The Evaluation Indicator System of Low-Carbon Parks in the Textile Industry

Abstract

Given that low-carbon development has become an important goal for the sustainable development of the industrial sector, the development of low-carbon industrial parks is conducive to advancing the low-carbon development of China’s industry. Based on the development status quo of industrial parks in China’s textile industry, this article adopted literature research, qualitative and quantitative analysis, and expert consultation to establish an evaluation indicator system for low-carbon industrial parks characterized by a scoring mechanism comprising three levels with thirty indicators. Meanwhile, considering the requirements of policies, documents, regulations, and standards related to energy consumption and low-carbon development in the textile industry, these indicators’ benchmark values and evaluation criteria were calculated and determined. Then, this article selected the parks dominated by garment, chemical fiber manufacturing, and textile industries for low-carbon evaluation demonstrations. The low-carbon zone evaluation index system developed in this article could realize the monitoring, assessment, and comparison of the low-carbon levels of industrial parks, thereby facilitating the planning, construction, and management of low-carbon parks in the textile industry.

1. Introduction

Energy consumption is the main cause of greenhouse gas (GHG) emissions, and China has made significant efforts to reduce GHG emissions and vigorously develop a low-carbon economy. The “Action Plan for Carbon Peaking by 2030” proposes that by 2030, China’s carbon intensity will drop by 60% to 65% compared to 2005 levels. The industrial sector, as a cornerstone of China’s rapid economic growth, accounts for a much larger share of GHG emissions than other domestic sources, with around 60% of domestic energy consumption and CO₂ emissions coming from this sector [1]. As effective organizational forms to promote China’s industrial development, industrial parks play a crucial role in this context. Currently, there are over 15,000 industrial parks of various types in China. Under the national strategy of China’s carbon peaking and carbon neutrality, the energy-saving and carbon-reducing initiatives in industrial parks are key to driving the low-carbon development of the industrial sector.

The development of low-carbon industrial parks in China dates back to the 2012 Guideline for the Development of Low-Carbon Industrial Parks, which has established an evaluation standard system for low-carbon industrial parks, encompassing four dimensions: energy efficiency and greenhouse gas monitoring, resource recycling, and ecological protection, industrial zone governance and support systems, as well as planning and land use. It has set forth a total of 23 specific indicators. In 2013, the State Council issued a Notice on Organizing the Pilot Projects of National Low-Carbon Industrial Parks, aiming to create model national low-carbon industrial parks by vigorously promoting low-carbon production, encouraging low-carbon technological innovation and application, and innovating low-carbon management practices, etc. However, this document only demonstrated the documentation required for park management to carry out low-carbon evaluations and failed to set up a unified evaluation indicator system. To advance the transition of industrial parks toward low-carbon development, it is crucial to establish a scientific and reasonable evaluation indicator system. Such a system must not only define the basic conditions for low-carbon industrial park evaluation but also demonstrate the effectiveness of the park in green and low-carbon construction. Wang et al. [2] developed a hierarchical evaluation indicator system based on planning, near-zero carbon technology, carbon emission management, and environmental health using a low-carbon park as a case study to discuss its upgrade path to a zero-carbon park. Liu et al. [3] explored the planning and design characteristics of low-carbon industrial parks, providing feasibility and decision-making support for their construction. Scholars such as Wang [4], Ma [5], and Li [6] established evaluation indicator systems for low-carbon parks based on regional characteristics and conducted low-carbon evaluation demonstrations in industrial parks in Wuhan, Qingdao, and Guangxi Province as cases for low-carbon evaluation demonstration; Wu et al. [7] developed an evaluation indicator system for low-carbon industrial parks framed around energy utilization, greenhouse gas emission control, recycling economy, environmental protection, park construction, and management, etc., with 18 detailed indicators. Cheng [8] developed an evaluation indicator system for low-carbon industrial parks structured around low-carbon output systems, low-carbon resource systems, low-carbon living indicators, and low-carbon policy indicators, including 16 detailed indicators. Zhang et al. [9] created a greenhouse gas emission inventory for different industrial parks to accurately quantify and assess their carbon reduction potential and improve their energy and environmental performance. Tan et al. [10] conducted an in-depth study on the accounting methods of greenhouse gas emissions in industrial parks, refining the accounting stages down to the specific departments’ project planning, construction, and operation within the park. They analyzed the rationality and applicability of these accounting methods in a selected park and, based on this, developed a low-carbon development plan. Huang et al. [11] explored the low-carbon practices of the Caohejing Industrial Park in recent years and evaluated the implementation effects of various energy-saving and emission-reduction policies in the park to provide a good example for the other industrial parks. Fang et al. [12] established an evaluation indicator system characterized by energy values in the study of the low-carbon performance of industrial parks.

Li et al. [13] established the index system of cleaner production in industrial parks based on the binary semantic evaluation method. They selected an industrial park for evaluation demonstration, analyzed the potential of cleaner production in the park, and put forward some suggestions for cleaner production. Sun et al. [14] divided the development of Kalenburg Eco-Industrial Park into three stages to reveal the evolution characteristics of intellectual property from the perspective of carbon neutrality. At the enterprise level in the park, this paper makes a quantitative analysis of the evolution of enterprises based on the enterprise correlation degree and the enterprise repeated correlation degree. Taking China and South Korea as examples, the evolution characteristics at the national level are analyzed. Ju et al. [15] used the coupling coordination degree model and the panel data of 14 cities in Xinjiang to analyze the synergistic effect between industrial cluster development, carbon emission, and economic growth in Xinjiang. The spatial disequilibrium of efficiency measures and efficiency cases is analyzed using the Gini coefficients based on the super-efficiency relaxation measure (SE-SBM) and Dagum. Yu et al. [16] developed a self-consistent methodology and framework for China’s industrial parks based on enterprise-level data.

China is the world’s largest producer of textile products, and the textile industry is a traditional pillar of China’s national economy and an important sector for livelihood, with more than 400 industrial parks in China’s textile industry [17]. The 14th Five-Year Plan for the Development of the Textile Industry outlines a vision for a green, low-carbon, and recycling economy driven by responsibility, highlighting the implementation of energy-saving retrofits in parks as one of the key projects for green manufacturing in the textile industry [18].

This article, based on the construction status quo of China’s textile industrial parks, utilizes methods such as literature research, qualitative and quantitative analysis, expert consultation, etc., to develop a low-carbon industrial park evaluation indicator system specific to the textile industry. This system is constructed by integrating the requirements of energy consumption and low-carbon development policies, documents, regulations, and standards related to the textile industry and calculating and defining the baseline and evaluation criteria for the low-carbon industrial park indicators. The low-carbon evaluation indicator system for textile parks developed in this article fills a gap in this field, providing the textile industry with tools to accurately assess and compare the effectiveness of low-carbon construction in textile parks.

2. Methodology

2.1. Framework of the Evaluation Indicator System

Through a comparative analysis of low-carbon evaluation indicator systems established at the park level by scholars both at home and abroad, as well as related low-carbon, green, and ecological evaluation standards, it has been found that low-carbon evaluation systems at the park level generally consist of two main parts. The first part stipulates the entry conditions (i.e., basic requirements) for low-carbon evaluation, while the second part sets up a hierarchical structure to evaluate the park across different dimensions, such as low-carbon planning, low-carbon management, low-carbon production, etc. In the construction of the low-carbon evaluation indicator system for textile industry parks, the expert consultation method was employed to gather input from experts in textile industry associations, enterprises, and universities. The hierarchical structure was divided into four layers: the target layer, the criteria layer, the sub-criteria layer, and the indicator layer. After defining the basic requirements for the evaluation indicator system, low-carbon management and low-carbon production were set as the criteria layers according to the aforementioned hierarchical principle. Low-carbon management was subdivided into three sub-criteria layers, and low-carbon production was divided into five sub-criteria layers. In terms of the composition of indicators, the low-carbon management criteria layer consists of 80% qualitative indicators and 20% quantitative indicators, whereas the low-carbon production criteria layer is comprised entirely of quantitative indicators. By setting cutting-edge policies and technological measures favorable to the low-carbon development in textile parks as incentive indicators, the system effectively guides these positive policies and technologies toward rapid adoption. The low-carbon evaluation indicator system for the textile parks is illustrated in Figure 1.

2.2. Weights and Scores of Evaluation Indicators

This article uses the Analytic Hierarchy Process (AHP) to precisely define the weight of indicators at all layers, and the framework of the low-carbon evaluation indicator system for the textile parks conforms to the recursive hierarchical structure, which can be directly used as the structural model for AHP. In this model, the low-carbon evaluation indicator system for the textile industry represents the target layer A. The basic requirements, low-carbon management, low-carbon production, and incentive indicators comprise the criteria layer B of the evaluation indicator system. Using low-carbon management and low-carbon production as the pivot points, sub-criteria layers such as low-carbon planning, organization and management, low-carbon publicity, and carbon emissions are set beneath them. Each sub-criteria layer is further broken down into indicator layer D. The recursive hierarchical structure model for low-carbon management indicators is shown in Figure 2.

A pairwise comparison is conducted between the importance of different indicators at each layer of the recursive hierarchical structure model. On this basis, a judgment matrix X is constructed, with scores assigned according to the relative importance of each pair:

$$X=\left[\begin{array}{ccc}\begin{array}{c}\begin{array}{cc}{x}_{11}& x_{12}\end{array}\\ \begin{array}{cc}{x}_{21}& x_{22}\end{array}\end{array}& \cdots & \begin{array}{c}{x}_{1n}\\ x_{2n}\end{array}\\ ⋮& \ddots & ⋮\\ \begin{array}{cc}{x}_{n1}& x_{n1}\end{array}& \cdots & x_{nn}\end{array}\right]$$

(1)

In the formula, taking $x_{ij}$ as an example, where i, j = 1, 2, …, n, it represents the relative importance of the indicator i compared to the indicator j, with $x_{ij}$ > 0 and $x_{ii}$ = 1. The judgment matrix is constructed using a 1-to-9 scale method for scoring, where specific numbers correspond to levels of importance, as shown in

Table 1. Judgment matrix scales and their meanings.

Serial No.	Meaning
1	Two factors are of equal importance when compared with each other.
3	The former is slightly more important than the latter when compared with each other.
5	The former is considerably more important than the latter when compared with each other.
7	The former is significantly more important than the latter when compared with each other.
9	The former is absolutely more important than the latter when compared with each other.
2, 4, 6, 8	It demotes the intermediate value of the above neighboring judgments.
Count backwards	Assuming that the ratio of the importance of Factor i to Factor j is xij, the ratio of the importance of factor j to facor i is xji = 1/xij

.

The judgment matrix constructed by the recursive hierarchical structure model is a positive reciprocal matrix. Therefore, the weight calculation involves solving the largest eigenvalue of the judgment matrix (${\lambda }_{max}$) and its corresponding eigenvector ($w$). In this article, the geometric mean method, also known as the square root method, is used for calculation. The calculation is divided into four steps: first, multiply all the elements in each row of the judgment matrix X to obtain m_i; then calculate the n^th square root of m_i to obtain $\overline{w_i}$; then normalize the eigenvectors $\overline{w_i}={(\overline{w_1},\overline{w_2},\dots ,\overline{w_n})}^T$ to get $w_i$; finally, based on $w_i,$ obtain the weight vector $w_i={(w_1,w_2,\dots ,w_n)}^T$ and $w_i={(w_1,w_2,\dots ,w_n)}^T$ calculate the largest eigenvalue ${\lambda }_{max}$. The formula is as follows:

$$m_{\mathrm{i}}={\displaystyle \prod }_{j=1}^n{x}_{ij} , i=1,2,\dots ,n$$

(2)

$$\overline{w_i}=\sqrt[n]{m_i} , i=1,2,\dots ,n$$

(3)

$$w_i=\overline{w_i}/{\displaystyle \sum }_{k=1}^n\overline{w_k} , i=1,2,\dots ,n$$

(4)

$${\lambda }_{max}={\displaystyle \frac{1}{n}}{\displaystyle \sum }_{i=1}^n{\displaystyle \raisebox{1ex}{${\left(Aw\right)}_i$}\!\left/ \!\raisebox{-1ex}{$w_i$}\right.}$$

(5)

where ${\left(Aw\right)}_i$ is the ith component of the vector Aw.

The final step of AHP is to perform the consistency check. When the decision-maker’s judgments are completely consistent, the importance ratio between indicator i and indicator j in pairwise comparisons should be unique and definite. However, in practical operations, due to the complexity of the judgment objects and the differences in cognitive judgments among different experts, inconsistencies often arise in the importance ranking of various elements. These inconsistencies are usually locally consistent but globally chaotic; that is, the overall ranking lacks satisfactory consistency. For example, you might encounter a situation where “Indicator A is more important than Indicator B, Indicator B is more important than Indicator C, but Indicator C is more important than Indicator A”. The consistency check is conducted to avoid such a situation. If the judgment matrix passes the consistency check, it indicates that the matrix has good logical coherence and is free from chaotic situations [19]. The formula for consistency check is as follows:

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(6)

$$CR={\displaystyle \frac{CI}{RI}}$$

(7)

where n represents the rank of the judgment matrix, CI is the consistency indicator, and RI is the average random consistency indicator, the value of which varies depending on n. The specific values of RI can be found in

Table 2. Average Random Consistency Indicator (RI).

n	1	2	3	4	5	6	7	8	9
RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45

. In addition, CR is used as the consistency indicator. If CR is less than 0.1, it means that the judgment matrix has passed the consistency check. Conversely, if CR is greater than 0.1, the judgment matrix needs to be fine-tuned until it meets the consistency check standard [20].

In this article, a questionnaire addressing the importance of evaluation indicators for low-carbon parks in the textile industry was designed and distributed to experts in the fields of textile and apparel as well as green low-carbon research. Among the 40 valid questionnaires received, the experts represented various sectors and institutions, including the China National Textile and Apparel Council’s Committee on Environmental Protection and Resource Conservation, the Social Responsibility Office, the Administrative Committee of the Industry Park, the Energy Conservation and Comprehensive Utilization Working Group of the Standards Committee, textile parks, and textile universities. They have extensive experience and professional knowledge in green and low-carbon development planning, standard setting, and industrial park management within the textile industry, ensuring the accuracy and reliability of the survey results. The composition of the experts and their years of experience in the field are shown in Figure 3.

The weighted geometric average method can maintain the reciprocity of the matrix. At the same time, when integrating the judgment matrices obtained from different experts, if these matrices are consistent, the weighted geometric average method can ensure that the final group judgment matrix is also consistent. Therefore, in order to obtain a more accurate and consistent consensus matrix of group decision judgment, the weighted geometric average method is chosen as the main calculation method in this chapter. This method can effectively integrate the opinions of each individual and ensure that the final judgment results are consistent [21]. The formula is as follows:

$$a_{ij}=\sqrt[n]{{\displaystyle \prod }_{k=1}^n{a}_{ij}^{\left(k\right)}}$$

(8)

where n represents the number of experts participating in the survey, and k represents the judgment matrix derived from the evaluation results of the kth expert.

The evaluation indicator system of low-carbon textile industry parks uses a scoring method for evaluation, and low-carbon management and low-carbon production are the main subjects of scoring in the whole evaluation system, with their respective scores set at 40 points and 60 points. Within the low-carbon management criteria, the qualitative evaluation indicators account for 80% of the total, while the low-carbon production criteria are entirely based on quantitative evaluation indicators. The score for incentive indicators should not be set too high, and the total score for incentive indicators is set at 5 points upon comprehensive consideration of experts’ opinions. The detailed scoring allocation for each layer of indicators in the low-carbon industrial park evaluation indicator system is presented in

Table 3. Indicator scores of low-carbon park evaluation indicator system.

Target Layer	Criteria Layer		Sub-Criteria Layer		Indicator Layer
A	B	Score	C	Value of a Score	D	Score
Low-Carbon Park Evaluation Indicator System in the Textile Industry	Fundamental requirements	No major safety or pollution accidents or major ecological damage occurred in the industrial park in the latest three years.				One-vote veto
		The infrastructure of the industrial park is well-developed and meets the requirements of production safety, environmental protection, and fire protection
		The industrial park has effectively implemented national and local environmental protection and green low-carbon related laws and regulations, policies, and standards.
	Low-carbon management	40 points	Low-carbon planning	20 points	Low-carbon infrastructure	7 points.
					Low-carbon development planning	6 points
					Inputs in low-carbon energy conservation projects	4 points
					Green coverage	3 points
			Organizational management	12 points	Low-carbon management institutions, personnel	4 points
					Low-carbon management system	3 points
					Carbon Emissions Information Platform	3 points
					Corporate Carbon Emissions Assessment	2 points
			Low-carbon publicity	8 points	Low-carbon publicity campaign	4 points
					Low-carbon training	4 points
	Low-carbon production	60 points	Carbon emissions	18 points	Carbon intensity	12 points
					Carbon intensity reduction rate	6 points
			Production inputs	14 points	Energy consumption per unit of industrial-added value	4 points
					Reduction rate of energy consumption per unit of industrial-added value	3 points
					Water consumption per unit of industrial-added value	3 points
					Fossil energy share of energy consumption	2 points
					Renewable energy use rate	2 points
			Resource recycling	11 points	Industrial water reuse rate	5 points
					Comprehensive utilization rate of industrial waste heat	4 points
					Comprehensive utilization rate of industrial solid waste	2 points
			Economic development	10 points	Industrial value-added growth rate	5 points
					Intensity of investment in fixed assets	3 points
					Output intensity of industrial land	2 points
			Verification and Certification	7 points	Percentage of enterprises accounting for greenhouse gases	4 points
					Management system certification rate	3 points
	Incentive indicators	5 points	Proportion of green buildings			2 points
			Implementation rate of clean production review			3 points

.

2.3. Evaluation Process and Ranking

The evaluation process for low-carbon industrial parks in the textile industry is illustrated in Figure 4. If a textile park fails to fulfill the basic requirements, it is disqualified for low-carbon evaluation. On the premise of satisfying the basic requirements, the evaluation process involves scoring the low-carbon management, low-carbon production, and incentive indicators. The overall evaluation score is composed of the sum of these three components. Based on the scoring results, the park is evaluated as a low-carbon industrial park if the total score reaches or exceeds 70 points. Further assessment will be then conducted on four key indicators: the carbon emission intensity, the carbon emission reduction rate, the energy consumption per unit of industrial added value, and the reduction rate of energy consumption per unit of industrial added value. If any of these four indicators scores a zero, the park cannot be evaluated as a low-carbon industrial park. However, if all four indicators score above zero, the park can be qualified for a low-carbon industrial park.

Based on the current industry standards and expert interviews, a score of 70 points is selected as the low-carbon baseline for low-carbon textile parks. The low-carbon parks in the textile industry are classified into three levels: Parks scoring 70–80 points are called “One-Star Low-Carbon Parks”, those scoring 80–90 points are labeled as “Two-Star Low-Carbon Parks”, and those scoring above 90 points are recognized as “Three-Star Low-Carbon Parks”, as shown in

Table 4. Classification of low-carbon parks in the textile industry.

Low-Carbon Rating	One-Star Low-Carbon Park	Two-Star Low-Carbon Park	Samsung Low-Carbon Par
Points (G) Requirement	70 < G ≤ 80	80 < G ≤ 90	90 < G

. The higher the low-carbon evaluation score indicates the better low-carbon development of the park.

3. Case Study

To verify the effectiveness of the constructed evaluation indicator system of low-carbon parks in the textile industry, three textile parks were selected for research, and their production data from the past three years was statistically analyzed. Then, they were scored and evaluated accordingly. Park A is located in Zhejiang Province and primarily focuses on the apparel industry. The park has 16 established enterprises, including 10 apparel production enterprises, 5 chemical fiber production enterprises, and 1 weaving enterprise. The park mainly produces apparel products related to chemical fibers, cotton, wool, silk, and synthetic fibers. Park B is located in Guangdong Province and is dominated by the chemical fiber manufacturing industry. The park has 14 established enterprises, including 3 printing and finishing enterprises, 5 chemical fiber production enterprises, 4 weaving enterprises, and 2 garment enterprises. The park mainly produces polyester, spandex, and other synthetic fiber filaments, synthetic fiber ropes, and thin taffeta fabrics. The park has distinctive features and a significant clustering effect, and in recent years, it has actively implemented the “cost reduction and efficiency enhancement” policy, strengthening its low-carbon management capabilities. Park C is located in Zhejiang Province and primarily focuses on the textile industry. The park has 14 established enterprises, including 2 printing and finishing enterprises, 2 weaving enterprises, 3 chemical fiber production enterprises, 3 home textile enterprises, and 4 apparel enterprises. The park’s main industries are centered on the downstream of the textile industry, producing functional home textile products, polyester woven fabrics, and cotton woven fabrics.

First, the demonstration parks were evaluated on the basic requirements. When conducting the basic requirements evaluation, the parks provided supporting documentation in the form of self-declared paper files. Over the past three years, Park A, Park B, and Park C have all maintained good safety and environmental protection records, without major safety incidents, pollution accidents, or ecological damage events. The three parks all have well-developed infrastructure, fulfilling not only the requirements for safety and fire protection but also environmental protection standards. In addition, all three parks have actively implemented national and local laws, regulations, policies, and standards related to environmental protection and green and low-carbon development, demonstrating a firm commitment to and practice sustainable development. Statistical analyses of the scoring details of these three demonstration parks at the low-carbon management criteria layer, the low-carbon production criteria layer, and the incentive indicator criteria layer were carried out separately, with the results shown in Figure 5 and Figure 6.

As shown in Figure 5, low-carbon infrastructure, low-carbon development planning, and investment in low-carbon energy-saving projects are the key elements contributing to the significant differences in the evaluation scores at the low-carbon management guideline level. In the past three years, Park A has steadily advanced its low-carbon infrastructure by continually replacing traditional lighting with energy-saving outdoor lighting fixtures in public areas. This earned Park A 4 points for this indicator. In contrast, Park B has not yet implemented any low-carbon infrastructure and, therefore, received no points for this indicator. Park C has a well-developed low-carbon infrastructure, including not only extensive use of energy-saving outdoor lighting in public areas but also the promotion of clean-energy shuttle buses for employee transportation within the park and clean-energy trucks for internal logistics. As a result, Park C scored 7 points for this indicator. Regarding the indicator of low-carbon development planning, all three zones have established annual medium- and long-term low-carbon development plans. However, Park A’s plan primarily focuses on low-carbon infrastructure, with the rest of the plan being more systematic and somewhat misaligned with the park’s actual development demands. In contrast, Park C’s development plan is more comprehensive, including both annual low-carbon development plans and a detailed three-year long-term plan. These plans cover the park’s low-carbon industry, energy, infrastructure, and transportation, ensuring the scientific and sustainable low-carbon development of the park. In summary, the scores for this indicator were 5 points for Park A, 4 points for Park B, and 6 points for Park C.

Each year, all three parks allocate a certain amount of funds for low-carbon energy-saving projects within the park. Park A invests in upgrading low-carbon infrastructure and introducing low-carbon production processes, with these investments accounting for 3.8% of the park’s annual budget, earning a score of 5 points. Park B annually invests in low-carbon production technology transformation, representing 2.6% of its annual budget, which earned it 4 points. Park C, which invests which invests the highest percentage of its budget (6.3%) in upgrading low-carbon infrastructure, introducing low-carbon production processes, and improving low-carbon production technologies, scored 6 points for this indicator.

As shown in Figure 6, the evaluation scores of the three parks on the indicators at the low-carbon production criteria layer show a trend that Park C performs the best, followed by Park A, and Park B performs the worst. The most significant factors contributing to the differences in the evaluation scores at the low-carbon production criteria layer are the carbon emission sub-criteria layer and the production investment sub-criteria layer. These differences can be attributed to two primary factors: First, the carbon emission sub-criteria layer and the production investment sub-criteria layer are the key assessment points of the entire low-carbon park evaluation indicator system, which account for a substantial weight in the overall score, leading to greater fluctuations in the scoring; Second, the level of low-carbon development across the parks varies significantly. The carbon emission intensity indicator is the core metric of the low-carbon park evaluation system, and the evaluation scores of this indicator vary greatly among Parks A, B, and C, with scores of 9, 7, and 12, respectively. Each park hosts companies from the textile industry, including the textile, apparel, and chemical fiber manufacturing sectors. The distribution of sub-industries varies across the parks. In Park A, the textile and apparel sector is the leading industry, followed by the chemical fiber manufacturing and textile sectors. The corresponding carbon emission intensities for companies in these sub-industries are 0.34 t CO₂/10,000 RMB, 2.68 t CO₂/10,000 RMB, and 1.58 t CO₂/10,000 RMB, respectively. Based on the scoring benchmark outlined in Table 3, Park A scores 9 points for this indicator. In Park B, the chemical fiber manufacturing sector is the dominant industry, followed by the textile and apparel sectors. The corresponding carbon emission intensities for the enterprises in these three sub-industries are 2.8 t CO₂/RMB 10,000, 1.6 t CO₂/RMB 10,000, and 0.35 t CO₂/RMB 10,000, respectively. Based on the scoring benchmark in Table 3, Park B scores 7 points for this indicator. In Park C, the textile industry is the primary industry, followed by the textile and apparel sectors and the chemical fiber manufacturing sector. The corresponding carbon emission intensities for companies in these sub-industries are 1.47 t CO₂/RMB 10,000, 0.33 t CO₂/RMB 10,000, and 2.27 t CO₂/RMB 10,000, respectively. Based on the scoring benchmark in Table 3, Park C scores 12 points for this indicator.

The scores of the three demonstration parks are shown in Figure 7. Park A received a total low-carbon evaluation score of 88 points, Park B scored 61 points, and Park C scored 103 points. According to the range of score intervals given in Table 4, the low-carbon ratings for the three demonstration parks are as follows: Park A qualifies as a two-star low-carbon park, Park B is rated as non-compliant, and Park C qualifies as a three-star low-carbon park. Low-carbon planning is the main factor affecting the low-carbon management of industrial parks, and carbon emission and production input are the main factors affecting the low-carbon production of industrial parks. The low-carbon infrastructure, low-carbon development planning, and low-carbon energy-saving project investment are the key factors that cause the large difference in the evaluation scores of the low-carbon management criteria of the park. Therefore, the management personnel in the park should plan to eliminate high-energy-consuming equipment in the park and improve the low-carbon development level of the park by eliminating or replacing backward production capacity, improving energy efficiency, and recycling waste heat [22].

4. Conclusions

This article has developed a low-carbon evaluation indicator system for textile parks, characterized by a scoring mechanism and consisting of 30 indicators across three levels. This system enables the low-carbon rating of demonstration parks while being objectively realistic, widely applicable, and practically operable. The distribution of scores within the evaluation results allows for the accurate identification of factors affecting the low-carbon level of the parks and highlights areas for improvement. For example, in the case study of the three parks, Park B was non-compliant, with low-carbon planning being the primary factor affecting its low-carbon management. Carbon emissions and production investment were the key factors influencing low-carbon production. Through in-depth research and exploration of the connotation of the establishment of the evaluation index system of low-carbon industrial parks in the textile industry, the internal mechanism and law of low-carbon development can be revealed, providing experience and reference for the further development of the low-carbon economic theory of the textile industry, and also providing reference for other industries to conduct low-carbon evaluation.

The development of low-carbon initiatives in textile industry parks can follow two development pathways: the “external introduction-digestion and absorption-transformation” model of technological upgrading. At present, the technological advancement of China’s textile industry still requires continuous innovation, and some textile production equipment, such as dyeing machines and cutting tables etc., rely on imports. Parks can acquire advanced low-carbon technologies through purchases or international cooperation and achieve rapid low-carbon transformation and upgrades through internal digestion and absorption. Second, The “internal innovation—process reengineering—enhancement” model of technological upgrading. For textile parks without substantial funds to directly purchase advanced low-carbon technologies, or where the available low-carbon technologies do not align with the park’s low-carbon development needs, collaboration with relevant research institutions or annual internal investments in developing low-carbon technologies tailored to the park’s specific circumstances can be a viable approach.

In the evaluation system of low-carbon industrial parks in the textile industry established in this paper, the benchmark value of the index is mostly selected for the macro-statistical data of the textile industry at the industrial level. In the future, the benchmark value of the index can be improved through the actual production data of low-carbon industrial parks in the textile industry.