Applied research on the building of the standardized evaluation system for low (zero) carbon industrial parks

Abstract

Against the backdrop of implementing the national carbon peaking and carbon neutrality strategy, this study proposes a standardized evaluation system for building low (zero) carbon industrial parks, which encompasses principles of construction, evaluation methods, indicators system, data collection, indicators calculation, evaluation results, and implementation steps. This proposal follows a deep understanding on the challenges facing industrial parks, such as trade barriers and discrepancies in technical standards, as well as opportunities under the context of high-quality development. Through quantitative analysis on the layout and distribution characteristics of government-approved industrial parks, it figures out the status quo of industrial parks in the development zone. Moreover, a case study of an economic development zone in the southeastern coastal area is made to validate the evaluation. Finally, this paper gives suggestions on the dynamic adjustment of indicators of the evaluation system and the collection of full-sample data.

Keywords

Low carbon industrial parks standardization indicator system

Introduction

Due to the severe reality of global climate change, the low-carbon economy has become an inevitable choice for world’s economic development. Low-carbon economic zones, therefore, play a crucial role as their construction influences regional even national low-carbon efforts.^1,2 In recent years, the research on low-carbon (zero) carbon industrial parks is increasing gradually, involving many aspects. For example, Sang et al. studied the integrated energy system optimization and risk resilience of near-zero carbon parks under the background of carbon tax, and proposed two low-carbon energy transition roadmaps based on natural gas and electricity, providing important references for energy system optimization.³ Wang et al. developed an industrial park carbon data management platform based on digital twin technology, which supports efficient energy conservation and carbon reduction decisions through real-time collection of energy consumption data and significantly improves the transparency of carbon data.⁴ Luo et al. carried out multi-objective optimization of the building envelope structure of office park, comprehensively considering energy consumption, thermal comfort, hidden carbon and economy, providing an important reference for low-carbon optimization at the building level.⁵ Fang et al. designed the energy storage scenario of the zero-carbon big data industrial park from the perspective of load-storage collaboration of the source network, analyzed the corresponding business model, and provided practical guidance for the application of energy storage technology.⁶ Yao et al. proposed a near-zero carbon park scheduling model based on the synergistic effect of waste treatment and carbon capture gas power plants, and verified the effectiveness of the model in improving system economy and renewable energy absorption capacity through simulation analysis.⁷ Yu et al. conducted classification and case study on the pilot low-carbon industrial parks in China, proposed classification methods of 3H, 3L and hybrid industrial parks, and analyzed different types of low-carbon development paths.⁸ Jiang et al studied the impact of material energy on low-carbon transformation of agricultural and rural industrial parks.⁹ He et al studied the design of park-level energy systems with near-zero emissions.¹⁰ Ju et al. studied the new micro-energy grid structure of power supply, heat source and electric dump gas.¹¹ Lyu et al. developed a land-industry-carbon comprehensive model, analyzed the energy consumption and carbon emission characteristics of industrial parks, and proposed two collaborative carbon peaking paths for industrial parks in China.¹² Huang et al. reviewed the development of China’s eco-industrial park standard system and studied the management mechanism of HJ/T 274-2015 on park environmental issues.¹³ Gao et al. have evaluated the eco-efficiency of 18 industrial parks in Central China and analyzed the influencing factors of eco-efficiency.¹⁴ Song and Zhou studied the impact of green industrial policies on industrial pollution emission.¹⁵

To sum up, the existing research has achieved some achievements in the aspects of energy system optimization, data management, building envelope optimization, energy storage technology application, waste treatment and carbon capture synergy in low-carbon (zero) carbon industrial parks, but there are still many shortcomings in the construction and implementation of standardized evaluation system. Therefore, this paper develops a standardized evaluation system for low (zero) carbon industrial parks to evaluate the carbon levels of these parks, guide their construction and management, address problems, enhance the parks competitiveness, all in an effort to facilitate the sustainable development of the regional economy.

Industrial park layout and current development status

Overview

The industrial parks in China are the cluster of enterprises from similar or related industries. This proximity facilitates interaction and collaboration among businesses, makes it easy to form a complete industrial chain, reduces transportation and inventory costs, and enhances the production efficiency across the entire chain. In addition, industrial clusters within the parks can benefit from economies of scale, which lowers the production cost per unit through large-scale manufacturing.

Enterprises in the same park can share infrastructure, public services and technological resources. For instance, technology firms can share laboratories that they cannot afford alone, and test equipment at different time, which makes it possible for them to expand business reach and conduct R&D without the need to invest heavily on fixed assets.

In 2018, the National Development and Reform Commission, along with the Ministry of Science and Technology and four other departments, including the Ministry of Land and Resources, the Ministry of Housing and Urban-Rural Development, the Ministry of Commerce, and the General Administration of Customs, jointly released the Catalogue of Approved Development Zones in China.⁴ Based on criteria such as land use, planning and construction, infrastructure, quantity control, industrial cluster, distinctive industrial features, development positioning, regional layout, environmental protection, and safety production, a total of 2543 development zones were approved, among which 552 were national level and 1991, provincial level. The distribution of these development zones accounts for the main characteristics of industrial parks. Figure 1 and Table 1 show the distribution of the approved national and provincial development zones.

Figure 1.

The distribution of approved national and provincial development zones.

Table 1.

Number of approvals issued by the development zone over the years.

Year	1984	1985	1986	1988	1989	1990	1991	1992	1993	1994	1995	1996
National level	10	1	2	2	3	2	28	64	14	3	3	1
Provincial level	1	0	0	3	0	0	3	104	62	65	25	21
Summary	11	1	2	5	3	2	31	168	76	68	28	22
Year	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008
National level	1	0	0	19	6	6	7	2	6	6	3	12
Provincial level	22	15	9	16	29	61	50	9	27	590	38	51
Summary	23	15	9	35	35	67	57	11	33	596	41	63
Year	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
National level	8	94	30	68	54	18	49	13	15	2
Provincial level	50	166	149	206	59	54	60	44	2	0
Summary	58	260	179	274	113	72	109	57	17	2

Approval numbers surged in 1992, 2006, and 2010–2013, aligning with national strategies to promote industrial clustering and regional growth.

Distribution characteristics

Development zones are calculated on a provincial basis, as shown in Table 2 and Figure 2. They are primarily concentrated in the southeastern coastal areas and first-tier cities such as Beijing, Shanghai, and Guangzhou. A scientific-based evaluation of low-carbon construction in these industrial parks is crucial for guiding the green development of related regions and cities.

Table 2.

Statistics of development zones by province.

Anhui	Beijing	Fujian^a	Gansu	Guangdong^a	Guangxi Zhuang autonomous region^a	Guizhou	Hainan^a	Hebei	Henan	Heilongjiang
117	19	97	66	132	65	64	7	153	150	91
Hubei	Hunan	Jilin	Jiangsu	Jiangxi	Liaoning	Inner Mongolia autonomous region	Ningxia hui autonomous region	Qinghai	Shandong	Shanxi
103	129	62	170	99	88	81	17	15	173	27
Shaanxi	Shanghai^a	Sichuan	Tianjin	Tibet autonomous region	Xinjiang Uyghur autonomous region	Yunnan	Zhejiang^a	Chongqing
56	59	134	33	5	84	78	120	49

^aMarked provinces are southeastern coastal regions, generally referring to the southeastern coastal areas in geographical and economic contexts.

Figure 2.

National development zone distribution trend chart.

Approved national and provincial development zones are primarily concentrated in southeastern coastal provinces (e.g., Guangdong and Zhejiang) and major cities (Beijing, Shanghai), reflecting regional economic development priorities. According to the National Economic Industry Classification (GB/T 4754-2017),⁵ a statistical analysis is made after classifying the leading industries in development zones approved by the National Development and Reform Commission (Table 3, Figure 3).

Table 3.

Industrial distribution in the development zone.

Major code	Category	Quantity	Proportion %
A	Agriculture, forestry, animal husbandry, and fisheries	5	0.2
B	Mining	94	3.7
C	Manufacturing	2352	92.9
D	Power, heat, gas, and water supply	3	0.1
F	Wholesale and retail trade	23	0.9
G	Transportation, warehousing, and postal services	20	0.8
I	Information and communication technology (ICT) services	21	0.8
J	Financial industry	2	0.1
L	Leasing and business services	11	0.4
M	Scientific research and technical services	1	0.0

Figure 3.

Industrial distribution map of the development zones.

The data indicate that manufacturing is a major focus, and 92.9% of the companies in the parks are dedicated to it. Among them, the processing of agricultural and sideline products is a primary sector (Table 4), followed by non-metallic mineral product manufacturing, chemical raw materials and chemical products manufacturing, petroleum, coal, and other fuel processing, as well as the textile and apparel industry. Thus, building manufacturing-based industrial parks in a low-carbon way is vital to achieving the nation’s low (zero) carbon and high-quality development in the future.

Table 4.

Manufacturing park distribution statistics.

Intermediate category code	Intermediate category	Quantity	Proportion %	Intermediate category code	Intermediate category	Quantity	Proportion %
C13	Agricultural and sideline food processing industry	874	37.14	C28	Chemical fiber manufacturing industry	3	0.13
C14	Food manufacturing industry	8	0.34	C29	Rubber and plastic products manufacturing industry	17	0.72
C15	Alcoholic beverages, soft drinks, and refined tea manufacturing industry	19	0.81	C30	Non-metal mineral products manufacturing industry	257	10.92
C16	Tobacco product manufacturing industry	2	0.08	C31	Ferrous metal smelting and rolling processing industry	47	2.00
C17	Textile industry	86	3.65	C32	Non-ferrous metal smelting and rolling processing industry	34	1.44
C18	Textile clothing, apparel, and accessories manufacturing industry	115	4.89	C33	Metal products manufacturing industry	22	0.93
C19	Leather, Fur, feather, and their products, and footwear manufacturing industry	13	0.55	C34	General equipment manufacturing industry	61	2.59
C20	Wood processing and wood, bamboo, rattan, palm leaf, and grass products manufacturing industry	35	1.49	C35	Specialized equipment manufacturing industry	151	6.42
C21	Furniture manufacturing industry	20	0.85	C36	Automobile manufacturing industry	38	1.61
C22	Paper and paper products manufacturing industry	21	0.89	C37	Railway, shipbuilding, aerospace, and other transportation equipment manufacturing industry	3	0.13
C23	Printing and recording media replication industry	15	0.64	C38	Electrical machinery and apparatus manufacturing industry	17	0.72
C24	Cultural, educational, artistic, sports, and entertainment goods manufacturing industry	15	0.64	C39	Computer, communications, and other electronic equipment manufacturing industry	7	0.30
C25	Petroleum, coal, and other fuel processing industry	94	3.99	C40	Instrumentation and meter manufacturing industry	1	0.04
C26	Chemical materials and chemical products manufacturing industry	184	7.82	C42	Waste resources comprehensive utilization industry	1	0.04
C27	Pharmaceutical manufacturing industry	192	8.16

Existing issues

Industrial Layout

In the early stages of industrial development, many industrial parks relied heavily on high-carbon industries. Traditional parks focused on heavy chemicals and energy-intensive and high-emission industries, such as steel and cement. These sectors consume vast amount of fossil fuels during production, resulting in high-carbon emissions and pose challenges to industrial upgrading.⁸ The long-established industrial system and infrastructure, built around these high-carbon industries stand in the way to low-carbon transformation.

Even if some industrial parks are equipped with well-aligned industrial chains, they lack initiative in pursuing low carbon. For instance, in electronics information parks, component manufacturing and assembly enterprises may address energy supply issues independently, without considering the optimization of energy use and collaborative carbon management across the entire chain. The absence of effective low-carbon cooperation between upstream and downstream enterprises limits the recycling of resources and hierarchical energy utilization. Regional disparities are evident: southeastern coastal parks face stricter national carbon emission standards, while inland parks struggle with outdated energy infrastructure and limited access to green technologies.¹¹

Energy

Despite the attempt to introduce clean energy to the industrial parks, fossil fuel is still predominant in the energy structure. Numerous enterprises within the parks fail to give up using coal and natural gas-driven equipment, resulting in a low proportion of new energy in the energy supply. Moreover, the intermittent and unstable nature of clean energy use, coupled with a lack of efficient storage and smart energy management, hampers large-scale replacement to traditional energy sources.

In terms of energy management, some enterprises are plagued by energy waste due to inadequate monitoring and management technologies, so they cannot master the energy consumption during production. Discrepancies in energy use efficiency among enterprises also prevent the formation of a unified mechanism for improving efficiency and monitoring waste, which impedes the overall low-carbon development within the parks.

Competitiveness

In the shift to low-carbon processes, enterprises need invest a lot in low-carbon technological research and innovation, such as developing new energy-saving processes and carbon capture technologies. This is undoubtedly burdensome for small and medium-sized enterprises. The financial strain results in slow move to the application of low-carbon technologies, and place these enterprises at disadvantage compared to their peers from low-carbon development zones.

Currently, the low-carbon product market is underdeveloped, with limited consumer awareness and acceptance. Low-carbon products might be priced higher than traditional high-carbon ones, but their green value is under recognized by the market. Consequently, the motivation for enterprises to produce low-carbon products is dampened, posing challenges to nurturing low-carbon competitiveness and development. High-tech clusters in developed regions have stronger low-carbon R&D capabilities, whereas inland SMEs lack financial resources for green innovation.

Construction of standardized evaluation system

Principles of construction

The principle of scientific accuracy requires evaluation indicators to reflect industrial parks’ actual conditions and low-carbon performance effectively and objectively. The choosing of indicators should be built on sound theoretical foundation and practical experience, and should cover all critical aspects of low-carbon development. For instance, the comprehensive energy consumption per unit of industrial added value measures the total energy used per 10,000 yuan of industrial output, which is expressed in tons of standard coal. This metric directly reflects the energy efficiency in production processes and the park’s commitment to low-carbon practices. The metric can be kept at a low level when advanced low-carbon parks implement energy-saving technologies and management strategies.

Moreover, the calculation of indicators should be scientifically valid, practical, and replicable. An example in case is the carbon emission intensity reduction rate, which calculates the percentage decrease in carbon dioxide emission intensity compared to the previous year. The intensity is defined as the ratio of total CO₂ emissions to the park’s GDP. Accurate measurement of CO₂ emissions and GDP are need to ensure the reliability and precision of the results, in addition to scientifically robust calculation methods.

Finally, the indicator system should be dynamically evolving with technological advancements and improved management. As renewable energy technologies progress, the renewable energy consumption ratio will be a more important indicator. The system should reflect the change of effective indicators so as to guide parks to use renewable energy sources.

The principle of comprehensiveness requires that the indicator system is based on economic, environmental and social dimensions. Economic indicators should include industrial added value per unit of construction land and the reduction rate of energy consumption per GDP unit to illustrate the economic development level and energy utilization efficiency of the park. For instance, industrial added value per unit area of construction land represents the industrial output value generated per square meter, which measures the land use efficiency and economic output capability of the park.

Environment indicators should cover aspects such as the reduction rate of carbon emission intensity, the proportion of renewable energy consumption, wastewater generation per unit of industrial added value, and solid waste generation per unit of industrial added value. These metrics measure the influence of a park on the environment and resource utilization efficiency. For example, the proportion of renewable energy consumption indicates the percentage of renewable energy in the total energy consumption of a park. As more global attention is averted to environmental protection, parks are increasing their use of renewable energy sources such as solar and wind power to reduce reliance on traditional fossil fuels and lower carbon emissions.

In the social dimension, indicators may include the rate of household waste sorting and the centralized treatment rate of domestic sewage, which reflect a park’s contribution to and responsibility for the society. For instance, the household waste sorting rate refers to the percentage of sorted waste out of the total waste collected. This metric measures a park’s environmental awareness and management level in waste handling, playing a significant role in promoting waste sorting and resource recycling across the society.

In summary, by implementing the principles of scientific accuracy and comprehensiveness, the comprehensive evaluation system for low-carbon parks can accurately reflect a park’s level of carbon reduction and overall development capacity. This provides scientific guidance and evaluation standards for the planning, construction, operation, and management of the industrial park.

Evaluation methods

To better illustrate the weight of evaluation indicators, the following methods are typically used, individually or combined.

Expert Scoring Method

Expert scoring is commonly used to evaluate low-carbon economic zones. Experts with extensive knowledge and practical experience in the field of low-carbon economics, energy, and environmental science are invited to score the evaluation indicators based on their professional insights and understanding of low-carbon parks.

Analytic Hierarchy Process (AHP)

Together with expert scoring, AHP can be used to determine the weight of each evaluation indicator. The evaluation system for low-carbon industrial parks is divided into a goal layer (evaluation of the park’s low-carbon development level), a criteria layer (such as energy utilization, carbon emissions, and industrial decarbonization), and an indicator layer (specific indicators). By constructing a judgment matrix through expert comparisons of the importance of each layer, the relative weight of each indicator concerning the goal can be calculated. This can avoid the unduly influencing of a single indicator to the evaluation results.

Fuzzy Comprehensive Evaluation Method

Considering the inherent ambiguities in evaluating low-carbon industrial parks, the results of expert scoring can be processed through fuzzy treatment. By establishing membership functions, how much an indicator is affiliated to different evaluation levels is quantified. The fuzzy evaluation results of all indicators are then synthesized to obtain a comprehensive assessment of a park’s low-carbon development level.

In this study, the expert scoring method is primarily utilized to determine the weight of evaluation indicators.

Evaluation indicator system

The standardized evaluation system for low-carbon parks includes six primary indicators: carbon emission intensity, energy utilization, ecological environment, resource recycling, infrastructure, and operational management, along with 21 secondary indicators (as shown in Figure 4).

Figure 4.

Standardized evaluation indicator system for low-carbon economic zones.

Weights are assigned entirely through direct expert scoring (as shown in Table 5). 10 experts independently rated the importance of each category. Final weights normalized to sum to 1.0. Secondary Weights: Experts ranked indicators within each category, and weights were averaged.

Table 5.

Weight distribution table.

Primary category	Primary weight	Secondary indicator	Secondary weight
1. Carbon emission intensity	0.1	1. CO₂ emission intensity (I_(CO₂))	0.05
		2. CO₂ emission reduction rate (R_(CO₂))	0.03
		3. CO₂ emission reduction amount (D_(CO₂))	0.02
2. Energy utilization	0.22	4. Reduction rate of comprehensive energy consumption per unit output (D_(E_unit))	0.06
		5. Renewable energy consumption ratio (P_ren)	0.06
		6. Electricity consumption ratio (P_elec)	0.06
		7. Proportion of new energy vehicles (P_new)	0.04
3. Water resource management	0.15	8. Fresh water consumption per unit output (E_fresh)	0.05
		9. Wastewater discharge per unit output (E_waste)	0.05
		10. Industrial water reuse rate (R_(W_repeated))	0.05
4. Resource recycling	0.23	11. Household waste sorting rate (R_(L_collected))	0.05
		12. Industrial solid waste utilization rate (R_(U_solid))	0.06
		13. Industrial waste heat recovery rate (P_(recover_y))	0.06
		14. Industrial waste gas recovery rate (P_(recover_g))	0.06
5. Green infrastructure	0.17	15. Proportion of green industrial buildings (P_(green_ind))	0.06
		16. Proportion of green public buildings (P_(green_pub))	0.06
		17. Charging station parking space ratio (P_charge)	0.05
6. Management certification	0.13	18. Environmental management system certification rate (P_EMS)	0.04
		19. Energy management system certification rate (P_(EMS_energy))	0.03
		20. Greenhouse gas verification rate (P_check)	0.03
		21. High-tech enterprise ratio (P_(high_tech))	0.03

Evaluation data collection

A total of 34 aspects of operation data of a park are collected to facilitate the calculation of relevant indicators within the evaluation framework (as shown in Table 6).

Table 6.

Data collection items for low (zero) carbon industrial parks.

No.	Name	Unit	No.	Name	Unit
1	Total carbon dioxide emissions ( $Q_{{C O}_{2}}$ )	Tons	18	Total number of enterprises with waste heat ( $N_{{produce}_{γ}}$ )	Item
2	Industrial added value ( $V_{ind}$ )	Thousand ¥	19	Enterprises utilizing waste gas resources ( $N_{{recover}_{g}}$ )	Item
3	Energy consumption per unit of industrial added value ( $E_{unit}$ )	Tons of SCE/Thousand ¥	20	Total number of enterprises emitting waste gas ( $N_{{prosuce}_{g}}$ )	Item
4	Renewable energy utilization ( $U_{ren}$ )	Tons of SCE, kWh	21	Number of green industrial buildings ( $N_{{green}_{ind}}$ )	Buildings
5	Total energy consumption ( $Q_{total}$ )	Tons of SCE, kWh	22	Total completed industrial buildings ( $N_{{total}_{nd}}$ )	Buildings
6	Electricity consumption ( $Q_{elec}$ )	kWh	23	Number of green public buildings ( $N_{{green}_{pub}}$ )	Buildings
7	Number of new energy vehicles ( $N_{new}$ )	Vehicles	24	Total completed public buildings ( $N_{{total}_{pub}}$ )	Buildings
8	Total number of operational vehicles ( $N_{total}$ )	Vehicles	25	Number of parking spaces with charging stations ( $N_{charge}$ )	Spaces
9	Fresh water consumption ( $W_{fresh}$ )	Cubic meters	26	Total parking spaces ( $N_{{total}_{park}}$ )	Spaces
10	Industrial wastewater discharge ( $Q_{waste}$ )	Cubic meters	27	Number of enterprises with environmental management certification ( $N_{EMS}$ )	Units
11	Sorted collection of household waste ( $L_{collected}$ )	Kg, tons	28	Total number of enterprises ( $N_{{total}_{energy}}$ )	Item
12	Total household waste generation ( $L_{total}$ )	Kg, tons	29	Number of enterprises with energy management certification ( $N_{{EMS}_{energy}}$ )	Item
13	Industrial recycled water use ( $W_{repeated}$ )	Cubic meters	30	Total number of enterprises ( $N_{{total}_{energy}}$ )	Item
14	Total industrial water use ( $W_{total}$ )	Cubic meters	31	Number of enterprises with greenhouse gas verification ( $N_{check}$ )	Item
15	Comprehensive recycling of industrial solid waste ( $U_{solid}$ )	Tons	32	Total number of enterprises ( $N_{{total}_{energy}}$ )	Item
16	Total generation of industrial solid Waste ( $Q_{solid}$ )	Tons	33	Number of high-tech enterprises ( $N_{{high}_{tech}}$ )	Item
17	Number of enterprises recovering waste heat ( $N_{{recover}_{γ}}$ )		34	Total number of enterprises ( $N_{{total}_{energy}}$ )	Item

Calculation of evaluation indicators

According to the standardized evaluation system established in Figure 4 and based on the data collection requirements stipulated in Table 6, each evaluation indicator value is calculated using the formulas specified in Table 7.

Table 7.

Calculation of evaluation indicators for low (zero) carbon industrial parks.

No.	Evaluation indicator	Formula	Unit	Explanation
1	Carbon dioxide emission intensity ( $I_{{C O}_{2}}$ )	$I_{{C O}_{2}} = \frac{Q_{{C O}_{2}}}{V_{i n d}} \times 100 %$	Tons/10 thousand yuan	Reflects carbon emissions per unit of industrial added value, evaluating low-carbon performance in production processes and aligning with national carbon intensity reduction targets
2	Reduction rate of carbon dioxide emission intensity $R_{{C O}_{2}}$	$R_{{C O}_{2}} = \frac{I_{{C O}_{2}} (t - 1) - I_{{C O}_{2}} (t)}{I_{{C O}_{2}} (t - 1)} \times 100 %$	%	Measures annual carbon intensity reduction, reflecting the effectiveness of low-carbon policies and serving as a core dynamic indicator for compliance assessment
3	Decrease in carbon dioxide emission intensity $D_{{C O}_{2}}$	$D_{{C O}_{2}} = I_{{C O}_{2}} (t - 1) - I_{{C O}_{2}} (t)$	Tons/10 thousand yuan	Quantifies absolute carbon intensity change, facilitating year-to-year emission reduction comparisons and supporting targeted policy adjustments
4	Reduction rate of comprehensive energy consumption per unit of industrial added value $D_{E_{u n i t}}$	$D_{E_{u n i t}} = \frac{E_{u n i t} (t - 1) - E_{u n i t} (t)}{E_{u n i t} (t - 1)} \times 100 %$	%	Reflects energy efficiency improvement, evaluating the effectiveness of energy-saving technologies and management measures in line with national energy consumption control policies
5	Proportion of renewable energy consumption $P_{r e n}$	$P_{r e n} = \frac{U_{r e n}}{Q_{t o t a l}} \times 100 %$	%	Measures the share of clean energy in total energy consumption, evaluating the greenness of energy structure and aligning with national renewable energy development strategies
6	Proportion of electricity consumption $P_{e l e c}$	$P_{e l e c} = \frac{Q_{e l e c}}{Q_{t o t a l}} \times 100 %$	%	Reflects the electricity share in energy consumption, identifying energy structure transformation potential and guiding electrification efforts
7	Proportion of new energy vehicles in operation $P_{n e w}$	$P_{n e w} = \frac{N_{n e w}}{N_{t o t a l}} \times 100 %$	%	Evaluates low-carbon transportation adoption, measuring new energy vehicle promotion effectiveness and supporting green mobility policies
8	Fresh water consumption per unit of industrial added value $E_{f r e s h}$	$E_{f r e s h} = \frac{W_{f r e s h}}{V_{i n d}}$	Cubic meters/Thousand ¥	Measures water resource consumption per unit of economic output, evaluating water use efficiency and aligning with national water conservation goals
9	Wastewater discharge per unit of industrial added value $E_{w a s t e}$	$E_{w a s t e} = \frac{Q_{w a s t e}}{V_{i n d}}$	Cubic meters/Thousand ¥	Reflects water pollution intensity per unit of economic activity, evaluating cleaner production levels and driving wastewater reduction
10	Household waste sorting collection rate $R_{L_{c o l l e c t e d}}$	$R_{L_{c o l l e c t e d}} = \frac{L_{c o l l e c t e d}}{L_{t o t a l}} \times 100 %$	%	Measures household waste sorting effectiveness, reflecting public environmental awareness and management capabilities to promote resource recycling
11	Industrial water reuse rate $R_{W_{r e p e a t e d}}$	$R_{W_{r e p e a t e d}} = \frac{W_{r e p e a t e d}}{W_{t o t a l}} \times 100 %$	%	Evaluates industrial water recycling efficiency, reflecting water resource management capabilities and supporting water-saving park initiatives
12	Comprehensive utilization rate of industrial solid waste $P_{U_{s o l i d}}$	$P_{U_{s o l i d}} = \frac{U_{s o l i d}}{Q_{s o l i d}} \times 100 %$	%	Measures industrial solid waste resource utilization, reflecting circular economy development and aligning with national “zero waste city” goals
13	Reuse rate of industrial waste heat resources $P_{r e c o v e r_{y}}$	$P_{r e c o v e r_{y}} = \frac{N_{r e c o v e r_{y}}}{N_{p r o d u c e_{y}}} \times 100 %$	%	Reflects waste heat recovery technology adoption, evaluating energy efficiency improvement potential and supporting energy-saving retrofits
14	Reuse rate of industrial waste gas resources $P_{r e c o v e r_{g}}$	$P_{r e c o v e r_{g}} = \frac{N_{r e c o v e r_{g}}}{N_{p r o d u c e_{g}}} \times 100 %$	%	Measures waste gas resource utilization, reflecting cleaner production technology adoption and reducing pollutant emissions
15	Proportion of green buildings in industrial buildings $P_{g r e e n_{i n d}}$	$P_{g r e e n_{i n d}} = \frac{N_{g r e e n_{i n d}}}{N_{t o t a l_{i n d}}} \times 100 %$	%	Evaluates green building adoption in industrial sectors, reflecting low-carbon infrastructure levels and supporting green building standards
16	Proportion of green buildings in public buildings $P_{g r e e n_{p u b}}$	$P_{g r e e n_{p u b}} = \frac{N_{g r e e n_{p u b}}}{N_{t o t a l_{p u b}}} \times 100 %$	%	Measures green building adoption in public facilities, promoting sustainable infrastructure and enhancing social sustainability
17	Proportion of charging station parking spaces $P_{c h a r g e}$	$P_{c h a r g e} = \frac{N_{c h a r g e}}{N_{t o t a l_{p a r k}}} \times 100 %$	%	Evaluates new energy vehicle infrastructure readiness, supporting green transportation systems and promoting EV adoption
18	Rate of environmental management system certification $P_{E M S}$	$P_{E M S} = \frac{N_{E M S}}{N_{t o t a l_{e n e r g y}}} \times 100 %$	%	Reflects the proportion of enterprises with EMS certification, evaluating environmental governance capabilities and aligning with international standards
19	Rate of energy management system certification $P_{E M S_{e n e r g y}}$	$P_{E M S_{e n e r g y}} = \frac{P_{E M S_{e n e r g y}}}{N_{t o t a l_{e n t e r}}} \times 100 %$	%	Measures energy management system adoption, promoting energy efficiency and fining management
20	Greenhouse gas check rate $P_{c h e c k}$	$P_{c h e c k} = \frac{N_{c h e c k}}{N_{t o t a l_{e n e r g y}}} \times 100 %$	%	Ensures GHG verification coverage, guaranteeing data accuracy and supporting carbon market mechanisms
21	Proportion of high-tech enterprises $P_{h i g h_{t e c h}}$	$P_{h i g h_{t e c h}} = \frac{N_{h i g h_{t e c h}}}{N_{t o t a l_{e n e r g y}}} \times 100 %$	%	Reflects the share of high-tech enterprises, evaluating low-carbon innovation capabilities and aligning with national innovation-driven strategies

Evaluation results

Indicator setting

Assume that the evaluation indicator system for the low-carbon industrial park comprises (n) primary indicators, denoted as $(l_{1}, l_{2}, \dots, l_{n})$ . Each primary indicator $(l_{i}) (i = 1, 2, \dots, n)$ contains ( $m_{i}$ ) secondary indicators, denoted as $(l_{i j}) (j = 1, 2, \dots, m_{i})$ .

Set the weight of a primary indicator $(l_{i})$ as $(W_{i})$ , which should satisfy $(\sum_{i = 1}^{n} w_{i} = 1)$ , and $(0 < w_{i} \leq 1)$ .

For the secondary indicator $(l_{i j})$ under corresponding primary indicator $(l_{i})$ , its weight is denoted as $(W_{i j})$ , satisfying $(\sum_{j = 1}^{m_{i}} w_{i j} = 1)$ , and $(S_{i j})$ .

Scoring of indicators

The score of the secondary indicator $(l_{i j})$ is denoted as $(S_{i j})$ . The scoring range is determined based on specific evaluation criteria, for example, $(0 \leq S_{i j} \leq 100)$ .

The score for the primary indicator $(l_{i})$ , denoted as $(S_{i})$ , is obtained through a weighted sum of the scores of its secondary indicators, calculated as follows equation (1):

S_{i} = \sum_{j = 1}^{m_{i}} w_{i j} S_{i j}

(1)

The total evaluation score for the low-carbon industrial park (S) is derived from a weighted sum of the primary indicator scores, calculated as equation (2):

[S = \sum_{i = 1}^{n} w_{i} S_{i}]

(2)

Representation of results

Industrial parks with scores below 60 points are classified as non-compliant low (zero) carbon parks, while those with over 60 points are categorized into the following three levels.

——Excellent: 90 points and above

——Good: 75 to 89 points

——Passable: 60 to 74 points

Case application

General overview

An economic development zone in the southeast coastal area was approved by the National Development and Reform Commission in 2006, whose major economic sectors cover both traditional and emerging industries. Traditional ones include textiles and apparel, footwear manufacturing, food and beverage, machinery manufacturing, and paper products. Among them, the total output of the textile and apparel cluster exceeds 100 billion, while the food processing cluster surpasses 50 billion. Many well-known leading enterprises have been nurtured in the park. By November 2024 (source: Qichacha), there are 2143 enterprises in agricultural and sideline food processing (C13), food manufacturing and liquor (C14), beverages, and refined tea manufacturing (C15), with total output exceeding 110 billion yuan. There are 218 high-tech enterprises, 6 specialized “Little Giant” enterprises, 53 provincial specialized enterprises, and 120 provincial innovative enterprises. The effects of industrial clustering and economic growth are significant, paving the way for the park to develop, showing great potential in standardization, industrial upgrading and digital transformation.

Data collection and indicator calculation

In the process of data collection and indicator calculation, we conducted field interviews and data verification to select 20 representative enterprises of various sizes. Based on a data model, we collected their data to estimate the overall sample. During this process, we encountered some key challenges, such as inconsistent reporting formats for energy consumption among enterprises and limited familiarity with low-carbon metrics among enterprise staff. To address these issues, we organized training sessions for over 50 managers to standardize data collection practices and enhance their understanding of evaluation indicators.

Table 8 is a detailed table of 21 indicators based on the expert scoring method, in which the full score is obtained according to the expert empowerment of Table 5, and the actual score of each indicator is obtained by the average score of the expert. In addition, we tested the consistency of experts, and if a certain indicator diverged too much (e.g., standard deviation >1.5), we organized experts to re-discuss the scoring basis. Based on the data collected and processed from these efforts, we gained a clearer understanding of the scores of various evaluation indicators.

Table 8.

Evaluation of a low (zero) carbon industrial park in an economic development zone focused on food processing.

No.	Evaluation indicator	Score	Full score
1	Carbon dioxide emission intensity $I_{{C O}_{2}}$	5	5
2	Reduction rate of carbon dioxide emission intensity $R_{{C O}_{2}}$	3	3
3	Decrease in carbon dioxide emission intensity $D_{{C O}_{2}}$	2	2
4	Reduction rate of comprehensive energy consumption per unit of industrial added value $D_{E_{u n i t}}$	4	6
5	Proportion of renewable energy consumption $P_{r e n}$	4	6
6	Proportion of electricity consumption $P_{e l e c}$	4	6
7	Proportion of new energy vehicles in operation $P_{n e w}$	3	4
8	Fresh water consumption per unit of industrial added value $E_{f r e s h}$	4	5
9	Wastewater discharge per unit of industrial added value $E_{w a s t e}$	4	5
10	Household waste sorting collection rate $R_{L_{c o l l e c t e d}}$	4	5
11	Industrial water reuse rate $R_{W_{r e p e a t e d}}$	4	5
12	Comprehensive utilization rate of industrial solid waste $R_{U_{s o l i d}}$	5	6
13	Reuse rate of industrial waste heat resources $P_{r e c o v e r_{y}}$	5	6
14	Reuse rate of industrial waste gas resources $P_{r e c o v e r_{g}}$	5	6
15	Proportion of green buildings in industrial buildings $P_{g r e e n_{i n d}}$	4	6
16	Proportion of green buildings in public buildings $P_{g r e e n_{p u b}}$	4	6
17	Proportion of charging station parking spaces $P_{c h a r g e}$	5	5
18	Rate of environmental management system certification $P_{E M S}$	4	4
19	Rate of energy management system certification $P_{E M S_{e n e r g y}}$	3	3
20	Greenhouse gas check rate $P_{c h e c k}$	3	3
21	Proportion of high-tech enterprises $P_{h i g h_{t e c h}}$	2	3
Total score		81	100

Presentation of results

The high scores for the indicators “Comprehensive Utilization Rate of Industrial Solid Waste,” “Reuse Rate of Industrial Waste Heat Resources,” and “Reuse Rate of Industrial Waste Gas Resources” can be attributed to the geographical advantages of the southeastern coastal area. The case study park is located in an economically developed region with a concentrated pool of capital, technological expertise, and talent resources, which facilitates the implementation of high-cost technologies and infrastructure. This enables efficient resource recycling, waste heat recovery, and emission reduction, aligning with national sustainability goals and driving superior performance in these metrics. The low scores of these indicators are mainly due to the industrial structure, technology application and management mechanism of the park. Traditional high-carbon industries (such as food processing and textile) rely on old and energy-consuming equipment, and their energy efficiency upgrading lags behind. The promotion of green building is limited by high renovation cost and insufficient policy incentives. The development of renewable energy is faced with the shortage of land resources and the bottleneck of power grid consumption. At the same time, the lack of refined energy management (insufficient real-time monitoring) and imperfect green electricity procurement mechanisms further restrict the progress of low-carbon transition. On the whole, by the standard in Representation of results, the economic development zone is rated as “Good” in the evaluation of the low (zero) carbon industrial park due toits successful implementation of national energy efficiency policies, evident in reduced energy consumption per unit of output, and adoption of energy-saving equipment in food processing enterprises. However, the park’s reliance on traditional industries constrained scores in low-carbon technology adoption, necessitating targeted R&D incentives to enhance green innovation.

Future prospects

How much an industrial park is aligned with the carbon peaking and carbon neutrality strategy can be known through the standardized evaluation system for low (zero) carbon industrial parks. The system offers valuable insights for coordinating low-carbon initiatives among enterprises and shaping the broader policies of the parks. However, given the diversity of industrial parks in China, a single set of fixed indicators and weights may not accurately reflect the state quo of low (zero) carbon development.

There remain several shortcomings in terms of comprehensive data collection and dynamic adjustment of the indicator system. Drawing on advanced experience both in China and abroad, we can enhance and refine the evaluation system for low-carbon industrial parks in an joint efforts with foreign peers. This serve as strong guidance and support for applying evaluation results to low-carbon development planning, management, and policy formulation of the industrial parks.

Footnotes

ORCID iD

Xue Bai

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by the Director’s Fund of the China National Institute of Standardization project “Research on the Indicator System and Path of Green and Low Carbon Transformation in Industrial Parks from the Perspective of the Whole Life Cycle” (542023Y-10363).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.*

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