Quantitative Evaluation of Policy Based on PMC Index Model: A Case Study of China’s Textile Industry Policy

Liu, Yu; Zhao, Hong

doi:https://doi.org/10.1155/2022/1870185

Mathematical Problems in Engineering

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Research Article | Open Access

Volume 2022 | Article ID 1870185 | https://doi.org/10.1155/2022/1870185

Quantitative Evaluation of Policy Based on PMC Index Model: A Case Study of China’s Textile Industry Policy

Yu Liu¹and Hong Zhao²

Academic Editor: Zaoli Yang

Received07 Feb 2022

Revised27 Apr 2022

Accepted30 Apr 2022

Published09 Jun 2022

Abstract

The intelligence information collection and analysis of textile industry policy, scientific evaluation of the effectiveness of the textile industry policy, and formulation scientific policy optimization path have practical significance to promote the development of textile industry. China’s textile and apparel exports are the largest in the world, and the healthy development of China’s textile industry not only promotes China’s economic development but also greatly affects the development of the world textile industry. However, most of the existing studies are based on content analysis of high-frequency words in policy texts and lack of quantitative evaluation of textile industrial policies. Based on 126 textile industry policy texts published from 2014 to 2020, this paper innovatively combines the grounded theory with the PMC index model to construct a policy effectiveness measurement index with the characteristics of the textile industry. Ten textile industry policy samples are selected to empirically study the PMC index policy evaluation model of textile industry policy effectiveness. Furthermore, using PMC curved surface to visualize the internal structure of textile industry policy and the measurement results of policy effectiveness. The results show that the average PMC index of 10 textile industry policy samples is 4.31, which is excellent. From the PMC scores of the nine primary variables of the sample, the content and functions of China’s textile industry policy formulation are relatively scientific and comprehensive. However, the policy nature and decision-making subject are single, and the incentive guarantee is relatively weak, which needs to be further improved. The quantitative evaluation of China’s textile industry policy provides a scientific basis for improving the content and effectiveness of the textile industry policy. This investigation offers a fresh perspective on policy evaluation research of other industries.

1. Introduction

The position of China’s textile industry is improving steadily in the global value chain, and the overall competitiveness of China’s textile industrial chain is gradually enhanced. In 2020, China’s textile and apparel exports reached US 299 billion dollars, accounting for more than one-third of the world’s share, ranking first in the world. Among them, textile exports increased from 36.6% in 2016 to 39.2% in 2019. China’s textile fiber processing reached 58 million tons, accounting for more than 50% of the world’s total fiber processing, and chemical fiber accounted for more than 70% of the world’s total. In addition, in 2020, the profits of China’s textile industrial enterprises with annual revenue of 20 million yuan or more from their primary business operations achieved an operating income of 4.52 trillion yuan, accounting for 4.3% of China’s industry, and a total profit of 206.5 billion yuan, accounting for 3.2% of China’s industry [1]. With the guidance of the sustainable development policy, the structure of China’s textile industry has continued to be optimized, and the energy utilization efficiency is improving continuously. The cumulative decline in wastewater discharge and major pollutants in the textile industry has exceeded 10%. The supply capacity of recycled chemical fibers is improving significantly, and the resource utilization level of waste textiles is further enhancing. The ecological environment for scientific and technological innovation in China’s textile industry is continuously improving, and the construction of innovation platforms has significant progress. And with the international development of the textile and apparel industry, the global influence of Chinese brands has increased, and the original ability of fashion design is significantly improving. With the adjustment of economic structure and the upgrading of technology, there are still some problems in China’s textile industry, such as overcapacity, structural imbalance, and insufficient innovation capabilities. To promote the transformation and upgrading of the traditional textile industry and the innovation and development of the textile industry, China has promulgated a series of related industrial policies to effectively promote the textile industry’s rapid growth. However, there are many problems in in-depth promoting the policy, such as imperfect policy guarantee and incentive measures, unclear implementation and supervision mechanisms, insufficient policy content, and inconsistency between policy objectives and practical results. Scientific policy evaluation is of great significance for optimizing the formulation of textile industry policy and realizing the growth goal of textile industry output value. Policy evaluation is the process of relying on scientific judgment and comprehensive examination of the policy system. Meanwhile, policy evaluation is also an indispensable part of policy formulation and implementation, the basis for rational allocation of policy resources, and effectively tests the effectiveness of policies [2]. More importantly, it provides a scientific basis for policy formulation and implementation adjustments [3]. In the early 1970s, policy evaluation tended to be empirical, emphasizing the use of content analysis and empirical analysis. As policy evaluation evolved, it gradually shifted from empirical criteria to normative quantification, emphasizing value judgments [4]. Research on the evaluation of China’s textile industrial policies mainly focuses on content analysis and evaluates policies based on comparative analysis of high-frequency words and subject terms in policy texts. China’s textile industry policies still lack scientific quantitative evaluation methods to explore the strengths and weaknesses of policy formulation. By evaluating the effects of existing textile industry policies, we can grasp the problems in the process of policy formulation and implementation and provide a theoretical basis for optimizing textile industry policies, which has a reference significance for researching and formulating policies for the transformation and upgrading of related traditional industries. The grounded theory research method is used to qualitatively summarize data and transform text content into concept by coding. It can overcome the monotony and limitation of traditional quantitative research, which makes the subsequent quantitative process more scientific and rigorous. At the same time, using grounded theory to extract the key words of policy text can avoid the quantity deviation of word frequency caused by the writing habit of policy writers and the abnormal expression of consent and greatly improve the scientific nature of subsequent policy quantification [5, 6]. On the other hand, PMC index is a single index that can evaluate the advantages and disadvantages of policy modeling research policy texts. Compared with other evaluation models, the PMC index model can not only intuitively show the advantages and disadvantages of a single policy according to the score but also intuitively obtain the score composition of each variable, which can provide specific optimization paths for policy optimization [7].

This paper innovatively combines the grounded theory and PMC index model to construct a policy effectiveness measurement index with the characteristics of the textile industry. Combining qualitative and quantitative analysis methods, a scientific quantitative method to the evaluation of textile industry policies is proposed. In the research process, this paper conducts text mining of policies through grounded theory and builds a policy evaluation index system with the characteristics of textile industry on the basis of fully mining the connotation structure of textile industry policy texts. It also constructs a PMC index model for policy effectiveness evaluation and obtains scientific and comprehensive evaluation conclusions through a mixed qualitative and quantitative analysis method. Finally, the PMC surface is used to visualize the internal structure of the textile industry policy and the measurement results of policy effectiveness and propose an optimization path for policy formulation. Based on the current research status, research objectives and significance of textile industrial policy evaluation, this study examines the following: firstly, this study systematically reviews relevant materials with the aim of evaluating the current development status of China’s textile industry. According to the index system, this paper constructs the PMC index model of policy evaluation and quantitatively analyzes the policy samples of textile industry. Finally, the study uses the PMC surface to visualize the advantages and disadvantages of textile industrial policies in the process of formulation.

The rest of the paper is structured as follows: Section 2 contains a systematic review of the previous work on textile industry policy quantitative evaluation, and based on the existing research, a framework for China’s textile industry policy quantitative evaluation is established. Section 3, based on grounded theory and social network analysis methods, extracts high-frequency words in policy texts, constructs a composite weighted co-word network of textile industry policies and keywords, and draws the co-occurrence network diagram of high-frequency words of textile industry policy to visualize the key layout and industrial development orientation of textile industry policy. Section 4 delineates the policy data sources, as well as the measurement method and variable parameters of the textile industry policy PMC index model. Section 5 selects 10 textile industry policies, empirically studies the quantitative evaluation method of the textile industry policy PMC index model, draws PMC surface of policy samples, visually analyzes the policy samples, and comparatively evaluates the shortcomings and advantages of the policy samples. Research conclusions summarizing and future research directions of this paper are drawn in Section 6.

2. Literature Review

The policy text is the behavior trajectory and development direction of the government in handling affairs. The policy text reflects the government’s future development direction planning and a key focus. Many studies have shown that industrial policies have a positive effect on industrial development. The content analysis of the policy text can quickly understand the industrial policy’s internal structure and development direction in this field. The measurement of the effectiveness of policy implementation can grasp the weaknesses in policy formulation and implementation.

With the development of the textile industry, the research on policies related to the textile industry mainly focuses on the analysis of the content of tax policies, industrial layout policies, and environmental protection policies. The study of textile industry policy taxation policy, such as Yong, reviewed the evolution of the implementation of export tax rebate policies and summarized the short-term and long-term effects of China’s export tax rebate policies on the textile industry [8]. This research has shown that the tax rebate policy can enhance the confidence of export enterprises, reduce the export cost, expand the export range, and promote the growth of domestic GDP in the short term. In the long run, increasing the export tax rebate rate will intensify trade friction, worsen competition in the international market, and increase the government’s financial burden. Yanli analyzed China’s textile and garment industry policy of export tax rebate rate adjustment from 2007 to 2013 and put forward policy suggestions to promote textile and garment export [9]. This research suggested that textile enterprises should optimize the structure of export textile products, raise prices, and reduce costs. The government should establish an incentive and restraint mechanism to encourage all departments to support the tax rebate policy. The study of textile industry development layout policies, such as Shuai, conducted the content analysis on the policies of “the Belt and Road” textile and apparel industry, and policies of China’s textile and apparel industry development clusters, and the strategies for the industrial development in his research [10]. The study of environmental policies in the textile industry, such as XU (2018), used the content analysis method to study 118 textile waste policies promulgated by the central government from 1991 to 2017 and systematically and comprehensively reviewed the development process of China’s textile environmental management policies [11]. Research shows that China’s textile environmental policies focus on combating water pollution, with most policies regulating textile production and lacking consumer-level regulatory policies. And the government needs to further improve the efficiency and flexibility of policy formulation and implementation and establish a complete textile environmental policy system. Through the current research, the existing textile industry policy research still stays in the study of policy text interpretation and countermeasures. There are relatively few historical studies in the effectiveness measurement of textile industry policies, which can be more scientific and systematic evaluation. Through the above analysis, it can be concluded that the existing textile industry policy research remains at the level of content analysis and puts forward countermeasure suggestions by interpreting the policy text. There is still a lack of scientific research on quantitative analysis methods for the evaluation of textile industry policies.

Policy evaluation is an indispensable part of policy formulation and implementation, which provides a scientific basis for policy formulation and implementation adjustment [12–14]. During the past 30 years, much more information has become available on policy evaluation. Early policy evaluation research mainly focused on semantic research, which was mainly based on content analysis [15], and this approach evaluates policies by conducting a comparative analysis of high-frequency words and subject words in policy texts [16]. For example, Abdul used the theory-based evaluation method to assess the effectiveness of the national biofuel policy of the transport sector in Malaysia [17]. Al Saleh reviewed the behavioral perspective between green policy instruments and the green business model based on a content analysis method [18]. These studies critically assess causal relationships in policy theory through critical examination of policy and regulatory documents. With the deepening of research, policy evaluation combines content analysis with expert evaluation. Sara Capacci screened 129 policies in the research, interviewed policy commentators, decision-makers, and policy researchers from five countries, and critically reviewed the effectiveness of the policy according to the evaluation of relevant researchers [19]. Haug reviewed the content of 262 European climate policy evaluation studies and concluded the difficulties of European climate policy [20]. In addition, Harmelink, Murphy, and Abdul Manan use the qualitative research method based on content analysis. However, the policy evaluation based on content analysis is too subjective. However, these studies are often based on fragmented evidence and insufficient data. Therefore, there is an increasing need for systematic evaluation of policy interventions. To address this issue, researchers have attempted to evaluate policies by using quantitative evaluation methods [18, 19]. Existing comparative studies are largely observational in nature, mostly relying on content analysis.

With the deepening of policy evaluation research, the content and methods of policy evaluation are more scientific and reasonable. The literature on policy evaluation is mostly descriptive and gradually changes from the single use of qualitative or quantitative research to the combination of qualitative and quantitative research [21]. The use of qualitative analysis to study initial inspection theories is considered to be the basis of project research, while data collection is designed in quantitative analysis to analyze and confirm these theories [22]. Decision-making and evaluation are no longer driven by subjective analysis but by scientific data analysis, which is what Porter calls “the pursuit of objectivity” [23]. Therefore, by combining qualitative and quantitative research methods, the advantages of content research and quantitative analysis can make a comprehensive and objective scientific evaluation of the policy [24]. In 2010, Ruiz Estrada creatively proposed a PMC index model based on Cartesian spatial application and Omnia Mobilis, a policy evaluation method combining content and quantitative analysis [25]. This method evaluates the policy from a multidimensional perspective and visualizes the advantages and disadvantages of the policy text through the PMC surface [25]. In recent years, the application of the PMC index model has attracted widespread attention in the academic community and has become a popular method to evaluate the effectiveness of policies. Yang quantitatively evaluated policies of the new energy vehicle industry by constructing a PMC index model and made suggestions for policy optimization of the new energy vehicle industry. However, the study only selects representative policies for the new energy vehicle industry, with a small sample size and incomplete coverage [26]. Dai used the PMC index model in the study to analyze the green development themed policy texts in China’s Yangtze River Economic Belt. However, only universal evaluation indicators were used in the study, and there is a lack of nonuniversal indicators containing policy characteristics. Therefore, it is necessary to further improve the accuracy of parameter variables according to the policy content [27]. Under the framework of the PMC index model, Yang put forward a modular policy evaluation system based on the combination of text mining and machine learning methods [28]. In the study, the author extracted high-frequency words from policy text to formulate the indicators of the PMC index model in existing policy evaluation research but lacked the mining based on the meaning and theoretical structure of the text content. Therefore, the accuracy of quantitative analysis needs to be further improved.

In summary, the current research on policy evaluation of the textile industry still has the following deficiencies: (a) there is a lack of scientific assessment and quantitative research on textile industry policy effectiveness. China has issued many textile industry policies, but there is a lack of intelligence analysis and policy effectiveness measurement of China’s textile industry policies. The content analysis of the industrial policy text can quickly grasp the necessary information, such as the structural layout and development planning of China’s textile industry. The quantitative evaluation and analysis of policy effectiveness can scientifically evaluate existing policies and industrial development shortcomings. Mastering the information of textile industry policy plays a good role in promoting the development of textile enterprises and the textile industry. The development of China’s textile industry has had a significant impact on China’s industrial and economic growth and the world. Therefore, the collection of China’s textile industry policy intelligence is essential. (b) The evaluation method of textile industry policy is single and subjective and lacks quantitative evaluation research based on the evaluation index system formulated by the textile industry policy content. The existing research on the evaluation of textile industry policy is primarily the fuzzy comprehensive evaluation method and analytic hierarchy process based on content analysis. The index weight assignment is subjective, and the influencing factors of the evaluation object are not complete. There is a lack of more comprehensive and scientific evaluation research that combines content analysis and quantitative analysis. (c) The extraction accuracy of policy effectiveness measurement indicators is insufficient. Generally, the variable index of policy evaluation is extracted by encoding high-frequency words in policy texts. Still, the word frequency has many influencing factors such as the writing habits of the policy writer, synonyms in different forms, invalid repetition, and so on, which leads to large errors in policy measurement. It is necessary to construct the index variable system based on mining the meaning and exploring the theoretical structure of the text content.

3. Construction of a Co-Word Network of the Textile Industry Policy

3.1. Policy Samples and Keyword Selection

In this paper, 167 policy texts are counted related to the development of the textile industry published on official websites of the General Office of the State Council, Ministry of Commerce, National Development and Reform Commission, Ministry of Industry and Information Technology, Ministry of Finance, the Ministry of Agriculture and Rural Affairs, the General Administration of Customs, the State Taxation Administration, the Standardization Administration, the General Administration of Quality Supervision, Inspection, and Quarantine, and the China National Textile And Apparel Council from 2014 to 2020. The incomplete and invalid policy texts are deleted, and finally, 126 policy texts are screened as the research object of this paper, based on the grounded theory to extract the variable of the PMC index system. Firstly, the content of the collected textile policy texts is analyzed. Based on the text content and concept, the semantics are combined and agreed to be different, and finally, 65 high-frequency keywords of textile industry policy texts are extracted. The content of textile industry policy is divided into 12 parts based on high-frequency words. Through the frequency of high frequency, we can understand the focus of textile industry policy. As shown in Figure 1, the high frequency of words such as intellectualization, information, high quality, competitiveness, and high-end product indicates that China’s textile industry policy focuses on the development and layout of the textile industry.

3.2. The Textile Industry Policy Co-Word Network

The textile industry policy keywords were extracted by grounded theory and counting the frequency of keywords, using Gephi software to draw the co-occurrence map of high-frequency words of the textile industry policy text (as shown in Figure 2). The larger the point area, the greater its influence in the whole network, and the greater the number of connections, the more contact between the keyword and other keywords. As shown in Figure 2, the keywords Transformation and Upgrading, Structural Optimization, System Reform, Informatization, Intelligence, Competitiveness, and Green have a high degree of centrality. We have summarized these keywords as the structural layout and industrial development direction of the textile industry. It shows that China’s textile policy in the recent five years focuses on adjusting the optimization of the structure of the textile industry and the layout of the future development direction. The administrative support keywords such as Policy, Policy Implementation, Guidance and Supervision, and Management System have a high centrality. According to the semantics, these keywords can be summarized as public service, social security, and administrative security, indicating that China has given excellent policy and organizational security to the development of the textile industry. The social network map of textile industry policy can reflect the policy text’s core structure and radiation degree. The keywords in the textile industry policy social network provide a basis for determining the primary and secondary variables of the PMC index in the next step.

4. PMC Index Modeling Approach

Mario Arturo Ruiz Estrada established the PMC (Policy Modeling Consistency) index model based on the Omnia Mobilis hypothesis. This paper divided the construction and calculation of the PMC index model into four steps: (1) classification of primary and secondary variables. According to the grounded theory, this paper analyzed the content of textile industry policy, extracted keywords, and summarized the level-one and secondary variables. (2) Establish multiple input-output tables. This study used nine primary variables and 47 secondary variables to construct the multi-input-output table of the textile industry policy PMC model. (3) PMC index calculation. This paper determined the value of the secondary variable by mining the text content of the textile industry policy. (4) Draw PMC exponential surface. According to the calculation results of the PMC index model, this study visualized the PMC index results of the policy text.

4.1. Assignment of Variables and Parameter Setting

This paper combines the keywords of textile industry policy with the PMC index model to construct a PMC evaluation index system for textile industry policies. The index system consists of 9 primary variables and 47 level-two variables, as shown in Table 1. The nine primary variables are nature of policy (X₁); policy timeliness (X₂); policy area (X₃); policy function (X₄); policy evaluation (X₅); policy content (X₆); guarantee and incentives (X₇); subject of policy (X₈); and policy strength (X₉).

In the construction, the textile industry policy PMC index model variables, we can conclude that the current textile industry policy plays the role of supervision, standard, planning, suggestion, description, and regulation from variables of primary variable policy nature (X₁). The primary indicator policy timeliness (X₂) analyses the timeliness of the textile industry policy, which has divided the policy timeliness into four policy timeliness ranges: more than ten years, 5 to 10 years, 1 to 5 years, and within one year. Policy area (X₃) consists of six secondary variables: finance and economy, social security, environmental protection, innovative development, adjusting the textile industrial structure, and promoting the development of textile enterprises. The primary variables policy area (X₃) consists of six secondary variables: public service, market regulation, financial and economy, social security, environmental protection, and innovation and development. This study has divided the policy function (X₄) into four parts: adjusting the structure of the textile industry, promoting the development of textile enterprises, regulating the market, and promoting trade cooperation. Policy evaluation (X₅) consists of aspects: clear objectives, suitable for the current situation of industrial development, precise implementation supervision mechanism, and sustainable development. Based on the induction and summary of the keywords in the textile industry policy text, the policy content (X₆) is divided into eight secondary variables: enterprise development guidance, financial and economy, taxation and subsidies, trade cooperation, green development, market environment, talent guarantee, industrial structure, industrial development plan, and administrative guarantee. The guarantee and incentives (X₇₎ include 8 secondary variables: foundation, tax reduction, exemption, cancel or exempt administrative fees, quota, allowance, and demonstration area. The primary variable policy subject (X₈) includes the textile enterprises of all and start-ups, micro-, small-, and medium-sized, and innovative. The number of ministries and commissions jointly issuing policies for the textile industry or referring to the textile industry reflecting the policy strength (X₉)

4.1.1. Textile Industry Policy PMC Model Variable Multi-Input-Output Tables

Multi-input-input is a data analysis framework that can store many data and measure any single variable. The multiple input-output tables consist of “m” primary variables and “n” secondary variables. The characteristic of multiple input-output tables is that it does not need to rank the importance of variables and accept any secondary variables related to primary variables and give the same weight to all secondary variables. Therefore, combined with nine primary variables and 47 secondary variables, this paper establishes the multiple input-output tables of the textile industry policy PMC model, as shown in Table 2.

4.1.2. PMC Index Calculation

The calculation of the PMC index was divided into four steps:(a)Put nine primary variables and 47 secondary variables in the PMC index model of textile industry policy into the multiple input-output tables in Table 2.(b)Assign values of secondary variables by text excavation. This paper used the binary system to assign the variables of the PMC model of textile industry policy. When the policy text content meets the secondary variable standard, the secondary variable value is 1; when the policy text content does not meet the level-two variable standard, the secondary variable value is 0. As shown in Table 3, the secondary variable assignment standard. As shown in (1) and (2), each distribution of the secondary variables is from [0, 1].(c)The primary variable value of textile industry policy is calculated according to (3).(d)The PMC index is obtained by summing up the primary variable of the policies to be evaluated, as shown in (4).where t = level-one variable, J = level-two variable, and N = number of level-two variables.

Among the nine primary variables established in this paper, the level-two variables contained in policy timeliness (X₂) and policy strength (X₉) are mutually exclusive, and the value of these primary variables should be “1/number of secondary variables “. From this, X₂ = 0.25 and X₉ = 0.33. The remaining seven primary variables’ value range should conform to X_i ≤ 1 (i = 1, 2, 4, 5, 6, 7, 8). Furthermore, this paper has calculated the specific PMC index value according to (4). According to the calculation of the PMC index, we have divided the policy evaluation results into four grades. And the PMC index scoring standards are shown in Table 4.

4.1.3. PMC-Surface Construction

PMC surface can present the PMC index in the form of graphical and reflect the advantages and disadvantages of various policies in the multidimensional coordinate system. The construction of the PMC matrix is the basis of forming the PMC surface. The matrix contains the scores of all primary variables and constructs a third-order square matrix, showing the calculation method of the PMC-surface matrix in

5. Empirical Analysis of Quantitative Evaluation of Textile Industry Policy

5.1. PMC Index Analysis of Textile Industry Policy

5.1.1. Textile Policy Selection

Based on the PMC index model of textile industry policy evaluation, this paper selects ten policy texts to focus on the textile industry or refer to the textile industry from the textile industry policy database for empirical analysis, as shown in Table 5.

5.1.2. Data Analysis of PMC Index Model for Textile Industry Policy Evaluation

We analyzed the content of the policy text based on grounded theory and scored the policy used the assignment standards of textile industry policy PMC model variable evaluation. According to the score from the data in Tables 6–8, we valuated the PMC index of the textile industry policy sample. It can be seen from the data in Table 4 that the policies are classified. As shown in Table 8, the scores of 10 textile industry policies are in two grades: excellent and acceptable. Policies 1, 2, 4, 5, 6, 7, 8, and 10 are excellent, and policies 3 and 9 are acceptable. The PMC value of policy 6, the “13th Five-Year Plan” of Textile Industry Development issued by the Ministry of Industry and Information Technology, is 5.67, the highest score of the ten policies. It shows that the policy is comprehensive, scientific, and reasonable in the formulation process.

Policy 9 is the Administrative Measures for the Pilot Demonstration Park (Platform) of Textile and Apparel Creative Design issued by Ministry of Industry and Information Technology. Its PMC value is 2.72, which is the lowest score of the ten policies. The score of the level-one variable policy content (X₆) is lower than the average, indicating that the policy content is single, directional, and minor in scope in the process of policy formulation. Overall, the average score of the ten policies is 4.31, which is at an excellent level, indicating that most of China’s textile industry policies are relatively superior in terms of policy content and policy functions. The policy objectives of the textile industry are clear and in line with the current situation of industrial development and adhere to the principle of sustainable development [11].

5.2. PMC-Surface Analysis of Textile Policy

The advantage of the PMC surface of textile industry policy is that it can visualize the advantages and disadvantages of each dimension of the policy. Different color of the PMC surface represents different values of index scores. The convex part of the surface indicates that the policy has a higher score on the corresponding evaluation index, and the concave portion indicates a lower score on the corresponding evaluation index. Figure 3 shows the PMC surface of 10 textile industry policy samples. According to the PMC surface visualization results, we can evaluate the perfection degree of textile industry policy.

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(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

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From the PMC value of ten primary variables in Figure 4, the scores of the policy area (X₃), policy function (X₄), policy evaluation (X₅), and policy content (X₆) are high. It is apparent that China’s textile industry policies involve many policy fields and cover a more comprehensive range in the formulation process. The policy’s goal is scientific, reasonable, and in line with the current industrial development situation. Further analysis of the PMC values of nature of policy (X₁), guarantee and incentives (X₇), and the subject of policy (X₈) this three primary variables reveals that the nature and subject of the existing textile industry policy are relatively single, and the guarantee and incentive of policies are relatively weak.

As shown in Figure 3(a), the PMC value of the policy sample 1 is 4.53, ranking 3rd comprehensively, and the rating level is excellent. The PMC value of the policy is slightly higher than the average level, and the score of the level-one variable policy function (X₄) is higher than the intermediate level. The policy sample has played a good guiding and regulating role in adjusting the textile industrial structure, promoting the development of textile enterprises, regulating the market, and promoting trade cooperation these four aspects. The PMC value of the policy sample 2 is 4.9, ranking 2nd comprehensively, and the rating level is excellent. The PMC value of the policy text is at a relatively high level. Among them, the scores of level-one variable policy function (X₄), policy evaluation (X₅), and policy content (X₆) are higher than the average level, indicating that the policy content is comprehensive, scientific, and reasonable. The PMC value of the policy sample 3 is 3.6, ranking 8th comprehensively, and the rating level is acceptable. The PMC index of the policy text is slightly lower than the average level. Among them, the policy function (X₄) is much lower than the average value. This policy is somewhat lacking in regulating and promoting industrial development and needs to be further improved. The PMC value of policy samples 4 and 5 is 4.08 and 4.28, ranking 6th and 5th comprehensively, and the rating level is excellent. Except that the score of the level-one variable policy function (X₄) is lower than the average, the scores of other level-one variables are all average. The PMC value of policy samples 6 and 7 is 5.67 and 4.9, ranking first and second in the comprehensive ranking, and the scoring rating is excellent. The scores of level-one variables of these two policies are higher than the average level. Overall, these two policy texts are the most comprehensive and scientific among all the policy samples. The PMC value of policy sample 8 is 4.47, ranking 4th comprehensively, and the rating is excellent. The scores of other level-one variables are at the middle class, except that the guarantee and incentives (X₇) are slightly lower than the average level. From this data, we can see that the policy mentioned little about the assurance and motivation during the textile industry policy’s formulation process, and it needs further improvement. The PMC value of policy sample 9 is 2.72, ranking 9th overall, and the rating level is acceptable. As shown in Figure 3(i), the PMC value of policy sample 9 is much lower than the average level, and its depression degree is the largest. Among them, the scores of level-one variable policy function (X₄), policy evaluation (X₅), and policy content (X₆) are far lower than the average level, which can show that the function and content of the policy in the formulation process are not comprehensive and reasonable. As a strong content orientation policy, the policy samples can be optimized and adjusted appropriately with the actual situation. The PMC value of the policy sample 10 is 3.97, ranking 7th overall, and the rating level is excellent. Among them, the PMC value of four level-one variables of policy nature (X₁), policy area (X₃), guarantee and incentives (X₇), and subject of policy (X₈) is slightly lower than the average level, and this policy can be further improved from these four aspects.

5.3. Results and Discussion

The PMC index score of policy sample 9 is the lowest. Policy sample 9 is the Administrative Measures for the Pilot Demonstration Park (Platform) of Textile and Apparel Creative Design issued by the Ministry of Industry and Information Technology. From the data in Figure 3(i), except for the nature of policy (X₁), policy timeliness (X₂), and policy strength (X₉), the other six level-one variables are all lower than the average. According to the policy text of 9, the policy area (X₃) involves two parts: public service (X_3.1) and market regulation (X_3.2). The policy mentioned promoting the construction of textile and garment creative design pilot demonstration zones in public services. Market regulation part, the policy said to promote the innovative development of textile and garment enterprises and effectively promote the construction of a textile power. Policy function (X₄) part, the sample 9 scored in promoting the development of textile enterprises (X_4.2) and regulating the market (X_4.3). The policy aims to encourage the development of innovative textile and garment enterprises and control the application and management of demonstration zones. Still, it does not mention the development of the textile industry or foreign trade cooperation. Policy evaluation (X₅) part only scored in clear implementation and supervision mechanism (X_5.3). The policy focuses on the management and assessment mechanism of textile and apparel creative design pilot demonstration zones, failing to reflect scientific objectives, green and sustainable development, etc. Policy content (X₆) part, the policy sample 9 scored in three parts: enterprise development guidance (X_6.1), market environment (X_6.6), and administrative guarantee (X_6.10). The results indicate that this policy focuses on supporting the creative design of textile and garment enterprises by providing platforms and demonstration zone with strict assessment management. Zhang [29] also believes that innovation in the textile industry is the core of the industrial sustainable development and an important issue worthy of attention in China’s textile industry. Then, the author studies the relationship between R&D model and innovation performance in the textile industry. He considers that China’s textile industry may lack market-oriented innovation. Therefore, the government should formulate targeted measures to improve the conversion rate of R&D results. In addition, it is necessary to guide enterprises to overcome the problems related to technological innovation as much as possible and increase the speed of commercialization of new products. Guarantee and incentives (X₇) part, the guarantee and incentive measures of policy sample 9 are to set up demonstration zones, which provide an ideal development environment, rich creative design resources, and perfect supporting services for textile and garment enterprises. However, there is no mention of tax relief, subsidies, and other security incentives. At the same time, to understand the development path of green supply chain management, Sheng [30] studied relevant policies using systematic literature review, analysis, and comparison methods. The results showed that policies need to be improved in terms of tax subsidies and incentive mechanisms. In addition, this conclusion is similar to the results obtained in this paper.

In terms of depression degree, as shown in Figure 5, the depression index of policy function (X₄) is 0.2, and the depression index of guarantee and incentives (X₇) is 0.1, which is at a low depression degree. The depression index of policy evaluation (X₅) is 0.5, and the depression index of policy content (X₆) is 0.42, indicating a high degree of depression. Therefore, taking policy sample 9, we can adjust the order of policy improvement path appropriately in combination with the actual situation of the policy text in policy evaluation (X₅)-policy content (X₆)-policy function (X₄)-guarantee and incentives (X₇).

6. Conclusion

In order to construct a quantitative evaluation model of textile industrial policy and study the optimization path of textile industry policy formulation, this paper takes 126 textile industry policy texts released from 2014 to 2020 as the research object, combines the root theory with the PMC index model, and constructs a policy effectiveness measurement index with the characteristics of textile industry. To reduce the subjectivity of policy evaluation and make the evaluation of policies more scientific and comprehensive, this study integrates modeling methods. Immediately after, 10 textile industry policies were selected for empirical analysis. Finally, PMC surface is used to visualize the internal structure of textile industrial policies and the measurement results of policy effectiveness. In addition, the following conclusions can be drawn from the present study:(1)The policy effectiveness of China’s textile industry policy sample is at an excellent level. In general, the average PMC index value of 10 policy samples is 4.31 at an excellent level, indicating that most of China’s textile industry policies are relatively superior in policy content and policy functions. The textile industry policies have clear objectives in formulation and implementation, conform to the current industrial development situation, and adhere to the sustainable development principle.(2)The nature and subject of China’s textile industry policies are relatively single, and the measures for industry guarantees and incentives are relatively weak.

According to the analyst of the average PMC index value of the level-one variables of 10 policy samples, the PMC index scores of the four level-one variables, policy area (X₃), policy function (X₄), policy evaluation (X₅), and policy content (X₆), are all relatively high. The nature of the policy (X₁), guarantee and incentives (X₇), and the subject of policy (X₈) scored low. It shows that the formulation of China’s textile industry policy involves a wide range of fields and covers a more comprehensive content. The goal of policy formulation is scientific, reasonable, and in line with the current industrial development situation. However, the nature and subject of textile industry policy are relatively single, and it is relatively weak in terms of the guarantee and incentive policy. An implication of this is the possibility that the policymakers need further to improve the guarantee and incentives protection.

Overall, this research has established a quantitative framework for detecting the policy evaluation. One of the more significant findings to emerge from this study is that combined with the actual situation of the policy text content, the depression degree of the level-one variable of the policy sample formulated the improvement path of the policy content more scientifically and reasonably. By drawing the PMC surface, the advantages and disadvantages of each dimension of the policy sample are visualized. The higher the depression degree of the level-one variable, the more significant the gap between the policy and the perfect policy. The present study will serve as a base for future policy quantitative evaluation and policy structure visualized studies. In terms of directions for future research, further work could research on the influence of policy optimization route on industry.

Data Availability

All data included in this study are available from the corresponding author upon request.

Conflicts of Interest

The author declares that there are no conflicts of interest regarding the publication of this article.

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Copyright © 2022 Yu Liu and Hong Zhao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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