Promoting Sustainable Development in Urban–Rural Areas: A New Approach for Evaluating the Policies of Characteristic Towns in China

Abstract

The Sustainable Development Goals, adopted by all United Nations Member States, emphasize sustainable cities and communities, aiming to strengthen development planning and foster positive links between urban and rural areas by 2030. As the newly leading platform for urbanization development, characteristic towns (‘Tese Xiaozhen’ in Chinese) contribute to the target of sustainable urbanisation, wherein town policies are especially important for leading the sound development of characteristic towns. However, the effect of these characteristic towns policies remains uncertain. Additionally, most related studies have utilized qualitative methodologies in policy evaluation, which may be inadequate to guide actual practice. Thus, in order to accurately acknowledge the effect of characteristic towns policies, a policy evaluation framework is established in this paper, utilising the multiple streams theory, text mining and the Policy Modelling Consistency Index model (PMC-Index model). Then, taking 225 policy documents promulgated in China from 2015 to 2022 as the research objects, policy evaluation indicators were selected by combining the multiple streams theory to improve the PMC-Index model and a text mining method. Thereafter, an empirical analysis was conducted to evaluate the consistency of 10 characteristic towns policies chosen. The results indicate that the mean value of the PMC indexes of the 10 examined policies is 7.13 in the total of 9 points, which means that the general performance of the characteristic town policies is satisfactory, yet the low scores in terms of effectiveness, content, and guarantee. In the end, some recommendations and suggestions are proposed for clarifying administrative attributes, optimizing the integration of urban and rural resources, and adjusting the policy hierarchy to a bottom-up approach. This study not only provides a policy evaluation framework to comprehensively understand the consistency of global urbanization policies but also offers a beneficial reference for promote urban–rural sustainable development.

1. Introduction

China has undergone an unprecedentedly rapid urbanisation since the new century. In fact, the urban expansion in China was currently ranked first internationally, accounting for 47.5% of the total urban expansion worldwide [1]. However, urbanisation is often accompanied by a series of ‘urban diseases’, such as traffic congestion [2,3], urban sprawl [4], growing urban population [5], and housing crowding [6]. Throughout the world, these consequences have led to planning innovations initiatives, such as the retrofitting of towns in the United States [7] and Australia [8]. Additionally, the characteristic town initiative, proposed by the Chinese National Government in 2016, aimed to facilitate the achievement of sustainable development at regional, national, and international levels [9].

The Sustainable Development Goals, supported by all United Nations Member States, were designed to promote sustainable cities and communities. The initiative aims at improving planning and fostering good relationships between urban and rural areas to achieve harmony and sustainability by 2030. In the analysis of sustainable development, some scholars, such as Manolis Manioudis and Giorgos Meramveliotakis, highlighted the critical role of historical inheritance, the necessity of interdisciplinary methods, and the primacy of social class analysis [10]. In addition, Klarin’s core views about the sustainable development concept was the characteristic of evolution, adaptation, and persistent relevance in addressing global environmental, social, and economic challenges [11].

In order to achieve the goals of sustainable urbanization, characteristic towns initiatives tailored by the Chinese national governments, aims to advance the development of environment, society, and economics accompanied with the process of urban–rural integration. Characteristic towns can contribute to sustainable urban–rural development. Firstly, characteristic towns can be made for ecological protection and the sustainable use of resources, aiding in constructing a harmonious relationship between humans and nature. Secondly, characteristic towns can improve the quality of local infrastructure and public services, which helps to promote stability and harmony in both urban and rural societies. Lastly, by leveraging advantageous local resources to foster distinctive industries and encourage innovation, characteristic towns have the potential to drive economic growth and secure sustainable development over the long term.

Therefore, governments attached great importance on construction of characteristic towns, especially by issuing a large number of policies. In July 2016, the ‘Notice on the Cultivation of Characteristic Towns’ was issued. Subsequently, local governments (provinces and municipalities) have been promulgating policies as regards characteristic towns under the guidance of the national government. Until December 2022, 29 out of 34 provinces have released distinctive town policies. A total of 225 policy documents have been promulgated, with 31 of them being at the national level.

However, the rapid increase in the number of policies within a short period has led to a significant inconsistency in policy texts. For instance, most of the local policies merely conform to national policies, disregarding specific local characteristics. The abundance of similar policy texts results in the homogenization of characteristic town development [12]. There is also a mismatch between policies and local practices [13], which has attracted considerable attention from scholars in evaluating the policies related to characteristic towns. These evaluations mainly include content, the situation of achieving targets, and the effects on resources [14]. Therefore, three research gaps are found. Firstly, the multiple streams theory, which emphasizes the analysis of policy formulation, overlooking the process of policy implementation, is missing in the characteristic town policy evaluation [15]. Secondly, previous research has utilized qualitative methodologies in policy evaluation, which may be inadequate to guide actual practice. Currently, the origins and rationale of policies are being extensively explored through qualitative analysis [16], a method that may overlook aspects of policy consistency. Therefore, it is necessary to remedy the shortcomings of policy evaluation for characteristic town policies by combining quantitative and qualitative analysis [17]. Thirdly, most of the present research on characteristic town policy evaluation focuses on the provincial level, which may lead to a lack of understanding of the impacts and effectiveness of these policies at the national or local level. Consequently, this approach often neglects the influence of local characteristics and needs on policy results. From the perspective of policy consistency, a comparative evaluation of an individual characteristic town policy, conducted at national, provincial, and municipal levels, would offer a new way for policy improvement [18]. Therefore, it is necessary to establish a complete scientific framework for policy evaluation to determine the effectiveness of a policy and identify problems in policy formulation.

Therefore, this study analyses the development of characteristic towns based on multi-source flow theory, integrating the PMC-Index model with text mining techniques to propose an innovative policy evaluation framework. Next, it identifies deficiencies in the structure of policy indicators, and examines the consistency between policy measures and development needs, and proposes specific policy optimization countermeasures required for characteristic towns. The innovation in this study lies in its exploration of the effectiveness of characteristic town policies, adopting a perspective that combines both quantitative and qualitative analyses. Moreover, this study not only considers different stages but also national, provincial, and municipal levels. The study aims to achieve the following objectives: (1) identify indicators associated with the formulation of characteristic town policies based on the multiple streams theory; (2) conduct a quantitative analysis of characteristic town policies using the PMC index model; (3) provide recommendations to enhance the consistency of characteristic town policies.

2. Literature Review

2.1. Characteristic Towns

Characteristic towns are defined as a collection of distinctive industries, culture, tourism, and community functions located in the suburbs [19]. In 2016, characteristic towns in China originated in Zhejiang and were rapidly implemented throughout the country. Under the guidance of urban–rural integration policy, characteristic towns have become the regional links between urban and rural areas [20]. Characteristic towns have been noted to play a prominent role in attaining resource optimal allocation between urban and rural regions, sharing urban and rural resources, and promoting urbanization sustainable development [21]. However, the quality of characteristic towns was unsatisfactory to the public due to issues of blind homogenisation, serious real estate tendencies, and insufficient support for industrial characteristics [12], which led to a total of 419 ‘problematic towns’ eliminated or rectified in 2018. Although characteristic towns have developed from the exploration stage and comprehensive promotion stage to the regulation and rectification stage, policy consistency in practice does not meet expectations [15].

The best outcomes are most likely when appropriate external help is available to support local capacity for change [22]. Unfortunately, few countries have included this initiative in their national regional approach as part of wider spatial or territorial development strategies. Powe argues that the Government of India is aloof in this regard, and there is an increasing tendency to exclude characteristic towns from the nets of urban development programmes [23]. For developing countries, several scholars agree that strong policy intervention is required in favour of the characteristic towns [24]. Therefore, characteristic towns must have a guided development from ‘scaled orientation’ to ‘high quality’ in order to evaluate the effectiveness of characteristic town policies and adjust promptly.

2.2. Characteristic Town Policies

The rapid promulgation of characteristic town policies has brought a multitude of problems, attracting the attention of researchers and politicians. It has been argued that these policies would encourage real estate firms to explore new space for land financialisation, leading to a new round of real estate issues [25]. To advance the construction of characteristic towns, local governments frequently take on debt, thus heightening their risk of indebtedness [26].

Characteristic town policies are a series of comprehensive policy tools that promote the construction of characteristic towns [19]. These policies are deemed positive for new urbanisation [27] and urban–rural integration [28]. However, several research gaps need to be filled. First, there is a lack of broader reflections on the origins and credentials of characteristic town policies. Characteristic towns are possibly an extreme example of explosive growth following the promulgation of policies [29]. Rushed development can result in a mismatch between policies and practices. Thus, policy implementation should revisit this conundrum through the lens of policy process theory. Moreover, previous research has focused on qualitative analysis, which took a broader macro view to determine the evolution of characteristic town policies and sort out the evolution and trend of urbanisation policies since the reform in 1978 [30]. This content analysis method is used to objectively analyse the guidelines for promoting the construction of characteristic urban policies from the perspective of policy tools [31]. The research perspectives of characteristic town policies are limited, which focused on the provincial perspective, analysing regions such as Jiangsu and Zhejiang provinces [32]. In particular, there is no sufficient comparative analysis of national, provincial, and municipal policies. To fill these gaps, this study adopts multiple streams theory to analyse the origins and credentials of characteristic town policies and uses the PMC model for quantitative research and comparatively analyses the evaluation results based on national, provincial, and municipal perspectives.

2.3. Multiple Streams Theory

Kingdon deals with how issues emerge and proposes the use of the multiple streams theory [33]. Kingdon analyses policy processes as a function of three streams, that is, problems, policies, and politics [34]. The multiple streams theory has been applied by researchers to explore 65 different countries worldwide [35]. This theory may be radical and eruptive. Therefore, explosive characteristic town policies fit with this framework.

However, the multiple streams framework emphasizes the analysis of policy formulation, overlooking the process of policy implementation. Therefore, its effectiveness in explaining the connection between policy and practice is limited [36,37]. Sabatier (1999) suggests that the multiple streams theory does not inspire many empirical applications [38]. Policy sciences often develop pragmatically, and so our approach here is an incremental step in a new direction. To fully address the entire policy process, significant adaptation and extension of the theory are required beyond Kingdon’s initial vision [39,40]. Therefore, we combine the PMC-Index model with the multiple streams theory to add a policy evaluation link to scientifically and rationally analyse the effect of policy implementation and provide reference, thereby forming a closed loop.

2.4. Policy Modelling Consistency Index Model

The PMC-Index model has been widely used in ecological policies, such as carbon neutrality [41,42], solid waste policies [43], and green development [44]; industrial policies, such as new-energy vehicle industry policies [45] and shipping decarbonisation policies [46]; and livelihood policies, such as high-tech industry [47], land protection [48], and vehicle polies [49]. Each of these has high applicability. Therefore, the widespread application of the PMC-Index model in policy evaluation has enabled a quantitative assessment of characteristic town policies in China.

3. Research Methodology

This study establishes a policy evaluation framework, utilising the multiple streams theory, text mining, and PMC-Index model (Figure 1). First, the whole process of formulating characteristic town policies adopts the multiple streams theory for in-depth analysis, which provides the basis for the selection of PMC-Index model variables and helps improve the all-round improvement of planning preparation, planning management, basic research, and so on. Second, text mining is used to identify high-frequency words for characteristic town policies, providing a basis for variables. Third, the PMC-Index model is established to evaluate characteristic town policies.

3.1. Theoretical Framework

This study constructs a characteristic town policies assessment framework based on the multiple streams theory. Kingdon’s model is based on the idea that decision making involves the coordination of three distinct elements: problems, politics, and policies [34].

3.1.1. Problems Stream: Social Issues Drive Policy Actions

The problems stream commonly emerges from social problems, mainly explaining how some specific social problems are acknowledged by decision makers. Towns characterised by low-tech, labour-intensive, and export-oriented manufacturing are usually less adaptable and lack social dynamism [50]. Zhejiang Province first began to propose characteristic towns. At present, the main problems faced by characteristic towns are as follows: Ignoring local specificities in the socio-economic context; overlapping and conflicting contents and confusing policies involving land, finance, and other aspects. The promulgation of characteristic town policies results from the accumulation of social problems in various fields in the process of urbanisation to a certain extent, which then creates a pressing root cause of the problem and drives the policy action. Thus, the policy flow dimension involves policy nature, content, and area.

3.1.2. Policy Stream: National, Provincial, and Municipal Tripartite Promotion

Stakeholders including those at all levels of government, academic experts, the public and others are actively involved in advancing issues onto the policy agenda. Zhejiang has demonstrated a positive role in promoting the official awarding of characteristic town policies. The key issue in policy formulation is how national, provincial, and municipal governments guide and regulate the development of characteristic towns. Policy programmes determine the policy effectiveness, government actions determine the policy orientation, and community statements determine the policy evaluation. Thus, the policy flow dimension involves policy effectiveness, evaluation, and orientation.

3.1.3. Politics Stream: Political Strategy Affects Political Dynamics

The political stream flow mainly includes the political climate and national sentiment. The political climate, such as urbanisation development strategy, sustainable development strategy, regional coordinated development strategy, and new development concept, forms a political environment for the introduction of characteristic town policies. With people being the main factor affecting the atmosphere, the national sentiment is expected to represent the general interests of the majority; thus, government departments need to understand the national sentiment with the help of the media to create an environment suitable for the implementation of the policy. Policy receptors and role levels need to consider the national mood, with reasonable incentives for guidance. Therefore, at the level of political sources and flows, factors such as policy guarantee, perspective, and disclosure are important.

These three streams interact and accumulate under the influence and impetus of governments at all levels, leading to the opening of the ‘policy window’.

3.2. Text Mining

To determine the influencing factors of characteristic town policies, high-frequency policy words of characteristic town policies were extracted using the ROSTCM 6.0 software package. It is a computing platform designed by Wuhan University in China and is used for text analysis, such as website analysis and word frequency statistics. This involves an in-depth analysis of texts to identify and count the frequency of each word. It helps in understanding trends, common themes, and patterns in language use.

3.3. PMC-Index Model

In this research, the application of the PMC-Index model is meticulously conducted through a structured, four-step process. Initially, the first step involves the precise determination of the classification and parameters of the variables. The second step is the construction of a multi-input/multi-output table. The third step is the measurement of the PMC index and constructing the PMC surface. Finally, the fourth and last step involves constructing the PMC surface. This step is about visualizing the data and the results of the PMC index in the graph.

4. Data and Model

4.1. Data Sources and Research Samples

Characteristic town policies in China are mainly distributed in the documents issued by the national government and relevant departments. The issuance of papers concerning these policies evolved to begin in 2015. To systematically and comprehensively obtain policy texts related to characteristic town policies, the following steps have been adopted. Policy texts were searched in the authoritative policy and regulation database, where the ‘Magic Weapon of Peking University’ (http://www.pkulaw.cn/ accessed on 5 May 2023) was created by China’s Peking University as the main information source. ‘Characteristic town’ and ‘small town’ were retrieved as keywords from the released policies related to characteristic towns from January 2015 to December 2022. In total, 225 relevant policies were obtained that include all 32 provincial-level administrative regions (comprising 23 provinces, 5 autonomous regions, and 4 municipalities directly under the central government), in addition to national policies. Given the interdependence and mutual influence of national, provincial, and municipal policies on the development of characteristic towns, all are crafted with the unified objective of guiding and regulating their sustainable and healthy growth. Therefore, analysing policies across these various levels proves beneficial for their optimization and adjustment. Additionally, the selection of ten policies covers a broad spectrum of characteristic town types and stages of development, providing a comprehensive overview of the policy landscape and its evolution in the context of characteristic towns.

The Ministry of Housing and Urban–Rural Development of the country has announced two batches totalling 403 characteristic towns, and various provinces have announced lists of provincial-level characteristic towns totalling 979; county and municipal-level characteristic towns are also actively being developed, with the total creation plans exceeding 2000.There are a total of 403 national-level characteristic towns in China, with a notable concentration in provinces such as Jiangsu, Zhejiang, Shandong, Guangdong, and Sichuan. Characteristic towns are less prevalent in economically disadvantaged regions such as Gansu, Hainan, Ningxia, Qinghai, and Tibet; yet there is an active effort towards their development. To ensure the scientific integrity of our study, in addition to selecting policies from Shandong, Jiangsu, and Zhejiang provinces, we have also chosen policies from the economically disadvantaged region of Tibet.

The selection of policies aims to systematically and comprehensively obtain policies with regard to the levels, types, and stages of development. The 10 policies included national, provincial, and municipal policies. The data for this study were from the full roll-out phase and the high-quality development phase of characteristic towns. Certain characteristic towns, such as financial characteristic towns, sports and leisure characteristic towns, and forest characteristic towns, were included for the general requirements. For a comprehensive screening scope, the original policy texts on the official websites of relevant departments were also chosen for comparative analysis to ensure data accuracy. The 10 policies obtained after screening and the basis for screening are summarised in

Table 1. Sample characteristic town policies.

Code	Policy Name	Release Time	Level	Reason
P1	Notice on the cultivation of characteristic towns	July 2016	National	The first characteristic town policy promulgated by the central government
P2	Notice on the promotion of policy-based financial support for the construction of small towns	October 2016	National	The first policy on the type of finance for characteristic towns
P3	Notice promoting the construction of sports and leisure towns	May 2017	National	The first sports and leisure characteristic town policies
P4	Notice on printing and distributing the guidelines for the standardised and healthy development of national characteristic towns	September 2021	National	The latest policy
P5	Guidance on accelerating the planning and construction of characteristic towns	April 2015	Provincial	The first characteristic town policies issued by Zhejiang Province, which has the largest number of characteristic towns
P6	General office of the people’s government of Inner Mongolia Autonomous Region’s guidance on the construction of characteristic towns	September 2016	Provincial	The characteristic town policies of autonomous regions
P7	Notice on the issuance of Jiangsu provincial characteristic town acceptance and naming measures	July 2020	Provincial	The characteristic town policies of the experience demonstration area
P8	Notice several measures to promote the standardised and healthy characteristic town development	September 2022	Provincial	The latest characteristic town policies of fruitful regions
P9	Jiujiang City characteristic town construction work program	June 2017	Municipal	Jiujiang characteristic towns ranked second in 2021
P10	Hangzhou implementation plan for promoting high-quality characteristic town development	April 2021	Municipal	Hangzhou’s characteristic town construction has significant innovation

.

4.2. Establishing the PMC-Index Model

The PMC-Index model is considered a quantitative tool designed for the multi-faceted evaluation of policies, aiding in the clear understanding of their structural consistency and effectiveness. It can provide a suitable assessment of their merits and areas needing enhancement. By utilizing the PMC-Index and PMC-Surface analyses, this model can express the inherent strengths and shortcomings of policy documents and also offer practical insights. The calculation steps of the PMC-Index model include determining the classification and parameters of the variables, constructing a multi-input/multi-output table, measuring the PMC index, and constructing the PMC surface. On the basis of the above analysis, the PMC-Index model was utilised using four steps (Figure 2).

4.2.1. Determining the Classification and Parameters of the Variables

To ensure the scientific validity of the evaluation, variables were chosen using a combination of extracted high-frequency policy words. The 225 characteristic town policy texts obtained were subsequently imported into ROSTCM 6. After counting the word frequency of the document text of the policy content and sorting them in a descending order, the top 60 high-frequency words were compiled (

Table 2. Sample characteristic town policies for the PMC-Index model.

Number	High-Frequency Words	Frequency	Number	High-Frequency Words	Frequency
1	characteristic	7136	31	Space	530
2	Small Towns	6413	32	Related	526
3	Construction	4120	33	Funding	518
4	Development	3205	34	Mechanisms	496
5	Industry	2877	35	Protection	491
6	Planning	1658	36	Main Body	489
7	Work	1404	37	Sports	484
8	Town	1354	38	Urban and Rural	480
9	Culture	1337	39	Guidance	476
10	Tourism	1188	40	Public	470
11	Services	1169	41	Implementation	456
12	Projects	1106	42	Carry out	451
13	Creation	1024	43	Organise	438
14	Innovation	1003	44	Science and Technology	431
15	Enterprise	995	45	National	427
16	Support	920	46	Improve	427
17	Incubation	880	47	Accelerate	426
18	Facilities	880	48	Leisure	409
19	Investment	787	49	Lead	404
20	Ecology	726	50	Technology	401
21	Reform	709	51	Unit	392
22	Environment	678	52	Layout	392
23	Management	673	53	Social	391
24	Resources	664	54	Land use	386
25	Policy	634	55	Establishment	384
26	Function	610	56	Clustering	383
27	Foundation	601	57	Insist	380
28	Sector	586	58	Provide	372
29	Government	560	59	Promotion	371
30	Economy	560	60	Quality	370

), representing the words that appeared with the greatest regularity across the analysed material.

To further determine the critical influencing factors of characteristic town policies, curve fitting was conducted, with the frequency variable plotted against an ordinal number of the top 60 words. The coefficient of determination R² was 0.97548, which was deemed a good fit (Figure 3).

The integration of the multiple streams theory, along with methodologies like word frequency analysis and long-tail curves, has been instrumental in refining the policy evaluation variables for the PMC-Index model. This refinement has resulted in a well-structured model with 9 primary and 34 secondary variables in

Table 3. Characteristic town policies variables.

Primary Variable	Secondary Variable	Source
Policy nature (X1)	Supervision (X1:1)	[21]
	Suggestion (X1:2)
	Guidance (X1:3)
	Description (X1:4)
	Else (X1:5)
Policy effectiveness (X2)	Short term (<3 years) (X2:1)	[21]
	Mid-term (3–5 years) (X2:2)
	Long term (>5 years) (X2:3)
Policy area (X3)	Environment (X3:1)	[51]
	Social (X3:2)
	Economy (X3:3)
Policy content (X4)	Development plan (X4:1)	[52]
	Boot specification (X4:2)
	Cultural services (X4:3)
	Ecosystem (X4:4)
	Urban and rural resources (X4:5)
	Innovation (X4:6)
	Finance (X4:7)
Policy guarantee (X5)	Laws and regulations (X5:1)	[53]
	Bottom-line constraint (X5:2)
	Error correction (X5:3)
	Pilot demonstration project (X5:4)
	Policy incentives (X5:5)
	Tax and subsidy incentives (X5:6)
	Special fund (X5:7)
Policy evaluation (X6)	With a clear purpose (X6:1)	[54]
	Well founded (X6:2)
	Programme science (X6:3)
	Clear rights and responsibilities (X6:4)
Policy orientation (X7)	Positive incentive (X7:1)	Text mining
	Negative incentive (X7:2)
Policy perspective (X8)	Macroscopic (X8:1)	Text mining
	Microscopic (X8:2)
Policy disclosure (X9)		Text mining

, providing a robust framework for assessing characteristic town policies.

4.2.2. Building a Multi-Input/Multi-Output Table

To effectively quantify the values of the main variables, a comprehensive multi-input/multi-output table was developed, as it provides a systematic approach to dissect and understand these variables (

Table 4. Multi-input/multi-output table of quantitative evaluation of characteristic town policies.

Main Variables	X1	X2	X3	X4	X5	X6	X7	X8	X9
Subvariables	X1:1	X2:1	X3:1	X4:1	X5:1	X6:1	X7:1	X8:1
	X1:2	X2:2	X3:2	X4:2	X5:2	X6:2	X7:2	X8:2
	X1:3	X2:3	X3:3	X4:3	X5:3	X6:3
	X1:4			X4:4	X5:4	X6:4
	X1:5			X4:5	X5:5
				X4:6	X5:6

).

4.2.3. Measuring the PMC Indexes

First, two-level variables are confirmed. The value of the second-level variable is calculated. As shown in Formulas (1) and (2), the secondary variables met the rule of the [0, 1] distribution. The value can be judged as 0 or 1. When the relevant secondary variable information appears in the policy text, the value is 1; otherwise, the value is 0.

Second, the Delphi method is used to assign values to secondary variables. Next, experts in the field of characteristic town policies were strictly selected. To ensure the accuracy of the PMC index model results, 10 experts were selected and the Delphi method was used to assign values to the secondary variables. Experts must have been engaged in characteristic town management-related research for more than three years. The results were recovered separately for comparison. After three rounds of comparing the results, a consensus score was reached.

$$X~N\left[0,1\right]$$

(1)

$$X=\left\{XR:\left[0~1\right]\right\}$$

(2)

The first-level indicator score of the policy portfolio was the sum of the second-level variable scores divided by the number of those scores, which was strictly between 0 and 1.

Third, each first-level indicator of the policies to be evaluated was calculated, as shown in Formula (3) below.

Finally, according to Formula (4), the PMC indexes were calculated by adding up the values of each first-level index of the policies to be evaluated.

$$X_t\left({\sum }_{j=1}^n{X}_{tj}/T(X_{tj})\right)t=1,2,3,4,5,6,7,8,9,10,\cdots ,\infty$$

(3)

In Formula (3), t is the primary variable and j is the secondary variable.

$$\mathrm{PMC}=\left[\begin{array}{c}{X}_1\left({\displaystyle \sum _{i=1}}{X}_{1i}/5\right)+X_2\left({\displaystyle \sum _{j=2}}{X}_{2j}/2\right)+X_3\left({\displaystyle \sum _{k=1}}{X}_{3k}/2\right)+\\ X_4\left({\displaystyle \sum _{l=1}}{X}_{4l}/3\right)+X_5\left({\displaystyle \sum _{m=1}}{X}_{5m}/7\right)+X_6\left({\displaystyle \sum _{n=1}}{X}_{6n}/8\right)+\\ X_7\left({\displaystyle \sum _{o=1}}{X}_{7o}/4\right)+X_8\left({\displaystyle \sum _{g=1}}{X}_{8g}/2\right)+X_9\end{array}\right]$$

(4)

Based on existing research, the following criteria were used to grade the policies: 0–3.99 (poor), 4–5.99 (acceptable), 6–7.99 (good), and 8–9 (excellent) (as shown in

Table 5. Policy scoring ratings.

Score	9–8	7.99–6	5.99–4	3.99–0
Evaluation	Excellent	Good	Acceptable	Poor

).

4.2.4. Constructing the PMC Surface

A PMC surface is constructed in order to calculate the PMC matrix and establish a 3 × 3 matrix consisting of nine first-level variable values (5). X₁₀ takes the value 1; therefore, it was ignored when constructing the PMC surface.

$$\mathrm{PMC}=\left[\begin{array}{ccc}{X}_1& X_2& X_3\\ X_4& X_5& X_6\\ X_7& X_8& X_9\end{array}\right]$$

(5)

5. Results and Analysis

According to the evaluation results of the PMC-Index model on characteristic town policies, the policy setting has also gradually evolved from clarifying cultivation goals and guiding and encouraging the development of characteristic towns to correcting errors. However, the existing policy system still has some shortcomings.

The results showed that two policies were rated as ‘excellent’, whereas eight policies were rated as ‘good’. The average value of the PMC indexes of the 10 policies was 7.13, whereas that of the other five policies was below 7.13 (

Table 6. Summary of improved PMC-Index values for the 10 characteristic town policies.

	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	Average
X1	1.00	0.60	0.80	1.00	1.00	0.80	1.00	1.00	0.80	1.00	0.90
X2	0.67	0.67	0.67	0.67	0.67	0.67	0.67	1.00	1.00	0.67	0.73
X3	1.00	0.67	0.67	1.00	1.00	1.00	1.00	1.00	0.67	1.00	0.90
X4	0.57	0.29	0.71	1.00	0.71	0.57	0.86	1.00	0.86	0.86	0.74
X5	0.43	0.29	0.43	0.43	0.29	0.29	0.57	0.57	0.43	0.29	0.40
X6	1.00	1.00	0.75	1.00	0.75	0.75	1.00	1.00	0.75	0.75	0.88
X7	1.00	0.50	0.50	1.00	1.00	0.50	0.50	1.00	0.50	1.00	0.75
X8	0.50	1.00	1.00	1.00	1.00	0.50	1.00	0.50	1.00	0.50	0.80
X9	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
PMC	7.17	6.00	6.53	8.10	7.42	6.08	7.60	8.07	7.00	7.06	7.13
Rank	5	10	8	1	4	9	3	2	7	6	—
Grade	Good	Good	Good	Excellent	Good	Good	Good	Excellent	Good	Good	—

). The order of policy improvement followed the principles that were evaluated in line with the average value, and adjustments were conducted considering specific situations.

The three lowest-scoring variables were X₂ (policy effectiveness) (0.73), X₅ (policy guarantee) (0.40), and X₄ (policy content) (0.74) (Figure 4). These were the key aspects that needed adjustment.

5.1. National Policies Analysis

Four characteristic town policies at the national level were selected. These four policies were issued by institutions such as the National Development and Reform Commission of the People’s Republic of China and the Ministry of Housing and Urban–Rural Development of the People’s Republic of China, and they carry high effectiveness and authority. Them cover different development stages and types of characteristic towns, providing a certain degree of representativeness. Moreover, this study has also incorporated policies from distinctive towns in other provinces and cities to enrich the analysis. National-level characteristic town policies were determined to be better overall; however, policy content and policy guarantee need to be improved. Among the 10 policies, P₄ is ranked 1st with a score of 8.10, and P₂ is ranked 10th with a score of 6.00. Meanwhile, P₁ and P₃ had scores of 7.17 and 6.53, ranking 5th and 8th, respectively. The PMC surface of each policy allowed the visualisation of the assignment of each level variable (Figure 5, Figure 6, Figure 7 and Figure 8).

Being the first characteristic town policy introduced by the national government, P₁ paved the way in exploring the healthy development of characteristic town construction. Among the nine first-level variable variables, only the scores of X₄, X₅, and X₈ were below average, indicating that while these policies were designed to be scientific and comprehensive, there is also some scope for improvement. As it was in the primary stage of characteristic town development, this policy has elaborated the guiding ideology, principles, and goals of characteristic towns from a macro perspective and has proposed specific requirements for cultivating characteristic towns, specifying responsible subjects and designating supporting policies and safeguards. Nevertheless, there are issues that could not be resolved in terms of characteristic town development. Thus, the policy system must be further enriched, and the content related to bottom-line constraints should then be increased.

As special characteristic town policies, P₂ and P₃ both focus on a characteristic industry and lack of comprehensive planning and guidance for the construction of characteristic towns, which led to a low overall ranking. The score of these policies was low in X₅. The variable special fund must be strengthened, bottom-line constraints and corrective measures should be established in terms of negative incentives, and the supervision in the characteristic industry must be improved. As the first financial characteristic town policy, P₂’s score was noted to be lacking in X₄. Hence, the content of policies is incomplete and should be supplemented to guide and regulate the healthy development of financial characteristic towns.

However, a series of issues have been gradually exposed. Since 2017, policies that aim to correct the mistakes of characteristic towns have successively emerged to ensure a sustainable and healthy characteristic town development. As the latest national policy, P₄ stipulated the comprehensive policy text regarding bottom-line constraints and correction of mistakes to cultivate and guide the development of characteristic towns. Particularly, X₂ and X₅ were lower than the average and still imperfect, as the positive incentives for development were ignored. This result was deemed not favourable for the influx of fresh blood into the construction of characteristic towns.

5.2. Provincial Policies Analysis

There have been large disparities in policies for different provincial characteristic towns. However, they are all consistent with national-level policies, demonstrating a top-down approach. To ensure the comprehensiveness of the policy evaluation, the policy of the less developed regions, such as the Inner Mongolia Autonomous Region (P₆), was added to provincial policies. The PMC index of P₆ was 8.10, with a low overall evaluation, ranking 9th among the 10 policies (Figure 9). The characteristic town policies of Jiangsu, Guangdong, and Shandong provinces (P₅, P₇, and P₈) had higher scores of 7.42, 7.60, and 8.07, ranking 4th, 3rd, and 2nd, respectively, indicating policies that were comparably more scientific and reasonable (Figure 10, Figure 11 and Figure 12). However, X₅ was noted to be low overall, lacking bottom-line constraints, error and deviation correction, and other regulatory measures. For P₅ and P₆, X₆ was low, indicating that it was difficult to reflect the full basis for policy formulation in the policy text document. Therefore, attention should be paid to supplying evidence when formulating policies to make them more authoritative and reliable than at present.

5.3. Municipal Policies Analysis

Based on our findings, municipal policies scored low, making it difficult to develop regional specialisation. Jiujiang and Hangzhou cities are known to be prominent regions for the development of characteristic towns, with PMC indexes value below the average (7.00 and 7.06), ranking 7th and 6th in the 10-item policy (Figure 13 and Figure 14), respectively. X₅ and X₆ should thus be improved. The characteristic town policies of Jiujiang City were deemed imperfect in terms of policy nature and policy area due to the early release and thus should be combined with the features of characteristic town development in recent years to be more targeted.

In terms of policy protection, Jiujiang City put forward the requirements of dynamic supervision of daily operations and final acceptance of the characteristic towns. However, there was no clear emphasis on error correction and bottom-line restraint mechanisms. Hangzhou City proposed to strengthen guidance and supervision and fully implemented the list management and dynamic management of characteristic towns. Specific description of the mechanism for correcting deviations nevertheless remains lacking. The requirements were only proposed in the form of slogans.

6. Discussion

In response to the evaluation results, the following policy recommendations are made in areas of policy guarantee, effectiveness, and content, with suggestions for clarifying administrative attributes, enhancing the integration of urban and rural resources, and shifting to a hierarchical down-top approach.

6.1. Clarifying Administrative Attributes

With regard to the problem stream, the focus should be on policy effectiveness as characteristic town policies mainly focused on the short- and medium-term scope. This is consistent with our result, which states that characteristic towns are utilised as a way for local governments to promote construction for the sake of profits, even at the risk of incurring significant debts [26]. Many western studies argue that urbanisation policy approaches should be developed within particular national and regional contexts in the long term [55,56].

The administrative attributes of characteristic towns should be further clarified to make the duration of characteristic towns longer and promote the refinement and implementation. The construction of policies should be incorporated into China’s current administrative division management system and thus be given complete administrative power to make it more rigorous and authoritative. Simultaneously, the administrative management efficiency should be improved; the administrative management system of towns and cities should be actively promoted and characteristic towns should be given more universal autonomy in terms of land, finance, taxation, and financing through reforms to stimulate their development potential.

6.2. Enhancing the Integration of Urban and Rural Resources

As demonstrated in the policy stream, little attention is paid to the integration of urban and rural resources. Characteristic towns ought to be viewed as a crucial medium for the integration of urban and rural components. This echoes other studies wherein strengthening the role of towns and cities as service centres for rural areas and as population absorbers in urban areas has become a policy objective [56,57]. The development of the concept of characteristic towns aligns with the practical enhancement of the cultural and scientific capabilities of the rural workforce, as well as the expansion of its industrial chain [58].

Characteristic towns are optimally exploited to establish regional links between urban and rural areas and incorporate their location advantages and industrial characteristics. First, the role and positioning of characteristic towns in the integrated development of urban and rural resources should be clarified. Second, the important tasks and responsibilities of characteristic towns should be confirmed, and the requirements of characteristic towns’ construction in urban–rural integration should be formulated. Finally, policy support to strengthen characteristic town construction in urban–rural integration should be emphasised, including finance, taxation, investment, finance, industry, land, ecological construction, and talents. Characteristic towns can serve as a platform for innovation. They can break through existing institutional framework of urban–rural division and accelerate the pace of integrated urban–rural development.

6.3. Shifting to a Hierarchical Down-Top Approach

Towards politics streams, the down-top approach is adopted to guarantee that the rising characteristic towns have their own advantages. In fact, traditional regional development policies were often dominated by a top-down approach, and deemed to be of limited effectiveness. Combined with the analysis of policies at the central, provincial, and municipal levels, the national programme creates a dilemma for its local implementation: promoting strengths with a place-based approach directly contradicts the idea of ensuring one standardised policy in a place-neutral manner [26]. The geography and history of the region determine the characteristic town’s industries. A top-down policy approach can hardly make all characteristic towns characteristic and may even result in a proliferation of characteristic towns similar to each other.

In the process of policy formulation, development strategies should be tailored to the spatial and geographical characteristics of different regions. For characteristic towns with unique landscapes and natural resource advantages, the focus should be on developing the eco-tourism industry [59]. Moreover, policies should take into account the unique historical and cultural features of each area, aiming to protect historical and cultural heritage while integrating modern progress with traditional practices, promoting the development of unique crafts, festivals, and cultural activities in characteristic towns [60]. Additionally, considering the industrial and technological development stages of different areas, industries that align with the economic strengths and market potential of characteristic towns should be developed [61]. Innovation and entrepreneurship should be encouraged to adapt to the economic pattern of the towns.

Therefore, policies aiming to encourage a down-top approach should effectively get rid of homogeneous development among characteristic towns. This challenge calls for more local government autonomy in terms of decision making and policy evaluation, as well as more comprehensive policy frameworks.

7. Conclusions

Policies have played a crucial role in developing characteristic towns, guiding the implementation phase, indicating the direction, and providing strong support for their construction. The integration of text analysis, the multiple streams theory, and the PMC-Index model provides a universally applicable policy evaluation framework. By considering the content (through text analysis), process (through the multiple streams theory), and valuation (through PMC-Index model), this framework facilitates a nuanced understanding and evaluation of policies across diverse settings. This approach is particularly useful in identifying opportunities for policy innovation and reform. Promoting the integration of urban and rural resources, characteristic towns showcase their impact through economic interdependence, social interaction, and environmental co-management. Leveraging advanced urban technologies alongside the abundant resources of the countryside, characteristic towns not only spur local economic growth but also generate employment opportunities for neighbouring villages, thereby narrowing the developmental divide between urban and rural locales. Concurrently, they enhance the flow of people between urban and rural areas, fostering cultural exchanges and strengthening social bonds. Moreover, the development of these towns emphasizes ecological conservation and environmental sustainability, advocating for a shared green ecological environment. This approach not only contributes to creating habitable natural surroundings but also supports the concurrent advancement of urban and rural areas towards economic and social harmony.

Nevertheless, there are marked disparities in the consistency presented by the characteristic town policies, owing to varying contexts, distinct external situations encountered at various periods, and the varying stages of development of the characteristic towns. Thus, this study aims to improve these policies using the multiple streams theory and the PMC-Index model to analyse 10 representative characteristic town policies in China since 2015. For policies with lower ratings, most showed weaknesses in areas of policy effectiveness, guarantee, and content. Our findings demonstrate three key conclusions.

First, the administrative attributes of characteristic towns should be clarified, and short-term speculative characteristic town policies should be replaced by long-term characteristic town programming and management. Second, policies have not yet clearly reflected the significance of characteristic towns for the integration of urban and rural resources, which should be considered in future policy formulation. Third, to avoid the problem of homogenisation, the top-down approach should be adopted and local governments should be appropriately decentralised.

This approach of combining the multiple streams theory, text mining, and the PMC-Index model for policy evaluation is not only an extension of the multiple streams theory but also an improvement of the PMC-Index model, as it provides lessons for China and other developing countries in terms of their policy analyses. In light of this study, two limitations need to be addressed. A limitation of our study lies in the uniform treatment of evaluation variables, which may not accurately reflect their varied impacts on policy effectiveness. Assigning differential weights to these variables could yield a more nuanced and scientifically robust evaluation outcome. Furthermore, our analysis could benefit from a more rigorous selection of policy texts for extracting high-frequency words. Delving deeper into the semantic relationships among these words through network analysis could uncover more profound connections between variables, enriching our understanding of policy dynamics.