Multi-Dimensional Evaluation of the Operational Benefits of Integrated Energy Systems in Zero-Carbon Parks Using Game Theory and Fuzzy Comprehensive Evaluation

Against the zero-carbon strategy and the “14th Five-Year” modern energy system planning, achieving the double carbon goal has become a vital driving force for energy transformation and social development. Facing this global challenge, the simplification of the traditional park energy system is no longer adapted to the requirements of low-carbon development [1]. As a consequence, it is of considerable value and significance to construct a comprehensive energy planning framework with zero carbon as the objective to encourage the decoupling of economic growth from carbon emissions in the park and enhance the zero carbon transformation of urban parks [2].

With financial support, capital has been directed to low-carbon and zero-carbon initiatives in international communities, especially in developed countries [3], [4]. The Chinese government has explicitly decided to support the “dual carbon” target in the “carbon peak action plan by 2030.” It has recommended supporting 100 representative pilot cities and parks in terms of policy, capital, and technology to further the goal of reducing peak carbon dioxide emissions.

In China, constructing a ‘zero carbon’ park is a strong response to the purpose of ‘double carbon’. Nevertheless, the park’s ‘zero-carbon’ transformation is now confronted with challenges in many ways, such as technology, planning, and policy [5]. Existing industries and energy infrastructures are still highly dependent on traditional fossil fuels, which makes it hard to adapt to the transition to clean energy and low-carbon technologies. Furthermore, the park’s ‘zero carbon’ transformation entails the overall design of each unit's growth, which necessitates extensive top-level design and scientific decision-making.

IES plays a pivotal role in accomplishing the objectives of safe energy usage, environmental protection, and zero-carbon by offering clean, efficient, and dependable energy supply and energy management services. Additionally, IES helps establish a “zero-carbon closed loop.” On the other hand, most of the research now available focuses on the green and low-carbon renovation of structures as well as striking a balance between the park economy [6], [7], and emission reduction [8], [9]. Relatively little research has been done on the use of energy-efficient systems (IES) and their benefits in zero-carbon parks. These studies concentrate primarily on how IES can decrease carbon emissions and promote energy efficiency in parks. Furthermore, the research is mainly focused on optimizing a particular energy system; it does not evaluate the full advantages of IES, nor does it assess its applicability and benefits in different types of parks.

Therefore, the innovation of this paper is applying IES to the evaluation of the operational benefits of zero-carbon parks for the first time and making up for the excessive attention to optimizing a single energy system. Based on the game theory combination weighting-fuzzy comprehensive evaluation model, the IES evaluation method as well as the evaluation index system of environment, technology, economy, energy, and sustainability, which is established in this paper, will scientifically evaluate the operation benefits of the three stages of planning, construction, and operation of a zero-carbon park. Moreover, game theory is applied to integrate the objective weight of CRITIC and the subjective weight of the AHP to overcome the drawbacks of single weighting. To guarantee that the combination weighting in every phase is reasonable, a sensitivity analysis of the model is performed, strengthening the evaluation's accuracy and scientific rigor. This research will offer theoretical and practical recommendations for planning, constructing, and operating zero-carbon parks. It will also provide decision support for policymakers and park managers and encourage the establishment and growth of zero-carbon parks.

The rest of this paper is organized as follows: A literature review is done in the second Section to assess the state of research on zero-carbon parks and IES both domestically and internationally. The evaluation index system is built in the third Section using the following five criteria: environmental impact, technical benefit, economic benefit, energy benefit, and sustainability. The steps and methods of the evaluation model used in this paper are presented in the fourth part. In Section 5, IES is evaluated during the zero-carbon park’s planning, construction, and operation stages. Sensitivity analysis serves to verify the model’s accuracy. The summary and prospect are addressed in Section 6.

In the last few years, since worldwide climate change has become more severe and the energy crisis has loomed large, the imperative to foster an energy revolution and construct an energy framework that is clean, low-carbon, secure, and efficient has become paramount to meeting the goals of ‘carbon peak and carbon neutrality’ [10], [11]. As a crucial means of achieving carbon neutrality, the imperative to actualize a ‘net-zero emissions’ transformation within the park is both pressing and essential.

As the vanguard of sustainable urban development, zero-carbon parks incorporate sophisticated environmental strategies and leading-edge technologies, intending to reduce carbon emissions to a minimum or zero carbon emissions [12]. Its development goes through the stages of low carbon emission, near-zero emission, and net zero emission, and finally realizes the self-balance of carbon emission and absorption within the park. To accomplish this goal, the construction and management of zero-carbon parks should consider energy efficiency, renewable energy utilization, carbon capture and storage technologies, and sophisticated energy management systems [13]. At the same time, the development of zero-carbon parks also needs joint efforts in many aspects, such as policy support, technological innovation, capital investment, and public participation. These parks are pivotal in facilitating the shift to a low-carbon economy on a global scale and aligning with international environmental objectives exemplified by the Paris Agreement. Additionally, there are notable examples of such parks both domestically and internationally, as depicted in Table 1 [14], [15].

Internationally, the research and development of zero-carbon parks has made groundbreaking progress in various regions [16]. The obstacles associated with the development of zero-carbon industrial parks are examined and forecasted. North American researchers concentrate on improving energy efficiency and decreasing carbon emissions with digital technology while researching policies and incentives to support low-carbon industrial transformation [17]. Domestic research mainly emphasizes exploring the balance between emission reduction and the economic benefits of the park, as well as the green and low-carbon transformation of park buildings. Concerning policies, the Chinese government has issued China’s National Program on Climate Change and China’s Carbon Market Development Plan. The European Union has also set a goal of becoming carbon neutral by 2050 through the Green Deal. It has introduced policies such as the European Climate Act to encourage the creation of zero-carbon parks.

To promote the development of zero-carbon parks, researchers at home and abroad have researched the optimal operation of IES to meet the energy efficiency and development needs of zero-carbon parks. It mainly focuses on three core areas: model construction, algorithm development, and mechanism design, to improve the accuracy of prediction models, the efficiency of optimization algorithms, and the flexibility of market mechanisms [18]. Regarding model construction, Irham et al. [19] proposed a multi-level optimization model based on model predictive control (MPC) to achieve multi-objective optimal energy management in IES. To improve equipment coordination ability and operation economy in IES [20], a multi-time scale optimal scheduling approach based on distributed model predictive control (DMPC) is suggested, which decreases costs while increasing economy and reliability. In terms of algorithm development [21], thinking about the complexity of multi-energy trading and the uncertainty of renewable energy output, an improved coyote optimization algorithm is proposed. Its usefulness in increasing income for operators, lowering energy expenditures, and improving energy usage economics has been demonstrated via case studies of industrial parks in northern China [22]. Aiming at the demand response and renewable energy uncertainty in IES, an interval optimal scheduling method based on soft actor-critic (SAC) deep reinforcement learning (DRL) is proposed, which improves the decision-making ability of IES optimal scheduling and effectively deals with the constraints of related equipment. With market mechanism design [23], a near-zero carbon IES (NZC-IES) optimization scheduling strategy is proposed. Through the establishment of a demand response and ladder carbon trading model, a new perspective is provided for the low-carbon operation of NZC-IES, and the case study results demonstrate the effectiveness of achieving waste-to-energy and low-carbon emissions while maintaining economic performance.

On this basis, IES has been widely used in many fields, such as industry, agriculture, and campus [24]. Aiming at the problem of relatively rough hydrogen energy storage modeling in traditional planning of IES in industrial parks, an IN-IES planning model was established to effectively improve the economic benefits of the system and reduce carbon pollution and power abandonment [25]. Aiming at the optimal scheduling model of multi-energy coupling agricultural IES, the low-carbon economic scheduling of agricultural IES is effectively optimized [26]. Aiming at the operation optimization of the IES of the school, the SIES technical and economic evaluation model based on the G1-anti-entropy weight TOPSIS method is proposed to effectively evaluate the IES of the school scientifically. This shows that the combination of IES and zero-carbon parks in in-depth research and optimization is an important direction for future research.

An IES is a platform for integrating multiple energy forms and technologies, with significant features such as source-grid-load-storage integration, multi-energy complementarity, and supply-demand coordination [27]. The central element of the system is the “source-network-load-storage” paradigm, which encompasses the full cycle of energy generation, conveyance, utilization, and conservation, ensuring a fluid interconnection of essential stages. Embedding the IES within the zero-carbon parks, offers the parks a clean, efficient, and dependable source of energy and energy management, while also making a notable contribution to the park’s sustainability objectives, particularly in the realms of low-carbon energy consumption and ecological preservation [13]. There are different roles in the IES of a zero-carbon park.

“Source” refers to the point of origin of energy and relates to the generation and provision of clean energy, including solar, wind, hydro, and biomass, to reduce the use of fossil fuels and lower the carbon footprint of the zero-carbon park.

The “network” element of the system pertains to the infrastructure for energy conveyance and dissemination, encompassing a variety of interconnected networks, including electrical, thermal, gaseous, and hydrogen conduits. Engineered to augment energy efficiency and sophistication, these networks aim to refine the distribution of resources and uphold the consistency and dependability of the energy supply chain.

The “load” aspect signifies the demand side of energy usage, encompassing a range of sectors including industrial, commercial, and residential. By implementing demand-side management strategies and enhancing energy efficiency, the goal is to streamline energy utilization, maintain equilibrium in energy demand, and elevate overall energy efficiency.

“Storage” refers to energy storage facilities and technologies, such as battery energy storage, thermal energy storage, and water energy storage. Energy storage systems are critical to maintaining a balance between energy supply and demand, by buffering the intermittency of renewable energy, improving energy efficiency, and enhancing system stability.

The “source-net-charge-storage” model of the IES emphasizes the integration and collaborative optimization of the energy system. Through the effective management and technological innovation of these four links, the zero-carbon park can improve energy efficiency and promote the sustainable development of the environment.

The study follows the principles of science, operability, representativeness, and relevance as the basis for screening and identifying evaluation indicators. By analyzing the dynamic change of the operating parameters of the IES in the zero-carbon park and predicting future development demand, the paper aims to select the key system evaluation indicators and build a comprehensive energy system evaluation framework [28], [29]. The indicator selection process is shown in Figure 1.

Following the aforementioned principles, this study initially gathers pertinent indicators of the IES utilizing sources such as the Web of Science, CNKI, and government official websites [30], [31]. Subsequently, the collected indicators were preliminarily screened by combining theoretical analysis and frequency statistics to ensure the relevance and importance of the indicators. On this basis, the Delphi method was adopted to carry out two rounds of expert consultation, aiming to extract key indicators through expert consensus, and finally build a comprehensive evaluation index system of the IES low-carbon operation efficiency of zero-carbon parks, as shown in Figure 2.

Energy efficiency is one of the core indicators used to measure the operating efficiency of IES in zero-carbon parks. This paper will show how IES can optimize energy use, reduce energy waste, and improve the overall efficiency of energy utilization by evaluating indicators such as energy conversion efficiency, energy intensity, and clean energy consumption rate in the park. Table 2 describes the specific indicators.

In terms of technical benefits, the innovative application of IES in zero-carbon parks is discussed to ensure that indicators can reflect the efficiency and reliability of energy systems. Indicators such as equipment operating life, load shedding probability, and mature technology are taken to show how IES brings significant advantages at the technical level to the park. The specific indicators are shown in Table 3.

The economic benefit is an important dimension in evaluating the operation of IES in Zero Carbon Park. This paper selects the system equipment investment cost, return on investment, and other indicators to explain the value and potential economic benefits of IES for the park at the financial level, see Table 4.

Environmental impact is an indispensable consideration in the construction of zero-carbon parks. In this part, this paper evaluates the positive impact of IES on the environment and its role in promoting the park’s development through the primary energy utilization rate, pollutant discharge rate, and carbon emission change rate, see Table 5.

Finally, the potential for long-term application of IES in zero-carbon parks is explored from a sustainability perspective. The environmental load rate, long-term operation, and maintenance capability, and sustainability index of the system are selected to analyze how IES can provide long-term stable energy solutions for the park and support its sustainable development goals, see Table 6.

The AHP is a decision support tool that enables decision-makers to synthesize qualitative and quantitative indicators and calculate the weight of each evaluation indicator through hierarchical processing and paired comparison of indicators. When assessing the operational advantages of a zero-carbon park’s IES, AHP adeptly consolidates measurable and difficult-to-quantify indicators, offering a structured analytical approach to devising an all-encompassing assessment framework. The detailed computational procedures are outlined below.

(1) Utilizing the hierarchical structure depicted in Figure 1 for the IES framework of the Zero Carbon Park, the 1-to-9 scale is applied for pairwise comparison of indicators, leading to the formulation of the respective judgment matrix. The significance of each scale value is detailed in Table 7.

(2) Computed weight vector

The judgment matrix of each level is normalized, and the arithmetic mean value of each factor is used as the ranking weight of the factor of the level.

(3) Consistency test of the judgment matrix

The consistency index CI of the n-order matrix is calculated. The larger the value is, the more inconsistent the results of pairwise comparison of matrix elements are CI. The formula for calculating the CI value is as follows:

where, $\lambda_{\max }$ is the maximum eigenvalue of the matrix; n is the order of the matrix. The matrix consistency ratio is as follows:

In the formula, RI is the average random consistency test index, and different matrix orders have different values. $\mathrm{CR}<0.1$ is required to ensure the consistency of the judgment matrix; Otherwise, adjust the judgment matrix until the consistency test is met.

As an objective weighting technology, the CRITIC algorithm can effectively deal with the comparison and conflict between multi-dimensional evaluation indexes of IES in zero-carbon parks, and then comprehensively determine the objective weight of each index. In addition, the algorithm also considers the variability and correlation of the indicators and makes full use of the intrinsic objective attributes of the data for scientific evaluation. The calculation steps are as follows:

Assuming that there are $m$ evaluation objects and $n$ evaluation indexes, the evaluation matrix can be expressed as:

Step 1: Dimensionless treatment of evaluation index. The aim is to eliminate the potential impact of the difference in the dimension and numerical range of the original data on the evaluation results. This process involves converting all indicators into dimensionless values and achieving dimensional unity through appropriate mathematical transformation to ensure that different indicators can be effectively compared consistently.

Step 2: Determine the index variability. The standard deviation is used to quantify the fluctuation of the index value. The larger standard deviation usually indicates a higher index fluctuation and information content. Therefore, the corresponding index should obtain a higher weight in the comprehensive evaluation.

where, $S_j$ is the standard deviation of the j-th index.

Step 3: Determine the index conflict. By calculating the correlation coefficient, the relationship between the evaluation indexes can be quantified. Highly relevant indicators tend to show lower conflict, which reflects that they can provide more abundant information in the evaluation system, thus effectively reducing unnecessary repetition in the evaluation process. The calculation formula is as follows:

where, $r_{i j}$ is the correlation coefficient between evaluation indexes $i$ and $j$.

Step 4: Calculate the objective weight:

where, $C_j$ is the amount of information contained in the j-th evaluation index; $\beta_j$ is the objective weight of the index.

Game theory has many forms of evaluation models for the common planning and scheduling problems of IES, such as the game theory model, the non-cooperative game theory model, and the Stackelberg game theory model [44]. There are obvious limitations to applying subjective and objective weighting methods alone, and their combined use can significantly improve the effect. Therefore, this study adopts the game theory combination weighting method, combining the AHP and the CRITIC method to determine the index weight. This method takes NASH equilibrium as the coordination goal to ensure that in the scoring decisions of all experts, the scoring behavior of any single expert will not have an advantage over its decision weight, achieving a stable decision equilibrium. Through this method, we can minimize the sum of the deviations between the combined weights and the basic weights and then obtain more accurate combined weights.

The basic weight vector set $\omega_k$ is constructed. Assuming that G weight calculation methods are used, the n evaluation indexes in the index evaluation system are combined and weighted based on the game theory, and the corresponding weight vectors are obtained.

Further, an arbitrary linear combination of weight sets of $n$ weights is obtained:

where, $\alpha_k$ is the weight coefficient.

By optimizing the weight coefficient, an optimal linear combination is constructed to minimize the deviation between the target weight vector W and each candidate weight vector $\omega_k$. Construct the optimal linear combination.

Using the differential characteristics of the matrix, the optimal first derivative condition can be obtained as follows:

The corresponding system of linear equations is:

The optimal linear combination is $\left(a_1, a_2, \cdots , a_G\right)$ and normalized.

Solve for the final portfolio weights:

Considering the challenges of acquiring precise data and the presence of incomplete indices, the study employs a fuzzy comprehensive evaluation approach to assess the operational advantages of the IES within zero-carbon parks. Utilizing fuzzy mathematics, this approach adeptly addresses the inherent uncertainties within the system, proving particularly apt for evaluating complex systems [45]. By constructing a membership function to quantify the benefit of each evaluation index, and combining the index weight, a fuzzy synthesis algorithm was applied to synthesize the scores of each index, to obtain the overall operating benefit score of IES. Using the fuzzy comprehensive evaluation method, the main steps of IES system operation benefit evaluation are as follows:

Step 1: Construct a comprehensive factor set encompassing all pertinent factors, followed by the establishment of an evaluation set comprising the factors designated for assessment. By applying normalization techniques, the raw data is transformed into dimensionless numerical values. The specific evaluation criteria are delineated in Table 8.

Step 2: Calculate the membership degree and construct the membership matrix R.

This paper uses the membership function in fuzzy logic to quantify each index. Specifically, the membership of each indicator is calculated based on the scores of experts in the relevant field, and the formula is as follows:

where, x is the score of respondents in different professional fields, and $x_0$ is the optimal score.

Step 3: The third step is to use the sum product operator in fuzzy logic to synthesize the fuzzy weight vector and fuzzy relation matrix, then calculate the comprehensive evaluation value F.

Located in North China, the park is rich in renewable resources and smart grid systems and is equipped with digital infrastructure, including wind turbines, photovoltaic panels, energy storage batteries, and other hardware facilities. The Park covers an area of about 0.267 square kilometers and is a typical “zero carbon” park. 80% of the energy demand in the park is directly supplied by wind power, photovoltaic panels, and energy storage systems, while the remaining 20% is optimized by the intelligent Internet of Things, which is balanced by selling electricity to the grid when there is excess power production. In addition, the park also realizes the traceability, analysis, and visualization of carbon emission and energy consumption indicators, and conducts real-time monitoring, early warning, and optimized closed-loop control through a unified data management platform. This study aims to assess the importance of each evaluation indicator in the three stages of planning, construction, and operation of this park. By calculating the weight of each indicator and combining the combination assignment method, this study will use a fuzzy comprehensive evaluation model to quantify the operational benefits of IES in the zero-carbon park.

In our practical research, we established a panel of 15 experts, including energy system planners, economists, architects and urban planners, renewable energy technology specialists, and operations management professionals. Using the LIKERT 5-point scale method, experts scored and assigned independently. Based on the evaluation results of each expert, we construct the judgment matrix A of the first-level index to the target layer and calculate the weight. Then, the corresponding judgment matrix is also constructed for the secondary indicators, and their weights are obtained. In the end, by multiplying step by step, we get the combined weight of each secondary indicator at the target level.

$A=\left[\begin{array}{ccccc}1 & 2 & 4 & 3 & 1 \\ 1 / 2 & 1 & 2 & 1 / 3 & 1 / 2 \\ 1 / 4 & 1 / 2 & 1 & 1 / 4 & 1 / 3 \\ 1 / 3 & 3 & 4 & 1 & 1 / 2 \\ 1 & 2 & 3 & 2 & 1\end{array}\right]$

Through subjective calculation weight and consistency test, it is concluded that the subjective weight of the first-level index is $\omega$ = (0.3557, 0.1027, 0.0513, 0.1911, 0.2991), $\lambda_{\max }$=5.2565, CI = 0.0573 $<$ 0.1, which passes the consistency test. The subjective weight and consistency test results of the secondary indicators are shown in Table 9.

In this study, the CRITIC method has been deployed to ascertain the intrinsic weights of the various components within the IES of a zero-carbon park. The experts invited by the previous AHP, based on a standardized quantitative table, which covers the two dimensions of competitiveness and conflict of indicators, score the indicators. To ensure the comprehensiveness and consistency of expert opinions, the collected data were subjected to two rounds of questionnaire surveys, screened and standardized, and sensitivity analyses of different expert opinions were conducted to ensure the robustness of the results. The weights of each secondary indicator on the target layer were then determined through the formula, as shown in Table 10.

Based on the theory of game theory, the subjective weight and objective weight in Table 8 and Table 9 are determined by the formula to determine the weight of each level index to the target layer, and then the correction coefficient of the first level index and the second level index is obtained by the linear equation formula (12-16). They are $\left(\mathrm{a}_{11}, \mathrm{a}_{21}\right)=(0.70950,0.29050),\left(\mathrm{a}_{12}, \mathrm{a}_{22}\right)=(0.69708, 0.30292)$, and then the final optimal combination weight is obtained from Eq. (17). The results are shown in Table 11.

It can be seen from the weight distribution maps of the first-level and second-level indicators that there is a certain deviation in the weights obtained by the AHP and the CRITIC method, especially the two aspects of economic benefit indicators and sustainability indicators. The subjective weight values of the energy conversion coefficient (0.0974) and the green power use ratio (0.1282) are about 7 times and 10 times the objective weight, respectively. The objective weight of the system equipment investment cost and operation and maintenance cost index is 5 times and 8 times larger than the subjective weight.

From the comparison of the weights of the three algorithms in Figure 3 and Figure 4, it can be seen that the combined weight algorithm combines subjective and objective weights, and the results obtained are similar to the results of the other two independent methods, thus verifying their credibility. In addition, the weight distribution of the algorithm on technical benefits and sustainability indicators is higher than that of traditional objective algorithms, which better meet the high standards of economic and technical safety in contemporary energy development. Therefore, the proposed comprehensive weight method based on game theory has significant advantages in rationality and accuracy.

The fuzzy comprehensive evaluation method is used to evaluate the benefit of IES operations in zero-carbon parks. The scores of each index are obtained from relevant people in different fields and questionnaires, and calculated and normalized according to the membership function. A total of 130 questionnaires were distributed, of which 118 were valid, with a recovery rate of 91%. The data in the questionnaire are analyzed and sorted to obtain the fuzzy relation matrix and the normalized fuzzy comprehensive evaluation vector, as shown in Table 12.

According to the table data and the importance level $\mathrm{V}=\{5,4,3,2,1\}$ = {very large, large, moderate, small, very small}, the comprehensive evaluation value of IES in the planning stage of zero carbon park operation benefit is calculated.

$\mathrm{F}_{\mathrm{P}}=\mathrm{B} \times \mathrm{V}^{\mathrm{T}}=\omega_{\text {first grade indexes }} \times\left(\mathrm{Y}_1, \mathrm{Y}_2, \mathrm{Y}_3, \mathrm{Y}_4, \mathrm{Y}_5\right) \times(5,4,3,2,1)^{\mathrm{T}}=3.5375$

The above is the process of solving for the operational benefits of IES in the zero-carbon park in the planning stage. Similarly, the composite scores for each indicator in the construction and operation phases are obtained, and the results are shown in Table 13 and Table 14.

This study conducted a comprehensive benefit assessment of the three key phases of the IES zero-carbon park’s planning, construction, and operation. The results show that the planning and operation phases are crucial in achieving the park’s low-carbon operation goals.

(1) Impact analysis of first-level indicators

In the first-level indicator impact analysis, by comparing the data in Figure 5 and Figure 6, we found that the environmental impact and technical benefit scores in the planning stage were the highest, which were 4.3898 and 4.3220, respectively. It is worth noting that the weighted value of environmental impact (1.5109) is significantly higher than the weighted value of technical safety benefits (0.5182). This difference indicates that environmental factors have a higher priority in decision-making at the planning stage. The score of economic benefit was the lowest (3.4068), but the weighted value (0.3522) was not the lowest in the construction stage, the weighted value of environmental impact is still the largest, but it is slightly lower than that in the design stage, and the economic benefit score (4.1441) score becomes the highest, which is significantly higher than that in the planning stage. In the operation stage, the sustainability score (4.2881) and the weighted value (1.1703) are the highest. The energy efficiency (4.2203) score is higher than the economic benefit (3.8559), but the weighted value is 0.1268 (24%) smaller.

(2) Influence analysis of secondary index analysis

It can be seen from Figure 7 and Figure 8 that the comprehensive energy utilization rate and technology maturity maintain a high level in all stages. The score (3.7881) and weighted value (0.0524) of energy intensity in the operation stage were significantly higher than those in the planning and construction stage. The weighted value of the clean energy consumption rate (0.0665) and user-side energy quality (0.0851) in the park is the highest in the three stages of the operation stage. The weighted value of the investment cost of system equipment is the highest in the planning stage (0.1115), while the weighted value of the return on investment of the energy system is the highest in the operation stage (0.1575). The three indicators of pollutant emissions, green power use ratio, and carbon emission change rate have significantly improved in the operation stage. The weighted value of pollutant emission environmental protection tax is the highest in the construction stage (0.1816), while the weighted value of long-term operation and maintenance capacity of the system in the operation stage (0.3805) is much higher than that in the design and construction stage.

(3) Comprehensive evaluation and analysis

Multi-stage consideration: The key to the successful realization of a zero-carbon park is a comprehensive consideration of the planning, construction, and operation stages. This includes in-depth analysis and extensive assessment of multi-dimensional factors such as environmental impact, technological application, economic efficiency, energy efficiency, and sustainability. Throughout the life cycle of the park, environmental impact and energy efficiency, such as the integrated energy utilization rate and the park's clean energy consumption rate, are always high-priority considerations, especially during the planning and operational stages.

Energy efficiency should be a long-term concern: Energy efficiency and environmental benefits are ongoing concerns throughout the life cycle of the park, especially in the design and operation phases, key metrics such as energy intensity, energy conversion efficiency, and energy unit function cost.

Technical safety and economic return: Technology maturity and equipment operating life always get high scores and significant weights in the evaluation of the operation stage, which can be seen that technical benefits and economic benefits play a crucial role in the long-term operation of the park.

To verify the influence degree of each main factor on the benefit, so that decision-makers can carry out targeted management, this section has carried out the sensitivity analysis. This paper uses game theory to obtain accurate combination weights, with Nash equilibrium as the goal. However, in practice, due to subjective and objective factors, the decision-making attitude of experts cannot be completely rational. Therefore, this paper changes the combination weights under the five first-level indicators in the operation stage by ± 10%, ± 20%, and ± 30%, respectively, to simulate the deviation from reality and analyze the impact of these changes on the results. The process uses the percentage formula:

Ensure that the sum of weights is 1, where i is a first-level indicator and j is a second-level indicator. The results are shown in Table 15 and Figure 9.

Figure 9 shows the changes of the comprehensive benefit caused by the change of different weights in the range of [-30%, +30%]. Among them, the reduction and increase of environmental weight have almost the same impact on comprehensive benefits. Technology exhibits greater sensitivity to lower weights, whereas energy and economic factors are more responsive to higher weights. Specifically, the energy index score demonstrates an ascending trend in line with the change percentage, increasing from 3.4961 at -30% change to 3.5416 at 30%. This suggests that the energy index is particularly sensitive to substantial weight variations. In contrast, sustainability scores remain relatively consistent, fluctuating minimally from 3.5248 at -10% to 3.5154 at 30%, indicating higher stability within a moderate range of weight changes. However, sustainability may exhibit sensitivity to more extreme weight adjustments. These findings underscore the importance of prioritizing energy efficiency within the benefit evaluation framework for zero-carbon park operations while placing a relatively lower emphasis on environmental impact. Decision-makers are thus advised to focus on enhancing energy efficiency, leveraging renewable energy sources, advancing energy technologies, and reducing energy intensity. They should implement effective energy strategies, optimize operational mechanisms, and minimize energy wastage. Notably, the operational efficiency of the IES during the zero-carbon park's operational phase remains consistently stable, indicating a lack of significant variability and thereby affirming the reliability of the study's outcomes.

Based on the combination weighting-fuzzy comprehensive evaluation (GRA-FCE) model of game theory, this study evaluates the benefits of IES in the three stages of planning, construction, and operation of zero-carbon parks. Using the weighted values in the model, the attention to key indicators at different stages is deeply revealed, and a clear quantitative standard is provided for the evaluation process through its scoring mechanism. It also provides a comprehensive analytical framework for the benefit evaluation of IES in zero-carbon parks. Based on this model, the following are key conclusions:

(1) First of all, we utilized the expert survey approach to develop an IES assessment indicator system for zero-carbon parks, which included five first-level indicators for environmental effect, technical benefits, economic benefits, energy benefits, and sustainability, as well as 28 second-level indicators. The system’s structure is versatile and widely adaptable.

(2) Secondly, game theory is used to optimize the combination of subjective weights of the hierarchical method and objective weights of CRITIC. It avoids the limitation of a single weight and greatly improves the rationality of the combination of subjective and objective weights, making the evaluation more scientific and accurate.

(3) Finally, based on the questionnaire and the combination weight data, the sensitivity analysis of the combination weight is carried out to verify the reliability of the model, and the importance degree and influence level of each index are obtained by the fuzzy comprehensive evaluation method. Combined with the case to verify the feasibility of the evaluation model, the evaluation result of the low-carbon benefit of zero-carbon park construction is obtained. Based on the evaluation results, project managers and decision-makers are helped to identify priorities at different stages and allocate resources rationally to achieve the goal of a zero-carbon park.

Facing a large number of domestic parks and the diversity of types, the IES is confronted with many challenges in the process of helping the park achieve the goal of ‘zero carbon’, such as the difficulty of technology integration, the balance between energy storage and supply and demand, and the difficulty of operation and management. For this reason, in future development, against the different regions and energy carrier characteristics of the park, it is necessary to further study the research and evaluation model of IES to meet the requirements of the optimal design and operation planning of the zero-carbon park at all stages of construction. An intelligent energy system is processing that integrates multiple technologies and optimizes the IES to a greater extent by introducing diversified modeling and simulation of the Internet and digital twin technology to help the park achieve the goal of ‘zero carbon’.

The evaluation system utilized in this study has not been able to fully capture the complexity of the actual situation. During the sustainable development process of zero-carbon parks, this indicator system should be continuously improved in future studies. To effectively address the challenges, future development should focus on formulating comprehensive top-level planning and deeply integrating the concept of carbon neutrality into every stage of park development. Furthermore, due to the lack of a unified standard system for constructing zero-carbon parks, it is a priority to study and construct a comprehensive evaluation system covering multiple dimensions such as technology, market, policy, and environment for in-depth risk assessment and management. Government departments should further complete the operational mechanism of the carbon trading market and utilize market incentives to promote enterprises in the park to achieve carbon emission reduction.

In summary, this study suggests that project managers should integrate the zero-carbon concept into all aspects of planning, construction, and operation. They should also formulate effective strategies according to the characteristics of each stage to enhance the overall benefits of zero-carbon parks. Additionally, the construction of zero-carbon parks not only offers a wide range of IES application scenarios but also serves as a testing ground for system innovation and progress. Looking ahead, the development of IES requires multi-dimensional joint efforts, including technological innovation, policy support, capital investment, talent training, community participation, and international cooperation. Through these comprehensive efforts, IES is expected to become a major driver in achieving the goal of zero-carbon parks and playing a central role in global sustainable development processes.