184), the Harbin Normal University Ph.D. Startup Fund Project (No. XKB201815)
More Information
  • Corresponding author: ZHANG Peng. E-mail: zhangp575@nenu.edu.cn
  • Received Date: 2021-01-15
  • Accepted Date: 2021-04-09
  • Available Online: 2021-05-27
  • Publish Date: 2021-07-04
  • With the advent of the era of big data and artificial intelligence, new infrastructure construction (NIC) has attracted the attention of many countries. The development of NIC provides an opportunity to bridge the digital divide and narrow the regional gap, providing continuous impetus to further promote economic development. Here, we considered 31 provincial-level administrative units in China (not including Hong Kong, Macao, and Taiwan of China due to data unavailable) and established comprehensive evaluation indicators for the development potential of NIC. Afterward, we used the entropy-weight TOPSIS model to determine the development potential of NIC and analyze its spatio-temporal evolution characteristics. Furthermore, the GeoDetector model was applied to explore the driving mechanism of the NIC development potential. The conclusions were as follows: 1) The Chinese NIC development potential is generally low. The eastern China was the region with the highest development potential year by year, while the development potential in the central China was found to be in an accelerating phase. 2) The evolution of the Chinese NIC development potential’s spatial pattern has been characterized by an inland extension and coastal agglomeration. Moreover, we identified a superior development zone, a rising development zone, an inferior development zone, and a declining development zone. 3) The scope of Chinese NIC development potential agglomeration areas has gradually expanded and its degree has gradually deepened. The range of high-value agglomeration in eastern area gradually expanded and its degree gradually deepened. 4) Investment in innovative talents appears as the core factor affecting the Chinese NIC development potential. Whether acting alone or synergistically with other factors, its promoting effect on Chinese NIC development potential is the strongest.
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Spatio-temporal Evolution Characteristics and Driving Mechanism of the New Infrastructure Construction Development Potential in China

doi: 10.1007/s11769-021-1214-8
Funds:  under the auspices of the National Natural Science Foundation of China (No. 41601553), the Heilongjiang Province General Undergraduate High School Youth Innovation Talent Training Program Project (No. UN-PYSCT-2017193, UN-PYSCT-2017184), the Harbin Normal University Ph.D. Startup Fund Project (No. XKB201815)

Abstract: With the advent of the era of big data and artificial intelligence, new infrastructure construction (NIC) has attracted the attention of many countries. The development of NIC provides an opportunity to bridge the digital divide and narrow the regional gap, providing continuous impetus to further promote economic development. Here, we considered 31 provincial-level administrative units in China (not including Hong Kong, Macao, and Taiwan of China due to data unavailable) and established comprehensive evaluation indicators for the development potential of NIC. Afterward, we used the entropy-weight TOPSIS model to determine the development potential of NIC and analyze its spatio-temporal evolution characteristics. Furthermore, the GeoDetector model was applied to explore the driving mechanism of the NIC development potential. The conclusions were as follows: 1) The Chinese NIC development potential is generally low. The eastern China was the region with the highest development potential year by year, while the development potential in the central China was found to be in an accelerating phase. 2) The evolution of the Chinese NIC development potential’s spatial pattern has been characterized by an inland extension and coastal agglomeration. Moreover, we identified a superior development zone, a rising development zone, an inferior development zone, and a declining development zone. 3) The scope of Chinese NIC development potential agglomeration areas has gradually expanded and its degree has gradually deepened. The range of high-value agglomeration in eastern area gradually expanded and its degree gradually deepened. 4) Investment in innovative talents appears as the core factor affecting the Chinese NIC development potential. Whether acting alone or synergistically with other factors, its promoting effect on Chinese NIC development potential is the strongest.

GU Tianshi, ZHANG Peng, ZHANG Xujia, 2021. Spatio-temporal Evolution Characteristics and Driving Mechanism of the New Infrastructure Construction Development Potential in China. Chinese Geographical Science, 31(4): 646−658 doi:  10.1007/s11769-021-1214-8
Citation: GU Tianshi, ZHANG Peng, ZHANG Xujia, 2021. Spatio-temporal Evolution Characteristics and Driving Mechanism of the New Infrastructure Construction Development Potential in China. Chinese Geographical Science, 31(4): 646−658 doi:  10.1007/s11769-021-1214-8
    • Infrastructure is one of the core sectors that directly or indirectly determine the socioeconomic development condition of a region (Holtz-Eakin and Schwartz, 1995). The World Bank delivered evidence that infrastructure has played a crucial role in urban transformation (Kessides, 2014); moreover, substantial linkages have been found between infrastructural services and socioeconomic development (Esfahani and Ramírez, 2003; Mangone, 2016). At the same time, with the advent of the era of big data and artificial intelligence, information infrastructure construction has attracted considerable attention from many countries. Therefore, research on traditional infrastructure construction alone is no longer suitable for the current era. At the 2018 Central Economic Work Conference, it was proposed to ‘strengthen new infrastructure construction, such as artificial intelligence, industrial Internet, and the Internet of Things’. This, as a matter of fact, was the first time the concept of new infrastructure construction (NIC) was formally proposed. NIC is an infrastructure system guided by new development concepts, driven by technological innovation, based on information networks, facing the needs of high-quality development, and providing services, such as digital transformation, intelligent upgrading, integration and innovation (Shi, 2020). Contrasted with traditional infrastructure construction, NIC has five major characteristics: digital technology is its core, emerging fields are its main body, technological innovation is its driving force, virtual products represent its main form, and platforms are its main carriers (Li, 2020). Notably, this new type of construction project adapts to the changes of the current era and promotes an intelligent development of countries and regions. Moreover, being related to the future comprehensive competitiveness of countries and regions, NIC is becoming their focus of development.

      Despite being an important element of regional development, infrastructure construction has not been considered as an independent research object in view of long-term development processes. In fact, it was not until the middle and late 18th century that Quesnay’s ‘Original prepayment’ (Coats et al., 1972) and Smith’s ‘Public Works’ (Smith, 2008) theories led to new ideas about urban infrastructure construction; furthermore, in 1943, Rosenstein-Rodan’s ‘Social Overhead Capital’ (Rosenstein-Rodan, 1943) first introduced the concept of infrastructure. The concept of infrastructure construction was clarified through a series of studies (Miyake, 1954; Hirschman, 1991; The World Bank, 1994). After the 1970s, the economic growth decline in Western countries has led to an increase in research on the causes of economic growth; in particular, the possible promotion of economic recovery by infrastructure has received widespread attention. Some scholars have used the production function (Shell, 1971; Duggal et al., 1999), the sector analysis method (Rostow, 1988), the Cobb-Douglas production function (Aschauer, 1989), the vector error correction model (Pradhan and Bagchi, 2013), and other methods to quantitatively analyze the role of infrastructure in the promotion of economic development, leading to an acceleration of the pace of infrastructure construction in various regions. Considering the rapid development of infrastructure construction, the associated social impact has gradually attracted attention from all kinds of parties. Initially, research mainly focused on the social problems possibly arising during the implementation of large-scale infrastructure projects: the purpose was to minimize any negative impacts and ensure the smooth implementation of such projects. As the positive impact of infrastructure projects on social development started to be recognized, the social benefits began to attract the attention of scholars. The role of infrastructure in reducing poverty (Fan et al., 2002), increasing employment (Zhu et al., 2009), alleviating traffic pressure (Hof et al., 2012; Knudsen and Rich, 2013), and improving the ecological environment (Aenab and Singh, 2012; Chang et al., 2012) has gradually been confirmed. Research has also demonstrated that a single aspect of infrastructure cannot fully reflect its comprehensive benefits. This finding effectively prompted the development of a comprehensive infrastructure evaluation (Démurger, 2001; Kumar, 2006; Calderón and Servén, 2004; Elgendy, 2008).

      In short, the vast amount of research conducted on infrastructure has led to a certain consensus among researchers on basic issues: infrastructure appears to play a positive role in urban and regional development; moreover, the social benefits of infrastructure outweigh its economic benefits. This not only highlights the importance of conducting research on infrastructure, but also shows that NIC is particularly in line with current trends: NIC provides technical and service support for the digital information economy and has been identified as the main driving force for future worldwide economic development (Zhao, 2020). This paper summarizes the findings of previous research on infrastructure and provides an overview of the current developments in the field, shifting from the perspective of traditional infrastructure research and exploring the development of NIC.

      Some multi-factor comprehensive evaluation methods, such as the full array polygon graphic indicator method (Zinatizadeh et al., 2017), the projection pursuit model (Huang and Li, 2017), the entropy-weight TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution ) model (Li et al., 2018), and some space technologies (e.g., Global Moran’s I, Geary’s C, Local Indicators of space Association (LISA) and Getis-Ord (Gi*) model) (Duncan et al., 2012; Gutiérrez and Delclòs, 2016; Marín et al., 2018) can be used to link indicators with time and space, allowing analyses on the spatio-temporal evolution of the research object; however, the above technical methods have not been applied to the investigation of NIC. Research on this topic is in its infancy, and it is based on the results of Chinese scholars, who have basically focused on the concept (Liu et al., 2020) and role (Guo et al., 2020; Tian and Yan, 2020) of NIC, as well as on related policy recommendations (Li, 2020). Most studies have been conducted in the field of the social sciences, while very few empirical investigations were focused on NIC; moreover, there is no related research on NIC from a spatial perspective.

      Due to the short period of time for which NIC has to be proposed, it is difficult to explore its development quality; moreover, construction projects associated with NIC are still in their infancy. In this study, we explored the NIC development potential (i.e., the realistic development basis that a region or city can provide for the continuous construction and development of new infrastructure within a certain period of time). This potential specifically depends on, for example, the local economic development level, the network development level, the social resource base, and the innovation capabilities. Although the development potential does not correspond to the actual development quality, the construction and development of regional NIC rely on the comprehensive utilization of resource elements. Therefore, by clarifying differences in the development potential of NIC and analyzing its influencing factors, it is possible to provide a theoretical basis for the construction and development of NIC in various Chinese provinces, facilitate the overall planning, and scientifically guide the development and construction of NIC.

    • Our study selected 31 Chinese provincial-level administrative units (not including Hong Kong, Macao, and Taiwan of China) as study area. Spatio-temporal evolution characteristics of the Chinese NIC development potential were assessed by considering seven major Chinese geographic regions as the basic unit, which were defined based on geographical proximity. The long-term development of different regions highlighted a particularly close degree of economic relevance and technology sharing with each of them. The country’s macro policies also tended to integrate construction within each region and promote cross-regional collaborative development (Fig. 1, Table 1).

      Figure 1.  The seven geographical divisions of China

      RegionUrban area / km2Population / 104 peopleGDP / 108 yuanInformation industry
      annual income / 108 yuan
      Year by year growth rate of the information
      industry revenue / %
      The northern China 34084.77 17478.22 112205.08 12406.93 0.12
      The eastern China 69010.96 40898.13 320111.56 22340.41 0.17
      The southern China 24068.80 16980.00 112691.03 8042.11 0.21
      The central China 17807.63 22321.15 113933.88 2939.13 0.08
      The southwestern China 22744.08 20095.00 87633.04 4361.63 0.09
      The northeastern China 20488.36 10874.70 54256.45 1690.82 0.02
      The northwestern China 10152.57 10186.00 46309.06 1811.98 0.07

      Table 1.  Basic overview of the seven Chinese regions in 2017

    • In this study, we analyzed annual indicator data collected in Chinese provinces, autonomous regions, and municipalities from 2011 to 2017. Since NIC was first proposed in 2018, the development time and the sample size considered here are extremely limited. Hence, we discussed the development potential of NIC rather than its actual development level; it is a realistic basic condition that was already in place before the start of the NIC. In order to investigate future trends in the NIC development potential and provide a solid base for future construction projects, changes in the development potential were analyzed from the perspective of its dynamic evolution within a period of time before the start of NIC. To do so, we selected indicators (obtained from https://www.jpgnet.com.cn/index.html#/Home, https://www.wind.com.cn/, and https://www.cnrds.com) among those identified before 2018.

    • By referring to a previous study (Han et al., 2020), we considered the accuracy and availability of data. Afterwards, we systematically constructed an indicator evaluation system (Table 2). First, it is important to consider that NIC represents a huge project related to very large investments and that puts forward higher requirements for the economic strength of the entire society, especially concerning the local social and economic development, the government’s financial strength, and the public construction funds. Second, NIC is built around 5G, intercity high-speed rail, UHV, and other projects. These large-scale physical projects require the support of a strong local industrialization and of an informative industrial environment: the development level and the number of enterprises are crucial. Considering the digital technology at the core of NIC, the development level of the local information industry must be considered. Third, the construction of any piece of infrastructure requires the cooperation of resource elements of the whole society. Among them, it is particularly critical to consider the consumption of population, land, energy, and other resources. Fourth, NIC is driven by digital technology at its core: the digital and informatization industries are knowledge-intensive. Improvements in this aspect are largely dependent on the development of local innovation capabilities. Among them, innovative talents, innovation investment funds, and innovation achievement outputs are the most important measurement criteria.

      Target levelDimension levelIndicator levelIndicator description
      Development potential of new
      infrastructure construction
      Development basis Economic strength Regional GDP per capita / (yuan(RMB)/person)
      Financial basis Total capital formation per capita / (yuan /person)
      Investment capacity General public service expenditure / 108 yuan
      Industry support Industrial base Industrial value added above designated size / 108 yuan
      Enterprise cluster Number of industrial enterprises above
      designated size / enterprise(s)
      Information industry Main business income of information industry / 108 yuan
      Ability to undertake Population base Urban population density / (people/km2)
      Land use Unit investment in fixed assets consuming
      new construction land / (km2/108 yuan)
      Energy consumption Regional electrical power consumption
      per capita / (kW·h/person)
      Innovation development Innovator Full-time equivalent of R&D personnel / (person/year)
      Innovation funds R & D internal expenditure / 104 yuan
      Innovation results Number of patents granted / patent(s)

      Table 2.  Comprehensive evaluation indicator system of the NIC development potential

    • This paper uses the entropy-weight TOPSIS method (i.e., essentially an improvement of the traditional TOPSIS evaluation method) to comprehensively evaluate the development potential of the Chinese NIC. This method is typically used to determine the weight of the evaluation indicator and, subsequently, to determine the order of the evaluation objects by approximating the ideal solution. The entropy weighting can not only objectively reflect the importance of an indicator in the indicator system during decision-making, but also prominently reflect any temporal changes in the indicator weight (Du et al., 2014). These characteristics made the above method particularly suitable for our research. The specific calculation steps are described by the reference (Wei and Li, 2018).

    • In this study, the Getis-Ord (Gi*) model was used to analyze the evolution of the Chinese NIC development potential’s spatial clustering. The Getis-Ord (Gi*) analysis consists in a cluster distribution mapping tool typically applied to the identification of statistically significant spatial clusters (i.e., hot and cold spots) (Getis and Ord, 1992). The specific formula is as follows:

      $$G_i^*(d) = \sum\limits_{j = 1}^n {{W_{ij}}} {x_j}/\sum\limits_{i = 1}^n {{x_j}} $$ (1)

      where n is the number of research units, xi and xj the attribute values of spatial units i and j, and Wij the spatial weight matrix.

    • After considering a variety of mathematical models for exploring possible driving factors, we selected the GeoDetector model (Wang et al., 2010, 2016), which has been previously used for the same scope. The comprehensive score of the Chinese NIC development potential was considered as the dependent variable (Y), while the driving factor as the independent variable (X). In the GeoDetector model, the dependent variable should be represented by a numerical quantity, while the independent variable by a type quantity. Therefore, when the independent variable happens to be a numerical quantity, a discretization is required and the numerical quantity has to be converted into a type quantity through classification methods (Wang and Xu, 2017). Here, we applied the Jenks method to divide the driving factors of each year into five levels. The specific formula used for factor detection was as follows (Wang et al., 2016, http://geodetector.org/):

      $$q = 1 - \dfrac{{\displaystyle\sum\limits_{h = 1}^L {{N_h}\sigma _h^2} }}{{N\sigma _{}^2}}$$ (2)

      where q is the explanatory value of the detection factor X, L the stratification of factor X, N the number of samples in the area, Nh the number of samples in the detection area, σ2 the variance of the Y value in the area, and σh2 the variance of the Y value in the detection area.

    • The average value of the NIC development potential can initially reflect the development potential characteristics of different regions in the country over time (Fig. 2). Generally speaking, the NIC development potential in the study area was low (total average: 0.257) and showed a relatively slow upward trend. Moreover, its growth rate gradually slowed down over time: the NIC development potential rose from 0.228 in 2011 to 0.260 in 2017 (average annual growth rate: 0.46%). The overall development potential of NIC across the country appeared insufficient, and we noticed large gaps between districts in terms of development trends. The eastern China had the highest development potential and was far ahead of other regions, followed by the southern China and the northern China. Moreover, the accelerated development of the central China led it to gradually reach the national average level, while the northern China showed a declining trend. Finally, the southwestern China, the southwestern China, and the northeastern China all showed a low potential. In particular, the southwestern China grew steadily in terms of growth rate, while the northwestern China and the northeastern China showed signs of decline. Correspondingly, the average annual growth rates of the NIC development potential in the northern China, the eastern China, the southern China, the central China, the southwestern China, the northwestern China, and the northeastern China were 0.39%, 0.99%, 0.99%, 0.79%, 0.51%, −0.39%, and −0.29%, respectively. We hence infer that the eastern and the southern China have been the main drivers of the growth in NIC development potential, while the central China is the main potential drivers of future development. This can be explained by the fact that the eastern and the southern China have been the most economically developed regions of the country for a long time and, consequently, have a solid foundation in innovation, information technology, education, etc. The rise of the central China can be explained instead by the presence of core developing locations in that region: the Beijing-Tianjin-Hebei Economic Band, the Yangtze River Delta, and the Pearl River Delta. Moreover, in recent years, the strategic position of Wuhan, the Hubei Province (which has a core role within the national main functional zone strategy), and the integrated construction of Changsha, Zhuzhou, and Xiangtan have accelerated the local development of the central China.

      Figure 2.  Temporal evolution characteristics of the Chinese NIC development potential

    • The evolution of the spatial pattern of Chinese NIC suggested inland extension and coastal agglomeration characteristics (Fig. 3). The southern regions showed a relative stability, while the northern regions experienced large-scale shrinkage.

      Figure 3.  Spatial evolution characteristics of the Chinese NIC development potential

      We defined the following four zones, type zones are divided according to the calculation of the NIC development potential level of each provincial administrative unit in the three years under the premise of geographical proximity.

      1) The superior development zone, which includes Shandong, Shanghai, Jiangsu, Zhejiang, Fujian, and Guangdong, corresponds to the eastern coastal area. This has been in a state of high-quality development, due to its superior basic conditions. In fact, the superior development zone presently corresponds to the most economically developed region of China: it is characterized by strong financial foundations, high-tech industry clusters, important research hubs and education facilities, and frequent external connections along the coastline. Overall, this zone provides a good foundation for NIC and has the belt distribution pattern with the highest value among all zones. 2) The rising development zone, which gradually expands from the Beijing-Tianjin-Hebei Economic Band toward south and southwest (including Beijing, Tianjin, Hebei, Henan, Hubei, and other provinces), is the largest and has a medium NIC development potential; moreover, it is characterized by the largest-scale and fastest development potential growth. This zone has a good NIC development potential, and is very likely to become the main center of Chinese NIC development in the future, while currently it is simply in a state of sound development. Notably, due to its relatively central location, this region has been the development hub of the country and has been relying on the resources of the Beijing-Tianjin-Hebei Economic Band, the Yangtze River Delta, the Pearl River Delta, and ‘The development of the western region in China’, and the ‘One Belt One Road’. 3) The inferior development zone, which includes Yunnan, Guizhou, Hainan, Tibet, and other provinces in the southwestern China and has a weak NIC development potential. Despite a showing certain upward trend, the NIC development here seems to be negatively affected by topography and a weak the foundation of economic development, explaining the small observed improvements. 4) The declining development zone, which includes Liaoning, Jilin, Heilongjiang, Xinjiang, Inner Mongolia, and other provinces of the northern China, is characterized by a relatively obvious decline in development potential. This decline is related to resource depletion, a lagging economic development, and brain drain.

    • In order to further analyze the spatial agglomeration characteristics of the Chinese NIC development potential, we analyzed its spatial clustering evolution characteristics by applying the Getis-Ord (Gi*) model. The corresponding analysis results showed (Fig. 4): 1) a high-value agglomeration in east area and a low-value agglomeration in the southwestern China; 2) a gradual expansion of the range of high-value agglomeration in east area, accompanied by a gradual increase of the expansion degree. These observations suggest that the focus of the Chinese NIC development potential will continue to shift to the southeastern coastal area, spreading to Beijing, Tianjin, Hebei, Henan, Hubei, and other rising development zones.

      Figure 4.  Spatial clustering evolution of the Chinese NIC development potential

    • The variables affecting the evaluation indicators of the development potential of the NIC were selected as driving factors. Notably, changes in the driving factors led to changes in the evaluation indicators of the development potential of NIC; change in the latter, in turn, led to variations in the development potential level of NIC. The development potential of the Chinese NIC seemed strictly related to factors like the local finance, the basic network construction, the technological level, and innovative talents. For this study, we selected 11 indicators from four dimensions (i.e., financial investment, network investment, knowledge investment, and talent investment) as independent variables: Financial investment (including the per capita investment in fixed assets (X1), the new government bond issuance (X2), the local fiscal expenditure (X3)), the network investment (including the number of Internet sites (X4), the number of Internet pages (X5), the number of Internet access ports (X6)), the knowledge investment (including the average years of education(X7), the number of papers (X8), the number of universities and scientific research institutions (X9)), the talent investment (including the number of R&D personnel per 10 000 individuals of the labor force) (X10), the number of college or students or more per 10 000 individuals of the personnel (X11)). The data relative to the 11 indicators were obtained from the National Bureau of Statistics (http://www.stats.gov.cn/), the Provincial Statistical Yearbooks (https://www.jpgnet.com.cn/index.html#/Home) , and the Wind Financial Database (https://www.wind.com.cn/). The NIC development potentials in various regions of China and the 11 driving factors were hence used as dependent and independent variables, respectively, to quantitatively analyze the spatio-temporal evolution mechanism of the NIC development potential.

    • The Jenks method was adopted to carry out a five-level stratification of the selected independent variables, which were converted from numerical to type quantities; then, factor detection was achieved by applying the GeoDetector model. The results (Table 3) showed that 5 out of the 11 independent variables in the three time sections passed the significance test. These five independent variables were represented by the local fiscal expenditure (X3), the number of Internet sites (X4), the number of Internet access ports (X6), the number of papers (X8), and the number of R & D personnel per 10 000 individuals of the labor force (X10). Overall, these results show that the temporal and spatial distribution of the Chinese NIC development potential was affected by the following aspects: finance, network, scientific research, and talent reserves. The q-values of the above significant factors also indicate that the number of R & D personnel per 10 000 individuals of the labor force (X10) was the leading driving factor of the Chinese NIC potential: the explanatory power of this factor during the three years considered in this study was > 70% (much higher than that of the other factors).

      Variable201120142017
      q-valueP-valueq-valueP-valueq-valueP-value
      X10.1170.5480.0590.8770.2340.251
      X20.4710.4820.5730.2010.5770.075
      X30.6700.007**0.5350.014*0.6480.025*
      X40.6710.016*0.6380.013*0.7200.000**
      X50.5580.0890.7290.005**0.7010.011*
      X60.6100.008**0.6350.006**0.5550.004**
      X70.2330.3170.2580.3300.1670.504
      X80.5460.032*0.5490.039*0.5630.024*
      X90.3810.2660.3280.0590.5890.013*
      X100.7480.000**0.8740.000**0.8450.000**
      X110.2470.2870.2310.7270.1260.667
      Notes: the symbols ** and * next to the P-values indicate that the corresponding q values had statistical significances of 1% or 5%, respectively

      Table 3.  Influence and significance test of the driving factors of the Chinese NIC development potential

    • In this study, we also explored the pairwise interaction and synergy of the driving factors on the NIC development potential. This was achieved through the interactive detection (the GeoDetector model’s ‘another detection’) of the five independent variables that passed the significance test for 2011, 2014, and 2017 (Tables 4, 5, 6).

      VariableX3X4X6X8X10
      X30.670
      X40.8480.671
      X60.7430.8420.610
      X80.7820.8530.7740.546
      X100.8600.8480.9140.8320.748

      Table 4.  Interactive detection of the driving factors for 2011

      VariableX3X4X6X8X10
      X30.535
      X40.8070.638
      X60.7380.9140.635
      X80.6920.9020.8260.549
      X100.9760.9370.9650.9130.874

      Table 5.  Interactive detection of the driving factors for 2014

      VariableX3X4X6X8X10
      X30.648
      X40.7990.720
      X60.7690.8050.555
      X80.8130.8960.8090.563
      X100.9380.9480.9540.8940.845

      Table 6.  Interactive detection of the driving factors for 2017

      The results indicate that the effects of the respective variables in the three time sections were indeed not independent of each other: they interacted with each other; moreover, when paired together, they enhanced the explanatory power of the dependent variable, showing a two-factor enhancement effect. In other words, the development potential of the Chinese NIC is not the result of independent and direct effects of the five significant factors, but rather the product of their two-to-one interactions (resulting in enhanced effects).

      Specifically (Table 7, Fig. 5), after the interactions between X3 and X4, between X4 and X6, the explanatory power dropped from midstream to the bottom, demonstrating declining auxiliary roles of these factors. Also, power after the interaction between X3 and X6, the explanatory power remained stable at the bottom, demonstrating a stable auxiliary role of these factors. After the interactions between X3 and X8, between X6 and X8, the explanatory power rose from the bottom to midstream, demonstrating the rising important roles of these factors. After the interactions between X8 and X10, the explanatory power remained stable in the midstream, demonstrating a stable important role of these factors. Furthermore, after the interaction between X4 and X8, the explanatory power dropped from forefront to midstream, demonstrating a declining important role of these factors. After the interaction between X3 and X10, between X6 and X10, the explanatory power remained stable at the forefront, demonstrating the stable leading roles of these factors. Finally, after the interaction between X4 and X10, the explanatory power rose from midstream to forefront, demonstrating the rising leading role of these factors.

      Interactive types201120142017
      X3X4489
      X3X610910
      X3X88106
      X3X10213
      X4X6648
      X4X8364
      X4X10432
      X6X8977
      X6X10121
      X8X10755

      Table 7.  Interactive detection explanatory power ranking of the driving factors

      Figure 5.  Schematic diagram of the dual-factor interactive driving mechanism of the Chinese NIC development potential

    • The synergy effect of the number of R & D personnel per 10 000 individuals of the labor force is the most obvious; moreover, the explanatory power of this factor (combined with any other factor) on the dependent variable is strong. This indicates that the synergy between the number of R & D personnel and other driving factors per 10 000 individuals of the labor force, rather than the number of R & D personnel per 10 000 individuals of the labor force, is the main driver behind the observed spatio-temporal pattern of the Chinese NIC development potential.

      Although the synergy effect of the number of papers is not as prominent as the former combination of factors, it is indeed significantly higher than the explanatory power of the other interaction modes and partly explains the evolution of the Chinese NIC development potential.

      In the future, the improvement of the development potential of the Chinese NIC will likely mainly rely on the investment in innovative talents; meanwhile, knowledge investment will likely largely affect the NIC development potential. In contrast, the impact of financial and knowledge investment on the NIC development potential is still on the rise.

    • NIC provides technical and service support for the digital information economy and has been identified as the main driving force for future worldwide economic development. Due to the short period of time for which NIC has to be proposed, it is difficult to its development quality; moreover, construction projects associated with NIC are still in their infancy. In this study, we explored the NIC development potential (i.e., the realistic development basis that a region or city can provide for the continuous construction and development of new infrastructure within a certain period of time). By clarifying differences in the development potential of NIC and analyzing its influencing factors, it is possible to provide a theoretical basis for the construction and development of NIC in various Chinese provinces, facilitate the overall planning, and scientifically guide the development and construction of NIC, thereby promote the regional digital economy and the improvement of comprehensive competitiveness. With the aim of quantitatively determining the spatio-temporal evolution of the NIC development potential of Chinese provincial administrative units, we constructed an indicator evaluation system through the application of the entropy-weight TOPSIS method; furthermore, we used the GeoDetector model to analyze the driving factors and mechanisms of the NIC development potential.

      Our conclusions are listed below:

      (1) The Chinese NIC development potential was generally low, with significant regional differences. This parameter, whose overall average value was 0.257, showed slow upward trend over time, while its growth rate gradually slowed down. The eastern China was the region with the highest development potential, while the development potential in the central China was found to be in an accelerating phase. Finally, the overall development potential of the southern China was relatively stable, while in the northern China it experienced a large-scale decline.

      (2) The evolution of the spatial pattern of the Chinese NIC was characterized by inland extension and coastal agglomeration. The overall development of the southern regions was relatively stable, while the northern regions experienced a large-scale shrinkage. Specifically, we identified a superior development zone, a rising development zone, an inferior development zone, and a declining development zone.

      (3) The spatial clustering of the Chinese NIC development potential presented a high-value agglomeration in east area and a low-value agglomeration in southwest area; moreover, the range of high-value agglomeration in east area gradually expanded and its degree gradually deepened.

      (4) The number of R&D personnel per 10 000 individuals of the labor force in the talent investment is the core factor affecting the development potential of the Chinese NIC. In the three years covered by this study (i.e., 2011, 2014, and 2017), the explanatory power of the number of R&D personnel per 10 000 individuals of the labor force (when this single factor acted on the dependent variable) was >70%. This value of explanatory power is much higher than that of other factors. Additionally, when the number of R&D personnel per 10 000 individuals of the labor force interacted in combination with other factors on the dependent variable, the explanatory power of eight of the 12 interaction results exceeded 90%, while that of two of the 12 interactive results exceeded 85%. Therefore, the number of R&D personnel per 10 000 individuals of the labor force was the most important driving factor for the development potential of NIC.

      The above findings have the following implications:

      (1) The current increase of the NIC development potential will continue to accelerate in developed areas of the southeastern China, and this trend will gradually extend to the central China. Meanwhile, this increase will be slow in the western China, while a three-tier development pattern is expected in the eastern, central, and western China.

      Although the Chinese NIC development potential is generally relatively low, the timing of the current Chinese NIC is not inappropriate. In fact, the analysis results suggest that Jiangsu, Zhejiang, Guangdong, and other places along the eastern coast actually already have a good foundation for NIC. The effect of driving factors in these areas has been beginning to show in the form of a growth pole, which is driving the construction of inland areas. The NIC development potential in the central inland areas close to the eastern coastal area has been steadily increasing year by year: these areas are likely to become keys for NIC.

      Eastern areas, which have a strong NIC development potential, should take the lead and stimulate a growth also in inland areas. The accelerated development of the central area requires special attention: the improvement of the NIC development potential should be promoted here, so that this area can become a hub of development potential for China. In this way, it would be possible to narrow the gap between high- and low-potential areas and achieve a coordinated development of the NIC development potential. Regions with weaker potentials need to rely on the resource advantages of neighboring regions with better development potential in order to address issues (e.g., resource depletion, economic recession, brain drain, and imperfect industrial systems).

      (2) Innovative talent is a core factor guiding NIC development; however, the synergy between the input of innovative talents and other factors will be more important in its promotion. Therefore, it is necessary to continue conducting collaborative evaluations to improve the overall synergy between different factors and maximize the benefits of NIC development.

      Innovative talents need to achieve a good level of talent training and talent attraction to play a significant role in their local area. The main disadvantages of underdeveloped regions in terms of talent training derives from faults in the existing integrated education system (from pre-school to higher education), while the main disadvantages in terms of talent attraction derive from the fact that the local social and economic development is not enough to attract high-quality talents in the area.

      Although the synergy strength of other influencing factors is not as strong as that of innovative talents, their enhancement effect after pairwise synergy is still relatively significant. To increase the NIC development potential, in addition to focusing on investments in innovative talents, it is also necessary to analyze which influencing factors have the best synergy effects over certain periods of time and in different regions. Once clarified, such differences can be used as references for a targeted development.

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