Study on the Coupled and Coordinated Development and Evolution of Digital Economy and Green Technology Innovation

Abstract

Green technology innovation offers a new dynamic support and realization path for the comprehensive transformation and upgrading of the digital economy and intensive development mode. The study of green technology’s coupled and coordinated development and evolution with the digital economy is conducive to promoting the quality and efficiency of the digital economy. With the aid of the coupling coordination degree, nuclear density, and the Gini coefficient, this paper develops a digital economy and green technology innovation evaluation index system based on the theoretical mechanism of coupling and coordination and empirically investigates the spatial and temporal evolution of the dynamic coupling and coordination of the digital economy and green technology innovation in Chinese regions from 2011 to 2021. The results show that there is an imbalance in the coupled and coordinated development of the regional digital economy and green technology innovation, and the overall strength needs to be improved; the empirical results of nuclear density show that the regional development gap is further revealed, and the driving mechanism needs to be optimized; the empirical results of Gini coefficient show that there are obvious differences in contribution rates between regions, and the coordination and collaboration mechanism needs to be improved. Finally, it is suggested that, in the future, green technology innovation capabilities should be comprehensively promoted, regional digital development drive mechanisms should be optimized, inter-regional coordination and regulatory mechanisms should be improved, and the coupled and coordinated development of the digital economy and green technology innovation at a higher level should be realized as soon as possible.

1. Introduction

Currently, China is going through a stage of transition from rapid economic growth to high-quality development. This is a crucial phase in the change from a model of basic economic development to one of intensive, superior economic development. In the past, production activities driven by factor inputs led to the unsustainable use of natural resources. The potential of data factors should be utilized to promote the digital transformation of the production mode, lifestyle, and governance mode, according to China’s 14th Five-Year Plan. This demonstrates how crucial it is for the growth of the digital economy that production variables change and production technology is upgraded. Meanwhile, innovation in green technology with a focus on achieving green development, focusing on innovation to provide new products, technology, services, and market solutions, is vital. This can, on the one hand, reduce the legitimacy costs of enterprise production and end-of-production management pressure, which in a particular sense reduces the pressure on the production environment; on the other hand, green technology innovation can improve resource efficiency and reduce pollution emissions at the source. Green technology innovation reduces the consumption of natural assets, reduces ecological and environmental damage [1], and increases resource allocation efficiency, which can provide new power support and a path of implementation for the intensive high-quality economic development model in China and its complete digital economy transformation and upgrade. The digital economy and green technology innovation development will become the mainstream of China’s economic development. Therefore, a thorough examination of how the two interact is crucial for formulating economic development policies.

Are the digital economy and green technology innovation coupled and coordinated in China, and are the evolutionary trends the same? The literature that is now available does not contain the answers to these queries. On the basis of this, this work develops a coupled and coordinated model mechanism of the digital economy and green technology innovation, develops a system of assessment indexes for these two fields, and determines the weights of the indicators across all levels using the entropy weight approach. By drawing the kernel density curve and calculating the Gini coefficient, China’s digital economy and green technology innovation’s degree of dynamic connection and coordination are investigated. Last but not least, strategies and recommendations are offered to enhance the degree of coupling and coordination between the digital economy and green technology innovation as well as to coordinate the regional digital economy and green technology innovation in order to serve as a reference for promoting the deep integration of the digital economy and green technology innovation for high-quality development.

2. Literature Review

2.1. Related Literature on the Digital Economy

Don Tapscott [2] first proposed the idea of the digital economy. Environmental effects [3], energy efficiency [4], and industrial structure [5,6] are the main topics of the current academic research about the effects of the digital economy.

Many academics are also researching the connection between the growth of green and low-carbon industries and the digital economy at the same time. At the theoretical level, Liao Zhenzhen et al. established an inverted U-shaped relationship between the digital economy and CO₂ emissions by building a system dynamics (SD) model, showing that the digital economy exacerbates CO₂ emissions primarily by promoting energy consumption, but reduces CO₂ emissions by encouraging the structural upgrading of energy consumption and lowering energy consumption intensity [7]. By analyzing the effects of the interaction between low-carbon industries and finance using a DEA model, Li Xuetao et al. indirectly demonstrated the connection between the digital economy and the driving mechanism of low-carbon industry development and confirmed that the digital economy can drive that mechanism [8]. At the empirical level, based on Chinese urban panel data from 2000 to 2019, Wang Xingan et al. used an interleaved difference (DID) model to examine the effects of the digital economy and the green low-carbon economy in the spatial and temporal dimensions, and they discovered that, in the latter, the initial development of the digital economy may increase the intensity of urban carbon emissions [9], while Zhao Chuanyu et al. used a regression model to verify the mechanism of the effects of the digital economy’s growth on the shift to green products, and their results suggested that higher levels of the digital economy can help increase urban green total factor productivity [10]. Yang Guoge et al. empirically examined the influence of the digital economy on low-carbon inclusive urban development based on panel data from 286 Chinese cities and discovered that the digital economy will support low-carbon inclusive urban development by encouraging entrepreneurship and promoting industrial upgrading, as well as reducing development imbalances [11]. Dong Ruiyuan et al. used spatial econometric and multiple threshold effect models to study the non-linear and spatial spillover effects of the digital economy on carbon emissions based on the panel data of 56 cities in the middle reaches of the Yangtze River urban agglomeration from 2011 to 2019. The findings revealed that the digital economy has a sizable negative spatial spillover effect on carbon emissions [12].

Numerous studies have also looked at the connection between technological innovation and the digital economy. On a theoretical level, Raju [13] and Xinwei Zhang [14] examined, respectively, the economic implications of blockchain technology in the context of cryptocurrency proliferation and the process of change in the way innovation resources are distributed and organized under the conditions of the digital economy. At the empirical level, Chen Ye used a panel vector autoregressive model with data from 30 Chinese provinces between 2011 and 2019 to analyze the dynamic relationship between the digital economy, scientific and technological innovation, and carbon emissions [15]. Based on panel data for 30 provinces in China from 2011 to 2020, Wu Jun et al. measured the impact of the digital economy on the degree of sustainability of the double cycle, and the mechanism analysis revealed that the expansion of technological innovation capacity could amplify the effect of the digital economy on the double cycle [16]. To study the effects of the development of the digital economy on firm innovation, Peng Siying et al. analyzed the data of 30 provinces from 2012 to 2020 and built fixed-effects and mediated-effects models; they discovered that the digital economy can drive firm innovation by reducing financing constraints [17]. Through the Moran index and panel regression models, Wang Qiong et al. studied the effect of the digital economy on enterprise technological innovation from 2016 to 2020 using a sample of Chinese manufacturing enterprises. They came to the conclusion that the digital economy has a double promotional effect on enterprise technological innovation [18].

2.2. Related Literature on Green Technology Innovation

The following two characteristics were highlighted in the literature that already exists on the in-depth research on the elements that influence green innovation:

The first category is the enterprise micro-influencing element. The amount of green technology innovation within a firm can be greatly increased by the tone of corporate management debate and analysis, according to research by Guo Siliang et al. on the impact of the top management team’s fundamental qualities [19]. Utilizing data from publicly traded Chinese manufacturing companies from 2008 to 2018 and a panel regression model, Peng Yulian et al. discovered that corporate IT skills can encourage the development of green technologies [20]. The driving influence of digital finance on green technology innovation was more obvious among state-owned enterprises and businesses in the eastern area, according to research by Tang Decai et al. using information from Chinese A-share listed companies from 2011 to 2020 [21].

The second is the macro-influencing factors of government environmental regulation. Behera, Puspanjali, and colleagues used combined mean groups, random effects models, and GMM models to empirically analyze the connections between environmental regulation, FDI, and green technology innovation in OECD countries. They emphasized the necessity of implementing an appropriate and effective environmental policy, particularly concerning FDI, to have a positive spillover effect on promoting green technology in the host country, specifically in OECD countries [22]. In resource-based cities, the average value of the net effect of green technology innovation is ranked in descending order by declining, mature, regenerating, and growing cities. The effect of environmental regulation on the development of green technologies varies depending on the life cycle of a resource-based city; Li Chuang and colleagues claim that there is instrumental heterogeneity in the incentive effect of environmental regulation on businesses’ adoption of green technology. They built a game model of green technology innovation between businesses and the government based on prospect theory, dynamically analyzed the decision-making and the best course of action under various scenarios, and used numerical simulations to pinpoint the influencing factors. Comprehensive environmental legislation is the most effective tool, followed by penalties and subsidies [23]. Leading businesses will prefer the route of internal independent research and the development of green technology rather than external green technology introduction when the government approves the green technology innovation subsidy, according to Wang Manman et al. Furthermore, they developed a double-agent game model of various environmental regulating tools on the choices of industrial businesses’ green technology innovation paths [24]. Vicki Norberg-Bohm examined how public policy mechanisms can be designed to stimulate rather than discourage pollution prevention and technological innovation and made the argument that unless policies provide stronger political or economic incentives and send clearer signals about future environmental performance requirements, we are unlikely to drive technological innovation in industries with longer-term or uncertain returns [25]. Zhang Jijian et al. used a panel of 1558 non-financial Chinese listed enterprises from 2015 to 2020 to perform an empirical investigation utilizing a multi-period twofold difference model. The findings imply that green bond issuance can considerably enable businesses to innovate in green technology, but the impact lags [26].

In conclusion, there is a dearth of research that organically connects green technology innovation and the digital economy, and just a few studies have looked at the direct [27] or indirect [28] effects of the digital economy on this field. As a relatively new technological paradigm in the information age, for high-quality economic development in China, it is important to understand how the digital economy and green technology innovation are coupled and coordinated, as well as their evolutionary patterns. The minor contributions of this paper to the body of literature consists of the following: first, by using information from 31 provinces (municipalities and autonomous regions) in China from 2011–2021, this paper divides the regions and comprehensively measures the coupling and coordination degree of China’s digital economy and green technology innovation from a system theory perspective, which is conducive to the national adoption of diverse development initiatives and narrows the gap between regions to achieve balanced development; second, by performing kernel density analysis on the coupling coordination level of the digital economy and green technology innovation systems determined by the coupling coordination model, the results can more quickly and accurately provide an intuitive and scientific description of the spatial aggregation characteristics of the coupling coordination level of the digital economy and green technology innovation nationwide, enabling macro-control and providing technical support; lastly, the Gini coefficient is determined as a data source to serve as a point of comparison for the resource allocation between China’s digital economy and green technology innovation.

3. Indicator Constructions, Research Methods, Research Area, and Data Sources

3.1. The Theoretical Mechanism of Coupling Digital Economy and Green Technology Innovation

3.1.1. Green Technology Innovation Has a Solid Foundation and Demands Thanks to the Digital Economy

Firstly, the digital economy helps to strengthen the evolution of information technology and the development of Internet-related infrastructure; for example, by increasing the volume of telecommunication services, permeation of mobile devices, and the quantity of Internet ports, which provides a good material basis for the process of green technology innovation. Secondly, in the development process of the digital economy, with the progressive increase in users of various platforms, there will be a consistent stream of feedback needed for the development of green technology. Green technology must innovate following user needs to provide greater value to consumers, which successfully encourages the growth of green technology; additionally, the growth of the digital economy has encouraged the use of digital information networks, improved regional connectivity, decreased the cost of talent exchange, provided more affordable access to technical assistance, and facilitated the easy flow of capital. It has also facilitated the development of green technology innovation across regions by providing human resources and technical support for such innovation [29].

3.1.2. The Digital Economy Will Grow in a High-Quality Manner Thanks to Green and Cutting-Edge Technologies

The main engine behind the development of the digital economy is innovation in digital technologies. First of all, green technology innovation provides a wide range of application scenarios for the digital economy, which can quickly boost the evolution of the digital economy. Secondly, the transformation of green technology innovation results in a reduction of the transaction costs of the digital economy, expanding the profit margin, and encouraging more people and capital to invest in green technology innovation. With the increase in input and output, the digital economy also develops. Third, through the transformation of traditional industries, green innovation technology is conducive to breaking the technical resource bottleneck that restricts the digital conversion of traditional industries, promoting cross-border integration and innovation of traditional industries, and creating new advantages for the process of the digital economy [30].

In summary, the digital economy influences the standard of green innovation technology by providing a material basis, feeding back demand, and delivering human resources as well as technical support, while green innovation technology accelerates the high-quality development of the digital economy by providing application scenarios, reducing transaction costs, and creating new advantages in the digital economy. The diagram of the theoretical mechanism is shown in Figure 1.

3.2. Indicator Constructions

Following the principles of systematicity, scientificity, objectivity, and data availability when putting together the evaluation index system, and drawing on the research results of Li Nanbo [31], Du Yueping [32] and other scholars, based on the definition and characteristics of the digital economy and green technology innovation, the evaluation system of digital economy system and green technology innovation system is constructed (

Table 1. Digital Economy and Green Technology Innovation Indicator System.

Objective	First-Level Index	Second-Level Index	Unit	Nature of Indicators	Variable
Digital Economy System	Degree of information development	Mobile phone exchange capacity	Million households	+	X1
		Telecommunications business volume	Billion	+	X2
		Postal network points	Department	+	X3
	Level of Internet development	Number of Internet access ports	Million	+	X4
		Mobile phone penetration rate	%	+	X5
	Degree of digital development	China Digital Inclusive Finance Index		+	X6
		E-commerce sales	Billion	+	X7
		Computers per 100 people	Units	+	X8
Green Technology Innovation System	Innovation input	Number of R&D staff	per person per year	+	X9
		R&D expenditure	Million	+	X10
		Number of R&D projects	Item	+	X11
	Innovative outputs	Revenue from new product sales	Million	+	X12
		The number of domestic patent applications accepted	Item	+	X13
	Economic performance of industrial enterprises	Total asset contribution margin	%	+	X14
		Gearing ratio	%	+	X15
		Cost Margin	%	+	X16

), and the entropy value approach is used to determine the index layer weights.

The digital economy in thirty-one provinces (autonomous areas and municipalities directly under the Central Government) in China is based on eight indicators chosen from three dimensions: the degree of information development, the level of Internet development, and the degree of digital development. The degree of information technology development is measured by a combination of mobile telephone exchange capacity and telecommunications business volume, as well as postal network points. The number of Internet access ports and the rate of mobile phone penetration are used for an extensive evaluation of the level of Internet development, one of the important indicators demonstrating the sustainable development of the digital economy. The degree of digital development, which include the China Digital Inclusion Index, e-commerce sales, and the number of computers per 100 people, is an indicator of how far along the digital economy is.

Since the primary goal of green technology innovation is the realization of green development, eight indicators from three dimensions—innovation input, innovation output, and the economic performance of industrial enterprises—have been chosen for a thorough evaluation. The level of innovation input is measured by the number of R&D personnel and R&D spending, as well as R&D projects; the level of innovation output is reflected by the sales revenue of new products as well as the number of domestic patent applications received; the level of the economic efficiency development of industrial enterprises is shown by the percentage of total assets contributing, the asset-liability ratio, and the cost–profit ratio. In terms of the economic efficiency of industrial enterprises, the total asset contribution ratio is the ratio of the sum of the total profits, total taxes, and interest expenses to the average total assets, the gearing ratio is the percentage of the total liabilities of the enterprise to the total assets of the enterprise, and the cost margin is the ratio of the total profits to the total costs and expenses of the enterprise in a certain period.

3.3. Research Area and Data Sources

The sample data period is from 2011 to 2021 because official statistics on China’s digital inclusive finance index are only available in more detail from that point on. To increase the credibility of the research findings, this paper makes an effort to use provincial panel data for analysis. Thus, the research sample was derived from the panel data of 31 provincial (municipality directly under the Central Government and autonomous region) level units across China from 2011 to 2021. The data used were from the China Statistical Yearbook, the National Bureau of Statistics, provincial (municipality directly under the Central Government and autonomous region) level statistical bureaus, and the Peking University Digital Inclusive Finance Index (2011–2021) from 2012 to 2022.

The thirty-one provinces (municipalities directly under the Central Government and autonomous regions) in China were divided into three regions: east, central, and west, with the specific division criteria shown in

Table 2. Regional relationship by province nationwide.

Region	Province
Eastern	Beijing Municipality, Fujian Province, Guangdong Province, Hainan Province, Hebei Province, Jiangsu Province, Liaoning Province, Shandong Province, Shanghai Municipality, Tianjin Municipality, Zhejiang Province
Central	Anhui Province, Henan Province, Heilongjiang Province, Hubei Province, Hunan Province, Jilin Province, Jiangxi Province, Shanxi Province
Western	Gansu Province, Guangxi Zhuang Autonomous Region, Guizhou Province, Inner Mongolia Autonomous Region, Ningxia Hui Autonomous Region, Qinghai Province, Shanxi Province, Sichuan Province, Tibet Autonomous Region, Xinjiang Uygur Autonomous Region, Yunnan Province, Chongqing Municipality

. This was done in order to improve the readability of the findings of the empirical analysis.

3.4. Research Methods

In this study, after standardizing the data indicators, this paper uses the entropy weight method and coupling and coordination model to calculate the scores of the digital economy and green technology innovation as well as the degree of coupling and coordination in 31 Chinese provinces (municipalities directly under the Central Government and autonomous regions) from 2011 to 2021.

3.4.1. Data Standardization

As each indicator has a different unit scale, to reduce the discrepancy in scale between the data, the data are first standardized by a linear transformation so that the values fall in the [0, 1] interval. The only indicators selected in this paper are positive, so no distinction needs to be made in the treatment of indicators, and the standardization formula is:

$$x_{ij}^{\prime }=\frac{x_{ij}-min\left(x_j\right)}{max\left(x_j\right)-min\left(x_j\right)}+0.00001$$

(1)

In Equation (1), $x_{ij}^{\prime }$ denotes the standardized indicator data, $min\left(x_j\right)$ and $max\left(x_j\right)$ are the minimum and maximum values of the j-th year of the system, respectively, and $x_{ij}$ denotes the value of the j-th year of the i-th indicator of the digital economy system or green technology innovation system.

3.4.2. Entropy Weighting Method to Obtain Indicator Weights

The entropy approach, which sets the indicator weights following the degree of variation in the values of each indicator, is an objective weighting method that can prevent bias brought on by human factors. The weights of individual indicators can be calculated by using information entropy, giving a foundation for the thorough assessment of several indicators. Referring to the research results of Gan Longxiong, Zhang Huaizhi, Lu Talent, etc. [33], the entropy method was carried out to calculate the weights of each indicator of the two systems, respectively, and the specific steps were as follows:

Calculate the ratio, information entropy, and weight of each indicator of a given system by the following formula:

The proportion $y_{ij}$ for the j-th year of the i-th indicator is:

$$y_{ij}=\frac{x_{ij}^{\prime }}{\sum _{i=1}^m{x}_{ij}^{\prime }}$$

(2)

In Equation (2), m stands for the system’s total number of indicators.

The j-th option’s information entropy is:

$$e_j=-K{\displaystyle \sum }_{i=1}^m{y}_{ij}\ln y_{ij}$$

(3)

$$K=\frac{1}{\ln m}$$

(4)

In Equations (3) and (4), K is a constant.

The weight of the j-th option is:

$$w_j=\frac{1-e_j}{{\sum }_{k=1}^n\left(1-e_k\right)}$$

(5)

In Equation (5), n is the number of options.

Calculate the combined score of the whole system by the formula:

$$u_i={\displaystyle \sum }_{j=1}^n{x}_{ij}^{\prime }{w}_j$$

(6)

In Equation (6), $u_i$ denotes the combined score of the digital economic system and the green technology system in a given year, respectively (I = 1, 2).

3.4.3. Coupling Coordination Model

As they quantify the degree of benign coupling between system components as well as the degree to which coordination is good or bad, coupling and coordination models are frequently used to analyze the degree of coordination and development. The specific calculation steps are:

Calculating the coupling C value:

$$C=2\sqrt{\frac{u_1{u}_2}{{\left(u_1+u_2\right)}^2}}$$

(7)

Calculating the coordination T value:

$$T=\alpha u_1+\beta u_2$$

(8)

In Equation (8), the weight coefficients for the green technological innovation system and the digital economic system are indicated by the letters $\alpha$ and $\beta$, respectively. In this paper, the digital economy development system and the green technology innovation system are considered equally important, thus the values of $\alpha$ and $\beta$ are both taken as 0.5 in this thesis.

Calculating the coupling coordination D value:

$$D=\sqrt{CT}$$

(9)

3.4.4. Coupling Coordination Assessment Criteria

Regarding the classification of the coupling coordination levels by the research results of Wang Yuan [34] et al., the coupling coordination degree of the development of the digital economy system and green technology innovation system was divided into ten levels after determining the coupling coordination degree of the two systems from 2011 to 2021, as shown in

Table 3. Judgement criteria for coupled coordination relationships.

Coupling Coordination D-Value Interval	Coordination Level	Level of Coordination
(0.0~0.1)	1	Extreme disorders
[0.1~0.2)	2	Severe disorders
[0.2~0.3)	3	Moderate disorder
[0.3~0.4)	4	Mild disorders
[0.4~0.5)	5	On the verge of disorder
[0.5~0.6)	6	Barely coordinated
[0.6~0.7)	7	Junior coordination
[0.7~0.8)	8	Intermediate coordination
[0.8~0.9)	9	Good coordination
[0.9~1.0)	10	Quality coordination

:

3.4.5. Non-Parametric Kernel Density Estimation

Unbalanced distributions can be studied using the non-parametric test approach known as kernel density estimation. The kernel density distribution studies the distribution features of the data from the data itself, overcoming the subjectivity of the function form set in the estimation of the parametric model, and therefore more objectively reflects the facts [35], and at the same time can determine the regional level distribution, shape, and evolution of the development of the digital economy and green technology, etc. The basic form of its kernel density estimation is:

$$f_n\left(x\right)=\frac{1}{nh}{\displaystyle \sum }_{i=1}^{n}K\left[\frac{x_i-\overline{x}}{h}\right]$$

(10)

In Equation (10), n is the total number of elements, $\overline{x}$ is the mean value of element attributes, $K\left(x\right)$ stands for the kernel function, while the small positive value h denotes the bandwidth. The greater the bandwidth, the smoother the curve, but the greater the difference between the probability density and the actual distribution. Contrarily, the estimates will stray from the true values less as the bandwidth and band narrow, but an excessively tiny bandwidth will significantly lower the computational efficiency of the kernel density estimations [36]. The selection criteria for the optimal bandwidth must thus be traded off between the bias of the kernel estimate and the efficiency of the calculation.

In this paper, with the help of StataSE 16 software, the more general Silverman method is chosen to determine the optimal bandwidth h. The Epanechnikov curve with the least loss efficiency is chosen to determine the kernel function $K\left(x\right)$. The bandwidth h and kernel function equations are:

$$h=0.9kmin\left(s,\frac{Q_3-Q_1}{1.34}\right){\left(n\right)}^{-\frac{1}{5}}$$

(11)

$$k=\left\{\begin{array}{c}\frac{3}{4}\left(1-x^2\right)\left|x\right|\le 1\\ \hspace{1em}0\hspace{1em}others\end{array}\right.$$

(12)

In Equation (11), s is the standard deviation, $Q_3-Q_1$ denote the quartic distance, and the factor k is the standard bandwidth transform, which is used to adjust the bandwidth so that the density estimates for different kernel functions are approximately equally smooth.

3.4.6. Gini Coefficient Decomposition

Regional differences have frequently been investigated using the Dagum Gini coefficient. This study applies Dagum’s subgroup decomposition method and the Gini coefficient to decompose the overall variation in the coupled coordination of China’s digital economy and green technology innovation system into three components: intra-group variation, inter-group variation, and hyper-variance density [37]. The Gini coefficient is then calculated using stataSE 16 software based on the coupled coordination model results and the overall Gini coefficient is given as follows:

$$\mathrm{G}=\frac{\sum _{\mathrm{j}=1}^{\mathrm{k}}\sum _{\mathrm{h}=1}^{\mathrm{k}}\sum _{\mathrm{i}=1}^{{\mathrm{n}}_{\mathrm{j}}}\sum _{\mathrm{r}=1}^{{\mathrm{n}}_{\mathrm{h}}}\left|{\mathrm{y}}_{\mathrm{j}\mathrm{i}}-{\mathrm{y}}_{\mathrm{h}\mathrm{r}}\right|}{2{\mathrm{n}}^2\mathsf{\mu }}$$

(13)

In Equation (13), k indicates how many areas there are, n denotes the number of all provinces (municipalities directly under the Central Government and autonomous regions), three and thirty-one, respectively, $\mathsf{\mu }$ denotes the national average, $y_{ji}$, $y_{hr}$ denote the degree of coupling and coordination of the digital economy and green technology innovation in the province (municipality directly under the Central Government and autonomous regions) iI within region j(h), and $n_j$, $n_h$ denote the number of provinces (municipalities directly under the Central Government and autonomous regions) within region j(h).

The following is how the area j Gini coefficient $G_{jj}$ is written:

$$G_{jj}=\frac{\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\left|y_{ji}-y_{hr}\right|}{2n_j^2{\mu }_j}$$

(14)

The following is then used to represent the Gini coefficient $G_{jh}$ for regions j and h:

$$G_{jh}=\frac{\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\left|y_{ji}-y_{hr}\right|}{n_j{n}_h\left({\mu }_j+{\mu }_h\right)}$$

(15)

In Equation (15), ${\mu }_j$, ${\mu }_h$ denote the average value of the coupled coordination of the digital economy and green technology innovation within regions j, h. To explore the deeper reasons behind the regional differences, this paper decomposes the overall Gini coefficient G into intra-regional variation contribution $G_{\omega }$, inter-regional net value variation contribution $G_{nb}$, and over-editing density contribution $G_t$, then we have:

$$\mathrm{G}={\mathrm{G}}_{\mathsf{\omega }}+{\mathrm{G}}_{\mathrm{n}\mathrm{b}}+{\mathrm{G}}_{\mathrm{t}}$$

(16)

$$G_{\omega }={\displaystyle \sum }_{j=1}^k{G}_{jj}{p}_j{s}_j$$

(17)

$$G_{nb}={\displaystyle \sum }_{j=2}^k{\displaystyle \sum }_{h=1}^{j-1}{G}_{jh}{D}_{jh}\left(p_j{s}_h+p_h{s}_j\right)$$

(18)

$$G_t={\displaystyle \sum }_{j=2}^k{\displaystyle \sum }_{h=1}^{j-1}{G}_{jh}\left(1-D_{jh}\right)\left(p_j{s}_h+p_h{s}_j\right)$$

(19)

$$p_j=\frac{n_j}{n}$$

(20)

$$s_j=\frac{n_j{\mu }_j}{n\mu }$$

(21)

$$p_h=\frac{n_h}{n}$$

(22)

$$S_h=\frac{n_h{\mu }_h}{n\mu }$$

(23)

$D_{jh}$ represents the relative influence of the coupled and coordinated development of the digital economy and green technology innovation between areas j and h, as illustrated in Equations (18) and (19), and is defined as follows (24).

$$D_{jh}=\frac{d_{jh}-p_{jh}}{d_{jh}+p_{jh}}$$

(24)

$$d_{jh}={\int }_0^{\infty }d{F}_i\left(y\right){\int }_0^y\left(y-x\right)d{F}_h\left(x\right)$$

(25)

$$p_{jh}={\int }_0^{\infty }d{F}_h\left(y\right){\int }_0^y\left(y-x\right)d{F}_j\left(x\right)$$

(26)

In Equations (25) and (26), $d_{jh}$ denotes the difference in the degree of coupling coordination between the digital economy and green technology innovation between regions, which can be interpreted as the mathematical expectation of the sum of all sample values of $y_{ji}-y_{hr}>0$ in regions j and h. $p_{jh}$ is defined as the hypervariable first-order moment, which can be understood as the mathematical expectation of the sum of all sample values of $y_{hr}-y_{ji}<0$ in regions j and h. $F_h$, $F_j$ denotes the cumulative density distribution function of the h and j regions.

4. Empirical Results

4.1. Digital Economy and Green Technology Innovation Development Index

4.1.1. Digital Economy and Green Technology Innovation Indicator System and Weights

According to

Table 4. Digital Economy System Evaluation Indicator System.

Objective	First-Level Index	Weight	Second-Level Index	Weight
Digital Economy System	Degree of information development	0.4156	X1	0.0752
			X2	0.2207
			X3	0.1197
	Level of Internet development	0.1643	X4	0.1109
			X5	0.0534
	Degree of digital development	0.4201	X6	0.0464
			X7	0.2554
			X8	0.1183

, in the subsystem of the digital economy, the weights of information technology development, Internet development, and digitalization development differ to some extent. Among them, the degree of informatization and digitization development plays the most significant role on the digital economic system, while the degree of Internet development has the weakest influence on the digital economic system. That is, the number of postal network points, the size of the telecom industry, the China Digital Inclusive Finance Index, e-commerce sales, and computers per 100 population all have a bigger influence on the digital economic system.

According to

Table 5. Green Technology Innovation System Evaluation Indicator System.

Objective	First-Level Index	Weight	Second-Level Index	Weight
Green Technology Innovation System	Innovation input	0.5205	X9	0.1716
			X10	0.1598
			X11	0.1891
	Innovative outputs	0.4346	X12	0.1833
			X13	0.2513
	Economic performance of industrial enterprises	0.0449	X14	0.0332
			X15	0.0091
			X16	0.0025

, in the green technology innovation subsystem, innovation input, innovation output, and the economic efficiency of industrial enterprises have greater differences in the weight of the green technology innovation system. Among them, innovation input and innovation output have a more vital influence on green technology innovation, i.e., the number of R&D employees, R&D expenditure, the number of R&D projects, income from new product sales, and domestic patent application acceptance have a greater contribution to green technology innovation.

4.1.2. Study of the Digital Economy’s and Innovation’s Development Trends

After using the entropy weighting method to determine the scores of the 31 provinces (municipalities directly under the Central Government and autonomous regions) in China, the comprehensive score of the digital economic system and the comprehensive score of the green technology innovation system were determined by averaging the comprehensive scores of each province within the region (Table 2). The composite scores are shown in

Table 6. Digital Economy System Composite Score Table.

Year	China	Eastern	Central	Western
2011	0.1163	0.1680	0.0973	0.0814
2012	0.1391	0.1980	0.1179	0.0993
2013	0.1008	0.1526	0.0811	0.0665
2014	0.1160	0.1729	0.0951	0.0777
2015	0.1355	0.1975	0.1154	0.0922
2016	0.1502	0.2117	0.1321	0.1060
2017	0.1738	0.2485	0.1518	0.1201
2018	0.2084	0.2953	0.1817	0.1465
2019	0.2448	0.3446	0.2141	0.1738
2020	0.2716	0.3772	0.2402	0.1957
2021	0.2526	0.3680	0.2164	0.1711

and

Table 7. Green Technology Innovation System Composite Score Table.

Year	China	Eastern	Central	Western
2011	0.0762	0.1281	0.0657	0.0355
2012	0.0845	0.1456	0.0710	0.0374
2013	0.0911	0.1597	0.0779	0.0371
2014	0.0958	0.1693	0.0799	0.0390
2015	0.0941	0.1706	0.0768	0.0356
2016	0.1015	0.1853	0.0817	0.0377
2017	0.1325	0.2422	0.1048	0.0504
2018	0.1428	0.2656	0.1137	0.0496
2019	0.1510	0.2806	0.1232	0.0506
2020	0.1702	0.3190	0.1353	0.0572
2021	0.1595	0.2927	0.1384	0.0516

.

The trends in the overall digital economy system scores by region across the country from 2011–2021 are shown in Figure 2.

Regarding time series, the overall score of the national digital economy system shows a fluctuating growth trend from 2011 to 2021, which is related to the transformation in China’s macroeconomic situation and the importance attached to the digital economy recently. In conjunction with each distinct developmental stage, the scores of the national digital economy system from 2011 to 2012 exhibit a straight-rising trend and quick growth, illuminating the positive development momentum of China’s digital economy. The national digital economy system score declined between 2012 and 2013, and from 2013 to 2020, China’s digital economy development increased and grew more quickly than it did between 2011 and 2012. This, according to Liu Yang and other academics, is because China’s digital economy during this period increased its integration with diverse industries and progressively moved into a new stage of expanding application, standardized development, and inclusive sharing [38]. The highest overall score of the digital economy system in 2020 is attributed to the fact that, in 2020, the Central People’s Government released the Opinions on Building a More Perfect Institutional Mechanism for Market-based Allocation of Factors, the Implementation Plan on Fostering the Development of the New Economy by Promoting the Action of “Going to the Cloud and Using Data to Empower Wisdom”, and the Opinions on Supporting the Healthy Development of New Industries and New Modes, Activating the Consumer Market, and Activating the New Consumer Market. The policies and opinions on fostering the healthy creation of new business models, stimulating customer demand, and expanding employment were issued, which proposed to create new business models in the digital market with energy, strongly support enterprise digital transformation, actively explore new modes of online services, activate new consumer markets, further encourage the creation of a contemporary economic structures, and achieve the high-quality development of the digital economy.

In terms of spatial sequence, with an overall tendency of high in the east and low in the west, the level of development of the digital economy is uneven across the region, and the phenomena of polarization are becoming more and more visible. This is consistent with the research of academics such as Guo Chuan [39]. It is mainly because the eastern coastal region has a good base of science and technology and talent reserves and has taken the lead in digital construction. According to the current situation and distinct advantages of the regional digital economy, local governments have released several policy documents or organized many conferences to regulate and support the growth of the digital economy. For example, on 13 April 2022, Fujian Province released the “Action Plan for Fujian Province to Make the Digital Economy Bigger, Stronger and Better (2022–2025)”, proposing eight actions such as digital information infrastructure. At a meeting on the promotion of the development of the digital economy on 17 May 2022, the Secretary of the Jiangsu Provincial Party Committee emphasized that “efforts should be made to build a new highland for the innovation and development of the digital economy, to add strong new momentum to carry a new mission and write a new chapter”. The “14th Five-Year Plan for the Growth of Shanghai’s Digital Economy” was issued by the Shanghai Municipal People’s Government on 13 June 2022, with the stated objective of “becoming a worldwide digital capital with world influence by 2035.” The main reasons why the western inland region is far lower than the country’s average are the lack of overall planning for the digital economy at the regional level in the west, bottlenecks in upgrading network infrastructure, a weak digital industry base, the insufficient sharing and use of data resources, and a scarcity of digital talent.

Trends in the overall Green Technology Innovation System scores by region across the country from 2011–2021 are shown in Figure 3.

In terms of time series, the overall increase in the national average green technology innovation system composite score from 2011–2021 is not high. The main reasons for this are the weak capacity for innovation in energy science and technology, the fact that essential technologies in important fields are still restricted, and the lack of dynamism in energy development due to institutional mechanisms.

The entire scores of green technological innovation systems show geographic variance in terms of spatial sequences, with the east having the best scores, the middle having the second-highest scores, and the west having the lowest scores. Green Road—China’s Economic Green Development Report 2018—points out that the majority of the western provinces are still in the developmental stage where decoupling economic growth from resource and environmental loads is the goal, which prevents them from realizing the inherent unity of “green water and green mountains are golden mountains” and impedes the advancement of green technological innovation. By contrast, the eastern coastal regions have done well in decoupling economic growth from resource and environmental loads.

4.2. Analysis of the Coupling and Coordination of Digital Economy and Green Technology Innovation

According to the coupling coordination degree calculation formula (Formula (9)), the coupling coordination degree of each province (municipality directly under the Central Government and autonomous region) in China from 2011 to 2021 was calculated, so as to obtain the coupling coordination degree of each region in China, and the line graph of the changing trend of the coupling coordination degree was drawn, and the results are shown in

Table 8. D-values for the coupling and coordination of digital economy and green technology innovation by region, 2011–2021.

Region	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	Average
Eastern	0.3687	0.3944	0.3752	0.3907	0.4030	0.4177	0.4627	0.4932	0.5170	0.5456	0.5271	0.4450
Central	0.2809	0.2998	0.2784	0.2908	0.3002	0.3148	0.3466	0.3679	0.3912	0.4106	0.4056	0.3352
Western	0.2259	0.2404	0.2152	0.2264	0.2322	0.2426	0.2658	0.2783	0.2927	0.3073	0.2894	0.2560

,

Table 9. Level of coordination between the digital economy and green technology innovation coupling by region, 2011–2021.

Region	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Eastern	4	4	4	4	5	5	5	5	6	6	6
Central	3	3	3	3	4	4	4	4	4	5	5
Western	3	3	3	3	3	3	3	3	3	4	3

,

Table 10. D-values for the coupling and coordination of digital economy and green technology innovation by eastern provinces, 2011–2021.

Province	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Beijing	0.3271	0.3489	0.3455	0.3602	0.3555	0.3652	0.4291	0.4399	0.4677	0.4860	0.4374
Fujian	0.3354	0.3597	0.3264	0.3370	0.3491	0.3576	0.3927	0.4289	0.4482	0.4670	0.4702
Guangdong	0.5261	0.5745	0.5703	0.5936	0.6160	0.6334	0.7467	0.8248	0.8761	0.9197	0.8694
Hainan	0.2107	0.2177	0.1961	0.1950	0.2013	0.2160	0.2107	0.2208	0.2264	0.2305	0.2190
Hebei	0.2981	0.3194	0.3023	0.3163	0.3211	0.3460	0.3753	0.4001	0.4339	0.4662	0.4416
Jiangsu	0.4833	0.5268	0.5148	0.5388	0.5569	0.5724	0.6303	0.6894	0.7418	0.7903	0.7936
Liaoning	0.3074	0.3250	0.3088	0.3015	0.3158	0.3121	0.3399	0.3684	0.3615	0.3952	0.3662
Shandong	0.4282	0.4600	0.4431	0.4597	0.4773	0.5111	0.5714	0.5928	0.5964	0.6406	0.6497
Shanghai	0.3874	0.4015	0.3654	0.4084	0.4085	0.4251	0.4640	0.4690	0.4932	0.5189	0.5057
Tianjin	0.3142	0.3323	0.2920	0.3066	0.3111	0.3221	0.3222	0.3346	0.3390	0.3549	0.3503
Zhejiang	0.4373	0.4724	0.4622	0.4809	0.5207	0.5342	0.6078	0.6569	0.7032	0.7323	0.6945

,

Table 11. D-values for the coupling and coordination of digital economy and green technology innovation by central provinces, 2011–2021.

Province	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Anhui	0.2995	0.3283	0.3073	0.3209	0.3482	0.3623	0.4115	0.4431	0.4700	0.5012	0.4889
Henan	0.3235	0.3379	0.3293	0.3543	0.3680	0.3880	0.4257	0.4636	0.4877	0.5171	0.4868
Heilongjiang	0.2617	0.2714	0.2479	0.2578	0.2542	0.2618	0.2715	0.2736	0.2818	0.2930	0.3319
Hubei	0.3041	0.3354	0.3149	0.3291	0.3505	0.3774	0.4038	0.4310	0.4545	0.4722	0.4686
Hunan	0.3007	0.3286	0.3151	0.3241	0.3381	0.3495	0.3832	0.4132	0.4454	0.4742	0.4670
Jilin	0.2525	0.2651	0.2327	0.2451	0.2344	0.2421	0.2692	0.2653	0.2849	0.2902	0.2768
Jiangxi	0.2600	0.2747	0.2492	0.2603	0.2744	0.2952	0.3351	0.3589	0.3959	0.4213	0.4082
Shanxi	0.2452	0.2574	0.2308	0.2347	0.2337	0.2423	0.2730	0.2944	0.3095	0.3156	0.3162

and

Table 12. D-values for the coupling and coordination of digital economy and green technology innovation by western provinces, 2011–2021.

Province	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Gansu	0.1999	0.2163	0.1888	0.2000	0.2013	0.2039	0.2332	0.2440	0.2567	0.2627	0.2437
Guangxi	0.2669	0.2764	0.2328	0.2458	0.2554	0.2901	0.3050	0.3134	0.3279	0.3503	0.3325
Guizhou	0.2230	0.2435	0.2174	0.2251	0.2330	0.2626	0.2737	0.2982	0.3180	0.3363	0.2999
Inner Mongolia	0.2370	0.2547	0.2393	0.2296	0.2409	0.2517	0.2598	0.2660	0.2802	0.2941	0.2848
Ningxia	0.1810	0.1978	0.1734	0.1740	0.1801	0.1827	0.1906	0.2013	0.2026	0.2036	0.2105
Qinghai	0.1756	0.1875	0.1656	0.1644	0.1714	0.1753	0.1733	0.1781	0.1967	0.1840	0.1678
Shaanxi	0.2771	0.2900	0.2621	0.2757	0.2751	0.2843	0.3283	0.3421	0.3629	0.3870	0.3490
Sichuan	0.3013	0.3271	0.3181	0.3356	0.3420	0.3481	0.4230	0.4423	0.4702	0.5063	0.4696
Tibet	0.1246	0.1320	0.1003	0.1232	0.1395	0.1471	0.1469	0.1486	0.1588	0.1618	0.1451
Xinjiang	0.2212	0.2334	0.2125	0.2187	0.2131	0.2148	0.2303	0.2415	0.2466	0.2554	0.2364
Yunnan	0.2335	0.2473	0.2241	0.2401	0.2511	0.2460	0.2826	0.3039	0.3280	0.3420	0.3046
Chongqing	0.2693	0.2793	0.2479	0.2848	0.2835	0.3044	0.3431	0.3606	0.3638	0.4043	0.4288

and Figure 4.

As can be seen from Figure 4 and Table 12, due to distinct regional development statuses and advantages, the degree of coupling and coordination between the digital economy and green technology innovation varies greatly among Chinese areas. Specifically, the eastern region has the highest average coupling coordination degree between the digital economy and green technology innovation, and its average coupling coordination degree is the sole region among the three regions that exceed 0.44; the central region’s coupling coordination degree between the digital economy and green technology innovation has increased from 0.2809 in 2011 to 0.4056 in 2021, second only to the eastern region; the lowest coupling coordination degree between the digital economy and green technology innovation is the western region, with an average value of 0.256.

The three areas’ digital economies and green technological innovation all display varied degrees of growth from the trend of the coupling and coordination degree, showing that the synergy between the two is constantly expanding; from the absolute value, the regional coupling and coordination degree is less than 0.6, indicating that the coupling and coordination of the digital economy and green technology innovation in China is generally at a low level. According to the particular data, the “digital gap” brought on by uneven regional growth is what accounts for China’s overall low level of coupling and coordination between the digital economy and green technology innovation. Taking the eastern region as an example, the degree of coupling and coordination between Guangdong Province, Hainan Province, Jiangsu Province, and Tianjin City exhibits a “Matthew effect”, as shown in Figure 5. The reason for this is that Hainan Province is unable to enjoy the dividends of digital economy development due to its relatively backward infrastructure, further widening the “digital divide” between provinces, while the regional disparity in digital economy development tends to exacerbate the imbalance and imbalance of green technology innovation between regions, ultimately forming a resistance to innovation in green technology development, resulting in certain regions being in a “low-end equilibrium” state in terms of the digital economy and the development of green technology innovation. At the same time, Guangdong Province and Jiangsu Province, with their rich resource advantages, are well supplied with factors for the expansion of the banking and technological sectors. As a result of these provinces’ early and rapid economic development, environmental pollution and damage brought on by economic growth have been identified earlier than in other provinces, and environmental regulations have been implemented to a higher degree than in other provinces and cities. This has led to a highly balanced degree of development for both green technology innovation and the digital economy.

4.3. Kernel Density Curve Analysis

The proposed cooperation graph was created using StataSE 16 software, and data from the first, last, and intermediate years (2011, 2016, and 2021), which are representative, were selected to plot. This allowed for the analysis of the dynamic evolution pattern of the coupling of the digital economy and green technology innovation in 31 provinces (municipalities directly under the Central Government and autonomous regions) in China, as shown in Figure 6. The figure’s vertical axis denotes the kernel density of the coordination between the digital economy and green technology innovation, while the horizontal axis displays the degree of coordination between the two.

Figure 6 demonstrates how the level of coordination and coupling between China’s green technology innovation and the digital economy tends to range from low to high. Following are some features of the evolution of the distribution of the degree of connection and coordination between the digital economy and green technology innovation:

In terms of shape, the country’s kernel density curve evolves from a bimodal to a single-peak pattern from 2011 to 2021, while the western region continues to show an obvious “single-peak” characteristic in its shape, and the eastern and central regions show an obvious single-peak to a weak single-peak evolution, indicating that the polarization of the standard of coupling and coordination between the digital economy and green technology innovation in the country is gradually slowing down, and the eastern, central, and western regions do not show an obvious divergence and there is no heterogeneous group within the region. In 2011, the graph of the national coupling coordination degree shows an obvious bimodal distribution, indicating that the national coupling coordination level of the digital economy and green technology innovation has been significantly graded. By 2021, the graph shows a single-peaked distribution to the right, with the peaks corresponding to a further rise in the level of the digital economy and green technology innovation and a tendency to disperse, indicating that the number of provinces with medium and higher levels of coupling and coordination of digital economy and green technology innovation increases and the number of provinces with low levels decreases, resulting in a growth in the level of coupling and coordination of the digital economy and green technology innovation nationwide; the nuclear density curve of the digital economy and green technology innovation in parts of the east, center, and west in 2011 presented a “single peak” feature, indicating that the coupling coordination level of the digital economy and green technology innovation in parts of the east, center, and west was relatively convergent in 2011, but the nuclear density curve’s maximum point in the eastern and central regions tended to be gentle from 2016 to 2021. It is proved that the coupling coordination level of the digital economy and green technology innovation in the central region tends from convergence to divergence.

From the position of the wave crest, the coupling coordination degree level of the digital economy and green technology innovation corresponding to the wave crest of the nuclear density curve across the entire nation as well as the eastern, central, and western regions showed a gradually increasing trend during 2011–2021, indicating that the coupling coordination level of the digital economy and green technology innovation has been improved across most of China’s regions. Although the national nuclear density curve in 2011 showed bimodal characteristics, the strong peak was in the low-value area, indicating that, in 2011, there was not much coordination between the development of green technologies and the nation’s digital economy.

From the change of wave width, the wave width of the nuclear density curve in China and all regions gradually widened during 2011–2021, indicating that the degree of difference between regions and provinces in the coupling coordination level of the digital economy and green technology innovation in China gradually increased. In 2011, the unimodal width of the eastern, central, and western nuclear density curves was wider, indicating that, although the coupling degree of the provinces in the region did not reach the state of polarization, the difference was great, and the middle and low-level coupling accounted for more provinces.

In terms of how much the left and right tails have shifted, the shift of the left tail to the right is tinier than the shift of the right tail in both the national and regional nuclear density curves in 2011 compared with 2021, indicating that areas which exhibit strong coupling and coordination between the digital economy and green technology innovation are growing faster than those with a low level during the period 2011–2021, in China as a whole and in each region.

4.4. Dagum Gini Coefficient Decomposition Dagum

Based on measuring the degree of coordination of the coupling of the digital economy and the green technology innovation system, the results of measuring and analyzing the overall variation, regional variation, cross-regional variation, and contribution rate of China’s high-quality digital economy and green technological innovation were presented in

Table 13. Gini coefficients and their decomposition results.

Year	Overall Gini Coefficient	Intra-Regional Disparities			Inter-Regional Disparities			Contribution Rate/%
		Eastern	Central	Western	Eastern–Central	Eastern–Western	Central–Western	Intra-Regional	Inter-Regional	Super Variable Density
2011	0.1620	0.1340	0.0540	0.1190	0.1330	0.1889	0.1098	23.8550	70.1830	5.9620
2012	0.1660	0.1410	0.0590	0.1160	0.1382	0.1920	0.1106	24.0440	69.5400	6.4160
2013	0.1870	0.1560	0.0760	0.1320	0.1547	0.2151	0.1296	24.2680	68.9650	6.4160
2014	0.1890	0.1620	0.0810	0.1360	0.1597	0.2165	0.1329	24.7850	66.8490	8.3660
2015	0.1920	0.1660	0.0970	0.1280	0.1655	0.2171	0.1359	24.8170	66.4180	8.7660
2016	0.1920	0.1650	0.1020	0.1340	0.1644	0.2155	0.1411	25.2230	65.5370	9.2410
2017	0.2070	0.1850	0.1010	0.1580	0.1779	0.2340	0.1540	26.3350	62.0610	11.6050
2018	0.2140	0.1920	0.1150	0.1600	0.1851	0.2403	0.1619	26.5200	61.8250	11.6540
2019	0.2160	0.2000	0.1140	0.1590	0.1881	0.2428	0.1638	26.7310	60.7350	12.5340
2020	0.2220	0.2000	0.1200	0.1760	0.1907	0.2490	0.1748	27.0470	59.5910	13.3620
2021	0.2260	0.2040	0.1080	0.1820	0.1879	0.2567	0.1779	26.6960	60.7890	12.5150
Average	0.1975	0.1732	0.0934	0.1455	0.1677	0.2244	0.1447	25.4837	64.7721	9.7444

.

The general variation in the degree of connection and coordination between green technology innovation and the digital economy: As can be seen from Table 13, between 2011 and 2021, China’s digital economy and green technology innovation had a level of coupling and coordination that, on average, had a Gini coefficient of 0.1957. From the perspective of variation tendency, the overall Gini coefficient continues to rise from 0.1620 in 2011 to 0.2260 in 2021, with an average yearly increase of 3.59%, making clear that the overall regional gap between the coupling coordination level of the digital economy and green technology innovation keeps expanding during the sample study period.

Intra-regional discrepancies in the level of coupling and coordination between the digital economy and green technology innovation: As can be seen from Table 13, the eastern, central, and western areas of China all exhibit nearly the same trajectory of change in terms of the degree of coupling and coordination between the digital economy and green technology innovation. Thanks to the robust economies of Beijing, Shanghai, Zhejiang, Guangdong, and Jiangsu, which are significant drivers of green technology innovation and the digital economy, the eastern region for 2011–2021 has the greatest intra-regional variance mean (0.173); Li Liu [40] et al. have described this phenomenon as the result of the high degree of digital transformation in green industries such as Chongqing, Sichuan, and Shaanxi since the mean value for the western area is somewhat lower than that of the eastern region at 0.1677. With a score of 0.093, the center region has a mean value that is much lower than both the eastern and western regions. In terms of trend, the intra-regional gaps in the eastern, central, and western regions are generally fluctuating upwards.

Inter-regional variations in the degree of coordination between green technology innovation and China’s digital economy: As demonstrated by Table 13, the mean values of the inter-regional discrepancies between east and central, east and west, and central and west from 2011–2021 are 0.1677, 0.2244, and 0.1447, respectively, which shows that regional contrasts between the east and west are large and the differences between the east and central and central and west regions are comparatively small. Trend-wise, there is a persistent upward trajectory for the gap between the eastern and central regions, increasing from 0.1330 in 2011 to 0.1879 in 2021, with an average annual growth rate of 3.75%; the overall upward trend in the difference between the eastern and western regions, rising from 0.1889 in 2011 to 0.2171 in 2015, then gradually declining to 0.2155 in 2016, before continuing to rise to 0.2567 in 2021, with a 3.26% increase every year on average; the trend in the variation between the central and western regions is more in line with that between the eastern and central, also showing an upward tendency, from 0.1098 in 2011 to 0.1779 in 2021, with an average annual increase of 5.76%.

The overall variations in the level of coupling and coordination of the digital economy and green technology innovation can be disassembled into three parts: inter-regional contribution differences, intra-regional contribution differences, and super-variable density contribution differences. Regarding the magnitude of the contribution, inter-regional variation is dominant, with a contribution of 64.77%, followed by intra-regional variation with a contribution of 25.48% and, to a lesser extent, super variable density, with a contribution of 9.74%. From the dynamic change of the contribution rate, the inter-regional contribution rate demonstrates a clear decreasing trend, with the largest inter-regional contribution rate at 70.18% in 2011 and the smallest at 60.79% in 2021; the intra-regional variation contribution rate increases from 23.86% in 2011 to 27.05% in 2020 and then decreases to 26.70% in 2021, showing an overall “upward-falling” fluctuating upward trend. The trend in the contribution rate of the super-variable density is the same as the contribution rate of the intra-regional differences. Therefore, focusing on inter-regional disparities and fostering a coordinated regional development of the digital economy and green technology innovation would help China’s digital economy and green technology innovation achieve greater levels of coupling and coordination.

5. Conclusions and Policy Recommendations

5.1. Conclusions

Using data from a panel of thirty-one provinces (municipalities directly under the Central Government and autonomous regions) over the period of 2011 to 2021, this paper divides thirty-one provinces into three areas by region and measures the development level of the digital economy and green technology innovation by combining the coupling coordination degree model, kernel density curve analysis, and Gini coefficient decomposition. A coupling coordination model has been used to first determine the level of regional coupling coordination and to investigate the temporal trend and spatial association between the digital economy and green technology innovation. Next, the dynamic evolution pattern of the coupling coordination degree of the digital economy and green technology innovation system was investigated by plotting the kernel density curve; decomposing the Gini coefficient also yielded the variation trend of the degree of linkage between the green technology innovation system and the digital economy. The following research findings are derived from an investigation of the linked and coordinated development and evolutionary patterns of the digital economy and green technology innovation:

(1)

Regional development is manifestly unbalanced, and the overall strength needs to be improved

In China’s three regions, there are noticeable variances in the degree of coupling coordination between the digital economy and green technology advancement. From the perspective of time series, the comprehensive score of China’s digital economy system showed a fluctuating growth trend during 2011–2021, while the overall score of the green technology innovation system did not increase much. From the perspective of spatial sequence, the development level of the digital economy is not developing at a level in the region, and the overall situation is high in the east and low in the west. Regional heterogeneity is what gives the green technology innovation system its overall score, which is highest in eastern China, second in central China, and lowest in western China.

(2)

Regional development gaps have become more apparent, and driving mechanisms need to be optimized

The coupling coordination degree of the digital economy and green technology innovation in the three regions all showed various degrees of increase, but in absolute terms, there is a very low level of cooperation between green technology innovation and the digital economy in China. By drawing the kernel density curve, it is clear that the coupling coordination level of the digital economy and green technology innovation in China changes from low capability to high capacity. The growth rate of regions with high coupling coordination levels of the digital economy and green technology innovation was faster than that of regions with a low level, and the degree of difference between regions and provinces within the region gradually increased. Further investigation reveals that the “digital divide,” poor digital infrastructure, and ineffective policy implementation are the primary causes of China’s low level of overall coupling and coordination between the digital economy and green technology innovation as well as the main challenges to the lack of capacity for both in various regions.

(3)

Coordination and cooperation procedures need to be enhanced because regional contribution rates clearly vary

The overall regional gap between the levels of coupling coordination of the digital economy and green technology innovation in China during the sample study period is growing and the levels of coupling coordination in the eastern, central, and western areas are significantly different within the region but exhibit roughly the same trend of change. On the geographical variations, the disparities between the eastern and western areas are substantial, whereas others are very small. Regional disparities, however, are generally becoming more pronounced. The contribution rate of regional difference is in the dominant position, and the contribution rate of the super variable density is the least, based on the total difference. The inter-regional contribution rate showed an obvious downward trend; meanwhile, the intra-regional difference contribution rate and the contribution rate of the super variable density showed an “up–down” type fluctuation upward trend.

5.2. Policy Recommendations

The following countermeasure recommendations are provided in light of the aforementioned conclusions:

(1)

Scientific understanding of the digital economy and green technology innovation in terms of scope of content and realistic value

At this time, the level of green technology innovation in China is lagging, and there is uneven regional growth. The primary reasons are the insufficient investment in innovation resources and the lack of a good social incentive influence. First of all, local governments should start from a top-down perspective and use digital platforms to ensure the smooth flow of financial support for green technology research and development by developing hierarchical financial incentives and subsidies and green credit services [41] to improve the environment for industrial investors to invest in. In the meantime, they should use green technology innovation technology to quicken conventional industrial businesses’ transformation and modernization pace, cut back on their production expenses and cycles, and increase their ability for innovation and productivity [42]. Additionally, this could improve the country’s access to the outside world, foster collaboration and exchanges between worldwide research teams, and introduce and use cutting-edge technology from other countries.

(2)

Optimize the driving mechanism for regional digital development and stimulate the innovation capacity of the digital economy and green technology

The progress of the digital economy and green technology innovation is bound to experience a process of changing from imbalance to equilibrium. Local governments need to specify targeted policy planning to boost the coordinated development of the digital economy and green technology innovation according to the current situation of regional development. In light of the conditions for the development of the digital economy and green technology innovation in each region, the competitive advantages ought to be accurately assessed and policies should be formulated according to local conditions. On one hand, one area might take the metropolitan area and urban agglomeration as the main carrier, enhance the dividend effect and multiplier effect generated by the cluster of regional digital and green technology innovation enterprises, and enhance the driving force of provincial capital cities to the surrounding cities. On the other hand, based on their resource endowment and low-cost advantages, the western region and other regions may think about constructing substantial data processing centers to raise the level of digital infrastructure [43].

(3)

Improve inter-regional coordination and oversight mechanisms and narrow differences in contribution rates between regions

It is necessary to establish coordinated development management departments through local governments to collect data for the development evaluation index of the degree of coupling between the digital economy and the green technology innovation of local businesses in e-commerce sales, R&D expenditure, new product sales income, etc. Furthermore, this will be needed to implement the green development principle that “green water and green mountains are the silver mountains of gold” by stopping pollution-intensive industries, eliminating backward production capacity [44], limiting development-related environmental pollution to reasonable levels, and avoiding the effects of congestion. On the one hand, the government should empower the ecological environment supervision department to carry out independent supervision and ensure its effective performance of duties. On the other hand, through the monitoring, assessment, and strengthening of supervision, comprehensive law enforcement, and other systems, the ecological environment department has enacted behaviors that play a significant role in the ecological environment into the scope of its supervision, in order to develop ecological policies with local flavor, encourage the development of new green towns and communities, and introduce green innovation components into different industries [45].