Quantitative Evaluation of Carbon Reduction Policy Based on the Background of Global Climate Change

Abstract

High-quality carbon reduction policies play a crucial role in tackling global climate change and reducing carbon dioxide emissions. China, as the world’s largest emitter of carbon dioxide, has committed to peaking its carbon emission by 2030. This study focuses on the evaluation of 12 carbon reduction policies implemented by the Chinese government. A quantitative evaluation index system for carbon reduction policies was designed. Next, the policy modeling consistency (PMC) index method was utilized to assess the quality of these policies. The findings are as follows: Firstly, the average PMC index value of the 12 carbon reduction policies is 6.75, indicating a good performance overall. Secondly, the carbon reduction policies established by the Chinese government are generally effective; among the twelve policies analyzed, one policy received a perfect evaluation grade, four policies were graded as excellent, six policies were graded as good, and one policy received a bad evaluation grade. Thirdly, the indicators Z₃, Z₄, Z₅, and Z₉ in the evaluation index system for carbon reduction policies scored relatively low, suggesting that there are some deficiencies in terms of policy timeliness, issuing institutions, policy tools, and policy evaluation within the 12 policies. Fourthly, this study presents a PMC curved surface diagram to visually illustrate the strengths and weaknesses of the carbon reduction policies. Finally, based on the research findings, recommendations are provided to enhance the quality of carbon reduction policies.

1. Introduction

Since the year 2000, humanity has made significant progress in both the economy and technology. However, this progress has come at a cost of consuming a large amount of fossil energy. According to the statistics of International Energy Agency (IEA), global oil demand was 99.4 million barrels per day in 2022. This heavy reliance on fossil energy has resulted in record-high greenhouse gas emissions worldwide. The statistics of IEA show that the total global energy-related greenhouse gas emissions reached 41.3 billion tons in 2022. Consequently, the climate change caused by massive greenhouse emissions has become the most critical challenge facing human society, which directly threatens the sustainable development of humans [1]. Over the past century, the average temperature of the global ocean surface has increased by 0.89 °C. With the continuous increase in greenhouse gas emissions, global climate change is creating adverse impacts on human production and life. These impacts include more frequent extreme weather events, grain reduction, and melting glaciers. For instance, in August 2023, an extreme rainstorm hit Beijing, affecting 1.23 million people, resulting in a loss of 33 lives, and causing direct and indirect property damages amounting to more than 10 billion yuan. In order to combat climate change and achieve sustainable development of human beings, the 2015 Paris Agreement requires countries around the world to reduce greenhouse gas emissions [2]. Given that carbon dioxide is one of the most significant greenhouse gases, many countries have announced their own carbon dioxide emission reduction plans and implemented a series of carbon reduction policies [3]. For example, the United States government has introduced the American Clean Energy Act, the Rebuilding a Better Future Act, and the Infrastructure Investment and Jobs Act. Similarly, the Chinese government has formulated the Implementation Opinions on Promoting Green and Carbon Reduction Development of the Postal Express Delivery Industry, as well as the Opinions on Strengthening the Green Carbon Reduction Construction of County Towns. Additionally, the European Union (EU) has introduced numerous carbon reduction policies, such as the European Green New Deal and the European Climate Law. Moreover, EU has pledged to reduce its carbon emissions by 62% compared to 2005 levels by 2030. High-quality carbon reduction policies are essential in addressing global climate change to achieve sustainable development. However, the quality of the carbon reduction policies implemented by governments remains unknown. Therefore, evaluating these policies becomes particularly important.

The evaluation of public policy can be divided into two different types: qualitative evaluation and quantitative evaluation. Qualitative evaluation of public policy can lead to subjective conclusions and personal biases [4]. Current research on carbon reduction policies primarily focuses on evaluating the outcomes of policy implementation and there is limited research on quantitatively evaluating policies from a policy-making perspective [5]. To address this gap, this paper utilizes the PMC index method to analyze carbon reduction policy. The carbon reduction policies examined in this paper are formulated by the Chinese government. This choice is justified because China’s carbon emission of 9.894 billion tons in 2020 ranked first in the word. Furthermore, in order to achieve sustainable development goals and reduce the adverse effects of climate change, in 2020, the Chinese government announced that by 2030, China’s carbon emissions per unit of gross domestic product (GDP) would be reduced by 60% to 65% compared with 2005, so as to achieve a carbon peak [6]. To achieve this goal, the Chinese government has introduced a series of carbon reduction policies, which provide abundant research data for this study. Therefore, this paper has decided to conduct a quantitative evaluation of China’s carbon reduction policies by using the PMC method. The PMC index can be used to analyze the internal consistency and pros and cons of policies. The specific research process of this paper is as follows: First, collect the carbon reduction policies formulated by the Chinese government and carefully review them. Next, screen the collected carbon reduction policies based on their relevance and significance. Then, design an evaluation index system for carbon reduction policies based on policy texts and academic research results. Finally, conduct a quantitative evaluation of carbon reduction policies.

In summary, this paper aims to evaluate the carbon reduction policies formulated by using the PMC index method. Although many countries have implemented numerous carbon reduction policies, there is a lack of studies that quantitatively evaluate these policies from the perspective of policy formulation. Therefore, this paper makes two contributions. Firstly, it designs a quantitative evaluation index system for carbon reduction policies from the perspective of policy formulation. This index system aims to provide a comprehensive evaluation of the quality of carbon reduction policies. Secondly, the paper uses the carbon reduction policies implemented by the Chinese government as an example to conduct a quantitative evaluation. This evaluation helps to assess the quality of these policies and find their strengths and weaknesses. By doing so, it can provide valuable insights for improving the quality of carbon reduction policy. The rest of this paper consists of four sections: the literature review, research design, research findings, and conclusions.

2. Literature Review

The purpose of the investigation of this paper is to provide a quantitative assessment of carbon reduction policy to reveal the quality of carbon reduction policy issued by the Chinese government. The literature review is carried out from two aspects: carbon emission and public policy assessment.

In terms of the research of carbon emission, there has been an extensive study about the impact of greenhouse gas emissions, particularly carbon dioxide, on global climate change. This research spans across multiple disciplines, such as environmental science, management, economics, and biomedicine. Currently, the research mostly focuses on the carbon footprint, influencing factors of carbon emissions, and suggestions for carbon reduction. Firstly, regarding the carbon footprint, many scholars have conducted assessments on the carbon emission footprint; assessing the carbon footprint refers to the measurement of greenhouse gas emissions, including CO₂, CH₄, and N₂O. Various sectors have been assessed for their carbon footprints, such as agriculture, tourism, construction, and healthcare. For example, Tennison et al. assessed the carbon footprint of the health sector in UK (United Kingdom) and found that it contributed 26% less greenhouse gases in 2019 compared to 1990 [7]. Safaa et al. assessed the carbon emission footprint of Morocco’s tourism industry and found that the global tourism footprint in Morocco from 2010 to 2018 was 7148.9 tons, then, they proposed policies to reduce the carbon footprint, including the use of electric vehicles [8]. Additionally, scholars have studied the influencing factors of the carbon footprint taking many countries, such as Canada, Japan, China, and South Korea as examples. They found that technological innovation can increase a carbon footprint, while the use of nuclear energy can reduce a carbon footprint [9]. Overall, scholars have employed various methods to evaluate carbon footprints in different fields and have proposed targeted recommendations based on their research. Secondly, aside from carbon footprint analysis, exploring the influencing factors of carbon emissions is another area of interest among scholars. For instance, Rehman et al. analyzed the relationship between industrialization, energy import, and carbon emissions in Pakistan by using the unit root test method. They found that industrialization, energy import, and economic growth all contributed to the increase in carbon emissions in Pakistan [10]. Bano et al. used the autoregressive distributed lag method to analyze the impact of human capital on carbon emissions in Pakistan and discovered that improving human capital through education development not only promotes economic growth but also helps reduce carbon emissions [11]. Sharif et al. used panel data from 74 countries to analyze the dynamic relationship between renewable energy, non-renewable energy, and carbon emissions. They found that the use of renewable energy can improve the ecological environment and reduce carbon emissions, while the use of non-renewable energy can worsen the ecological environment and increase carbon emissions [12]. Finally, some scholars have proposed numerous suggestions for carbon reduction, including establishing carbon trading markets, developing green transportation and agriculture, and utilizing renewable energy [13,14,15,16].

As public policy continues to be introduced by governments around the world, there has been increasing research on public policy evaluation. The evaluation of public policy originated in developed countries, such as the United Kingdom and Japan. The key to conducting effective policy evaluation lies in obtaining sufficient policy information and using appropriate evaluation tools. Currently, most research on public policy evaluation focuses on evaluating the outcomes of policy implementation. This means that the majority of studies are post-evaluations, with limited research conducted prior to evaluations. Scholars have primarily focused on evaluating environmental policies [17], agriculture policies [18], tax policies, and climate policies [19]. For instance, Kiss and Popovics evaluated the effectiveness of carbon reduction policies on carbon emissions in 25 countries. They found that different types of carbon reduction policies have significantly different effects on reducing carbon emissions [20]. Bayer and Aklin analyzed the impact of the EU emissions trading system on carbon emissions by using the synthetic control method. They discovered that the EU emissions trading system has the ability to significantly reduce carbon emissions [21]. Karim et al. conducted a qualitative assessment of Bangladesh’s renewable energy policy and put forward recommendations, such as strengthening public awareness and increasing investment in renewable energy to promote the renewable energy industry [22]. Additionally, some scholars treated policy implementation as a quasi-natural experiment and analyzed the impact of innovation demonstration zone policies on the level of innovation in cities by using the difference method and found that the establishment of innovation demonstration zones can enhance the level of innovation [23]. Furthermore, it is found that, in recent years, some scholars have adopted the PMC–AE model to conduct quantitative evaluation of manufacturing policies [24].

In summary, there is a considerable amount of the literature about carbon emissions and the evaluation of public policy. While some scholars have examined carbon emission policies, providing valuable insights for this paper, these studies specifically targeting carbon reduction policies focus more on their implementation effects. In other words, scholars analyze the effectiveness of specific carbon emission policies on carbon emissions, rather than evaluating these policies in advance. However, the quality of carbon reduction policy is directly related to implementing effectiveness. Although many countries have introduced various carbon reduction policies, their quality remains unknown. Therefore, this paper aims to use the PMC index method to assess the carbon reduction policies implemented by the Chinese government, taking China as an example, since it is the world’s largest carbon emitter. This evaluation not only seeks to shed light on the quality of carbon reduction policies but can also provide inspiration to improve the quality of carbon reduction policies.

3. Research Design

3.1. Introduction of PMC Model

The PMC index method is a quantitative approach used for policy evaluation. It enables the analysis of the consistency level within policies by constructing a binary scoring model and without limiting the number and weight of indicators [24]. The PMC index method has become one of the important methods for policy quantitative evaluation research, and its application scope covers many fields, such as environmental science [25] and economics [26]. It can analyze the strengths and weaknesses of a single policy and display the advantages and disadvantages of different policy dimensions through a three-dimensional PMC surface figure [27]. Compared with other methods, the PMC index model has some advantages. Firstly, the construction of the PMC index model is based on policy text, which avoids the subjectivity of the evaluation results, to a large extent, and makes the results more accurate [28]. Secondly, the PMC index model is used to analyze the internal heterogeneity and the degree of pros and cons of policies from multiple dimensions, and the PMC curved surface figure is used to visually display the indicator variables at all levels of policies, thus improving the effectiveness and feasibility of policy evaluation [28]. Thirdly, the construction and calculation process of the PMC index model is relatively simple and easy to operate [29].

The steps for evaluating a policy using the PMC index method mainly involve four parts. Firstly, it requires designing the index system for policy evaluation by classifying variables and identifying parameters. Secondly, a multi-input–output table needs to be established to calculate the value of the first-level index. Thirdly, the PMC index is calculated, and the policy is evaluated based on the calculation results. Finally, a PMC surface figure is drawn to visually evaluate the policy.

3.1.1. Design of Carbon Reduction Policy Evaluation Index System

This paper presents an index system that evaluates carbon reduction policy by referring to the research of Nkoua and Yiqun. Nkoua and Yiqun designed an index system for evaluating advanced manufacturing development policy, which can provide inspiration for designing an index system to evaluate carbon reduction policy [24]. The policy evaluation index system consists of 10 first-level variables, namely: policy nature (Z₁), policy area (Z₂), policy timeliness (Z₃), issuing agency (Z₄), policy instrument (Z₅), policy function (Z₆), policy object (Z₇), effect level (Z₈), policy evaluation (Z₉), and policy disclosure (Z₁₀). Under each first-level evaluation index variable, corresponding second-level variables were selected (see

Table 1. Carbon reduction policy evaluation index system.

First-Level Index	Secondary Index	Evaluation Content	Evaluation Criteria
Policy nature (Z1)	Forecast (Z1:1)	Whether the policy has a forecast character	If yes, it is 1; If not, it is 0
	Supervise (Z1:2)	Whether the policy has a supervise character
	Suggestion (Z1:3)	Whether the policy has a suggestion character
	Oriented (Z1:4)	Whether the policy has an oriented character
	Support (Z1:5)	Whether the policy has a support character
Policy domain (Z2)	Political (Z2:1)	Whether the content of policy involves political
	Economy (Z2:2)	Whether the content of policy involves economy
	Environment (Z2:3)	Whether the content of policy involves environmental
	Society (Z2:4)	Whether the content of policy involves society
	Technology (Z2:5)	Whether the content of policy involves technology
Policy time (Z3)	Long-term (Z3:1)	Whether the policy involves short-term impact (terms <3 years);
	Medium-term (Z3:2)	Whether the policy involves medium-term impact (3–5 years);
	Short-term (Z3:3)	Whether the policy involves long-term impact (terms >5 years)
Issuing authority (Z4)	The State Council (Z4:1)	Whether the subject of the policy is the State Council
	State ministries and commissions (Z4:2)	Whether the subject of the policy is State ministries and commissions
	Local government (Z4:3)	Whether the subject of the policy is local government
Policy instrument (Z5)	Investment (Z5:1)	Whether the policy instrument is investment
	Technology development (Z5:2)	Whether the policy instrument is technology
	Legal guarantee (Z5:3)	Whether the policy instrument is legal
	Personnel training (Z5:4)	Whether the policy instrument is personal training
	Publicity and education (Z5:5)	Whether the policy instrument is publicity and education
Policy function (Z6)	Environmental protection (Z6:1)	Whether the policy function is environmental protection
	Carbon reduction (Z6:2)	Whether the policy function is carbon reduction
	Resource saving (Z6:3);	Whether the policy function is resources saving
	Green development (Z6:4)	Whether the policy function is green development
Policy object (Z7)	Government (Z7:1)	Whether the object of policy is government
	Enterprise (Z7:2)	Whether the object of policy is enterprise
	Public (Z7:3)	Whether the object of policy is public
	Environment (Z7:4)	Whether the policy is recommended
Policy role (Z8)	National development (Z8:1)	Whether the policy is recommended
	Regional economy (Z8:2);	Whether the policy role is region economy
	Technological innovation (Z8:3)	Whether the policy role is technological innovation
Policy evaluation (Z9)	Clear goal (Z9:1);	Whether the policy has a clear goal
	Scientific plan (Z9:2);	Whether the policy has a scientific plan
	Reasonable measures (Z9:3)	Whether the policy has reasonable measures
	Good reason (Z9:4)	Whether the policy has good reason
Policy disclosure (Z10)	Whether the policy is open to access

). A binary assignment was conducted for each first-level indicator sub-variable, where a value of 1 is assigned if the policy text contains the corresponding content of the evaluation indicator, and a value of 0 is assigned if it does not (see Table 1).

3.1.2. Establish Multiple Input–Output Table to Calculate First-Level Index

Using the established evaluation index system composed of primary and secondary indexes, the multi-input–output table adopts a binary scoring form (see

Table 2. Multi-input–output table.

First-Level Index	Secondary Index
Z1	Z1:1;Z1:2;Z1:3;Z1:4;Z1:5
Z2	Z2:1;Z2:2;Z2:3;Z2:4;Z2:5;Z2:5
Z3	Z3:1;Z3:2;Z3:3
Z4	Z4:1;Z4:2;Z4:3
Z5	Z5:1;Z5:2;Z5:3;Z5:4;Z5:5
Z6	Z6:1;Z6:2;Z6:3;Z6:4
Z7	Z7:1;Z7:2;Z7:3;Z7:4
Z8	Z8:1;Z8:2;Z8:3
Z9	Z9:1;Z9:2;Z9:3;Z9:4
Z10	No secondary index

). The specific interpretation of secondary indicators is used to assign values. All secondary variables adhere to the [0, 1] distribution and are assigned values of 0 or 1. The value of all primary indicators is equal to the sum of the values of secondary indicators and then divided by the number of secondary indicators contained in each primary indicator.

3.1.3. Calculation of PMC Index

In this paper, only by calculating the PMC index of each policy can evaluate each policy. The specific steps are as follows:

Firstly, to assign values to the secondary indicators in the carbon reduction policy evaluation system, see Formulas (1) and (2) for details.

Z:N [0~1]

(1)

Z = {ZR:[0~1]}

(2)

Formula (1) indicates that the values of all secondary indicators are greater than or equal to 0 and less than or equal to 1. The ZR in Formula (2) means that the value of the secondary variable is an integer, that is, the value of the secondary variable is either 0 or 1.

Second, the value of the first-level index in the carbon reduction policy evaluation system are calculated, as shown in Formula (3)

$$\mathrm{Z}={\sum }_{\mathrm{j}=1}^{\mathrm{n}}\frac{{\mathrm{Z}}_{\mathrm{i}\mathrm{j}}}{\mathrm{T}\left({\mathrm{Z}}_{\mathrm{i}\mathrm{j}}\right)}$$

(3)

where i = 1, 2, 3, …, n, i is the first-level variable, j is second-level variables, T represents the number of secondary indicators in each first-level indicator.

Finally, the PMC value of the carbon reduction policy are calculated, as shown in Formula (4).

$$\begin{gathered} \mathrm{P}\mathrm{M}\mathrm{C}={\mathrm{Z}}_1\left(\sum _{\mathrm{j}=1}^5\frac{{\mathrm{Z}}_{1\mathrm{i}}}{5}\right)+{\mathrm{Z}}_2\left(\sum _{\mathrm{j}=1}^5\frac{{\mathrm{Z}}_{2\mathrm{i}}}{5}\right)+{\mathrm{Z}}_3\left(\sum _{\mathrm{j}=1}^3\frac{{\mathrm{Z}}_{3\mathrm{i}}}{3}\right)+{\mathrm{Z}}_4\left(\sum _{\mathrm{j}=1}^3\frac{{\mathrm{Z}}_{1\mathrm{i}}}{3}\right)+{\mathrm{Z}}_5\left(\sum _{\mathrm{j}=1}^5\frac{{\mathrm{P}}_{5\mathrm{i}}}{5}\right){+\mathrm{Z}}_6\left(\sum _{\mathrm{j}=1}^4\frac{{\mathrm{Z}}_{6\mathrm{i}}}{4}\right){+\mathrm{Z}}_7\left(\sum _{\mathrm{j}=1}^4\frac{{\mathrm{Z}}_{1\mathrm{i}}}{4}\right) \\ +{\mathrm{Z}}_8\left(\sum _{\mathrm{j}=1}^3\frac{{\mathrm{Z}}_{1\mathrm{i}}}{3}\right)+{\mathrm{Z}}_9\left(\sum _{\mathrm{j}=1}^4\frac{{\mathrm{Z}}_{9\mathrm{i}}}{4}\right)+{\mathrm{Z}}_{10} \end{gathered}$$

(4)

In Formula (4), Z₁₀ is equal to 1, because Z₁₀ represents the openness of the policy, and all carbon reduction policies are open access.

According to the calculation results of the PMC index and referring to the research of Ma et al., the policy evaluation of the PMC index can be divided into four grades [26] (see

Table 3. Classification of policy evaluation grades.

PMC index	[0,5)	[5,7)	[7,9)	[9,10)
Evaluation grade	IV (Badness)	III (Good)	II (Excellent)	I (Perfect)

).

3.1.4. PMC Surface Drawing

The nine first-level evaluation indicators are substituted into Equation (5), and a 3 × 3 surface matrix can be constructed to draw the PMC surface figure. Through the PMC index surface figure, the advantages and disadvantages of various policies in three dimensions can be more directly displayed, so as to conduct a comprehensive evaluation and analysis of carbon reduction policy. Given that this paper is based on the PMC index value and the PMC surface figure, the carbon reduction policies can be judged.

$$\mathrm{P}\mathrm{M}\mathrm{C}=\left[\begin{array}{ccc}{\mathrm{Z}}_1& {\mathrm{Z}}_2& {\mathrm{Z}}_3\\ {\mathrm{Z}}_4& {\mathrm{P}}_5& {\mathrm{P}}_6\\ {\mathrm{Z}}_7& {\mathrm{Z}}_8& {\mathrm{Z}}_9\end{array}\right]$$

(5)

3.2. Data Sources

Based on the timeliness of policy release and the availability of policy texts, the research period of this paper is from 2020 to 2023. “Carbon reduction” and “carbon reduction and emission reduction” were the keywords and titles for searching in “Peking University Magic Weapon”. In addition, the department websites of the central government and the websites of local governments were also searched to find related policies for confirmation and supplementation. In total, 110 policy texts on carbon reduction from across the country were collected. To ensure the authority and the representativeness of the policy samples, a preliminary screening of the collected policy texts had been conducted. Policy texts that are not strongly related to greenhouse gas emission control, pollution prevention and control, energy and resource conservation, and carbon reduction policies were excluded. Additionally, informal policy texts, such as notifications, approvals, reports, and letters were filtered out. As a result, a total of 60 effective carbon reduction policy documents were obtained. Based on the evaluation framework of carbon reduction policies mentioned above, 12 representative policy samples were selected for quantitative evaluation from the collected and sorted 60 carbon reduction policies (see

Table 4. Sample carbon reduction policies.

Number	Policy Name	Issuing Authority	Release Date
A1	Issued a notice on several measures to promote the high-quality development of green carbon reduction industry in Shenzhen	Shenzhen Municipal Government	12 December 2022
A2	Opinions on improving the system, mechanism, and policy measures of energy green carbon reduction transition	National Development and Reform Commission	25 August 2022
A3	Opinions on supporting Shandong to deepen the transformation of old and new kinetic energy and promote green carbon reduction and high-quality development	The State Council	25 August 2022
A4	Notice on further promoting green certification to promote green carbon reduction cycle development opinions	Jiangsu provincial government	19 March 2022
A5	Opinions on strengthening green carbon reduction construction in county	Ministry of Housing and Urban-Rural Development	25 May 2021
A6	Notice on the issuance of the Action Plan for Green and Carbon Reduction Development of the Information and Communications Industry (2022–2025)	Ministry of Industry and Information Technology	22 August 2022
A7	Implementation opinions on promoting green and carbon reduction development of postal express delivery industry	State Post Bureau	10 July 2023
A8	Notice on the issuance of “Green Carbon Reduction Development National Education System Construction Implementation Plan”	Ministry of Education	26 October 2022
A9	Notice on the issuance of Anhui Province building energy saving and carbon reduction action plan	Anhui provincial government	21 September 2022
A10	Guiding opinions on accelerating the establishment of a sound Green Carbon Reduction and Circular Development economic system	The State Council	2 February 2021
A11	Guiding opinions on promoting green carbon reduction and high-quality development of “two high” industries	Shandong provincial government	11 May 2022
A12	Notice on implementing new development concepts in the field of cultural relics and implementing green and carbon reduction development measures	Administration of Cultural Heritage	2 December 2021

). This paper only chose these 12 policies because of the time of policy release; among the 60 policies, some policies were released before 2020. The research period of this paper is from 2020 to 2023, so low-carbon policy issued before 2020 were excluded. In addition, to enhance the representativeness of policies and the diversity of policy-making bodies, among the 12 low-carbon policies, some are formulated by The State Council, some by provincial governments, some by prefecture-level city governments, and some by functional departments of The State Council.

4. Analysis Results

4.1. Calculation of PMC Index of Carbon Reduction Policy

The evaluated results were calculated by using Formulas (1)–(4), and the grade evaluation of 12 representative policies were conducted according to Table 3, then the final evaluation results of 12 carbon reduction policies were obtained. The evaluation grade of 12 carbon reduction policies is divided into 4 grades (see

Table 5. Calculation results of PMC index of 12 carbon reduction policies.

Poilcy	First-Level Index										Evaluation Result
	Z1	Z2	Z3	Z4	Z5	Z6	Z7	Z8	Z9	Z10	PMC Index	Grade	Ranking
A1	0.80	0.80	0.33	0.33	0.80	1.00	0.50	0.75	0.75	1.00	7.06	Excellent	4
A2	1.00	1.00	0.67	0.33	1.00	1.00	1.00	1.00	1.00	1.00	9.00	Perfect	1
A3	0.80	1.00	0.67	0.33	0.80	0.75	1.00	0.75	0.75	1.00	7.85	Excellent	3
A4	0.80	0.60	0.33	0.33	0.60	0.75	0.50	0.75	0.50	1.00	6.16	Good	9
A5	0.80	0.60	0.33	0.33	0.40	0.50	0.75	0.50	0.50	1.00	5.71	Good	10
A6	1.00	1.00	0.33	0.33	0.80	0.75	1.00	1.00	1.00	1.00	8.21	Good	2
A7	0.80	0.60	0.33	0.33	0.80	0.75	0.75	0.75	0.75	1.00	6.86	Good	6
A8	0.80	0.60	0.33	0.33	0.60	0.75	0.50	0.75	0.75	1.00	6.41	Good	8
A9	0.80	0.80	0.33	0.33	0.80	0.75	0.75	0.50	0.50	1.00	6.56	Good	7
A10	0.80	0.80	0.33	0.33	0.80	0.75	0.75	0.75	0.75	1.00	7.06	Excellent	5
A11	0.40	0.60	0.33	0.33	0.40	0.50	0.75	0.50	0.50	1.00	5.31	Good	11
A12	0.40	0.60	0.33	0.33	0.40	0.50	0.50	0.50	0.25	1.00	4.81	Badness	12
Mean value	0.77	0.75	0.39	0.33	0.68	0.73	0.73	0.71	0.67	1.00	6.75	Good

).

First of all, the average value of the PMC index for 12 carbon reduction policies is 6.75, which indicates a good evaluation level. Among the 12 carbon reduction policies, one policy receives a perfect evaluation grade, four policies receive an excellent evaluation grade, six policies receive a good evaluation grade, and one policy receives a bad evaluation grade. Among the 12 carbon reduction policies, 11 policies are rated as good or better, accounting for 91.67% of the total policy samples. This demonstrates that, overall, the quality of carbon reduction policies is good, but only 5 policies were rated as excellent or perfect, accounting for only 41.67% of the 12 carbon reduction policies, indicating that these carbon reduction policies still have room for improvement to achieve a perfect grade. Secondly, the 12 carbon reduction policies are ranked from high to low as follows: A₂ > A₆ > A₃ > A₁ = A₁₀ > A₇ > A₉ > A₈ > A₄ > A₅ > A₁₁ > A₁₂. The policy with the highest PMC index value is A₂, with a value of 9, and it receives an excellent evaluation grade. On the other hand, the policy with the lowest PMC index value is A₁₂, with a value of 4.81, and it receives a badness evaluation grade. Hence, there exist significant differences among the carbon reduction policies formulated by the Chinese government.

In terms of the first-level indicator values (see Table 5 and Figure 1), the value of indicator Z₁₀ (policy disclosure) is 1. From Table 5 and Figure 1, it can be found that the values of 10 first-level indicators are also quite different. The average value of Z₁₀ is 1, and the average value of Z₄ is 0.33. Additionally, Table 5 shows that the values of indicators Z₃ and Z₄ are significantly lower than those of indicators Z₁, Z₂, Z₅, Z₆, Z₇, Z₈, and Z₉ for all 12 policies. This suggests that the comprehensiveness of carbon reduction policies needs improvement. Specifically, the value of Z₁ for policy A₁₁ and policy A₁₂ among the 12 carbon reduction policies are 0.4, which are much lower than the Z₁′s value of other policies. Policy A₁₁ is a guideline formulated by the People’s Government of Shandong province on promoting high-quality development of green carbon reduction in the “two high” industries, while policy A₁₂ is a notice formulated by the State Administration of Cultural Heritage on implementing new development concepts in the field of cultural relics and implementing green carbon reduction development measures. This indicates that policy A₁₁ and policy A₁₂ have deficiencies in policy prediction, supervision, recommendation, guidance, and support. Moreover, the other indicators of policies A₁₁ and A₁₂ are significantly lower than those of other policies (except for indicators Z₂, Z₃, Z₄, and Z₅), highlighting the need for improving the quality of policy A₁₁ and policy A₁₂.

4.2. PMC Surface Drawing

The PMC surface figure is a symmetric surface based on the values of nine first-level indexes, which can show the pros and disadvantages of various policies, so as to better evaluate carbon reduction policies formulated by the Chinese government. Since the values of policy disclosure (Z₁₀) of 12 carbon reduction policies are all 1, the policy disclosure index variables are eliminated, and a 3 × 3 matrix is constructed according to the values of the remaining indicators (see Formula (5)). Since the 12 policies are divided into four different grades, this paper selects one representative policy for each grade of perfect (A₂), excellent (A₁), good (A₉), and badness(A₁₂), and then, this paper uses Matlab 2022 software to draw three-dimensional surface figures of four representative policies of different grades (see Figure 2, Figure 3, Figure 4 and Figure 5).

Policy A₂ is titled “Opinions on Improving System Mechanism and Policy Measures for Energy Green Carbon Reduction Transition”. It was formulated by the National Development and Reform Commission in 2022. This policy focuses on energy conservation and carbon reduction in the energy sector. It has a PMC index value of 9, which is evaluated perfect. Among the 12 carbon reduction policies, policy A₂ ranked first in terms of its PMC index. Figure 2 shows that most of the aspects of policy A₂ are positively evaluated, indicating a high level of internal consistency. Except for the indicators of issuing agency (Z₄) and policy timeliness (Z₁₀), the other indicators of policy A₂ are significantly higher than those of other carbon reduction policies. This demonstrates that policy A₂ is a high-quality carbon reduction policy that can effectively promote energy conservation and carbon reduction in China’s energy sector. In combination with Table 5, it can be found that, except for the issuing authority (Z₄) and policy timeliness (Z₁₀), other indicators of policy A₂ are significantly higher than those of other carbon reduction policies, but the values of indicators Z₄ and Z₁₀ are 0.33 and 0.67, respectively. This shows that policy A₂ still has shortcomings in issuing authority and policy timeliness, so policy A₂ needs to improve in the issuing authority and policy timeliness to improve the quality of the policy.

Policy A₁ is titled “Notice of Several Measures to Promote the High-quality Development of Green Carbon Reduction Industry in Shenzhen”. It was formulated by the Shenzhen Municipal People’s Government in 2021. The policy focuses on the development of Shenzhen’s carbon reduction industry. With a PMC index value of 7.06 and an excellent rating, policy A₁ ranks second among the 12 carbon reduction policies. Figure 3 shows that the positive evaluation of policy A1 is slightly lower compared to Policy A₂, with more areas exhibiting a negative evaluation. Table 5 also indicates that, except for the indicator Z₁₀, the values of the other indicators of policy A₁ are lower than those of policy A₂. It can be found from Table 5 that the values of Z₃ (policy timeliness), Z₄ (issuing institution), and Z₇ (policy object) of policy A₂ are relatively low, which means that policy A₁ needs to be improved in terms of policy timeliness (Z₃), issuing authority (Z₄), and policy object (Z₇) to improve the quality of policy A₁.

Policy A₉ is titled “Notice on the Anhui Province Building Energy Conservation and Carbon Reduction Action Plan”. It was issued by the Anhui Provincial People’s Government in 2022. The policy focuses on energy conservation and carbon reduction in the construction industry in Anhui province. Policy A₉ has a PMC index value of 6.56, ranking seventh among the 12 carbon reduction policies. Figure 4 shows that the positive evaluation of policy A₉ is even lower compared to policy A₁, with fewer areas exhibiting a positive evaluation. Table 5 also indicates that the values of indicators Z₃, Z₄, Z₈, and Z₉ in policy A₉ are lower compared to those of policy A₁ and policy A₂. This suggests that the quality of policy A₉ still has room for improvement when compared to policy A₂ and policy A₁. Especially, policy A₉ needs to be improved from the aspects of policy time (Z₃), issuing authority (Z₄), policy role (Z₈), and policy evaluation (Z₉) to enhance the quality of policy A₉.

Policy A₁₂ is titled “Notice on Implementing New Development Concepts in the Field of Cultural Relics and Implementing Green and Carbon Reduction Development Initiatives”. It was formulated by the State Administration of Cultural Heritage in 2021 and focuses on the green and low-carbon development of the cultural heritage sector. Policy A₁₂ has a PMC index value of 4.81, ranking last among the 12 carbon reduction policies. Figure 5 shows that the positive evaluation of policy A₁₂ is quite low, with fewer areas exhibiting a positive evaluation compared to other policies. Table 5 also indicates that, except for indicator Z₁₀ and indicator Z₂, the values of the other indicators of policy A₁₂ are relatively low. This suggests that the quality of policy A₁₂ has significant room for improvement. In particular, indicators of Z₉, Z₃, Z₄, Z_1, and Z₅ are all below 0.5, with values of 0.25, 0.33, 0.33, 0.4, and 0.4, respectively. This means that policy A₁₂ needs to comprehensively improve the quality of policies from many aspects, such as policy timeliness, issuing authority, and the nature of policies.

5. Conclusions

Global climate change has become one of the most significant challenges for humans, which directly threatens the sustainable development of human beings. The main contribution to this phenomenon is the emission of greenhouse gases, particularly carbon dioxide. To actively address climate change, many countries have implemented numerous carbon reduction policies. However, the quality of these policies remains unknown. Therefore, this paper aims to evaluate the quality of 12 carbon reduction policies. The carbon reduction policies promulgated by the Chinese government are chosen because the Chinese government has introduced a large number of carbon reduction policies to deal with climate change, which provide rich data to evaluate the quality of carbon reduction policies. This paper designs an index system and utilizes the PMC index method to assess carbon reduction policy. Unlike previous studies that focus on evaluating the effect of policy implementation, this paper evaluates the quality of carbon reduction policies from the perspective of policy formulation. The study reveals the following findings: Firstly, the average PMC index value for the 12 carbon reduction policies is 6.75, indicating a good grade. This suggests that the carbon reduction policies implemented by the Chinese government in 2020 to 2023 generally contribute positively to China’s carbon reduction efforts. Secondly, among the 12 policies, one policy is evaluated perfect grade, four policies are evaluated excellent grade, six policies are evaluated good grade, and one policy is evaluated bad grade. This indicates significant variances in the quality of different carbon reduction policies. Thirdly, the values of indicator Z₃, indicator Z₄, indicator Z₅, and indicator Z₉ in the evaluation index system of carbon reduction policies are low, which indicates that the 12 carbon reduction policies still have shortcomings in policy timeliness, issuing authority, policy tools, and policy evaluation.

Although the average PMC value of the 12 carbon reduction policies is 6.75, which is considered good, there is still room for improvement to achieve perfect grade. This paper proposes some suggestions. Firstly, Table 5 shows that the average value of indicator Z₃ among the 10 first-level indicators is 0.39, ranking the second lowest. This indicates that there are shortcomings in policy timeliness (Z₃) in carbon reduction policies. To enhance the quality of carbon reduction policy, it is necessary to clearly define the timeliness of policy when making carbon reduction policy. Carbon reduction policies should include clear short-term goals, medium-term goals, and long-term goals. Secondly, Table 5 also reveals that the average value of indicator Z₄ is 0.33, ranking the lowest among the 10 indicators. This is because some carbon reduction policies are not formulated by the central government, national ministries and commissions, or local governments. Therefore, it is recommended to strengthen the coordination among multiple government departments during the formulation of carbon reduction policies, and the involvement of departments with higher administrative levels should be encouraged. Thirdly, Table 5 shows that indicator Z₅ (policy tool) has an average value of 0.68, ranking fourth from the bottom among the 10 first-level indicators. This indicates that there are still shortcomings in policy tools. Therefore, it is suggested to fully utilize policy tools, such as investment, technology, law, talent, and publicity. Finally, Table 4 shows that among the 10 first-level indicators, the average value of indicator Z₉ is 0.67, ranking third from the bottom. This indicates that there are deficiencies in carbon reduction targets, programs, measures, and basis in the carbon reduction policies. Therefore, it is suggested that carbon reduction targets should be clearly defined when formulating carbon reduction policy, and reasonable and scientifically formulated carbon reduction programs and measures should be implemented. In addition, there should be sufficient basis for carbon reduction policy.

The quality of policy making is crucial for improving the effectiveness of policy implementation. Numerous factors influence the quality of policy making and its implementation outcomes. This paper focuses on 12 carbon reduction policies formulated by the Chinese government. By utilizing the PMC index method, a quantitative evaluation of the quality of these carbon reduction policies is carried out. However, this study has a limitation as it solely evaluates China’s carbon reduction policies. Therefore, it is imperative to expand the scope of carbon reduction policies to further enhance the evaluation and understanding of carbon reduction policies. Furthermore, investigating the influencing factors of carbon reduction policy formulation and studying the implementation outcomes also merits attention in future research.