Digital Economy and Green Development—Based on the Empirical Research on the Resource-based Prefecture-Level City

doi:10.21203/rs.3.rs-1983324/v1

Green development is the core content of the high-quality economic development, and the digital economy is the new driving force to accelerate the realization of high-quality economic development. In this regard, this paper selects 248 resource-based prefecture-level cities in China from 2011 to 2019 as the research objects and uses the fixed effect model and mediation effect model to test the relationship between digital economy and regional green development. We get the following conclusions: first, there is the nonlinear relationship between digital economy and regional green development, and this relationship shows the inverted U-shape. Secondly, digital economy can influence regional green development through the path transmission mechanism of regional technological innovation, industrial structure and environmental regulation. Finally, the effect of digital economy on regional green development is heterogeneous, especially in the eastern region and cities with high human capital level. Based on the digital economy, the findings of this paper provide theoretical basis and reference suggestions for promoting the green transformation development of regional economy.

digital economy，green development，technological innovation，industrial structure

environmental regulation

The State Council's "14th Five-Year Plan for Digital Economy Development" stated that the digital economy is the main economic form after the agricultural economy and the industrial economy. As the factor of production in normal economic activities, digit plays the important role in the era of digital economy. With digital technologies such as Big Data, the Internet, Supercomputing and the Internet of Things, it has become the core driving force for high-quality economic development. The rise of the digital economy has injected new impetus into the development of the traditional economy, and the application of digital resources and digital technologies has gradually become an important engine for promoting sustainable economic development. At the same time, the important strategic position of green development in China's high-quality development is increasingly prominent, and the construction of Beautiful China is imperative.

China's digital economy is developing at the faster pace and the scale of digital economic development is expanding. In 2020, the overall added value of digital economy will exceed 19 trillion RMB, accounting for about 18.8% of GDP. The new round of scientific and technological revolution represented by information technology and Internet infrastructure in the digital economy continues to promote traditional economic development and changes in industrial scale, and has become the important symbol of the new development pattern. The thriving development of the digital economy can not only transform the traditional production model, but also create the efficient operation process, empower the regional green development with ecological, green and low-carbon development paradigm, and accelerate the realization of high-quality development of the green economy. However, the existing research on the relationship between the digital economy and green development is still in the status quo, and there is little research on the mechanism of how the digital economy can promote the regional green development. The marginal contribution of this paper lies in the following three points, firstly, combined with theoretical analysis, from the perspective of empirical analysis, the path for the digital economy to promote regional green development is revealed. Second, resource-based cities are selected as the research objects to empower green development for the digital economy. It provides the realistic basis, and lays the theoretical foundation for further protecting the green and ecological development of resource-based cities. Thirdly, based on the research conclusions, it proposes the realization path of China's digital economic development to promote the regional green development, and then puts forward corresponding policy suggestions for the digital economy to empower green economic development.

The remainder of this paper is organized as follows. Section II discusses and sorts out the literature content related to this paper. Section III uses relevant research and theories to explain the theoretical mechanism of the digital economy affecting green development, and then put forward the research hypothesis of this paper. Sections IV discusses data, variables and the model in the paper. The results of empirical test are shown in the Sections V. Section VI concludes our paper and puts forward some policy suggestions.

The concept of digital economy was established by Tapscoot (1995), he thought the digital economy was the activity based on the wide use of Informational Connection Technology (ICT). Han and Chen (2022) divided the digital economy into four parts that are consisted of Data Value, Digital Industrialization, Industrial Digitalization and Digital Governance. There are some related things close to the digital economic, such as E-commerce, Internet Plus and digital construction. With the development of digital economy, its contents keep growing and its advantages also keep exerting (Zhang et.al, 2019). Research on the digital economy can be analyzed from both macro and micro perspectives. On the one hand, the digital economy can promote the growth of regional economy and the high-quality economic development (Zhao et.al, 2020). Not only can it improve the regional TFP, but also it would have the spillover effect on the neighboring cities. The integration of the digital economy and manufacturing industry has the positive effect on the industrial structure (Xu et.al, 2020), resource configuration (Zuo et.al, 2020) and the capacity of regional innovation (Wen et.al, 2019). Lechman and Popowska (2022) thought that the digital economy has the positive effect on the educational development, and then reduce the urban poverty. Digital economy could simplify the process of the education and improve the quality of the education and the level of human capital (Song et al.,2022). Common prosperity as an important symbol of building a powerful modern socialist country with Chinese characteristics. In the process of achieving Common Prosperity and development, the digital economy has played an important role, and one of the most obvious manifestations is that it can significantly reduce the urban-rural income gap (Fan et al.,2022). On the other hand, digital economy has caused the direct effect on the enterprises. Shi (2022) used the Chinese listed manufacturing enterprises to study and found that the digital economy can significantly improve the enterprises performance. At the same time, the technological innovation and the level of innovational capacity as the core of the enterprise performance, the digital economic development has the positive effect on them. In the field of personal employment and employee salary levels in the manufacturing enterprises, there is the development speed in the same direction between the development of the digital economy and personal employment and employee salary (Zhu and Ma, 2022). Moreover, the digital economy also can improve the total factor productivity of companies by optimizing the industrial structure and technical production process (Xu et.al, 2020).

At present, green development has been a research hotspot in economics. Regional green development is closely related to economy, environment and trade, the concept of green development was firstly established in An Essay on the Principle of Population (Thomas Robert Malthus, 1798). What factors can affect urban green development? Most studies put the perspective on environmental regulation, but they got different conclusions about the effect of environmental regulation on green development. Some studies thought it could help urban economy to realize green and sustainable development (Zhang et.al, 2022), others thought it has negative effect on developing green economy (Li and Ramanathan,2018). In addition to environmental regulation, Raza et al. (2020) thought the financial development could reduce the carbon emission by promoting green investment, promoting enterprise innovation, providing capital support, and upgrading industrial structure. Financial development can also improve the energy consumption structure (Puyang et al., 2011). Foreign trade as an important pole of economic growth, low-level export trade expansion is unconducive to industrial green transformation, while import trade is conducive to industrial green transformation and contributes to the green economic development (Xing and Bin, 2015).

Economic development cannot be at the expense of the ecological environment. The status of the digital economy in the national economy is increasingly prominent (Bejakovic and Mrnjavac, 2020). How does the digital economy influence the green development? Obviously, there is no consensus on the relationship between digital economy and green development. Existing researches mostly focus on the relationship between digital economy, green technological innovation and carbon emission, most of the literature conclusions have verified that the digital economy can promote green technology innovation and carbon emission reduction. (Wang et al.,2021, Li et al.,2022).

With the rapid development of AI, Big Data and Internet of Things, digital economy is characterized by sharing, easy access, high penetration, and innovation driving force (Murthy et al., 2022), whether it can promote green development is a practical issue that is worthy of attention. Although the academic community has conducted extensive and in-depth discussions on the influencing factors of green development and the promotion effect of the digital economy, which provides certain theoretical support for explaining the impact of the digital economy on the green development of cities, the above research still cannot provide information on the impact of the digital economy on the green development of cities. Reliable evidence. On the one hand, the theoretical discussion on the impact of the digital economy on green development in the existing research is still insufficient. On the other hand, the empirical test of the impact of the digital economy on green development also needs to be improved. The relationship between the digital economy and regional green development needs to be further to research and verify.

In view of this, based on the panel data of China's resource-based prefecture-level cities from 2011 to 2019, this paper adopts the global principal component analysis method to construct the evaluation system for the development level of the digital economy, and uses the dynamic panel model, the mediation effect model and the spatial Durbin model to analyze the impact of the digital economy, the impact and transmission mechanism of regional green development. The possible innovations in this paper are as follows: (1) Based on the existing studies, this paper deeply analyzes the internal mechanism of the digital economy enabling the green development of cities, sorts out the existing transmission mechanism and conducts empirical tests. (2) In the past, the research objects mostly focused on the sample data at the provincial level or the general data at the prefecture-level city level, but this paper turns the research object to the resource-based prefecture-level cities that are closely related to green development. This research object has more practical significance and research orientation. (3) Considering the regional heterogeneity of the digital economic development, which may lead to the spatial spillover effects. This paper constructs the Spatial Durbin Model to study the external spillover effects of the digital economy, which provides an entry point for further clarifying the promotion of green development by the digital economy.

3.1 The digital economy has the positive effect on green development

Digital economy is a new type of economy characterized by easy availability, high penetration, and strong externalities. Compared with traditional economic development, the digital economy is a new development model. Its development model is the rapid development of new digital technologies and the Internet, which can release dividends for green technology innovation (Kim, 2006). Digital technology is the core part of the digital economic development, which has smaller marginal effect compared to traditional land and other natural resource and is becoming the new emerging dividend to improve green development gradually (Tang et al., 2021). Therefore, in the process of integration and development of the digital economy and the real economy, modern digital technology has penetrated into the traditional production process. These shifts can speed up the flow of production factors significantly, further improve the overall production level of the city, and thus promote the city to accelerate the realization of green development.

However, excessive development of the digital economy may have the inhibitory effect on green development. First, the excessive expansion and development of the digital economy will consume a lot of labor, capital and other resource elements, which will not only cause an imbalance in the distribution of limited resources, but also lead to restrictions on the authorization of corresponding green technology applications and increase in product prices, thus weakening the momentum of green development. Second, the excessive expansion and development of digital economy will make regional economic development face problems such as information overload, information quality and information security, which is not conducive to the rapid formulation and accurate implementation of scientific decisions, but also causes the cost of information processing to far exceed the benefit, and increases the capital burden of economic development, thus hindering the process of green development. Finally, the excessive expansion and development of the digital economy is integrated into the national economic production, and real enterprises will have vicious competition, which will further complicate the economic green transformation and is not conducive to regional green development. Based on the above analysis, this paper proposes Hypothesis 1:

Hypothesis 1

There is an inverted U-shaped relationship between the digital economy and regional green development.

3.2 Channel analysis of the impact of digital economy on green development

From the macro perspective. On the one hand, the level of regional technological innovation has the positive relationship with the green development, digital economy has the technological spillover effect that can improve the technological innovation (Li et al., 2022). The digital economy provides the channel for the dissemination of innovational information and the popularization of knowledge. Internationally advanced technical information and production technologies can be efficiently applied to the actual production process with the development of the digital economy. On the other hand, the development of the digital economy promotes the formation of digital government. In the era of digital economy, government departments actively use digital tools and digital technologies to accurately monitor environmental pollution, enterprise production and other economic activities. The digital economy has improved the level of technological innovation in the government governance environment (Fu and Geng, 2019). At present, many governments have set up digital supervision platforms, on which they can monitor the clean production and air quality of enterprises in real time, and can take a series of measures when necessary to prevent destructive pollution of the environment, which promotes the green development of cities.

From the micro perspective, firstly, the digital economy can reduce the information search cost communication cost and management cost in day-to-day business operations, so that it could provide more financial support for technological innovation and research and development of enterprises. Secondly, the digital economy has the characteristics of self-innovation and sharing, which can accelerate the acquisition of technology by enterprises, increase the effective path for enterprises to absorb technology spillovers, and thus improve the basic level of technological innovation of enterprises (Cai and Wang, 2022). The improvement of the technological innovation level of micro-enterprises is conducive to accelerating the development of green economy in cities. Finally, the application of new technologies such as artificial intelligence and big data to production factors has improved the efficiency of the industrial supply chain, optimized the allocation of resources, and encouraged enterprises to release more resources for independent research and development. The development of the digital economy is conducive to breaking the boundaries of innovation, enhancing the technological integration capabilities of enterprises, helping traditional resource-based enterprises to improve collection efficiency, optimize technological processes, and accelerate the transformation of industrial chains into sustainable high-quality green chains. Correspondingly, we argue:

Hypothesis

a: The digital economy can promote green development by improving the level of regional technological innovation.

The reasonable industrial structure is the core content of improving resource utilization efficiency and reducing environmental pollution, which will further promote the sustainable ecological development of regional industrial economy. The main reason why the digital economy affects the industrial structure is its own high penetration. The digital economy is the new driving force for the development of the traditional economy, which can promote the transfer of the industrial structure dominated by labor-intensive and heavy industry to the industrial structure dominated by high technology content and environmental friendliness (Chen and Yang, 2021). In the context of the rapid development of digital technology, Big Data, Artificial Intelligence and the Internet of Things have gradually become important production and application tools by virtue of their characteristics of openness, collaboration, sharing, and connection. upgrade. The digital economy has formed a new type of economic platform, and its own competitive effect and scale effect can further improve the efficiency of enterprise resource utilization and overall operational efficiency, thereby forcing some backward enterprises to transform and upgrade (Zuo Pengfei et al., 2020, Li Zhiguo et al., 2021). At the same time, the development of the digital economy has effectively alleviated the financial constraints faced by enterprises and promoted the adjustment of the industrial structure of enterprises (Yuan et al., 2021). Therefore, we propose:

Hypothesis

b: The digital economy can promote green development by optimizing the regional industrial structure.

From the perspective of the long-term macroeconomic development process, appropriate environmental regulation can promote technological innovation of enterprises, change the original production technology and production method with heavy pollution and low efficiency, so as to improve the technical level and production efficiency (Shen Chunmiao and Zheng Jianghuai, 2021). From the perspective of micro-individual economy, market-based environmental regulation can promote the improvement of enterprise performance output, and at the same time, there is the mutual adjustment effect between heterogeneous environmental regulations (Chen et al., 2022). The development of the digital economy integrates digital elements such as advanced digital technology and digital infrastructure, and then builds the digital economy platform. On the one hand, the development of the digital economy has promoted the disclosure of industry information and broadened the channels of environmental supervision. Digital technology has opened up the traditional environmental supervision path. It has strengthened environmental supervision, strengthened the awareness of corporate environmental responsibility, and forced the economy to achieve green development. On the other hand, the vigorous development of digital economy has promoted the transformation of environmental regulation from the traditional form of vertical supervision under the leadership of the government to the form of multi-faceted collaborative supervision between the government, the masses and the society. The channels for participating in environmental governance have effectively improved the environmental governance capacity and the effect of environmental regulation, thereby promoting the development of regional green economy.

Hypothesis

c: The digital economy can promote green development by improving the regional environmental regulation.

4.1 Data

This paper uses the panel data of 248 resource-based prefecture-level cities in China from 2011 to 2019, and all data are selected from the WIND database, China City Statistical Yearbook, China City Information Industry Yearbook, China Environmental Statistical Yearbook. Some missing data were supplemented by interpolation and linear fitting.

4.2 Variables

4.2.1 Explained Variable

Green development (gd). Regional green development refers to improving the level of economic development on the basis of minimizing environmental damage, carbon emissions and natural resource loss, promoting sustainable and healthy economic development and harmonious and ecological social development (Lin and Zhou, 2022). It has become a consensus that most researches use Green Total Factor Productivity (GTFP) as the measure of the level of regional green development. Following Long et al. (2021), we apply the directional distance function including undesired output to the Global Malmquist model, and calculate the Global Malmquist-Luenberger (GML) productivity index as the measure of regional GTFP. Since the GML index reflects the growth of GTFP, it is necessary to further adjust the GML index accordingly. The specific calculation method is as follows: assuming that the GTFP is 1 in 2011, the GTFP in 2012 is the 2011 base period value is multiplied by the 2012 GML index, the 2013 GTFP is the 2012 GTFP multiplied by the 2013 GML index, and so on. To sum up, this paper uses the GTFP of each city as a proxy variable for the level of green development, denoted as gd. In the process of calculating GTFP, the following related data will be used.

(1) Input indicators. The indicators include labor input, energy input and capital input. The labor input is measured by the total number of employees in regional units. The energy input is measured by the total number of urban electricity consumption. The capital input is measured by regional fixed asset stock. Following Zhang Jun et al. (2004), this paper uses the perpetual inventory method to calculate the level of fixed asset stock and chooses the depreciation rate of 0.096. The calculation process requires price deflator of the data, and the price deflator index is selected from the fixed asset investment price index of each region in the China Statistical Yearbook.

(2) Output indicators. The indicators include expected and unexpected outputs. Among them, the expected output selects the GDP of each city, and the unexpected output selects the total number of the wastewater discharge, SO₂ discharge, soot discharge, and PM2.5 concentration of each city.

4.2.2 Core Explanatory Variable

Digital economy (de). Scholars at home and abroad have adopted the value-added method, comprehensive index method, principal component analysis method, weight addition method, entropy value method, and spatial analysis method to measure the development level of the digital economy. Chen P. (2022) measured the development level of the digital economy from four dimensions: digital infrastructure, digital equipment application, digital technology application, and digital business scale. The degree of variation and fluctuation of the measurement index is assigned a weight ratio using the entropy weight method. The more the degree of variation and fluctuation is, the more the weight ratio is. The metrics used in this paper are shown in the Table 3.

Table 1

*Measurement system of digital economy development level*
Target level	Standard level	Index level	Indicator attribute
Digital Economy	Digital infrastructure	Number of Internet broadband access ports	Positive
	Digital infrastructure	Optical cable line length	Positive
	Digital equipment application	Internet broadband penetration rate	Positive
		Mobile phone penetration rate	Positive
		Fixed telephone penetration rate	Positive
	Digital technology application	R༆D investment of informational industry	Positive
		Proportion of enterprises in E-commerce transaction activities	Positive
		Number of people with bachelor degree or above	Positive
	Digital business scale	Total revenue of information service industry	Positive
		Number of employees in the information service industry	Positive
		Number of express parcels	Positive

Table 3 shows the indicator system for measuring the development level of the digital economy, for which the steps are as follows:

(1) In order to eliminate the differences between each index, this paper adopts a standardized method to standardize each data. The data involved in the process of processing the development level of the digital economy are all positive promotion indicator data.

(2) Standardize the positive indicators:

\(Yij=\frac{{Xij - \hbox{min} (Xij)}}{{\hbox{max} (Xij) - \hbox{min} (Xij)}}\) ①

(3) Calculate the information entropy value of each indicator E_j:

\(Ej=\ln \frac{1}{n}\sum\limits_{{i=1}}^{n} {[({Y_{ij}}} /\sum\limits_{{i=1}}^{n} {{Y_{ij}}} )\ln ({Y_{ij}}/\sum\limits_{{i=1}}^{n} {{Y_{ij}}} )]\) ②

(4) Calculate the weight of each indicator W_j:

\(Wj=(1 - Ej)/\sum\limits_{{i=1}}^{m} {(1 - {E_i})}\) ③

(5) Based on the above calculation results E_j and W_j, the weighted function is used to calculate the digital economy development index DE_i of each resource-based prefecture-level city:

\(DEi=\sum\limits_{{j=1}}^{m} {WjEj}\) ④

In equations ①-④, i represents the province, j represents the measurement index. Max (X_ij) and min (X_ij) represent the maximum and minimum values of X_ij respectively. n represents the number of resource-based prefecture-level cities, and m represents all indicators quantity. The larger the value of the digital economy development level indicator is, the higher the digital economy development level of the resource-based prefecture-level cities are. On the other hand, the lower the digital economy development level is.

4.2.3 Mediating Variable

Technological innovation (ti). Technological innovation is one of the important factors for developing green and sustainable economy (Debrah et al.,2021). In order to highlight the role of innovation in promoting green development, it can also reflect regional innovation capabilities. This paper selects the number of patents granted in a region in one year as the proxy variable of technological innovation, and performs logarithmic processing on it.

Industrial structure (is). Regional industrial structure can not only reflect the evolution process of more coordinated development among different industries and more effective resource allocation, but also show the process of the gradual upgrading of the regional industrial structure from lower level to higher level (Han and Chen, 2022). The reasonable industrial structure can promote the sustainable development of the industry (Zhang et al., 2022). Therefore, this paper selects the ratio of the output value of the tertiary industry and the secondary industry as the proxy variable of the regional industrial structure.

Environmental regulation (er). At present, China's environmental regulation is mainly based on government departments and related functional departments, which supervise and manage the pollution discharge and pollution control behaviors of various industries. Government regulation requires fiscal expenditure, so this paper selects the ratio of the investment amount of industrial pollution control in the GDP of each province as the proxy variable of environmental regulation.

4.2.4 Control Variables (CV)

Following Liu Minlou et al. (2022) and Yu Shan et al. (2022), the selection of control variables is as follows. The environmental regulation (er) of each city can be represented by the number of laws and regulations promulgated by each city. The urbanization level (urb) is expressed using the urbanization rate of each city. The level of opening to the outside world (fdi) is expressed by the amount of FDI in each city. The level of human capital (ed) is expressed by the number of college graduates or above in each city. The statistical description of each variable is shown in Table 1.

Table 2

*Variables and Statistical Description*
Variable type	Variable noun	Notation	Sample size	Mean	Standard deviation	Minimum value	Maximum value
Explained Variable	Green development	gd	2232	1.009	0.029	0.960	1.067
Explanatory Variable	Digital economy	de	2232	0.032	0.728	-1.119	9.802
Mediating Variable	Technological innovation	ti	2232	0.196	0.042	0.005	0.372
	Industrial structure	is	2232	0.470	0.102	0.019	0.821
	Environmental regulation	er	2232	0.652	0.119	0.134	1.322
Control Variable	Economic development level	eco	2232	10.775	0.567	9.213	15.675
	Fixed asset investment	fe	2232	3.227	0.539	-0.003	6.184
	Opening level	fdi	2232	0.021	0.028	0	0.775
	Urbanization level	urb	2232	0.112	0.126	0.008	1.390
	Human capital	hc	2232	0.241	0.088	0.013	0.732

4.3 Model

According to this paper’s theoretical analysis and research assumptions, there is a link between the digital economy and the regional green development. As a result, this paper builds the following basic model.

\({\text{gd}}it=\alpha 0+\alpha 1d{e_{it}}+\alpha {\text{2}}de_{{it}}^{{\text{2}}}+\sum {\alpha X{\text{controls}}} +\theta {\text{it+}}\mu {\text{it+}}\varepsilon it\) ⑤

In model ⑤, i represents the city, t represents the time, gd is the green development level of each city, de is the digital economy development level of each city, X is the collection of a series of control variables, θ_it is the city effect, µ_it is the time effect and ε_it is the random disturbance term.

In order to test the internal mechanism of the digital economy affecting regional green development, this paper constructs the following model by referring to Lin and Feng (2022), namely the Mediation Effect model.

\(Medit=\lambda 0+\lambda 1d{e_{it}}+\lambda {\text{2}}de_{{it}}^{{\text{2}}}+\sum {\lambda Xcontrols+\theta {\text{it+}}\mu {\text{it+}}\varepsilon it}\) ⑥

\({\text{gdi}}t=\beta 0+\beta 1d{e_{it}}+\beta {\text{2}}de_{{{\text{it}}}}^{2}+\beta 3Medit+\sum {\beta Xcontrols+\theta {\text{it+}}\mu {\text{it+}}\varepsilon it}\) ⑦

In models ⑥ and ⑦, Med_it is the mediating factor of city i at time t that represents technological innovation (ti) industrial structure (is) and environmental regulation (er) respectively. First, verify the coefficient α₁ in Model 5, the coefficient λ₁ in Model 6, and the coefficient β₂ in Model 7 in turn. If all three coefficients are significant, it means that the mediating effect of technological innovation or industrial structure is significant, otherwise, use the bootstrap method to verify again; Next, verify the coefficient β₁ of Model 7. If it is significant, it means that the direct effect of the digital economy and green development is also significant. Otherwise, only the mediating effect is established. Finally, compare the signs of β₁ and λ₁β₂. If the two have the same sign, it is considered that technological innovation or industrial structure plays the partial intermediary role. If the two have different signs, it is considered to be the masking effect. At the same time, if the coefficient β₁ of model 7 is lower than the coefficient α₁ of model 5, it means that technological innovation or industrial structure is variables that affects the result of green development.

5.1 Analysis of benchmark regression

Column (1) of Table 3 shows the regression results without adding control variables. The results show that digital economy presents a significant inverted U-shaped relationship at the significance level of 5%, which confirms the nonlinear relationship between digital economy and regional green development. Columns (2) and (3) in Table 3 respectively show the random effect and fixed effect regression results after adding control variables. Based on the results of Hausman test, this paper selects the fixed effect model for regression analysis. The regression results of the fixed effects model still show that there is the significant inverted U-shaped relationship between digital economy and regional green development. In addition, the calculated inflection point value is 8, and the average value of the current regional digital economy development level is 0.032, which is less than the corresponding inflection point value, indicating that the development level of the digital economy is on the left of the inflection point of the inverted U-shaped curve, so the digital economy will promote regional green development. Meanwhile, the regression results of control variables also show that regional economic development level, fixed asset investment, opening to the outside world level and urbanization level are conducive to promoting regional green development.

Table 3

Benchmark regression results
	(1) gd	(2) gd	(3) gd
de	0.0386^** (1.96)	0.0017 (0.23)	0.0400^** (2.01)
de²	-0.0024^** (-2.13)	-0.0001 (-0.33)	-0.0025^** (-2.16)
eco		0.1077 (0.70)	0.0253 (0.13)
fe		0.0033 (0.44)	0.0027 (0.28)
fdi		0.0043 (0.20)	0.0021 (0.08)
urb		0.0018 (0.29)	0.0065 (0.54)
hc		-0.1041 (-0.76)	-0.0571 (-0.18)
_cons	0.8591^*** (10.12)	1.0063^*** (29.23)	0.9795 (1.34)
R²	0.003	0.002	0.004
N	2232	2232	2232
chi (2)			87.9760
p-value			0.0000
TFE	Yes	No	Yes
RFE	Yes	No	Yes
Note: The-t statistic of parameter estimation is in parentheses, *, and*are represented at the 1%, 5%, and 10% significant levels, respectively. TFE is Time Fixed Effecct, RFE is the Regional Fixed Effect.

5.2 Analysis of robustness test

In order to ensure the accuracy of the results of this paper, the following series of robustness tests are conducted: (1) The explained variable is replaced. In this part, the data envelopment method (DEA) is adopted to measure the regional green development level, and the explained variable is replaced with a new measurement result, denoted as gd-dea. The regression results are shown in column (1) in Table 4. The regression results in column (1) of Table 4 are consistent with the above benchmark regression results, showing the significant inverted U-shaped relationship between digital economy and regional green development. (2) Delete abnormal data. Considering that China launched the second broadband speed increase in 2011, in order to avoid the impact of this external event, this part deletes the research sample in 2011 and then returns. The regression results are shown in column (2) of Table 4, which are still consistent with the above conclusions. (3) Test for endogeneity. In order to deal with the possible endogeneity problems in regression analysis, 2SLS method was used for testing in this paper. With reference to Liu et al. (2022), the lagged first-stage value of digital economy is selected as the instrumental variable (IV). The test results are shown in columns (3), (4) and (5) of Table 4. Columns (3) and (4) are the regression results of the first stage. The results show that IV promotes both primary and secondary terms of the current digital economy, and IV is effective. The results in column (5) are consistent with the above conclusions, indicating that there is indeed the significant inverted U-shaped relationship between digital economy and regional green development, and the conclusions of this paper are robust.

Table 4

Robustness Test Results
	(1) gd-dea	(2) gd	(3) first de	(4) first de²	(5) second gd
de	2.8544^*** (3.52)	0.0475^* (1.90)			0.092^* (1.94)
de²	-0.8704^* (-1.63)	-0.0029^** (-2.00)			-0.006^** (-2.09)
L.de			0.315^* (1.73)	-2.416 (-0.77)
L.de²			0.008 (0.74)	0.598^*** (3.27)
CV	Yes	Yes	Yes	Yes	Yes
R²	0.003	0.004			0.002
N	2232	1984	2232	2232	2232
TFE	Yes	Yes	Yes	Yes	Yes
RFE	Yes	Yes	Yes	Yes	Yes
Note: The-t statistic of parameter estimation is in parentheses, *, and*are represented at the 1%, 5%, and 10% significant levels, respectively. TFE is Time Fixed Effecct, RFE is the Regional Fixed Effect.

5.3 Analysis of mechanism test

In order to verify the transmission mechanism of digital economy affecting regional green development, regional technological innovation, industrial structure and environmental regulation level were selected as mediators for empirical test. The regression results are shown in Table 5. According to the results in column (1) of Table 5, there is also an inverted U-shaped relationship between the development of digital economy and regional technological innovation, which is consistent with the research conclusions of Cheng Qiongwen and Lu Siyu (2022). The results of column (2) indicate that the development of digital economy can promote the improvement of regional technological innovation level, and then affect regional green development. The results in column (3) of Table 5 show that there is the positive U-shaped relationship between digital economy and regional industrial structure at the significance level of 1%, indicating that low development level of digital economy is difficult to play optimization effect on industrial structure, but with the continuous improvement of the development level of digital economy, regional industrial structure becomes more reasonable. The results of column (4) show that digital economy can exert nonlinear influence on green development by optimizing industrial structure. The results in column (5) of Table 5 show that digital economy has the positive U-shaped relationship with regional environmental regulation at the significance level of 5%, indicating that low level of digital economy development is difficult to promote regional environmental regulation behavior. Meanwhile, it can be seen from the results in column (6) that the development of digital economy can have nonlinear impact on regional green development by improving the level of environmental regulation. The results in columns (2), (4) and (6) of Table 5 show that regional technological innovation, industrial structure and environmental regulation all play a part in the mediating role of digital economy in the process of regional green development.

Table 5

Mechanism Test Results
	(1) ti	(2) gd	(3) is	(4) gd	(5) er	(6) gd
de	0.0104 (0.71)	0.0390^** (1.98)	-0.1814^*** (-4.82)	0.0394^** (1.99)	-0.0524 (-0.88)	0.0390^** (1.98)
de²	-0.0012^* (-1.45)	-0.0025^** (-2.17)	0.0115^*** (5.28)	-0.0025^** (-2.16)	0.0067^** (1.95)	-0.0025^** (-2.17)
ti		0.0437^* (1.45)
is				0.0050^* (1.21)
er						0.0080 (1.05)
CV	Yes	Yes	Yes	Yes	Yes	Yes
R²	0.023	0.004	0.021	0.003	0.046	0.004
N	2232	2232	2232	2232	2232	2232
TFE	Yes	Yes	Yes	Yes	Yes	Yes
RFE	Yes	Yes	Yes	Yes	Yes	Yes
Note: The-t statistic of parameter estimation is in parentheses, *, and*are represented at the 1%, 5%, and 10% significant levels, respectively. TFE is Time Fixed Effecct, RFE is the Regional Fixed Effect.

5.4 Analysis of heterogeneity test

Based on the above discussion, we believe that there may be heterogeneity in the nonlinear impact of digital economy on regional green development. China's regional economic development level differences and resources endowment may be the cause of digital economy influence to the diversity of regional green development, descriptive statistics also can be seen that the regional digital economy development level has the big gap, so this article will divide research samples into eastern, central and western cities. The regression results are shown in columns (1), (2) and (3) of Table 6. The results show that digital economy has the significant nonlinear impact on green development in the eastern cities, while this effect is not significant in the central and western cities, indicating that the impact of digital economy on regional green development is heterogeneous among different cities.

Digital economy is the new type of economy following agricultural economy and industrial economy, the development of digital economy needs high human capital (Zhutong and Ma Guowang, 2022). Developed countries have a high level of human capital, therefore, their digital economy development level is in the forefront of the world. In this paper, the research samples are divided according to the median of regional human capital level. The research samples higher than the median are taken as the sample group of high human capital region, and the research samples lower than the median are taken as the sample group of low human capital region. Regression results are shown in the column (4) (5) of Table 6. The nonlinear effect of digital economy on the green development of high human capital regions is more significant, and the effect on the green development of low human capital regions is not significant. It indicates that the level of human capital has great impact on the process of regional green development influenced by digital economy, and the high level of human capital in a region will promote the role of digital economy.

Table 6

Heterogeneity Test Results
	(1) eastern cities	(2) central cities	(3) western cities	(4) higher human capital cities	(5) lower human capital cities
de	0.0794^*** (2.49)	0.0108 (0.37)	0.0148 (0.19)	0.0811^** (2.43)	0.0127 (0.35)
de²	-0.0048^*** (-2.54)	-0.0009 (-0.56)	-0.0012 (-0.27)	-0.0049^*** (-2.59)	-0.0007 (-0.34)
CV	Yes	Yes	Yes	Yes	Yes
R²	0.017	0.001	0.011	0.020	0.001
N	981	918	333	1116	1116
TFE	Yes	No	Yes	Yes	Yes
RFE	Yes	No	Yes	Yes	Yes
Note: The-t statistic of parameter estimation is in parentheses, *, and*are represented at the 1%, 5%, and 10% significant levels, respectively. TFE is Time Fixed Effecct, RFE is the Regional Fixed Effect.

6.1 Contributions and policy implications

This paper uses the data sample of 248 resource-based prefecture-level cities from 2011 to 2019 to theoretical and empirical study the relationship between the digital economy and regional green development. The main contribution of this paper is to measure the level of regional digital economy and green development, construct the systematic framework to test the impact of digital economy on the regional green development and enrich the existing research. The results indicates that the digital economy can effectively improve the regional green development. Through the empirical regression results of the mediation effect model, we found that digital economy can promote the regional green development by improving the technological innovation, optimizing the industrial structure and environmental regulations. Moreover, the positive effect of digital economy on the regional green development has the regional heterogeneity among different cities, and the effect is more efficient in the eastern cities and higher human capital cities. After a series of robustness tests, the above conclusion still holds.

By combing the conclusions of this paper, the following relevant policy recommendations are obtained. Firstly, while promoting green development, relevant policies and measures must be taken to ensure the stable and efficient development of the digital economy. The government needs to continue to expand the construction of digital infrastructure, ensure the sustainable operation of the industrial structure, and continuously improve the level of regional technological innovation and innovative manufacturing. Secondly, regarding the heterogeneity of digital economic development in various regions. Less developed regions should learn from the experience of developed regions, continuously strengthen regional development advantages, and improve their ability to absorb digital dividends. Finally, the government and relevant functional departments should increase investment in digital governance, and be able to accurately monitor regional economic production, environmental protection, and regional construction, improve government efficiency, and strive to become digital government. At the same time, we must also be alert to potential risks in the development of the digital economy to prevent risk spillovers.

6.2 Weaknesses and future research

Some weaknesses in this paper should be addressed, it can promote the future research.

First, this paper expounds the theoretical logic and influence mechanism of the paper using words, and uses the method of econometrics to verify the theoretical hypothesis. In the next research, this paper believes that it is possible to combine mathematical modeling and use mathematical derivation to carry out logical deductions on the impact of the digital economy on green development, and finally draw mathematical conclusions. Second, this paper expounds the theoretical logic and influence mechanism of the paper using words, and uses the method of econometrics to verify the theoretical hypothesis. In the next research, this paper believes that it is possible to combine mathematical modeling and use mathematical derivation to carry out logical deductions on the impact of the digital economy on green development, and finally draw mathematical conclusions. Third, this paper only considers the two effective paths of technological innovation and industrial structure in examining the content of the mechanism by which the digital economy affects green development. Obviously, in the process of real economic development, the digital economy has many influence paths. Therefore, this paper suggests that relevant mechanism paths, such as resource allocation, environmental regulation level, etc., can be added in the follow-up research. Finally, due to data limitations, this paper only studies the sample range from 2011 to 2019, and there is a time span between the research data and actual economic development. Therefore, in future related research, the research time range can be extended to 2022.

Data availability: Not applicable.

Funding: The research work was supported by Zhejiang Xinmiao Talents Program in China, No.2022R429B032. The title is “Digital Economy and Enterprise Export Product Quality: A Study on the Mediation Effect Based on Heterogeneous Technological Innovation”.

Affiliations

School of Business, Wenzhou University, Wenzhou 325035, China

Shi Chen, Tingting Wu

School of Economics and Management, Hefei University, Hefei 230601, China

Yang Yang

Contributions

Conceptualization, methodology, formal analysis writing and data curation, S.C., rewriting and review, T.W., review and analysis, Y.Y. All authors have read and agreed to the published version of the manuscript. The authors declare no conflict of interest.

Corresponding author

Correspondence to Yang Yang.

Ethical Approval

I would like to declare on behalf of my co-authors that the work described was an original research that has not been published previously and not under consideration for publication elsewhere, in whole or in part.

Consent to Participate

Informed consent was obtained from all individual participants included in the study. Consent to Publish

The participant has consented to the submission of the article research to the journal. Competing Interests

The authors declare no competing interests.

Availability of data and material

Not applicable.

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Digital Economy and Green Development—Based on the Empirical Research on the Resource-based Prefecture-Level City

Status:

Journal Publication

Version 1

Abstract

1. Introduction

2. Literature Review

3. Theoretical Mechanisms And Assumptions

3.1 The digital economy has the positive effect on green development

3.2 Channel analysis of the impact of digital economy on green development

4. Data, Variables And Model

4.1 Data

4.2 Variables

4.2.1 Explained Variable

4.2.2 Core Explanatory Variable

4.2.3 Mediating Variable

4.2.4 Control Variables (CV)

4.3 Model

5. Empirical Test

5.1 Analysis of benchmark regression

5.2 Analysis of robustness test

5.3 Analysis of mechanism test

5.4 Analysis of heterogeneity test

6. Conclusion And Discussion

Declarations

References

Status:

Journal Publication

Version 1