Research on the Coupled Coordination of the Digital Economy and Environmental Quality
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Xiaomei Luan
,
Chibo Chen
Abstract
Few studies provide direct evidence that there is a coupling coordination relationship (CCR) between the digital economy and environmental quality. This study tries to fill this gap. Using panel data from 31 provinces in China from 2011 to 2020, we conclude that there is a symbiotic CCR with an increasing degree over time between China’s digital economy and environmental quality and it is more visible in eastern, central than other areas. Further study shows that the regional heterogeneity of coordination is mainly due to inter-group rather than intra-group. The environmental regulations (ERs) play a crucial role in shaping future levels of coupled coordination and the conclusions are robust. Policymakers should simultaneously promote the development of the digital economy, improve the quality of the ecological environment, and pay attention to the inclination of ERs to the western and north-east regions. This analytical framework reveals the complex interaction between the digital economy and environmental quality, while the coupled coordination evaluation system developed here can provide valuable experience for developing countries to open new paths towards high-quality development.
1 Introduction
With the development of urbanization and industrialization, environmental problems have become increasingly serious. For a prolonged period, China’s economy has traditionally relied on a growth model characterized by resource-intensive and labor-intensive practices. Although economic development has occurred, it has led to environmental pollution and ecological destruction, thus not only restricting the sustainable development of the economy but also seriously affecting the health and safety of the people and making social contradictions increasingly prominent. The improvement of environmental quality requires the transformation of the economic development mode. China is experiencing a surge in digital economy development across various regions. The digital economy represents an advanced economic paradigm. It has played a considerable role in resource allocation, penetration and integration, collaborative governance, and other processes. The digital economy and ecological environment influence and constrain each other. Sustainable development must be anchored in their synergistic advancement.
Few academics have examined the relationship between the digital economy and ecological environment, but there are two academic views. One is that the digital economy can significantly reduce ecological environmental pollution index, improve environmental governance performance (Ma et al., 2023; Tang & Wang, 2023), enhance product production efficiency, and reduce resource waste (Rusch et al., 2023). The other is that the digital economy has a significant inhibitory effect on environmental performance (Zhang & Zhong, 2022). Fewer academics have studied the coupling of the digital economy and environmental quality (Han et al., 2023; Liu et al., 2024). However, the theoretical framework of the coupling and coordination between the two systems in the existing literature is not perfect, the research method is also incomplete, and the comparative study of different regions and different stages of development is lacking. In addition, the relationship between the digital economy and environmental quality is dynamic, the previous studies lack an in-depth analysis of this dynamic, without revealing the long-term impact and change trend between these two systems. The analysis of dynamic factors related to the degree of coupling coordination is not included. Based on this, the potential for win–win cooperation between the digital economy and the ecological environment, as well as the regional variations and space-time evolution in their coordinated development, warrants an in-depth exploration.
The marginal contributions of this article are as follows. First, a comprehensive evaluation index system is established for the digital economy and environmental quality individually, and the coupled and coordinated relationship between the digital economy and environmental quality is measured. It enriched the relevant contents of digital economy and ecological environment research. Second, Kernel density estimation is used to analyze the dynamic evolution trends of the digital economy and environmental quality. The Dagum Gini coefficient is used to analyze the variations in regional differences in the coupled coordination index. Third, exogenous and endogenous driving factors of the two systems are discussed, the core and major driving factors affecting the coupled coordination degree are verified with an individual fixed-effect model and dynamic panel GMM, and the temporal correlation of coupling coordination degree between digital economy and environmental quality is revealed. The above in-depth analysis provides a full understanding of the coordinated development status and regional heterogeneity of the digital economy and environmental quality in China.
Compared to previous studies, this analytical framework uncovers the intricate interplay between the digital economy and environmental quality. Conducting a comparative analysis of the coupling and coordination relationships between these two systems in different regions and at various stages of development clarifies the dynamic evolutionary process and changing trends of their coupled development. Notably, unlike prior research, this article introduces a dynamic factor measurement model for the coupled development of the digital economy and environmental quality, thereby further revealing potential obstacles to their coordinated development. Such analysis can contribute to enhancing high-quality economic development and restoring the ecological environment, and it also provides relevant policy recommendations for promoting the coordinated development of the digital economy and ecological environment in China as a whole, inter-regional and intra-regional.
The rest of the study is organized as follows. A review of the relevant literature is presented in Section 2. Section 3 provides a description of the research design, samples and data sources, index system, and research methods. Section 4 analyzes and discusses the empirical results. Section 5 analyzes dynamic factors related to coupled and coordinated development. Section 6 provides conclusions. Section 7 proposes relevant policy recommendations. Section 8 addresses the research limitations and prospects.
2 Literature Review
2.1 Digital Economy Overview
The research on digital economy includes the concept and connotation of the digital economy and the measurement of the digital economy. The concept of the digital economy first emerged in the 1990s with the publication of The Digital Economy: Promise and Peril in the Age of Networked Intelligence by Tapscott. Tapscott (1996) sees the digital economy as a new set of economic relationships that have emerged with the advent of Internet technology and then attracted wide attention around the world. Moulton (2000) put forward that the digital economy encompasses e-commerce and information technology. Zalutskyy (2019) defines the digital economy as an economy based on digital technology that provides inclusive socioeconomic development and prosperity. Zhang et al. (2022) believe that the digital economy includes digital industrialization and industrial digitalization. Sadik-Zada et al. (2022) and Niftiyev (2022) view e-government as an application form of digital economy. Gurbanov et al. (2022) consider digital crisis management tools can greatly improve business performance. In summary, the definition of the digital economy has gradually shifted from the early emphasis on the digital technology industry and its market-oriented application to the interpretation of digital technology functions and the transformation of digital technology to production relations. Even though scholars do not fully agree with the definition of the digital economy due to the different perspectives and ways of perceiving it, they generally accept that intelligence and platformization are the main forms of the digital economy; digital economy has unique advantages in improving the speed of information transmission and precise allocation of resources.
International standards for measuring the digital economy are not yet unified. Scholars make useful attempts to measure the digital economy through the methods of value-added accounting and satellite account construction. However, due to the lack of statistical data and the difficulty in defining the scope of the digital economy, different institutions and scholars have greatly different definitions of the measurement scope of the digital economy and the measured results. Therefore, the construction of a comprehensive evaluation index system for the digital economy becomes an effective way to examine the development level of the digital economy. Among them, the European Union proposes the Digital Economy and Economic Society Index, which includes dimensions such as human capital, connectivity, digital technology integration, and digital public services. The United Nations International Telecommunication Union (ITU, 2009) proposes the Information and Communication Technology (ICT) Development Index; it includes three dimensions: ICT access, ICT use and ICT skills. Luo et al. (2023) administrate the principal component analysis (PCA) to evaluate the advancement level of the urban digital economy.
2.2 Environmental Quality Overview
The term ‘ecosystem services’ was first used in the 1960s to refer to the various benefits that humans derive from ecosystems. Martin and Anthony (2008) define ecosystem services as the web of life that represents the interdependence of human society with other species. Later, Bruno et al. (2014) point out on this basis that the happiness of human society depends on the public goods and services provided by the ecosystem, and the vast majority of ecological services are generated from environmental resources. Chinese scholars often use ecological environment instead of ‘ecosystem services’. Environmental quality is a further expansion of the concept of ecological environment, which analyzes the status of the ecological environment and its quality changes within a certain time and space, so as to reflect the quality of the ecological environment and its suitability for human survival and economic and social development (Zhou et al., 2022a). Ye and Liu (2000) believe that environmental quality refers to the degree of ecological environment, reflecting the suitability of ecological environment to human survival and sustainable social and economic development, and is an evaluation of the changes in the quantity and structure of ecological environment caused by human activities and social development.
Environmental quality research mainly focuses on the development and evaluation of indicators. Comprehensively speaking, urban environmental quality can include air quality, sound environmental quality, solid waste, soil environmental quality, water environmental quality, and other aspects (Cao et al., 2024; Esau et al., 2021; Wu et al., 2024). In 1990, the Organization for Economic Cooperation and Development of the United Nations adopted the PSR system, which represents the pressure caused by human activities on the ecological environment, as an ecological environment indicator (Balfors et al., 2005; Nakamae et al., 2001). This model well reveals the relationship between the ecological environment and social and economic development. Based on the PSR model, Lv and Zhou (2023) discussed the spatial and temporal evolution trends of ecological environment quality in each province and six regions in China in 2005–2020.
There are abundant research studies on the concept and evaluation of environmental quality. The existing research has greatly filled the research methods of environmental quality from multi-scale and different scopes. The detection methods of the ecological environment have shifted from relying on human statistics and ground monitoring to advanced satellite remote sensing monitoring, and the environmental quality evaluation system has also transitioned from a single pollutant emission index or a composite pollution emission index (Wang & Lu, 2020) to the establishment of a comprehensive index system.
2.3 The Relationship between Digital Economy and Ecological Environment Overview
Aghion et al. (1998) introduced environmental pollution and resource constraints into Schumpeter’s model and found that continuous technological innovation could promote the outwards movement of the economic equilibrium point and enhance output. Ma et al. (2023) incorporate an environmental index and a digital economy index into the Environmental Kuznets model and find that the impact of the digital economy on the environment displays a positive ‘U’ shape. Notably, the digital economy makes a significant contribution to improvements in the environment in China during the investigation period. Tang and Wang (2023) use the SDM model and intermediary effect model to study the impact of the digital economy on ecological resilience and the corresponding mechanism and find that the digital economy is characterized by a significant spatial spillover effect that promotes the ecological resilience of regions and neighboring regions. Zhao (2022) finds that industrial production has a considerable impact on the ecological environment, and the increase in tertiary industry has promoted the green economy. Using panel data for 72 countries from 2003 to 2019, Shahbaz et al. (2022) demonstrate that the digital economy has a positive impact on the energy transition. Lv et al. (2022) propose the main path for the digital economy to promote the realization of the value of ecological products. Dong et al. (2023) and Shen et al. (2018) use an improved coupled coordination degree model to verify the coupled relationships between socioeconomics and carbon emissions, urbanization, and air quality. Yan et al. (2019) verified that energy, the economy, and the environment interact, restrict, and promote each other. Weng et al. (2022) conducted an empirical analysis of the coupled and coordinated development of the economy, environment, and society in 31 provinces and regions in China. The results show that the comprehensive index and coupled coordination degree are highest in the eastern region, although the degrees of coupling are similar in the eastern, central, and western provinces. Han et al. (2023) and Liu et al. (2024) explore the coupled coordination relationship among the regional digital economy, level of technological innovation, and ecological environment in detail; they applied spatiotemporal and geographical weighted regression models and verify that human capital is the main force promoting coupled and coordinated development. Liu et al. (2024) propose that the economic development level, technological innovation level, and environmental driving mechanism are the main drivers of the coupled development of the three systems above in China’s provinces and cities. Fu et al. (2022) calculated the coordinated development level of the digital economy and environment by using an entropy method and a coupled coordinated development (CCD) model, and the results showed that the coordinated development levels of the two systems presented regional heterogeneity. Zhang (2021) proposed that attention be given to the integration of the digital economy and agricultural development, as the digital economy is an important driver that can promote economic and ecological development in rural areas.
In summary, scholars’ research on the digital economy and environmental quality mainly focused on the following two aspects. First, the measurements of the digital economy and environmental quality are obtained. The digital economy is a new economic model driven by advancements in digital technology, and it can significantly reduce resource and environmental loss (Guo & Luo, 2016), support green development, and protect the ecological environment. Quantitative assessments of the digital economy have focused on added value (Bakhshi, 2016) and the compilation of development indices. Notably, scholars tend to base the measurements of the digital economy on the levels of Internet access and digital financial inclusion (Zhao et al., 2020). Environmental quality research mainly focused on the development and evaluation of indicators. Comprehensively, urban environmental quality can include air quality, the quality of the sound environment, solid waste, soil quality, water quality, and other factors (Esau et al., 2021). With the gradual increase in the popularity of the concept of sustainable development, many countries propose series of indicator systems to support sustainable urban development (Musse et al., 2018). In general, due to the different research purposes and approaches of scholars, there are extensive differences in the construction of environmental quality indicators.
Second, the relationship between the digital economy and environmental quality has been discussed. This approach is mainly manifested in two aspects. First, the impact of the digital economy on environmental quality is explored. The digital economy has an important impact on the quality of the environment[ (Ma et al., 2023; Tang & Wang, 2023; Zhao, 2022; Pang & Xie, 2024). Notably, the digital economy has an inhibitory effect on energy consumption in the short term and a growth effect in the long term. The digital economy is an important driver of ecological development, which can limit environmental pollution (Vidas-Bubanja, 2014) and enable green development by enhancing innovation (Lu et al., 2024). The second is the interaction between the digital economy and the environment. The two systems are characterized by a coupled and coordinated development relationship (Dong et al., 2023; Han et al., 2023; Lv & Zhou, 2023; Yan et al., 2019; Weng et al., 2022). Human capital (Han et al., 2023), the economic development level, the technological innovation level, and environmental mechanisms (Liu et al., 2024) are the main drivers that promote the coupled and coordinated relationship between the digital economy and environmental quality.
Although research on the digital economy and environmental quality has attracted increasing attention from scholars, there is still room for further expansion regarding the coupled and coordinated development of the digital economy and environmental quality. First, existing studies focused on the coupled relationship between economic development and environmental quality, and have given insufficient attention to the connection between digital economy and environmental quality. Second, the existing studies mainly focused on the role and mechanism of the digital economy in improving the environment, including in specific regions through case studies. Empirically, panel data econometric models have been used to analyze the scope and intensity of the effect of the digital economy on environmental pollution at the provincial and city levels, and the coupled and coordinated relationship between the digital economy and environmental quality has not been comprehensively discussed.
Compared with those of previous studies, the main contributions of this article are as follows: (1) We put forward a theoretical framework for the coupled and coordinated development of digital economy and environmental quality, and expound the basic mechanism of the mutual promotion and collaborative progress of digital economy and environmental quality. (2) We measure and analyze the degree of coupled and coordinated development of the digital economy and the ecological environment in the context of rapid digitalization, and we reveal the regional heterogeneity and evolution of the coupling coordination degree between digital economy and environmental quality. (3) Exogenous and endogenous driving factors of the two systems are discussed, and the core and major driving factors affecting the coupled coordination degree are verified with an individual fixed-effect model. (4) We choose China as the study area, and the findings are representative. The purpose of this article is to provide a scientific basis for relevant research and for various departments to formulate relevant policies related to the digital economy and environmental protection. Subsequently, the trend of the coupling and coordination of the two systems could be assessed over time.
3 Materials and Methods
3.1 Connotation and Mechanism of the Coupling Coordination
Coupling is a concept commonly used in physics, biology, systems science, and chemistry, which refers to the dynamic correlation between two or more systems and modes of motion, which are interdependent, mutually coordinated, and mutually promoted, and the result of their final development. American scholar Weick (1976) took the lead in introducing ‘coupling’ into the field of social science and studied the interrelationships between educational organization systems based on the theory of loose coupling. ‘Coordination’ refers to the positive interaction between the systems or the internal components of the system (Liao, 1999). The coupling coordination degree is an important measure of the harmonious and consistent state of the system in its development and evolution (Rusch et al., 2023) and reflects the diachronic change and future development trend of the collaborative evolution of the system. Under the guidance of the idea of high-quality development, coupling coordination theory is an important theory and method to explain the coordinated development of complex ecosystem and economy, as well as a significant component of sustainable development. This theoretical model is feasible and relevant in the research of coupled coordination between digital economy and ecological environment system.
There exists a complex and interdependent relationship between the digital economy and environmental quality, as depicted in Figure 1. The digital economy pushed forward industrial upgrading and optimization, business model innovation, green technology advancement, and human capital development, which in turn enhances environmental quality. Simultaneously, the ecological environment facilitates the diffusion of digital technologies and data elements through enhanced consumer demand, improved infrastructure, resource, and environmental constraints, as well as heightened public awareness towards the environment. Consequently, this synergy fosters mutual influence, integration, and coordination between the digital economy and environmental quality for their shared development.
Coupling mechanism of digital economy and environmental quality. Note: The digital economy fosters industrial upgrading, business model innovation, green tech advancements, and human capital development, thereby improving environmental quality. Conversely, the ecological environment boosts the spread of digital tech and data, driven by consumer demand, better infrastructure, resource constraints, and heightened environmental awareness.
3.2 Samples and Data Sources
In this article, 31 provinces and autonomous regions in China (excluding Hong Kong, Macao, and Taiwan) are selected for analysis, and the sample investigation period is 2011–2020. The data are mainly from the 2012–2021 China Statistical Yearbook, China Environmental Statistical Yearbook, China Energy Statistical Yearbook, Tertiary Industry Statistical Yearbook, Information Industry Yearbook, Peking University Digital Financial Inclusion Index, and provincial statistical yearbooks. Some ecological and environmental data (expenditures on energy conservation, environmental protectio, Marketization index) are from the EPS data platform. Additionally, data gaps were filled with linear interpolation and the ARIMA model.
3.3 Index System
In accordance with the principles of data availability and index representativeness and with reference to relevant research results (Dong et al., 2020; Ge et al., 2022; Shen et al., 2022), we construct an evaluation index system for regional environmental quality by selecting 12 one-way indicators, including the amount of general industrial solid waste, the forest coverage rate of each region, the urban gas penetration rate, etc., based on the four first-level indicators of pollution emissions, ecological protection, the level of human activities and environmental governance, as shown in Figure 2.
Evaluation indicator system of coupled coordination. Note: The digital economy and environmental quality promote each other and develop in a coordinated manner.
The development level system of the digital economy refers to the research of scholars (Li et al., 2021; Zhao et al., 2020) and examines the development level of the digital economy from the two first-level indicators of Internet development and digital financial inclusion, as well as the five dimensions of Internet penetration rate, number of Internet employees, Internet-related output, mobile Internet users, and digital financial inclusion index. This practice has also been widely recognized by scholars. Specifically, the proxy index of Internet penetration rate is the number of Internet broadband access users per 100 people. The proxy index of the number of Internet employees is the proportion of the number of computer service and software industry employees; The proxy index of Internet-related output is the total telecommunications business per capita; The proxy index of mobile Internet users is the number of mobile phones per 100 people; Digital Financial Inclusion Select Digital Financial Inclusion Index of scholar (Guo et al., 2020), as shown in Figure 2.
3.4 Research Methods
3.4.1 Entropy Weight TOPSIS Method
The entropy weight method is used to determine the weights of indicators, and the ranking of evaluation objects is determined according to the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method. The Entropy Weight TOPSIS method can combine the advantages of both the Entropy Weight Method and the TOPSIS Method, through objective weighting and quantitative scoring, making the evaluation results more objective, fair, and reliable.
The calculation steps in the entropy-weighted TOPSIS method are as follows:
Assuming that the research object is m provinces, each province has an evaluation indicator, and a judgment matrix is constructed:
The following expressions are used to standardize the indicators:
Information entropy is calculated as follows:
Among them,
The weight of indicator j is determined as:
The weight matrix is obtained as follows:
The Euclidean distance between each value and the best and worst solutions is given by:
The comprehensive evaluation index is expressed as:
In this case, the larger
3.4.2 Coupling Evaluation
With formulas (1)–(7), the environmental development index (EDI) and digital economy development index (DDI) can be calculated for each factor, according to the method of Cong (Cong, 2019). The following formula is used to construct an evaluation model for the coupling degree of the regional digital economy and environmental development:
where
3.4.3 Coordination Degree Evaluation
To further explore the coordinated development level of the digital economy and environmental quality, a coupled coordination degree evaluation model of the provincial digital economy and environmental quality is constructed. The formulas are as follows:
3.4.4 Kernel Density Estimation
The probability density function of coupled coordination degree D is established as follows:
3.4.5 Dagum Gini Coefficient
To reveal the trend of the relative differences in evaluation indicators, the Dagum Gini coefficient decomposition method is used to calculate the regional differences in evaluation indicators, and the calculation is as follows:
Here, q and m are the number of regions and the number of provinces, respectively, l and h are the inner sets for region m, i and r are different provinces, and
where
3.4.6 Econometric Methodology
Based on the panel data from 31 provinces in China from 2011 to 2020, a two-way fixed effects panel data model of the dynamic factors of coupled coordination degree is constructed as follows (Shen et al., 2022; Zhou et al., 2022b):
where i represents each province (city), t represents time,
In view of possible endogenous problems, especially when heteroscedasticity exists, this article will further use dynamic panel GMM to estimate the impact of core dynamic factors (environmental regulations) on the coupling coordination degree of the digital economy and environmental quality. The GMM estimation method effectively addresses endogeneity, individual heterogeneity, and serial correlation issues in the time dimension by introducing lagged dependent variables and instrumental variables, thus enhancing the consistency and efficiency of the estimation. The model is as follows:
where i represents each province (city), t represents time,
3.5 Research Procedure
The following steps were followed to verify the coupling and coordination relationship between digital economy and environmental quality: (1) A comprehensive evaluation index system of the digital economy and environmental quality is constructed. The entropy weight method is employed to measure the digital economy index and ecological environment development index, respectively. (2) The coupled coordination model is utilized to assess the coupling degree between the digital economy and environmental quality, while the coordination degree evaluation model is applied to measure their coupling coordination level. These steps provide a clear understanding of the overall status of the coupling coordination level between the digital economy and environmental quality, as well as the coupling development stage in each region. (3) The kernel density estimation method is used to estimate the distribution and dynamic evolution trend of the coupling coordination degree between the digital economy and environmental quality. This approach allows for a more precise presentation of sequential evolution characteristics at both national and regional levels. (4) The Dagum Gini Coefficient is employed to decompose differences in coupling coordination degrees between the digital economy and ecological environment. By calculating difference coefficients across different regions in China, an analysis could be conducted on sources contributing to variations in this aspect. (5) Econometric models are constructed to analyze the driving factors that influence the interconnection and coordinated development of the digital economy and environmental quality. The two-way fixed effects panel data model is used to estimate the impact level of each driving factor. The GMM estimation method is employed to address potential endogeneity issues, while also investigating the presence of temporal correlation between the coupled coordination degree of the digital economy and environmental quality.
4 Results
4.1 Coupled Coordination Level Analysis
The regional EDI and DDI were calculated according to formulas (1)–(7), and the coupling degree
Evaluation index, coupling coordination degree of each region. Note: The coupling coordination degree between the digital economy and environmental quality varies across different regions, but the overall spatial distribution pattern is similar, with the coupling coordination level in the eastern region being higher than the national average.
First, although the value ranges of the environmental quality index and digital economy index are different in different regions, the distribution patterns of the two systems are relatively similar, forming the basis for the coupled relationship between the two systems. Second, the level in the eastern region is above the national average level, and Beijing, Guangdong, Shanghai, and Zhejiang display the highest levels, reflecting good coupled coordination degrees. Third, the levels in the central region and the northeast region are at the national average level, and those in Shanxi and Liaoning are lower. Fourth, the level in the western region is generally lower than the national average level, especially in Inner Mongolia, with the lowest observed value, but the measurement level in Tibet reflects a good system coupling effect. Overall, the development level of the digital economy in the eastern region is relatively strong, and the degree of correlation and mutual promotion between the two systems of the digital economy and environmental quality displays regional heterogeneity. The average value of the coupling degree C in Inner Mongolia is 0.757, ranging from 0.5 to 0.8, indicating that the digital economy and environmental quality systems in Inner Mongolia are in the run-up stage, and the C values in other regions are higher than 0.8, with many close to 1; this suggests that the two systems in other regions are in a high-level coupling stage. The average coupled coordination degree from 2011 to 2020 ranks as follows: eastern (0.59311) > central (0.50455) > western (0.49988) > northeast (0.49367). Therefore, from a data perspective, the coupled coordination degree of the digital economy and environmental quality systems displays obvious regional heterogeneity. The coupled coordination degree of the digital economy and environmental quality in the eastern region is higher than the national average level. Hebei Province experienced a brief decline in this degree in 2012, but a rapid growth trend has since occurred. In 2017, the coupled coordination degree of Hebei reached 0.535, an increase of 2.9 times that in 2012, among the highest values nationally, reflecting a jump in the coupled coordination level from low to high. The coupled coordination levels in Hainan, Jiangsu, and Shandong provinces also increased considerably by 81.59, 81.86, and 92.56%, respectively. The lowest increase was in Tianjin at 47.56%.
The development trend of the coupled coordination level in the central region is good. The gap between the coupled coordination level of Shanxi and the other provinces after 2013 varies, but the levels in the other five provinces are similar. The increase in the coupled coordination level in the six central provinces is especially higher than that in most of the eastern regions, ranging from 1.2 to 2, with the largest increase in Henan Province. In 2020, the coupled coordination degree of the two systems in Henan Province is 0.639, twice that in 2011, indicating that the correlation between the digital economy and environmental quality in Henan Province has improved over time.
In the northeast region, the degree of coupled coordination in Jilin and Heilongjiang demonstrates a consistent enhancement, whereas the degree of coupled coordination in Liaoning experiences a decline during 2015 and 2016. Additionally, the growth rate slowed after 2018, indicating that the coupled coordination level of the digital economy and environmental quality is not stable.
In the western region, the coupled coordination level of Tibet far exceeds the levels in other areas, outpacing the western average level by approximately 74%. The coupled coordination level in Gansu Province increases the most, approximately 4.37 times the original value. Second, the coupled coordination level in Guizhou increases by more than a factor of 2, and the coupled coordination levels in other provinces and cities increase to different degrees. Ningxia displays a low coupled coordination level before 2013, with subsequent reductions in 2014 and 2015, indicating that the coupled coordination between the digital economy and environmental quality in some provinces and cities is still in the run-up period. Thus, it is necessary to work together to improve the coupling of the two systems.
During the study period, the coupled coordination level of 31 provinces (autonomous regions) in China exhibits significant improvement, culminating in 2020. The levels of coordination in all regions range from 6 to 9. In 2011, no province achieved primary coordinated development or above; only one region in Beijing reached slight coordinated development with a coupled coordination degree of 0.532. This suggests that nationwide efforts towards coordinated promotion of the digital economy and environmental quality had not yet commenced. By 2020, Zhejiang and Tibet had reached a good level of coupled coordination, leading the country with values of 0.807 and 0.872 respectively. Beijing, Shanghai, Jiangsu, Fujian, Guangdong, Hainan, and Chongqing – accounting for approximately 23% of the country – achieved an intermediate level of coordinated development between the two systems which exhibited remarkable growth and interaction dynamics within these seven provinces experiencing rapid development overall. Additionally, seventeen regions including Tianjin and Hebei – representing around 55% of the country – reached a primary level of coordinated development between both systems indicating coupling across more than half the nation’s territory while five remaining regions demonstrated partially coordinated development implying significant potential for further promoting environment-digital economy synergy within these provinces specifically. Overall findings indicate varying degrees of improvement in coupled coordination levels between digital economy and environmental quality across thirty-one provinces/regions during this study period.
In order to further analyze the spatiotemporal variation trend of the coupling coordination degree between the digital economy and environmental quality, a detailed analysis was conducted on the coupling coordination situation in 2011, 2016, and 2020 (Figure 4). The specific findings are as follows: In 2011, the overall coupling coordination level between the digital economy and environmental quality in China was low. No city reached a coordinated development level, except for Beijing which achieved slight coordinated development. This indicates that there existed certain obstacles in the transformation mechanism between the digital economy and environmental quality. In 2016, six provinces and cities entered into good coordination while dysfunctional cities disappeared. This achievement can be attributed to the local deployment of digital economy strategies along with ecological and environmental protection policies. Under government guidance, both sectors have gradually promoted positive development. Most regions in China exhibit varying degrees of coordination stages with evident regional heterogeneity. By 2020, most regions entered into a stage of primary coordination, two regions demonstrated good coordination while only five regions showed slight coordination.
Spatial distribution of coupled coordination level analysis. (a) 2011, (b) 2016, and (c) 2020. Note: We use Yitu mapping software to generate this set of maps. From 2011 to 2020, new provinces and cities across the country continuously entered the coupled development state of the two systems. By 2020, all regions in the country had entered the coupled coordination stage, among which Zhejiang and Tibet had entered a good coordination stage.
4.2 Dynamic Evolution Characteristics of the Coupled Coordination Degree
In this article, Python 3.6 software was used to estimate the kernel density of the coupled coordination degree between the digital economy and environmental quality from 2011 to 2020, the built-in Gaussian kernel density function of the Python software is utilized for estimation, with the default bandwidth selected. The resulting output is displayed in Figure 5.
Dynamic evolution characteristics of the coupled coordination degree. (a) Kernel density in Nationwide. (b) Kernel density in Eastern region. (c) Kernel density in Central region. (d) Kernel density in Western region. (e) Kernel density in Northeast region. Note: Except for the Northeast region, the core curves are gradually shifting to the right, and the coupling coordination level between the digital economy and environmental quality continues to improve and dynamically converge. The distribution of coupling coordination levels within each region is balanced, but there is a significant difference in resilience between regions.
Nationwide, notably, the kernel curve gradually shifts to the right, indicating a continuous improvement in the overall coupled coordination level of the country. From a distribution pattern perspective, slight trailing phenomena are observed in 2012, 2014, 2017, and 2018 on the kernel curve, suggesting spatial disparities in nationwide coupled coordination levels during those years. Certain provinces have taken advantage of their resource endowments and advanced development experience to lead in enhancing the coupled coordination level of the digital economy and ecological environment system. The variations in the kernel curve demonstrate a narrowing trend to some extent, implying that spatial gaps in coupled coordination levels across regions are gradually diminishing. Based on this distribution pattern analysis, it can be observed that while vertical height remains unchanged for curves representing different years, horizontal width decreases along with a reduction in peak numbers. This indicates a tendency towards numerical reduction and dynamic convergence characteristics within national coupled coordination degrees.
In the eastern region, the kernel curve gradually shifts to the right, indicating a progressive shift towards higher values of coupled coordination degree. The distribution pattern reveals a consistent upward trend in the height of the main peak and a gradual narrowing of its width. Moreover, there is a reduction in interprovincial disparity regarding coupled coordination degree within the eastern region. Notably, no significant tailing phenomenon is observed in the right tail of the kernel curve, suggesting relatively small spatial differences in interprovincial coupled coordination levels. The unimodal peak maintained by the kernel density curve implies an even distribution of interprovincial coupled coordination levels without any multipolar or bipolar phenomena.
In the central region, the kernel curve shifts towards the right, accompanied by an upward trend in the coupled coordination degree. The kernel density curve of the coupled coordination degree maintains a unimodal peak, indicating a balanced distribution of interprovincial coupled coordination levels without any multipolar or bipolar phenomena. Regarding the distribution pattern, the height of the main peak demonstrates an ‘up-down’ trend while its width exhibits an ‘enlarging-narrowing’ trend, suggesting that initially there was an increase followed by a slight decrease in the coupled coordination level within the central region. Furthermore, there is an initial increase and subsequent decrease in interprovincial differences regarding their coupled coordination degrees within this region.
In the western region, the kernel curve gradually shifts towards the right, indicating a general upward trend in coupled coordination degree. From the distribution pattern perspective, the height of the main peak exhibits an ‘up-down-up’ trend, while the width of the main peak shows a ‘narrowing-enlarging-narrowing’ trend. Considering distribution ductility, there is no evident tailing phenomenon in the right tail of the kernel curve, suggesting relatively small spatial disparities in interprovincial coupled coordination levels within Western China. The kernel density curve of the coupled coordination degree demonstrates a unimodal state, implying an even distribution and absence of multipolar differentiation among provinces in Western China.
The kernel curve in Northeast China exhibits a three-stage pattern of ‘right shift-left shift-right shift’, while the increase in coupled coordination degree shows breakpoints during the periods of 2014–2017 and 2018–2020. Furthermore, the primary peak demonstrates a multistage trend of ‘rising-sharply declining-slightly rising’. These findings indicate a downward trend in the coupled coordination level between the two systems in Northeast China, with variations observed in interprovincial coupled coordination levels during the periods of 2015–2017 and 2018–2020.
4.3 Regional Differences in and Decomposition of the Coupled Coordination Degree
4.3.1 Overall and Intraregional Differences
Further investigating the spatiotemporal evolution of the Gw value of the Gini coefficient within the four regions in each year. Notably, the intragroup Gini coefficient Gw is high in the western region and the eastern region, reflecting heterogeneity in coupled coordination. However, the Gini coefficient in the northeast region is small, reflecting relatively uniform coupled coordination. The value of the Gini coefficient in the central region is moderate. In addition, the intragroup Gini coefficient value in the eastern region displays a downward trend, which suggests that the heterogeneity of the coupled coordination degree between the digital economy and environmental quality in the provinces in the eastern region has been decreasing in the past 10 years. However, this heterogeneity is increasing in the western, central, and north-eastern regions.
4.3.2 Inter-Regional Differences
As shown in Figure 6, the regional differences between the eastern and central regions showed a significant downward trend, with a decrease of 73.7%; the regional differences between the northeast and eastern regions and the eastern regions and western regions displayed an oscillating downward trend, with decreases of 61.9 and 64.8%, respectively. Notably, various regions have established multiple measures to manage and protect the environment and vigorously develop the digital economy. With spatial spillover and the regional coordination of the development level of the two systems, the regional differences in the coupled coordination degree have gradually declined. The inter-regional differences fluctuate greatly between the northeast and the west and frequently between the central and the western regions.
Inter-regional differences in the coupled coordination degree. Note: The inter-regional heterogeneity of the coupling coordination degree between the digital economy and environmental quality systems has decreased to varying degrees, with the greatest decline in regional differences observed in the eastern and central regions, showing a cliff-like drop.
4.3.3 Sources of Differences
The overall Gini coefficient of the coupled coordination degree between the digital economy and environmental quality exhibited a consistent downward trend, ranging from 0.155 in 2011 to 0.061 in 2020. Apart from minor fluctuations in 2015, there has been a gradual reduction in regional differences and imbalances. In particular, intergroup Gb has emerged as the primary source of disequilibrium. Over the past decade, intergroup contribution has consistently exceeded 40%, while the average intragroup contribution (Gw) stood at approximately 24.3% and the average contribution (Gt) was around 25.4%. This implies that regional imbalance in China’s coupled development of digital economy and environmental quality systems primarily stems from disparities among regions rather than sample overlap within or between regions/among regions. Since 2020, there has been a significant narrowing of the gap between the intragroup contribution rate and intergroup contribution rate for the coupled coordination degree, with the intergroup contribution rate dropping to 40.35% and the intragroup contribution rate rising to 27.18%. This indicates a decrease in interregional differences regarding national digital economy-environmental quality coupling coordination while highlighting an increase in intraregional disparity instead. Notably, variations exist across regions concerning strategies for robustly developing digital economy and promoting environmental restoration, thus constituting major discrepancies observed within coupled coordination degrees of these two systems on an overall scale. Therefore, it is crucial to pay attention to regional disparities when promoting digital economy growth while effectively enhancing environmental quality.
4.4 Analysis of Dynamic Factors Related to Coupled and Coordinated Development
4.4.1 Variable Selection and Model Construction
This article is based on the previous research results (Dong et al., 2021), combined with the actual situation of the coordinated development of the digital economy and environmental quality and the opinions of relevant experts. Finally, the level of economic development, industrial structure upgrading, level of openness, government intervention intensity, financial development level, technological innovation capability, MI, and ER were selected as the detection factors. Economic development level (PECO), industrial structure upgrading (STR), financial development level (FIN), and technological innovation capability (PINN) are defined as internal driving factors, while level of openness (TRADE), government intervention intensity (GOV), MI and ER are defined as external driving factors, as shown in Table 1.
Dynamic factors related to the coupled coordination degree
| Variable name | Variable symbol | Indicator specification | Mean | S.D. |
|---|---|---|---|---|
| Level of economic development | PECO | GDP per capita (logarithm) | 9.306 | 0.463 |
| Upgrading of industrial structure | STR | The proportion of the value added of tertiary industry in the value added of secondary industry | 1.335 | 0.720 |
| Technological innovation ability | PINN | Number of patent applications granted (logarithm) | 9.964 | 1.620 |
| Financial development level | FIN | Total deposits and loans by financial institutions as a proportion of GDP | 3.282 | 1.213 |
| Level of openness | TRADE | Value of imports and exports of goods as a proportion of GDP | 0.261 | 0.293 |
| Degree of government intervention | GOV | Fiscal expenditure as a percentage of GDP | 0.281 | 0.195 |
| Marketization index | MI | China’s Marketization Index Report by Province (2019) is expanded and calculated | 7.734 | 2.191 |
| Environmental regulation | ER | The proportion of completed investments in industrial pollution control in relation to industrial-added value | 0.004 | 0.004 |
Note: Blue text represents internal driving variables, which possess spontaneity, endogeneity, and directness, serving as the endogenous power in the development process of the digital economy and ecological environment. Black text represents external driving variables, which have externality, indirectness, and uncertainty, influencing the external environment and conditions of the system, and indirectly affecting the coupling coordination of the digital economy and ecological environment.
4.4.2 Baseline Regression
Table 2 shows that the two-way fixed effects panel data model yields a 0.01 level of significance in terms of the economic development level (t = 4.364, p = 0.000 < 0.01), and the regression coefficient is 0.171 > 0, indicating that the level of economic development has a significant positive influence on the coupled coordination degree. In terms of the degree of openness to the outside world, significance was observed at the 0.01 level (t = 3.656, p = 0.000 < 0.01), and the regression coefficient was 0.100 > 0, indicating that the degree of openness to the outside world has a significant positive influence on coupled coordination degree. A 0.05 level of significance (t = 2.244, p = 0.026 < 0.05) was observed for ER, and the regression coefficient was 2.24 > 0, indicating that ERs have a significant positive impact on coupled coordination degree. Additionally, the level of financial development displayed significance at the 0.01 level (t = 3.22, p = 0.001 < 0.01), and the regression coefficient was 0.031 > 0, indicating that the level of financial development has a significant positive impact on coupled coordination degree. Therefore, ER is the core driver of the coupled coordination of the digital economy and environmental quality, and the economic development level, openness degree, and the level of financial development are the main factors that drive the coupled coordination level.
Intermediate process values for the two-way fixed effects and SYS-GMM model
| Terms | TWFE | SYS-GMM | ||
|---|---|---|---|---|
| Coef | Std. Err | Coef | Std. Err | |
| Intercept | −1.433** | 0.393 | ||
| PECO | 0.171** | 0.039 | 0.029 | 0.073 |
| PINN | 0.019 | 0.011 | 0.091** | 0.019 |
| STR | 0.003 | 0.017 | 0.208* | 0.097 |
| TRADE | 0.100** | 0.027 | 0.009 | 0.024 |
| GOV | 0.048 | 0.125 | 1.352 | 2.359 |
| ER | 2.240* | 0.998 | 1.716 | 1.197 |
| MI | 0.003 | 0.005 | 0.185 | 0.116 |
| FIN | 0.031** | 0.010 | 0.256** | 0.084 |
| L1.CCD | 0.741** | 0.141 | ||
| L2.CCD | −0.125 | 0.079 | ||
| L1.ER | 0.240** | 0.089 | ||
| AR (1) | −1.679* (p = 0.093) | |||
| AR (2) | 0.174 (p = 0.862) | |||
| within R 2 | 0.312 | |||
Notes: *p < 0.05 **p < 0.01; The first step uses a two-way fixed effects model for estimation, and the results show the environmental regulation is the core driver of the coupled coordination of the digital economy and environmental quality, and the economic development level, openness degree and the level of financial development are main factors that drive the coupled coordination level. In the second step, the Hausman test indicates an endogeneity issue. This article further employs the dynamic panel GMM model for estimation. It uses the lagged terms of the explanatory variables as instrumental variables, and the conclusions are similar to those of the two-way fixed effects model regression, indicating that the GMM model has good robustness.
4.4.3 GMM Estimation
In the Hausman test, p = 0.000 < 0.05 indicates an endogeneity problem. In order to solve the possible endogeneity problem, this article further uses the GMM model of the dynamic panel system for estimation. The lag term of explanatory variables is selected for instrumental variables, and some variables are estimated after logarithms are taken. Compared with the results of the two-way fixed effects model above, it is shown that the coupling coordination degree of the digital economy and environmental quality has a very strong time series correlation, and the previous coupled coordination degree has a significant impact on the current value (shown in Table 2). Among which the coupled coordination degree lagging one period has a high influence coefficient of 0.741 on the current occurrence value. It can be seen that the lag 1 period is an ideal setting to examine the dynamic change of coupled coordination degree. According to the above model setting, the environmental regulation level ER has delayed by 1 period and 2 orders, and the regression of the system GMM dynamic effect is significantly positive after 1 period, indicating that ER has an important impact on the coupled coordination degree in the later period, and its dynamic effect is 0.24 after 1 period.
For the Hansen overrecognition test, the null assumption is that instrumental variables are not correlated with error terms. As can be seen from Table 2, the model rejects the Hansen overrecognition test (p = 1.000 > 0.05), which means that the instrumental variable has nothing to do with the error term, indicating that the current model is well constructed. For the AR(2) root test, the null hypothesis is that there is no autocorrelation in the model. As can be seen from Table 2, the model accepts the AR(2) test (p = 0.862 > 0.05), which means that there is no autocorrelation in the model, indicating that the current model is well constructed. It can be seen from the GMM model that industrial structure upgrading, technological innovation ability, financial developmental level, and ER all have significant impacts on the coupled coordination degree of the two systems. The above conclusions are close to the two-way fixed effects panel model regression results above, indicating that the GMM model has good robustness.
5 Discussions
5.1 Conclusions
Utilizing panel data from 31 provinces and cities in China spanning from 2011 to 2020, this study initially assesses the comprehensive development levels of both the digital economy and environmental quality separately. Subsequently, a coupled coordination evaluation model is employed to measure the degree of coupled coordination between these two systems based on their respective comprehensive development indices. This approach enables us to elucidate the overall status of coupled coordination levels between the digital economy and environmental quality, as well as track changes in the coupling development stage of these two systems across different regions over time. Furthermore, kernel density estimation and Gini coefficient models are utilized to analyze dynamic trends in the coupling coordination level between the digital economy and environmental quality. Sequential evolution characteristics at both national and regional levels are also examined for insights into their coupled coordination level. Additionally, a two-way fixed effects panel data model is established to analyze driving factors influencing the coupled coordination degree of digital economy and environmental quality. To address potential endogeneity issues, GMM estimation is further employed to observe temporal correlations among study variables. Consequently, this research not only validates mechanisms underlying coupled and coordinated developments between the digital economy and environmental quality but also explores key factors impacting their relationship. The resulting conclusions can enhance our understanding of China’s economic operations while shedding light on how China balances ecological protection within its economic processes towards achieving sustainable economic development.
The conclusions of this article can be summarized as follows: First, China’s digital economy has experienced consistent growth alongside improvements in environmental quality. However, the development level of the digital economy surpasses that of the ecological environment. Secondly, there is an increasing trend in the coupled coordination between the digital economy and environmental quality across all regions year by year, with a similar trend observed at a national level. In 2018, the country achieved a state of coordination, starting with the eastern region. Inner Mongolia is currently in its preparatory stage while other regions have already reached a high level of coupled coordination. Thirdly, there has been a gradual reduction in spatial disparities regarding coupled coordination levels nationwide. Although dynamic convergence characterizes the overall national coupled coordination level, regional imbalances still exist when it comes to coupling and coordinating the digital economy with environmental quality. Fourthly, multiple factors contribute to determining the degree of coupled coordination between China’s digital economy and environmental quality. Among these factors, external drivers such as ER plays a significant role. Internal factors including economic development levels, openness degree, and financial developmental level also serve as major internal driving forces behind achieving coupled coordination. Fifthly, the preceding level of coupled coordination significantly influences the current value, while ERs play a crucial role in shaping future levels of coupled coordination.
The research findings align with existing literature on the digital economy’s role in promoting ecological improvement. The case study of China further demonstrates that the digital economy serves as a primary driver for enhancing the ecological environment, indirectly validating its positive impact within the Kuznets curve framework. Additionally, this study introduces a novel research framework emphasizing the interdependent and mutually reinforcing relationship between the digital economy and environmental quality, challenging previous academic perceptions. These conclusions offer a fresh perspective on exploring this relationship and hold significant implications for China’s ongoing economic transformation towards low-carbon sustainable development.
5.2 Policy Recommendations
Based on the aforementioned research findings, the following policy recommendations are proposed: (1) Continuously expand the scope and depth of digital technology applications, enhance the allocation mechanism for digital resources, coordinate regional development efforts, and effectively acknowledge the government’s positive role in strengthening relationships, improving public service provision, optimizing business environments, and fostering internal driving forces. These measures will facilitate innovation in high-quality digital economic development and ensure its sustainable growth. Such endeavors can also provide the impetus for enhancing environmental quality. For instance, regions such as Guangxi, Guizhou, and Yunnan have leveraged their superior internet development infrastructure. With the government’s high attention to the digital economy and good e-government service capabilities, these areas have jointly improved their environmental quality levels. (2) Strengthen ecological and environmental protection as well as restoration efforts by establishing stringent local-specific environmental standards. Furthermore, focus should be placed on addressing pollution discharge issues, promoting ecological preservation, improving living conditions, and enhancing overall environmental quality. For example, Zhejiang Province has taken the “Ten Million Project” and the “811” ecological and environmental protection initiative as its focal points, deepened the reform of “integrating multiple plans into one,” improved ecological and environmental protection policies, actively and steadily advanced carbon peak and carbon neutrality goals, and enhanced the quality of the ecological environment. (3) Promote the simultaneous development of the digital economy and enhancement of the ecological environment. Optimize the factor supply system to facilitate the dual advancement of the digital economy and environmental quality. Establish a rational and efficient resource allocation mechanism, ensuring continuous support for coordinated progress in both the digital economy and environmental quality, while forming a long-term mechanism to foster their coupled coordination. (4) During the process of transforming towards a digital economy and improving environmental governance, all regions should prioritize achieving balance within and among regions, especially in the Northeast region, promoting regional coordinated development to optimize overall efficiency in both systems. (5) Foster openness and sharing of public data, and establish a collaborative regional governance mechanism for environmental issues. Policy barriers that hinder coordinated development between the digital economy and environmental quality are actively eliminated. External driving forces for their harmonious progress are fully considered while further exploring internal driving forces to promote coordination and mutual advancement between these two systems.
6 Research Limitations and Prospects
The limitations of this study include the following: First, although the coupled coordination model represents a novel research approach for analyzing the interaction and evolution of the digital economy and environmental quality, it fails to consider intermediary variables that influence the coupled coordination relationship. Consequently, this oversight neglects the internal mechanism underlying the coupling between digital economy and environmental quality, potentially resulting in an incomplete interpretation. Secondly, this article solely focuses on inter-provincial panel data at a macro-level examination level while insufficiently exploring city-level dynamics. As a result, its relevant conclusions possess limited guiding value for urban policy formulation and planning. Additionally, given disparities between China’s reality and other developed countries, these conclusions may vary.
The rapid development of the digital economy has garnered increasing attention towards its relationship with the ecological environment. Future research directions and key areas for further exploration include: analyzing environmental protection policies and regulations underpinning the digital economy, and exploring applications and prospects of digital innovation in ecological protection. Particularly within the context of coupling and coordinating efforts between the digital economy and environmental quality, important issues such as mechanisms facilitating their coupled coordination, regional coordination pathways for their coupled development with respect to both economic growth and ecological preservation, as well as evaluating effects resulting from this coupled development await further investigation by academia.
Acknowledgements
Thanks to the editors and reviewers for their suggestions for revisions to this article. Of course, the authors are fully responsible for the content.
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Funding information: This research was funded by the National Social Science Foundation of China (22BJY242).
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal. All authors have read and agreed to the published version of the manuscript. Conceptualization, X.L.; methodology, X.L. and Z.Z; software, X.L. and C.C.; validation, X.L., Z.Z. and C.C.; formal analysis, J.C.; investigation, J.C.; resources, J.H., Z.Z. and C.C.; data curation, X.L.; writing – original draft preparation, X.L.; writing – review and editing, X.L. Z.Z. and C.C.; visualization, X.L.; supervision, X.L.; project administration, X.L.; funding acquisition, J.H.
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Conflict of interest: Authors state no conflict of interest.
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Data availability statement: The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
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Article note: As part of the open assessment, reviews and the original submission are available as supplementary files on our website.
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