Research on the Effect of Information Infrastructure Construction on Low-Carbon Technology Knowledge Flow

Abstract

The cross-regional diffusion of low-carbon technology knowledge is conducive to the development of low-carbon technology and increases the emission reduction contribution of the wider land. However, the flow of low-carbon technology knowledge faces many obstacles such as information, geography, and local protectionism. This paper focuses on whether and how information infrastructure construction can promote the flow of low-carbon technology knowledge under the digital wave. Through the low-carbon technology patents of category Y02 under the classification standard of Cooperative Patent Classification (CPC) and by matching them to the city level to measure the low-carbon technology knowledge flow, empirical research finds that: information infrastructure cannot only directly promote the low-carbon technology knowledge flow, but also indirectly promote the low-carbon technology diffusion by increasing foreign direct investment and human capital. The role of information infrastructure in promoting low-carbon technology knowledge is more obvious in the central and western regions and non-resource-based cities. Finally, in view of the current development situation of low-carbon technology knowledge flows in China, the paper puts forward policy recommendations to vigorously promote the construction of information infrastructure, to gradually increase the network density and the number of network nodes, release the digital economic dividend, and form a spatial pattern of coordinated development of regional knowledge flows.

1. Introduction

The importance of technological means in meeting the challenge of climate change is generally recognized [1,2]. Against this background, the question of how to stimulate low-carbon technological innovation has received a lot of economic research focus. As a necessary component of the complete process of technological activities, the knowledge flow of mature low-carbon technologies has received less attention [3]. If the knowledge flow of low-carbon technologies is insufficient, the potential of innovation will not be fully realized and the value of innovation will be greatly reduced. The report of the 20th National Congress of the Communist Party of China stressed the important strategic deployment of “accelerating the construction of a new development pattern with domestic major circulation as the main body and domestic and international circulation promoting each other”, which provided strategic guidance for the diffusion of low-carbon technology knowledge. Some existing empirical studies show that behind the miracle of China’s growth is the huge benefits from international technology spillovers. For example, in the field of green manufacturing, since 2010, China’s export volume of green equipment products has exceeded 30% of the global share. The “cheap” version of “Made in China” has rapidly risen in the forefront of photovoltaic, wind power and other fields within a few years, becoming the world market leader [4]. In this connection, 59% of clean development mechanism (CDM) projects owned by China involve technology transfer [5]; China accounts for three-quarters of the transfer of climate mitigation technologies from OECD to non-OECD countries [6]. China’s knowledge, gained as a result of international green technology diffusion, and its effect on economic growth are more than twice that of the United States and Europe, and far more than other developing countries such as India and Brazil [7]. In China, a country with a vast territory and high carbon emissions, the technological means to achieve the “double carbon” target should not only pay attention to the innovation of technology, but also pay attention to the cross-regional diffusion of technology.

A new round of information and technological revolution with information technology as its core has sprung up. New digital infrastructure, such as 5G communications, broadband internet, and big data centers, have gradually integrated into the whole process of economic and social development in all fields, becoming a supporting force for steady investment, promotion of upgrading and cultivation of new momentum. At the same time, digital infrastructure plays an important role in breaking the time—space barrier of knowledge dissemination, accelerating the interregional flow of research and development elements, and realizing cross-city collaborative innovation [8]. According to the “China Digital Economic Development Report (2022)”, China’s digital economy made a new breakthrough in 2021. The scale of the digital economy reached RMB 45.5 trillion, accounting for 39.8% of GDP, and the growth rate of the digital economy was more than three times that of GDP (guizhou.gov.cn (1 July 2022)). The scale of industry digitalization reached RMB 37.2 trillion, accounting for 32.5% of GDP, which became the key driving force for stable economic growth, and information infrastructure construction played an important role in it [9]. More complex low-carbon technology knowledge is vulnerable to geographical distance and information asymmetry. The flow of low-carbon technology knowledge is more dependent on the policies and economic environment of the micro-region where it is located, but the research has paid less attention to the urban level. Most of the existing research has taken the flow of technology knowledge as an intermediary mechanism, and few studies have explored the mechanism of information infrastructure affecting the flow of low-carbon technology knowledge.

Based on this, this paper innovatively conducts an empirical study from the perspective of information infrastructure construction, using the data of 285 prefecture-level cities in the country from 2006 to 2019, trying to find empirical evidence on the relationship between the development of information infrastructure construction and the flow of low-carbon technology knowledge. The contributions of this paper are as follows. First, the existing literature mainly evaluates the effectiveness of information infrastructure construction through changes in some macroeconomic variables, such as economic growth, urban innovation, enterprise transformation, etc. [8,10,11]. This paper innovatively links the construction of information infrastructure with the flow of low-carbon technological knowledge. Secondly, we use patent citation (backward citations) and cited (forward citations) information to construct two indicators of knowledge flow: knowledge inflow and knowledge overflow. Third, the basic assumption of the current research on knowledge flow based on patent information is to use the developed countries such as the United States and other western countries as new knowledge sources to study the knowledge flow between developed countries and the knowledge spillovers to developing countries. This paper takes China as the knowledge source to study the characteristics of low-carbon technology knowledge flow in China.

2. Literature Review and Theoretical Analysis

2.1. Literature Review

• (1). Knowledge flow measurement-related research

The flow of knowledge occurs when an idea is passed between two subjects. These processes represent the process of learning from the ideas of others, effectively creating an accessible (or “borrowed”) inventory of ideas and knowledge [12]. At present, there are still few theoretical and empirical studies on knowledge flow. One important reason is that the externalities of innovation or knowledge spillovers are difficult to measure. The previous methods of measuring knowledge spillovers mainly explored the degree of diffusion of international technology elements to host countries from the perspective of FDI knowledge spillovers, and indirectly measured the scale of local technology diffusion by the turnover of the technology market. Some scholars also use model methods, such as the Bass model, the evolutionary game model, the epidemic disease model, etc. However, the model approach mostly starts from the macro level. For example, the Bass model is mainly aimed at the adoption and diffusion of innovative products and technologies and is often used as a market analysis tool to predict the demand for new products and technologies. The innovation diffusion of new products refers to the process of new products from creation and development to market promotion and final use, which is manifested by the behavior of consumers from awareness, interest, evaluation, and trial to final adoption of new products. The Bass model is usually associated with the theory of innovation diffusion. In the Bass model, the dissemination and adoption of new technologies or products are mainly divided into two categories. One is innovation adoption, that is, such people are not affected by anyone who has used new products or technologies. They independently choose to adopt new products and technologies, and the other type of adopters will refer to the evaluation and experience of the people who have used new products to make choices. Jaffe [13] creatively believes that the knowledge flow can be presented in the form of patent citation, which contains the information of “learning” from other innovators, and to some extent can intuitively reflect the knowledge flow. Narin [14] et al. proposed the concept of patent metrology and believed that the number of patent citations can also be used to measure the diffusion of technology. Since then, more and more scholars have begun to use this more direct measure of geographic location information in patent citations to study technology diffusion between regions [15]. This method strongly promotes the research of knowledge flow. However, at present, the research of knowledge flow based on patent information is basically based on Western developed countries such as the United States as the source of newly created knowledge, and less on the patented technologies applied by developing countries and China as the source of knowledge.

• (2). Relevant research on information infrastructure construction

At present, most research on information infrastructure are focused on the macro-economy and micro-enterprise. On the macro-economic level, Chen et al. [16] found that the construction of information infrastructure is positively promoting the economic growth of China and has a stronger role in promoting the western region through an empirical analysis of 31 provinces in China. Koutroumpis [17] used panel data from 22 OECD countries to prove that there is a significant positive causal relationship between broadband infrastructure construction and economic growth. Other studies have found that information infrastructure can promote the upgrading of China’s foreign trade by accelerating the flow of technological knowledge [18] and improve labor productivity while improving the city’s trade growth and export performance [19]. Zhang et al. [20] found that information infrastructure can effectively promote the flow of information, capital, labor, and other elements, thus promoting regional coordination and upgrading of industrial structure. In addition, a small amount of literature has found that information infrastructure can promote the city’s green technological innovation through the information support effect and the scale aggregation effect [21] and drive the city’s emission reduction effect [22]. At the micro-enterprise level, Lyytinen et al. [23] discussed the impact of information technology on enterprise innovation capability and found that enterprise information technology investment is beneficial to improve enterprise innovation capability. The internet can break the resource constraint, promote information sharing and technology diffusion, and thus improve the technological innovation capability of enterprises. At the same time, the accompanying technology diffusion and knowledge spillovers in the design and construction of information infrastructure will also promote the technological progress of relevant industries and promote the upgrading of technological standards of enterprise products and equipment [24].

• (3). Relevant research on the influencing factors of knowledge flow

Part of the literature emphasizes the role of policies in promoting the flow of low-carbon technology knowledge. Jaffe and Stavins [25] found that stricter environmental restriction policies can promote the flow of technology knowledge. If the intensity is too low, it will actually have little or no effect. Warren et al. [26] found through a series of case studies that environmental policies will encourage enterprises to adopt a series of pollution prevention and control measures, which is beneficial to enterprises to innovate in environmental protection technology and spread knowledge. Jacobsson and Lauber [27] explored the reasons for the rapid popularization of wind turbines and solar cells in Germany and found that various environmental policy tools have promoted the knowledge flow of clean technologies in Germany, and their effects are different in different periods. Similarly, research shows that environmental regulation, warrant trading, and public pressure can all motivate firms to adopt clean technologies [6].

There are two documents similar to the study in this paper, which discuss the impact of transportation infrastructure on technology diffusion. Liu and Zhu [28] and Shen [29] found that the improvement of high-speed rail infrastructure can promote the technology diffusion and innovation in cities along the line. The main reason is that the rapid development of public infrastructure has broken the barriers of space distance to a certain extent, expanded the scope of knowledge spillovers and promoted technological innovation and diffusion. Dong and others [30] took the research results of scholars as the research object and found that the opening of the high-speed railway reduced the communication cost among scholars, promoted academic cooperation and improved the quantity and quality of papers. Research such as Cui et al. [31] based on municipal data in China shows that the higher the road density in a city, the higher the technological innovation level of local companies, because the transportation infrastructure promotes the flow of technological knowledge, i.e., there is a knowledge spillover effect.

2.2. Theoretical Analysis

• (1). Analysis of the direct effect of information facilities construction on the flow of low-carbon technology knowledge

Low-carbon technology knowledge flow is also a kind of technology diffusion, which also follows the general rules of knowledge flow. As a carrier of the flow of technological knowledge, technology is often limited by factors such as geographical distance, trade and investment, and transmission cost. Marshall [32] has long recognized that the shortening of geographical distance can improve the efficiency of knowledge spillovers. Over the past few decades, the international transfer of knowledge spillovers has gradually increased, mainly due to the increase in investment in transportation and information technology equipment, which has lowered the barriers to technology transfer between regions [33]. The rapid development of public infrastructure, such as roads, railways, airports, and communications, has to a certain extent broken down the barriers of spatial distance, reduced commuting cost, and accelerated the exchange of personnel between regions, thus having a positive impact on the diffusion of technology. With the wide application of modern digital technology with the internet as the core, the encoding of knowledge and information is accelerated [34], which promotes the spread of knowledge and information and reduces the cost of dissemination [35]. The construction of modern information infrastructure connects networks in a series to solve the problem of information transmission, which can become an important method of knowledge flow [36]. Specifically, the construction of modern information infrastructure enables interpersonal communication to break through the limitation of time and space and allow communication with each other through instant messaging, video conferencing, and other means, which greatly accelerates the speed of knowledge dissemination and reduces the cost of knowledge exchange and knowledge acquisition [37]. It can be seen that with the rise of the digital economy, the information infrastructure construction has been paid attention to with its unique organization and its effect on knowledge flow. Based on the above mechanism analysis, the following hypotheses are proposed.

Hypothesis 1 (H1). 

Information infrastructure construction can directly promote the flow of low-carbon technology knowledge between cities.

• (2). The impact of information facilities on the flow of low-carbon technology knowledge mechanism analysis

When the infrastructure is perfect, it will reduce the trade barriers between regions and strengthen the flow of human and capital factors, which will benefit the knowledge spillovers [38]. The development of transportation infrastructure such as high-speed rail facilitates the movement of knowledge and personnel elements, but the transportation cost and time cost are still high, which cannot guarantee the complete freedom of movement. In contrast, information infrastructure such as broadband can transfer knowledge by the flow of information elements without the physical displacement of elements, which saves more time and improves the efficiency of knowledge transmission more than transportation infrastructure such as high-speed rail [39]. The efficiency of resource allocation brought about by the improvement of information infrastructure in a city or region will increase with the cross-regional flow of knowledge, technology, talents, capital, information, and other elements, removing barriers to the flow of elements, promoting the flow of various elements such as labor, capital, information, and high value-added intermediate products, and further driving the dissemination and diffusion of knowledge, technology and other elements to the surrounding regions, providing a material carrier for the dissemination of technology [40,41]. With the improvement of information infrastructure, people and enterprises have more and more convenient connections. Through the connection of information sensing equipment, people, objects, and machines can be interconnected efficiently. The mechanism variables selected in this paper are mainly divided into two categories: labor factor flow and capital factor flow. The remarkable feature of human capital is its strong mobility and sensitivity to changes in external factors such as economy, environment, and policies. The improvement of infrastructure will definitely affect the change of human capital. For example, the opening of high-speed rail can increase the market scale, the number and scale of enterprises, and the attraction of cities to high-level talents, thus promoting the trans-regional flow of high-level talents [39]. This high-skilled labor workforce is itself an important carrier of knowledge communication and technological innovation, and its inter-regional flow can further accelerate the technology diffusion and knowledge flow [42]. Based on this, the following hypothesis is put forward.

Hypothesis 2 (H2). 

Information infrastructure construction promotes the flow of urban low-carbon technology knowledge by promoting the flow of human capital and increasing foreign direct investment.

3. Model Construction and Data Description

3.1. Model Construction

• (1). The establishment of a benchmark regression model

For Hypothesis 1, this paper tests the role of information infrastructure in promoting the flow of low-carbon technology knowledge by establishing a measurement model as follows:

(1)$Indif{f}_{it}={\alpha }_1+{\beta }_{1}IC{T}_{it}+{\phi }_{1}controls+cit{y}_i+yea{r}_t+{\epsilon }_{it}$

(2)$Outdif{f}_{it}={\alpha }_2+{\beta }_{2}IC{T}_{it}+{\phi }_{2}controls+cit{y}_i+yea{r}_t+{\epsilon }_{it}$

In Formulas (1) and (2), indiff_it and outdiff_it are the explanatory variables, which represent the level of low-carbon technology knowledge inflow and low-carbon technology knowledge overflow of City i in t year, respectively. The coefficients β₁ and β₂ indicate the impact of information infrastructure construction on the flow of technological knowledge. If β₁ and β₂ are both significantly regular, it indicates that the development of information infrastructure construction has a positive impact on the inflow and outflow of technological knowledge. Controls are a collection of control variables. Furthermore, city_i is an urban fixed effect and yrar_t, a time fixed effect respectively. ${\epsilon }_{it}$ is a random perturbation term. The fixed effect model in this paper has passed the Hausman test and the p-value is significant.

• (2). The mechanism of regression model building

For Hypothesis 2, this paper mainly uses the mediating effect model to verify the mechanism that factor flows promote the flow of low-carbon technology knowledge for information infrastructure. The specific model is as follows:

(3)$hu{m}_{it}={\alpha }_3+{\sigma }_{3}IC{T}_{it}+{\phi }_{3}controls+cit{y}_i+yea{r}_t+{\epsilon }_{it}$

(4)$fd{i}_{it}={\alpha }_4+{\sigma }_{4}IC{T}_{it}+{\phi }_{4}controls+cit{y}_i+yea{r}_t+{\epsilon }_{it}$

(5)$Indif{f}_{it}={\alpha }_5+{\theta }_{1}IC{T}_{it}+{\tau }_{1}hu{m}_{it}+{\phi }_{5}controls+cit{y}_i+yea{r}_t+{\epsilon }_{it}$

(6)$Outdif{f}_{it}={\alpha }_6+{\theta }_{2}IC{T}_{it}+{\tau }_{2}fd{i}_{\mathrm{it}}+{\phi }_{6}controls+cit{y}_i+yea{r}_t+{\epsilon }_{it}$

Among them, hum_it and fdi_it represent the level of human capital of city i in t year and the utilization level of foreign direct investment; other variables are the same as Equation (1).

3.2. Data Description

• (1). Low-carbon technology knowledge flows

When applying for a patent, the applicant must refer to all “prior art” related to the invention, while a new technology will also be referred to by subsequent “inventions”. This series of circular references can reflect the complete process of knowledge flow [43,44]. Although patent citation is an accurate but noisy indicator of the actual knowledge flow, it constitutes the most widely used measure of knowledge flow in economic, management, and policy literature [45]. Therefore, this paper tracks the inflow of external low-carbon technology knowledge with the number of patents per ten thousand people and evaluates the spillover of local to external low-carbon technology knowledge with forward citations. According to Yu et al.’s [46] method, the ratio of patent citations and citations to ten thousand permanent residents is used as an overall indicator to measure knowledge flow. Patent data used herein are derived from the incopat Patent Database (incoPat.com).

• (2). Information infrastructure construction

In this paper, the information infrastructure is divided into two categories. One is the telecommunications network infrastructure, which includes mobile phones and network infrastructure. The other category is the information service level, which refers to the local postal and telecommunication service level. The regional information infrastructure level refers to the method of Li et al. [19] to select four indicators. They are per capita internet users, per capita mobile phone users, per capita total telecommunications business, and per capita total postal business in the region. Per capita internet users and per capita mobile phone users are the reflection of the level of information infrastructure. Since entering the 21st century, mobile phones have produced an obvious substitution effect on fixed phones, so it is more appropriate to select mobile phones. The per capita telecommunication service and the per capita post and telecommunication service can represent the information service level. Bartlett’s sphere test and KMO test were carried out before the principal component analysis method was used. The Bartlett’s sphere test showed that the p-value was significant at the level of 1% and the coefficient of KMO test was 0.758 greater than the critical value of 0.6, indicating that the correlation between explanatory variables was strong and suitable for principal component analysis. Each of the two types of indicator variables is derived from the wind database.

• (3). Control variables and mechanism variables

Control variable: the level of economic development is measured using the logarithm of the city’s GDP per capita (logarithm of per capita GDP yuan). The population size is measured by the number of the resident population of the city (thousands of people per square kilometer take logarithm). The upgrading of industrial structure is measured by the ratio of the added value of the tertiary industry to that of the secondary industry. The level of financial development is measured by the ratio of the balance of deposits and loans of financial institutions to GDP at the end of the year. The degree of industrialization is measured by the proportion of the added value of the secondary industry to GDP.

Mechanism variable: human capital is measured by the ratio of the number of employees in urban units at the end of the period to the resident population in the city. Foreign direct investment is measured by converting the actual foreign capital utilized in the current year into RMB. Both the control variables and the mechanism variables are derived from the city statistical yearbook.

• (4). Variable descriptive statistics

Table 1 shows the descriptive statistical results of the main variables in this paper. The standard deviation of the descriptive statistical results of low-carbon technology knowledge flow (Indiff, Outdiff) is 2.34, and the variance is small, indicating that there is no significant gap in the level of low-carbon technology knowledge flow in various regions. The minimum value of information infrastructure construction (ICT) is1.48819, and the maximum value is 20.63911. From this, we can see that some cities have better information infrastructure, and some cities are weaker.

3.3. Positive Results

• (1). Benchmark Regression

Based on the above measurement models (1) and (2), this paper examines the direct impact of information infrastructure on the knowledge flow of low-carbon technologies. The results in Table 1 show that for every additional unit of information infrastructure construction in cities, an average of 0.406 low-carbon technology patents will be cited per ten thousand people, i.e., the flow of low-carbon technology knowledge. Similarly, this also shows that the impact of information infrastructure on the inflow of low-carbon technology knowledge has a significant positive incentive effect, which verifies the conclusion of hypothesis 1 above. However, information infrastructure has a certain inhibitory effect on knowledge spillovers of low-carbon technologies, but the inhibitory effect is not significant.

Further observation of the influence coefficient of its control variables shows that the coefficients of industrial structure upgrading, population size, and industrialization degree are all in line with the actual situation, while the economic development level and financial development level do not have a positive incentive effect on the flow of low-carbon technology knowledge as expected, and inhibit the flow of low-carbon technology knowledge at a significance level of 10%. The reason is that, for the level of economic development and financial development, although the investment from the city government with a higher economic development level will give the enterprises corresponding research and development subsidies to improve the enthusiasm of the low-carbon technology knowledge flow to a certain extent, at present, due to the lack of government supervision over the use of investment funds, capital abuse and improper use are often caused. At the same time, government intervention will also increase the demand for research and development resources of enterprises. In the short-term lack of flexibility, research and development costs will be further increased, which will force enterprises to transfer research and development projects, thus inhibiting the development of low-carbon technology knowledge flow to a certain extent. In cities with higher levels of financial development, it is more convenient for enterprises to obtain loan support, which may result in the mismatch of resources and the decrease in the utilization rate of funds, thus resulting in similar results to the level of economic development in inhibiting the flow of low-carbon technology knowledge.

• (2). Mechanism testing

The regression results of the mediating effect are shown in Table 2. For human capital and foreign direct investment, the regression results in the first step of the mechanism test show that information infrastructure can significantly promote the regional human capital flow and foreign direct investment increase. The regression results in the second step of the mechanism test show that both information infrastructure, human capital and foreign direct investment can significantly promote the flow of low-carbon technology knowledge. When the coefficients in the model are significant, the Sobel test is not required. Therefore, this paper argues that the mechanism test effect model is significant. Information infrastructure can not only directly promote the flow of low-carbon technology knowledge, but also indirectly promote the flow of low-carbon technology knowledge by promoting the flow of human capital and increasing foreign direct investment. Therefore, Hypothesis 2 holds.

• (3). Endogeneity Test

Considering the possible reverse causal relationship between information infrastructure and the diffusion of low-carbon technologies and the omission of variables, this paper intends to use the instrumental variable method to mitigate the endogenous problems. In this paper, the topographic relief of prefecture-level cities is selected as the tool variable. Firstly, the geographical gradient of cities meets the correlation condition of the tool variable. The greater the geographical gradient of cities, the higher the cost of information infrastructure construction will be, and the quality of the broadband network will be affected, thus affecting the operation efficiency of information infrastructure. On the other hand, geographical gradient as a tool variable satisfies exogenous conditions, because it belongs to natural geographical factors and will not be affected by socio-economic factors and will not affect the flow level of low-carbon technology knowledge. Column (1) of Table 3 shows the effect of stage 1 instrument variable (IV-Undulation) on independent variable (ICT), with a coefficient of 0.381, which is significantly positive at 1%, while stage 1 regression F statistic is greater than 10, rejecting the assumption of “weak instrument variable”. Column (2) is listed as the second stage regression of the instrumental variable, and the regression coefficient of the core explanatory variable is 0.557, which is significant at the level of 1%. After the possible endogenous problems are solved, the information infrastructure construction still significantly promotes the flow of low-carbon technology knowledge, which is consistent with the benchmark regression results. Hypothesis 1 has been verified again, indicating the robustness of the conclusions in this paper.

• (4). Heterogeneity test

Location heterogeneity test

According to the geographical location of the cities, the samples are divided into eastern and central-western cities and grouped for regression as shown in Table 4. The results show that the construction of information infrastructure in the eastern region does not show a good effect in promoting the flow of low-carbon technology knowledge. On the contrary, the construction of information infrastructure in the central and western regions has a significant effect on the flow of low-carbon technology knowledge. This is different from the conclusion that the low-carbon city pilot will play a stronger role in promoting the flow of low-carbon technology knowledge in the eastern region [47]. The possible reason is that in the central and western regions where the infrastructure is not perfect enough, the improvement of the information infrastructure can drive the regional linkage with other mature low-carbon technology development, strengthen the exchange of talents and capital, and thus play a role in promoting the flow of low-carbon technology knowledge.

In accordance with the “Circular of the State Council on Printing and Issuing the National Sustainable Development Plan for Resource-based Cities (2013–2020)”, the sample cities are divided into resource-based cities and non-resource-based cities, and then grouped for regression. The results in Table 5 and Table 6 show that the promotion effect of information infrastructure construction on low-carbon technology knowledge flow in non-resource-based cities is significantly positive, while the promotion effect on resource-based cities is not obvious. It indicates that the “resource curse” effect of resource-based cities may exist, which will cause difficulties in the transformation of resource-based cities, and the improvement of information infrastructure construction will not be able to promote the flow of low-carbon technology and knowledge.

4. Conclusions and Policy Recommendations

Focusing on the issue of whether information infrastructure construction can promote the flow of low-carbon technology knowledge, this paper constructs an index of low-carbon technology knowledge inflow and outflow by using patent information. It empirically analyzes the effect of information infrastructure construction on the flow of low-carbon technology knowledge by using the two-way fixed effect model method and carries out its mechanism analysis to explore the details of the effect. The main research conclusions are as follows.

• The construction of information infrastructure has significantly promoted the flow of low-carbon technologies in cities. It shows that the construction of information infrastructure is effective in stimulating the development of urban technology. However, the role of information infrastructure in promoting knowledge spillovers of low-carbon technologies has not been revealed. The reason may be that the improvement of the information infrastructure promotes the city’s active learning by reducing the transmission cost, lowering the regional barriers, and helping people to communicate more frequently. However, although the information infrastructure facilitates the technology communication, it still cannot improve the level of unconscious knowledge spillovers between regions.

• The improvement of the human capital level and the increase of foreign direct investment are helpful to strengthen the promotion of information infrastructure construction on the flow of low-carbon technology knowledge. It shows that with the improvement of information infrastructure, the existing level of human capital and the level of foreign direct investment in cities have been affected. As the main driving factors to promote the flow of knowledge, these two have further promoted the flow of knowledge.

• The promotion effect of information infrastructure construction on the flow of low-carbon technology knowledge is more significant in the central and western regions and non-resource-based cities. It indicates that better geographical and transportation conditions and more concentrated factor resources may amplify the effect of information infrastructure on the flow of low-carbon technology knowledge. However, dependence on natural resources is likely to form a “resource curse” effect, making it difficult to make a positive technological response to the development of information infrastructure.

Policy Recommendations

The above research conclusions provide an inspiration for further information infrastructure construction to promote the smooth flow and circulation of low-carbon technology knowledge.

The first recommendation would be to strengthen the radiating and driving function of the information infrastructure, and to seek a balanced exchange and development of knowledge flows among cities. The construction of information infrastructure broke through the physical space restrictions and had a positive impact on the development of the “local-neighboring” knowledge flow, but the effect of promoting the knowledge flow in different cities is still heterogeneous. Therefore, we should gradually increase the network density and the number of network nodes, release the digital economic dividend, and form a spatial pattern of coordinated development of regional knowledge flows. We will promote exchanges and cooperation within cities, expand the radius of knowledge spillovers, regional restrictions, and industry barriers by virtue of the advantages of platform scale, accelerate the formation of a high-end and intensive green industry system, realize the effective allocation of essential resources, and thus release its potential for environmental benefit development.

Secondly, the formulation of information infrastructure construction should be linked and matched with the policies of foreign direct investment introduction and talents, in order to enlarge the effect of information infrastructure construction on the flow of low-carbon technology knowledge. At the same time, according to the characteristics of different cities, relevant policies to promote the development of information infrastructure should be formulated according to local conditions. In the eastern and central regions and the pilot cities of the first and second lines, we should make full use of the comprehensive advantages of talents and industries, focus more on the development of low-carbon technologies, and lead the deep low-carbon transformation. The western region, small-scale and resource-based cities should make full use of the investment opportunities of new infrastructure such as information, intelligence, and innovation in policy formulation, and strive to improve the basic conditions conducive to the exchange of technological knowledge. In addition, these cities should pay more attention to the coordination with industrial and talent policies, and take the initiative to eliminate the policy barriers that hinder the flow of factors, forming a joint force to promote the flow and circulation of knowledge.

Author Contributions

Writing—original draft preparation, X.W.; writing—review and editing, W.W.; methodology, Y.W.; data curation, S.J. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Tables

Table 1

Descriptive statistical analysis.

Variable	Obs	Mean	Std. Dev.	Min	Max
Indiff	2587	0.940024	2.335527	0.003911	40.54286
Outdiff	1747	1.210503	2.992132	0.004463	39.94028
ICT	3989	−0.000228	1.685552	−1.48819	20.63911
FIN	3989	2.920515	5.600944	0.00945	135.1855
IND	3989	0.928845	0.526785	0.094317	5.340072
PGDP	3989	0.528506	0.320294	−2.004365	2.807654
DEG	3989	0.477536	0.110623	0.031	0.9097
POP	3989	−1.176223	0.912532	−5.36019	0.973846

Table 2

Benchmark regression results.

	(1)	(2)	(3)	(4)
	Indiff	Outdiff	Indiff	Outdiff
ICT	0.408 **	−0.255	0.406 **	−0.248
	(2.72)	(−1.44)	(2.81)	(−1.36)
FIN			−0.015 *	0.013
			(−2.28)	(1.18)
IND			0.063	−0.547
			(0.20)	(−1.51)
PGDP			−2.306 *	1.956
			(−2.57)	(1.72)
POP			0.865	−0.393
			(1.77)	(−0.79)
DEG			2.893	−6.822 **
			(1.39)	(−2.97)
_cons	0.150	0.198	0.072	3.179 **
	(1.10)	(1.15)	(0.07)	(2.92)
N	2587	1747	2587	1747
F	7.985	3.758	7.954	4.878
r2_a	0.217	0.132	0.237	0.147
N_g	285.000	285.000	285.000	285.000

Note: *, **, ***, respectively, indicate that they have passed the significance level test of 10%, 5%, and 1%, the same below.

Table 3

Mechanism Test.

	(1)	(2)	(3)	(4)	(5)	(6)
	Hum	FDI	Indiff	Outdiff	Indiff	Outdiff
ICT	0.058 ***	21.478 ***	0.332 *	−0.269	0.335 *	−0.229
	(4.48)	(3.97)	(2.34)	(−1.53)	(2.37)	(−1.58)
Hum			1.711 *	0.472
			(2.56)	(0.89)
FDI					0.003 *	−0.001
					(2.07)	(−0.45)
Controls	Y	Y	Y	Y	Y	Y
City	Y	Y	Y	Y	Y	Y
Year	Y	Y	Y	Y	Y	Y
_cons	3.379 ***	54.826 ***	−6.254 *	1.220	−0.125	3.226 **
	(31.59)	(3.95)	(−2.26)	(0.58)	(−0.12)	(3.10)
N	3986	3986	2587	1747	2587	1747
F	33.379	7.234	8.570	4.411	7.594	4.830
r2_a	0.424	0.190	0.275	0.151	0.248	0.148
N_g	285.000	285.000	285.000	285.000	285.000	285.000

Table 4

Endogeneity test.

	(1)	(2)
	First stage	Second stage
	Treatment × post	All-diff
IV-Undulation	0.381 ***
	(4.44)
Treatment × post		0.557 ***
		(4.75)
Controls	Y	Y
City	Y	Y
Year	Y	Y
_cons	−0.917	−3.44
	(−1.05)	(−0.57)
N	2485	2585

Table 5

Location heterogeneity Test.

	(1)	(2)	(3)	(4)
	Indiff	Outdiff	Indiff	Outdiff
ICT	0.327	−0.522	0.367 **	0.042 *
	(1.42)	(−1.55)	(2.79)	(2.00)
Controls	Y	Y	Y	Y
City	Y	Y	Y	Y
Year	Y	Y	Y	Y
_cons	−3.418	4.921 **	−0.024	0.672
	(−0.95)	(3.19)	(−0.04)	(1.25)
N	1012	832	1573	914
F	5.964	4.604	7.899	3.970
r2_a	0.254	0.178	0.456	0.324
N_g	83.000	82.000	172.000	148.000

Testing the heterogeneity of resource endowment.

Table 6

Test of heterogeneity of resource endowment.

	(1)	(2)	(3)	(4)
	Indiff	Outdiff	Indiff	Outdiff
ICT	0.003	0.085	0.350 *	−0.262
	(0.03)	(1.19)	(2.42)	(−1.41)
Controls	Y	Y	Y	Y
City	Y	Y	Y	Y
Year	Y	Y	Y	Y
_cons	0.544	−0.200	0.587	4.487 **
	(1.31)	(−0.39)	(0.31)	(3.28)
N	866	457	1719	1289
F	6.505	4.455	8.765	4.841
r2_a	0.280	0.143	0.272	0.164
N_g	99.000	82.000	156.000	148.000

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