Digital economy and urban green development: A quasi-natural experiment based on national big data comprehensive pilot zone

Abstract

As the engine of economic growth in the modern age, the digital economy plays an indispensable role in boosting the green development of cities. In this research, the difference in difference model and the spatial Durbin difference in difference (SDMDID) model are utilized to empirically examine the influence of digital economy on urban green development by employing the national big data comprehensive pilot zone as a quasi-natural experiment. In addition, its mechanism of action is further investigated from the standpoint of industrial structure upgrading, technical innovation, and human capital. The findings demonstrate that the degree of urban green development has consistently increased over the research sample, and there were clear disparities across areas; the implementation of the national big data comprehensive pilot zone has a significant role in promoting the green development of cities, and the policy effect on central cities and small and medium-sized cities is significantly higher than that of other types of cities; SDMDID model research found that the implementation of the national big data comprehensive pilot zone has a spatial effect; the mechanism test shows that the implementation of the national big data comprehensive pilot zone can promote the improvement of urban green development by promoting the upgrading of industrial structure and improving the level of technological innovation, while the mechanism of human capital has not yet emerged.

Keywords

Digital economy urban green development difference in difference spatial Durbin difference in difference national big data comprehensive pilot zone

Introduction

While the international economy continues to flourish, environmental concerns are also becoming worse.¹ In particular, the enormous production of greenhouse gases has led to the frequent occurrence of numerous severe weather events, presenting a major danger to human existence.² As a consequence, governments throughout the globe are actively working to combat environmental contamination.³ However, the issue of environmental contamination has not been properly tackled.⁴ Therefore, green development has gradually become a generally recognized development paradigm.^5,6

Due to its particular development position, China's economic growth and environmental pollution concerns have garnered great attention.⁷ Over the last 40 years of reform and opening up, China's economy has continued tremendous expansion, becoming the world's second biggest economy.⁸ However, as the economy moves from high-speed growth to high-quality development, China must overcome environmental restraints.⁹ As a consequence, the Chinese government has also taken aggressive attempts to alter the current environmental challenges. For example, during the 2015 Paris Climate Change Conference, the Chinese government explicitly put up a two-constraint aim within the framework of the Paris Agreement: seek to peak total carbon emissions by 2030, and strive to attain the peak ahead of schedule. By 2030, carbon emissions per unit of GDP will be lowered by 60–65% compared to 2005. However, data suggest that China's environmental performance level is poor¹⁰ and that environmental quality varies widely from city to city. Studies have demonstrated that green development is an efficient strategy to promote sustainable development.¹¹ Therefore, measuring the green growth of cities is a critical requirement for improving the ecological environment of cities or regions.¹²

In recent years, information and communication technologies (ICT) have evolved fast over the globe, generating a new economic form, the digital economy.¹³ As a new economic form, the growth of the digital economy in China has continuously developed. According to estimations, the volume of China's digital economy is expanding, from 2.7 trillion yuan in 2005 to 45.5 trillion yuan in 2021, and the share of digital economy in GDP is also increasing, with a proportion of 39.8% in 2021. It can be observed that the importance of the development of the digital economy to the present economic boom cannot be overlooked. China is efficiently promoting the comprehensive development of digital industrialization and industrial digitalization, promoting the deep integration of the digital economy and the national economic industry, vigorously improving the economic efficiency of the industry, and empowering the green development of the economy.¹⁴ With the vigorous development of the digital economy, data has become a new factor of production, which is not only a basic resource and strategic resource, but also a new driving force for modern economic development, so the world's major industrialized countries have successively launched big data development strategies. For example, the Federal Data Strategy and 2020 Action Plan produced by the United States in 2019 contains the main purpose of strategic development of data. In 2020, the European Union launched its EU Data Strategy, which seeks to become the world's most dynamic data-agile economy. The Chinese government also puts considerable emphasis to the growth of the big data sector. In 2014, big data was first written into the government work report, and in 2015, the State Council issued the Action Plan for Promoting the Development of Big Data, which comprehensively deployed big data as an important national strategy and proposed to carry out the construction of a national big data comprehensive pilot zone. In 2020, the State Council released the Opinions on Building a More Perfect Market-based Allocation System and Mechanism for Factor Factors, which legally designated the data as a factor of production.

Deeply digging the value of data pieces is a precondition for completely unleashing the digital economy to boost economic and social progress. In order to expedite the deployment of big data and deepen the application of big data, through continuous practice, a set of big data industry development experience that can be pushed and duplicated across the nation is described to accomplish digital development and green development. In 2015, Guizhou officially inaugurated the building of big data pilot zones, followed by the approval of the construction of the second batch of big data pilot zones, comprising Beijing, Tianjin, Hebei, Shanghai, Chongqing, Inner Mongolia, Guangdong, Henan and Shenyang. The big data pilot zone has done a lot of discoveries in data resource management and sharing, data center integration, data resource application and so on. The implementation of the big data pilot zone is not only to grow the big data sector itself, but also to further research and exploit data components and deepen the interaction of the digital economy and the real economy. Therefore, under the background of the implementation strategy of the big data pilot zone, the big data-related industries in various regions have developed rapidly, and the role of data elements in economic development has also been fully realized, which also provides a theoretical basis for this paper to evaluate the impact of the implementation of the big data experimental zone on the improvement of the level of green development.

Since the digital economy has the essential characteristics of dataization, networking, intelligence, and sharing, it is conducive to enterprises to improve the technical complexity of export products and deepen the degree of embedding in the global value chain in the transformation of green production methods, which not only promotes the transformation of enterprise green development, but also improves the resource and energy utilization efficiency of the city where the enterprise is located, and provides a micro foundation for urban green development. In the improvement of green supervision efficiency, the digital economy mainly relies on the application of digital technologies such as big data, cloud computing, artificial intelligence, and remote sensing to enable the government to conduct real-time dynamic monitoring of environmental data such as environmental quality, pollution discharge, river water quality, and environmental carrying capacity, which not only improves the government's supervision level of resources and environment, but also provides support for the transformation of urban green development. The digital economy has spawned new industries, new formats, and new models such as the sharing economy, telemedicine, online office, online education, and platform economy, effectively aggregating fragmented demand and supply information, and accelerating product matching and trading. This will reduce the search costs of enterprises and users caused by information asymmetry, greatly improve the efficiency of economic operation, and then promote the development of urban green economy.

The digital economy penetrates all parts of production, distribution and sales, and may rebuild the percentage of factor input, decrease resource mismatch, optimize industrial structure upgrading, etc., and is an essential support for green development. So, as a new driving force for economic growth, can the digital economy help the green development of cities? Through what method does it affect the green development of the city? The novelty of this study is largely evident in the following three aspects: Firstly, based on the actual evidence at the city level, the impact of the national big data comprehensive pilot zone on urban green development is systematically investigated for the first time, which provides new evidence and useful supplement for the research on sustainable urban development. Furthermore, by cleverly using the exogenous impact of the national big data comprehensive pilot zone as a quasi-natural experiment, it solved the problem of variable endogenous and data measurement, and provided new empirical ideas for related research. Finally, explain the impact mechanism of the national big data comprehensive pilot zone on urban green development from a theoretical level, and further quantitatively analyze its impact mechanism through the mediation effect model.

The structure of this paper is as follows: the second part is related to the literature review and policy background, the third part is the study design, the fourth part discusses the research results, and finally the research conclusions and policy implications. The specific technology roadmap is shown in Figure 1.

Literature review

The notion of green development is acknowledged and presented as a progressive process. With the growth of human consciousness of environmental preservation, the academic community has a varied perception of the meaning of green development. The United Nations Commission for Economic and Social Affairs for Asia and the Pacific (UNESCAP) has argued that green development is a requirement for a green economy created in the framework of sustainable development and poverty reduction, and is also a strategy to achieve sustainable development. Graedel et al.¹⁵ and Motloch et al.¹⁶ extended green development to the industrial sector, explaining the meaning of green development in the industrial sector.

Now, the current research on green development largely focuses on the measurement of green development and the contributing variables. Geospatially speaking, green development calculations include the measurement of green development in various countries around the world,^17,18 there are also estimates of green development in a country and its internal regions.^19,20 With the depth of study, the measurement of green development is further covered by industry, manufacturing, and service sectors.^21,22 Watanabe and Tanaka²³ analyzed the extent of green transformation of China's industrial sector based on green total factor productivity (TFP). Honma and Hu²⁴ employed stochastic frontier analysis to estimate the efficiency of industrial green development. There are two basic approaches to quantify green development: First, the amount of green development is measured by accounting for the green national economic index. For example, the OECD analyzes the degree of green development by establishing an indicator system that incorporates variables such as economics, environment, and human well-being. The second is to measure the green development index by establishing a multi-index measuring system. For example, Arcelus and Arocena²⁵ used the DEA model to measure the green production efficiency of each OECD country, and further studied the impact of four strategies on the green productivity of countries in minimizing pollutant emissions, maximizing expected output, increasing expected output while reducing pollutant emissions, and completely ignoring undesirable output. Nahman et al.²⁶ estimated and compared the degree of green economic development in numerous nations across the globe by creating a multi-dimensional and multi-indicator comprehensive indicator system of green economy performance.

In terms of influencing variables, previous research has established the influence of human capital, environmental legislation, international commerce, technical innovation, industrial structure, etc. on green development.^27–29 Walz et al.³⁰ believes that technical innovation is a vital aspect in green development and reaching sustainable objectives. Shironitta³¹ evaluates the influence of the industrial structure of 40 nations on the green development of each country. The research concluded that improving the industrial structure and making the industrial system more green would enhance the status of green development. The study outcomes of Liu et al.⁴ suggest that the introduction of high-speed rail has an essential function in encouraging the green growth of cities. Similarly, Gao et al.⁸ utilized China's 2003–2019 city panel data to study the influence of research and technology funding policies on urban green development. Shi et al.³² based on panel data at the city level in China, the influence of the implementation of the national civilized city strategy on the green growth of prefecture-level cities in China was analyzed.

In recent years, owing to the fast growth outcomes of the development of the digital economy, it has drawn considerable attention from the academic community. Tapscott³³ initially suggested the notion of the digital economy, pointing out that the digital economy is an economic system that makes considerable use of ICT. Some international organizations, government agencies, and academicians have consistently advocated and enhanced the concept of the digital economy. Different from conventional economies such as agricultural economy and industrial economy, the digital economy as an interconnected economy cannot be neglected in directing the role of green development. Hampton et al.³⁴ argues that depending on big data and cloud computing may successfully increase the efficiency of environmental regulation. Johansson et al.³⁵ indicates that the Internet may raise awareness of environmental protection and environmental monitoring by broadening the channels for citizens to engage in environmental protection activities. However, the digital economy not only enhances the efficiency of environmental control via information technology, but also produces an energy rebound effect through scale development, which exacerbates pollutant emissions. The results of Wang and Cao³⁶ imply that the expansion of the digital economy is non-linearly connected to the TFP, whereas the energy rebound impact of the digital economy increases carbon emissions.

Combining through the aforementioned literature, it can be noted that most of the studies examine green development calculations, influencing variables, and the influence of the digital economy on the environment. However, there are still the following inadequacies in the study on the growth of the digital economy and the green development of the city: First, since the existing measuring techniques for the digital economy are not mature, there are huge disparities in the calculation results acquired by various measurement methods, which will also have an influence on the study outcomes. Second, the literature employing proxy variables for empirical research unavoidably confronts inherent issues and estimate biases.^37,38 Therefore, in order to make up for the foregoing shortcomings, this research intends to explore whether the implementation of the national big data comprehensive pilot zone substantially improves the green growth of cities. Using the implementation of the national big data comprehensive pilot zone as a quasi-natural experimental condition, the difference in difference (DID) model is used to study the policy effect and transmission mechanism of the national big data comprehensive pilot zone on urban green development, with a view to providing empirical evidence of relevant policies for the realization of urban sustainable development goals.

Research design

Measure the level of green development

The measuring of green development level in present research is largely separated into two types: index system evaluation and efficiency measurement. Based on the study of the connotation of green development in the previous article, this work proposes the design of a multi-dimensional index system to thoroughly and correctly quantify the degree of urban green development. Therefore, according to the criteria of rationality, scientificity and data availability of the index system, this research designs a complete assessment index system for green development from the three levels of green production, green ecosystem, and green life, as indicated in Table 1.

Table 1.

Comprehensive evaluation index system for green development.

Index I	Index II	Unit
Green production	Real GDP per capita	Meta/person
	Industrial value added/GDP	%
	Tertiary industry growth value/GDP	%
	Average salary level	Ten thousand yuan
	Wastewater discharge per unit of GDP	t/ ten thousand yuan
	Sulfur dioxide emissions per unit of GDP	t/ ten thousand yuan
	Soot emissions per unit of GDP	t/ ten thousand yuan
	Total electricity consumption per unit of GDP	kw•h/ ten thousand yuan
	Total water consumption per unit of GDP	m³/ ten thousand yuan
Green ecology	Domestic sewage treatment rate	%
	Harmless treatment rate of domestic waste	%
	Comprehensive utilization rate of industrial solid waste	%
	Green area per capita	Hectares/person
Green life	Electricity per capita	kWh/person
	Water per capita	m³/ person
	Public transport passenger traffic per 10,000 population in cities and towns	10,000 people/time
	Green coverage rate in built-up areas	%

Since the complete evaluation index system of green development contains numerous indicators, it is required to define the weight of the indicators and examine the data cutting dimensions. Compared to the principal component analysis technique, the index weight calculated by the entropy approach minimizes the interference that is believed to be subjective and has the qualities of objective and accurate. Therefore, this work leans on the Yu,³⁹ Shao and Wang⁴⁰ technique to pick the entropy value method to allocate the index weight of green growth. The particular measuring steps correspond to Shao and Wang⁴⁰ practice.

Empirical model

In order to evaluate whether the digital economy with data components as the core may raise the degree of urban green development, this article treats the national big data comprehensive pilot zone as a quasi-natural experiment. The selected study samples were 239 cities at the prefecture level and above in China, of which 58 cities at the prefecture level and above under the jurisdiction of five provinces including Guizhou, Hebei, Guangdong, Henan, and Inner Mongolia in the experimental area, as well as Beijing, Shanghai, Tianjin, Chongqing, and Shenyang, were included in the experimental group, and the rest were included in the control group. The policy time node of the pilot zone was selected in 2016, mainly because although the Action Plan for Promoting the Development of Big Data issued by the State Council in August 2015 clearly proposed to carry out regional pilots and promote the construction of big data comprehensive pilot zones such as Guizhou, in February 2016, the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the Central Cyberspace Administration sent a letter to approve the construction of the pilot zone in Guizhou Province, and in October of the same year, the construction of the second batch of pilot zones was agreed. Therefore, this document sets the policy time node universally as 2016.

DID offers a suitable approach for analyzing policy impacts by recording relative differences between treatment and control groups before and after policy changes to control the effects of variables other than policy interventions.^38,41 This research employs the DID approach to use the national big data comprehensive pilot zone as the foundation for natural experiments, and empirically examines the influence of the policy on the degree of urban green development. The datum model is as follows:

U G D_{i t} = β_{0} + β_{1} N B D_{i t} + \sum β_{j} \times X_{i t} + γ_{i} + μ_{t} + ε_{i t}

(1)

Expressed in equation (1), i and t represent the city and year; UGD represents urban green development; NBD _it = Treat _i × Time _t , Treat _i represents whether it belongs to the city of environmental information disclosure, the value of 1 indicates that it belongs to the city of national big data comprehensive experimental zone, and the value of 0 indicates that it does not belong; Time _t is a time dummy variable, with a value of 1 indicating that the city in the treatment group has conducted national big data comprehensive experimental zone in that year, and a value of 0 indicating that the implementation of the national-level big data comprehensive pilot area has not yet been carried out; X indicates a set of control variables;

γ

and

μ

represent the control of individual fixed effect and time fixed effect, respectively. ξ_it represents the residual term.

Considering that the influence of the national big data comprehensive pilot zone on the degree of urban green development may have a geographic effect. Therefore, this article employs spatial Durbin difference in difference (SDMDID) model to further examine the policy impact of the national big data comprehensive pilot zone on urban green growth, and the econometric model is as follows:

U G D_{i t} = α_{0} + ρ_{1} \sum W_{i j} U G D_{i t} + α_{1} N B D_{i t} + ρ_{2} \sum W_{i j} N B D_{i t} + \sum α_{ω} \times X_{i t} + γ_{i} + μ_{t} + ε_{i t}

(2)

Where, W is the spatial weight matrix. In this paper, the geographic distance spatial weight matrix is used in the benchmark regression, and the economic distance spatial weight matrix is used for the robustness test; ρ is the spatial spillover coefficient; the meaning of the rest of the variables is as the same as equation (1).

Variables

The dependent variable in this article is urban green development, called UGD. The research considers the national big data comprehensive experimental zone built in 2016 as a quasi-natural experiment. The dummy variable Treat _i indicates if the city is in the treatment group, and the dummy variable Time _t indicates whether the city in the treatment group has implemented national big data comprehensive experimental zone in that year. Therefore, the cross-item Treat _i × Time _t is the primary explanatory variable of this work. While minimizing the impact of missing variables on model estimates, this paper uses the level of urban economic development (PGDP), financial development (FI), government intervention (GI), urbanization (UR), and population density (DP) as control variables based on existing research articles.^{8,13,32,42,43} Among these, the degree of urban economic development is assessed by the natural logarithm of the city's per capita GDP; the level of financial development is expressed by the ratio of the balance of deposits and loans of financial institutions to GDP at the end of the year; government intervention is expressed as a ratio of fiscal expenditure to GDP; urbanization is replaced by the proportion of urban population in the total urban population; population density takes the number of inhabitants per unit of land area of the city to take the natural logarithm.

Descriptive statistics

Table 2 outlines the data characteristics of each variable, and in order to tackle the issue of heteroscedasticity, the data processing usually uses the approach of taking natural logarithms and ratios. From Table 2, it can be seen that the average value of green development is only 1.7812, while the highest value is 2.0047 and the smallest value is only 1.0045, showing that the amount of green development across cities varies substantially.

Table 2.

Descriptive statistics of the main variables.

Variable	Mean	Max	Min	Std. Dev.	P25	P50	P75	P95	Obs.
UGD	1.7812	2.0047	1.0045	0.1765	1.6918	1.8328	1.9111	1.9646	2868
PGDP	10.5751	13.1758	7.5537	0.7547	10.0848	10.5480	11.0478	11.8639	2868
FI	2.4673	9.0916	0.0230	1.1981	1.6363	2.1379	2.9793	4.9350	2868
GI	0.1801	1.5000	0.0113	0.0983	0.1180	0.1582	0.2187	0.3611	2868
UR	0.3691	1.0000	0.0436	0.2367	0.1880	0.3039	0.4835	0.9859	2868
DP	5.7916	9.0857	1.6034	0.9312	5.2414	5.9603	6.4971	6.9242	2868

Results

Measurement results of green development level

In this research, the complete evaluation index system and entropy technique created above are utilized to quantify the level of green development, and Figure 2 provides the average change trend of green development level at the urban level in China from 2009 to 2020. As can be observed from Figure 2, the total level of urban green development is constantly rising, from 1.5535 in 2009 to 1.8622 in 2020, an increase of almost 20%. This shows that in the post-financial crisis era, China is practicing the path of green and sustainable development, and the government has strengthened the responsibility of local governments for environmental management by setting binding targets for energy conservation and emission reduction, and establishing a target responsibility system. The policy guidelines and the execution of the zero emission objective in production and living have fostered the improvement of the level of urban green development. From the viewpoint of several areas, the eastern region has the greatest degree of urban green development, followed by the central region and the lowest in the western region. The reason is that the center and west areas lag behind the east regions in terms of economic development level, technical innovation, and industrial structure. Moreover, the growth of the center and west areas is based on traditional industries that use a huge quantity of fossil fuels, while the east regions are predominantly based on the developed tertiary sector, which has a low need for energy consumption.⁴⁴

In order to further observe the difference between the green development level of cities in the national big data comprehensive pilot zone and the green development level of the cities in the non-national big data comprehensive pilot zone, Figure 3 of this paper reports the average change trend of the green development level of the two groups of cities. From Figure 3, it can be seen that whether it is the experimental group city or the control group city, the green development level shows a constant increasing trend, which is consistent with the changing trend of the overall green development level. However, after 2016, the green development level of the experimental group cities was much greater than that of the control group. It can be observed that the difference between the green development level of the experimental group cities and the control group has expanded since 2016, which suggests to a certain degree that the national big data comprehensive pilot zone policy has a certain function in encouraging urban green development. However, in order to further establish the causal link between the two, empirical research is still required. Existing studies have shown that large cities have a pronounced agglomeration effect,⁴⁵ which not only optimizes factor structures and reduces the marginal cost of public investment, but also leads to large city diseases such as energy consumption and resource shortages.³⁷ Therefore, according to the Notice on Adjusting the City Size released by the State Council in 2014, this article classifies the sample cities into small and medium-sized cities and big cities.⁴⁰ From Figure 4, it can be seen that the green development level of both large cities and small and medium-sized cities showed a steady upward trend, which was consistent with the overall green development level, but the green development level of large cities during the sample period studied was higher than that of small and medium-sized cities. The possible reason is that large cities have a scale effect that small and medium-sized cities do not have, and the infrastructure of large cities is perfect, and a large number of talents are gathered, and the level of technological innovation is high, thus promoting the level of urban green development.

Parallel trend test

An important premise for policy evaluation using difference in difference model is that the treatment and control groups are inherently different in addition to their own differences before they are subjected to policy shocks. Therefore, this paper refers to the practice of Jacobson et al.⁴⁶ to conduct a parallel trend test based on event research, and the estimation formula is as follows:

U G D_{i t} = β_{0} + \sum_{k = - 7}^{4} δ_{k} D_{i t 0 + k} + \sum β_{j} \times X_{i t} + γ_{i} + μ_{t} + ε_{i t}

(3)

Expressed in equation (3), D is a dummy variable, indicating the k-th year of implementation of the national big data comprehensive pilot zone;

δ

reflects the differences in urban green development between the treatment group and the control group in the k-th year of the national big data comprehensive pilot zone. Due to the selection of the study sample interval, the maximum value of k is taken as 4 and the minimum value is taken as −7.

The results of the parallel trend test are shown in Figure 5, which shows that before the implementation of the national big data comprehensive pilot zone, there is no significant difference between the urban green development level of the experimental group and the control group, and after the implementation of the pilot policy, the national big data comprehensive pilot zone obviously promotes the improvement of the urban green development level. This indicates that before the implementation of the national big data comprehensive pilot zone policy, the precondition of parallel trend is basically satisfied between the experimental group and the control group, that is, it is reasonable for this paper to adopt the DID model for empirical testing.

Benchmark model regression

Table 3 provides the results of a policy test of the DID model with urban green development as the interpreted variable, in which column (1) does not contain the control variable, and then progressively includes the control variable. From the regression findings, it can be observed that the implementation of the national big data comprehensive pilot zone has a major influence in encouraging the green growth of the city, regardless of whether the control variables are incorporated or not. This shows that the implementation of the national big data comprehensive pilot zone has accelerated the integration of digital technology represented by big data and artificial intelligence with traditional industries, and promoted the gradual transformation of traditional industries into industrial digitalization, intelligence, and green fields.⁴⁷ In addition, the establishment of the national big data comprehensive pilot zone has promoted the agglomeration of innovative elements such as high-end talents, high-tech enterprises, and research and development capital, thereby improving the city's green innovation level and ultimately improving the city's green development level. Judging from the findings of the control variables, the level of urban economic growth and financial development has considerably boosted the level of urban green development. Urbanization has considerably hindered the level of urban green development, whereas the influence of government action and population density on urban green development is not clear.

Table 3.

Benchmark regression results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
NBD	0.119*** (0.010)	0.098*** (0.010)	0.056*** (0.010)	0.056*** (0.010)	0.060*** (0.010)	0.059*** (0.010)
PGDP		0.097*** (0.006)	0.101*** (0.005)	0.100*** (0.005)	0.117*** (0.006)	0.117*** (0.006)
FI			0.051*** (0.003)	0.049*** (0.003)	0.056*** (0,.003)	0.056*** (0.003)
GI				0.030 (0.036)	0.018 (0.036)	0.032 (0.037)
UR					−0.190*** (0.027)	−0.196*** (0.028)
DP						0.012* (0.007)
_cons	1.769*** (0.007)	0.749*** (0.059)	0.584*** (0.058)	0.594*** (0.058)	0.464*** (0.061)	0.398*** (0.072)
Observations	2868	2868	2868	2868	2868	2868
R²	0.0507	0.1230	0.1260	0.1255	0.1533	0.1602

Note: ***p < 0.01, **p < 0.05, *p < 0.1.

Placebo test

The comparability between the control group and the experimental group is a hypothetical prerequisite for the analysis of the impact of the national big data comprehensive pilot zone on green development by using the DID method, and if there is no established fact that the national big data comprehensive pilot zone does not exist, the difference in green development level between the experimental group and the control group does not change with time. Therefore, on the basis of referring to Yang et al.⁴⁸ practice, this paper conducts 1000 sampling times in all 239 prefecture-level cities, and randomly selects 58 cities as virtual experimental groups and the remaining cities as control groups each time to participate in the regression analysis of the original model with the changed policy variables as the control group, and verifies the policy effect through the comparison of regression results. It can be observed from Figure 6 that the absolute value of the t-value of most sample estimate coefficients is within 2 and the p-value is above 0.1, suggesting that the environmental information disclosure has no significant influence in these 1000 random samplings. This demonstrates that the benchmark regression findings are resilient.

PSM-DID test

Another condition for adopting the DID approach is to satisfy that the selection of experimental and control groups is random. Although conceptually, the implementation of the national big data comprehensive pilot zone does not appear to be influenced by local green development, it is required to undertake a robustness test based on actual findings. Therefore, this article employs control variables as covariates, and uses one-to-one neighbor matching approach to match samples from experimental and control groups, so easing the issue of selectivity bias. The efficacy of the PSM approach depends on the equilibrium assumption that there is no substantial difference between the sample matching characteristic variables of the processing group and the control group. Therefore, the next article will test whether the pair's propensity score matching will make the variables balanced in the distribution of the processing group and the control group. From the test findings in Table 4, it can be observed that after matching, the mean of each covariate does not change substantially between the experimental group and the control group. At the same time, it can be intuitively observed from Figure 7 that the standardized deviation of most variables is decreased after matching. This illustrates that the propensity matching scoring system may successfully eliminate the probable endogenous and selection bias concerns. From the regression findings in Table 5, it is known that the symbols of the key explanatory variables are compatible with the results of the benchmark regression. In summary, despite taking into account the selective bias, the implementation of the national big data comprehensive pilot zone still has a major contribution to green development, which emphasizes the robustness of the empirical conclusions of this research.

Table 4.

Common support test of PSM-DID method.

Variables	Sample	Mean		t	p
Variables	Sample	Treatment	Control	t	p
PGDP	Unmatched	10.799	10.55	5.35	0.000
PGDP	Matched	10.795	10.762	0.46	0.644
FI	Unmatched	2.9405	2.4141	7.16	0.000
FI	Matched	2.931	3.0134	−0.69	0.491
GI	Unmatched	0.1903	0.1790	1.86	0.063
GI	Matched	0.1905	0.1982	−0.94	0.346
UR	Unmatched	0.4293	0.3623	4.59	0.000
UR	Matched	0.4273	0.4091	0.77	0.442
DP	Unmatched	6.1376	5.7528	6.72	0.000
DP	Matched	6.1274	6.0476	0.97	0.333

Table 5.

PSM-DID regression results.

Variable	(1)	(2)
NBD	0.108*** (0.014)	0.094*** (0.013)
PGDP		0.035*** (0.009)
FI		0.032*** (0.006)
GI		−0.081 (0.073)
UR		−0.101** (0.040)
DP		0.004 (0.010)
_cons	1.794*** (0.010)	1.367*** (0.116)
Observations	2256	2256
R²	0.1490	0.2118

Note: ***p < 0.01, **p < 0.05, *p < 0.1.

Robustness test

In order to obtain more robust empirical results, this paper conducts robustness tests from the following aspects: Firstly, replacing the explanatory variables, we use green TFP to represent the level of urban green development. Secondly, due to the uniqueness of the four municipalities, Beijing, Tianjin, Shanghai, and Chongqing are excluded from the overall sample, and the remaining sample size is re-substituted into the model for regression testing.⁴⁹ Thirdly, excluding other policy interference. During the implementation of the national big data comprehensive pilot zone, there are other city policies that will have an impact on the green development of the city and affect the accuracy of the conclusions of this paper. Therefore, this paper controls for other policies that affect the level of urban green development, including the smart city policy and the broadband China strategy. Specifically, this paper adds to equation (1) the two policy dummy variables described above, with the dummy variables set so that cities are scored as 1 in the year of policy implementation and thereafter, and 0 otherwise. The results of the above robustness test reveal that the signs of the core explanatory variable have not significantly changed, indicating that our research conclusions have desirable robustness (as shown in Table 6).

Table 6.

Estimation results of robustness test.

Variable	(1)	(2)	(3)
NBD	0.006*** (0.001)	0.062*** (0.010)	0.051*** (0.010)
Control variables	Yes	Yes	Yes
Other policies			Yes
_cons	−1.290*** (0.089)	0.375*** (0.073)	0.552*** (7.28)
Observations	2629	2820	2868
R²	0.1037	0.1645	0.2525

Note: ***p < 0.01, **p < 0.05, *p < 0.1.

Heterogeneity analysis

Due to the diverse geographies and urban sizes of cities, the influence of the formation of national big data comprehensive pilot zones on urban green development may vary substantially across different cities. Therefore, this article will split the size and scale of the city from which the city is situated, and examine the influence of the formation of the national big data comprehensive pilot zone on the green growth of the city. First of all, this article follows the division approach established in the 7th Five-Year Plan. Divide the 239 cities into three primary economic zones, namely the eastern, central, and western sectors. Secondly, the Notice on Adjusting the Scale of Cities published by the State Council in 2014 separated the sample cities into small and medium-sized cities and major cities.

From the return results of Table 7, it can be seen that the establishment of the national big data comprehensive pilot zone has a significant role in promoting the green development of the city, whether it is the division of the city or the type of city divided by the city size, but the policy effect of the central city and the small and medium-sized city is significantly higher than that of other types of cities. The possible reason for this is that the green development level of eastern cities and large cities is relatively high, and the marginal contribution of the policy effect set up by the national big data comprehensive pilot zone is small, while the western city is limited by its own economic development level, infrastructure, and other conditions, so it has not fully played the policy effect of the establishment of the national big data comprehensive pilot zone.

Table 7.

Heterogeneity test results.

Variable	East city	Middle city	West city	Big city	Medium and small city
NBD	0.050*** (0.019)	0.077*** (0.015)	0.044*** (0.016)	0.042*** (0.013)	0.063*** (0.013)
Control variables	Yes	Yes	Yes	Yes	Yes
_cons	0.620*** (0.129)	0.276** (0.122)	0.251** (0.121)	0.186 (0.128)	0.358*** (0.090)
Observations	720	960	1188	888	1980
R²	0.0862	0.2350	0.1952	0.1946	0.1525

Note: ***p < 0.01, **p < 0.05, *p < 0.1.

Spatial spillover effect test

The previous study demonstrates that the implementation of the national big data comprehensive pilot zone has a key role in encouraging the green growth of cities. However, the underlying assumption of the DID model is that no person would be impacted by the treatment of other people, hence the disregard of spatial correlations with the study individual might lead to bias in the estimate findings. The main difference between spatial econometrics and traditional economic economics is that spatial econometrics takes into account the spatial correlation between variables and the investigation of spatial heterogeneity, and reflects the spatial structure characteristics of variables in econometrics. Based on this, this article utilizes the SDMDID model to examine the influence of the implementation of the national big data comprehensive pilot zone on the green development of the local and territorial regions.

Spatial autocorrelation indicates the relationships or correlations that exist between entities. The test technique is essentially spatial autocorrelation test, and the above test is mostly based on Moran's I statistic, Geary C statistic, etc. However, Moran's I statistic is commonly used, and the value range of Moran's I is [−1, 1]. If its value is greater than 0, it indicates that the research samples have similar attribute values in the spatial location distribution; if its value is less than 0, it indicates that the research samples have dissimilar attribute values in the spatial location distribution; if its value is equal to 0, then it shows that the study samples are independent of each other in the spatial location distribution. From the spatial autocorrelation test findings in Table 8, Moran's I is less than 0, which preliminarily proves that there is a geographical negative correlation between UGD.

Table 8.

Spatial test.

Year	Moran's I	z-value
2009	−0.059	−22.804
2010	−0.040	−14.997
2011	−0.020	−6.415
2012	−0.017	−5.526
2013	−0.017	−5.193
2014	−0.018	−5.674
2015	−0.019	−6.059
2016	−0.020	−6.601
2017	−0.022	−7.592
2018	−0.037	−13.704
2019	−0.037	−13.703
2020	−0.022	−7.627

To examine whether the SDMDID model degenerates into spatial autoregressive difference in difference and spatial error difference in difference model. According to the test idea of Elhorst,⁵⁰ a simplified test of the spatial econometric model is carried out. Table 9 reports the specific test results. Both the Wald test and the LR test reject the null hypothesis, that is, the SDMDID model is appropriate. Combined with the results of the Hausman test, this paper uses the time and space fixed effects model for estimation. At the same time, considering that there is a certain endogeneity problem in the spatial autoregressive model, if the OLS estimation is used, the estimation results will be biased to a certain extent. Therefore, this paper selects the maximum likelihood method to estimate the SDMDID model.

Table 9.

SDMDID model suitability test.

Test type	Statistical value	p-value
Wald_ spatial_ lag	13.69	0.0333
Wald_ spatial_ error	14.49	0.0246
LR_ spatial_ lag	13.99	0.0297
LR_ spatial_ error	14.40	0.0255
Hausman Test	21.35	0.0016

The first two columns of Table 10 give the regression results with spatial weights at geographical distance, while the final two columns reflect the regression results with the spatial weights matrix of economic distances. The first two regression findings demonstrate that the implementation of the national big data comprehensive pilot zone has a beneficial influence on urban green development, which is consistent with the prior study conclusions. In addition, the coefficient of the spatial lag term of the national big data comprehensive pilot zone was significantly negative at the level of 1%, indicating that although the implementation of the national big data comprehensive pilot zone has a promoting effect on local green development, there is a significant negative spatial spillover effect. The main reason is that due to the local implementation of the national big data comprehensive pilot zone, it attracts the influx of talents from the surrounding areas, which has a certain siphon effect on the surrounding areas, so it inhibits the green development of neighboring areas to a certain extent. However, the regression findings of the geographical weight matrix at the economic distance demonstrate that the implementation of the national big data comprehensive pilot zone not only has a stimulating influence on local green development, but also has a strong positive spatial spillover effect. That is, it shows that cities with similar economic development have positive spatial spillover effects, which may be explained that cities with close economic ties have certain similarities in terms of technological level, infrastructure, industrial structure, etc., and usually have a series of cooperation between the two. Thus, when one of the cities provides green impacts via the implementation of the national big data comprehensive pilot zone, the close collaboration between the two has generated a certain good spatial effect.

Table 10.

SDMDID model estimation results.

Variable	Geographical matrix	Geographical matrix	Economic matrix	Economic matrix
NBD	0.009** (2.01)	0.010** (2.19)	0.005** (2.50)	0.018** (2.54)
$W N B D$	−0.179*** (2.74)	−0.324*** (−3.43)	0.128*** (−3.80)	0.001*** (−3.27)
Control variables	No	Yes	No	Yes
Spatial-rho	−2.065*** (−8.08)	−1.770*** (−6.05)	0.048** (−2.33)	0.047** (2.30)
sigma2-e		0.010*** (37.15)		0.011*** (37.46)
Observations	2808	2808	2808	2808
R²	0.1154	0.0368	0.1002	0.0327

Note: ***p < 0.01, **p < 0.05, *p < 0.1.

Mechanism analysis

The implementation of the national big data comprehensive pilot zone is a physical expression of the robust growth of the digital economy, and its essential purpose is to realize the sharing and openness of data and encourage the use of data resources. The empirical research above reveals that the implementation of the national big data comprehensive pilot zone has benefited the green growth of the city. So, what is the conduction mechanism involved? According to current research, the impact route of green development generally comprises structural impacts, technological effects, etc.^8,27 The fast expansion of the digital economy has necessitated the upgrading of industrial structure.⁵¹

First of all, the new formats of electronic information manufacturing and software service industry derived from the application of digital technologies such as 5G big data and cloud computing have accelerated the pace of transformation of traditional manufacturing to high-end, thus boosting the upgrading of industrial structure. Secondly, the development of the digital economy is conducive to accelerating the rational allocation of resource elements and the collaborative division of labor between industries, and gradually eliminating traditional industries with high energy consumption, high emissions, and high pollution through industrial association, industrial integration, and industrial innovation, which is conducive to the upgrading of industrial structure. At the same time, the structural dividend brought about by the upgrading of the industrial structure supports economic growth while improving the environmental quality, therefore raising the degree of green development. In the digital economy dominated by the Internet, cloud computing and big data, it has technical features itself, and the scale impact and diffusion effect of the digital economy have contributed to the enhancement of the degree of technological innovation. Technological advances have led to the creation of a spectrum of low-carbon technologies and technological spillovers, which have greatly decreased production redundancy and enhanced environmental impact.⁵² Anderson⁵³ study indicates the beneficial influence of technical innovation in decreasing environmental pollution. The digital economy broadens information dissemination channels, improves the speed of information acquisition, strengthens regional knowledge spillover, accelerates the accumulation of human capital, cultivates the quality of human capital, and provides intellectual support for the improvement of green development. With the steady improvement of the level of human capital, the physical capital of high technology matches the high-level labor force, which encourages the increase of productivity and hence the degree of green development. In other words, the ongoing rise in the quantity and quality of human capital has inspired a new round of ICT, thereby producing a positive cycle that will finally accomplish the objective of green and sustainable urban development. Therefore, this article explores the method of the execution of the national big data comprehensive pilot zone on urban green development from the three perspectives of industrial structure upgrading, technology innovation, and human capital. Based on the available research,⁵⁴ the following mediation model is set:

M E_{i t} = α_{0} + α_{1} N B D_{i t} + \sum α_{j} \times X_{i t} + γ_{i} + μ_{t} + ε_{i t}

(4)

U G D_{i t} = β_{0} + β_{1} N B D_{i t} + β_{2} M E_{i t} + \sum β_{j} \times X_{i t} + γ_{i} + μ_{t} + ε_{i t}

(5)

Among them, ME _it represents the mechanism variables, namely industrial structure upgrading (ST), technological innovation (TI), and human capital (HC). Industrial structure upgrading adopts the industrial structure upgrading index measure⁴³; technological innovation adopts the city's scientific research, technical services, and geological exploration practitioners⁵⁵; human capital is measured by the average college student.⁵⁶ Other variables have the same meaning as the equation (1).

Table 11 summarizes the findings of the impact mechanism test estimations. From the estimation results of the first three columns, it can be seen that the estimated coefficient of the national big data comprehensive pilot zone is significantly positive at the level of 5% and above, indicating that the national big data comprehensive pilot zone has a significant role in promoting industrial structure upgrading, technological innovation, and human capital. From the last three column results, it can be seen that after the benchmark regression model added the industrial structure upgrading and technological innovation of intermediary variables, although the significance of the regression coefficient of the policy impact variable did not change significantly, the absolute value of the coefficient decreased. It can be observed that the upgrading of industrial structure and technological innovation have certain intermediate impacts in the process of implementing the national big data comprehensive pilot zone to influence the green growth of cities. However, once the reference regression model introduced the intermediate variable human capital, the regression coefficient of the policy effect variable for urban green development did not change in terms of significance or coefficient size. It can be observed that this demonstrates that the implementation of the national big data comprehensive pilot zone would not hinder the green growth of the city via the human capital route. One probable explanation is that the enhancement of human capital is a long-term undertaking and will not alter dramatically in the near term. Therefore, for now, the implementation of the national big data comprehensive pilot zone has not yet had a substantial influence on urban green development via the human capital impact route.

Table 11.

Mechanism test.

Variables	ST	TI	HC	UGD	UGD	UGD
NBD	0.053*** (0.005)	0.599*** (0.098)	0.087*** (0.019)	0.040*** (0.010)	0.031*** (0.010)	0.059*** (0.010)
ST				0.354*** (0.035)
TI					0.006*** (0.001)
HC						0.001 (0.005)
_cons	1.599*** (0.041)	−8.430*** (1.057)	6.869*** (0.214)	−0.154* (0.089)	0.334*** (0.073)	0.394*** (0.075)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Observations	2868	2868	2868	2868	2868	2868
R²	0.5894	0.1511	0.3949	0.1728	0.1709	0.1600

Note: ***p < 0.01, **p < 0.05, *p < 0.1.

Conclusions and policy implications

Promoting green development has become one of the fundamental concerns confronting the present global economic growth process. As the engine of economic growth in the modern age, the data components play an indispensable role in promoting the green development of cities. The construction of the national big data comprehensive pilot zone offers an analytical policy basis for evaluating the influence of data components on urban green development. Therefore, this research takes the execution of the national big data comprehensive pilot zone as a quasi-natural experiment, and evaluates the influence of digital economic growth on urban green development. The major conclusions of the research are as follows: (1) The degree of urban green development has progressively increased throughout the research period, however there are clear disparities between various locations. (2) The execution of the national big data comprehensive pilot zone has a major role in supporting urban green development, and this result is still established after the placebo test and the PSM-DID method test. (3) Heterogeneity studies have revealed that the policy impact of the construction of the national big data comprehensive pilot zone on center cities and small and medium-sized cities is much larger than that of other kinds of cities. (4) The SDMDID model study indicated that the deployment of the national big data comprehensive pilot zone has a certain geographic impact. (5) The mechanism test shows that the implementation of the national big data comprehensive pilot zone can promote the improvement of urban green development by promoting the transformation of industrial structure and improving the level of technological innovation, while the mechanism of human capital has not yet emerged.

Based on the foregoing study results, this article focuses on specific ideas for further strengthening the action mechanism of the digital economy and boosting the green growth of cities. (1) Further carry out the construction of the national big data comprehensive pilot zone, give full play to the leading role of the national big data comprehensive pilot zone, enhance the green development effect of data elements, summarize the experience of successful areas, and do a good job in development planning according to local conditions, so as to enhance the inclusiveness and flexibility of the national big data comprehensive pilot zone policy. (2) Strengthen cross-regional collaboration in the digital economy and establish avenues for interregional conversation and cooperation. All regions should continue to foster the deep integration of the digital economy and the conventional economy, and improve cross-regional cooperation and support. Further, the benefits of the digital economy in information transmission and resource allocation are leveraged, so as to effectively play a positive geographical spillover effect. (3) Explore the building of a multi-dimensional route to support the green growth of cities in the national big data comprehensive pilot zone. Make full use of the convenience of data components, break down barriers, and encourage the efficient flow of resource elements to relieve the distortion of industrial structure and increase the degree of technological innovation. Now, the mechanism of human capital has not yet evolved, partly because talent training is a long-term effort and cannot be greatly enhanced in the near term. Therefore, the government should deliberately foster digital skills and enhance financial assistance, so as to establish a strong talent basis for the green growth of cities.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Social Science Foundation of China (No. 23CJY028; 22&ZD095).

ORCID iD

Lianghu Wang

Lianghu Wang’s research interests include environmental economics and digital economics.

Jun Shao is full professor of digital economics in Southeast University.

References

Chen

You

. How does environmental pollution erode political trust in China? A multilevel analysis. Environ Impact Assess Rev 2021; 88: 106553.

Han

Feng

, et al. Application of threshold regression analysis to study the impact of clean energy development on China’s carbon productivity. Int J Environ Res Public Health 2020; 17: 1060.

Rehman

Fareed

Shahzad

, et al. When would the dark clouds of financial inclusion be over, and the environment becomes clean? The role of national governance. Environ Sci Pollut Res 2022; 29: 27651–27663.

Liu

Zhang

Cao

. Can high-speed rail help achieve urban green development? From a spatial perspective. Front Environ Sci 2022; 10: 894697.

Ouyang

Song

Zheng

, et al. Using gross ecosystem product (GEP) to value nature in decision making. Proc Natl Acad Sci USA 2020; 117: 201911439.

Tan

. The impact of urban compactness on urban sustainable development in China: the case of Nanjing. Sustain Dev 2019; 27: 270–280.

Zhang

Song

Zou

. Transformation of pollution control and green development: evidence from China’s chemical industry. J Environ Manag 2020; 275: 111246.

Gao

Song

Wen

, et al. Effect of science and technology finance policy on urban green development in China. Front Environ Sci 2022; 10: 918422.

Shao

Tian

Fan

. Do the rich have stronger willingness to pay for environmental protection? New evidence from a survey in China. World Dev 2018; 105: 83–94.

10.

Pan

Cheng

Gao

, et al.

Is environmental regulation effective in promoting the quantity and quality of green innovation?

Environ Sci Pollut Res 2021; 28: 6232–6241.

11.

Lin

Benjamin

. Green development determinants in China: a non-radial quantile outlook. J Clean Prod 2017; 162: 764–775.

12.

Wei

Liu

, et al. Sustainable development and green gross domestic product assessments in megacities based on the emergy analysis method: a case study of Wuhan. Sustain Dev 2020; 28: 294–307.

13.

Zhu

Liu

, et al. Effects of the digital economy on carbon emissions: evidence from China. Int J Environ Res Public Health 2022; 19: 1–21.

14.

Ren

Chang

. Big data and green development. China Ind Econ 2019; 4: 5–22.

15.

Graedel

Allenby

Comrie

. Matrix approaches to abridged life cycle assessment. Environ Sci Technol 1995; 29: A134–A139.

16.

Motloch

Armistead

David, et al. Eco-economics in Texas: competitive adaptation for the next industry revolution. Texas Business Rev 2008; 18: 1–6.

17.

Heshmati

. A sequential Malmquist-Luenberger productivity index: environmentally sensitive productivity growth considering the progressive nature of technology. Energy Econ 2011; 32: 1345–1355.

18.

Jarvis

Varma

Ram

. Assessing green jobs potential in developing countries. Geneva: International Labour Office, 2011, 54–89.

19.

Wang

Xie

, et al. Measuring the performance of industrial green development using a non-radial directional distance function approach: a case study of Jiangxi province in China. Sustainability 2017; 9: 1757.

20.

Xiao

Guo

, et al. Measuring the dynamic efficiency of regional industrial green transformation in China. Sustainability 2018; 10: 28.

21.

Yao

Feng

Zhang

, et al. Measurement and decomposition of industrial green total factor water efficiency in China. J Clean Prod 2019; 198: 1144–1156.

22.

Zhang

Sangaiah

. A study of green development mode and total factor productivity of the food industry based on the industrial internet of things. IEEE Commun Mag 2018; 56: 72–78.

23.

Watanabe

Tanaka

. Efficiency analysis of Chinese industry: a directional distance function approach. Energy Policy 2007; 35: 6323–6331.

24.

Honma

. A panel data parametric frontier technique for measuring total-factor energy efficiency: an application to Japanese regional. Energy 2014; 78: 732–739.

25.

Arcelus

Arocena

. Productivity differences across OECD countries in the presence of environmental constraints. J Oper Res Soc 2005; 56: 1352–1362.

26.

Nahman

Mahumani

De Lange

. Beyond GDP: towards a green economy index. Dev South Afr 2016; 33: 215–233.

27.

Tan

Yang

, et al. Impact of urban innovation on urban green development in China’s Yangtze River Economic Belt: perspectives of scale and network. Environ Sci Pollut Res 2022; 29: 73878–73895.

28.

Guo

Liu

. Sustainable poverty alleviation and green development in China’s underdeveloped areas. J Geogr Sci 2022; 32: 23–43.

29.

Zou

Zhang

. Does environmental regulatory system drive the green development of China’s pollution-intensive industries? J Clean Prod 2022; 330: 129832.

30.

Walz

Pfaff

Marscheider-Weidemann

, et al.

Innovations for reaching the green sustainable development goals: where will they come from?

Int Econ Econ Policy 2017; 14: 449–480.

31.

Shironitta

. Global structural changes and their implication for territorial CO2 emissions. J Econ Struct 2016; 5: 1–18.

32.

Shi

Song

Rui

, et al. How the establishment of the national civilized city promotes urban green development: from the perspective of administrative competing theory—A quasi experiment study in China. Int J Environ Res Public Health 2022; 19: 11103.

33.

Tapscott

. The Digital Economy: Promise and peril in the age of networked intelligence. New York: McGraw-Hill, 1996.

34.

Hampton

Strasser

Tewksbury

, et al. Big data and the future of ecology. Front Ecol Environ 2013; 11: 156–162.

35.

Johansson

Epitropou

Karatzas

, et al. Fusion of meteorological and air quality data extracted from the web for personalized environmental information services. Environ Model Software 2015; 64: 143–155.

36.

Wang

Cao

. Research on the influence of internet development on China’s total factor energy efficiency and network effects. China Population Resour Env 2019; 29: 86–95.

37.

Cheng

Dai

, et al. Can low-carbon city construction facilitate green growth? Evidence from China's pilot low-carbon city initiative. J Clean Prod 2019; 231: 1158–1170.

38.

Qiu

Wang

Liu

. The policy outcomes of low-carbon city construction on urban green development: evidence from a quasi-natural experiment conducted in China. Sust Cities Soc 2021; 66: 102699.

39.

. Ecological effects of new-type urbanization in China. Renew Sust Energ Rev 2021; 135: 110239.

40.

Shao

Wang

. Can new-type urbanization improve the green total factor energy efficiency? Evidence from China. Energy 2022; 262: 125499.

41.

Shao

, et al. Environmental non-governmental organizations and urban environmental governance: evidence from China. J Environ Manage 2018; 206: 1296–1307.

42.

Shi

Huang

, et al. The impacts of freight trade on carbon emission efficiency: evidence from the countries along the “Belt and Road”. Complexity 2020; 20: 2529718.

43.

Wang

Shao

. The impact of foreign direct investment on China's carbon emission efficiency through energy intensity and low-carbon city pilot policy. Energy Environ 2022; 34: 1844–1866.

44.

Wang

. Technological innovation, resource endowment, and green total factor energy efficiency. Environ Sci Pollut Res 2022; 29: 79618–79633.

45.

Henderson

Abdel-Rahman

. Reply to comments on urban diversity and fiscal decentralization. Regional Sci Urban Econ 1997; 27: 799–800.

46.

Jacobson

LaLonde

Sullivan

. Earnings losses of displaced workers. Am Econ Rev 1993; 83: 685–709.

47.

Tariq

Mahmood

, et al. The nexus between digital economy and carbon dioxide emissions in China: the moderating role of investments in research and development. Technol Soc 2022; 68: 101910.

48.

Yang

Jiang

Pan

. Does China’s carbon emission trading policy have an employment double dividend and a Porter effect? Energy Pol 2020; 142: 111492.

49.

Wang

Shao

. Digital economy, entrepreneurship and energy efficiency. Energy 2023; 269: 126801.

50.

Elhorst

. Matlab software for spatial panels. Int Reg Sci Rev 2014; 37: 389–405.

51.

Zhong

. Can the digital economy facilitate carbon emissions decoupling? An empirical study based on provincial data in China. Int J Environ Res Public Health 2022; 19: 6800.

52.

Lange

Pohl

Santarius

. Digitalization and energy consumption. Does ICT reduce energy demand? Ecol Econ 2020; 176: 106760.

53.

Anderson

. Technical progress and pollution abatement: an economic view of selected technologies and practices. Environ Dev Econ 2001; 6: 283–311.

54.

Baron

Kenny

. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986; 51: 1173–1182.

55.

Shao

Shi

. Does financial agglomeration promote the increase of energy efficiency in China? Energy Pol 2020; 146: 111810.

56.

Liang

Zhang

Gong

. City size and labor productivity: are Chinese cities too small? Based on Chinese city-level data analysis. China Econ Quarterly 2015; 14: 1053–1072.