E-Commerce Development and Green Technology Innovation: Impact Mechanism and the Spatial Spillover Effect

Abstract

Green technology innovation (GTI) is critical for economic development and environmental protection. This paper investigates the influence of the National E-commerce Demonstration Cities (NEDC) policy on GTI using a multi-period Difference-in-Difference (DID) model and data from prefecture-level Chinese cities. The findings indicate that the NEDC policy considerably facilitates GTI in China. The conclusion withstands a comprehensive set of robustness tests and remains valid even after considering potential endogeneity issues. A dynamic analysis reveals an increasing influence of the NEDC policy on GTI over time. The paper identifies producer services agglomeration, internet development, and financial support as channels through which the NEDC policy affects GTI. A heterogeneity analysis demonstrates that the NEDC policy’s influence on GTI is more pronounced in larger cities with a higher degree of marketization and increased levels of human capital. Moreover, the NEDC policy exhibits spatial spillover effects, supporting GTI advancement in both local cities and neighboring regions. This study provides insights into how emerging market economies can leverage e-commerce for green development.

1. Introduction

Resource depletion and environmental issues have gained prominence recently [1], posing a severe threat to human life. It has become a global concern to ensure the sustainable growth of human society within natural restrictions [2]. The supply chains have become extremely volatile in recent years due to various crises and shocks [3,4]. China’s rapid industrialization and urbanization have brought about severe ecological and environmental problems while achieving leaps and bounds in economic development [5,6,7,8]. China’s economic growth, resources, and environment are under severe pressure, and green development is imperative [9], with many efforts in recent years tackling the “green” themes from various perspectives, such as green finance [10,11] and green purchase intention [12,13].

Green technology innovation (GTI) attempts to increase the effectiveness of resource usage and decrease pollution emissions during production. It serves as the primary strategy for addressing the issue of global environmental pollution and achieving sustainable growth [14,15,16].

With the profound integration of digital technology and the real economy, the digital economy has grown exponentially and has been considered a new engine of global economic development. The value of the digital economy in 47 nations in 2018 was 30.2 trillion U.S. dollars, or an average of 40.3% of GDP. In 2021, the cost of a digital economy across 47 countries was 31.8 trillion U.S. dollars, accounting for 45.3% of GDP on average [17]. The development of the digital economy boosts the degree of economic openness, optimizes the industrial structure, and expands the market potential, thus facilitating GTI [18]. It is now a vital driving force for the promotion of GTI [19,20,21,22]. As an essential component of the digital economy, e-commerce also develops rapidly. The value of the global e-commerce market exceeded 3.8 trillion U.S. dollars in 2021 [17]. According to the China Internet Development Report (2021), China’s e-commerce transactions totaled $37.21 trillion in 2020.

The rapid development of e-commerce benefited from the strong support of the National E-commerce Demonstration Cities (NEDC) policy [23]. The central objectives of the NEDC policy are to furnish the necessary infrastructure and foster an advantageous environment for e-commerce growth, with a primary focus on minimizing energy consumption and propelling green economies. The central government established the first pilot zone in Shenzhen to explore a more suitable e-commerce development mode for China in September 2009. The program expanded in 2011 to include Beijing and 21 National E-commerce Demonstration Cities (NEDC) pilot cities. By 2014, the initiative had extended to Dongguan and another 29 NEDC pilot cities. By 2017, the program encompassed a total of 71 cities. Figure 1 shows the distribution of the NEDC pilot cities.

E-commerce provides enterprises from developing countries access to international markets [24]. It can reduce transaction costs [25], increase the scope and frequency of contact between enterprises and their stakeholders, and facilitate resource integration and knowledge spillover. Previous studies emphasized the driving factors of e-commerce development based on a sample of developed countries [26,27] and their influence on carbon emissions and environmental pollution [28,29,30]. The influence of e-commerce on GTI is rarely studied. Li [31] demonstrated that the NEDC policy promotes GTI, and the concentration of scientific and technological talents can further enhance this positive effect. However, the mechanism by which the NEDC policy promotes GTI remains to be determined.

Furthermore, the digital economy has an apparent spatial correlation [18,32]. It can provide favorable external conditions for the cross-regional flow of innovative factors such as knowledge and talent, facilitating the optimal allocation of innovation resources across regions. Developing a digital economy may encourage GTI locally and in the surrounding area. However, to our knowledge, the literature needs to address the spatial spillover effects of the NEDC policy on GTI. Therefore, this paper focuses on exploring the channels through which e-commerce influences GTI and the spatial spillover effect of the NEDC policy on GTI to reveal the underlying mechanism of e-commerce development impacts on GTI and offer empirical evidence and policy suggestions for promoting green technological innovation to accomplish excellent economic growth in the age of the digital economy.

Following the quasi-natural experiment of the NEDC policy, we explore the influence of e-commerce on GTI, its transmission mechanism, and the spatial spillover effect. Firstly, we employ a multi-period Difference-in-Differences (DID) approach to investigate the influence and mechanism of the NEDC policy on GTI, using data from 240 prefecture-level Chinese cities spanning the years 2006 to 2019. Secondly, to probe the underlying mechanisms, we adopt the mediating effect model to identify the role of producer services agglomeration, internet development, and financial support in the relationship between the NEDC policy and GTI. Thirdly, we analyze the NEDC policy’s diverse effects on GTI in the contexts of urban scale, marketization level, and human capital. Lastly, we use a spatial panel data model to investigate the spatial spillover effect of the NEDC policy.

The following are the contributions of the study. First, this study expands the exploration into the economic effects of the NEDC policy. Unlike existing literature that examines the outcomes of the NEDC policy from income and environmental perspectives [23,30,33,34], this paper employs a multi-period Difference-in-Difference (DID) approach to investigate the impact of the NEDC policy on GTI. It also enriches the literature on innovation. Second, this research explores the mechanisms by which the NEDC policy influences GTI from the vantage points of the producer services agglomeration, internet development, and financial support, advancing the work of Li et al. [31]. Third, this study investigates the varied impacts of NEDC policy on GTI, taking into account variables such as urban scale, the level of marketization, and human capital investment. Lastly, this paper expands upon the research by employing a Spatial Durbin Model to analyze the spatial spillover impacts of the NEDC policy on GTI. The findings suggest that the NEDC policy stimulates local GTI and enhances GTI in neighboring areas. This paper provides insights that could inform the development of e-commerce and GTI globally, particularly in developing countries.

The remaining part of the article is structured as follows: Section 2 presents the literature reviews and research hypotheses. Section 3 introduces the methodology, variables, and data. Section 4 reports the empirical results. Section 5 analyzes the spatial spillover effect. Section 6 gives the article’s conclusion along with some policy suggestions.

2. Literature Review and Research Hypothesis

2.1. Literature Review

Due to high investment costs, long investment return times, and high risk, enterprises are reluctant to pursue green innovation [35]. Environmental protection subsidies, R&D subsidies, and talent subsidies encourage enterprises to increase their R&D investment, enhancing their GTI [36]. Environmental regulation is one of the critical factors driving GTI [37,38,39,40]. However, there has yet to be an agreement on how green technology innovation and environmental legislation are related [41]. The first perspective holds that environmental regulation raises the expenses associated with environmental compliance for businesses, impeding their desire and capacity for innovation and, as a result, impeding their green innovation [42,43]. The Porter Hypothesis, the second point of view, contends that more rigorous but carefully designed environmental regulations can spur business innovation, which could then wholly or partially offset the cost of compliance and encourage the development of green technologies. Peng et al. [44] demonstrate that environmental legislation boosts green innovation intention, and green innovation intention encourages green innovation activity. As a market-based environmental regulation, green credit policy stimulates green innovation in highly polluting industries [45]. Gao et al. [46] discover that appropriate command-control environmental regulations and high-intensity market-oriented environmental regulation promote GTI.

The digital economy is an essential factor influencing GTI. Digital infrastructure can improve informatization, increase media attention, and improve corporate governance, thus stimulating corporate green technology innovation [19,21]. Industrial internet platforms promote green innovation [47]. Chin et al. [20] find that blockchain technology favors green innovation performance, with value appropriation capacity acting as a moderator. By encouraging producer service agglomeration, accelerating financial growth, and lowering resource dependence, internet development can considerably improve urban green innovation efficiency in China [48]. Digitally inclusive finance can reduce corporate funding limitations while improving internal governance and promoting innovation in green technology [49]. Chen et al. [50] reveal that the digital transformation of nations’ economies can promote green technology innovation by improving internal controls and financing opportunities.

E-commerce is becoming increasingly important to the world economy and has become a crucial engine for the growth of the global economy [51]. Oliner and Sichel [52] first investigate the effects of e-commerce on U.S. productivity from 1996 to 1999 using the “back-of-the-envelope” calculation approach. The productivity of businesses can be significantly increased by e-commerce, according to several empirical studies [53,54]. Cao et al. [23] verify that China’s pilot policy of National E-commerce Demonstration Cities (NEDC) can improve green total factor productivity, with industrial structure upgrading, non-production cost reduction, and green innovation incentives being the fundamental mechanisms. Rural e-commerce can raise rural household income and the volume of online commodity transactions [55,56] and considerably reduce poverty [57]. E-commerce is also thought to have the potential to be economical and energy-saving. Taking Shenzhen as an example, Zhao et al. [58] demonstrate that the environmental cost of traditional retail is higher than e-commerce, and the increase in the proportion of e-commerce will help to reduce the overall retail-induced carbon emissions. Both Dong et al. [34] and Wang et al. [30] report that the NEDC pilot program reduces carbon emissions through resource allocation optimization, energy consumption reduction, industrial structure upgrading, and technical innovation based on data from the Chinese urban panel. Through marginal income enhancement, mechanization, and labor transfer, Ji et al. [59] find that China’s rural E-Commerce Demonstration County (REDC) strategy reduces the amount of chemical fertilizer sprayed in the county by 21%.

2.2. Research Hypothesis

The primary objectives of NEDC policy are to reduce energy consumption and promote the growth of a green economy by creating the necessary infrastructure and an atmosphere conducive to the development of e-commerce. As an institutional exploration of Chinese e-commerce development, e-commerce demonstration cities are bound to receive the attention and support of the central government, and the local government will actively develop e-commerce to obtain the central government’s permission and improve personal reputation [60]. This includes, for example, releasing green environmental protection and GTI planning documents [61], releasing fiscal policies for digital infrastructure and green supply chain construction, and developing physical platforms and cooperation mechanisms for green innovation exchange activities. Establishing digital infrastructure breaks the knowledge barrier, enhances the spillover effect of knowledge, and accelerates the establishment of innovative structures based on existing technologies [62]. Information dissemination from a hierarchical to a networked approach can be accelerated by digital technology, which also increases the effectiveness of GTI and information transmission. Physical platforms and cooperation mechanisms can strengthen the innovation knowledge spillover effect. Government fiscal support can alleviate the financing constraints of innovation subjects. These can provide the impetus to promote GTI. Therefore, the following hypothesis is suggested.

H1. 

The NEDC policy may promote urban GTI.

In the process of NEDC construction, the government provides active support in terms of industrial layout, capital guidance, and professional talent gathering. Through institutional innovation and business environment optimization, accelerating the reform of market-oriented systems and mechanisms will help to optimize the allocation of capital, technology, human capital, data resources, and other factors [63,64]. Through labor market sharing, intermediate input sharing, platform economy, information exchange, and dissemination, some producer service enterprises will reduce communication and transportation costs, improve the degree of knowledge spillover and innovation, and promote the centralized layout and specialized division of labor of businesses in the same producer service industry to promote the specialized agglomeration of producer services.

The accumulation of producer services can strengthen the interaction between the innovation subjects, improve the speed of knowledge dissemination, and accelerate the green innovation subjects’ innovation process [65]. The accumulation of innovative elements leads to the knowledge spillover effect, producing the innovation subjects’ learning, imitation, and incentive effects. Such effects can promote the derivative development of knowledge products and innovation outputs and promote GTI. In light of this, the following hypothesis is put forward.

H2. 

The NEDC policy enhances the accumulation of producer services, thereby facilitating GTI.

Some viewpoints on accelerating e-commerce development propose that the pilot cities integrate and use all kinds of resources to establish a support service system for e-commerce development. The Internet is the basis for e-commerce. The NEDC policy can boost urban internet development. Internet-related information technology facilitates the integration of fragmented knowledge. With the help of the Internet, innovation agents can accelerate the accumulation of knowledge and technology throughout society by searching, learning, integrating, and transforming existing knowledge. That can promote the green innovation potential of the production and R&D sectors. Internet popularization can increase the effectiveness of information distribution, lessen the emergence of knowledge monopolies, and increase the knowledge spillover effect. The characteristics of the Internet, such as connection, sharing, and collaboration, can effectively facilitate the integration of resources and promote collaborative exchanges between green innovation factors. According to the theoretical analysis, the hypothesis is the following:

H3. 

The NEDC policy promotes internet penetration, thereby facilitating GTI.

According to some viewpoints on speeding the development of e-commerce, the provincial (district) governments should support the creation of National E-commerce Demonstration Cities and expand their support in policies, funds, and other aspects. Compared with general enterprise operation projects, the characteristics of GTI projects are a significant investment, a protracted payback period, and a high level of risk. Government financial support is crucial for innovation [66]. Zhang et al. [67] demonstrated that government R&D expenditure promotes GTI by improving expected returns, reducing R&D risks, and ensuring efficiency. Green development is the final aim of the NEDC policy. The main force for green development is GTI. The governments of the pilot cities prioritize green innovation projects and increase fiscal expenditure on R&D. That can alleviate financing constraints and inspire employee creativity, thus boosting GIT. Therefore, the hypothesis is proposed as follows.

H4. 

NEDC policy increases financial expenditure on R&D, thereby facilitating GTI.

As a policy shock, constructing NEDC will induce the free flow of production factors. The flow of innovation resources will generate “technology flow” and “knowledge flow” between regions, promote the exchange and dissemination of technology, and trigger knowledge spillover through inter-regional learning [68]. With the help of information technology and network construction, the flexible scheduling of science, technology, and human resources between cities is realized. The key to innovation is the flow of resources, such as technology, among creative individuals. The development of National E-commerce Demonstration Cities will spur this flow of inter-regional innovation resources, which will, in turn, spread knowledge to nearby cities and improve those cities’ capacity for innovation. Following the analyses shown above, hypothesis 5 is suggested.

H5. 

The NEDC policy has a spatial spillover effect on nearby cities and promotes the improvement of green technology innovation levels in nearby cities.

The transmission mechanism of policy effects is shown in Figure 2.

3. Methodology, Variables, and Data

3.1. Econometric Model

To test hypothesis H1, that is, the impact of the NEDC policy on GTI, this paper establishes Equation (1).

GP_it = α₁₀+α₁Treat_i × Post_t + γ₁FDI_it + γ₂Wage_it + γ₃GDP_it + γ₄Is_it + γ₅Fin_it + μ_i + φ_t + ε_it

(1)

where i and t stand for city and year, respectively. GP represents the number of green invention patent applications of city I in the year t, which is the explained variable. Treat × Post refers to the core explanatory variable. FDI, Wage, GDP, Is, and Fin are control variables. μ_i is the city control effect. φ_t is a time control effect. ε_it is the error term.

According to [69], we establish the mediating model to test H2, H3, H4. The models are as follows.

Med_it = α₂₀ + α₂₁Treat_i × Post_t + γ₂₁FDI_it + γ₂₂Wage_it + γ₂₃GDP_it + γ₂₄Is_it + γ₂₅Fin_it + μ_i + φ_t + ε_it

(2)

GP_it = α₃₀ + α₃₁Treat_i × Post_t + β₁Med_it + γ₃₁FDI_it + γ₃₂Wage_it + γ₃₃GDP_it + γ₃₄Is_it + γ₃₅Fin_it + μ_i + φ_t + ε_it

(3)

Med represents producer services agglomeration (Aps), internet penetration rate (Inter), and government financial expenditure on R&D (Grd). We expect α₂₁, α₃₁, and β₁ to be significantly positive.

3.2. Variables

3.2.1. Explained Variable

GTI represents the dependent variable. In 2010, the World Intellectual Property Organization launched the IPC of green inventory. The International Patent Green Classification List divides green patents into seven categories, including alternative energy production, waste management, agriculture or forestry, energy conservation, and transportation, following the United Nations Framework Convention on Climate Change. According to the IPC Green Inventory’s classification number, each city’s green patent application data is retrieved, which is used as the primary index to measure GTI [70]. It is expressed by the number of green invention patent applications and green utility model patents (GP), calculated by 10,000 units due to the large number.

3.2.2. Explanatory Variable

The independent variable Treat × Post represents the policy effect of NEDC. Treat equals 1 if the city is a pilot city. Otherwise, it equals 0. Post equals 1 every year after this city’s designation as a pilot city; otherwise, it is equal to 0.

3.2.3. Control Variable

According to Deng et al. [71] and Feng et al. [16], we choose economic development (GDP), financial development (Fin), wages (Wage), foreign direct investment (FDI), and industrial structure (Is) as control variables.

3.2.4. Intermediate Variable

The agglomeration of producer services (Aps). According to Wang et al. [29], Aps is measured as follows:

Aps_ist = (E_ist/E_it)/(E_st/E_t)

(4)

where E_ist is the number of employees in producer service industry s of the city i in year t, E_it is the total number of employees of the city i in year t, E_st represents the number of employees in producer service industry s of all cities in year t, E_t is the total number of employees of all cities in year t.

Internet development (Inter). Following Yang et al. [72], Inter is measured by the number of broadband users per million people.

Financial support (Grd). Following Hu and Liu [73], Grd is measured by the logarithm of government financial expenditure on R&D.

The Measurement of variables are shown in

Table 1. Measurement of variables.

Variables	Measurement
GP	Number of urban green invention patent applications divided by 10,000
Treat × Post	Treat equals 1 if the city is a pilot city; otherwise, it equals 0; Post equals 1 for every year after this city’s designation as a pilot city; otherwise, it is equal to 0.
Fin	Natural logarithm of the proportion of total loans to the GDP
Wage	Natural logarithm of the average wage
FDI	Natural logarithm of the ratio of foreign direct investment
Is	The ratio of the output value of the tertiary industry to the secondary industry
GDP	Natural logarithm of regional GDP
Aps	Calculated by Equation (4)
Inter	Urban broadband users per 1,000,000 people
Grd	Natural logarithm of government financial expenditure on R&D

.

3.3. Data

3.3.1. The Sample and Data Source

This study investigates prefecture-level cities across China, excluding cities with missing data, resulting in a sample of 240 cities. Some prefecture cities where the data was collected include Hangzhou, Wuhan, Changsha, Nanjing, Hefei, and Suzhou. Due to data availability constraints, the sample period is from 2006 to 2019. The data for green patents are sourced from the CNRDS platform. The control variable data comes from the EPS and City Statistical Yearbook. To mitigate the influence of outliers, we conduct a winsorization process at the 1% level on the dataset.

3.3.2. Descriptive Statistics

The descriptive data are seen in

Table 2. Descriptive statistics.

Variable	Obs.	Mean	Std. Dev	Min	Max
GP	3360	0.064	0.205	0	3.658
Treat × post	3360	0.121	0.326	0	1
FDI	3360	0.020	0.020	0	0.199
Wage	3360	10.30	0.413	8.828	11.70
GDP	3360	16.42	0.977	13.46	19.76
Is	3360	0.909	0.484	0.131	5.168
Fin	3360	0.903	0.563	0.112	7.450
Aps	3360	0.985	0.975	0.013	11.04
Inter	3360	0.815	1.073	0	13.72
Grd	3360	9.704	1.554	0.113	15.18

. The mean of GP is 0.064, and the standard deviation is 0.205. The mean value of Treat × post is 0.121.

Table 3. Examination of difference.

Before the Policy	After the Policy	Difference
0.0245	0.3514	0.3269 ***

Note: *** denotes significance levels at 1%.

shows that the mean of GP is much higher following the NEDC policy than before. It can be preliminarily assumed that NEDC policy positively impacts urban GTI.

4. Empirical Results

4.1. Baseline Regression

The regression results of Equation (1) are presented in

Table 4. Baseline regressions.

Variables	GP	GP	GP	GP	GP	GP
	(1)	(2)	(3)	(4)	(5)	(6)
Treat × Post	0.1878 ***	0.1846 ***	0.1814 ***	0.1749 ***	0.1620 ***	0.1628 ***
	(0.0149)	(0.0149)	(0.0147)	(0.0145)	(0.0126)	(0.0127)
FDI		−0.6757 ***	−0.6252 ***	−0.7014 ***	−0.5559 ***	−0.5700 ***
		(0.1667)	(0.1654)	(0.1674)	(0.1729)	(0.1718)
Wage			−0.0768 ***	−0.1312 ***	−0.1394 ***	−0.1381 ***
			(0.0180)	(0.0193)	(0.0198)	(0.0199)
GDP				0.1171 ***	0.1951 ***	0.1900 ***
				(0.0146)	(0.0285)	(0.0275)
Is					0.1290 ***	0.1320 ***
					(0.0346)	(0.0356)
Fin						−0.0136
						(0.0101)
Constant	0.0414 ***	0.0555 ***	0.8458 ***	−0.5145 *	−1.8293 ***	−1.7493 ***
	(0.0025)	(0.0042)	(0.1850)	(0.2656)	(0.4836)	(0.4650)
City-FE	Yes	Yes	Yes	Yes	Yes	Yes
Year-FE	Yes	Yes	Yes	Yes	Yes	Yes
R-squared	0.686	0.688	0.688	0.694	0.705	0.705
Observations	3360	3360	3360	3360	3360	3360

Note: Robust standard errors in parentheses; ***, * denote significance levels at 1% and 10%, respectively.

. According to Column (1), the NEDC policy may encourage GTI at the significance level of 1%. We gradually add control variables, and the coefficients of Treat × Post are still cheerful at the significance level of 1% (Columns (2)–(6)). As shown in Column (6), the coefficient of Treat × Post is 0.1628, indicating that under the same conditions, the urban GTI in pilot cities is higher than that in non-pilot cities. Hypothesis H1 is verified primarily. This conclusion conforms with Cao et al. [23] and Li et al. [31]. The construction of the e-commerce demonstration city strengthens innovation knowledge spillover and increases government financial expenditure on R&D, thus boosting GTI.

4.2. Common Trend Test and Dynamic Analysis

The treatment and control groups must exhibit the same temporal trend before the policy as a requirement for employing the DID technique. This study determines whether the G.P. meets the common trend assumption between the pilot and non-pilot cities. Additionally, we examine the dynamic influence of the NEDC policy on GTI using the event study method. According to Figure 3, the coefficients of pre6, pre5, pre4, pre3, and pre2 are not significant, suggesting that the parallel trend hypothesis has been verified since there is no discernible difference in the changing trend of carbon productivity between pilot cities and non-pilot cities before the introduction of the policy. The dynamic test results show that the current coefficients are insignificant at the 5% significance level. At the same time, those of post1, post2, post3, post4, and post5 are all significantly positive, suggesting that the influence of the pilot policies of information consumption cities on carbon productivity has long-term effects.

4.3. Robustness Test

4.3.1. PSM-DID Estimation

The choice of e-commerce demonstration cities may be influenced by regional economic development. To overcome the effect of sample selectivity bias and to ensure as much data balance as possible between pilot and non-pilot cities, the PSM-DID method is used in this study to validate the accuracy of the benchmark regression results. We select economic development (GDP), financial development (Fin), wages (Wage), foreign direct investment (FDI), and industrial structure (Is) as matching variables and use one-to-one close matching to conduct PSM. Next, we estimate Equation (1) using the matched samples, and the results are reported in

Table 5. PSM-DID estimation results.

Variables	GP	GP	GP
	(1)	(2)	(3)
Treat × post	0.1238 ***	0.1097 ***	0.1073 ***
	(0.0112)	(0.0116)	(0.0149)
FDI		0.7023 ***	−0.6207 ***
		(0.1766)	(0.2020)
Wage		0.0422 ***	−0.1381 ***
		(0.0137)	(0.0252)
gdp		0.1027 ***	0.2395 ***
		(0.0051)	(0.0340)
Is		0.1304 ***	0.1226 ***
		(0.0078)	(0.0371)
Fin		−0.0169 **	−0.0034
		(0.0071)	(0.0082)
Constant	−0.0381	−2.2041 ***	−2.5731 ***
	(0.0367)	(0.1214)	(0.5461)
City-FE	YES	NO	YES
Year-FE	YES	NO	YES
Observations	2640	2640	2640
R-squared	0.754	0.480	0.768

Note: Robust standard errors in parentheses; ***, ** denote significance levels at 1% and 5%, respectively.

. The coefficient of Treat × Post is still significantly positive, indicating that the H1 is still verified.

4.3.2. Placebo Test

Following the implementation of the NEDC policy, there may be differences in GTI between the experimental and control groups due to additional guidelines or random events. That is, the NEDC policy may not cause changes in the city’s GTI. To eliminate this influence factor, this paper conducts a placebo test by randomly generating the establishment event of the NEDC policy. Repeat the drawing 500 times and plot the resulting coefficients. According to Figure 4, the coefficient of Treat × Post is symmetrically distributed with 0 as the center, and 01628 does not fall into the nuclear density distribution. The placebo test verifies the validity of the regression results.

4.3.3. Instrumental Variable Regression Results

The NEDC policy is not a completely exogenous event. The GTI of the urban area may influence the choice of the pilot cities. The baseline regression may omit some variables. These may lead to an endogeneity problem. Therefore, we adopt the instrumental variable (IV) method to re-estimate Equation (1). This paper chooses the city’s telephone number in 1984 (Tele1984) as the instrumental variable of NEDC policy in Cao et al.’s work [23].

On the one hand, Tele₁₉₈₄ meets IV’s correlation criterion. The foundation for the growth of electronic commerce is information technology. Cities with a high density of telephones are more likely to have developed information technologies, which is better for the development of e-commerce. On the other hand, as historical data shows, Tele1984 does not directly affect GTI, satisfying the exogenous condition. Since Tele1984 is cross-section data, the interaction term of Tele1984 and the time trend variable are used as instrumental variables (Tele₁₉₈₄_year) for endogeneity analysis.

Table 6. Instrumental variable regression.

Variables	Treat × Post	GP
	(1)	(2)
Treat × Post		1.8284 **
		(0.7560)
Tele1984_year	0.0001 **
	(0.0000)
Constant	−10.6717 **	−1.7139
	(4.3520)	(1.2729)
Control	YES	YES
City-FE	YES	YES
Year-FE	YES	YES
R-squared	0.677	−1.629
Observations	2629	2629

Note: Robust standard errors in parentheses; ** denote significance levels at 5%.

reports the results of the 2SLS-IV regression. The first stage regression of the 2SLS is shown in Column (1), demonstrating a highly positive correlation between the instrumental variable (Tele₁₉₈₄_year) and the NEDC policy (Treat × Post). The value of Wald F (19.33) is more than 10, suggesting that the weak instrumental variable null hypothesis is rejected. As reported in Column (2), the coefficient of Treat × Post is highly positive, indicating that H1 is still validated after considering the endogenous issue.

4.3.4. Discussion on the Heterogeneity of Treatment Effects

Research on the heterogeneity of treatment effects highlights that the differential impact of the same policy on different entities can manifest not only across different periods but also in the duration of treatment acceptance or the groups accepting treatment at other points in time. Under such multi-dimensional heterogeneity of treatment effects, the Two-Way Fixed Effect (TWFE) model may lead to biased policy effect estimations due to negative weights [74,75,76].

To solve this problem, a number of theoretical econometric researchers suggest “heterogeneity-robust” estimators to eliminate the bias in TWFE estimation. Calculating the “group-period average treatment effect” yields a reliable estimation in accordance with the approach suggested by Callaway and Sant’Anna [76]. This method chooses a “good control group” rather than employing treated entities as a “bad control group” to calculate the group-period average treatment effect. The average treatment effect (Average Treatment Effect of Treated, ATT) is then determined by averaging the weights over the group and time dimensions. The estimator provided by Callaway and Sant’Anna [76] can employ not-yet-treated samples as the control group when there are no never-treated samples in the dataset, coinciding with this research’s policy context.

Table 7. Heterogeneous-robust estimation based on group-period average treatment effect.

	DRIPW	DRIMP	STDIPW
Variables	GP	GP	GP
	(1)	(2)	(3)
ATT	0.0509 ***	0.0326 *	0.0472 ***
	(0.0176)	(0.0197)	(0.0154)
Control	YES	YES	YES
Year FE	YES	YES	YES
City FE	YES	YES	YES

Note: Robust standard errors in parentheses; ***, * denote significance levels at 1% and 10%, respectively.

presents heterogeneity-robust estimation results based on the “group-period average treatment effect”. Columns (1)–(3) employ Double Robust Inverse Probability Weighting (DRIPW), Improved Doubly Robust Inverse Probability of Tilting and Weighted Least Squares (DRIMP), and Standard Inverse Probability Weighting (STDIPW) to estimate the “group-period average treatment effect”. It is evident that, regardless of the estimation method used, the weighted average treatment effect of the E-commerce Demonstration City pilot policy is significantly harmful. This suggests that, for this study, the estimation bias issue of TWFE is not severe, and the estimation results in Table 2 are reliable.

4.3.5. Excluding the Impact of Other Policies

The Chinese government has implemented many reforms to promote green development, such as an innovative city pilot policy. These policies may boost urban GTI. To eliminate the possibility of other policies interfering with the experiment, we add four more policies to Equation (1): the low-carbon city pilot policy [77], the innovative city policy [78], the broadband China strategy [79], and the carbon emissions trading policy [39,40].

Table 8. Eliminate other policies.

Variables	GP	GP	GP	GP	GP
	(1)	(2)	(3)	(4)	(5)
Treat × Post	0.1521 ***	0.1377 ***	0.1564 ***	0.1549 ***	0.1187 ***
	(0.0120)	(0.0114)	(0.0132)	(0.0117)	(0.0107)
Low-carbon city policy	YES				YES
Innovative city policy		YES			YES
Broadband China strategy policy			YES		YES
Carbon emissions trading policy				YES	YES
Constant	−1.7593 ***	−1.6743 ***	−1.7904 ***	−1.6639 ***	−1.6471 ***
	(0.4638)	(0.4504)	(0.4625)	(0.4494)	(0.4338)
Control	YES	YES	YES	YES	YES
City-FE	YES	YES	YES	YES	YES
Year-FE	YES	YES	YES	YES	YES
R-squared	0.709	0.714	0.706	0.714	0.725
Observations	3360	3360	3360	3360	3360

Note: Robust standard errors in parentheses; *** denote significance levels at 1%.

summarizes the findings. All of the coefficients of Treat × Post are highly positive, indicating that the NEDC policy still facilitates GTI after excluding other similar or related policies. H1 is still validated.

4.4. Mechanism: Producer Services Agglomeration Effect

As we can see in Column (1) of

Table 9. Impact mechanism.

Variables	Aps	GP	Inter	GP	Rd	GP
	(1)	(2)	(3)	(4)	(5)	(6)
Treat × Post	0.0943 ***	0.1609 ***	0.6074 ***	0.1013 ***	0.0765 **	0.1613 ***
	(0.0222)	(0.0127)	(0.0555)	(0.0127)	(0.0328)	(0.0127)
Agglo		0.0204 ** (0.0087)
Inter				0.1008 ***
				(0.0127)
Grd						0.0197 ***
						(0.0051)
Constant	−2.3165 ***	−1.7021 ***	−8.2360 ***	−0.9107 **	−20.0283 ***	−1.3543 ***
	(0.8200)	(0.4672)	(1.4895)	(0.4100)	(1.7379)	(0.5002)
Control	YES	YES	YES	YES	YES	YES
City-FE Year-FE R-squared Observations	YES YES 0.928 3360	YES YES 0.706 3360	YES YES 0.844 3350	YES YES 0.749 3350	YES YES 0.934 3360	YES YES 0.706 3360

Note: Robust standard errors in parentheses; ***, ** denote significance levels at 1% and 5%, respectively.

, the coefficient of Treat×post is positive at the significance level of 1%, suggesting that the NEDC policy significantly improves producer services agglomeration. Benefiting from the national preferential policies, the pilot cities speed up the construction of digital infrastructure, encourage entrepreneurship, and create more job opportunities, which attracts capital, the labor force, and other elements to gather in the pilot cities. Column (2) of Table 9 shows that the NEDC policy and producer services agglomeration significantly promote GTI. According to Baron et al. [69], the agglomeration of producer services partially mediates between the NEDC policy and GTI. That is, the NEDC policy can aggravate economic agglomeration to promote GTI. Hypothesis H2 is verified. The NEDC policy can produce an effect of producer services agglomeration, reduce the cost of GTI, and improve innovation efficiency, thus promoting GTI.

4.4.1. Mechanism: Internet Development Effect

Column (3) in Table 9 shows that the NEDC policy promotes the internet penetration rate. The Internet is the basis of r-commerce. One of the tasks of the NEDC policy is to strengthen the construction of e-commerce infrastructure. Thus, the NEDC policy has an internet development effect. Column (4) states that the NEDC policy and internet development significantly promote GTI. According to Baron et al. [69], the Internet’s development partially mediates between the NEDC policy and GTI. That is, the NEDC policy can boost Internet development to promote GTI. Hypothesis H3 is verified. Improving the Internet penetration rate can reduce the threshold of information dissemination, intensify knowledge spillover, and thus promote GTI.

4.4.2. Mechanism: Financial Support Effect

According to Column (5) of Table 9, the NEDC policy significantly increases financial expenditure on R&D.

Financial expenditure provides support for green technology innovation and development. On one side, green development needs financial support. Conversely, the central government requires local governments in e-commerce demonstration cities to increase their financial support. Whether to achieve the green development goal proposed by the central government or for personal promotion, local governments will actively respond to the central government’s call. According to Column (6), the NEDC policy and financial support significantly boost GTI. According to Baron et al. [69], the financial expenditure on R&D partially mediates between the NEDC policy and GTI. That is, the NEDC policy increases the financial expenditure on R&D, thus promoting GTI. Hypothesis H4 is verified. Financial assistance can help companies overcome funding difficulties, improve investment in environmentally friendly R&D funds, and promote GTI.

4.5. Heterogeneity Test

4.5.1. Heterogeneity of Urban Scale

In accordance with Hypothesis H2, the NEDC policy is proposed to intensify economic agglomeration, subsequently fostering GTI. Compared with their smaller counterparts, large-scale cities assemble more capital and labor resources integral to GTI. This suggests that the economic agglomeration effect of the NEDC policy is more profound in large-scale cities. Consequently, we postulate that the influence of the NEDC policy on GTI fluctuates with city scale. Using city population as a determinant, the sample is bifurcated into two groups: small-scale and large-scale. Cities with a permanent resident population exceeding five million are categorized as large-scale, while the remainder falls into the small-scale category. As indicated in columns (1) and (2) of

Table 10. Heterogeneity analysis.

Variables	GP	GP	GP	GP	GP	GP
	Small Scale	Large Scale	Low Marketization	High Marketization	Low Human Capital	High Human Capital
	(1)	(2)	(3)	(4)	(5)	(6)
Treat × Post	0.076 ***	0.135 ***	0.1531 ***	0.0865 ***	0.119 ***	0.184 ***
	(0.0254)	(0.0229)	(0.0255)	(0.0189)	(0.0262)	(0.0350)
Constant	0.225	−5.792 **	−3.5466 ***	−2.5430 ***	0.344	−5.835 **
	(0.3276)	(2.4907)	(1.2065)	(0.5589)	(0.4066)	(2.3126)
Control City-FE Year-FE R-squared Observations	YES YES YES 0.631 2081	YES YES YES 0.780 1273	YES YES YES 0.800 1670	YES YES YES 0.814 1673	YES YES YES 0.769 1666	YES YES YES 0.754 1661

Note: Robust standard errors in parentheses; ***, ** denote significance levels at 1% and 5% respectively.

, the coefficients of Treat × Post are 0.076 in the small-scale group and 0.135 in the large-scale group. The p-value (Treat × Post) of the Fisher test stands at 0.000, denoting a variation in influence across the two groups. This signifies that the NEDC policy affects GTI more obviously in large cities. Large-scale cities possessing superior institutions and infrastructure lure high-quality talents and resources, providing indispensable elements for GTI. Conversely, the economic development level of small and medium-sized cities is comparatively feeble. Implementing the NEDC policy necessitates establishing e-commerce-related infrastructure, which consumes resources and leads to less growth of GTI in small cities than in large ones.

4.5.2. Heterogeneity of Marketization Level

The degree of marketization impacts the efficacy of social resource allocation. A higher degree of marketization typically leads to more efficient resource allocation, facilitating innovative entities’ access to financial services. Enhancing the availability of financial resources can alleviate the constraints on R&D funding and further stimulate GTI. We propose that the influence of the NEDC policy on GTI varies with a city’s level of marketization. Using the median of the marketization index, the sample is divided into two groups: the low-marketization group and the high-marketization group. According to columns (3) and (4) of Table 10, the coefficients of Treat × post are significantly positive. Fisher’s test’s p-value (Treat × post) is 0.000, indicating a variation in influence between the two groups. In other words, the impact of the NEDC policy on GTI is more pronounced for the low-marketization group. The NEDC policy can more effectively assist cities with a lower level of marketization by providing a more conducive environment for innovation activities. This policy can significantly improve resource allocation efficiency and is instrumental in fostering green innovation activities in these cities.

4.5.3. Heterogeneity of Human Capital

Human capital serves as a vital backbone for innovation. Highly educated individuals are more likely to acquire and utilize the latest technologies, facilitating knowledge diffusion and innovation generation. A city abundant in human capital is more conducive to fostering GTI. Taking inspiration from Su and Liu [80], this study employs the number of college students as a proxy variable for human capital. The sample is split into two groups based on median human capital: a group with low and high human capital. According to the regression results in columns (5) and (6) of Table 10, the coefficient of Treat × Post is highly favorable for both groups. However, the coefficient for the high-human capital group is higher. As innovation is primarily an intellectual activity, the NEDC policy can more effectively stimulate GTI when the investment in human capital is substantial.

5. Further Study: Spatial Spillover Effect Analysis

The foregoing confirms that the NEDC’s development considerably raised the city’s degree of green innovation. Still, due to the spillover and siphon effects, their impact on innovation in neighboring cities is being determined. To further evaluate national e-commerce model cities and whether construction benefits neighboring cities, this article establishes a spatial Durbin model to test the green innovation effect of demonstration city construction on neighboring cities.

The following model is set,

GP_it = α₄₀ + ρ₁W × GP_it + ρ₂W × Treat_i × Post_t + ρ₃W × FDI_it + ρ₄W × Wage_it + ρ₅W × GDP_it + Ρ₆W × Is_it + ρ₇W × Fin_it + α₄₁Treat_i × Post_t + γ₄₁FDI_it + γ₄₂Wage_it + γ₄₃GDP_it + γ₄₄Is_it + γ₄₅Fin_it + μ_i + γ_t + ε_it

(5)

where ρ is the spatial correlation coefficient and W is the N × N geographical distance weight matrix. If i = j, W = 0; If i ≠ j, other variables were set in accordance with model Equation (1). This paper uses four kinds of spatial weight matrices for estimation at the same time: first, we use the spatial adjacency matrix W₁. If city i and city j are adjacent, the matrix elements are assigned 1, otherwise 0. The second is the geographical distance matrix W₂, represented by the reciprocal of the geographical distance between two cities, ${\mathrm{W}}_2=\left\{\begin{array}{c}1/\left|{\mathrm{d}}_{\mathrm{i}}{\mathrm{d}}_{\mathrm{j}}\right|,\mathrm{i}\ne \mathrm{j}\\ 0,\mathrm{i}=\mathrm{j}\end{array}\right.$, where d_id_j is the geographical distance between the city and city. The third is the economic geography matrix W₃, multiplied by W₂, and the average annual urban per capita GDP matrix. The last one is the gravity model, the comprehensive weight matrix of the relation between geographical location and economy, ${\mathrm{W}}_4=\left\{\begin{array}{c}(\overline{{\mathrm{G}}_{\mathrm{i}}}\times \overline{{\mathrm{G}}_{\mathrm{j}}})/{\mathrm{d}}_{\mathrm{i}\mathrm{j}}^2,\mathrm{i}\ne \mathrm{j}\\ 0,\mathrm{i}=\mathrm{j}\end{array}\right.$, where $\overline{{\mathrm{G}}_{\mathrm{i}}} \mathrm{and} \overline{{\mathrm{G}}_{\mathrm{j}}}$ represent the real GDP per capita of the two cities, respectively.

5.1. Spatial Autocorrelation and SDM Applicability Test

The basic premise of the econometric model is that the variables have spatial autocorrelation. Consequently, this study examines the spatial correlation of urban employment levels through the global Moran’s I index, and

Table 11. Spatial autocorrelation test.

Year	Moran’I	p-Value	Moran’I	p-Value	Moran’I	p-Value	Moran’I	p-Value
	W1		W2		W3		W4
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
2006	0.068 *	0.070	0.002	0.205	0.002	0.306	0.025	0.106
2007	0.071 *	0.059	0.004	0.111	0.004	0.136	0.031 **	0.050
2008	0.059	0.102	0.005 *	0.072	0.005	0.108	0.028 *	0.060
2009	0.073 **	0.053	0.007 **	0.024	0.010 **	0.014	0.041 **	0.013
2010	0.106 ***	0.007	0.014 ***	0.001	0.021 ***	0.000	0.067 ***	0.000
2011	0.128 ***	0.001	0.019 ***	0.000	0.031 ***	0.000	0.094 ***	0.000
2012	0.131 ***	0.001	0.021 ***	0.000	0.034 ***	0.000	0.101 ***	0.000
2013	0.106 ***	0.005	0.013 ***	0.001	0.021 ***	0.000	0.072 ***	0.000
2014	0.101 ***	0.007	0.013 ***	0.001	0.020 ***	0.000	0.070 ***	0.000
2015	0.114 ***	0.004	0.016 ***	0.000	0.024 ***	0.000	0.080 ***	0.000
2016	0.124 ***	0.002	0.016 ***	0.000	0.024 ***	0.000	0.077 ***	0.000
2017	0.122 ***	0.003	0.016 ***	0.000	0.021***	0.000	0.065 ***	0.000
2018	0.122 ***	0.003	0.016 ***	0.000	0.021 ***	0.000	0.062 ***	0.001
2019	0.124 ***	0.003	0.017 ***	0.000	0.024 ***	0.000	0.070 ***	0.000

Note: ***, **, and * indicate the significance level of the coefficients at 1%, 5%, and 10%, respectively.

shows the Moran’s I index distribution over the years. It can be seen from the table that the spatial autocorrelation coefficient was not very significant before 2009, which may be due to the poor information, transportation, and other infrastructure in China at that time, and the information exchange between regions being complicated. Hence, the frequency of innovation exchange between areas is low, and the spatial correlation is weak. However, after 2009, China made significant progress in such infrastructure, and the corresponding p-values are all less than 0.010, indicating the existence of spatial autocorrelation at the urban level, which is suitable for spatial econometric analysis.

The spatial Durbin model (SDM) is also tested to see if it can degenerate into a spatial lag model (SLM) or a spatial error model (SEM). The Wald and L.R. tests reject the null hypothesis that SDM can degenerate into SLM or SEM. As a result, SDM is an appropriate estimation method for this paper. Simultaneously, based on the Hausman test, this research concludes that the two-way fixed SDM model is the best fit for estimating the green technology innovation spillover effect of national e-commerce demonstration city buildings.

5.2. Analysis of Test Results of Spatial Spillover Effect

Under four spatial weight matrices,

Table 12. Spatial spillover effect test.

Varibles	W1	W2	W3	W4
	(1)	(2)	(3)	(4)
Treat × Post	0.1637 ***	0.1648 ***	0.1663 ***	0.1645 ***
	(0.0089)	(0.0090)	(0.0091)	(0.0089)
rho	0.2158 ***	0.5448 ***	0.3000 ***	0.2740 ***
	(0.0221)	(0.0931)	(0.0943)	(0.0372)
LR_Direct	0.1663 ***	0.1688 ***	0.1679 ***	0.1660 ***
	(0.0093)	(0.0095)	(0.0094)	(0.0093)
LR_Indirect	0.0508 **	1.0248 ***	0.5496 ***	0.0987 **
	(0.0221)	(0.3693)	(0.1769)	(0.0501)
LR_Total	0.2172 ***	1.1935 ***	0.7175 ***	0.2647 ***
	(0.0263)	(0.3721)	(0.1792)	(0.0532)
Control City-FE Year-FE R-squared Observations	YES YES YES 0.377 3360	YES YES YES 0.313 3360	YES YES YES 0.360 3360	YES YES YES 0.412 3360

Note: Robust standard errors in parentheses; ***, ** denote significance levels at 1% and 5% respectively.

shows the spatial spillover effect of NEDC buildings on the degree of urban green technology innovation. It can be found that whether using the adjacency matrix, geographical distance matrix, economic distance matrix, and gravity model matrix, the spatial lag term coefficient rho of Treat × Post and urban green technology innovation level are also positive and significant, suggesting that the construction of NEDC has a significant positive spatial spillover effect on urban employment level. The calculated coefficient of the SDM model cannot directly reflect the marginal influence of explanatory factors, so the spatial result was further decomposed into indirect effect, direct effect, and total effect in this research. The indirect effect is the impact of the construction of NEDC policy on the level of green technology innovation in other regions; the direct result is the impact of the NEDC policy on the level of urban green technology innovation in the region; and the total effect is the average impact of the NEDC policy on the overall regional green technology innovation level.

The results suggest that the three effects based on the four spatial weight matrices are all positively significant at the 5% significance level. When considering spatial effects, NEDC construction can significantly improve green technology innovation in demonstration areas. It has also increased urban green technology innovation in surrounding, bordering, and economically similar places. The results verify hypothesis H5.

6. Discussion and Implications

GTI is an efficient means of achieving green development. E-commerce is a critical driver of economic growth. As a developing economy, China has implemented several changes to promote the development of e-commerce. The NEDC policy’s primary objective is to foster green development. The influence of the NEDC policy on GTI is investigated in this research using the multi-period difference-in-difference (DID) model and a sample of 240 prefecture-level cities in China from 2006 to 2019. The findings of the study suggest that the NEDC policy significantly facilitates GTI. Such is the result after a series of robustness tests, including the standard trend test, placebo test, the IV approach, heterogeneous-robust estimation based on “group-period average treatment effect”, and eliminating the impact of other policies. The dynamic analysis indicates that NEDC policy’s influence on GTI increases. The result is consistent with previous studies [23,31]. Second, the agglomeration of producer services, internet development, and financial support effects are vital mechanisms for NEDC policy to affect urban GTI. This conclusion extends the research of Li et al. [31]. This shows that NEDC policy promotes GTI by enhancing urban informatization, attracting scientific and technological talents, and optimizing the urban innovation and entrepreneurship environment. It also enriches the research on how the digital economy influences GTI [18,48]. Third, the influence of NEDC on GTI varies with city scale, marketization level, and human capital. The effect of NEDC on GTI is more pronounced for cities with a more extensive scale, higher levels of human capital, and a lower level of marketization. Finally, NEDC construction has a positive spatial spillover effect on neighboring cities and promotes the improvement of green technology innovation levels in neighboring cities.

This study holds practical implications for green development. Firstly, the government should broaden the coverage of digital infrastructures, such as broadband networks, and bolster the development of software and hardware infrastructures in all regions, as technological and research innovations are pushing the boundaries in different sectors [81,82,83,84,85]. Future practical and theoretical endeavors could consider how to implement innovative solutions to existing issues. The Internet of Things is where future research could be conducted [86].

Secondly, there is a need for increased governmental financial support for GTI activities as government decisions significantly impact critical economic and societal outcomes [87].

For GTI projects, it would be advantageous for the central bank to provide low-interest refinancing loans to financial institutions, thus reducing financing costs. Local governments should be encouraged to establish guarantee funds to lower the financing threshold for GTI. There is also a need to enhance the green bond issuance system to bolster enterprises’ green innovation financing. Appropriate financing and capabilities are highly conducive to various levels of performance [88,89,90].

Thirdly, the government should foster an exemplary institutional environment to attract the agglomeration of innovative factors. This could include further liberalization of the household registration system to facilitate labor mobility and improve e-commerce legislation, regulatory systems, and platform governance. These steps would go a long way in paving the way for a green and sustainable future.

7. Limitations, Future Studies, and Conclusions

There are some limitations to our study. First, this paper only analyzes the NEDC policy’s impact on the quantity of GTI. The volume of GTI reflects firms’ desire for green innovation [49]. The quality of GTI is more significant for economic development and environmental conservation, which is a win-win situation. Therefore, the influence of the NEDC policy on the quality of GTI needs further study. GTI is classified by Zhang et al. [91] into two categories: real green technology innovation (RGTI) and strategic green technology innovation (SGTI). We will investigate the influence of the NEDC policy on the quality of GTI, RGTI, and SGTI in the future. The research variables could also be explored in the context of labor skills and the future of work as the prominence of the research increases [92,93]. This study does not fully account for potential moderating variables such as environmental regulations, media discourse, and public environmental consciousness when examining the relationship between the NEDC policy and GTI. Moreover, the research needs to comprehensively assess the economic implications linked to the innovation outcomes triggered by the NEDC policy, notable among them being carbon emission efficiency (CEE), enterprise digital transformation (EDT), and corporate ESG benchmarks. Future research directions include a deeper exploration of how the NEDC policy influences CEE, EDT, and ESG.

The study highlights the influence of the National E-commerce Demonstration Cities (NEDC) policy on Green Technology Innovation (GTI) in China. Producer services agglomeration, internet development, and financial support are the channels through which the NEDC policy affects GTI. NEDC policy’s influence on GTI is more pronounced in larger cities. Emerging economies can harness e-commerce policies and strategies to cultivate environmental sustainability and achieve economic growth.