Quantitative Evaluation of China’s Energy Transition Policy Since the 14th Five-Year Plan, Based on the MLP-PMC Model

Abstract

Evaluating the effectiveness of China’s energy transition policies and formulating scientific policy optimization paths are essential for advancing China’s energy transition and global carbon reduction. This paper quantitatively evaluates China’s 22 key energy transition policies during the 14th Five-Year Plan period via text excavation, the Multi-Level Perspective (MLP), and the PMC index model. The research results show that the overall design of China’s energy transition policy is reasonable. Only two policies are graded as acceptable, while the rest of the policies are graded as good or higher. In addition, landscape policy, regime policy, and niche policy have a decreasing influence on energy transition projects in China. This research summarizes three common shortcomings in China’s energy transition policy: (1) the lack of explicit policy incentives and constraints; (2) the inadequate authority of policy releasers; and (3) the limited coverage of the policy focus.

1. Introduction

In recent years, global climate change has continuously intensified under the influence of the greenhouse effect, posing a serious threat to human survival and the sustainable development of society. The accumulation of anthropogenic greenhouse gases, particularly carbon dioxide from fossil fuels and industrial activities, is the primary catalyst for the global greenhouse effect [1]. Consequently, energy transition has emerged as a major issue over the past few decades, becoming one of the most urgent challenges of our time [2]. China has witnessed significant economic growth over the past 40 years; however, its “pollute first, clean up later” model has led to severe environmental crises and unsustainable socio-economic development [3]. Limited by resource endowment, fossil fuels remain the largest contributor to China’s economic development. According to the National Bureau of Statistics of the People’s Republic of China, the total energy consumption in 2023 reached 5.72 billion tons of standard coal, with coal consumption accounting for 55.3% of the total energy consumption. Correspondingly, China’s carbon emissions have also increased year by year, making it the world’s largest carbon emitter since 2007. According to the latest data from the IEA, China’s carbon dioxide emissions increased by 408.09% from 1990 to 2022.

Unsurprisingly, the Chinese government is aware of these changes in environmental indicators, and thus has set energy transition and climate change as the core of China’s energy policy [4]. In 2009, at the Copenhagen Climate Conference, China proposed, for the first time, the goal of reducing CO₂ emissions per unit GDP by 40–45% by 2020 compared with that in 2005, and eventually exceeded this goal. In October 2021, the Chinese government submitted a new Nationally Determined Contribution (NDC) to the United Nations, further emphasizing China’s commitment to achieving carbon neutrality by 2060 and continuing the 2030 carbon peaking target proposed in 2015. In addition to prescriptive polices, China has also developed renewable energy incentives to promote energy transition. For example, the Renewable Energy Law of the People’s Republic of China, issued in 2005, represents a significant step forward in supporting China’s renewable energy industry development. The Feed-in-Tariff (FIT) is a Chinese renewable energy price incentive policy implemented in 2006, which has significantly promoted the development of China’s renewable energy power generation industry during the past few decades [5]. In addition, China’s Five-Year Plan (FYP) for the past 15 years has also prioritized energy transition and climate change. The 12th FYP (2011–2015) was thus regarded as a “green revolution” for China [4]. In this plan, the Chinese government addressed climate change as a major challenge to China’s economic and social development [6]. In the 13th FYP (2016–2020), China has included green development as one of its five pillars of social and economic development, along with innovation, coordination, opening up, and sharing. Influenced by the strategic idea of “four revolutions and one cooperation” in energy development, China has deepened the energy revolution through focusing on promoting the transformation of the consumption, production, and utilization modes of the energy system. The 14th FYP (2021–2025) is a critical period for accomplishing the goal of carbon peaking and carbon neutrality [7]. Recently, China has developed a series of new energy transition policies aimed at restructuring the energy system to absorb new energy sources, and constructing a modern energy system that is clean, low-carbon, safe, and efficient.

The purpose of this study is to evaluate and analyze the policies that significantly impacted China’s energy transition during the 14th FYP period. The marginal contributions of this paper are as follows: (1) This study discusses the rationality and feasibility of the energy transition policies issued during the 14th FYP, and provides empirical evidence for subsequent energy transition policy development and improvement. Since the majority of previous studies have only focused on policy regarding a particular sector or industry, this study extends the scope of the research assessing China’s energy transition policies. (2) This study methodically improves on existing PMC policy evaluation studies. Firstly, this research takes the policies that are having significant impacts on China’s energy transition during the 14th FYP as evaluation samples to ensure their timeliness and non-iteration. Secondly, the Multi-Level Perspective (MLP) is used to categorize the policy samples into three levels, macro-level landscape policy, meso-level regime policy, and micro-level niche policy, to ensure fairness in policy evaluation and comparison. Thirdly, this study combines text excavation techniques to identify key indicators for policy evaluation, making the policy evaluation results more objective.

This paper is structured as follows: Section 2 presents a comprehensive review of the relevant literature; Section 3 introduces the data resources and the evaluation model; Section 4 presents the analysis of the results; Section 5 discusses the practical values of this study; and Section 6 concludes and interprets the key findings of this study.

2. Literature Review

2.1. Research on China’s Energy Transition Policies

Research on China’s energy transition policies can be broadly categorized into three types: market-oriented energy policies, incentive and support-based energy policies, and constraint and regulatory policies. Market-oriented energy policies have played a significant role in promoting China’s energy transition. Existing studies have explored this from perspectives such as carbon emission trading policies and energy consumption rights trading policies, revealing their multiple mechanisms and effectiveness in enhancing green technology innovation, energy efficiency, and industrial structure upgrades. Notably, carbon emission trading policies have significantly improved energy efficiency and carbon reduction effects [8], reduced carbon dioxide emissions in the power sector [9], and markedly enhanced energy efficiency in the industrial sector [10]. However, their impact on improving green total factor productivity still shows inter-industry differences [11]. Energy consumption rights trading policies have significantly boosted the green technology innovation capabilities of cities and enterprises by promoting industrial structure upgrades and enhancing corporate ESG performance [12,13], which have also proved to be effective in reducing regional carbon emission intensity [14]. Pilot policies for the electricity spot market have fostered green economic development by reducing coal-fired power generation capacity and increasing the utilization of renewable energy [15], while energy consumption quota trading (EQT) policies have further optimized carbon market performance by enhancing carbon reduction effects [16]. Together, these studies reveal the multiple mechanisms and comprehensive effectiveness of market-oriented policies in energy transition.

Incentive and support-oriented energy policies have also played a crucial role in promoting green technology innovation, renewable energy utilization, and low-carbon transitions through multi-layered funding and technological and regulatory support. Among these, renewable energy policies have shown remarkable effectiveness in carbon reduction and optimization of energy structures, particularly when synergized with policies such as grid connection pricing, carbon trading, and research and development subsidies [17]. However, over-reliance on financial support may lead to resource imbalances and market distortions [18], while significant differences in regional policy effectiveness further highlight the importance of tailoring approaches to local conditions [19]. Policies for new energy vehicles have notably increased the number of technological innovations [20], yet there is still a need to optimize the structure of policy support to achieve long-term development goals [21]. Meanwhile, central and local governments are aligned on battery recycling policy objectives, despite disparities in local policy capabilities [22]. The coal-to-gas policy has effectively reduced air pollution in most regions, but there are still potential risks such as pollution exaggeration and long-term failures in certain areas [3,23]. Photovoltaic poverty alleviation and coal reduction policies have achieved positive results in enhancing efficiency and developing renewable energy [24,25], providing practical experience and theoretical support for optimizing policy design and implementation. Overall, incentive- and support-oriented policies have shown significant effectiveness in facilitating energy transitions, but the regional and mechanistic differences in their impacts require further research and optimization.

Constraints and regulatory energy policies can effectively promote emission-reduction targets, optimize resource utilization, and promote technological innovation through means such as taxation, environmental protection, and energy conservation measures. Energy tax policy has proven to be an important means of saving water and reducing emissions [26]. Carbon tax policies achieve the simultaneous control of emissions and energy efficiency by supporting non-fossil energy subsidies, positively influencing the economy, the environment, and public health [27,28]. In terms of environmental protection policies, environmental inspections have significantly reduced local pollutant emissions, while energy-saving and emission-reduction demonstration city policies have effectively promoted green technological innovation, particularly in regions with stringent environmental goals [29]. Additionally, energy-saving and emission-reduction policies have notably enhanced pollution-control effectiveness [30], although they may have negative impacts on corporate ESG performance [31].

However, existing research on these energy policies tends to focus on single industries or sectors, lacking a comprehensive and cross-sectoral perspective. This often fails to capture the interactions and impacts of policies across different fields, making it difficult to reflect the multidimensional effects of policies, especially regarding potential complex conflicts or synergies between policy objectives. Therefore, to achieve more accurate and comprehensive policy evaluation results, it is essential to adopt cross-sectoral and multi-level assessment methods.

2.2. Research on Policy Evaluation Methods

The effectiveness of policies and evaluation methods holds a central position in energy transition research, providing important reference points for the formulation and adjustment of actual policies. Existing studies primarily utilize econometric and statistical methods, computational modeling, and optimization techniques, as well as text mining and network analysis methods, to assess energy policies, thereby gaining a deeper understanding of the effects of policy implementation and its shortcomings.

Econometrics and statistical methods provide a scientific and systematic analytical framework for evaluating energy policies. These methods offer crucial references for policy optimization through the quantitative measurement of policy effects and the in-depth exploration of their mechanisms. Firstly, the Difference-in-Differences (DID) approach and its extended models have proven to be exceptional in assessing policy effects. For instance, Wang et al. (2024a) [32] utilized a multi-period DID model to reveal that China’s energy policies significantly reduced urban CO₂ emission intensity while promoting a win–win scenario for economic and environmental goals. Similarly, Wang et al. (2024c) [33] demonstrated through a PSM-DID study that renewable energy policies improved land-use efficiency and indirectly facilitated industrial structure upgrades. Secondly, to address the heterogeneity and dynamic impacts of policy implementation contexts, synthetic control methods have become an important complement. Research by Qian et al. (2023) [34] revealed regional differences and dynamic evolution in policy effects. Furthermore, studies are gradually expanding into complex models such as dynamic panels and multivariable regressions, capturing the long-term and comprehensive effects of policies. For example, Zhao and Wang (2023) [35] quantitatively assessed the impact of low-carbon energy transition policies using ridge regression models, revealing the volatility between policy intensity and objectives. These methods have significantly advanced the scientific understanding of energy policies, from effect evaluation and mechanism analysis to heterogeneity research, laying a solid foundation for achieving carbon neutrality and energy transition goals.

Computational modeling and optimization techniques have shown remarkable advantages in energy policy evaluation, particularly in simulating policy interactions and multidimensional impacts. Firstly, recursive dynamic computable general equilibrium (CGE) models are widely used to assess the synergies of multiple policy combinations. For instance, Pan and Dong (2023) [36] found that the combination of green finance policies and energy trading rights effectively promotes low-carbon economic development. Hu and Dong (2023) [37] proposed that reducing coal consumption and increasing non-fossil energy capacity could achieve the carbon peak target by 2030 while ensuring economic growth. Secondly, system dynamics models can further reveal the dynamic evolution and mechanism impacts of policies. For example, Chen et al. (2024) [16] discovered through scenario analysis that energy consumption quota trading (EQT) policies can enhance the emission-reduction effects of carbon emission trading (CET) but may reduce market activity. Du et al. (2024) [21] pointed out that adjustments to new energy vehicle policies must be prioritized to meet sales targets by 2035. Additionally, research on regional policy differences has shown that multiple policy combinations have significant improvement effects in resource curse areas. Xu et al. (2024) [38] found through dynamic CGE models that energy substitution policies not only alleviated the negative economic impacts in high resource curse regions, but also promoted regional coordinated development. These techniques provide systematic support for the scientific evaluation and optimal design of energy policies, advancing the achievement of carbon neutrality goals.

As research on policy evaluation deepens and matures, the mixed use of qualitative and quantitative assessment methods has become a new trend. Combining content research with quantitative analysis through text mining and network analysis methods can make policy evaluation results more comprehensive and objective. The Policy Modeling Consistency (PMC) index model is an effective assessment method that integrates qualitative and quantitative analysis. This model was established based on the Omnia Mobilis hypothesis, which states that everything is constantly moving and inter-related, so all relevant variables should be considered [39]. Therefore, calculating the PMC index and plotting the PMC surface graph can help researchers and decision-makers to intuitively understand the strengths and weaknesses of the policies being evaluated. In recent years, the PMC index model has been widely applied in policy evaluation research. For example, Zhao et al. (2023) [40] assessed 14 energy security policies published from 2014 to 2023 using the PMC index model, finding that the overall policy ratings were good, and offering targeted improvement suggestions. Zhang et al. (2023) [41] employed text mining and content analysis methods to construct a PMC index model based on China’s three major coal power crisis policies, analyzing the evolution of coal power policies and finding that while policies are diversified, they lack coordination; they therefore recommended the strengthening of the implementation of coal power policies from multiple perspectives. Jiang and Gao (2024) [42] analyzed 308 Chinese green consumption policies using text mining methods and the PMC index model, finding that the policy targets need to maintain consistency, focusing primarily on promoting green products and efficient energy use, and suggesting improvements to policy incentive measures.

In conclusion, policy evaluation studies aim to minimize human influence and provide a comprehensive and objective evaluation of policies. Although the PMC index model has apparent strengths, it still needs to overcome the following inadequacies in order to be better applied to policy evaluation research: (1) The development and improvement of new policies are based on the implementation and feedback of old policies. Ignoring the independence between old and new policies will lead to a loss of value in the evaluation results. (2) The hierarchical evaluation of different policies is necessary because planning policies at the macro-level are usually more critical than sectoral and industrial policies at the middle and micro-levels. (3) Further text excavation is necessary to address the high subjectivity caused by high-frequency words or rooted coding in evaluating indicators’ selection and scoring process. To address these shortcomings, this study combined the Multi-Level Perspective (MLP) framework and the Policy Modeling Consistency (PMC) index model to enhance the objectivity and comprehensiveness of policy evaluation by directly tackling the identified inadequacies. Firstly, by focusing on policies that have a major influence on the energy transition during the 14th Five-Year Plan, we ensure independence between new and old policies, preserving the value of the evaluation results. Secondly, the MLP framework allows for hierarchical evaluation by categorizing policies into macro- (landscape policies), meso- (regime policies), and micro- (niche policies) levels. This acknowledges the varying importance of different policy types and ensures that macro-level planning policies receive appropriate emphasis compared to sectoral and industrial policies. Lastly, integrating text mining techniques reduces subjectivity in selecting and scoring evaluation indicators, mitigating the influence of high-frequency words or coding biases. This methodological improvement not only minimizes human influence, but also provides empirical evidence for policymakers to understand the rationality and enforceability of existing policies, aiding in the formulation and refinement of future energy transition policies.

3. Methodologies and Data Analysis

As shown in Figure 1, the major steps in quantitatively evaluating China’s energy transition policy are as follows. In step 1, according to the predefined criteria that have a significant impact on China’s energy transformation during the 14th Five-Year Plan, 22 representative policy samples were selected for evaluation. Subsequently, these policy samples were categorized into three types of policies, landscape policy, regime policy, and niche policy, by combining the Multi-Level Perspective (MLP). In step 2, some essential evaluation indicators were identified based on high-frequency words and Latent Dirichlet Allocation (LDA) topic models. Step 3 determined the ten main indicators and 38 sub-indicators used for the energy transition policy evaluation and their parameter settings. Step 4 calculated the PMC index for each policy to be evaluated. Step 5 analyzed the results of the policy evaluation.

3.1. Policy Selection and Classification

This study focuses on policies that have an important impact on China’s energy transition project during the 14th Five-Year Plan period. Therefore, the selection of policies needs to meet the following preset criteria: (1) These policies are the latest ones and have no iterative relationship with each other; that is, they are irrelevant in the timeline of the evaluation sample. (2) The goal of these policies is to promote China’s energy transformation during and beyond the 14th FYP period. (3) These policies are the main driving force of China’s energy transformation, and they act on different dimensions and levels. (4) Although it would be more beneficial to use the latest policy data, considering that there is a certain time lag in policy implementation effects, we cannot score and evaluate the latest policies in the current time period, and it will take a certain amount of time to observe their implementation based on these standards; therefore, we set the time interval to the first two years of the 14th Five-Year Plan, 2021 and 2022. In addition, although the Energy Supply and Consumption Revolution Strategy (2016–2030), and the Outline of the 14th Five-Year Plan (2021–2025) for National Economic and Social Development and Vision 2035 of the People’s Republic of China, were released in 2016 and 2020, they have important implications for China’s energy transition during the 14th Five-Year Plan period. Based on the above criteria, a search was conducted in the policy document database of the State Council of China, and 22 representative energy transition policies were finally selected as evaluation samples.

The Multi-Level Perspective (MLP) was used as the theoretical support for the choice and classification of the policy sample in this study, since it assumes that the energy system transition is driven by the interplay between the macro-level landscape, the meso-level socio-technical regime, and the micro-level niche [43,44]. Niche refers to the protected space that is the breeding ground for radical innovation. The socio-technical regime refers to the institutional structure of the existing system that leads to path dependence and incremental change. Landscape is the external influence that acts on the whole of the energy system and exerts pressure on the niche and regime. Based on the multi-level structure of the MLP, 22 policies are categorized as landscape policy (P1–P8), regime policy (P9–P15), and niche policy (P16–P22). Landscape policy is a major macro-level policy that will guide China’s energy transition. Regime policy drives change in high-carbon energy systems and institutions. Niche policy aims to promote the development of renewable energy technologies and various low-carbon innovations. The 22 most recent energy transition policies and typologies are shown in Table 1.

3.2. Indicators Identification Based on Text Excavation

3.2.1. High-Frequency Words and Word Cloud

In this section, the python3.9-based Jieba word segmentation technique is applied to conduct primary text excavation on the 22 energy transition policy texts. The top 60 key words with high appearing frequencies sorted and listed in descending order as shown in Table 2. The word cloud shown in Figure 2 was generated using the PyeCharts visualization technique. According to the results, the high-frequency keywords in China’s energy transition policies during the 14 FYP period are concentrated on topics such as energy, environment, institutions, and technological improvements.

3.2.2. Results of LDA Topic Model

The LDA topic model was trained on the policy text via python’s scikit-learn module to further validate and extract the focuses of the energy transition policies, aiming to provide essential indicators for the subsequent policy evaluation. This module can represent each document as a probability distribution over different topics, where each topic can be considered as a probability distribution over words [45]. Perplexity is a deterministic determination indicator taken by the LDA model in differentiating topics, i.e., determining the optimal number of topics [46]. As shown in Figure 3, the first inflection point of perplexity occurs when the number of topics is 5, which is thus determined as the optimal number of topics. Therefore, by resetting the number of topics to 5, the top 10 keywords with highest weights under each topic are rescreened and reclassified into five major topics: Energy Development, Institution Construction, Technology Innovation, Electricity Production, and Climate Change, as shown in Table 3.

3.3. Indicator Classification and Parameter Setting

This paper combines policy text excavation and related studies [47,48,49] to construct a PMC evaluation index system for energy transition policies in China. The indicator structure consists of 10 main indicators and 38 sub-indicators, as shown in Table S1 of the Supplementary Materials. The 10 main indicators are as follows: Policy nature (X1); Policy timeliness (X2); Policy evaluation (X3); Incentives and constraints (X4); Policy area (X5); Policy receptor (X6); Policy approach (X7); Policy releaser (X8); Policy focus (X9); and Policy accessible (X10).

After determining the selection and classification of policy evaluation indicators, it is also necessary to set the parameters of each indicator. In order to make sure that all the sub-indicators have the same importance and weight, a binary number of 1 or 0 is assigned to each sub-indicator. If the energy transition policy text matches the sub-indicator, the sub-indicator is assigned with 1, otherwise it is assigned with 0.

3.4. PMC Index Calculation

The calculation of the PMC index contains five steps:

• (1). Put 10 main indicators and 38 sub-indicators in the PMC index model of China’s energy transition policies into the multiple input–output tables in Table 4.

• (2). Assign values to the sub-indicators. This paper uses the binary system to assign the variables of the PMC model of China’s energy transition policies. When the policy text content meets the sub-indicator, the sub-indicator value is 1; otherwise, the sub-indicator value is 0. As shown in Equations (1) and (2), each distribution of the sub-indicators is from [0, 1]. It is worth noting that the values of the five sub-indicators of policy focus (X9) are determined based on the probabilities in Table 5. If the probability that the policy sample belongs to each topic is not 0, the sub-indicator value is 1; otherwise, the sub-indicator value is 0. Policy accessible (X10) defaults to 1 because all policy texts can be directly accessed in the Chinese State Council Policy Document Database. The parameter settings for all sub-indicators in this study are shown in Table S2 of the Supplementary Materials.

• (3). Calculate the main indicator values of China’s energy transition policies using Equations (3) and (4), and then obtain the PMC index by adding the main indicators according to Equation (4).

• (4). To visualize the PMC index results, the PMC surface can be created once the PMC index is obtained. The PMC surface calculation is shown in Equation (5). In this study, the PMC surface is shown in Figures S1–S9 of the Supplementary Materials.

• (5). Evaluate each policy according to the criteria proposed by Ruiz Estrada (2011) [50], as shown in Table 6.

(1) $X~N\left[0,1\right]$

(2) $X=\left\{XR:\left[0~1\right]\right\}$

(3) $X_t\left({\displaystyle \sum }_{j=1}^n{\displaystyle \frac{X_{tj}}{T\left(X_{tj}\right)}}\right) t=1,2,3,4,5,6,7,8,9,10$

(4) $\mathrm{P}\mathrm{M}\mathrm{C}=\left(\begin{array}{c}\begin{array}{c}{X}_1\left({\displaystyle \sum _{i=1}^5}{\displaystyle \frac{X_{1i}}{5}}\right)+X_2\left({\displaystyle \sum _{j=1}^4}{\displaystyle \frac{X_{2j}}{4}}\right)+X_3\left({\displaystyle \sum _{k=1}^3}{\displaystyle \frac{X_{3k}}{3}}\right)\\ +X_4\left({\displaystyle \sum _{l=1}^6}{\displaystyle \frac{X_{4l}}{6}}\right)+X_5\left({\displaystyle \sum _{m=1}^5}{\displaystyle \frac{X_{5m}}{5}}\right)+X_6\left({\displaystyle \sum _{n=1}^4}{\displaystyle \frac{X_{6n}}{4}}\right)\end{array}\\ +X_7\left({\displaystyle \sum _{o=1}^3}{\displaystyle \frac{X_{7o}}{3}}\right)+X_8\left({\displaystyle \sum _{p=1}^3}{\displaystyle \frac{X_{8j}}{3}}\right)+X_9\left({\displaystyle \sum _{j=1}^5}{\displaystyle \frac{X_{9j}}{5}}\right)+X_{10}\end{array}\right)$

(5) $\mathrm{PMC} \mathrm{surface}=\left(\begin{array}{ccc}{X}_1& X_2& X_3\\ X_4& X_5& X_6\\ X_7& X_8& X_9\end{array}\right)$

3.5. Policy Choices and Data Processing Overview

In this study, 22 key energy transition policies were evaluated and analyzed based on data published by the Chinese State Council Policy Document Database. The specific policy selection and data processing procedures were as follows:

• (1). Text Preprocessing. In the initial stage, we used Python to remove non-Chinese characters, numbers, punctuation marks, and other irrelevant symbols to clean the noise from the data. Utilizing a predefined list of Chinese stop words, common stop words were eliminated to focus the analysis on meaningful content. All text data were converted to UTF-8 encoding to prevent encoding errors during processing.

• (2). Word Segmentation and Part-of-Speech Tagging. We employed the Jieba library to segment the preprocessed text. To improve segmentation accuracy, a custom dictionary containing terms related to energy transition was added. After segmentation, part-of-speech tagging was performed to identify nouns, verbs, adjectives, etc., providing a foundation for subsequent analysis. By calculating the frequency of each word across all documents, high-frequency words were extracted to identify important concepts and themes in the policy texts.

• (3). Topic Modeling with LDA. Using Python’s Scikit-Learn library, we trained Latent Dirichlet Allocation (LDA) topic models to uncover potential topics within the policy texts. By adjusting the number of topics and based on the model’s topic coherence scores and interpretability, we determined the optimal number of topics. For each topic, we extracted the most contributing keywords and named the topics accordingly to facilitate understanding and analysis.

• (4). PMC Index Calculation. According to the main indicator and sub-indicator evaluation system of the PMC index model, we evaluated each policy document one by one. Referring to the formulas in the paper (Equations (3) and (4)), we implemented the calculation of the PMC index in Python. During the scoring process, we thoroughly read the policy content and assigned scores to each indicator according to the established evaluation criteria through group discussions.

4. Results

4.1. General Evaluation of China’s Energy Transition Policies

The 22 policies were ranked into three levels (perfect, good, and acceptable) and compared through the PMC index, as shown in Table 7. It can be found that among all policies, 2 policies are perfect, 18 are good, and the remaining 2 policies are acceptable. In particular, two landscape policies are evaluated as perfect, and the rest are evaluated as good; while one regime policy and one niche policy are each evaluated as acceptable, and the rest are evaluated as good.

The average value of the PMC index provides a more intuitive evaluation of China’s energy transition policy at the three levels of landscape, regime, and niche. The assessment of the energy transition policy for all three levels is good. The average values of the PMC index for landscape policy, regime policy, and niche policy are 8.698, 7.919, and 7.790, ranking 1st, 2nd, and 3rd, respectively. This rank implies China’s energy transition policy has a good top-level design and clear target directions. Further improvements are needed in driving changes in energy systems and institutions and developing renewable energy technologies and various types of low-carbon innovations. The three policy levels have a decreasing influence on China’s energy transition projects.

In terms of the policy improvement strategies for the three levels, as shown in Figure 4, landscape policy has excellent performance for policy evaluation (X3), policy area (X5), policy receptor (X6), and policy approach (X7), with an average value of PMC index over 0.9 and nodes close to the outer boundary of the network. However, since nodes X8 and X9 are in the recesses of the network, the landscape policy improvement can start from the aspects of policy releaser and policy focus, and the recommended reference improvement order is X8-X9. Likewise, the regime policy can be improved in the order of X8-X9-X4, and the niche policy can improve in the order of X8-X4. Of course, this order is not absolute and should be combined with the specific situation of policy implementation and feedback.

4.2. Comparative Evaluation of China’s Energy Transition Policies

4.2.1. Landscape Policy

The eight landscape policies were classified into two evaluation levels: perfect and good. P6 and P7 are the two landscape policies with the highest and lowest scores for PMC, respectively. The results of the comparative evaluation of P6 and P7 are shown in Figure 5.

The PMC index of policy P6, Working Guidance For Carbon Dioxide Peaking And Carbon Neutrality In Full And Faithful Implementation Of The New Development Philosophy, is 9.467, which is the highest score of all 22 policies. Among the nine main indicators, only policy nature (X1) falls below the average rank of landscape policies. This indicates that the policy is comprehensive, scientific, and reasonable in the formulation process, but there is room for improvement. The order of policy improvement is judged based on the scores of indicators below the average, with indicators with lower scores being given priority. For example, the score of X1 is 0.075 below the average, indicating that the policy nature should be improved.

The PMC index of policy P7, National Climate Change Adaptation Strategy 2035, is 7.783. Although P7 is evaluated as good, it has the lowest score among the eight land-scape policies. Four of the nine main indicators are below the average level: incentives and constraints (X4), policy receptor (X6), policy releaser (X8), and policy focus (X9). In particular, the index score for incentives and constraints (X4) is 0.354, indicating that the incentives and regulation of policies need to be improved. The index score for policy receptor (X6) is 0.219, indicating that the coverage of policy implementation agencies is not comprehensive enough. The index scores for policy releaser (X8) and policy focus (X9) are 0.209 and 0.450, suggesting that policy release requires a higher level of government agency and more policy content coverage. Since the policy has the lowest score on policy releaser and policy focus indicators, they are the areas that need the most attention.

4.2.2. Regime Policy

The evaluation results of the seven institutional policies were all within the good and acceptable ranges, among which P11 had the highest PMC index and P13 had the lowest PMC index. The comparative evaluation results for P11 and P13 are shown in Figure 6.

The PMC index of policy P11, Opinions on Improving Institutional Mechanisms and Policy Measures for Green and Low-Carbon Energy Transition, is 8.600, ranking first among all institutional policies and being evaluated as good. The policy aims to establish a green and low-carbon energy transition promotion mechanism, led by the control of total energy consumption and energy intensity of added industrial value, as well as a non-fossil energy target system, during the 14th FYP period to improve energy policies, standards, markets, and regulatory systems. Among all nine indicators, only the score of the policy releaser (X8) is lower than the average level, and the scores of all other indicators are above or equal to the average level. These comparison results show that the development and implementation of P11 are comprehensive, and the policy design is reasonable. However, to further enhance the policy, P11 should be released by a higher-level government agency.

The PMC index of policy P13, Notice on Further Deepening the Market-Oriented Reform of Electricity Pricing for Coal-Fired Power Generation, is 5.783, ranking 7th among all regime policies, and its evaluation level is acceptable. The policy aims to accelerate the market-oriented reform of electricity prices, improve the electricity pricing mechanism, and ensure a safe and stable electricity supply. Among all nine indicators, only policy timeliness (X2) exceeds the average level, and all other indicators are substantially lower than the average, indicating that the policy’s impact on China’s energy transition is only focused on power system reform. In addition, X4 and X9 have the lowest scores, with nodes located in the center of the network, so it is recommended that improvements should focus on clarifying incentives and constraint measures and extending the coverage of policy focus.

4.2.3. Niche Policy

Similar to the regime policy, the seven niche policies were all classified as good and acceptable, with P18 having the lowest PMC index and P22 having the highest PMC index. The comparative evaluation results for P18 and P22 are shown in Figure 7.

The PMC index of policy P18, Guiding Opinions on Promoting the Integration of Power Generation, Grids, Demand, and Storage, and the Development of Multi-Energy Complementarity, is 5.933, ranking 7th in niche policies, and its evaluation level is acceptable. This policy aims to promote the integration of source, network, storage, and multi-energy complementary development, enhance the level of renewable energy development and consumption and the proportion of non-fossil energy consumption, and promote China’s energy transition and economic and social development. Among the nine main indicators, only policy evaluation (X3) scored 0.048 higher than the average, indicating that the policy was designed on the premise of sufficient basis, clear objectives, and detailed measures. However, the other eight indicators scored below average, especially the node of X4, which is located in the center of the network, so it is recommended that the focus of improvement should be on clarifying the policy incentives and constraints.

The PMC index of policy P22, The Medium-Term and Long-Term Plan for the Development of the Hydrogen Energy Industry (2021–2035), is 8.633, ranking 1st in niche policies, and is evaluated as good. This policy puts forward the objectives of each stage of the hydrogen energy industry development and deploys essential measures to promote the high-quality development of the hydrogen energy industry. Only the policy releaser (X8) scored below the average of the nine main indicators. The low authority of the policy-releasing agency may lead to constraints in implementing the policy among agencies at the same level, so if improvements are desired, the policy could be released and advanced by a higher-level government agency.

5. Discussion

With the deepening awareness of environmental protection and the advancement of ecological civilization, China’s energy transition policies are continuously being refined and enhanced. Each energy transition policy formulated and implemented ensures the ongoing progress of energy transition projects in China during specific periods. This study employs text mining techniques, a Multi-Level Perspective (MLP) framework, and a PMC index model to systematically conduct a quantitative assessment of 22 significant energy transition policies during China’s 14th Five-Year Plan period. The results indicate that the overall design of China’s energy transition policies is generally well designed, receiving positive evaluations at the macro-, meso-, and micro-levels, reflecting the efforts and achievements of policymakers in promoting energy transition. However, the evaluation also revealed some areas that need improvement, such as the lack of clear incentives and constraints, insufficient authority of policy issuers, and limited policy coverage.

From a theoretical perspective, this study enriches the theoretical framework for evaluating energy transition policies. Traditional policy evaluation methods often focus on specific industries or sectors and tend to be subjective. By introducing the MLP framework, this research categorizes policies into landscape, regime, and niche levels, providing a multi-layered analytical perspective. This hierarchical evaluation method aids in a deeper understanding of how policies at different levels impact energy transition. Additionally, by integrating text mining techniques and utilizing the LDA topic model to objectively extract policy evaluation indicators, the study reduces the interference of subjective human factors, enhancing the objectivity and scientific rigor of the assessment. This innovative approach offers new theoretical support for policy evaluation research, contributing to a deeper understanding of policy effects and mechanisms, and advancing the methodology of policy evaluation.

From a practical standpoint, the findings of this study are of great reference value to policymakers. Firstly, through the overall and comparative assessment of the 22 energy transition policies, the strengths and weaknesses of the policies are clarified, providing a basis for policy optimization. Specifically, to address the issue of lacking clear incentives and constraints, it is recommended to establish explicit reward and punishment mechanisms within the policies to enhance their enforceability. Regarding the lower administrative level of policy issuers, it is suggested to elevate the administrative rank of the issuing bodies or to jointly release policies through multiple authoritative departments to enhance their authority and influence. To tackle the issue of limited policy coverage, it is advised to conduct broader stakeholder research during the policy formulation process to ensure that the policies address all key areas and links in the energy transition process. These improvement measures will help to enhance the effectiveness of the policies and promote the achievement of China’s energy transition goals.

In terms of methodology, this study presents significant innovations and improvements compared to previous research. Traditional PMC index models often rely on the subjective judgment of researchers during the policy selection and scoring process, leading to certain biases. This study establishes predefined selection criteria for policy samples, ensuring the timeliness and independence of the policies, and avoiding the iterative influence between old and new policies. By employing the MLP framework for hierarchical evaluation, it acknowledges the varying importance of policies at different levels, ensuring that macro-level policies receive appropriate attention in the assessment. Coupled with text mining techniques to objectively extract policy evaluation indicators, the study reduces subjectivity and enhances the reliability and validity of the evaluation results. This methodological improvement not only addresses the shortcomings of traditional PMC index models, but also provides a more objective and comprehensive analytical framework for policy evaluation research.

However, this study also has certain limitations. Firstly, although text mining techniques enhance the objectivity of the assessment, the determination of the number of topics and the methods of text preprocessing in the construction of the topic model may still be influenced by the researchers’ subjectivity. Secondly, this study only selected significant energy transition policies during the 14th Five-Year Plan period and did not conduct comparative analyses of policies from other periods. Additionally, due to the time lag in policy effects, the selected policy sample years primarily come from the early stages of the 14th Five-Year Plan. Future research could expand the range of policy samples to include policies from different time periods for longitudinal comparative studies. Furthermore, the actual effects of energy transition policies are also influenced by various external factors such as economic, technological, and social elements; future research could incorporate empirical data for a more in-depth evaluation and analysis of policy implementation effects.

6. Conclusions and Policy Implications

This study combined text excavation techniques, the MLP energy transition analysis framework, and the PMC index model to quantitatively evaluate 22 important energy transition policies in China, and draws the following conclusions.

The overall design of China’s energy transition policy is reasonable. Among all 22 policies, 2 policies were evaluated as perfect, 18 were evaluated as good, and the remaining 2 were evaluated as acceptable. The 22 policies were then categorized into landscape policy, regime policy, and niche policy. All three levels of policy were evaluated as good, with their influence on China’s energy transition projects decreasing in order.

These energy transition policies still need improvement. Ten main indicators and thirty-eight sub-indicators were set up in this study, and each indicator was assigned a score to evaluate energy transition policies. Scoring makes it possible to intuitively identify the specific shortcomings of each policy. The common shortcomings for current energy policies are summarized as follows: (1) Lack of clear incentives and constraint measures may result in policies not being taken seriously during implementation. (2) The low level of policy releasers leads to inadequate authority of energy transition policies. (3) The coverage of policy focus is not broad enough, which may limit the policy’s applicability. The strategy and order of policy improvement can be judged based on the scores of below-average indicators, but the order is not absolute, and it should also be judged in the context of policy implementation and feedback.

To address the identified shortcomings, several specific solutions have been proposed. Firstly, incorporating clear incentives and constraints into energy transition policies can enhance their effectiveness. On one hand, explicit financial incentives, such as subsidies, tax reductions, or grants, can be established for entities that meet or exceed policy requirements. Conversely, imposing penalties or sanctions for non-compliance can serve as a deterrent against ignoring policy directives. Secondly, it is crucial to elevate the authority of policy issuers; policies should be released by higher-level government agencies or in collaboration with multiple authoritative bodies to enhance legitimacy and ensure robust enforcement. This may involve support from the central government or joint announcements from key departments to strengthen the policy’s impact. Finally, broadening the scope of policy focus is a necessary measure to improve applicability. Policies should encompass a wider range of industries, technologies, and practices involved in the energy transition. This can be achieved through comprehensive stakeholder consultations and surveys to identify all the relevant areas that require guidance.

Author Contributions

Conceptualization, Y.C. and P.G.; Methodology, Y.C. and T.Z.; Software, T.Z.; Investigation, T.Z.; Resources, Y.C.; Data curation, Y.C.; Writing—original draft, Y.C.; Writing—review & editing, T.Z. and P.G.; Supervision, P.G.; Project administration, P.G. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Major steps in the quantitative evaluation of China’s energy transition policy.

Figure 2. Word cloud of China’s energy transition policies.

Figure 3. LDA calculates perplexity to determine the number of topics.

Figure 4. The spider graph of the average of the PMC index for the nine indicators of the three-level policies.

Figure 5. PMC index of landscape policies (P6, P7) compared with the average.

Figure 6. PMC index of regime policies (P11, P13) compared with the average.

Figure 7. PMC index of niche policies (P18, P22) compared with the average.

Table 1

China’s energy transition policy evaluation samples.

Level	Item	Policy Name	Release Time
Landscape(Macro)	P1	Action plan for carbon dioxide peaking before 2030	October 2021
	P2	the 14th Five-Year Plan (2021–2025) for the National cleaner production implementation plan	October 2021
	P3	the 14th Five-Year Plan for Modern Energy Systems	January 2022
	P4	the Comprehensive Work Plan for Energy Conservation and Emission Reduction for the “14th Five-Year Plan” Period	January 2022
	P5	Outline of the 14th Five-Year Plan (2021–2025) for National Economic and Social Development and Vision 2035 of the People’s Republic of China	October 2020
	P6	Working Guidance For Carbon Dioxide Peaking And Carbon Neutrality In Full And Faithful Implementation Of The New Development Philosophy	September 2021
	P7	National Climate Change Adaptation Strategy 2035	May 2022
	P8	Energy Supply and Consumption Revolution Strategy (2016–2030)	December 2016
Regime(Middle)	P9	14th Five-Year Plan for Scientific and Technological Innovation in Energy Sector	November 2021
	P10	Establish and Improve the Implementation Plan of the Standard Measurement System for Carbon Peaking and Carbon Neutrality	October 2022
	P11	Opinions on Improving Institutional Mechanisms and Policy Measures for Green and Low-Carbon Energy Transition	January 2022
	P12	Guiding opinions of the State Council on accelerating the establishment and improvement of a green and low-carbon circular development economic system	February 2021
	P13	Notice on further deepening the market-oriented reform of electricity pricing for coal-fired power generation	October 2021
	P14	Plan for improving the double Control system of Energy consumption intensity and Total quantity	September 2021
	P15	the Implementation Plan for Science and Technology Support for Carbon Dioxide Peaking and Carbon Neutrality (2022–2030)	June 2022
Niche(Micro)	P16	14th Five-Year Plan for Renewable Energy Development	October 2021
	P17	The Implementation Plan on Promoting the High-Quality Development of New Energy in the New Era	May 2022
	P18	Guiding opinions on promoting the integration of power generation, grids, demand, and storage, and the development of multi-energy complementarity	February 2021
	P19	Guiding Opinions on Accelerating the Development of New Energy Storage	July 2021
	P20	Medium and long-term development plan for pumped hydro storage (2021–2035)	September 2021
	P21	Smart Photovoltaic Industry Innovation and Development Action Plan (2021–2025)	December 2021
	P22	The medium- and long-term Plan for the Development of the hydrogen Energy Industry (2021–2035)	March 2022

Table 2

High-frequency word statistics for energy transition policies.

Word	Counts	Word	Counts	Word	Counts	Word	Counts	Word	Counts
Development	1654	Standards	471	Planning	366	Levels	315	Consumption	283
Energy	1634	Utilization	464	Power	361	Services	311	Coal	272
Construction	1247	Research	436	Energy Storage	357	Smart	309	Comprehensive	271
Technology	1179	Fields	430	Optimization	344	Policy	307	Targets	262
Green	865	Industry	421	Area	339	System	304	Cooperation	259
System	804	Enterprise	412	Projects	338	Technology	304	Reform	248
National	567	Resources	394	Climate Change	330	International	301	Monitoring	243
Focus	512	Power Generation	388	New Energy	324	Market	300	Social	235
Mechanism	498	Production	371	Systems	324	Management	295	Economy	234
Capacity	489	Ecology	366	Engineering	319	Regional	292	Strategy	228

Table 3

The important words under each topic in the LDA.

Topic1(Energy Development)		Topic2(Institution Construction)		Topic3(Technology Innovation)		Topic4(ElectricityProduction)		Topic5(Climate Change)
Word	N	Word	N	Word	N	Word	N	Word	N
Energy	1127.6	Development	613.2	Technology	833.2	Development	365.7	Climate Change	291.7
Green	704.4	Construction	451.2	Energy	358.2	Construction	284.0	Climate	184.7
Development	519.8	System	317.1	Research	297.2	Power Generation	261.2	Risks	129.9
Construction	355.4	System	200.0	Standards	265.0	Energy Storage	261.0	Building	105.7
Focus	260.6	Mechanism	193.4	Smart	258.6	New Energy	243.0	Capacity	104.1
Utilization	233.0	National	188.9	Metering	199.0	Electricity	235.0	Ecology	104.1
Production	229.1	Services	159.7	Systems	168.7	Projects	176.7	Monitoring	90.9
System	224.5	Industry	152.8	Technology	166.7	Hydrogen Energy	174.8	Assessment	72.7
Consumption	215.8	Economy	151.5	National	131.5	Industry	170.4	Highlights	71.2
Region	210.3	Capacity	130.9	Key Technologies	125.4	Planning	170.2	System	69.0

Table 4

The multi-input–output table of quantitative evaluation of China’s energy transition policies.

Main Indicators	Sub-Indicators
X1	X1.1	X1.2	X1.3	X1.4	X1.5	—
X2	X2.1	X2.2	X2.3	X2.4	—	—
X3	X3.1	X3.2	X3.3	—	—	—
X4	X4.1	X4.2	X4.3	X4.4	X4.5	X4.6
X5	X5.1	X5.2	X5.3	X5.4	X5.5	—
X6	X6.1	X6.2	X6.3	X6.4	—	—
X7	X7.1	X7.2	X7.3	—	—	—
X8	X8.1	X8.2	X8.3	—	—	—
X9	X9.1	X9.2	X9.3	X9.4	X9.5	—
X10	—	—	—	—	—	—

Table 5

The probability that a policy belongs to each topic in the LDA.

Policy	Topic1	Topic2	Topic3	Topic4	Topic5	Policy	Topic1	Topic2	Topic3	Topic4	Topic5
P1	0.052	0.070	0.815	0.064	0.000	P12	0.243	0.021	0.736	0.000	0.000
P2	0.076	0.000	0.794	0.108	0.022	P13	0.000	0.999	0.000	0.000	0.000
P3	0.015	0.299	0.562	0.124	0.000	P14	0.030	0.000	0.968	0.002	0.000
P4	0.098	0.000	0.861	0.040	0.000	P15	0.031	0.013	0.229	0.715	0.012
P5	1.000	0.000	0.000	0.000	0.000	P16	0.017	0.360	0.540	0.072	0.011
P6	0.101	0.076	0.799	0.000	0.023	P17	0.000	0.556	0.392	0.052	0.000
P7	0.000	0.000	0.000	0.000	1.000	P18	0.000	0.841	0.159	0.000	0.000
P8	0.128	0.064	0.714	0.095	0.000	P19	0.000	0.660	0.141	0.198	0.000
P9	0.000	0.101	0.000	0.899	0.000	P20	0.000	0.449	0.472	0.024	0.055
P10	0.000	0.000	0.211	0.789	0.000	P21	0.119	0.187	0.094	0.600	0.000
P11	0.000	0.271	0.694	0.035	0.000	P22	0.128	0.612	0.081	0.179	0.000

Table 6

Policy evaluation criteria.

PMC Score	9–10	7–8.99	5–6.99	0–4.99
Evaluation Criteria	perfect	good	acceptable	low

Table 7

The PMC index and its levels for China’s energy transition policies.

Level	Item	PMC-Index	Rank	Criteria	PMC–Average	Rank–Average	Criteria–Average
Landscape	P1	8.933	4	good	8.698	1	good
	P2	8.150	7	good
	P3	8.350	6	good
	P4	8.817	5	good
	P5	8.950	3	good
	P6	9.467	1	perfect
	P7	7.783	8	good
	P8	9.133	2	perfect
Regime	P9	7.333	6	good	7.919	2	good
	P10	8.000	5	good
	P11	8.600	1	good
	P12	8.567	3	good
	P13	5.783	7	acceptable
	P14	8.567	3	good
	P15	8.583	2	good
Niche	P16	8.500	2	good	7.790	3	good
	P17	8.267	3	good
	P18	5.933	7	acceptable
	P19	7.417	6	good
	P20	7.883	5	good
	P21	7.900	4	good
	P22	8.633	1	good

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/en17235990/s1, Figure S1: The PMC surface of landscape policy; Figure S2: The PMC surface of regime policy; Figure S3: The PMC surface of niche policy; Figure S4: The PMC surface of policy P6; Figure S5: The PMC surface of policy P7; Figure S6: The PMC surface of policy P11; Figure S7: The PMC surface of policy P13; Figure S8: The PMC surface of policy P18; Figure S9: The PMC surface of policy P22; Table S1: The PMC index model classification and parameter setting; Table S2. Multi-input/output table for all policies (P1 to P22).

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