1. Introduction
In pursuit of carbon emission reduction and climate change mitigation, the Chinese government has made a solemn commitment toward the goal of carbon peaking and carbon neutrality. As a national imperative, this goal embodies distinctive national attributes and contemporaneous relevance. In the era of digital economy, the scale of China’s digital economy is expanding rapidly, ranking second in the world. On the one hand, big data serves not only as a catalyst for industrial transformation and economic growth, but also plays a pivotal role in China’s low-carbon transition process. The widespread adoption of big data acts as a powerful enabler, facilitating the dissolution of enterprise barriers, fostering the exchange of eco-friendly knowledge and technology, nurturing inter-firm collaborations and refining carbon mitigation strategies, thereby propelling the green transformation of energy-intensive enterprises. Furthermore, big data enables centralized control management and the intelligent analysis of vast datasets, particularly in domains such as carbon footprints and sink. The application of big data in these domains empower enterprises to meticulously monitor and strategize the mitigation of carbon emissions at every stage of production and operation, thereby facilitating tangible reductions in carbon emissions. However, on the other hand, while big data shows the potential for energy saving and emission reduction, its own storage, processing and transmission processes also result in certain carbon emissions. The storage, processing and transmission of big data rely on computer hardware, computer software and significant electricity consumption, which consume energy and generate carbon emissions. With the increasing popularity of big data applications, the surge in activities associated with data storage, processing and transmission will inevitably exacerbate carbon emissions. According to the estimations by experts at the Environmental Planning Institute of the Ministry of Ecology, carbon emissions stemming from the nation’s data centers totaled 135 million tons in 2021, representing approximately 1.14% of China’s overall carbon emissions, with projections indicating an increase to 2% by 2035. This illustrates the “carbon effect paradox” of big data, whereby its utilization results in significant carbon emissions even as it facilitates emissions reduction within traditional industries. Against this backdrop, we understand the direction of big data’s impact on carbon emissions and elucidating the underlying transmission mechanisms assumes paramount importance and great guiding and theoretical significance for realizing the low-carbon transition and national high-quality developments driven by big data.
In September 2015, in an effort to promote the advancement of big data, the State Council issued the “Outline for the Promotion of Big Data Development” (hereinafter referred to as the Outline), thereby enshrining the elevation of big data development as a national strategic imperative. The Outline highlighted the developmental advantages and potential inherent in China’s big data landscape, while also acknowledging various shortcomings, such as inadequate data accessibility, sharing mechanisms and a nascent industrial foundation. Consequently, the Outline advocated for the enhancement of big data development through the implementation of regional pilot initiatives. Subsequently, in February 2016, Guizhou Province became the first recipient of approval for the establishment of a national big data comprehensive pilot zone (NBD-CPZ). Following this, in October of the same year, the regions of Shanghai, Chongqing, Henan, Shenyang, Inner Mongolia, Beijing-Tianjin-Hebei and the Pearl River Delta were also approved to initiate the construction of NBD-CPZs. The construction of NBD-CPZs can promote the overall development of the data factor market, which in turn, through their radiation effect and demonstration effects, will promote quality change, dynamic change and efficiency change across economic and social spheres. Given the current absence of standardized metrics for assessing big data comprehensively, the establishment of NBD-CPZs, as a pivotal policy endeavor aimed at propelling big data development, furnishes a conducive research conditions for investigating the relationship between big data and carbon emissions.
Since cities are the primary battleground for China’s low-carbon economic transformation and high-quality development, this study takes cities as its research object. It empirically explores whether big data has a carbon emission reduction effect, and if so, through what mechanism is it transmitted? Is there any heterogeneity? Is there a spatial effect? To accomplish this, the study empirically examines the impact and spatial effect of big data on carbon emissions using city panel data from 2012 to 2019. It leverages the establishment of NBD-CPZs as a quasi-natural experiment and employs various analytical methods including multi-period difference-in-differences (DID), difference-in-difference propensity score matching (PSM-DID) and spatial difference-in-difference (SDID) models. This study makes three significant contributions to the field. First, this study enriches the related research on the impact of digital technology on carbon emissions. Through a systematic analysis of the “carbon effect paradox” associated with big data, this study provides empirical evidence demonstrating that big data promotes carbon emission reductions, clarifying the paradox and elucidating the environmental implications of big data. Second, this study analyzes the mechanism of big data to improve the environment. By leveraging the exogenous policy of the NBD-CPZs, this study empirically evaluates the environmental impact of big data from a carbon emissions perspective, thereby elucidating the mechanisms through which big data influences carbon emissions and aiding in identifying the focal points for environmental improvement efforts. Third, this study delves deeper into the spatial effect of the impact of big data on carbon emissions. This study uses the SDID model to empirically analyze the spatial effect of NBD-CPZs on carbon emissions at different geographical distances. Our findings provide pragmatic evidence for optimizing the arrangement of data center nodes and fostering regional collaboration to effectively reduce carbon emissions.
The remainder of this study is structured as follows.
Section 2 summarizes the relevant studies at home and abroad.
Section 3 puts forward a research hypotheses based on theoretical analysis.
Section 4 provides a detailed description of the model’s construction, variable description and data source. In
Section 5, we analyze the empirical results.
Section 6 further examines the spatial effect of NBD-CPZs. Finally,
Section 7 lays out the main conclusions of this study and makes relevant policy recommendations.
2. Literature Review
In the digital economy era, big data has emerged as a novel production factor permeating all facets of societal production and life, serving as a pivotal catalyst for China’s economic metamorphosis. Scholars at home and abroad have extensively investigated the multifaceted impacts of big data, predominantly focusing on its economic implications. By releasing technological, institutional and innovative dividends, big data has been instrumental in expediting the digital transformation of enterprises [
1,
2], fostering green innovation [
3,
4], facilitating the upgrading and transformation of traditional industries [
5,
6], and augmenting regional total factor productivity [
7,
8], thereby fostering high-caliber economic development [
9,
10,
11]. Concurrently, big data has profoundly influenced employment [
12], labor income distribution [
13,
14] and common prosperity [
15]. Furthermore, some scholars have focused on the environmental effects of big data. The rapid development of big data offers a pivotal avenue for promoting green development in China [
16]. Cheng and Duan (2023) [
17] conducted a rigorous analysis to ascertain the environmental effect of big data from the dual perspective of haze pollution and carbon emissions. Chang et al. (2023) [
18] approached the subject from the angle of carbon emissions stemming from electricity consumption, revealing a substantial reduction facilitated by big data. This reduction predominantly stems from the promotion of technological innovation and the advancement of inclusive financial practices. Wei et al. (2024) [
19] took 277 cities in China as research samples and employed a dual machine learning model to investigate the impact of big data on urban PM2.5 concentration. Their findings underscored the significant reduction in urban PM2.5 concentration attributable to big data development, with urban development and land use planning playing an important role in this process.
In the context of the digital economy, in order to deal with the challenge of global warming, scholars have extensively researched the relationship between the digital economy and carbon emissions [
20,
21]. Among them, there are fewer studies focusing on the impact of digital technology on carbon emissions and no unanimous conclusions have been reached, delineating two predominant perspectives: the “promotion theory” and the “inhibition theory”. The “promotion theory” posits that the rapid development of digital technology and its associated industries engenders a surge in carbon emissions. For instance, the deployment of Information and Communication Technology (ICT) is often linked to heightened electricity consumption [
22]. As one of the main sources of global carbon dioxide, the increase in electricity consumption will inevitably promote carbon dioxide emissions [
23]. Conversely, the “inhibition theory” contends that digital technologies can foster reductions in carbon emissions, consequently improving environmental quality [
24]. For instance, Lu (2018) [
25] scrutinized 12 Asian countries and delineated that ICT can effectively reduce carbon emissions. Shen et al. (2023) [
26] utilized panel data of Chinese cities from 2006–2020 to confirm that digital technology plays a pivotal role in diminishing carbon emissions through the facilitation of green innovation and the mitigation of energy intensity.
A thorough examination of existing literature reveals a predominant focus on the economic effects of big data, with relatively scant attention directed towards its environmental effects, and there is an extreme lack of research on the environmental effect of big data from the perspective of carbon emissions. Furthermore, scholarly discourse on the relationship between digital technology and carbon emissions remains inconclusive. Consequently, this study firstly integrates both the direct and indirect effects of big data on carbon emissions into an analytical framework, and theoretically explores the impact of big data on carbon emissions. Subsequently, we empirically investigate the impact of big data on carbon emissions, based on the exogenous policy shock of the construction of NBD-CPZs, employing the multi-period DID model and the PSM-DID model. Finally, the spatial effects of NBD-CPZs are further analyzed through the employment of the SDID model.
6. Analysis of Spatial Effects
In this study, a spatial autocorrelation test of carbon emissions in Chinese cities was conducted, and the results are shown in
Table 8. As can be seen from the results, the Moran’s I value of each city from 2012 to 1016 are statistically insignificant, while those of each city from 2017 to 2019 are statistically significant, which indicates that the spatial correlation of the carbon emissions of each city is constantly strengthening. Therefore, it is necessary to further examine the spatial effect of the NBD-CPZ on urban carbon emissions.
Hence, there is a pressing need to investigate the spatial effect of the NBD-CPZ on urban carbon emissions. In view of this, this study initiates the construction of a spatial Durbin difference-in-difference model (SDM-DID). Through a series of rigorous tests, the specific form of the model is determined, with detailed results presented in
Table 9. Notably, the results of the LM, Wald, LR, Hausman and two-way fixed effects tests significantly reject the original hypothesis. Consequently, it is justifiable for this study to adopt the SDM model, which incorporates controls for both city-fixed effects and time-fixed effects. In summary, the model constructed in this research is delineated below:
where
ρ represents the spatial autocorrelation coefficient,
β signifies the impact coefficient of big data on local carbon emissions,
θ denotes the coefficient reflecting the influence of big data on carbon emissions in neighboring regions, and
γ symbolizes the coefficient of the control variables. Additionally,
W represents the spatial weighting matrix, which is used to describe the spatial correlation degree between the city
i and the city
j. In this study, we adopt the inverse distance squared spatial weight matrix, as shown in Formula (5), where dij2 denotes the squared distance between distinct cities:
Regressions (1)–(3) in
Table 10 show the regression results derived from three spatial models: SEM, SAR and SDM, respectively. Notably, the spatial autocorrelation coefficients, denoted by
ρ or
λ across the three models, exhibit statistically significant positivity. This observation underscores the pronounced spatial clustering tendencies within carbon emissions among cities, thereby reinforcing the inclusion of spatial factors within this study’s framework. Furthermore, the coefficients of
Did across three models manifest as significantly negative, thereby reaffirming the inhibitory impact of big data on carbon emissions within pilot cities. Upon scrutinizing the effect of decomposition findings from the SAR and SDM models, it is discerned that the coefficient of the spatial term of the policy dummy variable (
W ×
Did) registers as positive, yet statistically insignificant. This insignificance implies that the NBD-CPZ exerts no notable influence on carbon emissions within neighboring regions. One plausible explanation for this result may be that the NBD-CPZ has both siphon and spillover effects on neighboring regions.
In order to further identify the spatial structure of the siphon effect and spillover effect in the NBD-CPZ, this study draws on the study of Sun and Yuan (2019) [
53] to construct the spatial weight matrix in the form of inverse distance decay. To guarantee each city’s inclusion of at least one neighboring counterpart, this study sets the initial geographic distance threshold at 400 km, with a subsequent increment of 100 km. The computation of the spatial decay matrix follows this formulation, wherein
D represents the geographic distance threshold:
Table 11 shows the spatial impact of the NBD-CPZ on carbon emissions at different geographic distances. The results delineate three discernible trends: initially, within the 400–500 km threshold, the spatial effect of the impact of the NBD-CPZ on carbon emissions is significantly positive, indicating that at this time, affected by the shadow of agglomeration, the NBD-CPZ has a siphon effect on the resources of the neighboring areas. Subsequently, within the 500–900 km threshold, the spatial effect of the impact of the NBD-CPZ on carbon emissions is significantly negative, signaling a significant spillover effect from the NBD-CPZ. Finally, as the geographic distance exceeds 900 km, the spatial impact of the NBD-CPZ becomes no longer significant. Thus, it becomes evident that the NBD-CPZ engenders both siphoning and spillover effects, and exhibits a certain spatial structure that corroborates Hypothesis 3.
7. Conclusions and Policy Recommendations
7.1. Conclusions
Drawing on the panel data from Chinese prefecture-level cities spanning the period from 2012 to 2019, this study leverages the establishment of the national big data comprehensive pilot zone (NBD-CPZ) as a quasi-natural experiment to empirically scrutinize the impact and mechanisms underlying big data’s influence on carbon emissions. There are four key study findings. (1) Big data exerts a significant inhibitory effect on urban carbon emissions, a conclusion robustly validated through a battery of robustness tests. (2) Mechanism analysis underscores that big data operates along three distinct pathways to suppress urban carbon emissions: fostering green innovation, optimizing energy structure and mitigating capital mismatches improving public awareness of environmental protection. (3) An analysis of heterogeneity reveals disparities in the carbon emission reduction effects of big data across various city typologies. Notably, big data is more effective in reducing carbon emissions in cities with a high digital economic level, non-resource-based cities, cities with strong IPR protection and the Guizhou Province. (4) The analysis of spatial effects shows that within the span of 400–500 km, the NBD-CPZ manifests a siphon effect, resulting in increased carbon emissions. Conversely, in the range of 500–900 km, the NBD-CPZ manifests a spillover effect, resulting in reduced carbon emissions. Notably, beyond the 900 km, the spatial effect of the NBD-CPZ becomes statistically insignificant.
7.2. Policy Recommendations
Based on the findings of this study, we advance the following policy recommendations.
First, we should make every effort to promote the development of the big data industry. Empirical evidence shows that this can significantly reduce urban carbon emissions. Against the backdrop of the goal of carbon peaking and carbon neutrality, the comprehensive development of the big data sector holds profound strategic importance. Government intervention is pivotal in this effort, necessitating the establishment of a robust policy framework. Such a framework should not only include standardized management protocols for data resources, but also incorporate mechanisms pertaining to data resource rights. This approach serves to fortify support and provide clear guidance to the burgeoning big data industry, consequently fostering a conducive environment for its development. Furthermore, given the exponential growth of the big data sector, there is a palpable surge in demand for skilled professionals among enterprises and research institutions. Consequently, the government should undertake initiatives to cultivate a substantial pool of big data talent through educational programs and specialized training, thereby injecting significant momentum into the industry’s expansion.
Second, we should differentiate the focus point of big data carbon reduction. Given the variable impact of big data on carbon emissions across diverse cities, the strategic deployment of big data for emission reduction necessitates a comprehensive consideration of factors, such as the city’s digital economy level, resource endowment and IPR protection intensity. Tailoring interventions to local contexts becomes essential. For instance, in cities with a low digital economy level, enhancing digital infrastructure, fostering information integration and sharing and optimizing resource allocation efficiency are imperative. Resource-based cities require the expedited integration of big data into traditional industries to facilitate their transition toward sustainability and reduce reliance on fossil fuels. Additionally, cities with weak IPR protection must strengthen the construction of an intellectual property rights protection system, elevate the costs of IPR infringement and then enhance the green innovation willingness of innovation subjects.
Third, we should explore the multidimensional carbon reduction pathways of big data. It is evident from mechanism tests that big data primarily serves to reduce urban carbon emissions through the promotion of green innovation, optimization of energy structure and mitigation of resource mismatch. Henceforth, it is imperative to promote the integration of big data with various industries while promoting its widespread use. This strategic endeavor aims to catalyze the digital transformation of industrial sectors, augment the proportion of clean energy in energy consumption patterns and improve resource mismatches. Additionally, sustained efforts are necessary to augment investment in research and development pertaining to green innovation, enhance enterprises’ cognizance of green innovation imperatives and expedite the translation of green innovation outcomes into practical solutions, thereby effectuating the transition towards a low-carbon economy and society.
Fourth, regional cooperation aimed at reducing carbon emissions should be further promoted. From the analysis of spatial effects, it can be seen that the spatial spillover effect of the NBD-CPZ is significant in the surrounding 500–900 km. Consequently, municipalities ought to foster a collective commitment towards carbon emission reduction. Leveraging big data technology to surmount administrative barriers, enhancing inter-city communication and fostering cooperation is pivotal. Additionally, there is a pressing need to explore inter-regional collaborative carbon reduction mechanisms. Such endeavors not only bolster the spatial spillover effect of the NBD-CPZ, but also promote the low-carbon development of cities.
This study still has some limitations in exploring the relationship between big data and urban carbon emissions, mainly in two aspects. On the one hand, our study focuses only on the Chinese city level, ignoring other country samples. All countries around the world are responsible for combating global climate change. Therefore, subsequent studies can expand the scope of research and explore the relationship between big data and carbon emissions from an international perspective. On the other hand, our study is limited to the period of 2012–2019 and excludes city samples with a significant amount of missing data, which may lead to some bias in the results. If the missing data can be made up, the research conclusions will be more accurate.