A novel framework for the carbon reduction performance of power grids: A case study of provincial power grids within the China Central Power Grid

Abstract

Power grids play a crucial role in connecting electricity suppliers and consumers. They facilitate efficient power transmission and energy management, significantly contributing to the transition toward low-carbon practices across both upstream and downstream sectors. Effectively managing carbon reduction in the power industry is essential for enhancing carbon reduction efficiency and achieving dual-carbon goals. Recent studies have focused on the outcomes of carbon reduction efforts rather than the management process. However, when power grids prioritize the process of carbon reduction in their management, they are more likely to achieve better results. To address this gap, we propose an evaluation model for managing carbon reduction activities in power grids, comprising the carbon management efficiency (CME) module based on the maturity model and the carbon reduction efficiency (CRE) module based on the entropy method. The CME module provides a scorecard corresponding to a detailed and continuous evaluation model for carbon management processes to calculate its performance. Simultaneously, the CRE module relates carbon reduction results to the development direction of the government and power grid, allowing for effective adjustments and updates based on actual situations. The evaluation model was applied to provincial power grids within the China Central Power Grid. The results reveal that despite some fluctuations in carbon reduction performance, provincial power grids within the China Central Power Grid have made continuous progress in carbon reduction efforts. According to the synergy model, there is evidence suggesting that power grids are steadily improving their carbon reduction performance, and a more organized approach would lead to a greater degree of synergy. The evaluation model applies to power grids, and its framework can be extended to other industries, providing a theoretical reference for evaluating their carbon reduction efforts.

Graphical abstract

Keywords

power grid / carbon reduction / evaluation model / maturity model / synergy model

1 Introduction

In recent decades, China has experienced rapid industrialization and economic growth, resulting in a significant increase in greenhouse gas emissions. As a result, it has become one of the world’s largest emitter of greenhouse gases. Consequently, China is now facing the consequences of climate change and is under increasing international pressure to address its emissions. Notably, the power sector is responsible for a substantial 42% of China’s total carbon emissions, according to the National Energy Administration (Statistics BP, 2020). Therefore, it is crucial that the electrical industry focus on efficient carbon management and reduction to achieve China’s national goals for carbon peaking and neutrality.

As the power industry undergoes a transformative phase, the importance of the power grid as a central hub for power transmission cannot be underestimated. It plays a critical role in optimizing resource allocation and scheduling, bridging the gap between power supply and demand. However, there is still opportunity for improvement in the management processes and dynamic development plans for carbon reduction. By integrating corporate social, environmental, and economic responsibilities, power grids can embark on a sustainable, environmentally friendly, and low-carbon development path, fostering extensive and profound benefits for both economic growth and environmental quality.

By focusing on the development strategy of the power grid, its effectiveness in carbon reduction and green value can be scientifically evaluated. This evaluation serves as a valuable tool for power grids to assess their internal carbon management and promote coordinated carbon reduction throughout the entire power transmission chain. Ultimately, the goal is to achieve a transition toward a sustainable and low-carbon value model, leveraging the synergies between carbon reduction and electricity transmission. This evaluation not only assesses the current state but also guides future efforts toward a greener and more sustainable future.

The evaluation of power grid carbon reduction performance serves two main purposes. First, it encourages power grids to enhance their efficiency in carbon reduction initiatives, strengthening their motivation for such work. Second, it functions as an effective monitoring tool, providing a foundation for government departments to effectively manage carbon reduction efforts. The assessment of power grid carbon reduction performance is crucial for achieving low-carbon development goals and plays a pivotal role in providing decision-making and technical support for this transition. Additionally, it aids in attaining carbon peaking and neutrality targets with a high degree of quality.

Global low-carbon management has been developed for several years; however, there is a lack of universally accepted criteria for evaluating carbon reduction performance in the power industry (Liu et al., 2020). Previous studies have focused primarily on categorizing the factors influencing carbon reduction and developing carbon measurements and metrics (Yun et al., 2002; Tan et al., 2007). For instance, Hoffmann and Busch (2008) proposed an evaluation model consisting of carbon intensity, dependency, exposure, and risk for typical corporations. Qi et al. (2012) developed a model for low-carbon development and management by categorizing methods into three aspects: reducing carbon sources, planning carbon flows, and increasing carbon sinks. Kuo et al. (2015) utilized a fuzzy network evaluation method and the TOPSIS model to establish a supplier carbon management evaluation model. Similarly, Modak et al. (2017) built an evaluation framework based on the balanced scorecard (BSC) and the fuzzy analytic hierarchy process (FAHP) to analyze the suitability of mineral and mining sectors across financial, customer, internal operations, company learning, and growth perspectives. Myung et al. (2019) employed the balanced scorecard to create an index system for evaluating the competitiveness of enterprises in addressing climate change challenges, including learning and growth, internal processes, external stakeholders, and carbon reduction performance. Moreover, Zhang et al. (2018) constructed indicators such as the carbon fund turnover rate, carbon emission rate, carbon turnover rate, and fixed carbon asset profitability to demonstrate the positive role of carbon assets in carbon reduction. Currently, research is primarily focused on developing carbon emission evaluation systems tailored for individual towns and industrial zones, with the aim of promoting healthy and sustainable local economic development. Additionally, researchers have extensively examined various industries, particularly those with significant emissions and pollution levels, to explore the complex connection between corporate carbon management strategies and financial performance, thereby investigating the impact of carbon management on a company’s bottom line.

With the increasing importance of low-carbon development, subsequent research has built upon these foundations, resulting in notable progress. The evaluation of carbon reduction performance has undergone a significant transformation, moving from a reliance on isolated metrics to a comprehensive approach that incorporates a diverse range of indicators. Rietbergen et al. (2017) utilized questionnaires and a scorecard methodology to assess the proficiency of construction and engineering enterprises in low-carbon practices. Their comprehensive evaluation framework included organizational transformations, energy usage monitoring, CO₂ emission reduction analysis, the effectiveness of the plan-do-check-act cycle, management commitment, CO₂ emission reduction targets, and employee engagement.

Fan (2018) developed a DPSIR model, which stands for “Driver‒Pressure‒State‒Impact‒Response.” This model provides a framework for presenting the necessary metrics to offer feedback to policymakers on environmental quality and the effects of past and future political decisions. Luo (2018) devised a triangular model-based carbon reduction performance evaluation model that considered the “ecological efficiency” indicator and aligned it with China’s specific national conditions. Liu et al. (2019) developed a low-carbon management model for the coal industry based on low-carbon innovation. The model is guided by a low-carbon image, supported by low-carbon management, and centered around low-carbon production. Zhou et al. (2022) proposed a four-dimensional carbon reduction performance model that integrates circular economy principles and resource value circulation theory. They employed an entropy-weighted matter-element extension evaluation method to calculate the final score for enterprises. Zhang et al. (2023) applied macroeconomic indicators, energy input indicators, transportation supply indicators, and output indicators within an evaluation model for the transportation sector.

Considering the current research landscape, it is clear that in the context of the low-carbon economy, carbon management has become a crucial focus of study, providing administrators with valuable insights for various low-carbon assessments and transitions. Administrators must grasp the current state of carbon reduction efforts to formulate targeted and cost-effective policies that maximize their impact on carbon reduction. Therefore, a profound understanding of the carbon reduction process is essential for developing effective policies and regulations.

To address the limitations of current methods for measuring carbon performance, this study proposes a comprehensive evaluation model that encapsulates both the process and results of carbon reduction efforts in power grids. The model consists of two modules: the carbon management efficiency (CME) module and the carbon reduction effectiveness (CRE) module. These modules were initially piloted on the provincial power grids within the China Central Power Grid. Additionally, a synergy model was developed to validate the effectiveness of this evaluation framework.

The remainder of this paper is organized as follows: Section 2 describes the construction of the carbon reduction performance evaluation model. Section 3 applies this model to the provincial power grids within the China Central Power Grid and utilizes the synergy model to authenticate the indexing system. Section 4 provides further insights gained from the analysis. Finally, Section 5 concludes the paper.

2 Materials and methods

This study proposes a “qualitative + quantitative” evaluation model composed of the CME module and the CRE module and provides a synergy model for evaluation. The CME module consists of a qualitative carbon management index in six dimensions, based on a maturity model, while the CRE module consists of a quantitative carbon reduction index in four dimensions. The results from both modules can be verified using the synergy model, which is based on orderliness and the degree of synergy.

2.1 Principles of indicator selection

The evaluation model for the carbon reduction performance of power grids is based on the principles outlined in Tab.1 (Alizadeh et al., 2020; Nakthong & Kubaha, 2019; Aghdam et al., 2020). By incorporating these principles into the evaluation model, power grids can effectively measure and analyze their carbon reduction performance. This process enables them to identify areas for improvement, develop targeted strategies, and ultimately contribute to a more sustainable and environmentally friendly energy landscape.

2.2 Constructing the evaluation index system

The bottom-up approach is used to categorize and simulate the underlying factors that impact carbon management and carbon reduction in power grids. The significant influential factors are selected to establish an index system for carbon reduction in power grids by incorporating existing standards and technologies. Through a combination of self-testing and expert testing methods, the indexes are thoroughly adjusted and improved by modifying relevant elements.

2.2.1 CME module

The CME module is designed based on a maturity model, which consists of five levels, as illustrated in Fig.1. The initial level, being the lowest level, is characterized by a state of chaos, irregularity, and unpredictability. Due to the lack of an overall management system and detailed plan, carbon reduction work efficiency is low and susceptible to cost overruns and other issues. The second level, the managed level, introduces project-level management, enabling projects to be executed in a planned, predictable, and institutionalized manner. The third level, the systematic level, entails actively pursuing carbon reduction measures and establishing enterprise-level standards to guide carbon reduction projects. The fourth level, the improved level, signifies a stage where change is quantifiable and predictable. At this stage, the power grid adjusts behavior by setting quality goals to prevent significant deviations from the intended objectives. The highest level, the optimized level, primarily focuses on continuous improvement and enhancing productivity and quality while also providing guidance for other project processes. With China’s persistent enforcement of low-carbon policies, there is a pressing need to enhance carbon management. As a result, a comprehensive and thorough carbon management evaluation framework is required to effectively steer all aspects of carbon reduction work (Antunes et al., 2014).

Each level contains a carbon management environment and the Plan-Do-Check-Action Cycle, which consists of a carbon management system, administration, strategies, implementation, compliance, and optimization. The indicators of the CME module are provided in Tab.2. Among these indicators, the carbon management system evaluates the formulation and implementation of a comprehensive carbon management system. The administration dimension encapsulates key aspects of carbon management, including leadership and resource support, policy alignment and communication, and team structure and authority. The strategies dimension provides a comprehensive assessment of an organization’s carbon management plan, including risk mitigation and opportunity identification, planning and indicator management, carbon accounting, carbon performance monitoring and evaluation, supply-side and demand-side management mechanisms, and power transmission and distribution management mechanisms. The implementation dimension covers various internal aspects and processes of an organization’s functioning. Key factors within this dimension include resources and capabilities, training and communication, sustainable energy procurement practices, carbon-efficient power dispatching practices, transmission line loss management, and the application and innovation of efficient transmission technologies and equipment. The compliance dimension assesses the comprehensiveness of carbon management monitoring, evaluation, internal auditing, and review. Finally, the optimization dimension evaluates an organization’s ability to identify and rectify issues in the execution process.

2.2.2 CRE module

Given the unique characteristics of power grids, our study focuses on their transmission and distribution processes. The tailored indicators designed for this purpose allow for a nuanced evaluation of the carbon reduction efficiency of power grids, considering various structural configurations and technological advancements. This assessment provides crucial insights into the benefits of low-carbon initiatives and guides the identification of priority areas for future development. The comprehensive CRE module addresses the critical factors related to carbon reduction in power grids through four dimensions: input, flow, output, and economy. Each dimension features five well-developed evaluation levels designed to assess the carbon management work within power grids.

In the input dimension, this study examines the carbon resources utilized in the transmission and distribution processes of the power grid. Specifically, it evaluates the input of these power resources, focusing on key indicators such as renewable energy integration, electricity supply growth, and the regional net inflow power ratio. The flow dimension assesses the extent to which low-carbon practices are implemented in the transmission and distribution processes. This includes the use of low-carbon technology and equipment, with indicators such as the comprehensive line loss rate and SF6 recycling efficiency. The output dimension analyzes the results of carbon reduction and management improvements, considering factors such as carbon emissions growth and carbon emission intensity. The economic dimension evaluates the benefits derived from reduction activities, including increased infrastructure investment, revenue growth, and waste management optimization.

2.3 Determination of weights

In the context of the CME module, evaluating the carbon management efficiency of power grids requires a focus on time continuity due to the inherent medium- to long-term characteristics of carbon emissions management. This necessitates stability in the evaluation method and the weights given to different indicators. For the initial stages of management system development, this study assumes that all aspects of carbon management efficiency are equally significant. To simplify the evaluation process, a questionnaire was designed following specific guidelines. The maturity level of each indicator was scored on a scale ranging from 1 to 5, representing nonexistent, partly implemented, average, well implemented, and fully implemented, respectively. The average of these scores was then converted into a percentage to arrive at a comprehensive carbon management score. Initially, the scorecard assigns equal weights to each indicator. However, power grids have the flexibility to adjust these weights based on the current operational stage of carbon management, reflecting the varying priorities of carbon reduction initiatives.

In the case of the CRE module, its outcomes are closely aligned with the strategic directions of governments and power grids. Therefore, frequent adjustments and updates based on the specific situation of individual power grids can significantly enhance the efficiency of carbon reduction efforts. The entropy weight method was chosen because it provides a clear and intuitive representation of factors that significantly influence carbon emission reduction efficiency. This approach allows for measuring the progress and effectiveness of power grid emission reduction initiatives. Moreover, it facilitates timely updates and revisions to carbon reduction targets, enabling power grids to take appropriate measures and chart a course for future low-carbon development.

To determine the weight of each data point, the following specific calculation process is utilized:

(1) Construct the evaluation matrix Y: Set an evaluation matrix composed of m evaluation schemes and n indexes.

(1)Y= {\left(y_{ij}\right)}_{m\ast n} \left(i=1,2,\dots ,m;j=1,2,\dots ,n\right),

where Y is the calculation matrix and x_{ij} is the standard value of the jth index based on the ith item. When the larger the value is, the better the effect; then, y_{ij}={\displaystyle \frac{x_{ij}-min\left(x_i\right)}{max\left(x_i\right)-min\left(x_i\right)}}. When the smaller the value is, the better the effect; then, y_{ij}={\displaystyle \frac{\mathrm{m}\mathrm{a}\mathrm{x}\left(x_i\right)-x_{ij}}{\mathrm{m}\mathrm{a}\mathrm{x}\left(x_i\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left(x_i\right)}}.

(2) Normalize the original matrix P: Carry out the standardization of each index to eliminate the incommensurability between indicators

(2)P_{ij}=\frac{y_{ij}}{{\sum }_{i=1}^n{y}_{ij}},

where P_{ij} is the standardization of each index.

(3) Calculate entropy: Define the entropy of index i

(3)E_j=-\frac{1}{\ln n}{\sum }_{i=1}^n P_{ij} \ln P_{ij}(j=1,2,\dots ,n ),

where E_j is the entropy of the jth index.

(4)W_j=\frac{1-E_j}{k-\sum E_j}(j=1,2,\dots ,n ),

where W_j is the entropy weight of the jth index.

2.4 Synergy model

Regarding the synergy model, we adopt the methodology used in Jin’s research and design a coordination degree model specifically tailored for carbon emission reduction performance (Jin, 2022). It assesses the reliability of the evaluation model by utilizing the concepts of order degree and synergy degree to interpret its effectiveness.

The specific calculation process is as follows:

(1) Calculate the operation degree of the carbon reduction performance:

(5){OD}_{im}=\frac{{XA}_{im}-{XB}_{im}}{{XT}_{im}-{XB}_{im}},

(6){OD}_{jn}=\frac{{XA}_{jn}-{XB}_{jn}}{{XT}_{jn}-{XB}_{jn}},

where {OD}_{im} and {OD}_{jn} are the operation degrees of the CME module and CRE module, respectively; {XS}_{im} and {XS}_{jn} are the actual values of the corresponding indicators for the CME module and CRE module; {XB}_{im} and {XB}_{jn} are the baselines of the corresponding indicators; {XT}_{im} and {XT}_{jn} are the targets of the corresponding indicators; and m,n=1,2,3\dots.

The operation degree quantifies the contribution of each indicator to the orderliness of the model. In the process of carbon reduction, the objective is to progress from one structure to another in an orderly manner. Therefore, the goal of development is also to achieve an orderly state. The desired outcome of this process is to achieve a target level for carbon management efficiency and carbon reduction effectiveness from a baseline level.

(2) Calculate the orderliness degree:

(7){ORD}_E=\sum _{k=1}^m{OD}_{ik}\times W_{ik},

(8){ORD}_M=\sum _{k=1}^n{OD}_{jk}\times W_{jk},

(9)ORD=U_E^{WE}\times U_M^{WM},

where {ORD}_E, {ORD}_M, and ORD are the orderliness degrees of the CME module, CRE module and comprehensive model, respectively; W_{ik} and W_{jk} are the weights of each indicator for the CME module and CRE module, respectively; WE and WM are the weights of the CME module and the CRE module, respectively; and m,n=1,2,3\dots. It is noteworthy that this study considers that the total weights refer to the weights of the CME module and the CRE module. By employing simulation optimization, the orderliness curve of carbon reduction performance can more accurately align with the orderliness of the CME module and the CRE module, particularly when the weights of both modules are equal. This assumption corresponds to the notion that both the process and result of carbon reduction efforts hold equal significance.

The level of coordination within the model can be evaluated by assessing the level of orderliness. A positive synergistic effect is crucial for seamless progress toward orderliness, while a reverse synergistic effect implies the opposite. The orderliness degrees of the CME module and the CRE module are reflected by {ORD}_E and {ORD}_M, respectively.

(3) Calculate the carbon reduction synergy degree:

(10)SYD=\pm \sqrt[3]{\left|\left({ORD}_{E_t}-{ORD}_{E_{t-1}}\right)\left({ORD}_{M_t}-{ORD}_{M_{t-1}}\right)\left({ORD}_t-{ORD}_{t-1}\right)\right|},

where SYD is the carbon synergy degree of the power grid and t represents the year.

The synergy degree is a quantitative indicator of the effectiveness of carbon reduction performance in a specific year and indicates the development trend of the degree of orderliness. When the level of synergy decreases, the growth trend of orderliness will also slow. Moreover, when a power grid maintains the same growth momentum or rate, it suggests that there has been no new change in the synergy effect formed in its carbon management, and the driving force it brings to the carbon reduction effort of development also remains at a certain level.

When utilizing the formula, it is essential to note that the operator outside the square root should be interpreted as “plus” only if all three values within the brackets are positive. In any other scenario, the operator should be interpreted as “minus.” This is because a negative value indicates a counterproductive synergistic effect in the evaluation model. From a carbon reduction performance standpoint, indicators moving in the opposite direction of the target reveal that the power grid’s efforts are ineffective.

2.5 Data sources

For this study, we selected provincial power grids within the China Central Power Grid as pilot enterprises and analyzed their performance from 2012 to 2021. The primary data sources were the China Electricity Statistical Yearbook and the Comprehensive Statistical Compendium of the four power grids within the China Central Power Grid from 2012 to 2021. In addition, we conducted on-site research and questionnaires to gather data on carbon management practices and various economic indicators. We also reviewed information available on official departmental websites to ensure the completeness and accuracy of our data. The weights of the indicators in the CRE module are shown in Tab.4. The processed data on the carbon management and carbon reduction of the four power grids can be found in Electronic Supplementary.

3 Results

3.1 CME module

To assess the maturity of carbon management, the CME module employs a scorecard ranging from 1 to 5, with a higher score indicating a higher level of maturity. This score is then converted into a percentile scale for further analysis. As shown in Fig.2, the Hubei, Hunan, Henan, and Jiangxi power grids achieved scores of 0.76, 0.66, 0.63, and 0.77, respectively, in 2021. Over the past decade, these four power grids have made significant progress in carbon management, with each focusing on specific areas of improvement. In particular, the performance evaluation and implementation of the Hubei Power Grid experienced the most substantial growth. The Henan Power Grid has shown noteworthy improvement in overall carbon management. The Hunan Power Grid has also demonstrated leadership improvements. Meanwhile, the Jiangxi Power Grid has dedicated its efforts to planning and supporting operations. However, it is crucial for these two power grids to adopt a more balanced approach to carbon management efforts.

3.2 CRE module

Using the entropy method previously mentioned, the weights of the indicators within the CRE module were calculated. These results, depicted in Fig.3, highlight the significant advancements in carbon reduction achieved by all power grids, particularly those in Hunan and Jiangxi Provinces. In 2021, Hubei, Hunan, Henan, and Jiangxi attained scores of 0.66, 0.84, 0.70, and 0.80, respectively. It is important to acknowledge that the CREs of these four provinces exhibited a fluctuating pattern, which is not unexpected given the complexity and challenges of carbon reduction. Nevertheless, the overall upward trajectory in these provinces signifies the effectiveness of recent carbon reduction initiatives.

3.3 Comprehensive carbon reduction performance

Fig.4 presents a comprehensive evaluation of the carbon reduction performance of Hubei, Hunan, Henan, and Jiangxi Provinces in 2021. The scores for these provinces are 0.70, 0.75, 0.67, and 0.79, respectively. These figures reveal consistent yet nuanced progress toward carbon reduction efforts across these power grids, despite certain fluctuations. These outcomes signify the diligent commitment to reducing carbon emissions, despite encountering challenges in the carbon reduction process. While commendable strides have been made in these power grids, there is still ample room for improvement in both carbon management efficiency and carbon reduction effectiveness.

3.4 Synergy degree

This study utilizes the Hubei and Henan power grids as examples to validate the evaluation model. As depicted in Fig.5(a), the degree of orderliness of the carbon reduction in the Hubei Power Grid has consistently shown an upward trajectory, with a noticeable acceleration since 2020. This increasing trend in orderliness degree is a result of the increasing trends observed in both constituent modules. The synergy of carbon reduction performance reached its peak in 2020 and has since remained stable. The preliminary rule can be summarized as follows: the synergy of carbon reduction performance is complexly connected to the changing trends in the orderliness degree. As the growth rate of the orderliness degree fluctuates, so does the synergy. Specifically, a faster growth rate correlates with a greater level of synergy. With regard to the input‒output relationship, the Hubei Power Grid has consistently maintained a stable growth rate in enhancing carbon management, while the results obtained through carbon management have accelerated at a faster pace. This suggests that despite constant input, the output has increased, representing a highly desirable state. Enhanced carbon management efforts have thus produced increasingly positive results.

Since 2020, the carbon reduction performance of Henan Power Grid Collaboration has displayed a downward trend, aligning with the pattern shown in Fig.5(b). Regarding the input‒output relationship, while the Henan Power Grid has made progress in enhancing carbon management, the pace of achieving the desired carbon reduction results has slowed. Specifically, the input has decreased, and the output has not only slowed but has even declined. This result is unfavorable for the Henan Power Grid, indicating that the contribution of carbon management efforts to energy performance improvement has weakened since 2020. It is worth noting that carbon reduction performance is influenced by various factors, and the presence of adverse elements can hinder its progress. However, the strength of carbon management determines the power grid’s ability to steer the results. More effective carbon management and improved management skills enable the power grid to consistently adopt effective measures to achieve set goals. Conversely, when the grid lacks self-management abilities, its carbon reduction performance becomes unpredictable and susceptible to various external factors.

To provide further insight into the trends and patterns observed in the four power grids, specifically the Hubei Power Grid, it is evident that the carbon emission reduction performance in 2020 showed a steeper curve than that in previous years. This aligns with the principle of increased synergistic growth. A stronger synergistic effect between carbon management efficiency and carbon emission reduction efficiency indicates more effective management and implementation of emission reduction measures. Consequently, overall carbon reduction performance surpasses expectations. Conversely, the carbon emission reduction performance curve in 2020 is flatter than that in previous years, which corresponds to the principle of decreased synergistic growth. A weaker synergistic effect between carbon management efficiency and carbon emission reduction efficiency suggests less effective management and implementation of emission reduction measures. As a result, the overall carbon reduction performance may not meet the expected results.

4 Discussion

4.1 Hubei Power Grid

Concerning CMEs, the Hubei Power Grid has established a comprehensive carbon management system, focusing on data collection, storage, and the formulation of precise goals and action plans for carbon reduction. Additionally, the power grid has implemented a thorough and standardized review mechanism, conducting regular assessments of carbon management activities within its operations. During the “13th Five-Year Plan” period, the CME of the Hubei Power Grid significantly improved compared to that during the “12th Five-Year Plan” period. Notably, the administration dimension and optimization dimension demonstrated substantial growth, increasing from 0.33 and 0.40 in 2016 to 0.67 and 0.80 in 2021, respectively. This represents annual growth rates of 20.61% and 20.00%, respectively. These notable advancements highlight the considerable importance placed on carbon reduction efforts by leaders and their swift actions to enhance them. Throughout its decade-long development process, the Hubei Power Grid has established a comprehensive framework for carbon management, greatly facilitating future endeavors in carbon reduction.

In 2021, the evaluation of the importance of CRE in the Hubei Power Grid was predominantly based on four key dimensions: economic, flow, input, and output. Each dimension was assigned respective weights of 0.326, 0.239, 0.238, and 0.197, reflecting their relative significance. Within the economic dimension, particular attention was given to waste management cost efficiency, indicating the commitment of the Hubei Power Grid to minimizing costs in this area. Similarly, in the flow dimension, comprehensive line loss was deemed highly important, highlighting a focus on reducing power transmission losses. Despite the considerable advancement of hydropower in the Hubei Power Grid, with clean power constituting more than 50% of its energy structure and achieving an impressive low carbon level, the potential for further carbon reduction is limited, presenting a challenge for future endeavors. Nonetheless, the Hubei Power Grid has demonstrated its ability to effectively balance carbon emission reduction with annual power supply growth, making a significant contribution to carbon reduction efforts.

4.2 Hunan Power Grid

In its pursuit of carbon management, the Hunan Power Grid has exhibited a resolute commitment to administration and implementation. To streamline carbon reduction initiatives, an autonomous department has been established that grants decision-making authority to the carbon management department. This department prioritizes the procurement of clean power while simultaneously focusing on reducing losses and advancing transmission technology. The procurement strategy specifically favors clean energy sources with the aim of maximizing the efficiency of low-carbon transmission and loss reduction technologies. Moreover, careful consideration is given to selecting insulation media that can serve as viable alternatives to sulfur hexafluoride gas. During the “13th Five-Year Plan” period, various performance indicators related to carbon management improved within the Hunan Power Grid. The carbon management system has been established and gradually enhanced, with increased awareness among leaders and decision-makers. However, consistent implementation and continual refinement of carbon management efforts are crucial. To further improve its carbon management system, the Hunan Power Grid can start by setting specific goals and considering potential improvements in the future.

In 2021, the prioritization of CRE within the Hunan Power Grid was categorized as input, economic, flow, and output, with weights of 0.363, 0.314, 0.171, and 0.152, respectively. The regional net inflow power ratio and revenue growth are particularly influential in determining the carbon reduction benefits of the Hunan Power Grid. This alignment perfectly aligns with the emphasis on procuring low-carbon power in carbon management strategies, which directly shapes subsequent low-carbon investments made by the power grid. It is clear that the carbon reduction efforts of the Hunan Power Grid are primarily focused on regional power exchange and electricity sales revenue, as these sectors provide crucial financial support for carbon reduction initiatives. In recent years, the Hunan Power Grid has recognized the limitations of carbon management, and its importance has progressively increased on an annual basis. However, the rate of carbon reduction efficiency has decelerated and is nearing saturation. This indicates a need for further refinement and innovation in evaluation methods to accurately measure the effectiveness of carbon reduction and facilitate continued improvement.

4.3 Henan Power Grid

The evaluation of carbon management efficiency within the Henan Power Grid predominantly revolves around planning and implementing carbon reduction measures. Although the current framework provides guidance for demand-side carbon reduction, comprehensive planning for other areas is lacking; therefore, refinement of the baseline parameters is required to improve the evaluation process. Despite gradual progress in carbon management efficiency over the past few decades, the advancement of the Henan Power Grid has been relatively modest. While their efforts to strengthen carbon management during the “13th Five-Year Plan” period are commendable, there is still ample opportunity for further growth. The depth of carbon management initiatives will significantly influence the path of low-carbon development and the achievement of dual carbon targets. As a result, it is crucial to intensify the establishment of strategic plans and procedural documents to guide the systematic and standardized progression of carbon reduction initiatives.

As a province with significant electricity consumption, the Henan Power Grid established structured priorities for its carbon reduction efforts in 2021. These priorities consist of four key areas—input, output, flow, and economy—with corresponding scores of 0.310, 0.247, 0.236, and 0.206, respectively. Notably, the integration of renewable energy sources and the management of the line loss rate play vital roles in determining the carbon reduction benefits of the Henan Power Grid. During the “12th Five-Year Plan” period, the focus on carbon reduction was not given the necessary attention, which resulted in setbacks due to the concurrent increase in power supply and comprehensive line loss rate. This, in turn, led to a rise in total carbon emissions. However, in the subsequent “13th Five-Year Plan” period, the Henan Power Grid prioritized carbon reduction and implemented progressive strategies to enhance carbon management and reduction. The achievement of carbon reduction targets was deemed of utmost importance.

4.4 Jiangxi Power Grid

When evaluating the CME of the Jiangxi power grid, the scores for strategies, implementation, and optimization are commendable. The grid demonstrates a commitment to rigorous and standardized processes in reviewing and managing its strategic planning and various risks. It also ensures the maintenance and effective updating of relevant documents pertaining to risk response mechanisms. Additionally, it actively evaluates opportunities for carbon reduction and effectively implements measures to capitalize on them. However, there is a critical aspect that requires attention: the limited emphasis on planning for carbon reduction work. It should be noted that comprehensive internal audits of management systems have not been conducted, and no corrective measures have been proposed to address nonconformities identified in audit reports. Therefore, it is highly recommended that the audit procedures be reviewed and updated, if necessary, to address these gaps.

Over the past decade, the Jiangxi Power Grid has experienced substantial growth driven by increasing environmental concerns and policies. However, the lack of professional talent and a weak awareness of carbon management among management personnel may hinder the grid’s progress in low-carbon development. Thus, there is an urgent need to strengthen management efforts in this area.

The importance of CRE within the Jiangxi Power Grid in 2021 is clearly defined across four dimensions—economic, input, flow, and output—with respective values of 0.306, 0.275, 0.263, and 0.156. Among these factors, comprehensive line loss is the primary factor driving carbon reduction efforts, reflecting the grid’s focus on minimizing losses and costs during the power transmission process. During the “12th Five-Year Plan” period, the annual average growth rate of carbon reduction efficiency was strong at 15.38%, indicating a consistent and significant commitment to reducing carbon emissions. Even during the “13th Five-Year Plan” period, although the growth rate slightly slowed to 18.09%, it remained at a commendable level. This demonstrates that the Jiangxi Power Grid has been successful in establishing and maintaining robust evaluation and management systems for carbon reduction. Furthermore, it is noteworthy that the pace of development in CSR has significantly outpaced that of CME, highlighting the grid’s dedication to achieving carbon reduction targets.

5 Conclusions

This study proposes a novel dual-module evaluation model that combines a carbon management efficiency module and a carbon reduction efficiency module for power grids. This model provides a comprehensive assessment of carbon reduction efforts, filling a gap in the field. This evaluation model effectively captures the actual progress and advancements in carbon reduction efforts implemented by the power grid companies in these four provinces over the past decade.

In terms of the carbon management efficiency module, the provincial power grids within the Central China Power Grid have gradually improved their awareness of carbon management and optimized their carbon management practices over the past decade, transitioning from an initial level of management to a system-level approach. For the carbon reduction efficiency module, driven by the influence of both domestic and international green development policies, the advancement of renewable energy, and the internal demand for carbon reduction within these power grids, the carbon reduction efforts of the provincial power grids within the Central China Power Grid have shown a significant upward trend compared to those of the previous decade, with a noticeable year-on-year decrease in grid carbon emission factors. A comprehensive analysis of both carbon management efficiency and carbon reduction efficiency reveals a mutually beneficial relationship between the two. The effective implementation of carbon management practices in power grids enhances the low-carbon awareness of internal staff, facilitating the smooth progress of carbon reduction efforts.

Based on the case study, power grids have multiple avenues for mitigating carbon emissions. A key direction is to diversify the energy mix by exploring renewable sources, which will contribute significantly to environmental sustainability while reducing carbon emissions. Additionally, power grids should prioritize investments in smart grid technologies and infrastructure upgrades to minimize line losses. These measures will enhance transmission and distribution efficiency, leading to lower energy losses and reduced carbon emissions. Furthermore, there is a need to strengthen the power grid’s existing carbon management system. Regular audits and updates based on the latest research and best practices will ensure alignment with carbon reduction goals. Employee engagement is also crucial, and awareness-raising efforts through training and workshops can foster a culture of sustainability and promote internal innovations. Finally, monitoring and evaluating the progress of carbon reduction efforts are essential for identifying areas for improvement and refining policies and plans accordingly. Transparent communication about power grid achievements can serve as inspiration for other industries to adopt similar measures, contributing to a more sustainable future.

In conclusion, our study offers a novel and practical evaluation model for assessing the carbon reduction performance of power grids. The dual-module evaluation model offers a comprehensive assessment that serves as a valuable tool for monitoring and assessing the progress of carbon reduction performance. This can provide guidance for policymakers and power grids in formulating targeted carbon reduction policies and initiatives.

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