Analysis of measurement results

The measuring results analysis of composite index

Combined with the previous model construction and the classification criteria of coupling coordination stage in Table 2, the stata software is utilized to calculate the digitalization level, traditional industrial upgrading level index, the coupling coordination degree of digitalization and traditional industrial upgrading, and the relative development degree of digitalization and traditional industrial upgrading in the Yellow River Basin during 2011–2020. The measurement results are shown in Table 4. Meanwhile, it describes the time series trend chart of each composite index for a more intuitive comparative analysis in Fig. 1. Specifically, the evolution rate of digitization level in the Yellow River Basin was positive increasing from 21.60 in 2011 to 41.57 in 2020 with an increase of 93.06%. The overall upward trend was obvious. The traditional industrial upgrading in the Yellow River Basin showed a steady improvement trend. Except for a slight decline in 2016–2017 and 2018–2019, there was no significant fluctuation on the whole, which increased by 27.95% from 23.11 in 2011 to 29.57 in 2020. The exponential curve corresponding to coupling coordination degree increased steadily, while the relative development degree decreased gradually. It was in the disordered development stage of low level coupling coordination during 2011–2014. In general, it was in the low steady state of antagonism during 2015–2018, and it was in the high steady state of run-ins during 2019–2020. It can be seen that the coupling coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin is constantly optimized, while the latter lags behind the former and the two do not match.

Traditionally, the 18th National Congress of the Communist Party of China (CPC) in 2012 attached great importance to the development of the digital economy and elevated it to a national strategy. The 19th CPC National Congress in 2017 put forward a grand blueprint for building a “digital China” and a “smart society”. Under the great attention of the China’s government and a series of policy guidance, China’s digital economy has developed rapidly and ranked second in the world for a long time. The 20th CPC National Congress in 2022 further proposed the task of “Accelerating the development of the digital economy, promoting the deep integration of the digital economy and the real economy, and creating an internationally competitive digital industrial cluster”. With the rapid penetration of digital technology in various industries, industrial digitalization has gradually become the main engine of digital economy development and continues to consolidate its dominant position. Along with the continuous development of regional economy, China attaches more and more importance to the development of the Yellow River Basin. The phenomenon of “heavy industry and heavy energy” in the traditional industries of the Yellow River basin is serious, which hinders the high-quality development of the basin. Therefore, the ecological protection and high-quality development of the Yellow River Basin was subsequently elevated to a major national strategy in 2019, which clearly proposes to vigorously advance the construction of new infrastructure such as digital information, enhance the penetration rate of industrial Internet, artificial intelligence and big data to traditional industries, and foster the green transformation, intelligent upgrading and digital empowerment of the advantageous manufacturing industries in the Yellow River Basin. In line with the trend of digital development, the provinces and regions of the Yellow River Basin focus on digital technology infrastructure construction, strengthen the integrated development of digital technology, improve the digital technology governance system, scientifically layout digital technology to promote ecological protection, and promote the accelerated integration and development of industrial digitalization. However, the problem of unbalanced and inadequate development in the Yellow River basin is prominent⁶⁹. For example, the economic ties between provinces are not close, the regional division of labor coordination and cooperation awareness is poor, and the level of economic development is uneven, which seriously restricts the nine provinces along the Yellow River to fully realize high-quality development⁷⁰. Furthermore, the industries of the provinces in the Yellow River basin lack of emerging industrial clusters with strong competitiveness, which are dominated by energy, chemical industry, raw materials, agriculture and animal husbandry. Moreover, traditional enterprises such as coal, chemical industry and smelting have large stocks and prominent problems of low quality and efficiency. Therefore, there are many deficiencies in the digital support capacity of the Yellow River Basin, and the degree of social informatization and the development level of digital economy in the basin are still at the downstream level of the country. At the same time, there are still many problems in the construction process of “digital government” in the Yellow River Basin, such as insufficient top-level design force, low degree of intensification of information infrastructure, backward platform technology, weak basic support ability, and insufficient integration of online and offline, which leads to the failure of digitalization to effectively play the driving role of the optimization and reallocation of traditional production factor resources³⁰, and the lag in the upgrading and development of traditional industries is in the digital development.

The coupling and coordination process of digitalization and traditional industrial upgrading in the Yellow River Basin

As shown in Table 5, the coupling coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin gradually experienced a transformation process of “disordered state → disordered state co-existing with low steady state → low steady state → low steady state co-existing with medium steady state”. At present, it was still in the stage of low steady state co-existing with medium steady state. Moreover, t was in the disordered development stage in 2011, which was in the low-level coupling stage. The D value of the coupling coordination degree of the nine provinces in the Yellow River Basin is generally low, and the λ value of the relative development degree is greater than 1.1, which indicates that the digital development level lags behind the traditional industrial upgrading level. In general, digitalization is developing disordered at this stage, and the traditional industrial upgrading has not received key attention. From 2012 to 2015, the Yellow River Basin was in the disordered state co-existing with low steady state. In this stage, Sichuan (2013), Shaanxi (2013), Shanxi (2013) and Shandong (2012) gradually got rid of disordered state and entered the stage of low steady state. These provinces have a relatively advanced level of economic development and industrialization, a deep foundation for industrial development, and perfect digital infrastructure, which provides good conditions for digital development, and the dividends brought by the digital economy can be effectively released. Therefore, the coupling and coordination degree of digitalization and traditional industrial upgrading is relatively high. On the contrary, the location conditions and digital foundation are relatively weak, the degree of modernization of the economic system is not high, and the advantages of industrial digital transformation are not obvious⁷¹. The digital economy has failed to give full play to the advantages of dividends, and the coupling and coordination degree of digital and traditional industrial upgrading has a large room for improvement. Except for Gansu, the coupling coordination degree of the other 8 provinces was in the low steady state of the antagonistic stage until 2016. At this stage, the coupling degree of digitalization and traditional industrial upgrading gradually increased, the relative development degree began to decrease, and the speed of digitalization process increased. Since 2016, all the nine provinces in the Yellow River Basin have been out of the disorder stage, the coupling degree of digitalization and traditional industrial upgrading has continued to increase, the relative development degree has continued to decrease, and the speed of digitalization process has gradually increased. From 2017 to 2020, it will be in the stage of low steady state co-existing with medium steady state. At this stage, the overall coupling coordination degree continues to rise and the relative development degree continues to decrease. Among them, Sichuan and Shandong are outstanding, and their coupling coordination degree breaks through 0.6 to enter the running-in stage, while the other provinces are still in the low-steady and antagonistic stage at this stage. It can be seen that the coupling coordination degree of digitalization and traditional industrial upgrading in the Yellow River Basin is gradually optimizing, and this phenomenon is obviously reflected in all provinces. In addition, the coupling coordination degree and relative development degree of each province showed a steady increase and a gradual decrease trend respectively. It is expected that all provinces will get rid of the current situation of coexistence of low steady state and medium steady state, and turn to the medium steady state or high level coupling of high steady state stage.

Notably, it is worth noting that this measure result has some practical significance. According to the Research Report on the Development of Digital New Economy in the Yellow River Basin released by the China Institute of Electronic Information Industry Development in May 2022, the development of digital new economy in the Yellow River Basin shows a steady growth trend. Shandong ranks in the forefront of various indexes, and the overall development level of digital economy is the most prominent. Going back to the source, Shandong is a big province of real economy with traditional industries account for up to 70% from the perspective of industrial structure. Due to promote the traditional industrial upgrading, Shandong aims at the direction of intelligence and high-end, promotes the integration of industrial Internet into the park, carries out the digital transformation action of traditional industries, supports the development zone to vigorously develop digital core industries, and nurtures a number of provincial demonstration bases for new industrialization industries, such as the Shandong Yellow River Digital Economy Industrial Park. The rapid development of digitalization has made it the second industrial Internet demonstration zone in China after Shanghai. Shandong has 29 state-level professional platforms, 31 platform innovation navigation application cases, and 4 “digital navigation” enterprises, all of which rank first in the country in April 2023. Moreover, the application rate of industrial cloud platform is 63.3% and the comprehensive digitization rate of key business links is 70.4% in Shandong, which are among the top three in the country. In contrast, the traditional industrial structure of Sichuan Province exceeds 70%. In order to set a benchmark for China’s digital economy innovation and development, strengthen the digital economy, and effectively support high-quality development, the National Development and Reform Commission authorized six provinces (Hebei (Xiongan New Area), Zhejiang, Fujian, Guangdong, Chongqing, and Sichuan) as national digital economy innovation and development pilot zones in 2019. Sichuan is one of them and the only one among the nine provinces in the Yellow River basin. In response to the call of the state, Sichuan continued to promote the high-end of advantageous industries, the new type of traditional industries and the scale of emerging industries, and the final results were remarkable. For example, the industrial Internet platform in the electronic information industry cluster area of Sichuan has driven the regional economic increase of more than 20 billion yuan. Sichuan’s digital economy will exceed 2 trillion yuan and account for 40% of GDP until 2022. The integration of industrialization and information technology is developing rapidly, and the average annual growth rate of development ranks second in China. The numerical control rate of key processes and the penetration rate of digital R & D design tools reached 54.6% and 80.9% respectively.

Spatial–temporal evolution of coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin

The temporal evolution characteristics of the coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin

Furthermore, the non-parametric kernel density estimation method based on kernel function is employed to investigate the dynamic change trend of the coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin. The basic principle is as follows: Let X₁, X₂, …, X_n follows the same distribution, and its probability density f(x) must be obtained by sample estimation. Sample empirical distribution function F(X) is shown in Formula (21).

The probability density estimation of fixed bandwidth is shown in Formula (22).

where n is the number of samples, h is the bandwidth, and \(K\left( \cdot \right)\) is the kernel function. In order to maximize the fitting effect, the commonly utilized Epanechnikov kernel function was selected, and the data-based automatic bandwidth was further selected according to the principle of minimum mean square error⁷².

In this paper, five years (2012, 2014, 2016, 2018 and 2020) are selected as investigation profiles, and the 10-year data of nine provinces in the Yellow River Basin are decomposed into four stages. It depicts the kernel density curve of the coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin in Fig. 2. It can be seen that the dynamic evolution of the coordination degree distribution between digitalization and traditional industrial upgrading in the Yellow River Basin from 2011 to 2020 has a distinct feature. First of all, the coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin presents an obvious “right-skewed” distribution on the whole, which indicates that there are a few provinces with high coordination degree. Secondly, with the continuation of time, the peak height shows an obvious upward trend, the peak moves slightly to the left, the extension degree of the right tail continues to decrease, and its corresponding width narges, which indicates that the coordination degree of digitalization and traditional industrial upgrading continues to rise, and the inter-provincial gap has a trend of narrowing. Finally, the coordination degree of digitalization and traditional industrial upgrading presents a bimodal distribution in some years, which indicates that the overall development of the coordination degree of digitalization and traditional industrial upgrading is scattered, and the inter-provincial development is not coordinated and polarized. To find out its cause, the Yellow River basin runs through the east, middle and west of China. Some provinces are far apart, which results in great differences in economic strength and digitalization level within the basin. In general, the economy of the Yellow River Basin shows a pattern of “strong downstream and weak upstream”⁷³. In 2020, the GDP of the nine provinces in the Yellow River Basin will reach 25.39 trillion yuan, of which the regional GDP of Henan and Shandong in the middle and lower reaches of the Yellow River basin account for more than 50 percent, while the GDP of the upper Yellow River regions such as Qinghai, Gansu, Ningxia and Inner Mongolia excluding Sichuan only account for 13.11 percent. The inter-provincial digital economy in the Yellow River Basin also has the characteristics of unbalanced development of “strong in the east and weak in the west” and “strong in the south and weak in the north”. As early as 2018, the digital economy scale of Shandong exceeded 2 trillion yuan, while the digital economy scale of Ningxia and Qinghai was only between 60 and 90 billion yuan.

The spatial evolution characteristics of coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin

Due to more intuitively observe the spatial characteristics of the coupling coordination degree in each region of the Yellow River basin, this paper draws the global Moran’s I index table and the local Moran’s I index scatter chart of the nine provinces of the Yellow River Basin on the basis of the economic distance weight matrix. According to Table 6, the Moran’s I index of the coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin during the sample observation period is all positive, and the empirical results show that there is a positive spatial correlation between digitalization and traditional industrial upgrading coordination in the Yellow River Basin. The coupling coordination level of digitalization and traditional industrial upgrading among provinces is closely related to the coupling coordination level of neighboring regions, which is shown as a relatively stable spatial clustering feature.

Furthermore, limited by space, this paper focuses on analyzing the spatial agglomeration state of coupling coordination degree in the Yellow River Basin in 2012, 2016 and 2020 corresponding to Figs. 3, 4 and 5. Concretely, the provinces of Henan, Shanxi, Shandong and Shaanxi in the first quadrant are concentrated in the middle and lower reaches of the Yellow River, which showes a “high-high” (H–H) agglomeration distribution situation. However, the provinces of Ningxia, Gansu and Qinghai in the third quadrant are mainly concentrated in the upper reaches of the Yellow River, which showes a “low-low” agglomeration (L–L) distribution. Specifically, the coupling coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin has obvious spatial difference. Due to the superior geographical conditions, economic advantages and technical conditions, there is a large spatial connection and strong spatial effect among the regions in the lower Yellow River with high level coupling, which leads to a high-value agglomeration state in the transformation and traditional industrial upgrading driven by digitalization. Nevertheless, limited by location conditions and economic system, the digital technology level of the upstream region is not mature, industrial technology lags behind and structural transformation is hindered⁷⁴. These problems lead to the lack of internal impetus for regional industrial upgrading, and its digital development speed is lower than that of traditional industrial upgrading. Hence, the low level coupling region also has a certain spatial autocorrelation effect which is relatively small.

Interactive mechanism between digitalization and traditional industrial upgrading in the Yellow River Basin

Regression results of stationarity test, co-integration test and lag order selection

A prerequisite for the application of panel VAR model is the stationarity and co-integration relationship between variables. Therefore, this paper selected Fisher-ADF test and LLC test to test the common and individual unit roots. In addition, Fisher ADF and Fisher PP tests were also used to make the test results more accurate. The test results in Table 7 show that all ten variables are first-order unitary. On this basis, Kao test is continued to verify the co-integration relationship between variables. The panel co-integration test results of the two models are listed in Table 8, and the results show that there is a co-integration relationship between variables at the significance level of 1% and 5%. The panel VAR model can be consequently selected to examine the interactive relationship between digitalization and the traditional industrial upgrading. In addition, the length of the lag order is related to the accuracy of the estimated results, and the determination of the lag order is the second prerequisite for the application of the panel VAR model. In particular, the loss of degrees of freedom will bring significant deviation to the empirical results under the small samples in this paper. It lists the test results of lag order according to the commonly exploited AIC, BIC and HQIC criteria in Table 9, and the results show that the selection of second-order lag panel VAR model is more reasonable.

Analysis on the interactive effect of digitalization and traditional industrial upgrading in the Yellow River Basin

The in-group mean difference method and forward mean difference method were used successively to eliminate the time and individual effects respectively, and the numerical relationships among the study variables were estimated when the optimal lag period was 2 in Table 10, where the data in parentheses referred to the t statistic adjusted by white heteroscedasticity. According to the estimated results of model (19), at the significance level of 1%, the lag items of digital construction level and digital access level have a significant impact on the upgrading of 0.511 and 0.413 respectively on the traditional industrial upgrading, while the influence coefficient of digital circulation level and digital application level on the traditional industrial upgrading under the significance level of 1% is 0.003 and 0.001, respectively. Obviously, digital access level contributes the most to the traditional industrial upgrading, while digital circulation level and digital application level have little influence on the traditional industrial upgrading. Therefore, it is necessary to further strengthen the construction of digital infrastructure, increase the supply of network facilities and network services, and provide an important guarantee for the traditional industrial upgrading in the Yellow River Basin. In addition, the lag term of traditional industrial upgrading has the largest impact on the digital circulation level and the least impact on the digital access level. According to the estimated results of Model (20), at the significance level of 1%, the lag term of product upgrading and industrial ecological upgrading has a significant impact of − 0.098 and 0.085 on the digitalization level, respectively. Moreover, the lag term of process upgrading has a negative effect on the digitization level at the significance level of 5%, while the lag term of function upgrading has an insignificant negative effect on the digitization level. In addition, product upgrade, function upgrade and industrial ecological upgrade have significant effects on the digital level lag terms of − 0.410, 2.273 and − 0.119, respectively. In the above two models, digitalization level and traditional industrial upgrading are significantly affected by their own lag term at the significance level of 1%, which indicates that there is a certain inertia between digitalization level and traditional industrial upgrading⁷⁵.

The impulse response function of panel VAR model can quantitatively describe the current and future impacts of an endogenous variable on other endogenous variables after applying an orthogonalization pulse of one standard deviation, and obtain the dynamic correlation features between the two endogenous variables while other endogenous variables are controlled. Furthermore, the dynamic interaction effect between digitalization level and traditional industrial upgrading can be more thoroughly observed by impulse response function graph based on the interaction characteristics of describing variables. The first step is to obtain the impulse response function by Cholesky decomposition. The second step is to run 300 simulations through Monte Carlo method to obtain a confidence interval of two standard deviations. The horizontal and vertical axes in Figs. 6 and 7 respectively represent the number of lag periods (s) and the impulse response value. The red and black solid lines represent the impulse response curve and the 95% confidence interval, respectively⁷⁶.

1. 1.

   Dynamic changes of traditional industrial upgrading in the Yellow River Basin under the impact of digitalization (Fig. 6). The impact of digital construction level on the traditional industrial upgrading has always been positive, and this positive influence shows a trend of convergence with the increase of the response period. It shows that the continuous improvement of digital construction level will be conducive to the upgrading of traditional industries in the Yellow River Basin. Traditionally, the level of digital construction refers to the degree of infrastructure perfection that can reflect the characteristics of the digital economy. These infrastructures are based on information networks and integrated with emerging digital technologies, which can provide a new zero-distance contact platform for technological progress and product innovation, accelerate the flow of resources between traditional industries, and enhance information transparency. In addition, digital infrastructure can collect, store and utilize data across multiple systems and equipment, provide necessary network resources for traditional industrial enterprises, strengthen the link between product supply and demand, provide information and technical support for enterprises to grasp user demand information, and develop products in a targeted manner, which will promote the upgrading of traditional industries.

The positive impact of digital access level and digital circulation level on the traditional industrial upgrading is 0 in the initial stage. The former immediately produces a positive response and tends to be stable after reaching a certain threshold, while the latter continues to increase with the continuation of time. It shows that the continuous improvement of digital access level and digital circulation level has a positive impact on the upgrading of traditional industries in the Yellow River Basin. On the one hand, the level of digital access refers to the level of network connectivity, which is usually manifested as the level of Internet broadband access or ICT access. Digital network access can help enterprises to manage production. Businesses with broadband Internet access can participate in a variety of online media activities, which can help foster more digital skills and enhance access to information resources. On the other hand, the level of digital circulation reflects the development degree of digital industrialization. Digital circulation is an important manifestation of consumption. The new digital circulation platform can change consumption patterns, promote consumption upgrading, assist enterprises to identify customer needs, improve resource integration and market circulation efficiency, and thus promote the upgrading of traditional industries.

The impact of digital application level on the traditional industrial upgrading is in a state of fluctuation. Specifically, the impact response was 0 in the initial stage, reached a peak in the second stage, turned negative in the fifth stage, and the negative impact increased steadily. It shows that the digital application level has both promoting effect and hindering effect on the upgrading of traditional industries. Among them, the level of digital application plays a dominant role in promoting the upgrading of traditional industries before the fifth phase. Digital application level refers to the degree of IT technology mastery and popularization. Compared with the extensive large-scale production mode of traditional industry, the cross-time, strong link and instantaneous characteristics of digital technology can resolve the conflict between production cost, product diversity, production cycle and other multi-objectives to a certain extent, so as to alleviate the problem of overcapacity in traditional industries with flexible production methods. Therefore, it will help promote the upgrading of traditional industries by accelerating the promotion of the digital application of traditional industries, promoting the digital transformation and intelligent upgrading of the production methods and organization methods of traditional industries. However, the negative effect of the digital application level on the upgrading of traditional industries has emerged after the fifth phase. It indicates that the problem of “Tool sprawl” of an enterprise becomes very serious when an enterprise’s application of digital technology is too advanced or employees must rely on more and more digital systems or tools to ensure the normal completion of their daily work. This phenomenon has a serious impact on data center operation, network security protection, system reliability and application performance. The hindrance to the upgrading of traditional industries will gradually exceed the promotion effect.

1. 2.

   The dynamic change of digitization level in the Yellow River Basin under the impact of traditional industrial upgrading (Fig. 7). With the continuation of response time, the impact of process upgrading on digitalization level turns from 0 to positive, and the degree of positive influence is increasing. The impact from product upgrading always remains positive, but this positive influence gradually decreases to convergence with the continuation of time. The process upgrading of traditional industries refers to the improvement of production processes through the use of advanced production technology and production equipment. The most direct manifestation of product upgrading in traditional industries is that enterprises achieve promotion to both ends of the value chain by developing new products or transforming existing products. The process upgrading and product upgrading of traditional industries are conducive to promoting the spatial spillover of digital technology, promoting digital technology cooperation within and between industries, and effectively driving the digital development of other industries. At the same time, the optimization of traditional industrial structure can effectively promote the linkage development of upstream and downstream industries in the Yellow River Basin, and enhance the digitalization level of upstream industries.

The impact of industrial ecological upgrading on digitalization level turns from 0 to positive, and the degree of positive influence is increasing. Industrial ecological upgrading means that traditional industries choose production technologies with lower energy consumption and less pollution to optimize the allocation efficiency of energy in the process of production, so that traditional products continue to develop in the direction of low energy consumption and low pollution to achieve sustainable development. In the process of ecological upgrading of traditional industries in the Yellow River Basin, the production factors in the basin have shifted and the production efficiency has gradually increased, which will effectively improve the digital capability in the basin.

The impact from function upgrading starts from positive, and this positive impact decreases continuously until it turns negative and deepens in the third stage. After process upgrading and product upgrading, the industry has accumulated core technologies and the ability to develop new products. It will recombine and distribute the internal resources of traditional industries, gradually abandon low value-added links to produce high value-added links, in order to obtain the core competitiveness of traditional industrial enterprises and promote functional upgrading. Before the third phase, the technology spillover and promotion effect of the functional upgrading of traditional industries emerged, which could offset the cost effect of digitalization and help improve the digitalization level of enterprises. However, digital transformation is not a one-off project, but a continuous process of innovation. Many enterprises only carry out digital transformation in the early stage, lack of continuous investment and innovation, and eventually lead to the gradual weakening of the transformation effect in the later stage. The cost effect of digitization is higher than the compensation effect. Therefore, the overall effect was negative after the third stage.

Due to further analyze the structural impact of CSJ, JS, JR, LT and YY on CSJ in Model (19), and SZH, GY, CP, GN and STH on SZH in Model (20). The variance decomposition method is used to extract the more important information of variables affected by each random disturbance in this paper. According to the results of variance analysis in Table 11. CSJ, JS, JR, LT and YY all have a certain influence on CSJ in Model (19). On the whole, the contribution of CSJ shock to its own prediction variance is the largest and shows a decreasing trend. The peak contribution of JS, JR, LT and YY to CSJ reached 5.3%, 37.4%, 0.2% and 18.2%, respectively. In contrast, the contribution of JS to CSJ has a great impact in the short term, while the contribution of JR and YY to CSJ is stable in the long term, and the contribution of LT to CSJ has not been obvious. In Model (20), the contribution of SZH and GN shocks to the prediction variance of SZH firstly increased and then decreased, the contribution of GY shocks to the prediction variance of SZH maintained an increasing trend, while the contribution of CP and STH shocks to the prediction variance of SZH maintained a decreasing trend. The peak contribution of GY, CP, GN and STH to SZH reached 22.4%, 18.2%, 25.6% and 44.4%, respectively. Moreover, GY and GN make relatively large contribution to SZH. In addition, the interaction between digitalization and traditional industrial upgrading is asymmetrical, and the two contribute the most to their own impact value, which indicates that the “digitalization—traditional industrial upgrading” system presents significant positive feedback effect. Regions should reasonably allocate digitalized construction investment, digitalized access investment and digitalized application investment at different stages according to the actual situation, and avoid one-sided emphasis on digitalized construction investment.