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Article

The Evolution of Smart City Policy in China: A Quantitative Study Based on the Content of Policy Texts

by
Chongfeng Yue
1,
Hongyan Li
2,
Haocheng Mao
3 and
Aobo Yue
4,*
1
Power China Chengdu Engineering Corporation Limited, Chengdu 610030, China
2
School of Civil Engineering and Architecture, Jiangsu University of Science and Technology, Zhenjiang 212100, China
3
School of Electrical Engineering and Electronics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
4
School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(1), 7; https://doi.org/10.3390/buildings15010007
Submission received: 22 October 2024 / Revised: 14 December 2024 / Accepted: 22 December 2024 / Published: 24 December 2024
(This article belongs to the Special Issue Urban Planning and Construction Management Under Smart City Development)
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Abstract

:
As China is the largest developing country and the country with the largest volume of smart city construction, the Chinese government has promulgated a series of policies to develop smart cities vigorously. Thus, the understanding of smart city policies from the perspective of policy science theory is helpful in achieving a full understanding of the development stage and evolution path of smart cities and improving the implementation of smart city policies. In this study, text content mining and quantitative empirical analysis are used to investigate the structure and function of China’s smart city policy. The results demonstrate that China’s smart city policy has largely realized the evolutionary process of the policy keynote from “taking” to “giving” and the policy subject from “single” to “pluralistic”. The organizational structure has gradually turned from “multidisciplinary” to “cooperative governance”. The analysis results indicate that the policy guidance for smart cities should focus on the positive interaction between social needs and urban development regulations. It is also important to enhance citizen participation and redefine the role of the government in the development of smart cities.

1. Introduction

Given the growing complexity of the urban development scale and management dimension in recent years, numerous countries have paid increasing attention to urban innovation governance and sustainable construction [1,2,3]. As an important carrier of social and economic development [4,5], cities affect the efficient and sustainable development of society and the country. However, as the urbanization process continues to accelerate, the rapid expansion of urban development has shown several problems, such as overpopulation [6], soaring housing prices [7], uneven allocation of public resources [8], and industrial decline [9]. At present, as a powerful tool for resource allocation optimization and efficient information interaction, smart cities provide a new direction for the efficient governance of urban issues and have received great attention worldwide [10,11,12].
A smart city embodies an innovative paradigm of urban development [13,14], promoted by manpower, organization, and information technology [15,16]. The construction of smart cities in the United States is currently in a leading position in the world [17]. As a trailblazer in the domain of smart city development, IBM was the first to articulate a comprehensive vision for smart city construction in late 2008. The company emphasized the application of information technologies, such as the Internet of Things (IoT) and big data, as the core technological foundation for building smart cities [18]. In practice, in September 2009, IBM announced the construction of the first “smart city”. The basic principle is to use new technology to connect the physical resource elements and public services in the city to realize intelligent operation and maintenance [19]. Driven by the technological wave, various countries are actively responding to the needs of the times and are racing to implement relevant plans [20]. The United States, Singapore, Japan, and several other countries have elevated the concepts of “smart earth” and “smart city” to the level of national strategic priorities [21]. The representative country under the diversified target system is Japan, and its smart city construction focuses on smart energy and smart water [22,23]. The background of smart city construction in Japan is the lack of natural resources and frequent natural disasters. Therefore, it pays more attention to promoting the realization of energy conservation and efficiency and the progress of urban disaster prevention and reduction by constructing urban intelligence. The development of smart cities in Japan has achieved initial results, and Japan’s digital society has applied information technology more effectively in daily life [13]. Compared to Western nations, the development of smart cities in developing countries began relatively late. Among them, China and India are the most typical examples, with a larger scale of developments. The background of smart city construction in the two countries is converging. After the rapid acceleration of urbanization, these countries face traffic congestion, crowds, and numerous urban diseases [24]. They hope that the new development concept of smart cities can alleviate urban problems and improve cities. China and India have promulgated a series of strategies at the national level to lead and plan smart city construction [25,26].
In general, countries and cities have different starting points for smart city construction, as well as different construction content, path choices, and characteristics [27]. Unlike the United States’ smart city construction, which is led by large technology companies, and Japan’s smart city development model, which is led by government–enterprise cooperation, China’s smart city construction is led by the central government and implemented by multiple ministries [28]. As the world’s most rapidly urbanizing country, China is developing the world’s largest smart city initiatives to address the challenges associated with accelerated urbanization [27]. The rapid and large-scale urban transition, coupled with pronounced regional disparities and sustained government investment in both institutional frameworks and capital infrastructure, has resulted in significant evolutionary and non-linear transformations in China’s smart city development process [29]. These distinct characteristics position China as an exemplary case for the analysis of smart city development. Accordingly, as an emerging economy in the midst of a wave of smart city initiatives, China serves as a focal case study for examining and evaluating the progress and dynamics of smart city construction [30]. The empirical evidence obtained will hopefully provide a basis for additional research in this field. It should also provide certain reference suggestions for constructing smart cities in developing countries. The empirical evidence obtained will undoubtedly add a basis for this research field.
Thus, exploring the hidden content of the smart city policy text is important. However, combining existing research demonstrates that the current academic research on smart cities involves a wide range of areas but also focuses on the theoretical connotation [31], technical system, and construction performance evaluation of smart cities [32,33]. Moreover, the research on smart city policy, especially the quantitative analysis around the policy text, is still insufficient. The elaboration of the basic characteristics of China’s smart policy, the organizational structure of the publishing institutions, and the evolutionary logic in each period remain inadequate, thus providing a reference for this study. Given the above purposes, the present study analyses the policy from the three dimensions of policy content, policy organization structure, and policy effectiveness. Then, it uses Python, Gephi, and other tools to conduct text mining and quantitative analysis of smart city policies and sorts out policy expression and evolution stages.

2. Methodology

2.1. Analytical Framework

As research has become increasingly sophisticated, the policy science research paradigm has evolved from a qualitative approach, relying on empirical evidence, to a quantitative methodology based on data models. This shift has positioned the paradigm as a prominent approach within the field of policy research in recent years. This shift in the research paradigm offers a potential avenue for reducing the excessive subjectivity and uncertainty that have historically characterized the paradigm of policy science research. The information presented in the policy text illustrates the inter-sectoral collaboration and the extent of policy integration within the context of smart city development. The analytical framework of this study is comprised of three distinct parts: a content analysis of smart city policies, an analysis of the organizational structure of such policies, and an analysis of their effectiveness. The research methods employed primarily comprise text content mining analysis, social network analysis, and a quantitative analysis of policy effectiveness. Regarding the dimensions of policy effectiveness analysis, this study establishes a quantitative model of policy effectiveness comprising five dimensions: policy intensity, policy objectives, policy measures, policy feedback, and policy supervision. This study incorporates the three dimensions of smart city policy content analysis, smart city policy effectiveness analysis, and smart city policy organizational structure analysis into the smart city policy evolution analysis framework. This approach allows for a more comprehensive understanding of the goal orientation of the smart city policy system and its evolutionary trends [34]. The research framework is illustrated in Figure 1.

2.2. Data Acquisition and Preprocessing

2.2.1. Policy Sample Selection

Given the significant regional disparities observed in Chinese cities, it becomes apparent that there is considerable diversity in economic levels, information construction foundations, and other pertinent variables. As a result, there are significant differences in the stages of smart city development. This study does not conduct a thorough analysis of a representative sample of smart city laws released by every city in the nation in order to ensure the accuracy and logic of the research program design. Rather, it concentrates on policies pertaining to smart cities that are issued by national ministries and commissions or the federal government. The research frame for this study is set from 1 January 2012 to 1 January 2023, taking into account the previously specified factors. Using the term “smart city” as the search criterion, this study conducted a keyword search of Peking University’s “Peking University Fabulous Database” and government websites pertaining to urban development [35]. Following the screening, identification, and cleaning processes, a total of 239 policy documents pertaining to smart cities at the central government and national ministry level were ultimately retrieved, originating from 68 policy-issuing departments.

2.2.2. Policy Sample Code

The three-dimensional analysis framework is employed to identify and quantify the effectiveness of smart city policy tools. The “Date of issue-Code” method is utilized to construct a comprehensive coding table that encompasses the 239 policy samples presented in Table 1.
As shown in Table 1. The policy texts were coded precisely, and this study provides examples of how the codes were coded. For instance, the code “2016-12” corresponds to the 12th smart city policy document issued in 2016. The calculation of the annual policy effectiveness (APE) is elaborated upon in the subsequent section. To ensure the reliability of the coding process, two postgraduate researchers specializing in smart cities and urban governance were independently tasked with coding the selected policy documents. This independent approach enhanced the credibility of the policy text coding. Upon completing the initial coding, the two coders independently recorded their results and conducted a mutual assessment reliability test to validate the objectivity and precision of the coding outcomes. If the results of the pre- and post-coding were consistent, it was noted as “1”. If there is no such evidence, it is noted as “0”. If the coding results of the two are highly consistent, the reliability of the scorers in this study is higher [36]. Based on this approach, the agreement rates between the coders and the original coding results were 86.19% and 82.43%, respectively. Furthermore, the overall consistency between the two coders exceeded 80%, underscoring the credibility and reliability of the smart city policy text coding process [37,38].

2.3. Model Construction and Data Analysis

2.3.1. Quantitative Analysis of Policy Content

Quantitative policy content analysis is a research method that integrates quantitative and qualitative research on the content of policy texts. The method is based on text content mining, which enables the analysis and identification of salient features in the content of policy texts, thereby facilitating the discovery of the underlying principles governing policy choices and the trajectory of policy formulation within the text. Fundamentally, quantitative policy content analysis serves as a multidisciplinary synthesis of theoretical perspectives applied to the study of policy texts. By integrating qualitative and quantitative research, this approach addresses the limitations of traditional policy research, which tends to prioritize structural and behavioral aspects of organizational entities while neglecting the nuances of specific social contexts and content. Furthermore, this approach circumvents the potential for analytical bias resulting from the analyst’s personal background, experience, or preference. It thus facilitates a more objective and accurate analysis of policy texts.

2.3.2. Calculation of Policy Effectiveness Values

At present, research on the basis of policy texts is largely qualitative in nature and is based on empirical knowledge. However, this approach may be perceived as lacking objectivity and replicability. There is a clear need to develop quantitative research tools that are both objective and operational. There is a clear need for the development of quantitative research tools that are both objective and operational. Following the approach outlined by Yin, et al. [39], this study develops a quantitative model comprising five dimensions of policy effects, offering a robust framework for evaluating the content strength and impact of individual policy texts. This model addresses the limitations of existing policy analyses by providing a systematic approach to assessing the expression, effectiveness, and stage-by-stage evolution of China’s smart cities from a macro-perspective. While policies related to public services, urban planning, new urbanization, and other areas intersect with smart city development, their focal points and degrees of relevance to smart cities differ significantly. Based on the correlation between political strategies and smart city objectives, the scoring of policy goals is determined, ensuring an accurate and nuanced evaluation of policy impacts in the context of smart city construction. In addition, the lack of policy supervision and feedback channels will aggravate the “decline” of policy transmission. Improving the supervision feedback mechanism can effectively supervise and intervene in the policy transmission process. This study also adds two dimensions of policy supervision and policy feedback to quantify policy effectiveness. Ultimately, this study develops a quantitative framework for the efficacy of smart city policies, based on an analysis of the aforementioned perspectives and dimensions.

2.3.3. Social Network Analysis

Social network analysis is a research methodology that employs a systematic examination of the social relationships and interactions among stakeholders within a network [39]. The focus of social network analysis is on the associations between different stakeholders (nodes) rather than on the behavior of individual stakeholders, as is the case with other approaches. By analyzing the associations between stakeholders, collaboration can ascertain their position and role in the cooperation network in a more scientific and objective manner [40]. Given the intricate and specialized nature of smart city construction projects, the participants involved in this process in China are numerous and diverse, exhibiting a high level of complexity. Consequently, the policies devised by smart cities are formulated by government departments with disparate responsibility backgrounds, and the policy-issuing organizations of the numerous smart cities have established intricate inter-governmental relationships. The social network analysis methodology is thus appropriate in this study.

2.4. Depth Analysis, Results Analysis, and Discussion

This study conducted a comprehensive analysis from three perspectives: policy content, policy effectiveness, and policy organizational structure. A framework for the cross-analysis of policy subjects and tools, as well as policy objects and tools, is developed within the realm of policy content analysis. This framework examines the application of various policy tools by government departments and illustrates their utilization across different policy objects. The effectiveness of policy is systematically examined through the analysis of the APE and its average value, focusing on the power and influence of policy texts. In the dimension of policy organization structure, the software Gephi 0.9.2 was employed for the purpose of analyzing the cooperation network between smart city policy-issuing agencies and the characteristics of said cooperation.

3. Quantitative Analysis of Policy Texts

3.1. Policy Content Analysis

Policy content analysis is a standardized research method. The scientific nature and accuracy of the analysis results largely depend on the standardization of the method and process. Generally, the content analysis of a single policy text adopts three analysis dimensions: policy tools, policy subjects, and policy objects.

3.1.1. Policy Object

The diverse objectives of policy implementation and the varied targets of its actions give rise to a multitude of classifications of policy instruments in the practice of smart city construction. At present, scholars employ the categorization of policy instruments proposed by Roy Rothwell and Walter Ziegfeld [41]. In accordance with the disparate positioning of policy functions, scholars have devised a classification system for policy instruments comprising three principal categories: supply type, environment type, and demand type. Considering the existing literature, this study puts forth a three-dimensional framework for analyzing policy tools, encompassing supply type, environment type, and demand type. This analytical framework, which has a total of 14 policy tools, covers scientific and technological information support, public services, and inter-subjective communication to encourage publicity [34]. The definitions of the different types of policy instruments are shown in Table 2.
Different types of policy tools were used in the smart city policy documents, and the statistics are shown in Figure 2. The number of published policy documents indicates a trend of first increasing, then decreasing, and finally increasing. In 2016, the frequency of smart city policy tools was the highest with 43 items, followed by 2015 and 2020 with 31 and 30, respectively. From the perspective of policy tools in the initial stage of smart cities, the types of policy tools are mainly environmental and demand oriented. Moreover, demand-oriented policies account for the largest proportion. To some extent, this perspective shows that the development of smart cities starts from the urgent needs of urban construction and management. Moreover, since 2013, the number of supply-oriented policy tools has gradually occupied the majority and peaked in 2016 at 21. Moreover, the proportion of environmental policy tools with respect to the total number of papers has been maintained at a relatively stable level since the same year.
This characteristic may be indicative of a government focus on enhancing the efficiency and scope of smart city development through the expansion of information infrastructure and increased investment in information technology and human capital development [5]. To illustrate, the government has been actively engaged in the advancement of intelligent transport systems, has invested in the development of big data centers, and has encouraged universities and enterprises to cultivate relevant technical expertise. It is worth noting that this phenomenon may have some negative impacts on the smart city construction process. For instance, an excess of supply inputs that are not closely aligned with the actual needs of urban development may result in the waste of resources, such as the low utilization of certain information facilities or systems constructed. A paucity of demand-based policy instruments may result in a less comprehensive grasp of the genuine requirements of citizens and enterprises regarding smart city applications. Consequently, the outcomes of construction may diverge from the actual needs and fail to optimally leverage the potential of the smart city. In the absence of sufficient stimulation and guidance on the part of the government, citizens and enterprises may exhibit a low level of acceptance and willingness to utilize smart city services and applications. This may render the promotion and effective utilization of certain smart applications challenging [19].

3.1.2. Policy Tools

In terms of policy subjects, 239 smart city-related policies have been selected from 68 institutions and departments. As shown in Figure 3, the top five institutions for issuing documents are the National Development and Reform Commission (NDRC), Ministry of Industry and Information Technology (MIIT), State Council (SC), Ministry of Housing and Urban-Rural Development (MHURD), and National Administration of Surveying, Mapping, and Geo-Information (NASMGI). Except for the SC and NDRC, which are coordinating government departments, the others are directly involved in constructing smart cities. The policy tools used by these 10 departments are more concentrated on the supply type, and the frequency of use of the demand-type and environment-type policy tools is the same, both of which are 75. The distribution characteristics of policy tools in the dimension of policy subjects show that the departments that use more supply-oriented policy tools are the NDRC, MIIT, and SC. The most used environmental policy tools are the SC, NDRC, and Ministry of Science and Technology (MST). These departments coordinate the development of smart cities and formulate relevant target plans and technical standards around smart cities to improve the external environment for development. In terms of demand-based policy tools, the MIIT, NDRC, and MHURD are the most frequently used departments, whereas the Ministry of Commerce (MC), Ministry of Finance (MF), Ministry of Education (ME), and State Administration for Market Regulation (SAMR) rarely issue policies on the demand side to promote the development of smart cities.

3.1.3. Cross-Analysis of Policy Objects and Policy Tools

The policy object (goal) is the concentrated embodiment of the theme of the policy text [42]. To promote the development of smart cities, government departments have matched different policy tool combinations in each dimension of policy elements, presenting a diverse distribution. According to the two-dimensional cross-analysis matrix of the basic policy tools and objectives, a comprehensive statistical analysis of smart city policy texts is carried out, and the distribution results shown in Figure 4 are obtained.
As shown in Figure 4, among the 239 smart city policy texts, the policy articles released in the field of infrastructure account for the highest proportion, followed by specific industry, urban construction, scientific and technological innovation, and demonstration publicity. From the perspective of policy tools, only one or two policy tools are used for the three policy objects of talent training, social livelihood, and government service, and three types of policy tools are used for the remaining policy objects. Specific to various governance objects, the types of policy tools still have differences. Among them, for infrastructure, specific industry, scientific and technological innovation, talent training, and other objects, government departments are more inclined to use supply-oriented policy tools to promote the rapid development of infrastructure, scientific and technological innovation, or industry to boost the pace of smart city construction. The policy objects represented by standards and norms, urban construction, social livelihood, and information security frequently use environmental policy tools, thus reflecting that the perfection of standards and norms, the guarantee of information security, and the understanding and recognition of the public are the keys to creating a good developmental environment for smart cities. The formulation of comprehensive and systematic standards and norms is an essential aspect of smart city construction [43]. These standards and norms can facilitate the integration and interoperability of different smart systems, safeguard information security and privacy, improve construction quality and efficiency, promote fair competition and innovation, and facilitate the supervision of the construction process and the evaluation of construction effects [44]. In accordance with the considerations, the standards and norms in question should encompass the following elements: data management standards and norms, technical application standards and norms, information security standards and norms, project construction and management standards, urban governance and service standards, cross-sectoral collaboration standards, and data privacy protection norms.
Moreover, compared with the characteristics of the diversification of policy tools for other policy objects, the types of policy tools used by personnel training, social livelihood, and government services are relatively singular. Among them, talent training focuses on the supply dimension, and the environment and demand sides are not involved, indicating that the relevant policy texts lack the growth space, optimization of the development environment, and establishment of demand posts for talent cultivation oriented to the construction of smart cities. Regarding livelihood, the policy tools used by relevant policies are more evenly distributed in the supply and environment types. However, the demand side does not appear. This observation reflects that the coverage and strength of the policy papers on the popularization of smart cities among the public are not wide and are insufficient. Similarly, no demand-oriented policy tool is involved in government services. This absence reflects the role of the relevant government in ignoring social people’s livelihoods when considering the smart urban construction demand. That is, the existing policies lack the initiative, enthusiasm, and creativity to engage in public participation in smart cities. However, public participation is a self-evident crucial aspect of smart city construction [45], as public acceptance will be a key factor in determining the success or failure of smart cities [15]. It is, therefore, recommended that demand-driven policy tools be given greater emphasis in the context of talent training and public services. To illustrate, regarding the training of talent, a mechanism should be established for the assessment of the needs of talents for the construction of smart cities, the customization of educational programs, and the construction of continuous and comprehensive career development support programs. Regarding government services, it is essential to prioritize research into public demand, implement a feedback mechanism for smart city services, and enhance the transparency of decision-making throughout the construction process. Furthermore, policy incentives can be augmented to enhance the degree of public involvement in the development of smart cities. Firstly, efforts should be made to enhance the dissemination of information and the provision of educational resources about smart cities, with the objective of elevating the level of public awareness. Secondly, the implementation of a reward system is recommended to encourage public participation in the planning of smart cities. This system should recognize and reward those who contribute valuable suggestions or participate in actual projects. Thirdly, the number of interactive experience activities should be increased to stimulate public interest and encourage participation in the construction process. Thirdly, the establishment of a public advisory committee would facilitate the direct influence of public opinion on the formulation of policy and the implementation of projects within the context of smart cities [46].

3.2. Policy Organization Structure

As the process of smart city construction progresses, it gradually extends to the construction industry, the information technology industry, the financial industry, and other areas of development. This is accompanied by an increasing prominence of the “cross-boundary” characteristics of the practice. It is, therefore, essential to develop a rational smart city policy through inter-sectoral collaboration at the stage of policy formulation and implementation. This study employs social network analysis to examine the interconnections between the entities responsible for formulating smart city policies. It elucidates the pivotal roles and collaborative dynamics of the policies pertaining to the construction of smart cities.

3.2.1. Type of Publication

In addition to the form of policy release mentioned above, the type of publication should also include the cooperation of the main body of the paper. Furthermore, multi-department cooperation can reflect the status of smart city construction in social and economic activities to a certain extent. The results reveal that, among the 239 smart city policy texts collected, 189 were released individually, and 50 were released jointly. The specific time series distribution is shown in Figure 5. The figure shows that the number of independent papers in the research period is significantly greater than the number of cooperative papers, with the largest number of independent papers coming from the NASMGI. For a long time, the department has released a series of policies on the construction of and personnel training for spatiotemporal big data platforms for smart cities. However, its interaction with other government agencies is minimal, which is not conducive to forming policy synergies. The NDRC is the organization that cooperates and leads the greatest number of papers. This result is determined by the inherent overall planning and top-level design of the NDRC. Significantly, following the slowdown in smart city building, the annual production of collaborative papers on smart city-related policies has risen since 2018. This phenomenon is indicative of the holistic and synergistic nature of government work in the process of developing smart cities. Firstly, as different departments are typically responsible for distinct areas, but many social problems are complex and comprehensive, it is challenging for a single department to address them in isolation. The issuance of smart city-related policy documents through multi-departmental collaboration allows for the integration of resources and the complementation of strengths, thereby forming policy synergies and addressing challenges and problems in the process of smart city construction in a more comprehensive and systematic manner. Secondly, it facilitates the enhancement of policy science and efficacy. The expertise and data held by different departments can be fully integrated through collaboration, allowing for more in-depth analysis and research on the issues. This enables the formulation of more scientific, reasonable, and practical policy measures. This increases the likelihood of the policy achieving its intended outcomes and meeting its policy objectives during the implementation phase. Moreover, it bolsters the authority and credibility of the policy. The unified stance of multiple departments conveys a clear and unwavering commitment from the government on a given matter. This instills confidence and assurance in the public and other stakeholders, underscoring the government’s gravity and resolve in matters pertaining to the advancement of smart cities. Consequently, there is a greater inclination to trust and endorse the implementation of the policy. Furthermore, the sharing and communication of information between departments is facilitated. In the process of collaboration, frequent communication and exchange of opinions and data between departments is required. This breaks down the information barriers between departments, improves administrative efficiency, and lays a good foundation for future cooperation [47].

3.2.2. Totality Policy Organization Structure

The construction of smart cities is a highly complex process requiring the integration of expertise from multiple sectors and stakeholders. As the field of smart city construction continues to evolve, the departments involved and the forms of cooperation will inevitably undergo changes. To examine the interconnections between government departments engaged in smart city construction, this study employs Gephi software to construct a co-occurrence matrix and a cooperation relationship graph of issuing nodes, based on the cooperation situation of issuing organizations. The resulting network is illustrated in Figure 6.
The results show that the NDRC is the core node of the smart city-related policy issuance network, and the structure of the core network with the NDRC, MIIT, MC, MHURD, MF, and MST has basically been formed. Moreover, the width of the link path between nodes indicates the degree of cooperation between the institutions. The results show the closest cooperative relationship between the NDRC, MIIT, and MST. Among them, the MST and MIIT are the main departments responsible for technological innovation and informatization at the national level. However, they can only cooperate with comprehensive management departments, such as the NDRC, MF, and MC, as well as special financial projects, because of the limited ability of specific functional departments to mobilize policy resources. The cooperation of management departments can compensate for their lack of administrative power and achieve the effect of comprehensively exerting multiple policy tools across departments.

3.3. Analysis of Policy Effectiveness

Policy effectiveness represents the concentrated manifestation of the strength, content, and influence of policy texts [48,49]. A similar phenomenon can be observed in the context of smart city-related policies, where the degree of importance reflected in policy documents serves as an indicator of the government’s attention to the construction of smart cities and the strength of its investment. Based on the conclusions of previous analyses, this study presents a framework for evaluating the effectiveness of smart city policies. This framework encompasses five dimensions, including policy strength, policy objectives, policy measures, policy feedback, and policy supervision. As summarized in Table 3, policy intensity P is used to reflect the influence of Chinese smart city-related policies, which is determined by the publication size of the policy documents and the level of the issuing department. Policy objective G measures the correlation between policies and smart cities. The closer the objectives are to the smart city, the higher the score. Moreover, policy measure M represents the construction of smart cities in the policy documents. The more detailed the relevant measures are, the more specific the measures will be from a certain perspective of smart city construction. In addition, the score will be higher. Policy feedback F and policy supervision s, respectively, represent whether a feedback and supervision mechanism is present in the policy implementation process and its strict degree.
Equation (1) displays the calculation formula for the quantitative model of the annual policy efficacy (APE) of smart cities, which is constructed with reference to previous research [39]. This work develops a quantitative model for the annual average policy effectiveness (AAPE) of smart cities, as indicated by Equation (2).
A P E i = ∑ j = 1 n P j ( G j + M j + F j + S j )
A A P E i = ∑ j = 1 n P j ( G j + M j + F j + S j ) N i
where i is the year of issue; Ni is the sum of the number of smart city policies issued in the i-th year. The APE and AAPE of the stage of smart cities are shown in Figure 7.
The APE shows a trend of first rising and then falling. Then, it ultimately maintains a stable trend. The policy effectiveness value reached the highest level in 2016, with an effectiveness value of 448. The change trend graph shows that the effectiveness of the policies in the two phases from 2014 to 2015 and 2016 to 2017 has changed significantly. Looking back to the original policy database, in August 2014, eight departments, including the NDRC and MIIT, jointly released the “Guiding Opinions on Promoting the Healthy Development of Smart Cities.” This policy clarified the time node and specific contents of smart city construction. Moreover, it has stimulated governments’ enthusiasm at all levels for the construction of smart cities to a certain extent, thus leading to a sharp increase in the number of smart city policies released. After 2016, the APE and policy effect have declined significantly, thus reflecting the cold wave effect of the decline in the construction of smart cities. In November 2016, the General Office of the NDRC, the Central Cyberspace Administration of China, and the Office of the National Standards Commission jointly released the “Notice on organizing the evaluation of new smart cities and practically promoting the healthy and rapid development of new smart cities”, which called for the development of a new type of smart city, the establishment of a systematic smart city evaluation system, and a clearer direction for the construction of smart cities.
The AAPE is calculated by dividing the APE value by the number of policies issued in the year. This indicator provides an indication of the differences in the effectiveness of policies over the corresponding period. Figure 8 demonstrates that, although the general trend of the AAPE is the same as the overall effectiveness value, the overall effectiveness of the smart city policy in 2019 is not high. The AAPE value of the policy ranks first, with an effectiveness value of 11.47. Looking back to the original policy database, in early 2019, the Standardization Administration and the Ministry of Natural Resources released a series of notices on the technical outline of smart cities and the expansion of the scale of pilot cities. During this period, the theme of smart city policy announcements has gradually shifted from early target planning, publicity, and promotion to landing applications. Comparing the special points of the changes in policy effectiveness, the sharp increase in the overall effectiveness of the policy in the short term is due to the promulgation of iconic policies and their supporting policies. Policy aggregation drives the effectiveness of policies to reach a peak in a short period. However, given the timeliness of policies, the number of supporting policies gradually decreases over time, and the effectiveness of policies also decreases.

4. Analysis of Policy Evolution Stage

Figure 8 illustrates the trajectory of smart city policy evolution in China over the course of the study period. In conjunction with the findings of the preceding paper concerning the dimensions of the number of smart city issuances, policy effectiveness, and key features of changes in the issuance network, classifying the stages of smart city policy evolution, this study delineates three distinct phases in the evolution of China’s smart city policy during the study period: 2011–2013, 2014–2016, and 2017–2022. The specific evolution stages of smart cities are as follows.

4.1. 2012–2014 Was a Period of Exploration

In this stage, the MIIT and MST are the main contributors to smart city policies. Policy implementation is mainly based on demand-oriented policy tools for urban construction. The application scenario is mainly the construction needs of a city. By examining the policy text and studying the word cloud and word frequency statistics, we find that the policy-themed words in this period focused on cities, construction, information, development, and implementation, which, to a certain extent, reflect the urgency and necessity of the construction of smart cities in policy documents. To achieve these objectives, the policy papers involve various policy tools. Moreover, demand-oriented policy tools are mainly used in this stage, including popular science publicity for the public, smart city demonstration pilot projects for lower-level government departments, and multidimensional publicity and guidance for constructing smart cities.

4.2. 2015–2018 Is the Period of Development

At this stage, the National Development and Reform Commission (NDRC) and the Ministry of Industry and Information Technology (MIIT) are the main issuers of smart city policies. It is worth noting that the issuance characteristics of policy documents in this phase have gradually shifted from independent issuance by a single government department to joint issuance by multiple government departments. The use of policy instruments is mainly dominated by supply-type policy instruments supported by information technology. The application scenarios are mainly the massive data storage and processing in the urban construction and governance process and the use of information technology. The policy text shows that, after the initial establishment of the development goal of smart cities in the first stage, the construction of smart cities has made more positive progress in terms of the number of pilot projects and the construction scale. Furthermore, some problems emerge, including the lag of system and mechanism innovation, blind follow-up construction, network security risks, and prominent risks, which need to be strengthened during planning and guidance. Based on the problem orientation, in September 2014, eight ministries and commissions jointly released guiding opinions on promoting the healthy development of smart cities. They highlighted the overall direction and construction priorities of smart city development at the national level and proposed some basic and common requirements for constructing smart cities in China. Then, they established general behavioral guidelines for smart city construction.

4.3. 2019-Present Is a Period of Enhancement

In this stage, the main issuing bodies of relevant smart city policies are the MIIT, NDRC, MHURD, ME, MF, and Ministry of Human Resources and Social Security. Moreover, the implementation of the policies is mainly based on supply-based policy tools for science and technology infrastructure construction and environmental policy tools for multi-party cooperation. The application scenarios are mainly the massive data storage and processing in the urban construction and governance process and the use of information technology. After a long construction period, the policy focus of smart cities in this period has gradually shifted from planning and design to evaluation and guidance. The evaluation work shows that the management of norms should be strengthened through the evaluation results to promote the implementation of rectification to promote the healthy development of smart cities. Thus, in 2016, the three ministries and commissions jointly released a notice on organizing the evaluation of new smart cities and promoting the healthy and rapid development of new smart cities. They announced the design of an evaluation index for new smart cities. The focus was on considering the scale of platform and hardware construction but particularly on the application effect and the public opinion regarding and satisfaction with the information technology in the city. More than half of the evaluation contents concentrate on the services and experiences of the people. Examining the policy texts has resulted in finding the above characteristics. The policy themes of this period focus on the construction, information, service, and city. Compared with the policy focus of the previous stage, the change in the policy focus in this period, to a certain extent, reflects the control and correction of the smart city construction process by various departments of the central government and pays more attention to the construction of a smart city system centered on application services. Moreover, this focus strengthens the dominant position of the public; encourages enterprises to conduct basic, critical, and cutting-edge research on smart city data information platforms; and improves social initiative awareness, action ability, and participation in the construction of smart cities.

5. Conclusion and Discussion

5.1. Lessons Learned from Previous Policies

Focusing on the theme of smart city policy texts, this study relies on 239 policy texts released by various departments of the central government from 2012 to 2022. Moreover, it uses a variety of quantitative policy analysis tools to analyze the policy content, organizational structure, and policy effectiveness in the field of smart cities to establish the evolutionary process of the policy system systematically. The conclusions are as follows:
(1)
The policy content, organizational structure, and policy effectiveness are unified under the framework of policy evolution theory, which realizes the mutual confirmation of qualitative public policy analysis and quantitative policy text research and supports the empirical judgment of the evolutionary process of a smart city policy.
(2)
Different policy instrument classifications reflect policymakers’ perceptions of smart cities. Based on the cognitive logic of construction, this study proposes a two-dimensional matrix classification of smart city policy tools and policy objects (objectives). The in-depth dialogues between policy objects and policy tools and policy tools and policy subjects help influence and guide the continuous improvement of policymakers, ideas, and thinking.
(3)
Overall, the policymaking of smart cities at the central level tends to be environment- and supply-oriented. This aspect includes the use of policy tools, especially supply-oriented policy tools. The central government is more inclined to adopt the strategy of the direct expansion of the supply and indirect impacts to accelerate the pace of smart city construction. However, demand-oriented policy tools have not been given sufficient attention, and the overflow, shortages, or lack of tools have different degrees. In the smart city construction process in the future, we should make more active use of the catalyst of urban policy and give full play to its role in guiding and regulating the construction of smart cities to implement smart city policy and promote the policy effect better.

5.2. Suggestions for Future Policies

5.2.1. Emphasizing the Importance of the Citizen’s Position in the Development of Smart Cities

From previous research, China’s smart city construction policy system ignores the participation of all parties in society, especially the participation of citizens [50,51]. The main reason is that the government does not pay enough attention to public participation. In the construction of smart cities, local governments often cooperate with large-scale information technology companies by purchasing services [45]. Enterprises provide public products and services, and enterprises are only responsible for the government, not for the service objects. This phenomenon will inevitably weaken public participation enthusiasm and reduce participation channels [13]. Lack of public participation in smart city construction will create a disconnect with citizens’ needs. Therefore, the concept of governance must be reshaped, and modern information technology and methods must be used to expand public participation channels and improve public participation capabilities. First, we must strengthen the cultivation of public participation awareness. The popularization of the concept of a smart city and the education on public rights awareness guide the public to participate in urban construction actively [52]. Therefore, the government should constantly improve the organizational guarantee mechanism of public participation to provide favorable conditions for developing non-governmental organizations. Second, to improve the public’s ability to master information technology, the government can popularize smart city concepts and application scenarios to the public through social media, community promotion, and online lectures. In doing so, the public will gradually form a habit of thinking on the Internet and become familiar with online participation procedures [53]. For the poor, the current state’s vigorous implementation of the “Internet +,” poverty alleviation, and e-commerce poverty alleviation will help solve their political participation problems.

5.2.2. Improve Citizens’ Sense of Participation in the Construction of Smart Cities

From previous research, China’s smart city construction policy system ignores the participation of all parties in society, especially the participation of citizens [51]. Given the lack of public participation in the construction of smart cities, the construction of smart cities deviates from the purpose of putting people first. To realize a smart city, the concept of governance must be reshaped, and modern information technology and methods must be used to expand public participation channels and improve public participation capabilities [54]. First, we must strengthen the cultivation of public participation awareness [55]. Through the education of the public’s awareness of rights and the rule of law, the public is cultivated to become an independent subject with a sense of ownership and consciously assume the responsibility of participating in public governance. We must continuously improve the degree of organization of public participation and build an effective platform for public participation. Second, to improve the public’s ability to master information technology [56], the government can use news media, community propaganda on-site consultation meetings, and other forms to popularize the knowledge of smart technology and management to the public; in doing so, the public will gradually form a habit of thinking about the Internet and become familiar with online participation procedures [53].

5.2.3. Promote the Development of Smart Cities from Fragmentation to Systemic Development

From the perspective of policy release, smart city policy formulation at the central level is biased toward using environmental- and supply-based policy tools and especially lacks demand-based policy tools. Therefore, to form a relatively complete smart city construction system, we must strengthen the government’s use of demand-based policy tools. Government departments should appropriately adjust the proportion of policy tools used, attach importance to demand-based policy tools, and strengthen the market’s leading role. The government can create a good policy support environment by introducing relevant laws and regulations, intellectual property protection, assessment, and evaluation [57,58].

5.2.4. Improving the Level of Information Technology and Attracting Outstanding Talents

China’s existing smart city policies lack effective exploration and support for innovative information technologies. Regarding smart city construction, government agencies and technology companies have engaged in insufficient cooperation. We can learn from the technology leadership model represented by the United States. The government can actively cooperate with related technology companies; cultivate talents; encourage innovation; and improve the level of urban construction, management, and service by developing advanced technologies. Therefore, the government should actively cooperate with local technology companies, universities, and scientific research institutions in the process of policy release. It should also encourage joint technological research, absorb and train outstanding talents, and enhance its overall technical strength. In addition, government departments should focus on the overall planning and coordination of the cooperative relationship with social capital, strengthen the mining and utilization of existing innovative science and technology, and actively absorb all outstanding resources and talents to join the process of smart city construction [59].

5.2.5. Improve Relevant Information Disclosure and Privacy Safeguards to Increase the Level of Public Trust in the Government

With regard to the extensive deployment of AI technology in the context of smart city development, AI has the potential to enhance the efficiency and quality of urban services [15]. This may be achieved, for instance, through the introduction of intelligent transport systems, which could help to reduce congestion, and the implementation of accurate public service notifications, which could meet the needs of citizens more effectively [60]. Such developments could foster a perception among the public that the government is actively engaged in the management of the city, thereby enhancing trust. Furthermore, it reinforces public safety and security, augmenting the public’s perception of security through the intelligent monitoring and forecasting of criminal activity. Nevertheless, the deployment of AI in the context of smart city development may, on occasion, give rise to challenges in fostering trust between the public and the government. The potential leakage of personal data as a result of AI implementation may give rise to public concerns regarding privacy protection, which could in turn cast doubt on the government’s capacity to manage the situation effectively. Furthermore, the intricacy of these technologies may result in opaque decision-making processes, rendering it challenging for the public to comprehend and oversee, which could potentially erode trust [61]. Consequently, in the context of formulating policies pertaining to smart cities, it is imperative to prioritize enhancements in relevant information disclosure and privacy safeguards, thereby augmenting the level of public trust in the government.

5.2.6. Promote the Establishment of the “User Perspective” of the Smart City Construction Practice and Improve Public Access and Satisfaction

In the process of smart city construction, government departments should pay attention to the actual benefits of investment and should combine the fragmented needs of the public to realize the application of microscopic scenes of smart cities [5]. While building smart cities, they should also adhere to the humanistic nature of value judgments and pay attention to the digital survival situation of urban citizens, especially disadvantaged groups [62]. Government departments need to start by strengthening the attention and protection of disadvantaged groups and strengthening the supervision and service of the project construction process. By formulating equal, diversified, and inclusive service policies and exploring best practice paths, the city’s inclusive service capacity will be continuously improved and the digital divide bridged [63].

5.3. Limitations of This Work

The limitations of this study must be pointed out. (1) Although this study encompasses a vast array of smart city policy documents at the national level in China, it is not exhaustive, as it does not cover all policies related to smart city construction issued by every city in China. Furthermore, policy documents pertaining to smart city construction issued by developed countries, including the United States, Singapore, and the United Kingdom, warrant examination. Comparative analysis of policies across different countries on this basis represents a pivotal research avenue in the domain of smart city policy planning. (2) This study focuses on a series of studies on smart city strategies at the government level. Thus, government, private, or NGO information campaigns focused on smart cities may have been missed/excluded. This shortcoming does not mean the rest of the information activities are unimportant. On the contrary, these institutions or organizations related to the commercial layout of smart cities play a crucial role in developing smart cities. Analyzing their behavior in the development of smart cities and their collaboration with the government and examining the interaction mechanism will be an interesting area for future study.

Author Contributions

Conceptualization, C.Y.; methodology, A.Y.; software, H.L.; validation, A.Y.; formal analysis, C.Y.; writing—original draft preparation, C.Y.; writing—review and editing, H.M.; visualization, H.L.; supervision, A.Y.; and funding acquisition, A.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Junior Fellowships for Advanced Innovation Think-tank Program of the China Association for Science and Technology (Funder: China Association for Science and Technology; Funding number: 2021ZZZLFZB1207134).

Data Availability Statement

The data that support the findings of this study are openly available in the China Statistical Yearbook at http://www.stats.gov.cn/tjsj/ndsj/ (accessed on 11 January 2023).

Conflicts of Interest

Author Chongfeng Yue was employed by the company Power China Chengdu Engineering Corporation Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Statistics of the use frequency of smart city policy tools.
Figure 2. Statistics of the use frequency of smart city policy tools.
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Figure 3. Two-dimensional matrix of smart city policy subjects and policy tools.
Figure 3. Two-dimensional matrix of smart city policy subjects and policy tools.
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Figure 4. Two-dimensional matrix of smart city policy objects and policy tools.
Figure 4. Two-dimensional matrix of smart city policy objects and policy tools.
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Figure 5. Bubble chart of smart city policy issue types: (a) ranking of cooperative documents issued by government; (b) ranking of cooperative documents issued by government departments (greater than 5).
Figure 5. Bubble chart of smart city policy issue types: (a) ranking of cooperative documents issued by government; (b) ranking of cooperative documents issued by government departments (greater than 5).
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Figure 6. Cooperation network of smart city policy-issuing agencies (The labels in the figure refer to the government agencies at the centre).
Figure 6. Cooperation network of smart city policy-issuing agencies (The labels in the figure refer to the government agencies at the centre).
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Figure 7. Trend of smart city policy effectiveness.
Figure 7. Trend of smart city policy effectiveness.
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Figure 8. Evolution of smart city policy.
Figure 8. Evolution of smart city policy.
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Table 1. Smart city policy samples.
Table 1. Smart city policy samples.
Date Policy NameContent Analysis UnitAPETypes of Policy InstrumentsName of Policy InstrumentCode
……… ………
2014.08.27Circular of the National Development and Reform Commission and Eight Other Departments on the Issuance of Guiding Opinions on Promoting the Healthy Development of Smart Cities…should seize the opportunity of smart city construction, make positive suggestions, actively participate in, and promote the construction of smart ports20Supply type
Environmental type		Public service
Goal programming		2014-16
……… ………
2020.09.03Letter on supporting Wuhan to build China’s new generation artificial intelligence innovation and development pilot zone... accelerate the deep integration of artificial intelligence technology with smart cities and people’s livelihood9Environmental typeGoal programming2020-21
……...…………
Table 2. Classification and definition of policy instruments.
Table 2. Classification and definition of policy instruments.
CategoryPolicy ToolsDefinition
Supply typeScience and technology information supportThe government provides public science and technology support and information services for developing a smart city by building a data database, evaluating and releasing urban construction information, and establishing oyster products.
Science and technology infrastructure constructionSupport the opening of public information resources or build new infrastructure.
Investment in science and technologyThe government provides funds for R&D and construction through financial subsidies.
Public serviceSmart cities serve urban governance and public needs better by improving the types of public services they provide.
Personnel trainingCultivate smart city talents through professional education or skills training in information technology.
Environmental typeGoal programming The policy specifies the overall objectives and requirements for the construction of smart cities.
Regulations and standardsThe implementation of unified data formats and standardized business scenario interfaces ensures inter-connectivity between subjects, thereby enhancing the quality and efficiency of urban governance and public services.
Communication and cooperation among subjectsThe multi-agent communication platform and channel are built to promote communication and cooperation among multiple agents.
Finance, taxation, and financeThe government promotes the construction of a smart city through loans, financing, subsidies, venture capital, financial distribution or relaxation of financial restrictions, and creation of financing conditions.
Intellectual property protectionThe government protects intellectual property rights related to the smart city through judicial and administrative law enforcement.
Demand typeGovernment procurementPriority or directional procurement of products and services catalog for smart city construction are set.
International communication and cooperationCooperation happens between overseas and foreign organizations or groups on smart city construction in various forms.
Strengthening propagandaThrough policy or institutional means, the government encourages the government, enterprises, and the public to use public information resources actively to create a good atmosphere for the construction of a smart city.
Demonstration project Smart city policy pilot is set up.
Table 3. Calculation rules for assigning values to the effectiveness of smart city policies.
Table 3. Calculation rules for assigning values to the effectiveness of smart city policies.
IndexScoreScoring Criteria
Policy strength
P		5Laws enacted by the National People’s Congress or the State Council
4Laws and regulations issued by the State Council
3Policy documents or regulations and standards issued by departments under the State Council
2Guidance and regulations issued by agencies under the State Council ministries and commissions
1Circular from the ministries and committees of the State Council
Policy objectives G5All of the policy’s construction goals are centered on smart cities
3The state objectives in the policy are related to smart city construction
1The policy document does not set out any construction targets for smart cities
Policy measures
M		5Starting from the overall dimension of smart city construction, detailed construction planning and implementation measures have been formulated. And the responsible units and key time nodes are clearly defined.
4Starting from a certain aspect of smart city construction, a detailed planning arrangement is formulated, and relevant policy measures are clarified.
3Starting from certain aspects of smart city construction, approximate target plans are formulated, and relevant policy measures are proposed.
2Provide a brief implementation of the topic and a list of some fundamental steps.
1There is not a detailed operation plan, only from a macro-perspective.
Policy feedback
F		5The department in charge is clear, and feedback needs to be repeated regularly
3The department in charge is clear, but only one feedback is required.
1No feedback
Policy supervision
S		5The way of supervision is clear, and the results need to be returned regularly and repeatedly.
3The way of supervision is clear, but the supervision results are not specified or only need to be returned once.
1No supervision
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Yue, C.; Li, H.; Mao, H.; Yue, A. The Evolution of Smart City Policy in China: A Quantitative Study Based on the Content of Policy Texts. Buildings 2025, 15, 7. https://doi.org/10.3390/buildings15010007

AMA Style

Yue C, Li H, Mao H, Yue A. The Evolution of Smart City Policy in China: A Quantitative Study Based on the Content of Policy Texts. Buildings. 2025; 15(1):7. https://doi.org/10.3390/buildings15010007

Chicago/Turabian Style

Yue, Chongfeng, Hongyan Li, Haocheng Mao, and Aobo Yue. 2025. "The Evolution of Smart City Policy in China: A Quantitative Study Based on the Content of Policy Texts" Buildings 15, no. 1: 7. https://doi.org/10.3390/buildings15010007

APA Style

Yue, C., Li, H., Mao, H., & Yue, A. (2025). The Evolution of Smart City Policy in China: A Quantitative Study Based on the Content of Policy Texts. Buildings, 15(1), 7. https://doi.org/10.3390/buildings15010007

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