1. Introduction
Global demand for fossil fuels as a result of economic expansion increases greenhouse gas emissions, specifically carbon dioxide, and the resulting global warming and El Nino phenomena have begun to imperil the development of human civilisation. China has experienced a rapid economic growth since the Reform and Opening Up. This has placed significant pressure on the country to reduce its CO
2 emissions. China’s CO
2 emissions accounted for 27% of global emissions in 2019, surpassing the total emissions of OECD countries and making China as the world’s largest carbon emitter. China’s industrial sector accounts for more than 70% of total emissions (Data are obtained from
http://www.tanpaifang.com (15 February 2022)). Chinese government, in line with the Paris Agreement, has proposed a goal of reaching emission peak by 2030 and reducing carbon emissions per unit of GDP by 60–65% by 2030. It is critical to find a low-carbon development path that balances economic growth and carbon reduction.
Under China’s “dual circulation” development pattern,“new infrastructure” has emerged as a new engine for economic. The national 14th Five-Year Plan defines the measures for information infrastructure construction (IIC), leading the path of new technologies such as artificial intelligence, block chain, and big data. The Ministry of Industry and Information Technology (MIIT) released the “Communications Industry Statistical Bulletin 2021”, which indicates that China’s information infrastructure continues to evolve and upgrade, with the total number of mobile communication base stations nationwide reaching 9.96 million by 2021, including 5.9 million 4G base stations and 1.425 million 5G base stations; 5G investment totaled 184.9 billion yuan, accounting for 45.6% of total investment. Total broadband Internet access ports now exceed 931 million. The next generation of information infrastructure, as the bearer of information in the digital economy, is crucial for redesigning the modern economic system. During the COVID-19 epidemic, information infrastructure, in particular, was important in revitalizing enterprises. A crucial issue is whether information infrastructure can help cities reduce carbon emission intensity (CEI). If so, does this effect differ by city? Additionally, what are the strategies for carbon reduction? Due to a paucity of pertinent research, our present work has practical importance.
There are three major types of studies relevant to carbon emission reduction being hampered by information infrastructure: the first type concerns carbon emission. Numerous studies have been conducted to measure the carbon emissions produced by various sectors and regions [
1,
2,
3] and also the factors that affect carbon emissions. Economic growth, financial development, technological advancement, and reorganization of the energy sector all contribute significantly to carbon emissions [
4,
5,
6,
7]. Government regulations and policies have suppressed carbon emissions [
8,
9,
10]. There is no consensus on whether urbanization accelerates or retards carbon emissions, as different approaches of urbanization may affect energy demand [
11,
12,
13]. The second type of literature focuses on the environmental consequences of transportation infrastructure. High-speed railway reduces fossil fuel consumption and carbon emissions, but this effect is offset by the scale effect of passenger [
14,
15,
16]. Additionally, high-speed railway indirectly reduces pollution emissions by promoting knowledge spillover and factor mobility in a way that strengthens enterprises’ energy-saving and emission-reducing technologies [
17]. The third type of literature is concerned with the macroeconomic and microeconomic consequences of information infrastructure. While the development of information infrastructure facilitated by information communication technology (ICT) has benefited employment structure, export, and economic growth [
18,
19,
20,
21], but excessive informatization also results in labor mismatch and resource waste, lowering the labor share of national income [
22].
This present work uses panel data from 289 cities in China from 2011 to 2017 and examines the impact of IIC on CEI, in terms of direct impact, heterogeneity, and mechanism. The present work’s contributions are reflected in three areas. First, this present work, instead of focusing on the economic effects of IIC on total factor productivity (TFP) and urban innovation levels, focuses on effect of IIC on CEI. Second, our research is enriched based on regional technology, city scale, and traditional infrastructure construction. Thirdly, this present work provides empirical evidence for the continued development of the digital economy and provides a theoretic foundation to achieve the “dual carbon” goal – “emission peak and carbon neutrality”.
The remainder of the present work is organized as follows:
Section 2 reviews relevant literature on similar topics;
Section 3 presents the empirical design and data description, describing the econometric model’s and data selection;
Section 4 presents the empirical results and analysis, focusing on the model’s basic regression results, robustness, and heterogeneity; and
Section 5 examines the mechanism by which information infrastructure contributes to the reduction of urban carbon emission intensity;
Section 6 concludes the paper.
5. Mechanism Test
We have showed in previous sections that information infrastructure would suppress carbon emission intensity, but that is only the overall impact, behind which the black box of the mechanism is yet clear. We further explore the mechanism with respect to industrial structure optimization, producer service industry agglomeration, and green technology innovation.
5.1. Industrial Structure Effect
As the underlying foundation of the digital economy, information infrastructure provides a supporting role to promote industrial digitization. Information infrastructure, as the carrier of big data information, has significant positive externality, the more users, the more utility of information value, as information infrastructures are not mutually exclusive. Industrial structure optimization and transformation and upgrading constitute the main parts of low carbon economic. Due to the limited connection between service industry and traditional manufacturing industry, data isolation leads to business isolation between industries, which can be lubricated by information infrastructure through data transmission and information exchange that reduce information asymmetry and accelerate the flow of capital, technology and labor factors. With information infrastructure construction, the degree of tertiary industry development and whether the ratio of industries is reasonable and coordinated, can information infrastructure promote the optimization of industrial structure? Can it break through the industrial barriers to widen the industrial chain?
To test the industrial structure optimization from information infrastructure, we use the share of tertiary industry in GDP (Per3) and the ratio of tertiary industry to secondary industry (Ris) as proxy variables of industrial structure. Column (1) of
Table 7 tests the effect of information infrastructure on the tertiary industry in GNP, and the regression coefficient of IIC is 1.7414, which is significant at 1% level. Column (2) of
Table 7 tests the influence of information infrastructure on the relationship between the ratio of tertiary and secondary industries in the industrial structure, and the regression coefficient is 0.0573 and is significant at the 1% level. The results show that information infrastructure is positively related to the optimization of industrial structure, and the investing in information infrastructure construction promotes the optimization of industrial structure.
The mechanism by which information infrastructure reduces CEI by optimizing industrial structure lies in the following: on one hand, industrial structure optimization comes from the inter-industry resource reallocation within cities. The information infrastructure triggers knowledge spillover effects, facilitates the evolution of industries from natural resources and labor to knowledge- and technology-intensive industries, promotes the flow of resources from industries with a low degree of professional division of labor to those with a higher one, and promotes specialized division of labor coupling between industries. At the same time, the investment in information infrastructure unleashed a number of new industries and generated “creative destruction”, which has boosted the proportion of service industry inputs and increased the demand for service industry products. On the other hand, the optimization of industrial structure can be a result of the reallocation of resources among cities. The popularity of network and the speed of information services make information more transparent, which intensifies competition and requires more harshly on the city’s industrial layout and the capability of resource allocation. At the same time, information infrastructure reduces the cost of information collection from physical level, smooths the cross-regional integration of capital, manpower, and technology resources, improves resource allocation and supply chain, and thus promotes the re-integration and layout of industries and provide favorable conditions for the transformation to a low-carbon economy.
5.2. Producer Service Industry Agglomeration Effect
Manufacturing enterprises outsourced the intermediate manufacturing to specialist producer service manufacturers, which formed an agglomeration of the production service industry. This agglomeration realizes economies of scale in the production of intermediate services and products, embeds more low-carbon production technologies and services into the manufacturing, and embraces higher value-added and low-pollution production. Can information technology industry represented by cloud computing and big data accelerate the integration of high value-added service and manufacturing industries, so as to improve total factor productivity and reduce carbon emissions?
For this reason, we construct a producer services agglomeration model based on producer services specialization agglomeration index (
) and diversification agglomeration index (
), inspired by a relevant study [
46], to measure the agglomeration patterns of specialized and diversified producer service. Among these, the
of the production service sector indicates the degree to which the production service industry is specialized relative to the rest of the country.
reflects the region’s concentration of diverse production services. The index is constructed in the following manner: The index is constructed as follows,
where
denotes the producer service industry specialization concentration index for city
i,
denotes the producer services diversification index for city
i.
denotes the employed population in some producer services industry
s,
denotes the total employments in city
i. The symbols with an apostrophe indicate the corresponding indicator other than city
i.
denotes the the employment in producer services
s as a percentage of total employment all across the country.
According to the employment statistics of urban industries in China, seven industries in 19 industries are merged to represent producer services [
35]: transportation, warehousing and postal services, information transmission computer services and software, wholesale and retail, finance, leasing and commercial services, scientific research and technical services, environmental governance and public facilities management.
The regression coefficient of IIC in column (1) of
Table 8 is 0.1443, and it is significant at 1% level. The regression results show that information infrastructure significantly promotes the professional agglomeration of producer services. New business type with information technology and cloud computing enables producer service agglomeration and manufacturing industry, which breaks through their spatial limitations and helps outsourcing the intermediate services, pollution control and emission reduction. As a result, companies can thus concentrate on their core business and speed their transition to green manufacturing. The information infrastructure provides a favorable technology carrier to promote the knowledge spillover and technology transfer between different industries in the specialized agglomeration of producer service industries and form a development path of “high value-added and low pollution”. The regression coefficient of information infrastructure in column (2) of
Table 8 is not significant, indicating a weak relationship between information infrastructure and diversified agglomeration in producer services. The reason may be the effect of the heterogeneity in industry segments on the diversified agglomeration, that is, a large proportion of low-end producer services may dampen the spillover of information infrastructure technology and weaken carbon emission reduction.
5.3. Green Technology Innovation Effect
Carbon emission intensity is the carbon emission per unit of GDP, which is a concept about efficiency, and therefore pertaining to technological innovation. The higher the technological innovation capacity of cities, the more they can lead low-carbon technologies and environmental technologies, which curbs carbon emissions. From Metcalfe’s law, the marginal cost of information infrastructure and other hardware inputs will decrease with the expansion of user scale, resulting in significant economies of scale. Can the information infrastructure continuously release the technological spillover bonus of information technology, enhance green technology and thus reduce the carbon emission intensity?
Patent invention from R&D activities can reflect the development of urban innovation. We use the number of green inventions applied for that year (gpatent) and the number of green utility models applied for that year (ppatent) as the proxy variables of green technology innovation, and take logarithm of these variables to test the impact of information infrastructure on green technology innovation. Among them, green invention patents and green utility model patents are obtained by excluding non-green technology invention patents from the international green patent classification.
The estimated coefficients of IIC in the regression results of
Table 9 are 0.4684 and 0.3955, and both are significant at the 1% level, indicating that information infrastructure improves the green technology innovation in cities. The improved information infrastructure implies a better accessibility of information network which, due to the decreasing marginal network cost, reduces the cost of network-mediated technology innovation information transmission and enhances the flow of innovation factors and diversified information between regions. Information technology keeps generating technology bonus from an increasing information infrastructure investment, which promotes the green technology in cities, thus providing a feasible path to reduce the CEI during the industrial development.
6. Discussion and Conclusions
6.1. Discussion
Global warming is a growing problem that is inextricably linked to human economic activity. The paper’s contributions are reflected in three areas. First, the present work, instead of focusing on the economic effects of IIC on total factor productivity (TFP) and urban innovation, focuses on effect of IIC on CEI. Second, our research is enriched as performed based on regional technology, city scale, and traditional infrastructure construction. Thirdly, the present work provides empirical evidence for the digital economy and provides a theoretic foundation to achieve the “dual carbon” goal—“emission peak and carbon neutralit”.
However, the present work has some limitations. First, the sample of this study is from one single country, and future studies could extend the study to more countries, especially developing countries. Information infrastructure could be a double-edged sword for carbon emission reduction. Due to the fact that some developing countries are at a low economic level and are subject to stringent environmental regulations, the excessive investment of information infrastructure may have a serious “green paradox” effect, impeding economic development. Second, environmental pollution is not solely due to CO emissions; future research will include other greenhouse gases. Additionally, due to data availability, we conducted empirical testing using 2011–2017 urban panel data. If more global data were available, the findings of this study would need to be empirically tested again to ensure their robustness. Finally, the existing model can be developed further by incorporating additional exogenous variables such as environmental policies, corruption, and green technology innovation.
6.2. Conclusions
We examines the impact of information infrastructure on urban carbon emission intensity by constructing a fixed-effects model using 289 cities in China as a research sample from 2011–2017. It is found that: (1) Information infrastructure construction significantly reduces urban carbon emission intensity. This indicates that the more developed the information infrastructure, the more advanced the digitalization technology, which enables the transformation of industry to low-carbon manufacturing and increase resource utilization efficiency. This conclusion still holds after a series of robustness tests. (2) The present work on the mechanism of information infrastructure construction to reduce carbon emission intensity shows that information infrastructure achieves the purpose of carbon emission suppression through the paths of industrial structure optimization, producer service industry agglomeration, and green technology innovation. (3) It is discovered that the impact of information infrastructure construction on carbon emission intensity is heterogeneous. The carbon emission reduction effect is greater in mega and super cities, large cities. The carbon emission reduction effect are not significant in the subgroups of medium-sized and small cities. In other words, information infrastructure, with the Internet and big data as key symbols, is contingent on the traditional infrastructure, technology supply, and economic scale of cities. Disparities in initial resource endowment will affect the quality and effectiveness of information infrastructure in supporting low-carbon industries.
The findings not only provide theoretical analysis of how information infrastructure affects carbon emission reduction, but also deserve special attention from policymakers in China: first, this study finds that information infrastructure plays an important role in carbon emission reduction. Thus, it is critical to keep promoting emerging businesses such as big data, cloud computing, and artificial intelligence in order to reap the benefits of the digital economy. Information technology innovation, as a impetus for common construction and sharing of innovation platforms, enhances the city’s innovation. Second, regional diversities, such as city size, technology level, and traditional infrastructure construction, should be considered when formulating policies for information infrastructure construction. Due to the uneven initial endowment of cities’ resources, a single investment in information facilities may not reduce carbon emissions. For less developed cities, efforts should be made to improve traditional infrastructure, foster a healthy business environment and attract scientific talent to create a favorable environment for information infrastructure. Removing barriers to production factors flow and optimizing element allocation are necessary to maximize the technology spillover impact of information infrastructure across areas.