Smart city construction and urban green development: empirical evidence from China

Smart city construction represents an advanced stage of China's urbanisation process and plays an important role in promoting green economic growth and sustainable development. Propensity score matching is combined with the difference-in-difference method to analyse the data of 221 prefecture-level cities in China from 2006 to 2020 to assess the impact of smart city construction on urban green development. We found that smart city construction can significantly contribute to urban green development; this contribution has long-term benefits. Further analysis shows that smart city construction promotes urban green development via industrial structure and green technology innovation and that smart city construction has a significant positive spatial spillover effect, i.e., it promotes urban green development locally while significantly contributing to urban green development in neighbouring regions.

Since the reform and opening up, the quality of China's urban development has rapidly improved, but the various urban diseases that accompany this development cannot be ignored.Furthermore, while environmental pollution has become an increasingly serious problem, urban green development has been severely constrained.By the end of 2022, China's urbanisation level, driven by large-scale industrialisation, reached 65.22%, creating rapid economic growth while causing the urban ecology to severely deteriorate and environmental problems to increase; this traditional, unsophisticated urban development model needs to be changed 1 .The current trend for innovative urban development models addresses the deteriorating ecological environment.As an advanced stage of China's new urbanisation, smart city construction is characterised by innovation-driven and environmentally sustainable development."Smart" refers to a new urban development mode featuring intelligence, digitalisation and informatisation.The core of smart cities involves using big data, the Internet of Things, cloud computing and other information technologies to promote city development via digitalisation and intelligence and improve city operation efficiency 2 .To define smart cities, Yigitcanlar et al. state that smart cities represent a new concept in which technological progress and urban development are integrated; furthermore, they act as advanced systems that are socially driven, technologically driven, and policy driven 3 .Lom et al. categorise the concept of the smart city as an information-physical system, one of whose main objectives is to enable collaboration between sectoral systems and the systems to which they belong, thus improving the quality of urban life, saving energy and reducing carbon emissions 4 .

Literature review
Since the US company IBM put forwards the idea of a "Smart Earth" in 2008, countries worldwide have been gradually promoting the construction of intelligent cities.As early as 2012, China's Ministry of Housing and Urban-Rural Development officially began piloting smart cities and gradually expanded the scope of the pilot in 2013 and 2014 [5][6][7] .Most of the current academic research on smart city construction focuses on the concept and implementation of smart city construction; in assessing the effectiveness of smart city construction, economic benefits are mainly determined from the input-output perspective, with few studies focusing on the concurrent social and ecological benefits.We executed an empirical investigation regarding the impact of smart city construction on urban green development and its mechanism of action.Therefore, the relevant studies on smart cities are sorted out and grouped into three categories: economic effect, innovation effect and environmental effect.In terms of economic effects, some studies have shown that smart city construction can significantly improve the quality of urban development, adjust the industrial structure and promote sustainable economic development [8][9][10] .Jo et al. studied the impact of smart city construction on the industrial ecosystem in Korea and found that smart industries are becoming pillar industries that create new value chains; furthermore, smart OPEN 1 School of Economics Management, Xi'an Shiyou University, Xi'an 710065, Shaanxi, China. 2 School of Economics Management, Xi'an University of Technology, Xi'an 710054, Shaanxi, China.* email: liuyinke0729@163.com

Theoretical analysis and research hypothesis
Information technology and big data network platforms, which are intrinsic to smart cities, link the various economic agents in the city and fully emphasise the intelligence of governance, the convenience of residents' lives and the digitalisation of enterprise production.In terms of government governance, smart city construction promotes intelligent governance that can more effectively promote environmental regulation and energy management, improve the structure of energy consumption and promote the use of clean energy.In terms of residential life, smart cities provide residents with smart homes that reduce energy consumption by intelligently regulating indoor temperature, lighting and electricity consumption.In addition, smart cities improve the transport system and reduce traffic congestion, thereby reducing fuel consumption and carbon emissions.In terms of enterprise production, enterprises can invest in factors of production more efficiently through industrial IoT and big data analysis, expanding the stage of increasing returns to scale and thus reducing the waste of resources.On the other hand, smart city construction helps enterprises carry out supply chain management more effectively and use digital tools to conduct environmental impact assessments, thus promoting the transition to green and sustainable development.The above factors are conducive to high-quality economic and sustainable resource development and promote green urban development.We further classify the theoretical mechanisms of smart city construction for urban green development into industrial structure effects, technological innovation effects and policy spillover effects.
The industrial structure effect is an important channel through which smart city construction influences urban green development.Many scholars have noted that industrial structure upgrading is the basis for economic growth.This upgrading leads to a reduction in environmental pollution and an increase in production efficiency, thus promoting green total factor productivity 30 .Furthermore, smart city construction can use information technology and technological innovation to rationalise, and thus upgrade, industrial structures.In addition, smart city construction generates new technology that can impact traditional industries; new industries act in a "demonstration role", forcing traditional industries to adjust and upgrade.For example, traditional high pollution and high energy consumption enterprises can use modern information technology to adjust their industrial structure and adapt to the market, thus allowing them to move towards high technology, low energy consumption and low pollution.Therefore, the industrial structure of the city can be transformed and upgraded, thus promoting green economic development.
The effect of technological innovation is an important driving force that causes smart city construction to influence urban green development.To promote smart city construction, the government provides financial and policy support for technological innovation.It is willing to invest in multiple research and development resources to accelerate the Internet of Things, cloud computing and big data and promote the innovative development of a new generation of information technology.However, technological innovation will in turn stimulate the level of green economic development in cities 31 .In the framework of Schumpeter's theory, enterprises are the main body of innovation.To adapt to city development trends and gain competitive advantages, enterprises will seize development opportunities, maximise information technology and continuously promote technological innovation.These actions will reduce the cost and improve the ability of enterprises to treat pollution, which will benefit the development of the green economy.In addition, the government encourages green production and living and low-carbon consumption under smart city construction, which subtly forms and strengthens people's awareness of environmental protection.These actions generate new demand for green products and give rise to various new production processes and technologies while reducing the amount of resources consumed, improving energy use efficiency, providing new growth drivers for economic development and helping to promote urban green development.
From the perspective of economic geography, the implementation of smart city construction in the region will promote urban green development in other neighbouring regions.On the one hand, smart city construction originates from the Internet of Things, cloud computing and other big data network platform support.Information technology and other intelligent elements are not restricted by time and space, which can break the barriers to factor flow and is conducive to promoting cobusiness, cobuilding and sharing between regions and strengthening economic ties.On the other hand, the construction of smart cities can optimise the internal systems of cities, such as the financial development environment, education level and infrastructure construction in the pilot areas, continuously attracting the inflow of various resource elements.These elements then give rise to the clustering of new industries such as smart industries and modern service industries led by new business models, which in turn drive urban green development in neighbouring cities through knowledge, technology and other spillover radiation (Fig. 1).
Based on the abovementioned information, we propose the following hypotheses:

Variable selection
Dependent variable: Since urban green development data are not directly available, we construct an indicator evaluation system from three dimensions: socioeconomic, quality of life and ecological environment, based on the research literature, and use the entropy value method to measure green development data [32][33][34] , with the following calculation steps (Table 1).

a. Data standardisation
The aim of standardising the data is to remove the effects of dimensionality, thus making the data more comparable.In addition, data standardisation can make the data homogenous so that indicators of different natures have the same direction of action on the evaluation results.We use the extreme difference standardisation method for dimensionless processing with the following formula.
where i and j are prefectures and indicators, respectively x ij and x ′ ij denote raw data and extreme deviation normalised data, respectively, and Min(x j ) and Max(x j ) denote the minimum and maximum values of raw data, respectively.b.Calculating information entropy e j A sample of n prefecture-level cities and m evaluation indicators is assumed, and the weighting of the characteristics of prefecture-level city i under indicator j is shown below.
The information entropy value is calculated for indicator j.

c. Calculation of weights and overall score
The weight w j of the j-th index is calculated and the comprehensive score Score i of green development of prefecture-level city i is determined.
As the coefficient is too small due to the small indicator of the independent variable, we treat measurement A by multiplying it by 10 (Table 1).

Positive indicators
Table 1.Urban green development evaluation index system.www.nature.com/scientificreports/Core independent variable The core explanatory variable in this paper is the smart city pilot policy and is assigned a dummy variable in the form of a location dummy of 1 if the city is on the smart city list and 0 otherwise and a time dummy of 1 if the time is after the implementation of the smart city pilot policy and 0 otherwise, with the interaction term representing the policy variable for smart city construction, denoted as Smartcity.The coefficient of the interaction term is the estimated value that is the focus of this paper; if it is significantly positive, then the smart city pilot policy can significantly contribute to urban green development.

Socioeconomic
Control variables Based on a review of the literature, the control variables selected for this paper are as follows: (1) urbanisation rate, measured as the ratio of the urban population to the total population, denoted as town; (2) level of financial development, measured as the ratio of total loans of all balances of financial institutions to GDP at the end of the year, denoted as fin; (3) investment in scientific research, measured as expenditure on science and technology, denoted as rd; (4) level of education of the population, measured as expenditure on education, denoted as edu; (5) the degree of government intervention, measured using public budget expenditure in municipalities as a percentage of GDP, denoted govern; and (6) population size, measured using year-end population numbers, denoted peop.
Intermediate variables In this paper, industrial structure and green technological innovation are selected as mediating variables for analysis.Referring to Ye et al., the industrial structure is measured by the share of the secondary industry in GDP 35 .Green technological innovation is measured by adding one to the logarithm of green patent applications in prefecture-level cities and then analysing the mechanism of action involved in the impact of smart city construction on urban green development (Table 2).

Data use
China officially promoted the construction of smart cities in 2012, and the second and third batches of smart city pilots were announced in 2013 and 2014, respectively.This paper treats the smart city pilot policy as a quasinatural experiment and uses the asymptotic DID method to analyse the impact of smart city construction on urban green development (Fig. 2).
As the pilot list includes counties and urban areas, the following treatment is performed when selecting the empirical sample: to avoid underestimating the impact of the smart city pilot policy on urban green development and to make the regression results more robust, the prefecture-level cities where the counties and urban areas are located in the list are excluded from the study sample, and only prefecture-level municipal units are retained in the final sample (Table 3).
Based on the above procedure, this paper uses a panel of 221 prefecture-level cities in China from 2006 to 2020 to analyse the possible impact of smart city pilot policies on urban green development.The analysis in this paper does not consider Tibet or Hong Kong, Macao or Taiwan due to serious data deficiencies in these regions.While processing the data, data from relevant autonomous prefectures, county-level cities and urban areas are excluded, and only the city data are retained for analysis.A small amount of missing data is interpolated using the linear interpolation method.Data for the study were obtained from the wind database, the China Urban Statistics Yearbook and data publicly available from provincial statistical bureaus (Table 4).

Model setting
To analyse the impact of smart city construction on urban green development, this paper adopts a progressive DID method to treat smart city construction as a quasinatural experiment to analyse its treatment effects on urban green development.This paper further constructs a model to assess whether smart city construction has promoted urban green development by comparing the differences between pilot areas and nonpilot areas.The econometric model constructed in this paper is as follows.

Empirical analysis DID results analysis
The use of the DID method presupposes that the treatment and comparison groups should have the same trend of change before the policy shock, i.e., they should pass the parallel trend test.The parallel trend test conducted in this paper demonstrates the applicability of the double difference method in this study; the parallel trend test is shown in the robustness test below.Table 5 shows the baseline regression for this paper, and columns (1)- (7) show the regression results after the control variables are gradually added.The estimated coefficient of the policy variable Smartcity it is still significantly positive at the 1% level after the control variables are gradually added, indicating that smart city construction significantly promotes urban green development.Hypothesis 1 is argued in this paper.
Looking further at the regression coefficients of the control variables in this paper, the urbanisation rate and investment in research significantly contribute to green urban development, while financial development and the degree of government intervention have negative impacts on green urban development.The coefficient of the urbanisation rate (town) is significantly positive because smart city construction represents an advanced stage of China's new urbanisation process.The manner in which the ecological environment is emphasised in new urbanisation is different from that in traditional urbanisation, which develops the economy at the expense of the environment.The reason why scientific research investment (lnrd) plays a significant role in promoting urban green development is that scientific research investment promotes technological innovation in smart city construction and policy formulation, which is conducive to improving the efficiency of energy use, optimising the allocation of resources, and providing decision support for the green attributes of smart city construction.The estimated coefficient of the degree of government intervention (govern) is significantly negative because the government's public budget expenditure is aimed at safeguarding and improving people's livelihood and promoting the country's economic and social development.Along with infrastructure improvements and steadily developing economic levels, environmental problems are becoming increasingly serious, thus hindering urban green development to some extent.The reason for the significantly negative estimated coefficient on the level of financial development (fin) is that financial institutions are more inclined to focus on short-term returns on investment, whereas the green development of smart cities tends to require longer investment cycles and slower rates of return.Therefore, financial institutions may be reluctant to invest in long-term, potentially less rewarding green projects for smart city construction if they are overly concerned with short-term returns.In addition, the implementation and effectiveness of green projects in smart cities may be difficult for financial institutions to accurately assess due to information asymmetry, which in turn may curb investment willingness.

Parallel trend test
The core assumption of the DID model is that the treatment and comparison groups should have the same trend of change before the policy is implemented.The parallel trend test model constructed in this paper is shown below.where D k it is the dummy variable for the current period of smart city construction, k < 0 is the year before smart city construction, k > 0 is the year after smart city construction, T jt is the time trend variable, and the remaining variables are consistent with the previous expression.Figure 3 shows the parallel trend test graph.Before smart city construction began, the treatment group and the control group have a consistent trend of change; in 2012 for the current period of smart city construction and in 2013, urban green development has no significant impact, indicating that the policy has a time lag.After smart city construction, a long-term positive effect is found.Therefore, smart city construction can significantly promote urban green development, with a long-term effect.

Placebo test
Smart city construction may be influenced by unobservable factors; hence, this paper refers to Li et al. to conduct random sampling to ensure that the impact of smart city construction on a particular city is randomised, which eliminates the influence of unobservable factors 36 .The coefficient estimates of Smartcity it are as follows.
where X it is the control variable mentioned above and θ is an unobservable factor.If β is to be an unbiased esti- mator to avoid inconsistent estimates, then parameter θ = 0 should be made.However, direct testing of param- eter θ is less tractable; thus, random sampling is performed to generate smart city construction cities to make parameter β = 0 .If β = 0 exists, then parameter θ = 0 can be introduced 37 .Figure 4 shows the kernel density plots of the coefficient estimates obtained after 1000 (left panel) and 2000 (right panel) random samplings.The βrandom coefficient estimates are concentrated at approximately 0 and approximately obey a normal distribution after random sampling is conducted; thus, the parameter θ = 0 can be inferred, indicating that the findings of this paper are robust.

Nonrandom selection impact test
Although the use of the DID method to evaluate the impact of smart city construction on urban green development can, to a certain extent, avoid the unobservable endogenous factors that belong to the "common trend" between the treatment and control groups, smart city construction is policy-oriented, and the list of construction  www.nature.com/scientificreports/areas is not randomly generated.Based on this situation, the interaction term of urban factors and time trends is introduced and the following econometric model is constructed.
where C i denotes the city attribute factor, C i includes provincial capitals, subprovincial cities and areas east of the Hu Huanyong line, Tim t is the time trend variable, and the remaining variables are the same as in the previous statement.As shown in Table 6, the interaction term Smartcity it coefficient is still significantly positive after controlling for the city attribute factors, and the conclusions of this paper are robust.
In addition, to further demonstrate the robustness of the regression results, a combination of PSM and the DID method is adopted to analyse effect of smart city construction on urban green development, which can overcome the effect of selection bias.In this paper, three matching methods, kernel matching, radius matching and local linear regression matching, were used to perform PSM-DID estimation.Figure 5 shows the results of the equilibrium test after kernel matching, which shows that the standardised deviations of the covariates are reduced and concentrated to approximately 0 after matching (Fig. 5 left) and that the samples of the treatment and control groups mostly lie within the common range of values (Fig. 5 right), which is a good matching result.All other matching methods passed the balance test.
Columns ( 2)-( 5) in Table 6 show the regression results after radius matching, nearest neighbour matching, kernel matching and local linear regression matching.The coefficients of the policy variables are still significantly positive at the 1% level, indicating that the effect of smart city construction on urban green development is not affected by selection bias and that the regression results in this paper are still robust.

Heterogeneity analysis
As different locations of cities have different economic development, Chinese prefecture-level cities are divided into three subsamples, namely, eastern, central and western, based on their geographical locations and then separate regressions for geographical location heterogeneity analysis are performed.As shown in Table 7, smart city construction has a significant impact on urban green development in the eastern, central and western regions of China, with the greatest impact on urban green development in the eastern region, followed by the   central region and finally by the western region.The reason for this is that eastern China has a better foundation in high-tech industries and is gradually transforming and upgrading to smart manufacturing, the internet, artificial intelligence and other fields, which provides strong technological support for promoting the construction of smart cities.For example, the application of big data, the Internet of Things, artificial intelligence and other technologies can not only optimise city operations and improve efficiency but also promote industrial transformation and upgrading and green development.On the other hand, after a long period of high-intensity industrialisation and urbanisation, the ecological environment in eastern China is under greater pressure.The construction of smart cities can effectively prevent and repair ecological problems with information technology.In the new infrastructure of big data, Internet of Things, cloud computing, etc., the new infrastructure of the eastern part of the country is growing in parallel with the urban scale and industrial structure of the eastern part of the country, forming a coupling state, thus maximising the policy effect of smart city construction on urban green development.

Endogeneity issues
The nonrandom nature of smart city construction may lead to endogeneity problems when analysing the impact of smart city construction on urban green development.Therefore, in this paper, instrumental variable estimation (IV) is used to address possible endogeneity problems.Topographic relief (rdls) is chosen as the instrumental variable for the core explanatory variable Smartcity it .On the one hand, topographic relief is closely related to the location of smart city construction, and the relevance assumption of the instrumental variable is satisfied; on the other hand, topographic relief, as geographical data, does not affect urban green development, and the exogeneity assumption of the instrumental variable is satisfied 38 .Furthermore, as topographic relief data do not change over time and have research limitations, the interaction term T-rdls is introduced between the instrumental variables and each year's time dummy variable and is then used as the instrumental variable for the empirical analysis to measure changes in the time dimension.The instrumental variable model is constructed as shown below.
where parameters ξ 1 , ξ 1 1 and ξ 2 1 denote the ordinary least square estimates, IV estimates the regression coefficients for the first and second stages, and the remaining variables are the same as above.
Column (1) of Table 8 shows the results of the one-stage regression, where the estimated coefficients of the interaction term T-rdls for the topographic relief and time dummy variables are significantly positive.Column (2)  shows the results of the two-stage regression, where the regression coefficient of the interaction term Smartcity it is smaller than the case of the previous benchmark regression, indicating that the impact of smart city construction on urban green development will be overestimated if the endogeneity issue is not accounted for.

Mechanism analysis
In this paper, to further explore the possible mechanisms of the impact of smart city construction on urban green development, green technology innovation is measured by adding one to the number of green patent applications in prefecture-level cities and taking the proportion of secondary industry in GDP as the measurement variable of industrial structure.The mechanism effect model is shown below.www.nature.com/scientificreports/Column (1) of Table 9 shows the impact of smart city construction on green technology innovation, and the findings show that smart city construction significantly promotes green technology innovation.Column (2)  shows that the coefficients of policy variables and green technology innovation are both significant at the 1% level, indicating that green technology innovation is an important channel through which smart city construction affects urban green development and that smart city construction significantly contributes to urban green development through green technology innovation.Column (3) shows the impact of smart city construction on industrial structure, and the empirical results show that smart city construction has a negative impact on industrial structure, which is the same as theoretical expectations.Column (4) shows that the coefficients of both industrial structure and policy variables are significant, indicating that industrial structure is an important channel through which smart city construction affects urban green development and that smart city construction significantly contributes to urban green development through industrial structure.Hypothesis 2 of this paper is argued.

Spatial econometric analysis
In the above section, the causal relationship between smart city construction and urban green development is identified using the progressive DID model, but the spatial factors of smart city construction affecting urban green development are not considered.In this section, a DID spatial Durbin model (SDMDID) is established to introduce and decompose these spatial factors according to how their impact on smart city construction influences urban green development; this process can analyse the urban green development spillover effect of smart city construction.The model is set as follows.where W is the spatial weight matrix, the binary adjacency matrix is selected as the spatial weight matrix in this paper, W × UGD it denotes the spatial lag term of urban green development, W × Smartcity it denotes the spatial lag term of smart city construction, W × X ′ it denotes the spatial lag term of the control variables, and the remaining variables are the same as in the previous expressions.

Global spatial correlation test
The premise of using the SDMDID model is that the explanatory variables are spatially correlated, i.e., Moran's I should be significantly nonzero.This paper uses Moran's I to test the spatial correlation between smart city construction and urban green development.Table 10 shows that the Moran's I values of both urban green development and smart city construction are greater than zero, indicating that both are spatially correlated, i.e., the implementation of smart city construction will not only have an impact on the implementation of smart city construction and affect urban green development in this region but also affect other adjacent regions.

Spatial econometric model analysis
The paper further examines and analyses whether the DID spatial Durbin model degenerates into either a DID spatial lag model or a DID spatial error model.Table 11 illustrates that both the LR test and the Wald test significantly reject the original hypothesis, fully demonstrating the applicability of the spatial Durbin model in this study.Based on the results of the Hausman test, time and space double fixed effects are chosen to analyse the spillover effects of urban green development of smart city construction.
Column (1) of Table 12 shows the overall regression results, while columns ( 2)-( 4) show the results after decomposition by partial differencing, indicating the direct effect, the spatial spillover effect and the total effect, respectively.The direct effect indicates that smart city construction has a significant contribution to urban green development, which is consistent with the findings from the previous baseline regression.The spatial spillover effect indicates that the implementation of smart city construction will benefit urban green development in neighbouring cities.The implementation of smart city construction will cause neighbouring cities to change their industrial structure, eliminate inefficient production capacity and improve energy efficiency through imitation and learning, thus promoting urban green development in neighbouring cities.Therefore, smart city construction has a good spatial radiation effect on neighbouring areas.

Conclusions
Smart city construction has given rise to the emergence and concentration of new industries and injected new momentum into the development of the digital economy.With the in-depth development of the digital economy and information technology, smart city construction has gradually become an important trend in urban development.Therefore, this paper uses the panel data of 221 prefecture-level cities in China from 2006 to 2020 to employ the asymptotic double difference method to empirically analyse whether smart city construction will promote urban green development The following conclusions are drawn.First, smart city construction significantly promotes urban green development and has a long-term promotion effect.Second, an analysis of intermediary

Hypothesis 1 :
Smart city construction plays a significant role in promoting urban green development.Hypothesis 2: Smart city construction promotes urban green development through industrial structure and green technology innovation.Hypothesis 3: Smart city construction has a positive spatial spillover effect on urban green development in neighbouring areas.

Table 2 .
Variable definitions and descriptions.

Table 3 .
Sample distribution.Figures in brackets indicate the number of prefecture-level city samples in the treatment and comparison groups.

Table 6 .
Nonrandom selection effects test.Robust standard errors in parentheses.

Table 8 .
Instrumental variable estimation.Robust standard errors in parentheses.***p < 0.01, **p < 0.05, *p < 0.1;The Kleibergen-Paap rk LM statistic was used for the underidentification test, and the numbers in [] are their p values; the weak identification test refers to the Donald Wald-F statistic, and the Stock-Yogo test 10% level thresholds are in {}.

Table 10 .
Global spatial correlation test.