Influence Mechanism of Smart City Pilot Policy on Enterprise Green Technology Innovation: Evidence from China

Abstract

This study employs data from publicly listed enterprises spanning the years 2008 to 2021 and uses China’s Smart City Pilot Policy (SCPP) as a quasi-natural experiment to explore its impact on driving green technological innovation within enterprises. The results reveal that the SCPP significantly fosters improvements in enterprises’ green technology innovation. The policy’s impact is particularly notable for state-owned enterprises, those in high-pollution industries, and technology-intensive enterprises located in the eastern regions of China. The SCPP facilitates green technological innovation by promoting green finance development, advancing digital transformation within enterprises, and strengthening environmental regulations. Moreover, the business environment serves as a positive moderator in enhancing the policy’s effectiveness, with favorable conditions in government, market, and legal frameworks further amplifying the policy’s positive influence on green technology innovation in enterprises.

1. Introduction

As a rapidly developing emerging economy, China has experienced quick economic growth, but also faces serious challenges with energy usage and environmental pollution. According to the 2020 Global Environmental Performance Index study, which was co-published from Yale University and other research institutions, China scored 37.3, placing it 120th out of 180 nations and regions assessed. Faced with this status quo, national policies that guide green innovation in the primary sector could be a helpful instrument in addressing environmental issues. It is essential to employ institutional innovation to eliminate obstacles to enterprise innovation and scientific and technological innovation in order to provide new growth prospects that support the nation’s sustainable development. This is known as a “double-wheel drive” of innovation. At the start of the twenty-first century, the idea of “smart cities” first surfaced due to the quick growth of information technologies like the internet, IoT, cloud computing, and artificial intelligence. The idea of building smart cities has subtly become a revolutionary method of city development on a global scale [1]. For instance, the European Union launched the European Smart Cities Network Program in 2006, leveraging open network spaces to provide high-quality public services to residents. Singapore introduced the decade-long “Smart Nation 2015” initiative, emphasizing the use of information network development to drive the construction of a globally integrated smart metropolis. Similarly, countries like the United Kingdom, Ireland, and Germany have successively implemented initiatives such as the “Digital Britain” plan, the “T-CITY” experiment, and the “Smart Bay” project, highlighting their commitment to integrating technology into urban management [2]. The SCPP exhibits distinct characteristics compared to other policies. Firstly, it demonstrates comprehensiveness and cross-sectoral synergy, encompassing areas such as information technology, infrastructure, green development, and public services. By deeply integrating technology with urban management, it promotes overall development, whereas traditional policies often focus on a single domain; for example, environmental policies prioritize emission reduction but lack the systemic optimization of urban management. Secondly, the SCPP is driven by technology and digital transformation, leveraging big data and artificial intelligence for optimizing city management. Additionally, the SCPP adopts sustainable development as a strategic goal, promoting green growth through low-carbon and circular economies. For instance, Hangzhou’s “City Brain” enables efficient waste sorting and resource recycling. In contrast, traditional industrial policies tend to emphasize economic growth while neglecting environmental costs. By prioritizing digitalization, connectivity, and sustainability, smart city policies have become a universal paradigm for addressing urbanization challenges and advancing sustainable development. Smart cities have become a major component of China’s innovation-driven development strategy, serving as a tool for economic and social modernization and transformation, as well as an incubator for emerging business models. To study smart cities with different dimensions and qualities, China has selected hundreds of pilot cities nationwide for its Smart City Pilot Policy (SCPP), including first-tier cities like Beijing, Shanghai, Shenzhen, and Hangzhou. The Ministry of Housing and Construction released three rounds of lists by the end of 2021, increasing the total number of SCPP cities to 290. China, one of the most populated and urbanized countries in the world, offers a vast market area and a variety of application scenarios that serve as a testbed for the growth of smart cities, which has advanced as a result of the application of effective data processing technologies. By comparing the SCPP with global smart city initiatives, it becomes evident that China’s model prioritizes large-scale implementation and technological innovation and addresses environmental issues, whereas other countries emphasize connectivity, digitalization, and urban management optimization.

In addition to serving as a symbol of urban modernity, smart cities are a crucial strategic hub for businesses looking to innovate, upgrade, and expand into new markets, while also advancing sustainable development. Enterprises should take advantage of these strategic possibilities, innovate to drive industrial growth, and actively contribute to smart city development. While the innovation for Chinese enterprises has improved significantly in recent years, they still face a number of challenges. The effectiveness of enterprise innovation is influenced by policy implementation, market acceptance, and the flexibility of the regulatory environment, among other factors. Innovation activities require substantial upfront investment, and it can be difficult and costly for enterprises to finance innovation, given the large R&D expenditures and limited funding channels. The lack of an intellectual property information sharing platform leads to information asymmetry, and there are no smooth green technology investment and financing channels. Therefore, the development of smart cities should also address these issues. Under the “double-wheel drive” trend of technological innovation and institutional innovation, smart city development is necessary to improve the environment and conditions for enterprise innovation and to boost the innovation vitality of enterprises in line with the times.

This research has two marginal contributions. By combining the SCPP and green technical innovation into a single theoretical framework, it first innovates the research approach. This allows the study to validate the role SCPP plays in encouraging enterprise innovation, thereby enhancing the research findings related to the policy. Second, the paper broadens the research on SCPP and green technological innovation by introducing the business environment as a moderating variable. This clarifies the crucial importance of a favorable business climate in attracting green capital and fostering enterprises’ adoption of advanced green technologies. Furthermore, this paper provides factual data and quantitative evaluation tools to help enhance business environment regulations and promote the greening of the economy. In summary, the key contributions are (1) the integration of green innovation and SCPP into a unified framework to validate the policy’s role and (2) the expansion of the research by considering the business environment as a critical moderating factor, providing factual information to aid in the formulation of policies aimed at fostering an environment that encourages green technological innovation. This innovative approach strengthens the theoretical foundation in these important research areas.

2. Literature Review

2.1. Research on Smart Cities

The concept of smart cities first emerged in 1990 at an academic conference held in San Francisco, where discussions focused on how the integration of information and communication technologies (ICTs) could enhance urban competitiveness [3]. The formal introduction of the smart city concept can be traced back to IBM’s 2008 report, Smart Earth: Smart cities were positioned as a crucial part of the Smart Earth agenda by the Next Leadership Agenda. Since then, smart city development has progressed on a worldwide scale. Developed nations have been the primary focus of research on smart city efforts (Liu et al., 2024 [2]; Lau et al., 2019 [4]). For example, the European Union unveiled the “2000 Plan” in 2005 and the “Smart Cities and Communities European Innovation Partnership” in 2012, which focused on the energy and transportation sectors and involved a total investment of 81 million euros. Singapore had already proposed its “Smart Island” plan in 1992. Japan started the “I-Japan Strategy 2015” in 2009 to support smart city projects in the fields of education, healthcare, and e-government.

Based on the current body of research, the majority of academics concur that smart cities may help businesses grow at the enterprise level. In terms of enterprise performance, smart cities may boost local policymaking and cloud computing to increase enterprises’ capacity for innovation and, consequently, their performance [1], as well as facilitating investment in green innovations [5,6]. From a sustainability viewpoint, Yu et al. (2019) [7] measured energy efficiency through the non-convex common frontier (DEA) method, and found that smart cities can effectively improve energy efficiency, therefore drastically lowering enterprises’ carbon emission intensity [8]. In terms of production efficiency, the SCPP may improve company resource allocation efficiency, expand enterprise financing options, and accelerate the flow of talent and information through a strong information network [9]. Additionally, SCPP encourages enterprise digital transformation using two strategies: optimum resource allocation and technical innovation [10]. Additionally, intelligent management and operation may be realized in smart cities by fully using sophisticated information technology, which allows the enterprise intelligent management mode to play a larger role in enterprise production and operation [11]. It can be seen that most of the studies agree on the effectiveness of the SCPP in promoting enterprise development, providing the results presented in this study with theoretical backing.

2.2. Research on Green Technology Innovation

There is still disagreement about whether Porter’s Hypothesis is applicable in this situation, despite the fact that the majority of the work to far has focused on the connection between environmental rules and green innovation. Du et al. (2021) [12] found that, in regions with low economic development, environmental regulations tend to hinder green technological innovation, with minimal impact on industrial upgrading. On the other hand, such restrictions were shown to considerably stimulate both industry upgrading and green technology innovation in regions with greater levels of economic growth. Additionally, Du et al. (2023) [13] observed that environmental tax policies exert a negative influence by reducing the quantity and quality of green patents, ultimately inhibiting green technological innovation. This occurs because such policies accelerate capital turnover and environmental investment. On the other hand, Li et al. (2023) [14] argue that environmental penalties encourage substantive green technology innovations through both external pressures and internal incentives, although they found these penalties had limited effects on strategic green technology innovation. Beyond regulatory frameworks, various studies have explored factors influencing green innovation, focusing particularly on market dynamics in enterprises. According to Hu et al. (2022) [15], who divided green tech innovation into two categories—process and product—green process innovation establishes a comprehensive connection between GVC involvement and green product innovation, with stronger environmental legislation strengthening this relationship. Zhang et al. (2024) [16] also discovered that employing internal funding and bank loans as a “bridge” to support digital transformation encourages green innovation. The amount of information technology investment in state-owned enterprises, enterprises with a strong sense of environmental responsibility, and enterprises subject to strict environmental regulations is positively correlated with the increasing number of green patents in highly polluting enterprises. These mixed findings further highlight the complex and context-dependent factors that shape enterprises’ green innovation activities, underscoring the need for continued research to unpack the nuances of this important topic.

2.3. Research on Smart Cities and Green Technology Innovation

Existing research has placed a lot of emphasis on the connection between the creation of smart cities and green technology progress. Much of the recent work in this area follows a shared theoretical framework, primarily examining green technological innovation at the urban level. This body of research suggests that the creation of smart cities may encourage the development of green technologies, which, in turn, aids in the improvement of urban industrial structures and green efficiency [17]. For example, Song et al. (2021) [18] investigated the effects of China’s SCPP on the quantity and quality of innovation in green technology using the policy as a quasi-natural experiment. According to their results, the creation of smart cities greatly increases the amount and caliber of green technological innovation. Similarly, Tang et al. (2023) [19] found that the expansion of the digital economy enhances smart cities’ ability to foster green technological innovation. Furthermore, smart cities have a geographical diffusion effect that enhances the green technological innovation extent in nearby cities in addition to encouraging local green technological innovation.

Building on these insights, the existing literature underscores the positive effects for smart city policies in enterprise development, green technological innovation, and their integration, but several gaps remain that this study aims to address. First, while numerous studies have shown that the SCPP enhances enterprise performance, energy efficiency, and production efficiency, most focus on city-level outcomes, lacking in-depth exploration of how these policies influence green technological innovation at the enterprise level. Second, although smart cities are found to foster green innovation through technological advancement and optimized resource allocation, the mechanisms linking these policies to enterprise green innovation remain underexplored. Last, but not least, while research emphasizes how smart cities promote green innovation and industry restructuring, less focus has been placed on the moderating effect of the business environment. Dynamic business environments created by smart cities can support enterprises transitioning to green business models by providing capital and improving organizational structures, but the interaction between smart city policies, business environments, and enterprise-level green innovation is insufficiently explored. To address these gaps, this study will examine how smart city policies affect green technological innovation within enterprises and the business environment as a moderating factor, providing a comprehensive understanding of their interplay and informing sustainable urbanization strategies.

3. Background Analysis and Research Hypotheses

3.1. Policy Context

A major step in China’s investigation of the “digital city,” the smart city initiative aims to set standards for creative and environmentally friendly urbanization, provide a new blueprint for the sustainable development of modern cities, and establish a model for urban innovation. In late 2012, China formally launched the SCPP. Additionally, it conducted the second and third rounds of the SCPP in 2013 and 2015 in an attempt to significantly promote the growth of smart cities. A total of 290 smart city initiatives were under progress in the three batches of pilot city lists released by the government by the end of 2021. Smart cities seek to improve urban administration’s intelligence while attaining sustainable growth, effective resource allocation, and environmental preservation by utilizing big data and cutting-edge technology. A core aspect of China’s priority development industries, the SCPP denotes the thorough advancement and integrated use of information technology. Finally, it might be argued that the SCPP is a quasi-natural experiment designed to stimulate green technology innovation.

3.2. Research Hypotheses

3.2.1. SCPP and Green Technology Innovation

From a standpoint of green practices, the cornerstone of the smart city paradigm is the incorporation of innovative concepts such as intelligent, ecological, low-carbon, and intensive technology into urban planning. To comply with the SCPP, which aims to lower emissions of urban pollutants and enhance the natural environment, pilot cities would unavoidably tighten their environmental regulations. Local businesses are likely to increase their investments in environmental protection due to these stringent regulations. Examples of such investments include buying eco-friendly machinery or carrying out research and development on green technologies to foster innovation in this field. Enterprises that use green technology will save manufacturing costs by using less energy, which will encourage businesses to use green technology again and create a positive feedback loop. Tight environmental laws can serve as a powerful signal for green development by directing scarce R&D resources toward eco-friendly green technology [20]. Smart city construction applies green technological achievements (such as various types of sensors, intelligent monitoring equipment, etc.) and advanced pollution management models and technical means to the daily production activities of enterprises, bringing reference and new thinking to the traditional ways of sewage discharge and the management of enterprises [21]. In terms of informationization construction, building smart cities substantially facilitates the effective clustering and distribution of information resources, and raising the degree of urban informatization may overcome temporal and spatial constraints while lowering the cost of bringing in green technology components. Utilizing information technology fusion and integration, connectivity, and interaction to increase transaction efficiency among innovation topics allows for the optimization of the external innovation environment and enables enterprises to receive and absorb external knowledge at a reduced cost [22]. Last, but not least, in order to promote green technology in manufacturing, the government’s credit and investment funds for the development of “smart cities” will be utilized for the introduction of more sophisticated machinery and technology, as well as for the research and development of new technologies. As a result, the following hypothesis is put forth.

H1. 

The SCPP positively promotes enterprises’ green technology innovation.

3.2.2. The Role Mechanism of the SCPP in Influencing Enterprises’ Green Technology Innovation

The SCPP promotes green finance development through multiple mechanisms, thereby effectively enhancing enterprises’ green technological innovation capabilities. First, smart city development raises financial development levels by utilizing financial technology like smart payments or smart finance [23]. The deep integration of informatization and digitization optimizes financial service processes, reduces financing costs, and improves financing efficiency [24]. The aggregation of financial elements further promotes the development of green finance, which not only eases enterprises’ financing restrictions on clean projects, but also provides an economic foundation for green technological innovation. Furthermore, green financing strengthens businesses’ propensity to adopt green technological innovation by reducing the knowledge asymmetry that exists between businesses and financial institutions [25]. Furthermore, by removing financial barriers and reducing financing expenses, the growth of digital finance significantly promotes green innovation [26]. On this basis, smart city policies alleviate enterprises’ financial pressures through channels such as government environmental subsidies and support from financial institutions. With strong central financial support for smart city construction, governments can incentivize enterprises to engage in key technological research for smart cities by offering tax incentives and special subsidies [27]. In parallel, fiscal measures such as environmental subsidies, interest discounts, and rewards internalize the positive externalities of energy saving and pollution reduction [28] and provide long-term specialized funding support to enterprises that actively respond to policies [29]. Therefore, smart city policies enhance financial efficiency and governmental support, reducing enterprises’ financial burdens and strengthening their green technological innovation capabilities. Based on this, we put forward the following research hypothesis.

H2a. 

The SCPP positively promotes enterprises’ green technology innovation by promoting green finance development.

The SCPP encourages the quick creation of digital platforms, infrastructure, and technologies, which profoundly alters how businesses innovate [30]. In line with the SCPP’s requirements, the government will concentrate on enhancing the draw of digital projects by fostering the growth of digital industries within its borders and directing the digital transformation of already-existing industries. This will enable the digitalization of a number of urban construction sectors, including social services, finance, transportation, and networking [31]. Digital transformation reduces information asymmetry by facilitating smooth communication between enterprises and external stakeholders, enabling government decision-makers and investors to better understand internal enterprise information, thereby increasing funding and investment in green innovation projects [32,33]. Additionally, digital technologies allow enterprises to extract valuable insights from vast data sets to make strategic decisions, enhancing their ability to collect, process, and apply market information. This fosters collaboration across production, management, and R&D departments, unlocking green innovation potential [34]. Furthermore, digital transformation strengthens governance, maximizes human capital structures, creates organizational ecosystems, and facilitates knowledge spillovers, enhancing innovation capacity [35,36]. Finally, the deep application of digital technologies optimizes resource allocation and operational efficiency, driving the digitalization of production, R&D, and quality control processes, as well as improving demand forecasting, inventory management, and supply chain operations [37,38,39]. These advancements promote integrated, collaborative, and networked innovation models, providing the platform and environment for enterprises to achieve breakthroughs in green technology innovation [40]. Therefore, the following research hypothesis is proposed.

H2b. 

The SCPP positively promotes enterprises’ green technology innovation by driving digital transformation.

The SCPP significantly enhances enterprises’ green technological innovation through environmental regulation. On the one hand, these policies prompt governments to strengthen environmental oversight by implementing stricter inspections and measures for polluting enterprises. This creates a “deterrent effect”, compelling enterprises to standardize production processes, reduce pollutant emissions, and shift toward renewable energy use and green technologies [27,41]. Environmental regulations will limit the manufacturing sector’s ability to upgrade its structure inside its own industry, but they will encourage industry-wide manufacturing industry structural upgrading [42]. Smart city development bolsters the effectiveness of environmental regulations through institutional norms and technological advancements, providing robust support for green development. Institutionally, smart city initiatives emphasize “people-oriented” and “ecological civilization” principles, requiring local governments to enhance environmental regulation and adopt smart management practices to improve urban ecosystems. Technologically, regulatory agencies employ smart technologies such as sensors and intelligent monitoring systems in key enterprises and regions to collect real-time data on emissions and energy consumption. These data are analyzed and predicted using big data platforms, improving regulatory efficiency and encouraging enterprises to innovate in pollution management technologies and upgrade their emissions equipment to address the cost pressures brought about by environmental regulations. This, in turn, objectively advances urban green development [3]. However, as the digital economy grows, external environmental control becomes even more robust, driving enterprises’ green innovation. The implementation of digital technology, including e-government platforms, environmental monitoring systems, and urban big data platforms, significantly mitigates the government’s information disadvantage, enabling a shift from end-point regulation to proactive governance and enhancing regulatory efficiency. For instance, smart environmental platforms allow governments to directly access real-time video footage of enterprises’ wastewater, exhaust, and sludge disposal. Big data analysis can then identify issues in polluting enterprises’ operations, reducing regulatory costs while increasing the comprehensiveness and effectiveness of oversight [43]. As external regulatory intensity increases, the risks of polluting behaviors being detected and penalized grow. When the cost of penalties exceeds the gains from unsustainable practices, enterprises are compelled to adopt green innovation models, fostering sustainable urban development. Therefore, the following research hypothesis is proposed.

H2c. 

The SCPP positively promotes enterprises’ green technology innovation by strengthening environmental regulation.

3.2.3. Moderating Effects of the Business Environment

The business environment is the external environment that companies engaged in finance, investment, entrepreneurship, innovation, and other activities have to deal with [44]. Optimizing the business environment has two main advantages: it eliminates the impacts of enterprises looking to rent space and it promotes technological innovation [45]. The business environment is particularly separated into governmental, market, and legal environments for examination in conjunction with the Regulations on Optimizing the Business Environment [46].

From the standpoint of the governmental environment, the business climate is greatly influenced by the government. Government efficiency represents the transaction costs related to local governments as well as the quality of administrative services they offer [47]. A well-run government can effectively explain the objectives of the innovation-driven development strategy of the SCPP to enterprises, increase government oversight practices, and discourage enterprises from engaging in rent-seeking activities [48]; meanwhile, the frequency with which government officials change also affects how stable the political climate is [49]. Frequent changes in officials raise the risk of enterprise investment and stifle creative behavior by causing policy changes, an uncertain economic climate, and a deterioration in the credibility of government data.

H3a. 

The governmental environment positively regulates the promotion effect of the SCPP on enterprises’ green technology innovation.

In terms of the market environment, an orderly and competitive market will break the local administrative monopoly barriers and strengthen the incentives for market competition, which, in turn, will stimulate the intrinsic driving force for innovation [50]. The effective distribution of innovation resources, including money, talent, and technology, may be encouraged by the market environment, which is primarily governed by the market and enhanced by governmental regulation. In addition, through the adoption of the market access negative list, raising the access threshold, limiting the penetration of high-pollution and low-efficiency industries, and creating a market environment that tends to green science and technology, it not only inhibits the unhealthy competition, but also maintains the market order and creates a benign space for the enterprise’s green innovation.

H3b. 

The market environment positively regulates the promotion effect of the SCPP on enterprises’ green technology innovation.

In view of the legal environment, legal protection focused on intellectual property rights is essential for encouraging enterprises to innovate independently. An ideal intellectual property protection system might provide sufficient security to enable SMEs’ green innovation practices while reducing the detrimental consequences of plagiarism on their innovation processes. We can increase the awareness of environmental protection among enterprises, force them to develop green innovation technologies, and realize an advanced industrial structure if we fortify the improvement of environmental protection laws following the implementation of the SCPP, increase the penalties for high-polluting businesses, and subsidize high-tech and environmentally friendly industries.

H3c. 

The legal environment positively regulates the promotion effect of the SCPP on enterprises’ green technology innovation.

4. Research Design

4.1. Model Setting

Since the implementation of the SCPP was carried out in phases, the first batch of smart city pilot areas was designated in 2012, followed by additional regions being included in the pilot scope in 2013 and 2015. It can be observed that the SCPP was introduced at multiple time points and in a phased manner. Compared to the traditional DID (Difference-in-Differences) approach, which applies only to policies or events at a single point in time, the staggered DID method can address situations where policies or interventions are implemented gradually at different time points, better reflecting the progressive nature of policy implementation in reality. Furthermore, the staggered DID approach can control for time trend interference through dynamic treatment models, enabling the more precise identification of policy effects. Therefore, this paper adopts the staggered DID method, as this model can account for cases where the treatment time points are not fully consistent across individuals. The model settings are as follows:

$${GrePat}_{it}={\beta }_0+{\beta }_1{DID}_{it}+{\beta }_2{X}_{it}+Z_{jt}+{\rho }_i+{\tau }_t+{\epsilon }_{ijt}$$

(1)

where ${GrePat}_{it}$ is an explanatory variable indicating the level of green technological innovation of enterprise i in period t, expressed as the number of green invention patent applications plus one and taking the natural logarithm. ${DID}_{it}$ denotes the SCPP dummy variable; if the enterprise’s city is located in the SCPP area, it takes the value of 1, otherwise it is 0, and its coefficient ${\beta }_1$ embodies the policy effect of the implementation of the SCPP on the role of enterprises’ green technology innovation. $X_{it}$represents a set of control variables affecting the enterprise ’s green invention patent applications in year t. ${\rho }_i$ and ${\tau }_t$ denote industry fixed effects and year fixed effects, respectively, with model estimation adjusted for the clustering of standard errors at the city level. ${\epsilon }_{ijt}$ is a random perturbation term.

A stepwise testing method was employed to explore the mechanism by which the SCPP influences the level of green technological innovation. The specific steps are as follows: First, Formula (1) was used to verify the impact of the SCPP on the level of green technological innovation. Next, Formula (2) was applied to examine the effect of the SCPP on the mediating variable $M$. Finally, both the mediating variable $M$ and the SCPP were included in the model simultaneously. The validity of the mediation effect hypothesis was determined by testing the sign and significance of the coefficients for the mediating variable and SCPP Formula (3).

In order to further verify the mechanism of the impact of the SCPP on green technology innovation, the following model is constructed:

$$M_{it}={\alpha }_0+{\alpha }_1{DID}_{it}+{\alpha }_2{X}_{it}+Z_{jt}+{\rho }_i+{\tau }_t+{\epsilon }_{ijt}$$

(2)

$${GrePat}_{it}={\delta }_0+{\delta }_1{DID}_{it}+{\delta }_2{M}_{it}{+\delta }_3{X}_{it}+Z_{jt}+{\rho }_i+{\tau }_t+{\epsilon }_{ijt}$$

(3)

where $M_{it}$represents the mediating variable, which, in this paper, specifically contains three variables: the level of green financial development, digital transformation, and environmental regulation.

Further, the following model is set up to test the moderating effect of the business environment.

$${GrePat}_{it}={\gamma }_0+{\gamma }_1{DID}_{it}+{\gamma }_2{DID}_{it}\times E_{it}+{\gamma }_3{E}_{it}+{\gamma }_4{X}_{it}+Z_{jt}+{\rho }_i+{\tau }_t+{\epsilon }_{ijt}$$

(4)

$E_{it}$represents the moderating variable, i.e., business environment, which is measured by the total score of the marketization index. It is also categorized into governmental environment level, market environment level, and legal environment level.

4.2. Data Sources and Processing

The Shanghai and Shenzhen A-share-listed companies in China are chosen as the study sample for this article, which spans the years 2008 through 2021. The following is how the sample data are processed: (1) The financial category’s listed company sample is not included. (2) The ST and *ST listed company sample is not included. (3) Missing data are not included in the sample of listed firms. (4) All continuous variables are shrink-tailed at the 1% level to prevent outliers from influencing the regression findings. The CSMAR database provides enterprise-level data, whereas the China City Statistical Yearbook provides city-level statistics.

4.2.1. Dependent Variable

Referring to the methodology of Qi et al. (2018) [51], the number of green invention patent applications of listed firms is utilized to quantify the green technology innovation level of organizations in a more intuitive and accurate manner. The indication is processed by adding 1 and taking the logarithm, since the number of green patents held by companies is skewed to the right. To guarantee the robustness of the study, this research additionally uses the ratio of listed companys’ green patent applications to the total number of patent applications this year (ratioenvrinvpat) as an explanatory variable in the robustness test.

4.2.2. Independent Variable

The time dummy variable (DID = treat × post) and the SCPP dummy variable’s interaction term are employed as independent variables in this study. By reviewing the relevant documents of the State Council, manually collecting and organizing the list of incorporated SCPP zones, and assigning time according to the list of the three batches of the SCPP in 2012, 2013, and 2015, the cities that do not qualify as pilots are designated as the control group, while the cities that join the SCPP are designated as the treatment group. Enterprises in the pilot cities are given a value of 1 for the year of inclusion in the SCPP list and the years that follow, and 0 for the remaining years, due to the varying times of inclusion in the pilot zones; enterprises in non-pilot cities are given a value of 0 for the sample period.

4.2.3. Mediating Variable

Based on the methodology of Zhan et al. (2023) [52], green finance builds an indicator system that includes green credit, green support, green equity, green investment, green bonds, green insurance, and green funds. The entropy weight technique is then used to gauge the degree of green finance. Green credit is measured by the ratio of the total credit amount for environmental protection projects in the region to the total credit amount in the province. Green support is measured by the ratio of fiscal expenditure on environmental protection to general budgetary expenditure. Green equity is measured by the ratio of carbon trading, energy use rights trading, and pollutant discharge rights trading to the total trading volume in the equity market. Green investment is measured by the ratio of investment in environmental pollution control to the GDP. Green bonds are measured by the ratio of the total issuance of green bonds to the total issuance of all bonds. Green insurance is measured by the ratio of environmental pollution liability insurance revenue to total premium income. Green funds are measured by the ratio of the total market value of green funds to the total market value of all funds. We used the entropy weight method to comprehensively calculate the above indicators, and the resulting value represents the level of green finance.

According to Wu et al. (2021) [53], this study statistically analyzes the word frequency related to company digital transformation from the annual reports of A-share-listed enterprises in Shenzhen and Shanghai. In particular, we examine two facets of the statistical arrangement of the key words—the “underlying technology use” and the “digital technology application”—and uses them to gauge the degree of company digital progress. These include “digital technology application,” which includes mobile Internet, e-commerce, mobile payments, and so forth, with 34 keywords, and “underlying technology application”, which includes four technical fields: blockchain, big data, cloud computing, and artificial intelligence. The indicator used to gauge the extent of company digital transformation is the natural logarithm of the total number of word frequencies after one is added.

For environmental regulation intensity, the methods of Guo. (2019) [42] and Ye et al. (2018) [54] are used to establish the level of environmental control, which involve developing a municipal-level index of environmental regulation enforcement measured by the occurrence rate of terms associated with “environmental protection” into the government work reports of each province. Every year, during the “Two Sessions” held at the beginning of the year, governments at various levels present reports outlining the achievements of the previous year and their work plans for the upcoming year. These reports, shaped by the consolidation of diverse societal demands and consensus, play a key role in guiding governmental actions for the year. Consequently, the attention given to environmental issues within these reports reflects the government’s commitment to environmental governance and offers a comprehensive view of governance policies for that year. Moreover, utilizing provincial government work reports offers the advantage of “exogeneity”, as future plans from higher-level governments direct lower-level governments without being directly influenced by the lower-level governments’ interests. For this study, Python software (the version is python3.10) was employed to process and tokenize these reports, and the frequency of environmental-regulation-related terms in 317 local governments’ work reports between 2008 and 2021 was computed and compiled.

4.2.4. Moderator Variable

For evaluating the business environment, we adopt the approach of Li et al. (2024) [46], drawing on the China Provincial Marketization Index developed by Wang et al. (2003) [55]. This index serves as a comprehensive system to assess the relative progress of marketization across China’s provinces, autonomous regions, and municipalities. It currently covers all 31 provinces, autonomous regions, and municipalities. The index offers overall scores and rankings of marketization from 1997 to 2022, as well as detailed scores for various sub-indicators and component indices (though data for Tibet are missing in certain years). The marketization index includes a general indicator alongside several specific sub-indicators, which concentrate on topics including the expansion of market intermediary organizations, the maturation of product markets, the development of factor markets, the interaction between the government and the market, the non-state-owned economy, and the legal and institutional framework. In this study, the overall quality of the business environment is assessed using the total marketization index score, the “government-market relationship” sub-indicator score to assess the governmental environment, the “development of factor markets” sub-indicator score to gauge the maturity for market environment, and the “development of market intermediary organizations and the rule of law” sub-indicator score to measure the maturity of the legal environment.

4.2.5. Control Variables

The following control variables are chosen in accordance with Wang and Wang’s (2021) [43] methodology to limit the impact of outside unobservable influences on the study’s findings: gearing, which is determined by the ratio of the company’s total liabilities to its total capital at the end of the year; enterprise size, which is determined by the natural logarithm of the company’s yearly total assets; return on equity, which is calculated by dividing the company’s net profit by the average amount of owners’ equity; cash flow ratio, which is calculated by dividing the total assets by the net cash flow from operational activities; and gross profit margin, which is calculated as the difference between operating income and operating costs expressed as a percentage of operating income. The natural logarithm of the ratio of the first biggest shareholder’s shareholding to the total number of firm shares is used to calculate the shareholder’s shareholding ratio. Economic development, as determined by the natural logarithm of the city’s per capita GDP, industrial structure, as determined by the natural logarithm of the secondary industry’s added value as a percentage of the province’s GDP, the number of urban general higher education institutions, and scientific and technological input are all considered macro-level factors. The descriptive statistics for the study’s primary variables are displayed in

Table 1. Descriptive statistics.

Variables	Symbol	N	Mean	SD	Min	Max
Dependent Variable	GrePat	25,869	0.134	0.412	0	5.521
Independent Variable	DID	25,869	0.339	0.473	0	1
Mediating Variable	ed	25,869	2.863	3.653	0	26.11
	er	25,869	0.00349	0.00132	0.000294	0.0124
	finance	25,869	0.456	0.187	0.0576	0.897
Moderator Variable	envir	25,869	9.318	1.752	−0.161	12.39
	envir_g	25,869	7.798	1.662	−7.145	12.15
	envir_f	25,869	11.14	3.448	−1.448	17.97
	envir_l	25,869	9.927	3.471	−0.736	16.51
Control Variable	soe	25,869	0.428	0.495	0	1
	size	25,869	22.16	1.283	19.32	26.45
	lev	25,869	0.439	0.206	0.0274	0.908
	roe	25,869	0.0646	0.131	−0.926	0.435
	grossprofit	25,869	0.283	0.174	−0.0606	0.871
	cashflow	25,869	0.0469	0.0705	−0.222	0.283
	top1	25,869	35.14	14.88	8.087	75.84
	Second	25,869	41.58	11.17	11.70	90.97
	pergdp	25,869	96,632	53,640	5162	467,749
	school	25,869	35.12	29.62	1	93
	input	25,869	12.63	1.838	6.269	15.53

.

5. Empirical Analysis

5.1. Benchmark Regression

The SCPP model’s estimation findings on enterprises’ green technology innovation are shown in

Table 2. Benchmark regression: SCPP and enterprises’ green technology innovation.

	$\mathit{G}\mathit{r}\mathit{e}\mathit{P}\mathit{a}\mathit{t}$
	(1)	(2)	(3)
$DID$	0.0387 ***	0.0394 ***	0.0392 ***
	(0.0136)	(0.0136)	(0.0137)
_cons	0.1201 ***	−0.2722 **	−0.2121
	(0.0046)	(0.1369)	(0.1629)
Enterprise-level control variables	No	Yes	Yes
City-level control variables	No	No	Yes
Individual	Yes	Yes	Yes
Year	Yes	Yes	Yes
Observations	25,809	25,809	25,809
R-squared	0.5056	0.5060	0.5060

Note: *** and ** indicate significance at the 1%, 5%, and 10% statistical levels, respectively; t-values corrected for heteroskedasticity in parentheses.

. Regardless of the inclusion of control variables at various levels, the benchmark regression’s findings are displayed in Table 2, where the core independent variable DID’s coefficient is considerably positive at the 1% level. This suggests that the SCPP may greatly raise enterprises’ level of innovation in green technologies. According to the aforementioned study, the SCPP uses state-of-the-art information technology to provide enterprises with a robust technical support platform for attaining the best possible integration and resource efficiency. Developing smart cities facilitates green technology research, development, and application marketing for enterprises. Additionally, it makes it possible for them to implement more eco-friendly and effective technologies in the manufacturing and operation processes, which promotes the advancement of green technology innovation. Hypothesis 1 is thus put to the test.

5.2. Parallel Trend Test

The treatment and control groups must meet the parallel trend assumption, which states that they should show comparable temporal trends in the dependent variable before the policy intervention in order for the Difference-in-Differences (DID) model to be considered valid. In order for the control group to be a legitimate counterfactual for the treatment group in this study, the green technology innovation levels of businesses in the treatment and control groups under the SCPP should exhibit similar patterns. The baseline for this analysis is the first year of the study period (2008), and the model includes dummy variables for the first three and last three years of the smart city pilot. The parallel trend test findings, as shown in Figure 1, demonstrate that the SCPP has a favorable impact on businesses’ ability to innovate green technologies. The treatment and control groups’ innovations in green technology did not vary significantly before the policy was put into effect. However, the changes become statistically significant after the policy is put into effect. The model passes the parallel trend test, since this implies that there were no pre-existing discrepancies in the temporal trends of green technology innovation between the two groups.

5.3. Robustness Testing

5.3.1. Placebo Test

To assess the robustness of benchmark regression findings and address potential concerns regarding omitted variable bias, this paper performs a placebo test. Specifically, the core independent variable, the SCPP, is regressed using 500 randomly selected non-repeated samples by employing the DID method. This process generates a two-tailed distribution of the estimated coefficients and significance levels for the dummy random samples, as shown in the figure. As illustrated in Figure 2, the estimates for the placebo samples are normally distributed around zero, whereas the estimated coefficients from the benchmark regression are clearly distinct and fall outside the range of the placebo sample distribution. This suggests that the results of the 500 simulations in the regression model are correct with certainty, indicating that the benchmark regression results pass the placebo test. Moreover, this outcome implies that omitted variable bias does not compromise the validity of the benchmark regression estimates.

5.3.2. Control of Other Policies

Taking into account additional rules that were put in place during the research period may potentially raise the degree of green technology innovation in enterprises, which would disrupt the experimental findings, for instance, the “national big data comprehensive pilot zone” in 2016, the “broadband China” policy pilot in 2014, and the “low-carbon city” policy pilot in 2010. It gathers aforementioned policy data and creates the control variables of these policy shocks to be added to the model to control for the effects of these policies in the regression analysis. Column 1 of

Table 3. Robustness testing.

	(1)	(2)	(3)	(4)	(5)
	Control of Other Policies	Replacement of Dependent Variable	PSM-DID	Extreme Values Are Excluded	Changing Time Series
DID	0.0406 ***	0.0097 ***	0.0280 *	0.0273 *	0.0406 ***
	(0.0134)	(0.0026)	(0.0150)	(0.0159)	(0.0146)
DID 1	−0.0091
	(0.0101)
DID 2	0.0083
	(0.0106)
DID 3	0.0085
	(0.0116)
_cons	−0.2141	−0.0162	−0.0562	−0.2813 *	−0.4475 **
Control variables	(0.1626) Yes	(0.0351) Yes	(0.1712) Yes	(0.1651) Yes	(0.1872) Yes
Individual	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes
Observations	25,809	25,809	17,239	20,224	22,071
R-squared	0.5061	0.2377	0.4555	0.4135	0.4994

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% statistical levels, respectively; t-values corrected for heteroskedasticity in parentheses.

displays the regression results, which demonstrate that, even after controlling for these policy factors, the coefficients of this paper’s core independent variable remain significantly positive. This suggests that the SCPP is more independent of other policies and has a better exogenous nature, which enables it to eliminate the chance that the shocks of other policies may affect the trial outcomes.

5.3.3. Replacement of Dependent Variable

To guarantee the robustness of the benchmark regression results, the dependent variable in this study is replaced with the percentage of green invention patent applications submitted by companies compared to the total number of patent applications filed by enterprises. The regression results are shown in Table 3, column 2. Both before and after the control variables are included, the coefficients indicating the dependent variable’s effect on the independent variable are statistically positive at the 1% level, indicating that the results of the benchmark regression are reliable and strong.

5.3.4. Propensity Score Matching–Double Difference (PSM-DID)

Selectivity bias is still a possibility even if the DID model can partially solve the endogeneity issue. To lessen the effect of uncertainty brought on by non-random selection bias, this article employs propensity score matching (PSM) in addition to the benchmark regression. A DID test comes next, and Table 3’s column 3 displays the regression findings. The findings of the benchmark regression are solid and trustworthy, and the dependent variable’s coefficient of influence on the independent variable is significantly positive at the 10% level, suggesting that the development of smart cities continues to encourage businesses to innovate in green technologies.

5.3.5. Extreme Values Are Excluded

The political and economic circumstances in China’s municipal cities are clearly more favorable than those in other cities. This study eliminates the portion of the sample where businesses are situated in the aforementioned cities in order to prevent the impact of extreme values on the benchmark regression’s outcomes. In Table 3, column 4, the regression results are displayed. The benchmark regression findings are resilient and dependable because, even after eliminating the extreme values, the coefficient of the dependent variable’s effect on the independent variable is still significantly positive at the 10% level before and after the control variables are added.

5.3.6. Changing Time Series

Regression is carried out with one period of lag on the dependent variable, and it is hypothesized that it takes some time for the implementation of the SCPP to promote the role of green technological innovation to appear because enterprises must devote a significant amount of time to research and innovation. Column 5 of Table 3 displays the regression results. The attributable coefficient of DID is significantly positive at the 1% level, indicating that the promotion effect of the SCPP on green technological innovation is significant even when the dependent variable is regressed with one period of lag.

5.4. Mechanism Testing

The aforementioned findings demonstrate that the SCPP successfully raises enterprises’ degree of green technology innovation. In the sections that follow, we examine in more detail how the development of smart cities influences enterprises’ degree of green technology innovation from three perspectives: regulation of the environment, digital transformation, and green finance.

5.4.1. Green Finance

The approach of Zhan et al. (2023) [52] is used in this research to build an indicator system that includes green credit, green support, green equity, green investment, green bond, green insurance, and green funds. The entropy weight method is then used to gauge the degree of green financial development. Columns (1) and (2) of

Table 4. Mechanism testing.

Variable	Green Finance		Digital Transformation		Environmental Regulation
	(1)	(2)	(3)	(4)	(5)	(6)
	Finance	Envrinvpat	Ed	Envrinvpat	Er	Envrinvpat
DID	0.0259 ***	0.0345 **	0.2075 **	0.0381 ***	0.0005 ***	0.0360 ***
	(0.0024)	(0.0135)	(0.1025)	(0.0136)	(0.0001)	(0.0134)
finance		0.1800 **
		(0.0748)
ed				0.0049 ***
				(0.0017)
er						6.8681 ***
						(1.9898)
_cons	0.4129 ***	−0.2873 *	−14.8712 ***	−0.1407	0.0061 ***	−0.2550
	(0.0382)	(0.1646)	(1.6046)	(0.1635)	(0.0008)	(0.1634)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Individual	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Observations	25,809	25,809	25,809	25,809	25,809	25,809
R-squared	0.9702	0.5062	0.7473	0.5065	0.4038	0.5063

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% statistical levels, respectively; t-values corrected for heteroskedasticity in parentheses.

present the findings of the first-stage and second-stage regressions on the mechanism variable green finance, respectively, in the test technique, which is based on the practice of Lu et al. (2022) [56] employing a two-stage regression approach to determine the influence mechanism. In accordance with this empirical result, the SCPP has a positive impact on raising the development level of green finance, as evidenced by the estimated coefficients of the green finance index with the independent variable being positive and significant at the 1% significance level. When the green finance index is used as a control variable, the estimated coefficients are also positive and significant at the 5% significance level. This clearly shows that the SCPP’s purpose can be effectively fulfilled by the development level of green finance, and that SCPP’s implementation raises businesses’ levels of green technological innovation by encouraging green financing growth.

5.4.2. Digital Transformation

Python is used to statistically arrange the word frequency associated with the growth of corporate digital transformation from the text of annual reports of A-share-listed companies in Shenzhen and Shanghai. In order to create the indicator system of the digital transformation of enterprises, the word frequencies of important technology directions are first grouped and combined to create the final total word frequencies. After adding 1, the natural logarithm of the total number of word frequencies is used as the indicator for gauging the extent of the digital transformation of enterprises. The outcomes of the first- and second-stage regressions on the mechanism variable digital transformation are shown in Table 4’s columns (3) and (4), respectively. This empirical finding indicates that the SCPP has a positive impact on enterprise digital transformation, as evidenced by the estimated coefficients of digital transformation with the independent variable being positive and significant at the 5% significance level, and the estimated coefficients when digital transformation is the control variable that is positive and significant at the 10% significance level. This implies that enterprise digital transformation is a successful means of fulfilling the SCPP’s mandate, and that the SCPP’s implementation raises the bar for green technology innovation in businesses by encouraging enterprise digital transformation.

5.4.3. Environmental Regulation

The severity of municipal environmental restrictions may have an impact on businesses’ green innovation initiatives. Table 4’s columns (5) and (6) display the findings of the first- and second-stage regressions pertaining to the mechanism variable of environmental regulation. The findings reveal that the estimated coefficients for environmental regulation are both positive and statistically significant at the 5% level when the independent variable is considered. This indicates that the SCPP has a favorable impact on strengthening environmental regulation within the region. Furthermore, when environmental regulation is included as a control variable, the coefficients remain positive and significant at the 10% level. This implies that the SCPP’s effect is increased in large part through the digital transformation of enterprises, and that the policy further encourages green technology innovation by supporting this transition. It appears that, in response to rising costs of emissions associated with stricter environmental regulations, enterprises have proactively pursued green technological innovations to reduce pollution and achieve sustainable development. Consequently, as environmental regulations tighten, enhancing the quality of green technological innovation has become a key strategic priority for the future growth of enterprises.

5.5. Analysis of Moderating Effects

Table 5. Analysis of moderating effects.

	(1) Business Environment	(2) Governmental Environment	(3) Market Environment	(4) Legal Environment
DID	0.0392 ***	0.0412 ***	0.0392 ***	0.0392 ***
	(0.0137)	(0.0137)	(0.0137)	(0.0137)
DID × envir	0.0138 ***
	(0.0045)
DID× envir_g		0.0091 ***
		(0.0032)
DID × envir_f			0.0068 ***
			(0.0025)
DID × envir_l				0.0040 *
				(0.0022)
Control variables	Yes	Yes	Yes	Yes
Individual	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Observations	25,809	25,809	25,809	25,809
R-squared	0.5063	0.5064	0.5064	0.5061

Note: *** and * indicate significance at the 1%, 5%, and 10% statistical levels, respectively; t-values corrected for heteroskedasticity in parentheses.

illustrates how the business environment in the SCPP process influences businesses’ innovation in green technologies. In this study, the legal, market, and governmental environments are the three elements that make up the business environment. The first column states that, if the business environment in which the SCPP functions is better, it may make a greater contribution to the enterprise’s degree of green innovation. At the 1% level, there is a considerable positive correlation between the SCPP and the business environment as a whole, as indicated by the cross-multiplier term coefficient of 0.0138. This is because cities that have great business environments are more standardized, can better implement national policies, and provide the best infrastructure for the development of the digital economy. This increases the level of green technology innovation among businesses and speeds up the growth of the green digital sector. The second column demonstrates that the coefficient of the cross-multiplier term between the governmental environment and the SCPP is significantly positive at the 1% level, indicating that effective governmental services contribute to the advancement of the SCPP on the level of the green technological innovation of enterprises. In other words, when the government environment is better, administrative efficiency is higher, and the connection between the government and the firm is healthier, the SCPP promotes businesses to spend more on green innovation. The third column demonstrates that the coefficient of the cross-multiplier term between the market environment and the SCPP is significantly positive at the 1% level, indicating that an orderly market can effectively play a positive role in the SCPP to promote the level of green technological innovation of enterprises. In a market with fair competition, businesses can aggressively seek industrial transformation and upgrading while avoiding intense low-price competition. Finally, the third column demonstrates that, at the 10% level, the cross-multiplier coefficient between the market environment and the SCPP is considerably positive. This shows that, in order to properly control business behavior and successfully safeguard business rights and interests—both of which are beneficial to the growth and development of SMEs—the legal environment helps to govern a fair and transparent legal environment.

6. Further Analysis

6.1. Heterogeneity Analysis

6.1.1. Nature of Ownership

In terms of their degree of green innovation, different ownership firms may be impacted by the SCPP in different ways because of the notable variations in their operational systems and architectural designs. In this study, all observed samples of state-owned and non-state-owned businesses will be divided based on the type of ownership.

Table 6. Nature of ownership.

	(1)	(2)
	SOE	Non-SOE
DID	0.0568 ***	0.0009
	(0.0205)	(0.0163)
_cons	0.2562	−0.7453 ***
	(0.2781)	(0.2028)
Control variables	Yes	Yes
Individual	Yes	Yes
Year	Yes	Yes
Observations	11,024	14,702
R-squared	0.5416	0.4737

Note: *** indicate significance at the 1%, 5%, and 10% statistical levels, respectively; t-values corrected for heteroskedasticity in parentheses.

shows the outcomes of grouped regression. As the coefficient of the sample group grouped into state-owned enterprises is significantly positive at the 1% level (0.0568), the results indicate that the pilot policy has a more noticeable role in increasing the level of green innovation in state-owned enterprises. In contrast, the coefficient of the sample group grouped into non-state-owned enterprises (0.0568) is not significant at any level. From the standpoint of political skew, the reason might be because SOEs are more politically significant than non-SOEs and are therefore more likely to be given resources that are skewed by the city’s pilot program. The empowering impact of the pilot policies on SOEs is more substantial because, in contrast to the profit-oriented development pattern of non-SOEs, SOEs need to take a leading role in green innovation and are more socially responsible (particularly in the SCPP domains).

6.1.2. Nature of Industry

The involvement of enterprises in various industries should also be heterogeneous, given that smart cities have policies aimed at modernizing and transforming urban industry. The sample enterprises are split into heavy- and non-heavy-pollution industries for regression, in accordance with the methodology of Ni et al. (2016) [57]. The outcomes are displayed in

Table 7. Nature of industry.

	(1)	(2)
	Heavy-Pollution Industry	Non-Heavy-Pollution Industry
DID	0.0253	0.0436 ***
	(0.0279)	(0.0165)
_cons	−0.3426	−0.2843
	(0.3623)	(0.1861)
Control variables	Yes	Yes
Individual	Yes	Yes
Year	Yes	Yes
Observations	5984	19,771
R-squared	0.4410	0.5226

Note: *** indicate significance at the 1%, 5%, and 10% statistical levels, respectively; t-values corrected for heteroskedasticity in parentheses.

. While its influence on the green innovation of enterprises in the non-heavy-pollution sector is positive (0.014) but not statistically significant, the pilot policy can significantly boost the green innovation of enterprises in the heavy-pollution sector, as evidenced by the significantly positive coefficient of its independent variable (0.103). The reason might be that non-heavily polluted enterprises themselves are part of the electronic information, ecological environment, green services, energy saving and environmental protection, and other high-tech green industries that are more responsive to policies focused on green development. The SCPP can, therefore, better assist these enterprises in advancing green technology innovation. The bulk of heavily polluting enterprises includes coal, cement, iron and steel, thermal power, and other heavy chemical enterprises, making it difficult for beneficial green innovation technologies to play a more visible role in these enterprises. These traditional heavy industries are also dealing with issues like low technological content and challenges in industrial transformation.

6.1.3. Industry Factor Intensity

There are significant differences in the need for green technology development across various input industries, and, as a result, the pilot policy’s impact varies as well. Regression analysis is performed independently for the sample enterprises in this study, which are categorized into three groups based on the industry they are in: labor-intensive, capital-intensive, and technology-intensive.

Table 8. Industry factor intensity.

	(1)	(2)	(3)
	Labor-Intensive	Technology-Intensive	Capital-Intensive
DID	0.0059	0.0759 **	0.0424
	(0.0119)	(0.0333)	(0.0330)
_cons	−0.0974	−0.9049 **	−0.4013
	(0.2127)	(0.3646)	(0.4186)
Control variables	Yes	Yes	Yes
Individual	Yes	Yes	Yes
Year	Yes	Yes	Yes
Observations	9610	10,592	4882
R-squared	0.5119	0.5166	0.4270

Note: ** indicate significance at the 1%, 5%, and 10% statistical levels, respectively; t-values corrected for heteroskedasticity in parentheses.

displays the regression findings. Although it is evident that the SCPP has a major influence in raising the degree of green technology innovation in technology-intensive firms, its effects on labor-intensive and capital-intensive enterprises are comparatively minor and statistically insignificant. Based to this paper, this is because smart city construction better serves the needs of enterprises that rely heavily on technology, and, on the other hand, technical enterprises can make better use of the infrastructure construction facilities and digital agglomeration that smart city construction brings. High research expenditures, high worker cultural and technical levels, high product added value, quick expansion, and a big share of technological knowledge are all features of the production system. These enterprises have a greater need for green technology innovation; however, technology-intensive enterprises are more in line with the policy orientation after the construction of the smart city is finished as opposed to traditional labor-intensive enterprises or technology-intensive enterprises to receive government policy subsidies. As a result, the SCPP can more effectively promote green technology within the context of technology-intensive enterprises.

6.1.4. Regional Heterogeneity

It is expected that the SCPP’s effect on encouraging green technology innovation among enterprises may vary depending on the area, given the regional differences in China’s growth. Based on their geographic locations, enterprises in this research are divided into three groups: eastern, central, and western. While the effect on enterprises in the central and western areas is not statistically significant, the data shown in

Table 9. Regional heterogeneity.

	(1)	(2)	(3)
	East	West	Central
DID	0.0630 ***	0.0193	−0.0037
	(0.0214)	(0.0257)	(0.0200)
_cons	−0.4648 *	−0.2395	0.3695
	(0.2526)	(0.2415)	(0.3480)
Control variables	Yes	Yes	Yes
Individual	Yes	Yes	Yes
Year	Yes	Yes	Yes
Observations	17,663	4442	3688
R-squared	0.5248	0.4118	0.4425

Note: *** and * indicate significance at the 1%, 5%, and 10% statistical levels, respectively; t-values corrected for heteroskedasticity in parentheses.

show that the SCPP considerably raises the green technology innovation levels of enterprises in the eastern region. This discrepancy might be explained by the fact that the eastern part of the country is China’s political and economic center, making it more receptive to policy changes. Additionally, cities in the east benefit from advantages such as advanced technological capabilities, industrial concentration, and a well-developed human resource base, all of which facilitate the successful implementation for the policy. The central or western regions face challenges such as slower development, limited resources, weaker market dynamics, and slower policy-induced incentives. Consequently, the policy’s influence on promoting technical innovation is less noticeable in these areas over a brief period of time. Therefore, it is crucial for the central and western regions to prioritize improvements in digital infrastructure, promote digital applications, and capitalize on the digital dividends to close the gap in green technology development.

7. Conclusions and Policy Recommendations

7.1. Conclusions

This study uses a mechanism test to analyze how China’s SCPP influences enterprises’ green technological innovation from the viewpoints of digital transformation, green finance, and environmental regulation. The SCPP is seen as a quasi-natural experiment that uses data from public firms between 2008 and 2021. By including the external aspect of the business environment, the moderating influence of the policy implementation environment on the process of creating smart cities that encourage enterprises to innovate green technologies is investigated. For enterprises in the pilot area, the more favorable the governmental environment, the more organized the market environment, and the more equitable and transparent the legal environment, the stronger the promotion effect of the SCPP on the level of enterprise in green innovation. It has been found that the SCPP can effectively raise the level of green technology innovation in enterprises by promoting the development of green finance, the digital transformation of enterprises, and the strengthening of the environmental regulation path. There are variations in this effect, though, and the policy effect is particularly noticeable for state-owned enterprises, enterprises that pollute a lot, and enterprises that use a lot of technology in the eastern area.

By contrasting the study findings of this work with those of earlier studies, the following comment may be made: In line with the findings of earlier research on the beneficial impacts of smart cities on green technological innovation, this study first identifies that the SCPP considerably encourages the increase in enterprises’ levels of green technological innovation. Based on enterprise-level data, the results of this study further support these conclusions and offer further micro-level empirical support. Second, by encouraging the growth of green financing, propelling digital transformation, and fortifying environmental regulation, this study outlines the ways in which the SCPP raises the levels of green technological innovation in businesses. This discovery broadens the range of processes investigated in earlier studies. For example, Zhang et al. (2024) [16] investigated the effects of digital transformation on green innovation from the standpoint of information technology investment, while Hu et al. (2022) [15] investigated the relationship between process and product innovation in green innovation from the perspective of value chain participation. Building on existing research, this study offers a more thorough understanding of how green innovation is empowered by smart city building by including the routes of green financing and environmental regulation. Furthermore, this study reveals notable variations in the impacts of the SCPP among different enterprise categories and geographical areas. State-owned enterprises, enterprises that emit a lot of pollutants, and enterprises that use a lot of technology are all more affected by the policy, and enterprises in the eastern region have improved more than those in other regions. This finding is consistent with previous research on the varied impacts of environmental regulations on regional economic inequality. This study clarifies the geographical variations and variability in policy impacts by placing these findings within the framework of smart city construction, offering fresh perspectives for both theory and practice. Lastly, one of the paper’s main innovations is the addition of the external element known as the “business environment” as a moderating variable to examine how the environment of policy execution supports enterprises’ green technology innovation through the development of smart cities. The findings indicate that the impact of the SCPP on company green innovation is higher in environments with better governance, more structured markets, and more equitable and transparent laws. On the other hand, few studies currently in existence look at the SCPP’s implications from a business environment standpoint. This study closes this research vacuum and broadens the paradigm for assessing policies in this field.

7.2. The Necessary Conditions for Generalization

This study examines how the SCPP affects enterprises’ green technology innovation using data from Chinese listed firms from 2008 to 2021. The conclusions of this study have certain scopes of applicability and limitations. As the sample primarily focuses on listed companies in China, which typically have higher resource access and technological capabilities, the findings are more applicable to resource-rich and innovation-intensive enterprises, whereas their applicability to SMEs or non-listed enterprises requires further testing. Additionally, the policy effects are more pronounced in enterprises located in eastern regions, indicating that the findings may have weaker applicability in less economically developed areas. Furthermore, China’s unique policy environment—distinguished by the government’s significant influence over the distribution of resources and its highly centralized approach to implementing policies—limits the generalizability of the conclusions to other countries or regions, especially those with more market-oriented systems or less government intervention. This study identifies a significant moderating effect of the business environment on policy outcomes, with stronger effects observed in regions where government environments are superior, markets are well organized, and legal systems are fair and transparent. This suggests that, when extending the findings to other regions, the institutional and environmental conditions of the target areas should be carefully considered. Additionally, by analyzing the mediating effects of green financial development, enterprises’ digital transformation, and enhanced environmental regulation, the study reveals the internal mechanisms through which the policy operates. However, these mechanisms may be further influenced by industry characteristics, corporate governance structures, and complementary regional policy measures. Therefore, when applying the findings to other countries or regions, it is essential to verify the similarity of policy designs and assess whether mechanisms such as green finance, digital transformation, and environmental regulation are equally applicable. Considering that this study’s data span the years 2008–2021, the policy effects reflect short-to-medium-term impacts. Long-term effects may vary due to changes in economic conditions, technological advancements, and policy adjustments, necessitating further confirmation through subsequent studies. In summary, when extrapolating the findings, it is important to cautiously consider the characteristics of the sample, policy environments, levels of economic development, and spatiotemporal factors.

7.3. Policy Recommendations

This study’s conclusions strengthen our knowledge of market-oriented innovation behavior and company green innovation, and they offer a foundation for relevant policy. The environmental regulation’s method, premise, and financial development in fostering industrial green innovation should be emphasized by policymakers, who should also carefully craft policies to propel industrial green transformation. The following policy insights are presented in this paper: (1) national strategic policies should serve as a guide for green financial development [58]; (2) financial institutions should be encouraged to integrate green finance and innovative industries through the use of blockchain and other digital technologies; (3) credit support toward green energy, building materials, crafts, or other fields should be improved; (4) low-carbon transformation should be encouraged; and (5) the financing issue of green innovation should be resolved. In addition, local governments also need to further improve the green financial risk prevention system [59] and boost the examination and evaluation of financial institutions’ green financial operations and their capacity to lower the risk of enterprise financing, as well as create and enhance the risk compensation system for investments in green projects. (2) In order to promote digital transformation and upgrading, integrate internal and external resources, improve inter-enterprise synergies, and increase production efficiency, enterprises should make good use of the information platform built by the smart city. Additionally, the government should encourage enterprises to create and maintain a diverse digital green innovation ecosystem. The foundation for enterprise digital transformation is talent in order to jointly drive digital transformation and innovation, strengthen staff members’ training in digital skills and innovation, concentrate on developing data analysis and intelligent manufacturing specialists, bring in elites from outside the company, and collaborate with academic institutions, research centers, and tech companies. (3) Reasonable and appropriate environmental regulatory measures should be formulated. To motivate businesses to allocate greater resources to green technology, the government should implement specific measures, employ legislation, and set distinct green innovation objectives according to the municipal level [60]. In addition, it should encourage cross-sectoral, multifaceted, and effective green data integration and improve the evaluation and dissemination of environmental information [61]. To motivate enterprises to take the lead in implementing innovative green technologies and reducing pollution in the environment, distinct environmental policies for enterprises in various industries and with varying pollution levels should be implemented. (4) In the process of the SCPP, we should focus on building a perfect business environment for enterprises and adhere to the organic integration of orderly market and active government [62]. At the level of governmental environment, we should strengthen the transparency and stability of policies, ensure the openness and transparency of policies related to green technological innovation, avoid frequent changes in policies to bring unnecessary troubles to enterprises, and enhance the confidence of enterprises. At the level of market environment, we should have strict market access and exit rules and simplify the process of entering the market for green innovation enterprises. To direct social capital toward green innovation and boost the market, we should make use of incentives like tax breaks and green innovation funding. We should create a platform for green technology services that offers everything from technology transfer to market promotion, and hasten the commercialization of green innovation successes. At the legal level, we should enforce environmental protection laws more strictly, punish noncompliant businesses harshly, and preserve market order. We should enhance the legal framework pertaining to intellectual property rights, bolster the enforcement of the law, and encourage enterprises to develop and protect their ideas.

This study offers factual support for the idea that smart city development encourages enterprises to innovate green technologies. This study does, however, still have several shortcomings. First, while this analysis examines data from 2008 to 2021 based on public enterprises, it does not include data from earlier or more recent periods, which may not accurately reflect changes in patterns throughout all economic cycles. Additionally, the information of unlisted enterprises is excluded owing to data gathering limitations, which might have an effect on the analysis conclusions’ generalizability. Second, although China’s smart pilot cities were chosen for observation, this study may not have sufficiently considered the diversity of these factors due to the wide variations in the specific conditions of the various cities, particularly with regard to the extent of economic progress, industrial structure, and policy implementation.