Does Smart City Construction Inhibit Corporate Financialization? Evidence from China

Abstract

Against the backdrop of funds flowing from the real economy to the virtual economy, the trend of corporate financialization is becoming more and more obvious. Establishing how to guide enterprises to return to their main business is the key to guaranteeing the sustainable development of the economy. Considering the promulgation of new accounting standards in 2007 and the availability and completeness of listed company data, this paper takes A-share listed companies from Shanghai and Shenzhen stock markets in China from 2008 to 2022 as research samples. This paper takes China’s smart-city pilot policy as a quasi-natural experiment and constructs a time-varying difference-in-differences (DID) model to empirically analyze the impact of smart city construction on corporate financialization. According to the study, smart city construction can significantly inhibit corporate financialization. Specifically, this paper measures corporate financialization by the proportion of financial assets to total assets, and empirical results show that when the city where the enterprise is located is selected as a smart city pilot, the degree of corporate financialization decreases by 0.7%. After a series of robustness tests, this conclusion still holds. Mechanism analysis indicates that smart city construction can inhibit corporate financialization by alleviating financing constraints and improving profitability. Heterogeneity analysis shows that smart city construction has a stronger inhibitory effect on corporate financialization in the central and western regions, state-owned enterprises, management shareholdings, industries with a high degree of competition, and enterprises in the growth and maturity stages. On the one hand, the research results of this paper can help us to understand the influencing factors of corporate financialization, avoid the excessive financialization of enterprises, and promote the sustainable development of enterprises. On the other hand, it also tests the policy effect of smart cities and provides help for the subsequent policy formulation of smart city construction.

1. Introduction

As China’s economic development slows down, overcapacity in the real economy and the rise in production costs have resulted in a decrease in the return on real investment. Enterprises’ willingness to invest in entities has weakened, gradually deviating from production and business activities. Numerous enterprises have participated in the financial sector in pursuit of high returns, giving rise to the phenomenon of corporate financialization. Corporate financialization is the trend where enterprises detach from their core business operations and show a greater propensity to invest in financial assets, with a significant portion of their profits primarily derived from financial channels [1,2]. According to the Wind database, in 2023, over 1200 listed companies purchased financial products, with a total amount exceeding CNY 1.2 trillion. As can be seen from Figure 1, the scale of financial assets and the proportion of financial assets in total assets of China’s listed companies generally show an upward trend. The average financial assets held by listed companies in China increased from 100 million in 2008 to 980 million in 2022. The average proportion of financial assets in total assets rose from 2.4% in 2008 to 8.5% in 2022. Especially since 2013, the upward trend of financialization of China’s listed companies has become more evident, and the speed of the rise has accelerated from 2013 to 2019. The economic structural patterns for 2019–2021 were impacted by the COVID-19 pandemic and trade disputes. Financial market fluctuations intensified, and the risks of financial investment increased, which made corporate investment decisions more cautious. Therefore, the rate of increase in the financialization of listed companies slowed down after 2019.

Enterprises allocating appropriate financial assets can avoid risks and ensure stable cash flow [3,4]. Nevertheless, in the long term, corporate financialization will exert a “crowding out” effect on entity investment and impede corporate innovation and sustainable development [5]. It is also prone to cause systemic risk, resulting in the collapse of corporate stock prices, triggering financial crisis, which is not conducive to macroeconomic stability [6]. To stabilize the real economy and prevent the occurrence of corporate financialization, the 14th Five-Year Plan clearly proposed to “improve the capacity of financial services for the real economy”. The 2017 National Financial Work Conference also clarified that “finance must regard serving the real economy as the starting point and landing point, and serving the real economy is the fundamental measure to prevent financial risks”. The Question and Answer on Issuance Supervision-Regulatory Requirements on Guiding and Regulating the Financing Behavior of Listed Companies released by China Securities Regulatory Commission in 2020 mentioned that in addition to financial enterprises, restrictions are put forward on the allocation of financial assets when listed companies apply for refinancing. In this context, in order to steer enterprises back to their core operations, prevent financial risks, and foster the stable growth of the real economy, it holds certain practical significance to investigate ways to inhibit corporate financialization.

Numerous studies have explored the factors that influence corporate financialization. Scholars have proposed that corporate governance models [7], internal control [8], executive equity incentives [9] and digital transformation [10] affect corporate financialization. Corporate financialization is not only affected by the internal circumstances of the enterprise, but also by the external environment. Due to the uncertainty of the macro environment and economic policies, real enterprises exhibit a greater propensity to invest in financial assets characterized by higher liquidity and returns, thereby intensifying corporate financialization [11,12]. On the basis of the quasi-natural experiments of China’s value-added tax reform and accelerated depreciation policy of fixed assets, many scholars found that tax incentives slow down corporate financialization through the enhancement of returns on fixed assets and the stimulation of enterprises’ R&D investment [13,14]. Gao and Ren discovered that digital finance can inhibit the financialization of SMEs and reduce the risk of bankruptcy through financing constraints [15]. Therefore, whether smart city construction, as an important city-level external policy, has an impact on corporate financialization is worth discussing.

A smart city is the integration of technology and a city, representing a city that leverages ICT systems to provide advanced services for its inhabitants, thereby enhancing their standard of living [16,17]. Zanella et al. (2018) argued that in the context of smart city construction, advanced communication technologies like the Internet of Things (IoT) can improve urban management and provide added-value services to citizens [18]. According to the “China Smart City Market Forecast, 2023–2027” released by International Data Corporation (IDC), the scale of investment in the ICT market for smart cities in China in 2023 exceeded CNY 870 billion, which is an increase from the market investment scale in 2022. It is estimated that by 2027, it will reach CNY 1185 billion. The construction of smart cities enhances the operational efficiency of cities and contributes to urban sustainable development. Enterprises constitute vital economic entities within cities. The construction of smart cities will also entail various impacts and opportunities to enterprises. In an effort to boost domestic demand and facilitate industrial structure upgrading, the Ministry of Housing and Urban-Rural Development of China promulgated the smart-city pilot policy in 2012. Three batches of pilots involving more than two hundred cities have been carried out. The integration of new-generation information technology with urban economic and social development is primarily demonstrated in the construction of network infrastructure, digital management, and smart industries, among other aspects.

Scholars have also extensively explored the impact of smart city policies. Caragliu and Del Bo (2019) analyzed 309 EU cities through propensity score matching and noted that implementing smart city policies more than the EU average is conducive to urban innovation [19]. Jo et al. (2021) examined the changes in the smart city industrial ecosystem in Korea and found that the traditional labor-intensive manufacturing industry in smart cities has shifted towards the high-tech industry, which indicates that the construction of smart cities can upgrade the industrial structure [20]. From the micro perspective, the existing literature indicates that smart city construction can enhance the green innovation of enterprises [21], improve the quality of enterprises’ export products [22], and promote the digital transformation of enterprises [23]. Wen et al. (2022) discovered that smart city construction can significantly boost the total factor productivity of enterprises by diminishing transaction costs through their research on listed manufacturing companies in China [24]. Although the existing literature has studied the economic impacts of smart city construction from the micro level, research on the influence of smart city construction on corporate financialization has not been found. The investment decision of enterprises is largely influenced by the external environment. Smart cities represent an urban form based on digital technology. The construction of smart cities in the locations of enterprises is likely to have an impact on the financialization of enterprises. Studying the effects of smart city construction on corporate financialization is beneficial for governments, enabling them to implement differentiated smart city policies and improve the efficiency of urban governance. This research also helps enterprises to understand the policy dividends required to conduct business activities and solve their problem of financing constraints. It also guides enterprises to return to the main business, which is advantageous for the long-term development of enterprises and boosts the real economy.

On the basis of the smart-city pilot policy as a quasi-natural experiment, this paper employs a time-varying difference-in-differences (DID) model to explore the effect of smart city construction on corporate financialization and the transmission mechanism. The marginal contribution of this paper is manifested in the following aspects: First, this paper takes the implementation of smart-city pilot policy as an opportunity to empirically test the impact of smart city construction on corporate financialization. It expands the inspection of smart city construction at the micro level, examines the implementation outcomes of smart city policy from the perspective of corporate financialization, and supplements the research on the influencing factors of corporate financialization. Second, this study discusses the impact mechanism of smart city construction on corporate financialization from the perspective of financing constraints and profitability and clarifies how smart city construction can inhibit corporate financialization. In addition, it provides support for the subsequent formulation and implementation of policies for smart city construction and provides a decision-making reference for how to inhibit corporate financialization. Third, this paper takes the smart-city pilot policy as a quasi-natural experiment and constructs a time-varying difference-in-differences (DID) model to examine the effect of smart city construction on corporate financialization, which can better address the endogenous problem and improve the reliability of the research outcomes.

The rest of this paper is organized as follows: Section 2 presents the background of the smart city policy, continues the theoretical analysis and puts forward the research hypothesis. Section 3 provides the research design and introduces the data sources, model specification and variable descriptions of this paper. Section 4 comprises the empirical analysis and robustness test. Section 5 presents mechanism analysis and heterogeneity analysis. Section 6 presents the conclusion and discusses recommendations.

2. Policy Background and Theoretical Analysis

Section 2.1 introduces the policy background of smart cities to understand the origin of smart cities and the objectives of China’s smart-city pilot policy. Section 2.2 analyzes the impact of smart city construction on corporate financialization on the basis of the motivation of corporate financialization in theory.

2.1. Policy Background

IBM introduced the concept of “Smart Earth” in a speech titled “Smart Cities: An Agenda for the Next Generation of Leaders” in 2008. Subsequently, in 2009, IBM unveiled the plan “Smart Earth Winning in China”, which introduced the vision of a “Smart City”, aiming to contribute to urban development both globally and in China. IBM believed that a “smart city” is able to leverage ICT to perceive, analyze, and integrate crucial information from the key systems of urban operations, as well as provide intelligent responses to various demands like people’s livelihood, industrial and commercial activities. The notion of a smart city has garnered widespread interest from nations across the globe. In 2009, Dubuque, USA, cooperated with IBM to construct the country’s inaugural smart city. The EU initiated the European Smart Cities Initiative in 2009 and “the European Innovation Partnership Initiative for Smart Cities and Communities” in 2012. Simultaneously, the Chinese government has also begun to attach importance to the construction of smart cities.

In 2012, the Ministry of Housing and Urban-Rural Development of China mentioned in the “Notice on Doing a Good Job of National Smart City Pilot Work” that “smart city construction is a crucial approach to fostering the development of intensive, intelligent, green and low-carbon new urbanization, boosting domestic demand, and propelling industrial transformation and upgrading”. The “National Smart City (District, Town) Pilot Indicator System (Trial)” also stipulates that pilot cities should strengthen network infrastructure construction, digital city management, smart finance, and smart industry and economic development. Smart finance requires pilot cities to strengthen the intelligent construction and services of the urban financial system, encompassing the construction of the integrity supervision system, the investment and financing system, and the financial security system. Smart industries and economies require pilot cities to clarify the investment in urban innovation industry. China’s smart-city pilot policy began in 2012 with the establishment of the first batch of smart city pilots, involving 90 cities (districts and towns). Subsequently, the second and third batches of smart city pilots were set up in 2013 and 2014, respectively, involving 103 cities (districts, counties, towns) and 84 cities (districts, counties, towns).

2.2. Theoretical Analysis

The existing literature categorizes the motivation of corporate financialization into three types: “reservoir” theory, “investment substitution” theory and “entity intermediary” theory. The “reservoir” theory originates from Keynes’ (1936) precautionary savings theory, in which enterprises hold financial assets in order to prevent future capital shortages from adversely affecting production and business activities [25]. When the capital of enterprises is abundant, they can invest in financial assets in the short run to activate capital, because financial assets generally exhibit greater liquidity compared to fixed assets [26]. The uncertainty of future cash flow also makes enterprises more inclined to hold more financial assets to satisfy the capital requirements for future real investment and promote the development of enterprises’ core business [27]. The “investment substitution” theory holds that given a higher return on investment for financial assets compared to that of real assets, enterprises prefer financial assets, leading to the reduction of enterprises’ real investment [28]. In addition, due to the principal-agent conflict, managers of enterprises tend to prefer financial assets with higher return on investment and faster realization speed in pursuit of short-term interests, and they are unwilling to invest corporate funds in entities, thus ignoring long-term interests [29]. The “entity intermediary” theory is similar to the “investment substitution” theory. As a result of the discrimination in bank financing, enterprises with easy access to bank loans transfer funds to enterprises without easy access to loans, so that well-capitalized enterprises will earn interest spreads [30]. However, this behavior crowds out the real investment of enterprises and has implications for financial market stability [31]. Based on the motivation of corporate financialization, this paper analyzes the impact of smart city construction on corporate financialization.

Although the government bears the primary responsibility for the construction of smart cities, enterprises also participate in a large number of smart projects. The government provides financial support to draw enterprises into the construction process, and the enterprises actively participate based on the profit-seeking motive, which also provides an opportunity for the digital transformation of enterprises [32]. Substantial funds are allocated to the renewal and upgrading of production equipment as well as the refinement of business processes, thus increasing the entity investment of enterprises and diminishing the extent of corporate financialization. Additionally, the digital transformation of enterprises alleviates information asymmetry, enabling enterprises to more accurately grasp market demands. This subsequently optimizes sales forecasting and production arrangements and enhances the efficiency of real investment. Enterprises concentrate on the development of their core business and decrease financial investment. The business model changes and innovations spurred by smart cities are conducive to enterprises expanding their market share and exploring new markets, thereby increasing opportunities for real investment and avoiding the excessive financialization of enterprises. Smart city construction enables cities to gather more high-tech enterprises, high-end technologies and talents, and government subsidies, tax incentives and other related policy support to provide external conditions for enterprise innovation, which is conducive to corporate R&D. For instance, Hangzhou, in the process of constructing a smart city, provides preferential tax policies for high-tech enterprises to deduct research and development expenses and reduce income tax, which reduces the research and development costs of enterprises. The policy dividend has led the net inflow rate of talents in Hangzhou rank to first in the country for many years in a row, and the city’s research and experimental development funds are significantly higher than the national average. Apparently, numerous initiatives have optimized the business environment, attracted more high-tech enterprises, and stimulated the innovation vitality of enterprises. Increasing investment in innovation can optimize the resource allocation of enterprises and divert capital towards R&D initiatives as opposed to the financial market, which inhibits corporate financialization. Consequently, the subsequent hypothesis is put forward:

H1. 

Smart city construction can inhibit corporate financialization.

In the process of smart city construction, digital technology empowers financial services to alleviate the issue of information asymmetry and solve financing problems for enterprises. The “reservoir” motivation for corporate financialization weakens. Bank credit constitutes the primary source of enterprise financing. Due to information asymmetry, enterprises often need to meet some conditions to obtain loans, such as good business conditions and having tangible assets as collateral [33]. Banks need to assess the creditworthiness and repayment capacity of loan enterprises and supervise the utilization of loans. The construction of smart cities enables governments to introduce digital industries and industrial digital transformation, so as to digitally empower financial services and overcome the limitations of traditional financial services [32]. The application of digital technology in the process of smart city construction can alleviate the problem of information asymmetry between enterprises and banks and enhance the banks’ ability to obtain, identify and process relevant information about the lending enterprises, so as to improve the information transparency of both credit parties [34]. Banks widen financing conditions for SMEs in terms of collateral, credit rating, business status, etc., to improve the financing environment for enterprises [35]. When it becomes easier for enterprises to raise capital and future sources of funding are secured, there is less excessive financialization for financing purposes. The “reservoir” motivation for corporate financialization weakens. In addition, the financing cost of SMEs declines, and the loan objects of well-capitalized enterprises no longer exist, which indirectly inhibits the financialization of large-scale enterprises. As a result, the subsequent hypothesis is put forward:

H2. 

Smart city construction can inhibit corporate financialization by alleviating corporate financing constraints.

Digital projects and the transformation of business models brought about by smart city construction bring more business opportunities to enterprises. Simultaneously, the application of digital technology lowers the operating expenses of enterprises. The return on enterprises’ real investment rises, and the motivation of “investment substitution” of corporate financialization diminishes. The implementation of smart-city pilot policies necessitates the involvement of both the government and local enterprises. From a revenue perspective, the demand for digital technology in pilot cities can be transformed into business opportunities for enterprises [19]. With the construction of digital infrastructure, the business model is constantly changing, and the sales channels of enterprises are increasing. In addition, smart city construction helps enterprises achieve digital transformation. Digital transformation can provide personalized services to customers and improve product quality, thereby increasing the revenue of enterprises. From a cost perspective, nowadays, informatization is an important mechanism to save transaction costs [36]. Smart city construction can enhance the city’s information infrastructure and reduce the external transaction costs and internal control costs of enterprises [37]. The utilization of digital technology not only enhances the cooperation efficiency between the two parties involved in a transaction but also improves the internal management and production efficiency of the enterprise, ultimately causing a reduction in the enterprise’s operating costs. Backed by the development of smart cities, big data information platforms make corporate information transparent and reduce agency costs. The alleviation of agency problems makes the goals of managers and shareholders converge. Managers attach importance to real investment and pursue the long-term interests of enterprises. The degree of corporate financialization is eased. In short, smart city construction can increase enterprises’ profitability, narrow the disparity between real and financial returns, and diminish the enterprises’ inclination to invest in financial assets. Hence, the subsequent hypothesis is formulated:

H3. 

Smart city construction can inhibit corporate financialization by improving corporate profitability.

Figure 2 displays the theoretical framework of this paper.

3. Research Design

This section clarifies the research samples and data processing of this paper, then constructs a time-varying difference-in-differences (DID) model, and finally explains the measurement methods of the dependent variable, core explanatory variable and control variables.

3.1. Data Sources

On account of the promulgation of new accounting standards in 2007, this study selects A-share listed companies from the Shanghai and Shenzhen stock markets in China for the period 2008 to 2022 as research samples in order to ensure the uniformity of financial data. In this paper, the initial data are handled in the following manner: first, financial and real estate sectors are removed; second, samples with ST and *ST are deleted; third, samples with missing primary data are deleted; and fourth, samples with an asset/liability ratio exceeding one are deleted. Moreover, to avoid the impact of outliers on the regression outcomes, all continuous variables are treated with 1% winsorization. Through the above processing, 38,244 observations were obtained for this research. The data were obtained from the CSMAR database.

3.2. Model Specification

In 2012, China initiated its first batch of smart-city pilot programs, involving 90 cities (districts and towns). Subsequently, the second and third batches of smart city pilots were set up in 2013 and 2014, involving 103 cities (districts, counties and towns) and 84 cities (districts, counties and towns), respectively. The smart-city pilot list is determined by the National Smart City Expert Committee formed by the Ministry of Housing and Urban-Rural Development of China, so the smart-city pilot policy can be designed as a quasi-natural experiment and as an exogenous shock event. Since the smart-city pilot policies are implemented in three batches, this paper uses a time-varying difference-in-differences (DID) model to empirically examine the connection between smart city construction and corporate financialization. A time-varying difference-in-differences (DID) model is constructed as follows:

$${\mathit{Fin}}_{i,t}=\alpha +\beta {\mathit{Smart}}_{i,t}+\gamma X_{i,t}+{ \mu }_i+v_t+{ \epsilon }_{i,t}$$

(1)

In the model, ${\mathit{Fin}}_{i,t}$ denotes corporate financialization. ${\mathit{Smart}}_{i,t}$ denotes the smart-city pilot policy. ${\mathit{Smart}}_{i,t}$ is the multiplication term of $\mathit{Treat}$ and $\mathit{Post}$, where $\mathit{Treat}$ is the dummy variable of the treatment group and the control group, and $\mathit{Post}$ is the dummy variable of the policy implementation time. $X$ represents a group of control variables influencing corporate financialization. ${\mu }_i$ and $v_t$ stand for firm fixed effects and year fixed effects. ${\epsilon }_{i,t}$ refers to the random disturbance term. $\beta$, the coefficient of most concern in this paper, represents the net effect of smart-city pilot policies on corporate financialization. The initial pilot cities were set up at the close of 2012, and the actual publication of the second and third batch of smart city pilots took place in the latter half of 2013 and 2015, respectively. Taking into account the lag in policy implementation, this paper takes 2013, 2014 and 2015 as the implementation times of three batches of smart-city pilot policies. In the published smart-city pilot list, only districts, counties or towns in some cities are used as pilots, rather than the entire prefecture-level city. Therefore, this paper excludes prefecture-level cities with only districts, counties or towns as pilots in the process of determining the list of smart city pilots.

3.3. Variable Descriptions

3.3.1. Dependent Variable

Corporate financialization (${\mathit{Fin}}_{i,t}$). Drawing on the practices of Demir (2009) [28] and Du et al. (2017) [38], this paper uses the proportion of financial assets in total assets to measure corporate financialization. Financial assets include trading financial assets, derivative financial assets, net loans and advances granted, net available-for-sale financial assets, net held-to-maturity investments and net investment real estate. Due to the promulgation of the new standards for financial instruments, net available-for-sale financial assets and net hold-to-maturity investments after 2018 have been changed to debt investments, other debt investments, investments in other equity instruments and other non-current financial assets. Although monetary funds and long-term equity investments also belong to financial assets, monetary funds are usually used for operating activities, and long-term equity investments are mostly held to guarantee the smooth functioning of enterprise production and to disperse business risks. Therefore, monetary funds and long-term equity investments are not categorized as financial assets in this paper. Investment real estate is maintained with the aim of generating rental income or capital appreciation and has no direct relationship with the production and operational activities of enterprises. Therefore, this paper categorizes investment real estate as a type of financial asset.

3.3.2. Core Explanatory Variable

Smart-city pilot policy (${\mathit{Smart}}_{i,t}=\mathit{Treat} \text{×} \mathit{Post}$). This paper takes the smart-city pilot policy issued by the Ministry of Housing and Urban-Rural Development of China as a quasi-natural experiment. When the city where the enterprise is located is selected as a smart city, $\mathit{Treat} =1$; otherwise, $\mathit{Treat} =0$. $\mathit{Post}$ is a time dummy variable set to 1 for the year of policy implementation and subsequent years, otherwise $\mathit{Post} =0$.

3.3.3. Control Variables

Taking into consideration that other corporate characteristics factors may have an impact on corporate financialization, this paper draws on the previous literature and controls the following corporate characteristics variables: enterprise scale (Size); financial leverage (Lev); Tobin’s q (TobinQ); return on total assets (ROA); enterprise growth (Growth); cash flow (Cfo); proportion of fixed assets (Fix); ownership concentration (Top1); leadership structure (Dual). The specific variable definitions are shown in

Table 1. Variable definitions.

Variable Type	Variable	Variable Symbol	Variable Definition
Dependent variable	Corporate financialization	Fin	Financial assets/total assets
Core explanatory variable	Smart-city pilot policy	Smart	If the city where enterprise i is located is included in the smart-city pilot list in year t, Smart = 1; otherwise, Smart = 0
Control variable	Enterprise scale	Size	Natural logarithm of total assets
	Financial leverage	Lev	Total liabilities/total assets
	Tobin’s q	TobinQ	Market value/total assets
	Return on total assets	ROA	Net profit/average balance of total assets
	Enterprise growth	Growth	Revenue growth rate: Main business income of the year/main business income of the previous year − 1
	Cash flow	Cfo	Net cash flows from operating activities/total assets
	Proportion of fixed assets	Fix	Net fixed assets/total assets
	Ownership concentration	Top1	The ratio of the largest shareholder
	Leadership structure	Dual	If the chairman and general manager are the same individual, assign a value of 1; otherwise, assign a value of 0.

.

4. Empirical Results and Analysis

This section mainly presents the baseline regression results of the impact of smart city construction on corporate financialization under the premise of meeting the parallel trend test and conducts a series of robustness tests to improve the reliability of the empirical results.

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	N	Mean	Standard Deviation	Minimum	Median	Maximum
Fin	38,244	0.0490	0.0875	0.0000	0.0108	0.5480
Smart	38,244	0.1652	0.3713	0.0000	0.0000	1.0000
Lev	38,244	0.4080	0.2019	0.0283	0.4009	0.8919
Size	38,244	22.0939	1.2594	19.2804	21.9035	26.3784
TobinQ	38,244	2.0398	1.3362	0.8032	1.6196	15.6067
ROA	38,244	0.0446	0.0667	−0.3982	0.0427	0.2744
Growth	38,244	0.1667	0.3803	−0.6479	0.1105	3.8082
Cfo	38,244	0.0488	0.0688	−0.1912	0.0477	0.2913
Fix	38,244	0.2158	0.1578	0.0021	0.1827	0.7723
Top1	38,244	34.3001	14.8399	8.0600	32.1500	78.1400
Dual	38,244	0.2993	0.4579	0.0000	0.0000	1.0000

reports the descriptive statistics for the variables. The mean value of the corporate financialization (Fin) is 0.0490, the minimum value is 0, the maximum value is 0.5480, the median is 0.0108, and the standard deviation is 0.0875, which suggests that the average financialization degree of the sample enterprises is 4.9%, and the financialization of different enterprises varies greatly during the sample period. The mean value of smart-city pilot policies (Smart) is 0.1652, indicating that 16.52% of the cities in which the sample enterprises are situated are selected as smart city pilots. The descriptive statistics for control variables are basically reasonable.

4.2. Parallel Trend Test

The prerequisite for employing the time-varying difference-in-differences (DID) model for policy evaluation is to meet the parallel trend hypothesis. In other words, the change trend of the treatment group and the control group is consistent prior to the implementation of the policy. So as to test the parallel trend hypothesis of samples, this paper adopts the event study method to test parallel trends. The model is as follows:

$${\mathit{Fin}}_{i,t}=\alpha +\sum _{t=-4}^7{\beta }_t{D}_{i,t}+\gamma X_{i,t }+{\mu }_i+v_t+{ \epsilon }_{i,t}$$

(2)

In the model, $D_{i,t}$ is a set of dummy variables. If the city where enterprise $i$ is located is selected as a smart city pilot in year $t$, the value is 1; otherwise, it is 0. The symbolic meanings of other variables are in accordance with model (1). The coefficient ${\beta }_t$ reflects the difference in the financialization of enterprises in the pilot cities and non-pilot cities in the $t$ year of implementation of the smart-city pilot policy.

Since the sample observation period for the paper spans 2008–2022 and the policy implementation time of the first and last batch of smart city pilots is 2013 and 2015, respectively, there are no sample values less than period −5 or greater than period 7 in some treatment groups. Therefore, in this paper, with reference to the practice of Bai et al. [39], the time before period −5 and the time after period 7 are merged into the period −5 and period 7. Meanwhile, in order to avoid multicollinearity, the period −5 is designated as the base period and eliminated.

The result of the parallel trend test is depicted in Figure 3. It indicates that the coefficient estimates for each period prior to the implementation of the smart-city pilot policy are insignificant. Therefore, there is no substantial difference between enterprises in pilot and non-pilot cities before the policy was implemented, indicating that the study samples pass the parallel trend test.

4.3. Baseline Regression Results

Table 3. Baseline regression results.

Variable	(1)	(2)
	Fin	Fin
Smart	−0.0076 ***	−0.0070 ***
	(0.0026)	(0.0025)
Lev		−0.0359 ***
		(0.0061)
Size		−0.0043 ***
		(0.0016)
TobinQ		0.0022 ***
		(0.0006)
ROA		−0.0574 ***
		(0.0099)
Growth		−0.0014
		(0.0009)
Cfo		−0.0110 *
		(0.0066)
Fix		−0.0629 ***
		(0.0071)
Top1		−0.0001
		(0.0001)
Dual		−0.0021
		(0.0017)
Constant	0.0202 ***	0.1449 ***
	(0.0016)	(0.0340)
Firm FE Year FE	Yes Yes	Yes Yes
N	38,244	38,244
Adj. R2	0.1516	0.1659

Note: *** and * are significant at the level of 1% and 10%, respectively. Standard errors clustered at the enterprise level are in parentheses.

reports the baseline regression results of the impact of smart city construction on corporate financialization. Column (1) controls for firm fixed effects and year fixed effects without adding control variables. Column (2) controls for a range of control variables based on column (1). In addition, so as to reduce the impact of heteroscedasticity on the regression results, this research adopts the standard errors clustered at the enterprise level. The baseline regression results indicate that the estimated coefficients of smart-city pilot policy (Smart) are significantly negative at the level of 1%. This suggests that the implementation of smart-city pilot policy inhibits corporate financialization. H1 is verified. With regard to economic significance, in accordance with the regression results in column (2), when the city in which the enterprise is situated is chosen as the smart city pilot, the degree of corporate financialization decreases by 0.7%. This suggests that smart city construction exerts an inhibitory influence on corporate financialization.

4.4. Robustness Test

4.4.1. Replacing the Measurement of Corporate Financialization

To prevent the interference of the measurement method of corporate financialization with the estimation results, this paper refers to the practice of Krippner (2005) [2] and Zhang et al. (2016) [40] to measure corporate financialization with income instead of assets. Investment income, profit or loss from changes in fair value and other comprehensive income net of investment income in associates and joint ventures as a proportion of operating profit measure corporate financialization. Column (1) of

Table 4. Robustness test.

Variable	Replacing the Measurement of Corporate Financialization	Shortening the Sample Observation Period	Excluding Other Policies
	(1) Fin	(2) Fin	(3) Fin	(4) Fin
Smart	−0.0708 ***	−0.0039 **	−0.0072 ***	−0.0059 **
	(0.0238)	(0.0018)	(0.0025)	(0.0025)
Innovation			0.0022
			(0.0022)
Broadband				0.0072 ***
				(0.0021)
Constant	−0.3587	0.1576 ***	0.1451 ***	0.1467 ***
	(0.2277)	(0.0380)	(0.0340)	(0.0339)
Control variables	Yes	Yes	Yes	Yes
Firm FE Year FE	Yes Yes	Yes Yes	Yes Yes	Yes Yes
N	38,244	20,527	38,244	38,244
Adj. R2	0.0084	0.1079	0.1660	0.1667

Note: *** and ** are significant at the level of 1% and 5%, respectively. Standard errors clustered at the enterprise level are in parentheses.

presents the regression results. After replacing the measurement method of corporate financialization, the regression results show that the estimated coefficient of a smart-city pilot policy is still significantly negative at the level of 1%, suggesting that smart city construction does significantly inhibit corporate financialization. This aligns with the results of the prior study.

4.4.2. Shortening the Sample Observation Period

The sample observation period of this paper is 2008–2022. To avert estimation bias due to the long observation period, this study intercepts the sample data of three years preceding and following the policy implementation—that is, the sample observation period becomes 2010–2018. Column (2) of Table 4 presents the regression outcomes. After shortening the sample observation period, although the absolute value of the estimated coefficient of smart-city pilot policies decreases, it remains significantly negative at the 5% level. Therefore, the above regression results are robust.

4.4.3. Excluding Other Policies

Given the long observation period of the sample in this paper, there are other policies that have an impact on corporate financialization during this period, which may bias the results of the baseline regression. Therefore, this paper identifies two relevant policies that may affect corporate financialization by combing through government-issued documents and previous studies: the national innovative city pilot introduced in 2008 and the “Broadband China” demonstration city set up in three batches after 2014. Dummy variables of the two policies are, respectively, added to the baseline regression to control for the effect on the estimates. Innovation indicates whether the city in which the enterprise is located is chosen as the national innovation city pilot in the current year. If it is chosen, the value is 1; otherwise, it is 0. Broadband indicates whether the city in which the enterprise is situated is selected as the “Broadband China” demonstration city in the current year. The value is set to 1 if it is selected; otherwise, the value is 0. As shown in columns (3) and (4) of Table 4, after the influence of two policies is excluded, the regression results are similar to the baseline regression. The estimated coefficients for smart-city pilot policies are significantly negative at the levels of 1% and 5%, respectively.

4.4.4. Placebo Test

After implementing the smart-city pilot policy, the change in corporate financialization may stem from the impact of other unobservable factors, rather than from smart city construction. Therefore, this paper also performs a placebo test. Drawing on the research of Wei et al. [41], the placebo test is conducted through two methods: randomizing the smart-city pilot region and randomizing the time of implementing the smart-city pilot policy. Since both the pilot region and the policy implementation time are randomly generated, the smart-city pilot policy does not significantly affect corporate financialization. Otherwise, it suggests that there is a bias in the model setup of this paper. Specifically, the above random processes are repeated 500 times for model estimation, and the corresponding kernel density plots of the estimated coefficients for the smart-city pilot policy are drawn. It can be seen from Figure 4 that, in the above two random processes, the estimated coefficients are clustered around the 0 value and adhere to a normal distribution. In addition, the majority of the p-values are above 0.1, and most of the regression results are insignificant. The actual estimated coefficient of the smart-city pilot policy is −0.007, which is the low-probability occurrence in the kernel density plot of the placebo test. Accordingly, it can be excluded that the baseline regression results in this paper arise from unobservable factors. The inhibitory impact of smart city construction on corporate financialization is not an accidental event. The research results in this paper are reliable.

4.4.5. PSM-DID

Although the smart-city pilot policy, as an exogenous shock event, can mitigate the endogenous issue to some degree, the selection of pilot areas may not be random, resulting in sample selection bias. In addition, differences between the treatment group and the control group may result in bias in the estimated results. To mitigate sample selection bias, propensity score matching is performed between the treatment group and control group via control variables in the baseline regression. After matching, re-estimation is performed through a time-varying difference-in-differences (DID) model. The smart-city pilot policy is carried out in three batches. The cities in which the enterprises in the treatment group are located are selected as smart city pilots in various years. Therefore, referring to the practice of Böckerman and Ilmakunnas (2009) [42], the period-by-period matching method is adopted to match the enterprises in the control group in this paper. Specifically, this paper takes the control variables mentioned above as matching variables and adopts Logit model to calculate the propensity score values. Enterprises in the control group are matched year by year through nearest-neighbor caliper matching (1:1) without putting back (caliper = 0.05). Then, the data matched each year are combined. Finally, regression analysis is conducted on the combined data.

Table 5. PSM-DID.

Variable	(1)	(2)
	Fin	Fin
Smart	−0.0070 **	−0.0068 **
	(0.0029)	(0.0029)
Constant	0.0203 ***	0.1558 ***
	(0.0027)	(0.0539)
Control variables	No	Yes
Firm FE Year FE	Yes Yes	Yes Yes
N	11,109	11,109
Adj. R2	0.1394	0.1595

Note: *** and ** are significant at the levels of 1% and 5%, respectively. Standard errors clustered at the enterprise level are in parentheses.

presents the regression results of PSM-DID. The estimated coefficient of the smart-city pilot policy remains significantly negative, suggesting that smart city construction can significantly inhibit corporate financialization. There is no obvious deviation from the baseline regression results, indicating that the regression results are robust.

5. The Further Analysis

Section 5.1 takes financing constraints and profitability as mechanism variables to reveal the transmission path between smart city construction and corporate financialization and better grasp the relationship between the two variables. In Section 5.2, heterogeneity analysis is conducted to examine the variations in research results under different conditions, including regions, property rights, whether management holds shares, the degree of industry competition and life cycle.

5.1. Mechanism Analysis

It can be seen from the baseline regression results and a series of robustness tests that smart city construction significantly inhibits corporate financialization. On the basis of the analysis in the previous theoretical section, smart city construction can inhibit corporate financialization by alleviating financing constraints and improving profitability. Therefore, this paper sets the following model for mechanism testing:

$${\mathit{Mechanism}}_{i,t}={ \alpha }_{1 }+{\beta }_1{\mathit{Smart}}_{i,t}+{\gamma }_1{X}_{i,t}+{\mu }_i+{ v}_t+{ \epsilon }_{i,t}$$

(3)

$\mathit{Mechanism}$ is the mechanism variable including the financing constraint (FC) and profitability (Profit). The control variables align with model (1). ${\mu }_i$ and $v_t$ stand for firm fixed effects and year fixed effects. ${\epsilon }_{i,t}$ denotes the random disturbance term. ${\beta }_1$ represents the net effect of a smart-city pilot policy on mechanism variables.

5.1.1. Financing Constraint Mechanism

Drawing on the idea of Kaplan and Zingales (1997) [43], this study opts for the KZ index to gauge the degree of financing constraints faced by enterprises. The greater the KZ index, the more severe the financing constraints encountered by enterprises. The regression results in column (1) of

Table 6. Mechanism analysis.

Variable	(1)	(2)
	FC	Profit
Smart	−0.0758 **	0.0079 **
	(0.0386)	(0.0040)
Constant	−0.4153	−0.3220 ***
	(0.4543)	(0.0525)
Control variables	Yes	Yes
Firm FE Year FE	Yes Yes	Yes Yes
N	38,242	38,244
Adj. R2	0.6738	0.5883

Note: *** and ** are significant at the level of 1% and 5%, respectively. Standard errors clustered at the enterprise level are in parentheses.

show that the estimated coefficient of a smart-city pilot policy is significantly negative at the 5% level, suggesting that smart city construction can alleviate the financing constraints of enterprises. The utilization of digital technology can alleviate the problem of information asymmetry and enhance the financing environment of enterprises. Therefore, enterprises are less likely to engage in excessive financialization activities for financing purposes. The “reservoir” motivation of enterprises declines. The easing of corporate financing constraints can inhibit corporate financialization. H2 is verified.

5.1.2. Profitability Mechanism

In this study, with reference to the practice of Wang et al. (2020) [44], the profitability of enterprises is measured by the main business profit margin. It is calculated as the proportion of operating profit net of corporate financial income to operating revenue. Financial income includes investment income, profit or loss from changes in fair value and other comprehensive income net of investment income in associates and joint ventures. The regression results in column (2) of Table 6 show that the estimated coefficient of a smart-city pilot policy is significantly positive at the 5% level, suggesting that smart city construction can significantly improve the profitability of enterprises. Digital projects and the transformation of business models brought about by the construction of smart cities can foster the growth of enterprises’ operating incomes. Meanwhile, the operating costs of enterprises are reduced by the utilization of digital technology. The profit rate of enterprise real investment improves, and the “investment substitution” motivation for corporate financialization weakens. Improving corporate profitability can inhibit corporate financialization. H3 is verified.

5.2. Heterogeneity Analysis

5.2.1. Regional Heterogeneity

Given the significant disparities in economic development levels and resource endowments across different regions of China, the impact of smart city construction on corporate financialization varies from region to region. This study categorizes the samples into two sub-samples based on the provinces in which the enterprises are situated— the eastern region and the central and western region—and performs baseline model estimation for the two sub-samples, respectively. The regression outcomes are presented in columns (1) and (2) of

Table 7. Heterogeneity analysis.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	Eastern Region	Central and Western Region	State-Owned Enterprise	Non-State-Owned Enterprise	Management Shareholding	Management Non-Shareholding
Smart	−0.0029	−0.0052 *	−0.0075 ***	−0.0057	−0.0068 **	−0.0057
	(0.0041)	(0.0030)	(0.0028)	(0.0037)	(0.0029)	(0.0038)
Constant	0.1887 ***	0.0652	0.2353 ***	0.1562 ***	0.1101 ***	0.2250 ***
	(0.0460)	(0.0465)	(0.0483)	(0.0466)	(0.0404)	(0.0768)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	27,371	10,868	13,211	24,275	29,707	7382
Adj. R2	0.1774	0.1501	0.1372	0.1938	0.1731	0.1228

Note: ***, ** and * are significant at the level of 1%, 5% and 10%, respectively. Standard errors clustered at the enterprise level are in parentheses.

. The regression outcomes indicate that the estimated coefficient of a smart-city pilot policy in the eastern region is insignificant, while the estimated coefficient in the central and western region is significantly negative at the 10% level. Put another way, smart city construction can significantly inhibit corporate financialization only in the central and western regions, while smart city construction has no significant inhibitory effect on corporate financialization in the eastern region. This may be because the infrastructure in the central and western regions is relatively backward. In the early stages of smart city construction, the government needs to collaborate with enterprises, as the development of digital projects requires the support of businesses. Therefore, enterprises invest substantial funds in real investments to guarantee the smooth running of the project. The degree of corporate financialization declines. The eastern region boasts a relatively high level of economic development and a more active financial market. The policy impact brought about by smart city construction does not have a significant effect on the financial investment behavior of enterprises.

5.2.2. Property Rights Heterogeneity

In this study, the sample is classified into two sub-samples: state-owned enterprises and non-state-owned enterprises, based on enterprise property rights. The baseline model estimation is carried out on the two sub-samples, respectively, to investigate the impact of a smart-city pilot policy on corporate financialization with distinct property rights. Columns (3) and (4) of Table 7 present the regression results. For state-owned enterprises, the estimated coefficient of a smart-city pilot policy is significantly negative at the 1% level, while that of non-state-owned enterprises is insignificant. In the process of smart city construction, various projects need to be implemented by enterprises. State-owned enterprises hold an edge over non-state-owned enterprises in terms of information acquisition and government subsidies. State-owned enterprises are subject to government supervision. Therefore, state-owned enterprises are more likely to undertake the construction of various types of infrastructure in the smart city pilot. Since state-owned enterprises have to invest substantial funds in real assets in the early stage of construction, financial investments by state-owned enterprises decline. In addition, state-owned enterprises also possess more advantages in terms of financing, so state-owned enterprises lend idle funds to other enterprises and act as entity intermediaries. However, with the development of smart cities, digital technology empowers financial services and gradually improves the financial system. The relending business of state-owned enterprises decreases; they are more focused on their main business, and the degree of corporate financialization declines [31].

5.2.3. Whether Management Holds Shares

Due to the existence of agency problems, conflicts of interest arise between the management and shareholders of a company. Whether or not management holds shares affects their business decisions, and the enterprise’s investment behavior is affected accordingly [45]. Therefore, this study categorizes the samples into two sub-samples of management shareholding and management non-shareholding for regression analysis. From columns (5) and (6) in Table 7, it can be seen that when the management holds the shares of the enterprise, the estimated coefficient of the smart-city pilot policy is significantly negative at the 5% level, which indicates that smart city construction has a significant inhibitory effect on the financialization of the enterprise with management shareholding. During the construction of smart cities, pilot cities attract many high-tech enterprises and talents and create a good innovation environment. Government subsidies and tax incentives facilitate the innovation of enterprises. However, due to the difficulty and long cycle of innovation, managers of enterprises are reluctant to urge enterprises to innovate for their own interests. When management holds shares in the enterprise, the interests of management and shareholders are aligned. The management takes into account the sustainable development of the enterprise and increases R&D expenditure. In this case, the enterprise’s financial investments decrease.

5.2.4. Degree of Industry Competition

On the basis of the practices of Gaspar and Massa (2006) [46], as well as Huang and Jiang (2015) [47], this paper uses the Herfindahl–Hirschman Index (HHI Index) computed based on the enterprise’s main business revenue to gauge the degree of market competition in the enterprise’s industry. The HHI is the sum of the squares of the market shares of all enterprises in the industry. Then, according to the median, the samples are categorized into two sub-samples with a high degree of industry competition and a low degree of industry competition. If the HHI is below the median of the sample, it suggests that the degree of competition in the industry is high; if the HHI is above the median of the sample, it indicates that the degree of competition in the industry is low. Regression is performed on the baseline model based on sub-samples, and columns (1) and (2) in

Table 8. Heterogeneity analysis.

	(1)	(2)	(3)	(4)	(5)
Variable	High Degree of Industry Competition	Low Degree of Industry Competition	Growth Stage	Maturity Stage	Decline Stage
Smart	−0.0079 **	−0.0041	−0.0054 **	−0.0078 **	−0.0074
	(0.0034)	(0.0033)	(0.0024)	(0.0037)	(0.0071)
Constant	0.1571 ***	0.1229 **	0.0979 **	0.1080 **	0.2548 ***
	(0.0460)	(0.0481)	(0.0404)	(0.0544)	(0.0824)
Control variables	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
N	19,238	18,986	17,777	13,394	6837
Adj. R2	0.1695	0.1554	0.1435	0.1962	0.1856

Note: *** and ** are significant at the level of 1% and 5%, respectively. Standard errors clustered at the enterprise level are in parentheses.

present the results. When the degree of competition in the industry is high, the estimated coefficient of the smart-city pilot policy is significantly negative at the 5% level. When the degree of competition in the industry is low, the estimated coefficient is insignificant. This suggests that in industries characterized by high degree of market competition, smart city construction can significantly inhibit corporate financialization. In industries characterized by a high degree of market competition, the entity profit rate of enterprises is often relatively low. The risk of entity investment is high. The opportunity of entity investment is limited. Therefore, enterprises have to turn to financial investment to obtain higher returns to make up for the lack of real investment returns. At the same time, the higher operating risks of enterprises make financing more difficult, and enterprises prefer to retain financial assets to prevent future capital shortages. As mentioned above, the business opportunities and the improvement of operating efficiency brought about by the construction of smart cities improve enterprises’ profitability. The development of digital technology alleviates the financing constraints of enterprises. Obviously, smart city construction can weaken enterprises’ motivations of preventive savings and investment substitution, leading to a decrease in the proportion of financial assets held, while enterprises in industries characterized by higher competition exhibit stronger motivations. Therefore, in industries characterized by high market competition, smart city construction has a stronger inhibitory effect on corporate financialization.

5.2.5. Life Cycle

Enterprise life cycle theory states that enterprises in different life cycles differ in terms of capital allocation and financing needs. It is essential to examine the effect of smart city construction on corporate financialization in different life cycles. Referring to the studies of Dickinson (2011) [48] and Cao et al. (2010) [49], this paper classifies the enterprise life cycle into a growth stage, a maturity stage and a decline stage on the basis of cash flow patterns. Specifically, an enterprise life cycle is classified based on the different positive and negative combinations of the net cash flows resulting from operating activities, investment activities and financing activities. The cash flow combination for each stage is shown in

Table 9. Cash flow combinations in different life cycles of enterprises.

Cash Flow	Growth Stage		Maturity Stage	Decline Stage
	Introduction Stage	Growth Stage		Phase-Out Stage	Phase-Out Stage	Phase-Out Stage	Decline Stage	Decline Stage
Net cash flows from operating activities	−	+	+	−	+	+	−	−
Net cash flows from investment activities	−	−	−	−	+	+	+	+
Net cash flows from financing activities	+	+	−	−	+	−	+	−

Note: “−“ indicates that net cash flow is less than zero. “+” indicates that net cash flow is greater than zero.

. Regression analysis is carried out on the three sub-samples of growth stage, maturity stage and decline stage. Columns (3)–(5) in Table 8 present the regression results. When enterprises are in the growth and maturity stages, the estimated coefficients of a smart-city pilot policy are significantly negative at the 5% level. When enterprises are in the decline stage, the estimated coefficient is insignificant. This suggests that smart city construction can significantly inhibit the financialization of enterprises in the growth and maturity stages, while the effect on the financialization of enterprises in the decline stage is not obvious. Enterprises in the growth stage lack stable profits and sufficient internal funds and have strong dependence on external funds. The strict loan conditions of financial institutions limit the financing of enterprises, leading enterprises to make financial investments to meet future capital needs for the purpose of preventive savings. Smart city construction can digitally empower financial services to reduce the financing difficulty of enterprises and help enterprises in the growth stage to solve financing problems, thereby inhibiting corporate financialization. When enterprises are in the maturity stage, their profit margin and market share increase, and internal funds are abundant, which enhances the enterprises’ willingness to innovate. Smart city construction also provides enterprises with a good innovation environment, preferential policies and government subsidies. On this basis, the innovation risk of enterprises is reduced. Enterprises are inclined to invest in real assets and R&D. Relatively speaking, investment in financial assets declines. The business performance of enterprises in the decline stage decreases, and the financial condition deteriorates continuously. They are more eager to obtain high returns through financial investment to improve the financial condition of enterprises and are not sensitive to the implementation of various policies for smart city construction.

6. Conclusions and Recommendations

Corporate financialization exerts a “crowding out” effect on enterprises’ real investment, which is detrimental to the sustainable development of enterprises, but it also increases financial risks and hinders the development of the economy. How to inhibit corporate financialization and mitigate the macro economy “shifting from real to virtual” has become an important issue. Based on the smart-city pilot policy as a quasi-natural experiment, this paper employs a time-varying difference-in-differences (DID) model to explore the effect of smart city construction on corporate financialization and the transmission mechanism. The conclusions are as follows: (1) The estimated coefficients of smart-city pilot policy are all significantly negative. This indicates that smart city construction can significantly inhibit corporate financialization. Moreover, it satisfies the parallel trend assumption and passes a range of robustness tests, including replacing the measurement of corporate financialization, shortening the sample observation period, excluding the interference of other policies, a placebo test, and PSM-DID, which proves that the regression results are robust. (2) Mechanism analysis finds that smart city construction can inhibit corporate financialization by alleviating financing constraints and improving profitability. (3) Heterogeneity analysis indicates that the inhibitory effect of smart city construction on corporate financialization is more significant in the central and western regions, state-owned enterprises, management shareholding, industries with a high degree of competition, and enterprises in the growth and maturity stages.

Based on the findings of this study, the following recommendations are proposed. The government should continue to advance the construction of smart cities. Firstly, the government should accelerate the construction of urban digital infrastructure and actively develop emerging information technologies to lay the foundation for the construction of smart cities. The integration of digital technology with urban growth creates new business forms and improves the business environment for enterprises. At the same time, the government should promote digital technology to empower financial services and improve corporate financing issues. It is necessary for the government to steer enterprises towards their core business and increase investment in corporate entities so as to achieve sustainable development. Secondly, as enterprises play a crucial role in the construction of smart cities, the government should give enterprises more subsidies and tax incentives and set up digital-technology exchange platforms to break down information barriers. The government should guide enterprises to rationally allocate financial assets and distribute funds to R&D and production, to achieve the sustainable growth of enterprises and thus promote the development of cities. Thirdly, the government should formulate differentiated development policies. The eonomic development of different regions should be taken into account to gradually promote the construction of smart cities, which is beneficial for enhancing China’s urban governance model and promoting sustainable urban development. In addition, supporting policies should be formulated by the government in line with the actual circumstances of enterprises to meet the differentiated needs of enterprises and optimize the allocation of resources.

Enterprises should consider the current macroeconomic environment and their own development strategies when making investment decisions. Enterprises need to adjust their development strategies on the basis of the policies formulated by the government, as well as understand the specific content related to enterprises in the smart-city pilot policy. It is vital for enterprises to make full use of policy dividends, like tax incentives, government subsidies and innovation incentives, in order to better carry out their main business and stimulate their innovation ability to improve their competitiveness. Enterprises should also use digital technologies to improve production efficiency and optimize management processes to achieve improved product quality and reduced operating costs. This should enhance corporate performance and attain sustainable growth for the enterprises.