A Study on the Synergy of Renewable Energy Policies in Shandong Province: Based on the Coupling Coordination Model

Abstract

Renewable energy’s integral role in addressing the global climate crisis underscores the importance of crafting coordinated policies to bolster its growth. Shandong Province, as China’s largest carbon emitter, presents an intriguing case study. Leveraging policy text analysis and the coupling coordination model, this research investigates the interplay among the diverse policy instruments within Shandong Province’s renewable energy policies. The findings reveal a harmonious and varied array of policy instruments. Yet, notable disparities emerge when examining secondary policy instruments across different types of renewable energy. Consequently, this paper offers strategic recommendations to improve the coupling coordination and utilization of policy instruments across various types of renewable energy. The ultimate aim is to strengthen policy synergies, overhaul the energy structure, and make a meaningful contribution to global climate change mitigation efforts.

1. Introduction

Mitigating global climate change and safeguarding the environment necessitates a reduction in carbon emissions. As energy production contributes most of the total carbon dioxide on earth, it significantly fuels global warming. With a rising number of nations committed to achieving carbon neutrality by this century’s close, the march towards a low-carbon energy system carries increasing urgency. This transition demands the fostering and re-engineering of energy systems through a range of policies [1]. Acknowledging this, the Chinese government has prioritized carbon emission reduction and has made international pledges to that effect. Specifically, Shandong Province, one of China’s most populous and economically prosperous regions, demonstrates nationwide leadership in carbon emissions, with an estimated 936 million tons in 2020, accounting for approximately 9% of the nation’s total emissions [2]. Although Shandong trails behind Guangdong and Jiangsu economically, its carbon emission intensity overwhelmingly surpasses both, catalyzing heightened focus on its carbon reduction and renewable energy policies.

Renewable energy policies are crucial drivers in reducing carbon emissions [3]. As key facilitators of the transition from fossil fuels to cleaner and more sustainable energy sources, these policies champion the adoption of technologies such as solar, wind, hydro, and biomass. By propelling the shift from carbon-intensive energy generation methods, these measures not only curtail carbon emissions but also tackle associated environmental challenges precipitated by climate change. Furthermore, renewable energy policies catalyze investment, research, and technological development in the clean energy sector, fostering innovation and driving down costs. They also act as stimulants for job creation and economic growth in the renewables sector. All told, renewable energy policies are pivotal in combating the deleterious effects of carbon emissions and ushering in a sustainable, low-carbon future.

The transition of energy systems entails the tuning of internal interactions within the system, paired with the cumulative influence of external factors. Indeed, energy, economy, and environment constitute an interdependent network, with external factors from economic and environmental systems impairing the harmonization of energy policies. As internal and external factors attain equilibrium, the system’s evolution will noticeably hasten. Nevertheless, a clean, low-carbon energy system is improbable to come into existence solely through the system’s cooperative evolution [3,4]. Consequently, a methodology of “policy coordination”, competent in aligning varied external factors, is essential for steering the entire system’s progression towards low carbon.

Policy coordination is a process in which diverse decision-making bodies collaborate and implement variable measures to enhance coordination in policy execution. This strategy aligns the internal factors with the policy goals within the energy system [4]. Through the synergistic development among various institutions in the energy system, energy policy coordination effectively amalgamates external resources, environmental factors, policy objectives, and measures. It promotes a comprehensive low-carbon progression in the energy system. Viewed from external factors, energy policy coordination serves to elucidate the underlying logic and value principles behind policy decision-making, harmonizes the disparate interests among various stakeholders within the energy system, and fosters its coordinated evolution [5]. Nevertheless, the effectiveness of renewable energy policy implementation hinges on the collaborative impact of multiple factors rather than the influence of a solitary factor.

In summation, Shandong, bearing the distinction of the highest carbon-emitting province in China, should escalate its research efforts regarding the harmonization of renewable energy policies. Significant strides in the coherence and synergistic application of these policies are integral to effectively facilitating carbon reduction initiatives, thereby yielding substantial contributions to the sustainable development of not just Shandong Province but China at large. Consequently, a thoroughly engaged exploration of Shandong Province’s renewable energy policies’ integration is crucially imperative for fostering carbon reduction. This study introduces a framework for policy coordination analysis utilizing the coupling coordination degree model. This framework is instrumental during the early stages of policy development to proactively improve coordination. Furthermore, through case application, this research can serve as a reference for formulating energy policies in various countries and regions.

Section 1 delineates the significance of renewable energy policy synergy and rationalizes the selection of the case study. Section 2 presents a review of the pertinent literature and then proceeds to develop an analytical framework grounded on the review. In Section 3, the materials and methods utilized are detailed, explaining the case study process specifically. The outcomes of the case study are manifested in Section 4. A comprehensive interpretation of the results along with tailored suggestions are offered in Section 5. Lastly, Section 6 enumerates the overall recommendations and conclusions drawn from the study.

2. Analytical Framework

Policy synergy can ensure the systemic and coordinated nature of the policy system and maximize its effectiveness. Enhancing policy synergy can prevent the fragmentation of policies [6], optimize policy implementation [7], reduce conflicts and overlaps, integrate objectives, improve efficiency [8], and mitigate policy uncertainty and environmental complexity [9].

The current research on policy synergy includes international case studies, experience synopses, research overviews, current situation analyses, law explorations [10,11,12,13,14,15], proposals or frameworks for enhancing policy synergy [16,17,18], the interplay and synergistic effect among different types of policies [19], and the design of measurement models from policy instruments [20,21], policy subjects [22,23], policy structure [24], policy effectiveness [25], and policy objectives [22] to study policy synergy evolution [26,27,28,29,30,31].

Methods of policy text analysis necessitate the processing and interpretation of natural language within documents. Derived from a content analysis perspective, this method decrypts profound policy implications, such as policy stances, trends, and values [32]. Policy text analysis was employed to retrospectively scrutinize academic work on policy coordination, offering a specific emphasis on recent advancements in public administration [33]. Scholars gauge the consistency in policy by investigating whether existing sectoral policies share governance objectives both internally and collectively, complemented by the utilization of network analysis to comprehend the nexus between tools and individuals, thereby exploring policy coordination [34]. By analyzing energy policy texts, the quintessential relationship between energy policies and their implementation outcomes can be effectively quantified. Furthermore, the identification of these particular energy technologies permits an in-depth analysis of their impact on systemic evolution. Hence, this paves the way for an expanded research horizon in the future, establishing itself as a more versatile approach for studying energy policy coordination. Policy instruments represent concrete measures for achieving policy objectives and translating objectives into actions [35]. The coupling and coordination model, widely recognized as suitable for policy environmental assessment, is used to measure the degree of coupling and coordination of these instruments [36,37,38].

Scholars have undertaken analyses of policy coordination within the realms of renewable energy and reduction of emissions and energy use [39,40,41]. Despite these analyses, gaps in comprehension persist, particularly around the application of primary policy instruments. Further knowledge is needed regarding their effectiveness, as well as the role of secondary policy instruments and their applicability to each type of renewable energy. For instance, Lan et al. highlighted the dominance of command control policy instruments in Chinese renewable energy policy [39]. Yet, to increase policy coordination, a greater utilization of policies centered on demonstration guidance and economic incentives is suggested [39]. While these encompass primary policy instruments, there are numerous secondary policy instruments encapsulated within these, such as industrial recommendation catalogs, management methodologies for demonstration projects, and the promotion of project schemes. Notably, the necessity for enhancing different secondary policy instruments may not be uniform. Additionally, the domain of renewable energy includes numerous diverse types, such as: hydrogen energy, solar power, and wind power, each of which may require differing policy instruments. Therefore, overarching conclusions deduced from the overall coordination analysis may not apply universally to each unique project category of renewable energy, highlighting the importance of refined understanding of the practical construction of types of renewable energy.

In summary, this paper establishes a research framework, as outlined in Figure 1. Initially, we segregated the types of renewable energy in Shandong Province. Building on this foundation, we gathered related policy documents, and in tandem with pertinent research findings and the distinct attributes of renewable energy, ascertained categories of policy instruments for encoding analysis and measurement. This study procured efficacy scores for diverse policy instruments, performed policy coordination analysis, and yielded conclusions, along with cogent recommendations. The objective is to enhance the development of renewable energy, contribute to the improvement of energy infrastructure, and address the challenges of global climate change.

3. Materials and Methods

3.1. Collection of Policy Documents

This research utilized Nvivo12 software to conduct a statistical analysis of the policy instruments within the system, thereby investigating the system’s policy synergy. This policy system accounts for an array of energy, ecological environment, and socio-economic laws, rules, policies, plans, and regulations established by both the state and Shandong Province.

According to the policy “Opinions on Promoting the High-Quality Development of Renewable Energy in Shandong Province”, issued in 2021, the development of renewable energy in Shandong province covers the following major areas: offshore wind power planning and construction; site selection, construction scale, and regional confines for the integration bases of wind, photovoltaic, and storage in Northwestern and Southwestern Shandong; establishment of distributed photovoltaic power generation in urban areas and rural districts; specifications and distribution of in-operation and proposed channels for external power input to Shandong, and the requirements for management and control underneath and on both sides of the channels; and various forms of energy storage, including but not limited to pumped storage, electrochemical energy storage, and hydrogen energy.

The policy sample in this research chiefly comprised policies that guide and restrict the development of renewable energy, adhering to the following principles: first, their contents indeed include effective policy instruments; second, the policies fall under the bounds of information disclosure by governmental departments; third, the policies have direct relevance to renewable energy, typically involving the aforementioned types of renewable energy; and fourth, they are currently in effect, which implies that former policies overwritten due to the advent of similar new ones were not considered.

Policy-related data were aggregated via the official web portals of the Chinese Government and its various departments, the legal database of laws and regulations of Peking University, and the academic literature to establish a comprehensive policy collection. Finally, 58 valid policy documents were collected and examined in this study.

3.2. Formation of Policy Quantification Tables

Constructing a policy quantification table involves two primary steps: first, recognizing the types of primary and secondary policy instruments, and second, quantifying these policy instrument indicators.

Policy synergy measurement originates with the synergy of policy instruments, beginning with categorizing them into distinct groups, known as primary policy instruments. Primary policy instruments, usually classified as supply-side, environmental, and demand-side, have been widely used in policy synergy research [22,23,42,43,44,45]. Rothwell’s review of research and development policies across six nations, inclusive of the UK and Japan, proposed supply-side, environmental, and demand-side policy classifications. Under these divisions, supply-side includes technological process knowledge and highly-skilled humanpower; environmental incorporates the planning process, regulations, and fiscal measures; and demand-side envelops public procurement and a range of promotional measures for demand-generating areas [46]. This paper adopts Rothwell’s classification, partly due to its wide acceptance and application in policy synergy scenarios [22,23,42,43,44,45], and partly because the classification matches the research objective of policy coordination as it is based on the type of function and impact of the policy instrument. For secondary policy instruments, the classification approach in policy synergy research varies across different policy fields and depends on the policy’s characteristics and research requirements [36,37,47,48].

Renewable energy supply-side policy instruments aim to facilitate renewable energy development via supply expansion. Environmental policy instruments seek to create favorable conditions for renewable energy advancement, for instance, through tax incentives, subsidies, and regulations. Demand-side renewable energy policy instruments aim to stimulate renewable energy growth through demand-side management. The definitions of operationalization of policy instruments, taking into account the characteristics of renewable energy, are shown in

Table 1. Definitions of operationalization of policy instruments.

Primary Code	Secondary Code	Operationalized Definition
Supply-side	Scientific and technological input	Promoting renewable energy development by encouraging and promoting technology research and development
	Complementary industry	Development of supporting industries for types of renewable energy
	Supporting infrastructure	Construction of supporting infrastructure for types of renewable energy
	Talent and institution	Cultivate and introduce talents in the field of renewable energy and guide the sharing of institutional resources
	Information service	Establishment of an information platform in the field of renewable energy, or a mechanism for information disclosure and sharing
	Capital investment	Establishment of special funds for renewable energy development
Demand-side	Energy consumption	Promotion of renewable energy consumption through grid regulation, etc.
	Trade promotion	Promotion of energy trade, e.g., offshore development of renewable energy, energy imports, etc.
	Demonstration project	Construction of renewable energy demonstration projects and dissemination of experience
	Promotional incentives	Promotion of incentives for projects related to renewable energy development
	International cooperation	Promoting international cooperation on renewable energy-related industrial standards, certification systems, etc.
Environmental	Standard	Establishment and improvement of renewable energy-related technical standards and norms
	Regulatory control	Developing regulations on renewable energy or clarifying the legal responsibility for violating the corresponding regulations.
	Inspection and supervision	Inspection, supervision, etc. of renewable energy-related fields
	Financial support	Improving the loan approval process, encouraging green bonds and other financial instruments
	Target planning	Establishment of renewable energy development targets and integration of renewable energy development into planning
	Ecological risk control	Addressing ecosystem protection, carbon emissions, and security risks associated with renewable energy
	Tax incentives or subsidies	Tax incentives (tax exemptions, deductions, credits, etc.) or subsidies for projects related to renewable energy development
	System building	Renewable energy price mechanism, system operation mechanism, and other institutional development

, where the primary code corresponds to the primary policy instrument, and the secondary code links to the secondary policy instrument.

Indicator quantification is the process of judging the effectiveness of policy instruments within policy text coding. The effectiveness of corresponding policy instruments is obtained through indicator quantification. These can then be substituted into the policy synergy measurement model to calculate the degree of synergy. Existing policy synergy research typically refers to the indicator quantification table proposed by Peng et al. and augmented by Zhang et al. [21,49] This paper, taking into consideration the features of renewable energy and the aforementioned studies, and considering the support strength, standardization, comprehensiveness and detail, construct a quantitative table of policy instruments; see Appendix A 

Table A1. Quantitative table of policy instruments.

Secondary Code	Quantitative Standard	Value of a Score
Scientific and technological input	Adopt a series of measures to stimulate scientific and technological innovation, optimize processes, organize technical research, etc., the content is very detailed and specific	5
	Adoption of some of the above measures in more detail and specificity	4
	Adoption of some of the above measures, with more specific content; or from the adoption of a certain measure, with very detailed and specific content	3
	Adoption of certain scientific and technological inputs	2
	Mention of promotion of technological innovation, etc., no specifics	1
Complementary industry	Development of supporting industries, listing the corresponding types of industries or names of projects, with very detailed and specific contents	5
	Development of certain types of supporting industries, with more detailed and specific content	4
	Development of certain kinds of supporting industries, with more specific contents; or from the development of certain kinds of supporting industries, with very detailed and specific contents	3
	Development of some kind of supporting industry
	Attitudinal indication of development of supporting industries, no specifics	2
Talent and agency	Provide technical support for talents in a variety of ways, such as talent training, capacity training, expert pools, consulting organizations, etc., and formulate policies related to the support of specialized talents, with very detailed and specific content	5
	Provide technical support for talent in a number of areas, with more detailed and specific content	4
	Technical support for talent in a number of areas, with more specific content; or support in a particular area, with very detailed and specific content	3
	Supporting talent organizations in one way or another	2
	Mention only words related to talent, technology, etc.	1
Information service	Provide information services such as statistical services, establishment of sharing mechanisms, building information platforms, etc., with detailed and specific content	5
	Provide information services in a number of areas, with more detailed and specific content	4
	Information services are given in a number of ways, with more specific content; or support is given in a number of ways, with very detailed and specific content.	3
	Supporting information services in one way or another	2
	Attitudinal indication of information service support, no specifics	1

Note: This table lists the quantitative criteria for part of the secondary codes, and the quantitative criteria for other secondary codes are similar to those above. The presentation of the entire content would impose excessive spatial constraints.

.

3.3. Coding and Quantification of Policy Texts

Prior to policy text coding, a basic policy information coding table was developed to facilitate subsequent coding and effectiveness measurement. Using content analysis, the policy text was coded with supply-side, demand-side, and environmental as the primary nodes and renewable energy policy instrument types as the secondary nodes. The processed text was then imported for coding into Nvivo12 software.

The coding was organized by policy instrument, and the effectiveness scores were given by combining the policy instrument quantification table and the actual coding. The total effectiveness scores of the policy instruments, namely the indicator values, can be calculated using Formula (1) based on the effectiveness scores of each document and the document’s effectiveness level.

By independently summing the columns of policy instruments for each type of renewable energy, the indicator values for the supply, environment, and demand instruments for the various types of renewable energy can be obtained.

3.4. Policy Synergy Measurement Modeling

3.4.1. Model for Measuring the Validity of Policy Instruments

Related studies have generally used the policy synergy measurement model proposed by Peng et al. to measure policy instrument effectiveness and policy synergy effectiveness [21,43,44,45,49]. In Equation (1), TPM represents the policy instrument validity, PM_j represents the score of the j-th policy instrument, P_i represents the policy effectiveness level of the i-th policy, and n represents the number of policies.

$$\mathrm{T}\mathrm{P}\mathrm{M}={\sum }_{i=1}^{n}P{M}_j\times P_i$$

(1)

3.4.2. Coupling Coordination Degree Model

The coupling coordination degree model mainly includes three parts: the efficacy function, coupling degree, and coordination degree, in which the coupling degree reflects the degree of mutual influence, dependence, and constraints among the systems, and the coordination degree reflects the degree of benign coupling in the coupling relationship and characterizes the advantages and disadvantages of the coordination state [36,37,47,48,50]. Wang et al. pointed out the misunderstanding of the application of the coupling coordination degree model in the field of social sciences and proposed two correction methods. One is to modify the critical value of coupling degree antagonistic-coordination, and the other is to propose a new calculation formula [50]. This paper draws on the research of Wang et al. to study the degree of coordination of the three policy instrument subsystems with a modified coupling coordination degree model.

The efficacy function reflects the extent to which a given type of policy instrument contributes to the development of renewable energy policies. First, the data are normalized: the parameter of the i-th subsystem is denoted as x_i (i = 1, 2, 3, … n), x_ij values are X_ij (j = 1, 2, 3, … n), max_ij, min_ij are the maximum and minimum values of indicator j during the year, and i and j represent the ordinal number of subsystems and indicators, respectively. The ordered efficacy function of a positive indicator on subsystems is Equation (2), and the negative indicator is Equation (3).

$$x_{ij}=\frac{{\mathit{X}}_{ij}-{min}_{ij}}{{max}_{ij}-{min}_{ij}}$$

(2)

$$x_{ij}=\frac{{{max}_{ij}-\mathit{X}}_{ij}}{{max}_{ij}-{min}_{ij}}$$

(3)

x_ij is the magnitude of the efficacy contribution of indicator j (value X_ij) to the system, x_ij ∈. The contribution of each of the three subsystems to the development of renewable energy is shown in Equation (4).

$$U_{ki}={\sum }_{j=1}^m{\lambda }_{ij}\times {\mathrm{x}}_{ij},{\sum }_{j=1}^m{\lambda }_{ij}=1$$

(4)

where U_ki (i = 1, 2, 3, …, n) is the ordered contribution of subsystem i to the total system k, m is the number of indicators, and λ_ij is the weight of the indicators in the subsystem.

The fundamental concept of the entropy value method involves utilizing the information entropy of every indicator to determine its weight; whereby an indicator’s weight decreases as its entropy value increases. This method amalgamates the impacts of various indicators on the target, circumventing the adverse effects instigated by a single factor. It also negates the implications of subjective elements and offers robust stability and adaptability.

The steps of the entropy method calculation are shown in Equations (6)–(8).

With n uncertain information measures, an attribute matrix M is created:

$$\mathrm{M}=\left[\begin{array}{ccc}{x}_{11}& \cdots & x_{1n}\\ ⋮& \ddots & ⋮\\ x_{m1}& \cdots & x_{mn}\end{array}\right]$$

(5)

Use P_ij to denote the contribution of the j-th metric for the i-th attribute:

$$P_{ij}=\frac{x_{ij}}{{\sum }_{i=1}^m{x}_{ij}}$$

(6)

Denote by E_j the total contribution of all indicators to the M attribute:

$$E_j=-K{\sum }_{i=1}^m{P}_{ij}\ln \left(P_{ij}\right)K=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\mathrm{l}\mathrm{n}\left(m\right)$}\right.$$

(7)

where d_j is the consistency of the degree of contribution of each indicator under the j-th attribute, d_j = 1 − E_j, to obtain the attribute weights:

$${\lambda }_{ij}=\frac{d_j}{{\sum }_{i=1}^n{d}_j}$$

(8)

The generic coupling degree model for n system interactions is shown in Equation (8).

$$C_n={\left[\frac{U_1\times U_2\times U_3\times \dots \times U_n}{{\left(\frac{U_1+U_2+U_3+\dots +U_n}{n}\right)}^n}\right]}^{\frac{1}{n}}$$

(9)

Drawing on previous related studies, the modified coupling degree model is shown in Equation (9).

$$C_n=\sqrt{\left[1-\frac{{\sum }_{i>j,j=1}^n|U_i-U_j|}{{\sum }_{m=1}^{n-1}m}\right]\times {\left({\prod }_{i=1}^n{U}_i\right)}^{\frac{1}{n-1}}}$$

(10)

where C is the coupling degree of the policy instrument subsystem, and U_i is the contribution value of the policy instrument subsystem efficacy function. The coordination degree is calculated as shown in Equations (11) and (12). C is the coupling degree, while D is the coordination degree; T is the comprehensive evaluation index, and α is the coefficient to be determined. In the current policy synergy study, by applying the coupling coordination degree model, the application of the coupling coordination degree was mostly handled according to equal weights [36,37,47,48]. The three instruments had equal pending coefficients: α₁ =1/3, α₂ =1/3, α₃ =1/3, and the comprehensive evaluation index T was obtained by considering that the three instruments had a similar degree of promotion of the policy objectives.

$$D=\sqrt{C\times T}$$

(11)

$$T={\alpha }_1{U}_1+{\alpha }_2{U}_2+{\alpha }_3{U}_3$$

(12)

The degree of coordination was then generally categorized into five levels [48,51]. Referring to the related research, the division standard of the coupling coordination degree in this paper is shown in

Table 2. Criteria for the classification of degree of coupling C and degree of coordination D.

Coupling (C)	Coupling Level	Level of Interaction	Coordination (D)	Level of Coordination
0	Uncoupled	No interaction	[0, 0.2)	Severe disorder
(0, 0.3]	Low coupling	Low level of interaction	[0.2, 0.4)	Moderate disorder
(0.3, 0.5]	Low to medium coupling	Strong interactions	[0.4, 0.6)	Tenuous coordination
(0.5, 0.8]	Moderate coupling	Significant interactions and benign coupling	[0.6, 0.8)	Moderate coordination
(0.8, 1]	High coupling	High-level benign coupling, orderly development of the system	[0.8, 1)	High degree of coordination

.

4. Results

Prior to conducting the coupling measurement, the quantitative data from the policy instruments were standardized, resulting in x_ij. Given that a value of 0 influences the computation, in accordance with the general procedure of the entropy value method, this was replaced by 0.00001 to acquire standardized processing data. Using the entropy value method predicated upon the given formula, the weights of the three subsystems for supply, demand, and environmental policy instruments were determined.

According to the formula, C = 0.9694 and D = 0.8796. In accordance with the priorly referenced first correction mode of the coupling coordination degree model, when the subsystem count is at three, the maximum value is acceptable as two times that of the minimum value. Additionally, if the C value lies within a moderate or high degree of coupling (benign coupling), we can deduce that the policy instruments are functioning in benign coupling. However, the specific degree of coupling and level of coordination remains indeterminable. Following the second correction mode of the coupling coordination model, the calculated values of C = 0.6888 and D = 0.7415 signify a medium degree of coupling and coordination, aligning with the adjustments from the first method.

The above are the overall calculations for all renewable energy policy instruments. The different categories of the renewable energy types of renewable energy and their corresponding C-values, D-values, and levels of coordination are catalogued in

Table 3. C-values, D-values, coupling, and coordination level of each type of renewable energy.

Type of Renewable Energy	C-Value	D-Value	Coupling Level	Coordination Level
Overall	0.6888	0.7415	Moderate coupling	Moderate coordination
General energy	0.6482	0.6883	Moderate coupling	Moderate coordination
Hydrogen	0.6977	0.7430	Moderate coupling	Moderate coordination
Pumped storage	0.7374	0.7828	Moderate coupling	Moderate coordination
Electrochemical energy storage	0.6662	0.7005	Moderate coupling	Moderate coordination
Wind power	0.7087	0.7650	Moderate coupling	Moderate coordination
Photovoltaic storage integration	0.7128	0.7434	Moderate coupling	Moderate coordination
Photovoltaic (e.g., cell)	0.8199	0.8591	High coupling	High degree of coordination
Biomass	0.8267	0.8485	High coupling	High degree of coordination
External power input	0.8624	0.8874	High coupling	High degree of coordination

. It should be noted that “general energy” represents provisions for the development of renewable energy sources without reference to specific types of renewable energy.

Further analysis was necessary to explore the application of specific policy instruments for varying types of renewable energy.

Table 4. Effectiveness scores for renewable energy policy instruments.

Subsystems	Policy Instruments	General Energy	Hydrogen	Pumped Storage	Electrochemical Energy Storage	Wind Power	Photovoltaic Storage Integration	Photovoltaic (e.g., Cell)	Biomass	External Power Input	Overall Score
Supply-side	Scientific and technological input	166	43	6	12	14	2	2	10	3	258
	Complementary industry	55	63	-	6	36	10	22	27	3	222
	Supporting infrastructure	48	38	21	4	12	4	-	4	14	145
	Talent agency	26	43	-	4	4	2	-	-	-	79
	Information service	30	3	-	8	4	2	10	-	-	57
	Capital investment	51	3	2	2	4	2	8	2	-	74
Demand-side	Energy consumption	40	6	12	4	2	12	21	12	2	111
	Trade promotion	34	9	-	-	-	-	-	-	-	43
	Demonstration project	81	59	8	10	15	6	9	3	12	203
	Promotional incentives	192	5	5	2	2	2	29	23	3	263
	International cooperation	22	6	-	-	-	-	-	-	-	28
Environmental	Standard	37	20	2	8	8	2	15	1	-	93
	Regulatory control	81	-	-	-	2	2	1	-	3	89
	Inspection and supervision	61	8	8	6	13	3	10	2	9	120
	Financial support	40	3	-	2	-	-	-	2	-	47
	Target planning	158	30	37	12	52	30	8	29	24	380
	Ecological risk management	189	39	6	2	16	4	11	13	-	280
	Tax incentives or subsidies	49	3	-	10	6	-	8	7	-	83
	System building	117	9	4	-	9	-	19	6	-	164

presents the policy instrument efficacy scores corresponding to the various types of renewable energy noted. This study intended to suggest optimization strategies to boost policy instrument synergies based on analyzing the application of diverse policy instruments on distinct types of renewable energy. Observations from the distribution of primary and secondary policy assets reveal the balanced and diverse policy instruments within Shandong Province’s renewable energy policy systems. All kinds of primary and secondary policy instruments have a certain proportion, and different types of renewable energy focus on the application of policy instruments with their own emphasis, which presents good synergy. Secondary policy instruments are dominated by scientific and technological inputs, demonstration projects, promotional incentives, industry support, and ecological risk control. These instruments foster renewable energy development while considering socioeconomic benefits (industry support) and ecological effects (ecological risk control). Still, a comprehensive analysis is essential to ascertain whether Shandong Province can align renewable energy development, socioeconomic progress, and ecological preservation by evaluating policy instrument effectiveness and synergy for each type of renewable energy.

5. Discussion

The research shows that, from the perspective of primary policy instruments, the policy instruments of the policy system are diversified, which is manifested by the fact that all types of renewable energy contain three different types of primary policy instruments, but each has its own focus in terms of share, e.g., hydrogen energy uses more supply-side policy instruments, while photovoltaic (PV) uses more environmental policy instruments. Viewing the secondary policy instruments, discrepancies exist among different types of renewable energy. For example, wind power accentuates target planning and complementary industries, PV emphasizes promotional incentives and energy consumption, whereas pumped storage focuses on supporting infrastructure and target planning. Notably, some types of renewable energy lack emphasis on ecological risk control.

General energy policy instruments demonstrate a moderate level of coupling and coordination. In terms of the effectiveness scores and contribution values, environmental policy instruments have the upper hand. As for the variety of policy instruments, their applications are comprehensive, allotting greater focus towards ecological risk control. The most recurrently used policy instruments include promotional incentives, the input of science and technology, ecological risk control, and target planning. To bolster the synergy levels of general energy policies, lesser-used demand-side and supply-side instruments, such as international cooperation and information services, could be integrated. The “Belt and Road” initiative offers opportunities for international cooperation, emphasizing an increase in green and clean energy collusions and the fortification of high-carbon energy import–export management, alongside fostering foreign investment and introductions from renewable energy-related businesses, both domestic and foreign. Relative to information services, exploration into establishing a mechanism for sharing green, low-carbon energy information could be valuable. Creating a database for renewable energy, integrating types of renewable energy and industrial data from various sectors and regions, and enhancing exchanges and cooperation on renewable energy-related projects can offer suitable protection for enterprises, governments, and entities across diverse sectors and regions.

The coupling and coordination of policy instruments for hydrogen energy are at a medium level. In terms of the effectiveness score of policy instruments, hydrogen energy mainly uses more supply-side instruments, including complementary industries, talent and institution, scientific and technological input, and supporting infrastructure, taking into account environmental and demand-side policy instruments, with demand-side policy instruments focusing on demonstration projects, and environmental policy instruments taking into account more ecological risk control. To further enhance the coordination among hydrogen energy policy instruments, a focus on demand-side and environmental policy instruments is recommended. Strategies could involve bolstering international collaborations on hydrogen energy, deepening affiliations with international research bodies, educational institutions, and organizations, like the International Hydrogen Energy Association (IHEA), encouraging investments and technology exchange in the hydrogen energy sector, and refining the relevant regulations and a standard system for hydrogen energy. Flath et al.’s research underscores the significance of international collaborations within the European Union (EU) for advancing hydrogen energy, given that similar investigations present valuable reference points [52].

In the case of pumped storage, medium coupling and coordination are seen, with the highest effectiveness scores for environmental policy instruments and the lowest for demand-side policy instruments. There is a limitation in the diversity of policy instruments, primarily focusing on supporting infrastructure and target planning. To foster coupling and coordination in pumped storage policy, a broader range of policy instruments need to be incorporated, including scientific and technological input, tax incentives or subsidies, and particularly, additional ecological environment control policy instruments. Measures might include the research and development of intelligent mechanical equipment, the empowerment of technological support, and the promotion of autonomous, mechanized, and intelligent upgrading of pumped storage power stations; tax incentives or subsidies for the upstream and downstream processes of pumped storage project construction; scientific selection of construction addresses for pumped storage power station sites, and the preparation of project planning, design of engineering solutions, management of power station operation guided by ecological priorities, and the whole process of carrying out an environmental impact assessment for the project to reduce pollution and effectively protect the ecological environment.

The coupling and coordination of electrochemical energy storage policy instruments is at a medium level. In terms of the policy instrument effectiveness scores, environmental policy instruments are the most numerous, followed by supply-side policy instruments, while demand-side policy instruments are used less frequently, and there is a certain amount of consideration for ecological environmental protection. For example, the Guiding Opinions on Accelerating the Development of New Types of Energy Storage proposes to strengthen the research on safety technology for electrochemical energy storage, broaden the surface of energy storage technology, reduce the cost of electrochemical energy storage, and realize large-scale commercial application. To augment the synergy and coordination among electrochemical energy storage policy instruments, the implementation of demand-side policy instruments requires amplification, along with fortifying ecological environmental protection efforts. For example, with full consideration of the characteristics of electrochemical energy storage sites, targeted environmental impact assessments should be carried out for the operation and maintenance of battery modules, and for the storage, transport, and dismantling of electrolyte and gas–liquid raw materials; and the disposal and regulation of pollutants in the process of producing, constructing, and operating electrochemical energy storage sites should be strengthened, with low-cost treatment pathways explored to ensure that emissions meet standards.

Wind power has a medium level of coupling and coordination. The combined effectiveness scores of the three policy instruments show that the wind power policy instruments are more comprehensive, and more supply-side and environmental policy instruments are used, taking into account demand-side policy instruments. The research of Milton M et al. revealed the importance of the wind energy project supply chain for policy coordination, and they proposed the adoption of dynamic performance management measures. Considering that energy consumption is an important link in the supply chain, this is similar to the result of the lack of energy consumption policy tools found in this article [53]. Enhancing the synergy level of wind power policy instruments can be approached by focusing on aspects like trade facilitation, promotional incentives, and international cooperation. For example, measures can include incentivizing wind power businesses to expand internationally and fostering small-scale wind power technology development. Given that the overall energy consumption of renewable energy already includes wind power, there are few policy instruments specifically targeting wind energy consumption. Refining the energy consumption section could be beneficial, adding guidance on wind power consumption.

The degree of coupling and coordination of photovoltaic storage integration is at a medium level. The policy of photovoltaic storage integration applies ecological environment risk control policy instruments. For example, while promoting projects such as integrated generation, grid, and storage systems, the strict implementation of ecological conservation redlines should also be enforced. Optimizing the synergy of photovoltaic storage integration policy could be achieved by widening the types of policy instruments employed. For instance, for scientific and technological inputs, accelerate the research and development of new charging and switching technology and applications, and create a “photovoltaic storage charging and discharging” pilot; for system construction and supporting infrastructure, establish a market mechanism for storage and discharge of green electricity, develop intelligent green logistics, and promote vehicle-network integration, etc. Moreover, heightened focus should be given to ecological environmental protection. For example, the ecological restoration of wetlands and mines and the coordination and integration of photovoltaic storage in the subsidence zone and saline flats need to be clarified in the policy.

The coupling and coordination of PV are at a high level. Its policy instruments are more varied, and the effectiveness scores of the three policy instruments are at a balanced level, which indicates that distributed PV-related policies coordinate the development and protection of renewable energy, socio-economics, and the ecological environment, and that they have a certain degree of synergy. Distributed PV is characterized by being constructed mainly in the vicinity of user sites and is suitable for nearby consumption. Promotional incentives and energy consumption, supporting industries are the main secondary policy instruments, in line with the development needs of distributed PV. In addition, ecological risk control is also reflected. Applying promotional incentives, as an essential strategy for distributed photovoltaic development, still holds potential for further strengthening of its combination with demonstration creation and rural revitalization. For example, distributed PV power generation and the application of other renewable energy sources can be used as an indicator for demonstration creation or the construction of characteristic towns and beautiful villages. Chen et al. highlighted the emerging trend of a harmonious relationship between the agricultural system and photovoltaic power generation industry [54]. They also emphasized the importance of reinforcing the fundamental role of the agricultural industry and fostering the growth of agricultural photovoltaics. These insights align with this study’s proposal to integrate the photovoltaic sector with rural revitalization projects.

The coupling and coordination of biomass energy policy instruments are at a high level. In terms of the effectiveness of policy instruments, the three policy instruments are balanced; in terms of the types of policy instruments, there are diverse policy instruments, with target planning, promotional incentives, and supporting industries as the main policy instruments, focusing on ecological risk control. For example, for target planning, a number of policy documents set the corresponding time nodes that biomass energy should reach the scale of the target; supporting industries create a three-dimensional development model of forestry and promote the integrated development of planting, animal husbandry, processing, forestry, biomass energy, tourism and other industrial chains; for promotional incentives, biomass energy is encouraged by legal guarantee, and it is also encouraged to supply energy to renewable energy; for ecological and environmental protection, biomass boilers are not allowed to use fossil energy, and they must be retrofitted with air pollution purification and other environmental protection facilities and an air pollution emission monitoring system, and emissions should meet the standards; biomass boilers should take into account environmental protection, energy saving, and safety and stability, with the introduction of biomass fuels, boiler manufacturing, and other related local standards. For enhancing such a high degree of coordination, prospective studies could focus on the refinement of policy instrument categories. Such studies could adopt the stratification of direct and indirect incentive policies, as proposed by Zahra et al., and propose commensurate recommendations for comprehensive results [55].

The coupling and coordination of external power input is at a high level. In terms of the policy instrument effectiveness scores, external power input is more likely to use environmental policy instruments, taking into account both supply-side and demand-side. External power input adheres to the efficient use of stock and the high-quality development of incremental combinations, focusing on the creation of supporting a renewable energy power delivery base, with clear target planning, specific supporting base construction, and taking into account renewable energy power, proposing renewable power development goals. The two are coordinated and linked to form synergy in planning to jointly promote renewable energy power generation and generate synergistic benefits. In order to further improve the coordination of policy instruments for the entry of external power input, various types of policy instruments can be added appropriately, including financial inputs, inspection and supervision, and ecological risk control. For example, special funds for external power input should be increased, supervision of the construction of external power input projects should be strengthened, and particular attention should be paid to ecological risk control. Currently, regarding the possible conflict between the construction of the channel of external power input and ecological environmental protection, the relevant policies have not yet been specified in this regard, such as ecological environment control requirements under the channel and on both sides of the in-transit and the proposed channel, the ecological protection zones where the construction of the channel is prohibited, the minimum distance between the channel and the ecological protection zones, and so on.

6. Conclusions

Leveraging the coupling coordination degree model, this research devised a framework for measuring policy synergy, utilizing Shandong Province as a case study to appraise the synergy of renewable energy policies. The results demonstrate a primarily satisfactory coordination of renewable energy policies across all categories, either moderately or highly coordinated in Shandong Province. Concerning the secondary policy instruments, instruments such as target planning, demonstration projects, promotional incentives, supporting industries, and ecological risk control are prevalently utilized. Specific circumstances vary across different types of renewable energy. In the fifth section, this study proposes several suggestions aimed at bolstering policy coordination tailored to these specific scenarios. These comprise the optimization of demand and environmental policy instruments for hydrogen energy regulations.

In light of these findings, several policy implications can be inferred. Initially, Shandong Province should enhance and amend its renewable energy policies to preserve and augment policy coordination, thus maximizing the combined impetus of these policies. Secondly, related energy, environmental protection legislations, and technical guidelines could incorporate additional clauses concerning policy coordination. For instance, the policy analysis of environmental impact assessments could integrate an analysis of policy coordination, using this research’s proposed framework. Appraisal of the policy within its system was undertaken to elucidate the policy’s position and its relation to other policies, whereby insights into the environmental ramification of policy synergy were subjected to critique, with subsequent recommendations being proffered. With this, consideration was extended to the linear importance, comprehensiveness, and harmonization of policy instruments, thereby optimizing the configuration of policy instrument amalgamations. Lastly, when crafting energy-related policies, consideration should extend beyond the prevailing energy policy systems to encompass economic, industrial, and ecological policies closely associated with energy. This prevents policy duplication or conflicts and fosters policy integration and complementarity. The application for this can be realized in the policy analysis, as a step toward public policy evaluation or policy-based strategic environmental assessment. It can be conducted using two strategies. The first is shown in our study. The second strategy centers on an examination of the policy’s real-world impacts, securing applicable data from the affected sectors pre- and post-policy implementation or simulating various scenarios reflecting policy enforcement and lack thereof to construct analytical models of the policy’s influence.