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Systematic Review

The Impact of the Global Digital Economy on Carbon Emissions: A Review

by
Bingjie Liu
1 and
Fengyi Wang
2,*
1
School of Economics, Wuhan Business University, Wuhan 430056, China
2
Department of Computing and Artificial Intelligence, Beijing Technology and Business University, Beijing 102401, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(11), 5044; https://doi.org/10.3390/su17115044
Submission received: 11 March 2025 / Revised: 9 April 2025 / Accepted: 28 May 2025 / Published: 30 May 2025
(This article belongs to the Special Issue Industry 4.0, Digitization and Opportunities for Sustainability)
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Abstract

:
Based on the PRISMA systematic review framework, this study screened relevant literature from the Web of Science database and selected 102 studies for analysis. Using CiteSpace for bibliometric analysis, this study identified three core research areas: (1) measurement methods for carbon emissions and digital economy, (2) the nonlinear relationship between digital economy and carbon emissions, and (3) mediating mechanisms. The results demonstrate that while digital technologies can reduce carbon emissions by improving energy efficiency, promoting green technology innovation, and upgrading industrial structure, the energy-intensive nature of digital infrastructure may conversely increase emissions. Notably, the relationship may exhibit nonlinear characteristics (inverted U-shaped or N-shaped curves). Three key mechanisms are summarized: energy efficiency effects, green technology innovation effects, and industrial structure effects. Future research should focus on optimizing evaluation systems, applying remote sensing technology, conducting micro-level studies, and examining digital divide impacts. This study provides both a comprehensive theoretical framework and practical policy insights for understanding the digital economy-carbon emissions relationship.

1. Introduction

Carbon emissions represent a critical global issue with profound impacts on the climate. Global warming has led to frequent extreme weather events and ecosystem destruction, posing severe threats to human survival and development [1]. In 2022, the global total carbon dioxide emissions reached 36.8 billion tons [2], and in 2023, despite numerous policies, while some countries managed to reduce emissions, global total carbon emissions increased by 1.1%, reaching a new high of 37.4 billion tons [3]. The 28th United Nations Climate Change Conference warned that without effective measures to reduce carbon emissions, global temperatures will continue to rise, leading to even more severe climate change consequences [4].
In response, countries have implemented various measures to address carbon emissions, such as clean energy technologies and improving energy efficiency. These measures have already shown positive results in developed countries. For instance, the United States’ carbon emissions in 2023 decreased by 4.1%, while the European Union’s energy-related emissions fell by nearly 9%. Moreover, China has significantly curtailed the growth rate of carbon emissions in 2023 by adding photovoltaic power generation equivalent to the global total added in 2022 by using clean energy technologies [5]. However, despite these efforts, China’s total carbon emissions still experienced a considerable growth rate of 5.7% in 2023 [3]. In fact, carbon dioxide emissions can be influenced by various factors, such as population growth, energy efficiency, economic structure, and technological transformation [6,7,8,9]. Among these, technological revolution is opening up global emission reduction opportunities for all countries [10], and many are exploring the potential of digital strategies.
Currently, digital strategies have become a new engine for global economic growth [11], reshaping traditional economic structures and growth models, and altering the energy-consumption patterns of economic growth [12,13]. Innovations in digital technology and its widespread application have brought transformation opportunities to resource-based cities, enabling them to tackle environmental challenges. These cities contribute significantly to global carbon dioxide emissions and have profound effects on global warming trends and local socioeconomic development [14]. Digitalization has paved new paths for achieving sustainable development, with the potential to reduce carbon emissions and drive economic and social progress. This hypothesis has been widely discussed and explored [15,16].
However, the impact of the digital economy on carbon emissions is a complex process, and it may also introduce new energy demands and environmental challenges, particularly concerning the energy consumption of data centers [17,18]. The construction and operation of digital infrastructure require significant energy, potentially increasing carbon emissions [19]. The consumption of electricity during the digitalization process links the digital economy closely to high carbon emissions [20]. Energy transition and revolution constitute a systemic and monumental challenge [21]. While the digital economy has played a positive role in improving energy efficiency, its overall environmental impact remains uncertain.
In the current research, the impact of the digital economy on carbon emissions exhibits complex and multifaceted characteristics. Whether the development of the digital economy can effectively promote energy conservation and emission reduction remains inconclusive in the academic community. Against this background, this paper discusses the impact of the global digital economy on carbon emissions. Through a systematic literature review, it analyzes how the digital economy affects carbon emissions through various mechanisms and proposes directions for future research. Compared with existing literature, the contributions of this paper are as follows:
  • Theoretical Integration and Framework Innovation: This paper systematically combs through the complex relationship between the digital economy and carbon emissions, constructing an analytical framework of “measurement methods–decoupling of relationships–mechanism analysis–research prospects”. It breaks through the limitations of traditional single-path explanations and provides a new theoretical perspective for understanding the relationship between the two.
  • In-depth Analysis of Research Evolutionary Patterns: Using bibliometric methods, this paper conducts a thematic evolution analysis of 102 global publications. It identifies emerging research hotspots such as the “digital infrastructure carbon emission paradox” and the “mediating effect of green innovation”, revealing the dynamic evolutionary patterns of the knowledge structure in this field.
  • Proposing Forward-looking Research Directions: This paper proposes emerging directions such as “the impact of the digital divide on carbon reduction effects” and “using remote sensing technology for precise measurement of carbon emissions”. These suggestions provide more targeted policy recommendations for policymakers to ensure the inclusiveness and fairness of the digital economy on a global scale.
This paper is divided into seven sections, with the following structure: Section 1 is “Introduction”; Section 2 is “Article Selection and Methodology”; Section 3 reveals the current mainstream methods for measuring carbon emissions and the digital economy; Section 4 systematically combs through the relationship between the digital economy and carbon emissions; Section 5 summarizes the main mechanisms through which the digital economy affects carbon emissions; Section 6 looks forward to future research; and Section 7 concludes the paper.

2. Article Selection and Methodology

This study investigates the impact of the digital economy on carbon emissions, focusing on three core themes: the measurement of the digital economy and carbon emissions, their interrelationship, and the mechanisms of influence. Additionally, the “Future Directions” section discusses the potential of emerging technologies such as machine learning and remote sensing in the study of the digital economy and carbon emissions.
This study adopted the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to screen relevant literature and conduct systematic analysis of the selected studies (see Supplementary Materials). The PRISMA approach involves four key steps in literature screening: identification, screening, eligibility assessment, and inclusion [22].
Specifically, the Web of Science (WOS) international citation database was employed as the primary literature source. To comprehensively examine the relationship between the digital economy and carbon emissions, we conducted a topic-based search in WOS using the Boolean operator “AND” to combine the keywords “digital economy” and “carbon emissions”. This initial search yielded 896 relevant publications. Subsequent screening was performed by limiting the document type to “Article” and “Review Article”, resulting in 422 selected papers.
Next, a preliminary applicability check was conducted on all 422 screened articles. By reviewing titles and abstracts, we ensured alignment between each study’s research focus and our investigation objectives. This process retained 200 articles for further evaluation. Finally, through full-text review and synthesis, 102 representative studies were identified as the core research sample (Figure 1).
The selected literature encompasses the latest developments and key research findings in digital economy, carbon emissions, environmental science, and related technological fields. Preliminary analysis shows the selected publications are primarily concentrated in the 2020–2024 period. Based on these selected studies, this study performed a bibliometric analysis of the 102 publications using CiteSpace 6.3.R1 to identify current research themes. Keyword clustering results indicate that existing research on digital economy and carbon emissions primarily covers three areas: (1) Measurement of digital economy and carbon emissions, (2) Relationship between digital economy and carbon emissions and (3) Mediating effect mechanisms (Figure 2). Accordingly, this review will focus on these three aspects.

3. Measurement of Digital Economy and Carbon Emissions

3.1. Measurement of Carbon Emissions

Carbon emissions refer to the total emissions of greenhouse gases, such as carbon dioxide, generated during economic development and energy consumption [23]. It can be simplified as the overall emissions of a country or industry within a specific time frame, referred to as the total carbon emissions. Carbon emissions are influenced by multiple factors, including renewable energy policies [24], population growth, and macroeconomic determinants [7,25]. The academic focus on carbon emission research varies significantly across different study contexts, consequently leading to the absence of a standardized calculation methodology. Many scholars and research teams have delved into the measurement and assessment of carbon emissions, proposing various methods and techniques [26]. Carbon emissions are typically represented by total carbon emissions and carbon emission intensity [27].
Regarding the measurement of total carbon emissions, the emission factor method proposed by the IPCC (Intergovernmental Panel on Climate Change) currently represents the most widely applicable and commonly used approach. This method estimates emissions by multiplying the energy consumption of various emission sources with their corresponding carbon emission factors and has been extensively adopted for compiling national and regional carbon emission inventories due to its simplicity and relatively low data requirements [28]. Life Cycle Analysis (LCA), which assesses carbon emissions throughout the entire life cycle of products or services, constitutes another fundamental method for determining carbon emissions [29,30]. Building upon this foundation, more comprehensive measurement methodologies have been developed through the integration of social and economic factors, giving rise to approaches such as Social Life Cycle Analysis (SLCA) and Life Cycle Cost Analysis (LCCA) [31,32].
Among these two major categories of methods, the emission factor method proposed by Intergovernmental Panel on Climate Change is currently the most widely used and authoritative approach. It conducts accounting from the producer’s perspective, with a broad scope of application and relatively convenient data acquisition. This method is particularly suitable for calculating carbon emissions at large-scale spatial levels such as national or provincial levels. However, it tends to overlook the transfer of carbon emissions between regions due to economic and trade activities. In contrast, life cycle assessment (LCA) that account for carbon emissions from the consumer’s perspective can provide relatively more accurate and scientific results. Nevertheless, these methods have strict data requirements, and it is challenging to standardize the data used in the accounting process. Table 1 provides a comparison of these two major categories of methods.
As one of the earliest indicators applied, total carbon emissions intuitively reflect the status of carbon emissions. With the deepening of research, carbon emission efficiency [33], carbon emission performance has gained widespread recognition [26]. The focus of this indicator includes single-factor metrics, which primarily concentrate on individual indicators such as carbon productivity [34,35], carbon emission intensity [36], and per capita carbon emissions [37]. However, these single-factor metrics fail to establish relationships among various production factors. To comprehensively consider the relationship between input and output factors, the total-factor approach has been more widely adopted [38]. For example, methods such as the DEA–Malmquist index, super-efficiency SBM (Slack-Based Measure) model, super-efficiency EBM-GML (Enhanced Binary Model—Global Malmquist) model, and Stochastic Frontier Analysis (SFA) have been increasingly utilized. Measurements of Carbon Emissions are shown in Figure 3.

3.2. Measurement of the Digital Economy

The digital economy encompasses all economic activities driven by digital computing technologies, including big data, cloud computing, and the Internet of Things [39,40,41]. The United Nations Conference on Trade and Development further points out that the digital economy not only includes the production and trade of digital products and services but also involves the digital transformation of traditional industries [42]. The digital economy represents a new model of economic development, bringing profound changes to production, consumption, distribution, and trading methods. It has become a key force driving the comprehensive transformation of society, the economy, and the environment [43]. The current mainstream digital economy measurement indexes are shown in Table 2.
Currently, there are various perspectives in academia regarding the measurement of the digital economy (Figure 4), with the most mainstream approach being the construction of a digital economy index. The most commonly used indicators in the index system include household internet access or internet penetration rate [44,45]. Other indicators, such as digital infrastructure, digital industrialization, industrial digitization, and digital governance, have also received extensive attention from scholars [46,47,48]. Among the index measurement methods, the entropy weighting method is a primary evaluation approach [46,49], with TOPSIS and principal component analysis (PCA) also being widely used [50]. The main Digital Economy Indexes are shown in the table below. Nevertheless, digital economy estimation methodologies differ considerably depending on research purposes and settings [51].
In addition, some scholars have employed methods such as DEA to measure the improvements in production efficiency and economic structural changes brought about by digital technologies, thus indirectly measuring the development of the digital economy [52]. Among the various areas of digital economy research, one key focus is the relationship between the digital economy and carbon emissions [53], including studies on the impact of lower-level indicators of the digital economy on carbon emissions.

4. The Relationship Between the Digital Economy and Carbon Emissions

The impact of the digital economy on carbon emissions has not yet reached a consensus in academia. Numerous scholars have analyzed and empirically tested the influence of the digital economy on carbon emissions from different perspectives (Figure 5). In fact, the relationship between the digital economy and carbon emissions is a complex process. On one hand, it can reduce carbon emissions by promoting technological advancements and improving resource utilization efficiency; on the other hand, the construction and operation of digital infrastructure require significant amounts of energy, which may increase carbon emissions. Currently, the relationship between the two mainly involves three categories: reducing carbon emissions, increasing carbon emissions, and non-linear complex relationships.

4.1. The Digital Economy Reduces Carbon Emissions

From a positive perspective, the development of the digital economy and digital technologies has had a significant positive impact on reducing carbon emissions [54]. From a macro perspective, digital technologies such as information and communication technologies (ICT), the development of the digital economy, and digital trade can effectively suppress carbon emissions in countries or regions. For instance, Dogan and Pata (2022) argue that ICT positively impacts the environmental quality of G7 countries, reducing their carbon emissions [55]. Mei et al. (2023), in their study of 100 countries, found that the development of the digital economy is positively correlated with the suppression of carbon emission growth in those countries [56]. Li et al. (2024), based on panel data from 46 countries, discovered that digital trade has a clear low-carbon effect [43]. Chen (2022), using data from BRICS countries, demonstrated the inhibitory effect of digitalization on carbon emissions [57]. From a micro perspective, the development of the digital economy can enhance the technological level of enterprises, enabling energy-saving and emission-reduction during the production process [58,59]. Initially, the growth of the digital economy suppresses carbon emissions, but once a certain threshold is reached, this suppressive effect gradually weakens [60].
Furthermore, some scholars have begun to explore the spatial characteristics of how the digital economy reduces carbon emissions, and the spatial effect of the digital economy has been widely validated [61,62], demonstrating that digital economic development not only facilitates local carbon mitigation but also contributes to emission reductions in geographically adjacent regions [63]. However, the impact of the digital economy on carbon emissions shows clear regional heterogeneity. This regional difference exists not only between developed and developing countries [64], but also across different countries [65], as well as among various regions within the same country [61]. These studies collectively suggest that the digital economy plays a positive role in reducing carbon emissions at different levels and in various regions, providing strategic references for global low-carbon development.
In summary, these studies have confirmed that the digital economy plays a positive role in reducing carbon emissions, involving the digital economy and its core-related indicators, such as Information and Communication Technology (ICT), digital trade, and digital finance. The research perspectives cover both macroscopic and microscopic levels, with the macroscopic level being predominant. Moreover, as research progresses, in addition to verifying this relationship, many scholars have also examined the extent of its impact, regional heterogeneity, spatial spillover effects, and underlying mechanisms. These studies collectively demonstrate that the digital economy has a positive effect on reducing carbon emissions across different levels and regions, providing strategic references for global low-carbon development. Related literatures about the Digital Economy decreasing Carbon emissions are shown in Table 3.

4.2. The Digital Economy Increases Carbon Emissions

On the negative side, the development of the digital economy may also bring about new energy demands and environmental challenges, potentially exacerbating carbon emissions [67]. On one hand, the development of the digital economy relies on energy-intensive infrastructure such as data centers and telecommunications networks [68], and the construction of this infrastructure increases carbon emissions. For example, Che et al. (2024), using panel data from 83 countries between 2005 and 2021, found that the construction of digital infrastructure increased carbon emissions globally, particularly in Europe and the Asia-Pacific region [19]. Raihan (2024) summarized the challenges that the development of the digital economy poses to sustainable development, pointing out that the increasing demand for high-performance equipment and data-intensive facilities is confronted with the significant issue of high carbon emissions from infrastructure construction [69].
On the other hand, digital technologies consume large amounts of energy and are not inherently environmentally friendly, with global electricity consumption continuing to rise [18]. For instance, Salahuddin and Alam (2015), in their analysis of Australia, revealed that the use of digital technologies drives energy consumption, leading to increased carbon emissions [70]. In 2016, a further study by Salahuddin et al. found that internet usage showed a statistically significant but practically negligible positive effect on carbon emissions [71]. Although there exists potential for increased emissions, proactive utilization of digital technology is required to achieve broader emission reductions. A similar conclusion was reached in Wang et al.’s (2021) study focusing on OECD countries [72]. Usman et al. (2021), studying nine major economies in Asia, found that the application of information technology increased carbon emissions in these economies in the short term, and this effect was further strengthened in the long term [73]. Khan et al. (2023), in their study of 76 EMDE (Emerging Market and Developing Economies) countries, found a strong positive correlation between the development of digital inclusive finance and carbon emissions [16]. Arshad Z et al. (2020) found that Information and Communication Technology (ITC) will increase carbon dioxide emissions, and there is a significant degree difference between developed and developing countries [74].
In addition to increasing carbon emissions through infrastructure construction and electricity consumption, the rebound effect of energy consumption is also one of the factors contributing to the negative impacts of digital economic development. Improvements in energy efficiency may trigger a rebound in energy consumption, ultimately leading to higher carbon emissions [75]. Specifically, enhanced energy efficiency can increase the demand, thereby resulting in a situation where total energy consumption and total emissions do not decrease but actually rise. Moreover, although the digital economy can improve energy efficiency and potentially reduce carbon intensity, per capita carbon emissions may still increase [49].
In summary, the development of the digital economy is inevitably accompanied by the expansion of information infrastructure and the upgrading of related hardware facilities. This process increases the demand for energy-intensive products. Meanwhile, the production of digital products and the expansion of digital technologies consume a significant amount of energy, both of which lead to an increase in carbon emissions. Moreover, the digital economy accelerates technological progress, and the induced demand for energy may exceed the total amount of energy saved, resulting in a rebound effect that causes carbon emissions to rise instead of decline. Related literatures about Digital Economy increasing Carbon emissions are shown in Table 4.

4.3. Non-Linear Relationships

Recent studies have indicated that the relationship between the digital economy and carbon emissions may not be linear but rather exhibits an inverted U-shaped pattern, suggesting that after a certain threshold is reached, further development of the digital economy could lead to a reduction in carbon emissions and intensity [76,77]. This finding aligns with the well-known Environmental Kuznets Curve [78], which has been empirically supported by numerous contemporary studies. For instance, Zhang et al. (2024) examined 72 countries and uncovered an inverted U-shaped relationship between the digital economy and carbon emission intensity [79]. They noted that while the digital economy initially promotes carbon emissions, it leads to a decrease in emissions after reaching a tipping point. The study also highlighted the mediating roles of industrial upgrading and enhanced energy efficiency. Similarly, Wang et al. (2023) utilized panel data from 67 countries spanning 2005 to 2019 to explore the impact of the digital economy on carbon emissions and renewable energy development [80]. Their findings corroborated the inverted U-shaped relationship between the digital economy and carbon emissions, consistent with the Environmental Kuznets Curve hypothesis. Li et al. (2021) incorporated the digital economy as technological progress into the Solow growth model, empirically validating the nonlinear relationship between them [81]. Ma et al. (2025) applied panel fixed effects and spatial Durbin models at the prefecture level in China and found that the digital economy exhibits an inverted U-shaped relationship with carbon emissions, also identifying the presence of spatial spillover effects [82].
The investigation of the threshold point has been a focal point for scholars. Higón et al. (2017) discovered an inverted U-shaped relationship between digital technology and carbon emissions, noting that the tipping point for developing countries is higher than that for developed countries [83]. Li and Wang (2022) found that the impact of the digital economy on carbon emissions exhibits an inverted U-shaped relationship [84]. When the level of the digital economy is below a certain threshold, it significantly increases carbon emissions. However, once the digital economy exceeds this threshold, the promoting effect turns into an inhibitory effect. Notably, this threshold is lower in the more developed eastern region.
However, some scholars have argued that the inverted U-shaped curve hypothesis can only explain the increase in carbon emissions during the early stages of digital economic development but fails to account for the potential rebound effect in carbon emissions at later stages [54]. As a result, some researchers have turned to the N-shaped curve hypothesis, suggesting that environmental quality may experience another phase of deterioration with further development of the digital economy [85]. Research in this area is extremely limited and requires further refinement.
In summary, the positive and negative impacts may interact with each other. Therefore, some studies have confirmed that there is a nonlinear relationship between the digital economy and carbon emissions, typically characterized by an inverted U-shaped or N-shaped curve. This implies that when the level of digital economy development is below a certain threshold, it significantly increases carbon emissions. However, once the development of the digital economy exceeds this threshold, it significantly reduces carbon emissions. This threshold is often lower and more easily reached in developed regions, thereby more readily achieving the emission reduction effect. Related Literatures about Nonlinear relationship between Digital Economy and Carbon Emissions are shown in the Table 5.

5. Mechanisms by Which the Digital Economy Affects Carbon Emissions

The effect mechanisms of the digital economy primarily focus on how the application and development of digital technologies impact carbon emissions. A review of existing literature reveals that the main mechanisms empirically supported can be summarized into three categories: energy efficiency, green technological innovation, and industrial structure (Figure 6). In addition, several other pathways exist, such as urbanization level and economic development level [87].
Theoretically, the development of the digital economy and digital technologies can significantly influence resource utilization efficiency, thereby affecting carbon emissions. Several studies have emphasized the positive role of the digital economy in enhancing energy efficiency [88]. For instance, Mei et al. (2023) found that digital economic growth affects regional carbon emissions through intermediaries such as energy structure and efficiency [56]. Schulte et al. (2016), through an analysis of the relationship between Information and Communication Technology (ICT) and energy productivity in OECD countries, revealed that digital technology levels enhance energy productivity and are closely associated with a significant reduction in total energy demand [89]. Ma & Li (2025) confirmed the mediating role of energy efficiency in the relationship between the digital economy and carbon emissions through a mediation effect analysis, arguing that the development of the digital economy improves overall energy utilization efficiency, thereby alleviating the pressure of carbon emission growth to some extent [90]. However, some scholars have pointed out that the digital economy does not always lead to a reduction in carbon emissions. Zhang et al. (2024) indicated that the digital economy exerts an inverted U-shaped nonlinear effect on carbon emissions through energy efficiency [79].
The development of digital information technology exerts a significant promoting effect on green technology innovation [91]. As a result, another set of studies has shown that the development of the digital economy significantly promotes green technological innovation, thereby reducing carbon emissions through this mechanism. Wang and Zhang (2024)’s research indicated that the digital economy stimulates green innovation, leading to industrial upgrading and a significant reduction in carbon emissions [91]. Jiang et al. (2024), using a Panel Smooth Transition Regression (PSTR) model, found that the expansion of the digital economy can promote technological progress, foreign direct investment, urbanization, and industrial structure optimization, thereby curbing carbon emissions in the early stages [60]. Zuo et al. (2024), based on panel data from 80 countries and using a System-GMM model, pointed out that the development of the digital economy significantly promotes carbon emission reduction through technological progress, structural optimization, and enhanced education [64]. Zhu et al. (2024) argued that digital trade can promote corporate green innovation and change consumer behavior to reduce carbon emissions [92]. However, some studies have proposed different views, suggesting that the development of the digital economy may inhibit the efficiency of green technological innovation. Some studies have indicated that although digitalization offers opportunities for green innovation, its rapid development and application may lead to an over-concentration of resources in the digital field, thereby neglecting technological innovation in other key areas [93].
Industrial structure upgrading and transformation is an important mediating mechanism through which the digital economy promotes carbon emission reduction. Some studies have emphasized the positive role of the digital economy in promoting industrial upgrading and reducing carbon emissions. For example, Wang and Zhang (2024) argued that the digital economy significantly reduces carbon emissions by stimulating green transformation and development, especially in non-resource-based cities, large cities, and regions with better digital infrastructure [91]. Jiang et al. (2024), using a PSTR model, found that the expansion of the digital economy curbs carbon emissions in the early stages by promoting technological progress, foreign direct investment, urbanization, and industrial structure optimization [60]. Dong et al., using panel data from 60 countries, examined the impact of digital economic development on carbon emissions and the related transmission mechanisms, indicating that economic growth, financial development, and industrial structure upgrading play mediating roles between the digital economy and carbon emissions [49]. Industrial diversification constitutes a crucial dimension of industrial structure upgrading. Wang et al. (2023) developed an integrated causal mediation model based on extended structural equation modeling, demonstrating that the digital economy effectively curbed carbon emissions through low-carbon technological innovation and industrial diversification [94]. Related Literatures about mechanisms of how Digital Economy effects Carbon Emissions are shown in the Table 6.

6. Prospect

Both the digital economy and carbon emissions encompass extensive research domains, generating a multitude of research directions worthy of exploration. Future research may focus on the following aspects.

6.1. Optimizing the Evaluation System and Index Measurement Methods of the Digital Economy

With continuous breakthroughs in data acquisition methods, advancements in big data analytics, and the ongoing expansion of application scenarios, the evaluation system of the digital economy is entering a more multidimensional, precise, and dynamic developmental stage. To elaborate further, due to the widespread application of cutting-edge technologies such as artificial intelligence and blockchain, the digital economy is no longer confined to traditional information-based industries. Instead, it has deeply permeated fields such as artificial intelligence, new energy, and environmental protection. Consequently, the evaluation system of the digital economy needs continuous optimization and adjustment to more comprehensively reflect its development [96]. For instance, in addition to traditional indicators such as economic output, employment, and innovation capacity, new evaluation dimensions such as intelligence, green development, and digital technology adoption rates can be introduced to better capture the societal impact and sustainability of the digital economy [97]. The evaluation system of the digital economy will exhibit more precise, intelligent, dynamic, and globally integrated characteristics.
On the other hand, traditional measurement methods face limitations when addressing the complex and ever-changing structure of the digital economy. Machine learning technologies, particularly deep learning and reinforcement learning, can process complex data patterns and automatically learn critical features from data, thereby enhancing the accuracy and flexibility of index measurement. By employing these advanced methods, it is possible not only to effectively capture hidden relationships within the data but also to identify more reasonable weight distributions among multiple indicators, making the measurement of the digital economy index more scientific and precise [98]. At the operational level, researchers can utilize ensemble learning methods to improve prediction accuracy by combining different models. By integrating various machine learning algorithms, the strengths of multiple algorithms can be aggregated, reducing biases and errors that may arise from a single algorithm, thereby further enhancing the reliability of measurement results [99,100].

6.2. Enhancing the Accuracy of Carbon Emission Measurement Using Remote Sensing Technology

Remote sensing technology can improve the accuracy of measuring the scale and efficiency of carbon emissions. By integrating remote sensing data with ground-based observations, socio-economic data, and other multi-source data, a more comprehensive carbon emission information system can be established [101]. As such, remote sensing technology holds significant potential for future development in the field of carbon emission measurement. Specifically, the continuous advancement of remote sensing and other spatial technologies, along with their application in interdisciplinary integration and global collaboration, will significantly enhance the accuracy and real-time capabilities of carbon emission measurement. The widespread use of high-resolution monitoring satellites, LiDAR, and advanced sensors enables comprehensive observation from global to local scales [102]. The deep integration of remote sensing technology with artificial intelligence and the Internet of Things will drive the construction of intelligent and dynamic monitoring systems. Meanwhile, the promotion of remote sensing data-sharing mechanisms and deep technical cooperation will facilitate the transparency and widespread adoption of global carbon emission measurement. Remote sensing technology will also play a crucial role in zero-carbon city development and society-wide carbon neutrality initiatives, particularly in social applications such as urban management, individual carbon footprint tracking, and responding to extreme climate events [103]. Looking ahead, remote sensing technology will not only serve as a monitoring tool but also become a vital instrument for advancing global climate governance and green transformation, providing robust support for achieving a clean and sustainable future.

6.3. Enhancing Research at the Micro Level

With continuous breakthroughs in data acquisition methods, advancements in big data analytics, and the ongoing expansion of application scenarios, the evaluation system of the digital economy is entering a more multidimensional, precise, and dynamic developmental stage. To elaborate further, due to the widespread application of cutting-edge technologies such as artificial intelligence and blockchain, the digital economy is no longer confined to traditional information-based industries. The primary actors in carbon emissions or carbon reduction are enterprises. Focusing solely on macro-level research may lead to issues such as insufficient applicability and high implementation challenges. By concentrating on the meso- and micro-level impacts of carbon emissions across industries and individual enterprises, researchers can identify the characteristics of different industries and enterprises of varying scales, thereby addressing the limitations of macro-level studies. Specifically, methods such as field research, case studies, and questionnaires can be employed to examine the pain points, effectiveness, and successful experiences of digital carbon reduction at the micro-enterprise level, further enriching the research scope of how the digital economy empowers carbon reduction [104,105]. Enterprise-level carbon emission data serves as a critical foundation for the construction of carbon markets and carbon trading systems. Currently, the establishment of carbon trading systems still relies on relatively coarse industry-level data, which may significantly deviate from actual enterprise emissions. Therefore, refining carbon accounting methods at the enterprise level can improve the accuracy of carbon emission data, enhance the fairness and effectiveness of carbon markets, and provide more reliable data support for carbon trading policies [106].

6.4. Addressing the Digital Divide and Innovating Research Frameworks

With the vigorous development of the digital economy, the application of digital technologies has gradually emerged as a key driver of economic growth and carbon reduction on a global scale [107]. However, the existence of the digital divide poses challenges of imbalance and inadequacy in this developmental process, potentially undermining the digital economy’s ability to achieve sustainable development goals [108,109]. Future research must place greater emphasis on the digital divide and propose innovative research frameworks to more comprehensively understand the complex relationship between the digital economy and carbon reduction [110].
Future research frameworks should integrate factors related to the digital divide, particularly the disparities in digital access between developed and developing regions, urban and rural areas, and different income groups, alongside considerations of technological accessibility, availability, and acceptance. The digital divide not only restricts access to advanced digital technologies and carbon reduction solutions for certain regions and populations but also exacerbates social and economic inequalities. By developing new research models, the constraining factors of the digital divide in promoting carbon reduction can be quantified, thereby providing policymakers with more precise recommendations to ensure the inclusivity and equity of the digital economy on a global scale. The full potential of the digital economy in the field of carbon emission reduction can only be realized when digital technologies are widely disseminated to a sufficiently broad level.

7. Conclusions

Through a review, this paper systematically examines the impact of the digital economy on carbon emissions and explores the underlying mechanisms through a comprehensive review. The findings indicate that the relationship between the digital economy and carbon emissions is complex and multifaceted. Specifically, the digital economy contributes to carbon emission reduction by enhancing energy efficiency, fostering green technological innovation, and facilitating industrial structure upgrading. However, the energy-intensive nature of digital infrastructure construction and operation may simultaneously increase carbon emissions, presenting a dual-edged effect. Furthermore, the existing studies identify nonlinear relationships—often characterized by an inverted U-shaped or N-shaped curve—suggesting that the carbon mitigation effects of the digital economy become more pronounced only after surpassing a certain developmental threshold. Furthermore, the main mechanisms empirically supported can be summarized into three categories: energy efficiency, green technological innovation, and industrial structure.
Moving forward, research should focus on optimizing digital economy evaluation systems, enhancing carbon measurement accuracy using remote sensing technologies, and conducting micro-level analyses of corporate emissions behavior. Additionally, future studies should exam how the digital divide affects emission reduction outcomes to ensure inclusive and equitable sustainable development globally.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17115044/s1, PRISMA checklist. Reference [111] is cited in supplementary materials.

Author Contributions

Writing—original draft preparation, B.L.; Writing—review and editing, F.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The National Social Science Fund of China, grant number 22CTJ006.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The process framework of PRISMA for screening literature.
Figure 1. The process framework of PRISMA for screening literature.
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Figure 2. Bibliometric keyword clustering of the 102 sampled publications.
Figure 2. Bibliometric keyword clustering of the 102 sampled publications.
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Figure 3. Measurements of Carbon Emissions.
Figure 3. Measurements of Carbon Emissions.
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Figure 4. Measurements of Digital Economy.
Figure 4. Measurements of Digital Economy.
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Figure 5. Relationships between Digital Economy and Carbon Emissions.
Figure 5. Relationships between Digital Economy and Carbon Emissions.
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Figure 6. Main mechanisms of how the Digital Economy effects Carbon Emissions.
Figure 6. Main mechanisms of how the Digital Economy effects Carbon Emissions.
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Table 1. Comparison of Carbon Emission measurement methods.
Table 1. Comparison of Carbon Emission measurement methods.
Emission FactorLCA, SLCA, LCCA
SourceCarbon sources caused by human activities
TheoryCE = activity intensity × carbon emission factor
PerspectiveProducer PerspectiveConsumer Perspective
FeatureFrom top to bottomFrom bottom to top
AdvantagesConvenient data acquisitionAccurate and scientific results
DisadvantagesOverlook the CE transfer between regions due to economic tradeStrict data requirements and difficult to unify data standards
ApplicationLarge-scale spaces such as national or provincial levelMicro level domain, such as a product or activity
Note: CE = Carbon Emissions, LCA = Life Cycle Analysis, SLCA = Social Life Cycle Analysis, LCCA = Life Cycle Cost Analysis.
Table 2. Main Digital Economy Indexes.
Table 2. Main Digital Economy Indexes.
Indicator SystemPublish SourceCovering Regions
Digital Economy and Social Index (DESI)European Statistical Office (Eurostat)EU countries
ICT and Digital Economy Statistics Indicator systemOECDOECD members
Digital Economy Index (DEI)China Academy of Information and Communications TechnologyChina
Network Readiness Index (NRI)World Economic Forum (WEF)130 economies
ICT Development Index (IDI)International Telecommunication Union (ITU)ITU members
Note: ICT = Information and Communication Technology.
Table 3. Related Literatures about the Digital Economy decreasing Carbon emissions.
Table 3. Related Literatures about the Digital Economy decreasing Carbon emissions.
FindingScopeMethodStudy
ICT improve environmental qualityG7 countries, 1986–2017Cross-sectional ARDLDogan & Pata (2022) [55]
China’s DE has a noticeable CE reduction effect31 provinces in China, 2011–2029Spatial panel Durbin modeYi et al. (2022) [61]
DE is conducive to reducing the CEUrban cities in China, 2000–2019Staggered DID modelWang & Zhong (2023) [62]
National CE can be suppressed by DE development100 countries, 1990–2019Mediating effect modelMei et al. (2023) [56]
Digital trade can lower CE46 countries, 2007–2021Fixed effect modelLi et al. (2024) [43]
DE is conducive to CE reduction globally80 countries from 2010–2020System-GMM model Zuo et al. (2024) [64]
Digital transformation decreases CE in the manufacturing companiesListed companies in China, 2011–2019Mediating effect modelZhang et al. (2024b) [58]
Digital technology application promotes energy saving and emission reductionListed companies in China, 2011–2020Ordered logit modelZhang et al. (2024c) [59]
Digitalization significantly enhances carbon productivity and reduces CE136 countries, 2000–2020Quantile regressionYu & Liu (2024) [66]
DE growth has a significant curbing influence on CE30 provinces in China, 2011–2021Panel smooth transition regression Jiang et al. (2024) [60]
Note: DE = Digital Economy, CE = Carbon Emissions, ICT = Information and Communication Technology.
Table 4. Related literatures about Digital Economy increasing Carbon emissions.
Table 4. Related literatures about Digital Economy increasing Carbon emissions.
FindingScopeMethodStudy
The digital economy as a whole boost carbon emissionsAustralia, 1985–2012ARDL bounds testSalahuddin & Alam (2015) [70]
Statistically significant but practically negligible positive effect between Internet usage and CEOECD countries, 1991–2012Pooled Mean Group (PMG)Salahuddin et al. (2016) [71]
Technological innovation in the digital industry increases CE50 OECD economies, 2005–2015KPWW method and multiple panel regressionWang et al. (2021) [72]
Digital economy promotes increases in the per capita carbon emissionsSSEA region, 1990–2014K-means clusterArshad Z et al. (2021) [74]
Digital infrastructure, digital markets and technologies increase carbon emissionsThe whole worldCPERI/CSPKKunkel & Tyfield (2021) [75]
The increased use of ICT causes the CO2 emissions to risenine Asian economies, 1990–2018Non-linear ARDLUsman et al. (2021) [73]
ICT causes the CO2 emissions to rise60 countries, 2008–2018Intermediary effect modelDong et al. (2022) [49]
Data centers and networks will further accelerate global electricity consumption76 EMDE countries, 2014–2021Dynamic two-step system GMMKhan et al. (2023) [16]
Digital financial inclusion increases carbon emissions83 countries, 2005–2021Mediating effect modelChe et al. (2024) [19]
Energy efficiency also stimulates a rebound in energy consumption97 countries, 2003 to 2019Panel threshold modelWang et al. (2024a) [67]
Note: DE = Digital Economy, CE = Carbon Emission, ICT = Information and Communication Technology.
Table 5. Related Literatures about inverted “U”-shaped relationship between DE and CE.
Table 5. Related Literatures about inverted “U”-shaped relationship between DE and CE.
FindingScopeMethodStudy
ICT and CO2 emissions is an inverted U-shaped relationship142 economies, 1995–2010OLS regressionHigón et al. (2017) [83]
Non-linear relationship109 countries, 2005–2016Solow growth modelLi et al. (2021) [81]
Internet development on emission reduction efficiency is nonlinear196 cities in China, 2011–2018SDM, Threshold model, mediating model, DID Wu et al. (2021) [86]
Inverted U-shaped relationship between DE and CE274 cities in China, 2011–2018PTM, SDMLi & Wang (2022) [84]
Inverted U-shaped relationship between DE and CE271 cities in China, 2011–2019Panel threshold modelBai et al. (2023) [76]
Digitization’s impact on carbon emissions presents an inverted U-shaped curve281 cities in China, 2016–2019Spatial panel model and
Mediating effect model		Zheng et al. (2023) [77]
Inverted U-shaped relationship between DE and CE67 countries, 2005–2019FMOLS regression Wang et al. (2023) [80]
Inverted U-shaped relationship between DE and CE 72 countries, 2013–2020 Intermediary effect model, Spatial panel modelZhang et al. (2024) [79]
Inverted U-shaped relationship between DE and CECities in China, 2011–2019Threshold regressionsMa et al. (2025) [82]
Note: DE = Digital Economy, CE = Carbon Emission, ICT = Information and Communication Technology.
Table 6. Related Literatures about Mechanisms of how DE effects CE.
Table 6. Related Literatures about Mechanisms of how DE effects CE.
MechanismsScopeMethodStudy
Energy efficiency 10 OECD countriesSingle-equation modelsSchulte et al. (2016) [89]
Energy structure and efficiency100 countriesMediating effect modelMei et al. (2023) [56]
Renewable energy technology innovationIEA and OECD memberIntermediary effect modelLee et al. (2022) [95]
Economic growth, financial development, Industrial structure upgrading60 countriesIntermediary effect modelDong et al. (2022) [49]
Technological advancement, Structural optimization, and educational enhancement80 countriesSystem-GMM modelZuo et al. (2024) [64]
Energy efficiency, Industrial upgrading72 countriesIntermediary effect model Spatial panel modelZhang et al. (2024c) [79]
Technological advancement, Foreign direct investment, Urbanization, Industrial structure optimization30 provinces in ChinaPanel smooth transition regressionJiang et al. (2024) [60]
Green transformation, Industrial structure optimization275 cities in ChinaDID modelWang & Zhang (2024) [91]
Green innovation, consumption behavior30 provinces in ChinaFixed effect modelZhu et al. (2024) [92]
Energy efficiency, Industrial restructuring, foreign investment attractionCities in G20IV-GMM regressionMa & Li (2025) [90]
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Liu, B.; Wang, F. The Impact of the Global Digital Economy on Carbon Emissions: A Review. Sustainability 2025, 17, 5044. https://doi.org/10.3390/su17115044

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Liu B, Wang F. The Impact of the Global Digital Economy on Carbon Emissions: A Review. Sustainability. 2025; 17(11):5044. https://doi.org/10.3390/su17115044

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Liu, Bingjie, and Fengyi Wang. 2025. "The Impact of the Global Digital Economy on Carbon Emissions: A Review" Sustainability 17, no. 11: 5044. https://doi.org/10.3390/su17115044

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Liu, B., & Wang, F. (2025). The Impact of the Global Digital Economy on Carbon Emissions: A Review. Sustainability, 17(11), 5044. https://doi.org/10.3390/su17115044

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