The Role of Policy Narrative Intensity in Accelerating Renewable Energy Innovation: Evidence from China’s Energy Transition

Abstract

The energy transition is not only a technological or market-driven process but also a discursive and institutional challenge. While conventional research emphasizes financial incentives and regulatory frameworks, the role of policy narrative intensity in shaping renewable energy innovation has received limited empirical attention. This study addresses this gap by analyzing 8837 provincial-level policy documents (2005–2023) from 31 regions across China. We construct a policy narrative intensity index using the PMC framework to systematically assess how institutional discourse influences the direction and intensity of renewable energy development. The results reveal that a 1% increase in policy narrative intensity corresponds to a 4.60% rise in renewable energy innovation, as measured by renewable electricity generation, with robustness confirmed through IV and IHS methods. Regional heterogeneity is also evident: executive-led regions such as Jiangxi, Shandong, and Fujian exhibit higher narrative strength and stronger renewable energy outcomes, while market-driven provinces like Shanghai and Guangdong show weaker narrative alignment. Mechanism testing demonstrates that policy narratives enhance renewable energy innovation by (1) strengthening environmental regulation enforcement (β = 0.37), (2) increasing green patent activity by 23.6%, and (3) raising public adoption of renewable energy by 17.2 percentage points. This study highlights the governing value of policy narratives as institutional public goods and reveals their crucial role in aligning administrative capacity, corporate innovation, and public engagement to drive energy transition. These insights contribute to the broader discourse on SDG7/SDG13-aligned sustainability governance.

1. Introduction

As the world’s largest developing country, China has long faced the challenge of balancing economic growth with environmental protection. In recent years, rapid industrialization and urbanization have fueled China’s rapid economic development, making it one of the fastest growing economies in the world. However, this economic expansion has also come with significant environmental costs, including increased resource consumption, ecosystem degradation, and continued growth in greenhouse gas emissions. By the end of 2024, China’s population had reached 1.408 billion, with more than 60.2 million registered enterprises and a gross domestic product of 134.9 trillion yuan [1]. In recent decades, China has maintained an average annual GDP growth rate of about 6–10% [2]. Behind the high economic growth rate, the accompanying carbon emission problem is becoming increasingly serious, with China’s total carbon emissions exceeding 12.6 billion tons in 2024, accounting for 32% of global carbon emissions [3]. Against the backdrop of increasing global climate change and the gradual depletion of energy resources, China is under pressure to urgently transform itself.

Policy plays a critical role in facilitating the energy transition and renewable energy innovation. However, policy does not exist in isolation; it is deeply embedded in the institutional, social, and cultural fabric of a country. The success of any energy transition strategy depends not only on economic incentives, market regulation, technological innovation, and regulatory frameworks, but also on broader cultural and governance factors [4]. These factors are important but often overlooked. Political culture, historical narratives, and political discourse can significantly influence how energy policies are formulated, accepted, and implemented. In China, policy implementation and effectiveness are often closely linked to the country’s institutional culture, ideology, and collective governance orientation, which have promoted strong state capacity, centralized policy implementation, and high social responsiveness to national strategies [5]. This governance structure has enabled China to implement large-scale policies with astonishing efficiency, as reflected in the implementation of policies related to poverty alleviation, epidemic control, and climate adaptation [6].

In recent years, behavioral public policy has advocated the use of a combination of tools for better implementation results, of which the policy narrative process is an important component. Therefore, government agencies have widely mobilized through various means and effective policies to effectively address urgent issues such as environmental protection, energy transformation, and renewable energy innovation, which has gradually become a research hotspot in academia. Among various forms of social mobilization, it is a common practice for governments to mobilize society through the elaboration and interpretation of specific policies (such as narrative publicity) to promote voluntary public behavior. In academia, the narrative policy framework (NPF) has become a specialized field of research. It refers to the government’s use of a policy story, which includes key elements such as a storyline and characters, to help the public better understand and accept the policy, thus facilitating policy implementation. As shown in Figure 1, the keyword co-occurrence network based on the policy texts reveals the structural association between narrative expressions and renewable energy concepts, highlighting the dense semantic connections among terms such as "growth", "energy", "microalgae", "photosynthesis", and "innovation", which collectively reflect the discourse construction around green transformation. Although previous research has extensively examined the economic and technological factors driving the green energy transition, the role of policy discourse as an alternative driver of sustainable innovation has not been fully explored [7]. Policy narratives not only mobilize political and social actors but also create institutional legitimacy for sustainable transitions. Exploring how the strength of policy narratives affects the implementation of renewable energy innovation and energy policies provides a new perspective on China’s energy transition [8].

Despite increasing attention to the importance of political culture in national governance, the specific role of policy narratives in energy innovation remains understudied, and many mechanisms have not been clearly identified. Currently, most research focuses on the impact of policy discourse on political mobilization, social stability, and national governance, while less attention has been paid to how it shapes renewable energy development, technological progress, and local government policy responses [9]. This study systematically examines how policy narratives influence energy transitions and sustainable innovation through a combination of policy discourse analysis and econometric methods and provides theoretical and empirical analysis of the underlying mechanisms [10]. A deeper understanding of the role of policy narratives in China’s energy transition will not only provide new perspectives for discussions on global sustainable development governance but also provide reference and experience for other countries in finding a balance between state-led policies and market-driven sustainable transformation.

This study aims to bridge this gap by quantifying the impact of policy narrative strength on renewable energy innovation. By analyzing 8837 policy documents from 31 Chinese provinces (2005–2023) and constructing a policy narrative strength index using the PMC framework, we provide an empirical assessment of how government discourse can promote energy innovation and decarbonization strategies. The study further examines the regional differences in policy narrative strength, revealing how different provinces prioritize sustainability in their governance models. In addition, we explore three key mechanisms through which policy narratives promote renewable energy innovation: (1) improving government implementation capacity to ensure large-scale policy implementation [11]; (2) incentivizing corporate green research and development to promote owner-led innovation [12]; and (3) cultivating social trust and public participation to strengthen social support for renewable energy applications.

The innovation of this paper lies in it being the first systematic study of how policy narrative intensity influences renewable energy development and innovation, providing a novel theoretical framework for understanding the role of policy discourse in energy transitions. This study proposes that policy narrative intensity shapes energy innovation through three key mechanisms: enhancing government execution, fostering social trust, and strengthening local innovation culture. Using spatial econometric methods, this paper quantifies the impact of policy narrative intensity on renewable energy innovation and demonstrates how institutionalized policy discourse mobilizes social cooperation, reinforces governmental enforcement capacity, and fosters a culture of technological innovation in the renewable energy sector. Moreover, by integrating China’s unique political and institutional context, this study innovatively positions policy narrative intensity as a governance tool for promoting green development, expanding the research horizon of energy transitions. This perspective provides a new conceptual bridge between political discourse, sustainability governance, and technological innovation, offering insights into the role of institutionalized narratives in shaping long-term energy policy effectiveness.

2. Theoretical Framework and Research Hypotheses

This section constructs the theoretical framework explaining how policy narrative intensity influences renewable energy innovation. Based on the policy enforcement, corporate innovation, and social recognition mechanisms, we propose research hypotheses and illustrate the pathways through which policy narratives shape green technological advancements (Figure 2).

2.1. Policy Narrative Intensity and Renewable Energy Innovation

China’s state-led energy transition is heavily influenced by the core political values of collectivism, long-term strategic planning, autonomous innovation, and energy security [13]. Collectivism emphasizes the priority of the overall national interest over short-term market fluctuations. This allows governments to set more forward-looking targets for renewable energy development. The “dual carbon” strategy, for example, not only demonstrates China’s strong commitment to tackling climate change but also the government’s long-term planning to promote sustainable development [14]. Long-term strategic thinking has led to strong stability and continuity in renewable energy policy, ensuring that green technologies and infrastructure continue to receive financial investment and policy support [15].

Self-innovation is an important pillar of China’s industrial policy. Particularly in the renewable energy sector, this principle has promoted indigenous technology research and development (R&D), reduced dependence on foreign core technologies and intellectual property rights (IPR), and accelerated breakthroughs in solar, wind, and energy storage technologies [16]. In addition, energy security has been gradually integrated into the framework of the national security strategy to ensure the transition of the energy structure to renewable energy, while at the same time serving the stability of China’s energy supply in a broader context and reducing the geopolitical risks associated with dependence on traditional fossil fuels [17].

Transitions to renewable energy are influenced by these policy narratives in a number of significant ways. First, in addition to establishing explicit emission-reduction goals, the dual-carbon strategy promotes policy incentives such as market-based trading systems, subsidies, and regulatory requirements that hasten the adoption of renewable energy [18]. Second, the spread of green energy is better governed by the government thanks to China’s state-led energy strategy. Mandatory quota systems, long-term energy planning, and renewable energy subsidies are all largely controlled by organizations like the National Development and Reform Commission (NDRC) and the National Energy Administration (NEA) [19]. To speed up the implementation of renewable energy on a wide scale, state-owned energy companies including State Grid, China Three Gorges Corporation, and China General Nuclear Power are also immediately mobilized [20]. Lastly, policy narratives impact spatial energy transitions, as seen by policy prioritizing for historically important revolutionary locations. By incorporating historical political importance into modern sustainability initiatives, regions like Yan’an and Jinggangshan are given preferential policies and substantial financial incentives to support renewable energy projects [21].

Figure 2. Mechanism of policy narrative intensity promoting renewable energy innovation.

Figure 2. Mechanism of policy narrative intensity promoting renewable energy innovation.

Hypothesis 1. 

Higher policy narrative intensity positively influences renewable energy innovation by strengthening governmental implementation capacity, fostering technological self-sufficiency, and reinforcing energy security objectives.

2.2. Policy-Oriented Mechanisms

Government regulatory frameworks and the effectiveness of policy implementation are greatly influenced by policy narratives, especially when it comes to the transition to renewable energy. By enhancing regulatory monitoring and bringing local governance into line with national sustainability goals, the rigor of policy narratives supports state institutions’ capacity to carry out clean energy policies [22]. The policy-driven collectivist perspective and the emphasis on national strategic interests, which influence how local governments interact with energy transition programs, form the basis of this mechanism.

First, policy narratives shape governance behavior by reinforcing alignment between local and national priorities. When policies emphasize collective responsibility and national development goals, China’s governance system tends to demonstrate a high degree of cohesion and responsiveness, particularly when addressing major societal challenges [23]. In the context of renewable energy policy, local governments do not act solely as passive implementers of central directives; rather, they proactively integrate national energy strategies into regional governance. When policy narrative intensity is high, local governments mobilize resources beyond formal requirements, accelerating renewable energy deployment and infrastructure development. This proactive approach ensures that policy objectives are not just carried out but optimized at the regional level, leading to more effective and coordinated energy transition efforts [24].

Second, the emphasis on long-term national interests over short-term economic concerns—a recurring theme in policy narratives—guides local governments in structuring renewable energy policies [25]. This narrative perspective encourages regional policymakers to look beyond immediate economic trade-offs and adopt a broader, nationally integrated approach to green development. As a result, they are more inclined to facilitate cross-regional resource coordination, introduce regulatory innovations, and eliminate bureaucratic bottlenecks that hinder renewable energy expansion. For instance, in the development of clean energy infrastructure, local governments have displayed high levels of efficiency in managing resources, adjusting policies to fit regional conditions, and overcoming administrative inertia, ensuring that renewable energy projects are implemented at scale and integrated into the national energy system. The driving force behind these actions is not merely policy compliance but a broader commitment to energy security and climate objectives, reinforced by the institutional discourse surrounding sustainability transitions.

Such proactive engagement is particularly evident in regions with strong historical-political legacies, where revolutionary traditions have been institutionalized into governance practices. Figure 3 illustrates the spatial overlap between high policy narrative intensity and historically significant revolutionary bases, such as Jinggangshan, Yimengshan, and the New Fourth Army’s anti-Japanese base areas. These sites not only serve as symbols of political continuity but also help legitimize local governments’ assertive role in implementing national sustainability agendas.

Hypothesis 2. 

Higher policy narrative intensity enhances the execution of renewable energy policies by strengthening state regulatory capacity, aligning local governance with national energy strategies, and fostering proactive policy implementation.

2.3. Enterprise Innovation Mechanism

Corporate innovation behavior is greatly influenced by the ferocity of policy narratives, especially in the field of renewable energy. Enterprises are encouraged to actively seek independent innovation in green technologies by a robust policy discourse that emphasizes sustainability, national energy security, and technical self-reliance [26]. Businesses are encouraged to move away from conventional energy models and pursue innovative developments in new energy solutions by the policy-driven dedication to technical innovations. Policy narratives that include corporate social responsibility into innovation initiatives and push businesses to combine environmental sustainability and economic performance serve to further solidify this change [27].

A key component of China’s policy discourse is the emphasis on “self-reliance in science and technology”, which has led to substantial government investment in renewable energy technologies, including solar power, wind energy, and energy storage solutions [28]. Institutions that place strong narrative emphasis on green energy development typically channel significant subsidies into emerging energy industries, fostering rapid sectoral growth. In 2022 alone, China allocated 150 billion yuan in renewable energy subsidies, with a large portion directed to provinces where policy narratives strongly support clean energy initiatives. Additionally, state-owned enterprise (SOE) reforms have played a crucial role in accelerating renewable energy innovation, particularly in the fields of solar photovoltaics, wind power, and hydrogen energy technologies [29]. Major SOEs, including China Three Gorges Corporation(Yichang, China) and the State Power Investment Corporation(Beijing, China), have leveraged government backing to establish themselves as global leaders in wind and solar energy investment.

Furthermore, policy narratives that highlight ”strategic national projects” have enabled the government to mobilize extensive resources for large-scale infrastructure development. One example is the construction of ultra-high voltage (UHV) transmission grids, which allow renewable energy generated in western China to be efficiently delivered to high-demand industrial centers in the east [30]. Additionally, targeted government support has driven rapid advancements in energy storage solutions, particularly in lithium batteries and hydrogen storage, addressing one of the biggest challenges in renewable energy generation—intermittency.

Hypothesis 3. 

Higher policy narrative intensity promotes independent innovation in renewable energy technologies by enhancing government investment, supporting enterprise-led technological breakthroughs, and fostering large-scale infrastructure advancements.

2.4. Social Identity Mechanism

When it comes to influencing public opinion, social conduct, and group involvement with sustainability objectives, the ferocity of policy narratives is vital. A greater public commitment to the deployment of renewable energy is fostered by policy rhetoric that prioritizes fairness, social responsibility, and the well-being of all [31]. According to this concept, people are impacted by national development goals and a larger sense of social duty in addition to their own economic interests. Citizens are encouraged to make environmentally conscious decisions, promote the use of renewable energy sources, and connect their personal behaviors with sustainability policies via policy narratives that emphasize benevolence and a commitment to communal progress [32]. As a result, the adoption of renewable energy is now seen as an extension of civic duty and social commitment, creating a cultural climate where green development is seen as a shared national obligation.

Beyond individual behavioral shifts, policy narratives have also positioned renewable energy as a symbol of modernization and China’s global leadership in clean energy technologies [33]. The growth of China’s new energy vehicle industry, for instance, has been framed within the broader narrative of national rejuvenation, reinforcing the industry’s role in enhancing China’s global competitiveness. Traditional policy narratives—such as the “Jinggangshan Spirit” and the “Long March Spirit”—have been repurposed to promote renewable energy expansion, strengthening the industry’s role in national development. Many clean energy enterprises leverage policy-driven slogans, such as “Green Long March” and “Energy Revolution”, in their branding strategies, reinforcing a collective national identity linked to the green transition.

Policy narratives are also deeply intertwined with regional cultural identities, particularly in historically significant areas associated with major national development initiatives. In places like Jinggangshan and Zunyi, renewable energy projects have been directly tied to local economic revitalization and poverty alleviation efforts, such as solar-powered rural electrification and photovoltaic-based poverty alleviation programs [34]. Additionally, media portrayals of renewable energy pioneers—such as those featured in the documentary Great Power—have further strengthened public recognition of clean energy engineers and entrepreneurs as key contributors to national development, reinforcing the social identity of the renewable energy sector.

The emphasis on equity in policy narratives has also significantly influenced public acceptance and market demand for renewable energy. By framing sustainability as a collective societal goal, policy narratives encourage broad public consensus on environmental protection, positioning it as a shared responsibility rather than a government mandate [35]. This approach ensures that renewable energy adoption is not limited to government programs or elite-driven initiatives, but instead gains widespread support across various socioeconomic groups, creating a more inclusive green transition. As a result, consumer demand for renewable energy solutions continues to expand, driving market growth and accelerating the commercialization of clean energy technologies [36].

Hypothesis 4. 

Higher policy narrative intensity fosters renewable energy consumption and market demand by enhancing public environmental responsibility, embedding sustainability in national identity discourse, and promoting collective participation in the green transition.

3. Research Design

3.1. Sample Selection and Data Sources

This study takes 31 provincial-level administrative regions in China (excluding Hong Kong, Macao and Taiwan) as the research object and constructs provincial-level panel data from 2005 to 2023 to explore the impact of policy narratives intensity on renewable energy innovation. The choice of the provincial level as the unit of analysis is mainly based on the availability and representativeness of the data. Compared with data at the prefecture or county level, provincial data are more consistent in terms of statistical caliber and, at the same time, can better reflect regional policy differences and energy development levels. In addition, provincial governments have strong dominant power in policy formulation and implementation, and the formulation and implementation of their policy narratives directly affect the development direction of the local new energy industry. The time span of the study is set from 2005 to 2023, mainly considering that 2005 is one of the starting points of the Chinese government’s explicit renewable energy policy, which marks the gradual improvement of the national policy layout in the field of energy transition and environmental protection, and the integration of the policy narratives has begun to appear. At this stage, the long-term impact of policy narratives on new energy innovation and capture the latest results and dynamic changes in policy implementation. Therefore, this time interval allows a comprehensive assessment of the impact of policy narratives on renewable energy innovation, especially the effects and trends of policy implementation.

This study utilizes a diverse range of data sources to ensure analytical robustness and regional comparability. The core explanatory variable, policy narrative intensity, is derived from official policy documents issued by 31 Chinese provincial governments between 2005 and 2023. These documents were systematically collected from national policy databases, provincial government websites, government work reports, and the People’s Daily. After undergoing standard preprocessing procedures, text mining methods including latent Dirichlet allocation and term frequency–inverse document frequency analysis were applied to extract renewable energy-related topics and term weights. Based on this, a PNI index was constructed to quantify the rhetorical intensity of local policy narratives regarding renewable energy, enabling temporal and spatial comparison.

Renewable energy innovation is measured using provincial-level, non-fossil electricity generation (in billion kWh), covering power from wind, solar, hydro, and nuclear sources [37]. Data are sourced from the China Energy Statistical Yearbook, the National Energy Administration, and the Wind Financial Terminal. This indicator captures the actual deployment and output of renewable energy technologies, reflecting their application effectiveness across regions [38]. Compared with patent counts or R&D project counts, this metric more directly links policy narrative outcomes with renewable energy performance, avoiding potential lags or misalignment between innovation input and output [39].

Control variables were selected to reflect the broader institutional, economic, and technological context. These include per capita GDP, the proportion of renewable energy in total energy consumption, infrastructure investment, higher education enrollment rate, the proportion of the secondary industry in GDP, and the share of foreign-invested enterprises. Most data were obtained from the China Statistical Yearbook and the China Science and Technology Statistical Yearbook. Nevertheless, the possibility of residual bias remains, and this limitation is explicitly addressed in Section 5.3 of the manuscript.

In addition, this study introduces a series of control variables with data from the China Statistical Yearbook and China Science and Technology Statistical Yearbook. The main variables include GDP per capita, science and technology R&D expenditure, energy structure, and industrial structure, and the data for variables such as external cultural shocks come from the statistics of the Ministry of Finance, the Ministry of Commerce, and the Ministry of Culture and Tourism, in order to control the external cultural factors on the implementation of the policy narratives and new energy innovation possible impact.

3.2. Model Setting and Variable Description

3.2.1. Spatial Measurement Model

This study uses a spatial measurement model to explore the impact of policy narratives on renewable energy innovation:

$$Y_{i,t}={\beta }_0+{\beta }_{1}W{Y}_{i,t}+{\beta }_1{X}_{i,t}+{\beta }_k{Z}_{i,t}+{\mu }_i+{\alpha }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(1)

Bring in variables:

$$RE{I}_{i,t}={\beta }_0+{\beta }_{1}PN{I}_{i,t}+\sum {\beta }_k{Z}_{k,i,t}+{\mu }_i+{\alpha }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(2)

REI_i,t is renewable energy innovation using renewable energy generation. PNI_i,t is the intensity of the policy narratives. Z_k,i,t is a set of control variables. β₁ measures the direct effect of the region’s policy narratives on new energy innovation, and β_k represents the effect of the control variables on the region’s new energy innovation. μ_i is the individual provincial heterogeneity term, which is used to capture long-standing but unobservable regional characteristics, such as institutional environment, geographic resource endowment, and historical and cultural factors. These factors may not change over time but will affect the region’s capacity for new energy innovation. The model also includes regional fixed effects α_i and time fixed effects λ_t to control for structural differences across regions and time trends, and ε_i,t is a random error term.

To reduce the scale difference of variables and improve the fitting effect of the model, this study takes the logarithms of all variables to make the relationship more linear and at the same time reduce the problem of heteroskedasticity.

$$lnRE{I}_{i,t}={\beta }_0+{\beta }_{1}PN{I}_{i,t}+{\beta }_k{\displaystyle \sum ln{Z}_{i,t}}+{\mu }_i+{\alpha }_i+{\lambda }_t+{\epsilon }_{i,t}$$

(3)

Dependent variable: In this study, renewable energy power generation is selected as the dependent variable for measuring renewable energy innovation to reflect the real-world outcomes of technology application and policy support [40]. Renewable energy power generation includes the total power generation of wind, solar, hydro, biomass, and other non-fossil energy sources, which can measure the level of renewable energy development at the regional level in a more objective way [41]. This indicator has strong data availability and can reflect the final market transformation of new energy technology innovation. However, we acknowledge that generation alone may not fully represent the upstream innovation process. Therefore, in the mechanism analysis and robustness tests, we additionally incorporate (i) the number of renewable energy patent applications (ln REP) and (ii) renewable energy-related R&D expenditures (ln R&D) as complementary proxies. These variables reflect the technological inputs and innovation outputs at the enterprise and provincial level, allowing us to assess the broader spectrum of renewable innovation activity beyond generation capacity alone.

Independent variable: This study adopts the intensity of policy narratives as the core independent variable to quantify the policy intensity of regional governments in promoting the integration of policy narratives and new energy development. This indicator can effectively reflect the degree of policyization of the concept of policy narratives by different regional governments, as well as the strength of policy influence on the development of new energy industry.

This study introduces several control variables into the model to reduce omitted variable bias and enhance the robustness of the estimation (

Table 1. Variable definitions.

Variable Category	Variable Name	Variable Symbol	Definition
Dependent variable	Renewable energy innovation	REI	Logarithm of renewable electricity generation
Independent variable	Policy narrative intensity	PNI	Policy narrative intensity index
Control variable	Level of economic development	GDP	Logarithm of GDP per capita
	Infrastructure	Infr	Log of investment in local infrastructure development
	Education level	Edu	Enrollment rate in tertiary education
	Energy structure	Eng	Share of renewable energy consumption in total energy consumption
	Industrial structure	Ind	Share of secondary industry in GDP
	Foreign culture shock	FC	Proportion of foreign-funded enterprises

). The level of economic development (GDP_i,t), measured by GDP per capita, controls for the impact of regional economic strength on renewable energy development; infrastructure development (Infr_i,t) is measured by the degree of support for new energy innovation through the investment in local infrastructure development (e.g., transportation, communication, etc.); the level of education (Edu_i,t) measures the allocation of local educational resources and their support for technological innovation; energy structure (Eng_i,t) indicates the proportion of renewable energy in total energy consumption, considering the impact of the regional energy transition process on new energy generation; industrial structure (Ind_i,t) is measured by the proportion of the secondary industry, controlling the impact of the degree of industrialization on energy demand and new energy development. In addition, the foreign cultural impact (FC_i,t) is measured by the proportion of foreign-funded enterprises or the frequency of international cultural exchanges, reflecting the potential impact of external cultural factors on the implementation of local policy narratives and the development of new energy technologies, ensuring that the model can identify the real effects of policy narratives in a more comprehensive way.

Table 2. Descriptive statistics and Pearson’s correlation coefficient.

Variable	Obs	Mean	Std. Dev.	Min	Max	REI	PNI
REI	589	24.645	30.586	0	175	1.000
PNI	589	0.326	0.397	0.004	5.447	0.2221 *	1.000
GDP	589	62,048.6483	30,890.3731	22,089	190,313	0.1793 *	0.8694 *
Infr	589	8827.201	6361.883	3886	40526	0.1112	0.4021 **
Edu	589	0.4581	0.2694	0.0639	4.8594	0.1007 *	0.3998 *
Eng	589	0.1534	0.1641	0.0027	0.8360	0.1581 *	0.7772 *
Ind	589	0.2574	0.1053	0.0380	0.8590	0.3632 *	0.9208 *
FC	589	29.4798	54.3707	3.2806	100	−0.1834 *	−0.2822 *
Variable	Obs	GDP	Infr	Edu	Eng	Ind	FC
GDP	589	1.000
Infr	589	0.1326 *	1.000
Edu	589	0.1270 *	0.9987 *	1.000
Eng	589	0.6525 **	0.8051 *	0.7980 **	1.000
Ind	589	0.6238 *	0.2273 *	0.1439 *	0.2849 *	1.000
FC	589	−0.0963 *	0.0522	0.0848 *	0.0039	−0.1706 *	1.000

t statistics in parentheses, * p < 0.1, ** p < 0.05.

demonstrates the descriptive statistical characteristics and Pearson’s correlation coefficients of the main variables. From the descriptive statistics, the mean value of REI is 24.645 with a large standard deviation (30.586), the minimum value is 0, and the maximum value reaches 175, indicating that there is a significant difference in green energy innovation among provinces. The mean value of PNI is 0.326, and the maximum value is 5.447, indicating a large difference in the implementation of policy narratives among regions. Other variables, such as GDP, Infr, Edu, Eng, and Ind, all show large standard deviations, reflecting the unevenness of economic development, industrial structure, and the application of green energy across provinces (Figure 4).

In terms of correlation, PNI is significantly positively correlated with REI (0.2221, p < 0.1), indicating that regions with stronger policy narratives tend to invest more resources in renewable energy innovation, reflecting the fact that policy narratives may have played a positive role in promoting green energy development. In addition, GDP has a higher correlation with PNI (0.8694, p < 0.1), indicating that more economically developed regions tend to pursue stronger policy narratives, consistent with the government’s emphasis on cultural guidance and policy implementation in regions with higher levels of economic development. Meanwhile, industrial structure (Ind) shows a high positive correlation with PNI (0.9208, p < 0.1), indicating that regions with a higher proportion of secondary industries tend to pay more attention to the guidance of policy narratives, probably due to the fact that this type of regions is traditionally closely related to the national industrialization and energy policies.

Notably, FC is negatively correlated with PNI (−0.2822, p < 0.1), suggesting that regions with more foreign-funded enterprises or stronger international cultural influences tend to have relatively less-strong policy narratives. This may reflect that the influence of policy narratives is relatively weakened in a more open economic environment. In addition, FC is also negatively correlated with REI (−0.1834, p < 0.1), suggesting that regions with a higher proportion of foreign-funded enterprises may not necessarily promote renewable energy innovations, possibly because some foreign-funded enterprises are still on the sidelines in terms of energy transition or are not fully integrated into China’s green development strategy due to the influence of policy barriers. The above information provides the basis for our empirical analysis.

3.2.2. PMC Index Model

In order to quantify the intensity of policy narratives, this study adopts the policy monitoring and classification (PMC) index model, which constructs a metric for the intensity of policy narratives by systematically analyzing policy texts [42]. The PMC index model is based on the content features of policy documents, combines text mining and machine learning methods, and is measured through the following steps:

(1) Collection and preprocessing of policy documents

At first, all policy documents related to policy narratives issued from 2012 to 2023 at the provincial level and above are collected. The original text is collected from government websites, policy databases, and NLF and official announcement platforms, and the text is preprocessed, including the removal of deactivated words, punctuation marks, special symbols, etc., to ensure data cleanliness and accuracy of analysis.

(2) LDA topic modeling

The policy narratives texts are topic-mined using the latent Dirichlet allocation (LDA) model. LDA is able to effectively identify potential themes in the text and extract the core themes and keywords that represent the policy narratives by categorizing the keywords, phrases, and sentences that appear in the policy documents. Each policy document is assigned a thematic distribution that characterizes the policy content of the policy document in the context of policy narratives.

(3) Calculating the weight

Based on the thematic distribution extracted from the LDA model, the policy narratives intensity of each policy document is quantified, and the weight value of each provincial and municipal policy text is calculated [43]. Specifically, the intensity index of policy narratives documents in each region is obtained by calculating the word frequency (TF), inverse document frequency (IDF), and thematic distribution. The higher the value of this index, the stronger the strength of the region in promoting the integration of policy narratives and renewable energy.

(4) Constructing a quantitative policy indicator system

In order to more accurately measure the intensity of policy narratives, this study constructs a nine-dimensional indicator system, each dimension corresponding to a specific quantitative indicator, to comprehensively measure the intensity of policy narrative implementation, coverage, and impact at the policy level (

Table 3. Quantitative indicator system of China’s policy narratives.

Indicator	Score	Standard	Indicator	Score	Standard
(×1) Policy category	5	Laws	(×2) Issuing agency	5	Central government
	4	Regulations		4	State council
	3	Normative documents		3	Local government
	2	Working documents		2	Local people’s congress
	1	Administrative licensing approval		1	Local bureaus and departments
(×3) Policy status	5	Currently in force	(×4) Policy rank	5	National level
	4	Not yet implemented		4	Provincial
	3	Modified		3	Municipal level
	2	Partially invalid		2	County level
	1	Losing effectiveness		1	Village level
(×5) Emergency nature	5	Major emergencies	(×6) Policy nature	5	Compulsory
	4	General emergencies		4	Regulatory
	3	Intermediate		3	Hybrid
	2	Weaker		2	Introductory
	1	Non-emergency		1	Voluntary
(×7) Policy currency	5	Long-term (more than 10 years)	(×8) Strategic category	5	Environmental protection law
	4	Medium- to long-term (5–10 years)		4	Dual carbon strategy
	3	Medium-term (3–5 years)		3	One belt, one road
	2	Short-term (1–3 years)		2	Integrated regional strategy
	1	1 year		1	Free trade pilot zone
(×9) Object of action	5	More than 10 years
	4	5–10 years
	3	3–5 years
	2	1–3 years
	1	1 year

) [44].

(5) Calculate the strength of policy inputs

$$PM{C}_i={\displaystyle \sum _{j=1}^n{w}_j\times S_{ij}}$$

(4)

PMC_i denotes the PMC index of the ith policy, S_ij denotes the score of the ith policy on the jth dimension, and w_j is the weight of the jth dimension, reflecting the importance of different dimensions in evaluating policy strength.

Based on the nine dimensions, the PMC index model is used to comprehensively quantify the policies:

$$PMC=\left[\begin{array}{l}X1\left({\displaystyle {\sum }_{i=1}^5\frac{X1i}{5}}\right)+X2\left({\displaystyle {\sum }_{j=1}^5\frac{X2j}{5}}\right)+X3\left({\displaystyle {\sum }_{k=1}^5\frac{X3k}{5}}\right)+\\ X4\left({\displaystyle {\sum }_{l=1}^5\frac{X4l}{5}}\right)+X5\left({\displaystyle {\sum }_{m=1}^5\frac{X5m}{5}}\right)+X6\left({\displaystyle {\sum }_{n=1}^5\frac{X6n}{5}}\right)+\\ X7\left({\displaystyle {\sum }_{p=1}^5\frac{X7p}{5}}\right)+X8\left({\displaystyle {\sum }_{q=1}^5\frac{X8q}{5}}\right)+X9\left({\displaystyle {\sum }_{r=1}^5\frac{X9r}{5}}\right)\end{array}\right]$$

(5)

In this study, an equal weighting scheme was developed to ensure that the dimensions are equally important in quantifying the strength of policy narratives. In this scheme, all nine dimensions are equally weighted, meaning that each dimension contributes equally to the PMC index. This design scheme helps to avoid the excessive influence of any one dimension on the overall policy strength, maintains the fairness and balance of the assessment, and allows the role of each dimension to be fully represented. By equalizing the weights, we are able to ensure that each dimension plays an equal role in the overall assessment and fully reflects the strength of the policy narratives.

From 2005 to 2023, the policy intensity of policy narratives in Chinese provinces shows obvious inter-provincial differences (Figure 5). The policy intensity in Jiangxi, Shandong, Hunan, and Fujian is significantly higher than that in other provinces. As the important birthplaces of the Chinese Revolution, these regions have long been tasked with the inheritance of policy narratives, and the local governments have attached great importance to them in terms of policy. In contrast, some developed coastal provinces such as Shanghai, Zhejiang, and Guangdong, as well as economically underdeveloped places such as Qinghai and Tibet, have relatively lower intensity of policy narratives, which may be related to the different development strategies of the local governments. Economically developed regions are more inclined to prioritize the promotion of scientific and technological innovations and economic growth, whereas in some of the western regions, the policy resources are limited, and the construction of policy narratives relies more on guidance from the national level. In terms of policy averages, Jiangxi, Shandong, and Hunan not only have high policy totals but also show strong continuity over time, suggesting that local government support for policy narratives is not a short-term behavior but a long-term policy direction. On the other hand, provinces such as Sichuan and Inner Mongolia have relatively low mean values despite high policy totals, suggesting that policy narratives in these regions may be issued intensively during certain specific periods rather than being promoted stably over the long term.

Looking at the overall trends in the four regions, the total number of policy narratives is relatively high in the eastern and central regions, while the policy intensity in the northeastern region is significantly lower than in the other regions. The eastern region, as an economically developed region, maintains a high overall policy intensity despite the low policy intensity in some provinces, probably because Shandong and Fujian have taken more initiative in policy development. The central region, on the other hand, pulls up the overall average because of the high policy intensity in Jiangxi, while policy releases are smoother in Shanxi and Henan. The western region is more volatile in terms of overall policy intensity, reflecting more concentrated policy support in some provinces and weaker policy support in others. The northeast region has the lowest level of both total and average policy, suggesting that there are fewer red policies in the region, which may be related to its economic transformation and changing policy focus. These interregional differences suggest that policy narratives in China’s regions do not show a balanced development but rather are influenced by multiple factors, including historical, economic, and political factors, and show a clear geographical and historical dependence (Figure 6).

4. Empirical Results

4.1. Baseline Regression Results

Table 4. Baseline results.

	(1)	(2)	(3)	(4)
VARIABLES	LnREI	LnREI	LnREI	LnREI
PNI	0.0460 ***	0.0390 ***	0.0270 ***	0.0350 ***
	(0.0130)	(0.0115)	(0.0090)	(0.0085)
ln GDP			0.0014	0.0016
			(0.0023)	(0.0024)
ln Infr			0.0089 **	0.0090 **
			(0.0036)	(0.0034)
Edu			0.3800 ***	0.3880 ***
			(0.0430)	(0.0425)
Eng			0.0069 ***	0.0072 ***
			(0.0021)	(0.0020)
Ind			0.7300 *	0.7480 *
			(0.0355)	(0.0360)
FC			−0.0003	−0.0004
			(0.0018)	(0.0017)
Constant	1.5600 ***	1.7300 ***	1.9800 ***	1.7000 ***
	(0.3200)	(0.3000)	(0.2850)	(0.3100)
Controls	No	No	Yes	Yes
ID effect	No	Yes	Yes	Yes
Year effect	No	Yes	Yes	Yes
Province effect	No	No	No	Yes
N	589	589	589	589
R2	0.1500	0.4000	0.4200	0.4700

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

demonstrates the impact of policy narrative intensity (PNI) on renewable energy innovation (LnREI) using stepwise inclusion of control variables and fixed effects to test the robustness of the results. From the regression results, the promotion effect of PNI on renewable energy innovation (LnREI) maintains significance in all models, and the regression coefficients range from 0.0270 to 0.0460, indicating that the increase in PNI intensity can effectively promote innovation in green energy technologies. Among them, column (1) is the most basic estimation without controlling for any fixed effects, and the regression coefficient of PNI is 0.0460, which is significant at the 1% level, suggesting that the enhancement of the strength of policy narratives presents a strong positive relationship with renewable energy innovation without the interference of other control variables. With the inclusion of individual fixed effects (ID effect) and year fixed effects (year effect) in column (2), the PNI coefficient slightly decreases to 0.0390 but is still significant, suggesting that local government characteristics and yearly changes have an impact on the results but do not change the positive effect of policy narratives on renewable energy innovation.

A series of control variables are further included in columns (3) and (4), including the level of economic development (GDP), infrastructure (Infr), education level (Edu), energy structure (Eng), industrial structure (Ind), and foreign cultural shocks (FC), to minimize omitted variable bias. It can be seen that the regression coefficient of PNI decreases to 0.0270 in column (3) and rebounds to 0.0350 in column (4), which still maintains 1% significance, suggesting that the promotion of renewable energy innovations by policy narratives is still robust, even after taking into account economic, industrial, and social factors. In addition, in terms of control variables, education level (Edu), energy structure (Eng), and infrastructure construction (Infr) all show significant positive impacts on renewable energy innovation, indicating that scientific and technological inputs and infrastructure improvement can effectively promote the development of new energy technologies, while industrial structure (Ind) is positive in columns (3) and (4), indicating that the increase in the proportion of secondary industry is to some extent is not contradictory to green energy innovation. Foreign culture shock (FC) is not significant in any regressions, indicating that the entry of foreign enterprises does not directly affect green technology innovation.

In terms of the goodness of fit (R²) of the regression model, with the addition of control variables and fixed effects, R² rises from 0.1500 in column (1) to 0.4700 in column (4), indicating that the explanatory power of the model gradually improves. It is worth noting that the PNI remains robustly positive after adding provincial fixed effects in column (4), indicating that the impact of policy narratives is not driven by regional heterogeneity but has a strong endogenous effect. Overall, the baseline regression results indicate that policy narratives can effectively promote renewable energy innovation, and this effect remains significant after controlling for a variety of economic and social factors, suggesting its importance in China’s green energy transition.

4.2. Robustness Tests

4.2.1. Substitution of Dependent and Independent Variables

To further test the robustness of the baseline regression results, the model is re-estimated using dependent variable substitutions and independent variable transformations to ensure that the study results are not dependent on any particular variable measure. To ensure the robustness of the study results, various substitutions are made for key variables. First, at the dependent variable level, the dependent variable is replaced by the logarithm of renewable energy patent applications(REP) to reflect new energy technology R&D activities, as shown in column (1) of

Table 5. Substitution of measurement of dependent and independent variables.

	(1)	(2)	(3)	(4)
	Replace Dependent Variables		Replace Independent Variables
VARIABLES	Ln REI-Ⅰ	Ln REP-REI	Ln REI	Ln nREI
PNI	0.0420 ***	0.0340 ***
	(0.0110)	(0.0105)
PNIⅠ			0.0570 ***
			(0.0080)
PNIⅡ				0.0640 ***
				(0.0075)
Constant	6.5593 ***	3.8210 ***	1.3028 ***	0.9037 ***
	(4.30187)	(1.2544)	(0.3035)	(0.3219)
Controls	Yes	Yes	Yes	Yes
ID effect	Yes	Yes	Yes	Yes
Year effect	Yes	Yes	Yes	Yes
Province effect	Yes	Yes	Yes	Yes
N	589	589	589	589
R2	0.4873	0.9371	0.1012	0.5261

t statistics in parentheses, *** p < 0.01.

; furthermore, as shown in column (2) of Table 5, the Inverse Hyperbolic Sine (IHS) transformation is applied to the dependent variable to mitigate the effect of zero or extreme values.

The mathematical form of the REP transformation is

$$REP(PNI)=ln(PNI+\sqrt{PN{I}^2+1})$$

(6)

Second, for the independent variables, the number of policy narratives, recommendations, or regulations issued in the current year and the number of martyrs’ memorial facilities in each province are used as substitutes, and these two substitutions measure the impact of policy narratives from the perspective of policy supply and historical and cultural precipitation, respectively, in columns (3) and (4) of the table.

It can be seen that the estimated coefficients of the main independent variables are significantly positive after using different proxy variables, thus further validating the positive facilitating effect of policy narrative-related indicators on renewable energy innovation.

4.2.2. Ruling Out the Possibility of Reverse Causality

To mitigate concerns regarding potential reverse causality between policy narrative intensity and renewable energy innovation, we employ an instrumental variable (IV) approach for robustness testing. Specifically, we use the number of prefecture-level cities in each province as the primary instrument. This variable is exogenously determined by historical administrative divisions and remains stable over time, thereby satisfying the exclusion restriction, as it is unlikely to directly influence renewable energy innovation outcomes or be driven by them.

In addition, to further account for time heterogeneity and enhance the variation in the instrument, we construct an interaction term between the number of prefecture-level cities and year, following established practice in the literature. The underlying logic is that the administrative competition across prefecture-level cities can affect the relative emphasis on economic growth versus environmental priorities at the provincial level, thereby indirectly influencing the formulation and intensity of policy narratives.

Column (1) of

Table 6. Excluding the possibility of reverse causality.

	(1)	(2)
	First-Stage IV	Two-Stage IV
VARIABLES	PNI	Ln REI
City_number	−0.0003 **
	(0.0001)
PNI		0.1390 **
		(0.0115)
Constant	1.6000 ***	3.7200 *
	(0.4830)	(0.3300)
Kleibergen–Paap Wald F Statistics		17.5120
Cragg–Donald Wald F Statistics		125.6420
Stock-Yogo 10%		16.38
Controls	Yes	Yes
ID effect	Yes	Yes
Year effect	Yes	Yes
Province effect	Yes	Yes
N	589	589
R2		0.4700

t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01.

shows the IV estimation results using a single instrumental variable, while column (2) shows the estimation results after using the interaction term as an instrumental variable. Both sets of results indicate that the positive effect of policy narratives intensity on new energy innovation is significant, while the instrumental variable statistics are above the critical value, which rules out the weak instrumental variable problem and thus increases the credibility of the results of this study.

4.3. Analysis of Mechanisms

4.3.1. The Mediating Role of State Executive Power

Policy narratives promote the implementation of local government policies in the field of new energy by strengthening the implementation power of local governments. For example, policy narratives emphasize national collective interests and social responsibility, which encourages local governments to pay more attention to new energy technological innovation and actively introduce supporting policies (e.g., financial subsidies, tax incentives, etc.) [45]. Improving government execution is an important factor in promoting new energy innovation. The effective execution of the government plays a bridging role in promoting new energy technology innovation, and the policy narratives indirectly promote the actual implementation of new energy projects and technological research and development by promoting the improvement of government execution.

Table 7. Mechanism analysis 1: government implementation power.

	(1)	(2)
VARIABLES	Ln FS	Ln REI
PNI	0.2389 *
	(0.0213)
PNI × ln FS		−0.0823 **
		(0.019)
Constant	2.8950 *	4.2101 *
	(0.7264)	(0.5123)
Controls	Yes	Yes
ID effect	Yes	Yes
Year effect	Yes	Yes
Province effect	Yes	Yes
N	589	589
R2	0.5632	0.3920

t statistics in parentheses, * p < 0.1, ** p < 0.05.

column (1) and column (2) show the effect of this mechanism. Column (1) shows that the policy narratives significantly improve the implementation of local governments, especially in the implementation of green policies, while column (7) shows that the improvement of government implementation significantly intensifies the positive driving effect of the policy narratives on new energy innovation and promotes the R&D and application of green technologies.

4.3.2. Mediating Role of Enterprises’ Green Innovation

Table 8. Mechanism analysis 2: corporate green innovation.

	(1)	(2)
VARIABLES	Ln REP	Ln R&D
PNI	0.1032 *	0.0725 *
	0.0257	0.0183
PNI × ln REP	0.2126 **
	(0.0739)
PNI × ln R&D		0.1864 *
		0.0212
Constant	3.1543 *	2.9175 *
	(0.5973)	(0.5147)
Controls	Yes	Yes
ID effect	Yes	Yes
Year effect	Yes	Yes
Province effect	Yes	Yes
N	589	589
R2	0.7012	0.5934

t statistics in parentheses,* p < 0.1, ** p < 0.05.

demonstrates the mediating role of firms’ green innovation between PNI and LnREI, which is examined in terms of the number of green patent applications (ln REP) and green technology R&D investment (ln R&D), respectively.

First, in column (1), the regression coefficient of PNI is 0.1032 and significant at the 10% level (p < 0.1), indicating that the increase of PNI intensity can positively promote the number of green patent applications by enterprises. This indicates that under the guidance of the policy narratives, enterprises tend to increase the patent layout of green technology, reflecting the effectiveness of the policy in incentivizing enterprises’ green innovation. Meanwhile, the regression coefficient of the interaction term between PNI and enterprise green patent applications (PNI * ln REP) is 0.2126, which is significant at the 5% level (p < 0.05), indicating that enterprise green innovation further enhances the positive impact of the policy narratives on renewable energy innovation. In other words, the policy narratives not only directly promote the growth of corporate green patents but also further amplify the positive effect on renewable energy innovation through the mechanism of corporate technological innovation.

Second, in column (2), the regression coefficient of PNI on enterprises’ green R&D investment (ln R&D) is 0.0725, which is significant at the 10% level (p < 0.1), indicating that the higher the intensity of PNI, the relatively higher the green R&D investment of enterprises in the region. In addition, the regression coefficient of the interaction term between PNI and green R&D investment (PNI * ln R&D) is 0.1864 and is significant at the 10% level (p < 0.1), indicating that firms’ green R&D investment can further enhance the promotion effect of policy narratives on renewable energy innovation. This result implies that under the guidance of the policy narratives, enterprises not only increase green patent applications, but also increase R&D investment, which results in the accumulation of green innovation technology and plays a key role in the technological breakthrough and promotion of renewable energy.

In addition, the model fit (R²) is 0.7012 and 0.5934 in the two regression models, indicating that the model has strong explanatory power, while controlling for individual fixed effects, year fixed effects, and province fixed effects, which ensures the robustness of the estimation results.

4.3.3. The Mediating Role of Social Trust and Local Innovation Culture

The policy narratives promote new energy innovation by increasing trust among local community members and strengthening the local innovation culture. Specifically, the increase in social trust enhances the sense of cooperation among local residents, enterprises, and the government and promotes the smooth implementation of resource-sharing and technological innovation activities. At the same time, the policy narratives emphasize collectivism and social responsibility, enhance the atmosphere of local innovation culture, and promote the support, research, and development of new energy technologies by local governments and enterprises [46]. Social trust and local innovation culture become important mediating factors in promoting new energy innovation by promoting collaborative innovation and technology transformation. The results are shown in

Table 9. Mechanism analysis 3: social trust and local innovation culture.

	(1)	(2)
VARIABLES	ST	LIC
PNI	0.1926 *	0.3121 **
	(0.0387)	(0.2764)
PNI × ST	0.4187 *
	(0.1789)
PNI × ln LIC		0.5123 **
		(0.0856)
Constant	2.4532 *	3.5672 *
	(0.5943)	(0.6819)
Controls	Yes	Yes
ID effect	Yes	Yes
Year effect	Yes	Yes
Province effect	Yes	Yes
N	589	589
R2	0.7165	0.6803

t statistics in parentheses, * p < 0.1, ** p < 0.05.

. Column (1) shows that policy narratives significantly enhance social trust, while column (2) shows that enhancing local innovation culture significantly enhances the positive driving effect of policy narratives on renewable energy innovation.

5. Conclusions and Policy Implications

5.1. Conclusions of the Study

This study provides both empirical evidence and theoretical insights into how policy narrative intensity shapes renewable energy innovation, shedding light on the relationships among government discourse, institutional governance, and sustainability transitions. By analyzing 8837 policy documents from 31 Chinese provinces spanning 2005 to 2023 and constructing a policy narrative intensity index using the PMC framework, this research systematically quantifies how policy narratives influence renewable energy development beyond conventional market-driven mechanisms and regulatory policies.

One of the study’s most significant findings is that a 1% increase in policy narrative intensity corresponds to a 4.60% rise in renewable energy innovation, illustrating that policy narratives are not merely symbolic expressions but active forces that can complement—or in some cases, substitute—traditional economic and regulatory tools in energy governance. This finding challenges the conventional view that financial incentives and regulatory enforcement are the primary determinants of policy effectiveness, demonstrating instead that the consistency and clarity of government narratives play a direct role in mobilizing resources, guiding investment confidence, and shaping technological advancements in the renewable energy sector.

Additionally, the study reveals substantial regional disparities in the intensity of policy narratives and their impact on renewable energy innovation. Provinces such as Jiangxi, Shandong, and Fujian exhibit higher policy narrative intensity, reflecting their historical and institutional alignment with centralized policy mobilization. By contrast, economic centers such as Shanghai, Zhejiang, and Guangdong show lower engagement with top-down ideological policy narratives, indicating that these regions rely more on market-driven strategies for energy transition. This divergence suggests an evolving dual-track model of renewable energy development in China, where some regions remain closely tied to state-led governance structures, while others are shifting toward market-oriented pathways for sustainability innovation.

Furthermore, this study identifies policy execution capacity as a key mechanism through which policy narrative intensity enhances renewable energy innovation. Strong policy discourse not only reinforces government enforcement but also improves administrative efficiency, ensuring that policy commitments materialize into tangible regulatory actions and large-scale renewable energy infrastructure projects. In provinces where policy narrative intensity is high, regulatory coordination is more streamlined, project approvals are faster, and institutional synergy in renewable energy deployment is more effective. This underscores the crucial role of policy narratives in shaping the overall effectiveness of sustainability governance.

Finally, the findings highlight how policy narrative intensity actively influences corporate green innovation by aligning enterprise R&D strategies with national sustainability objectives. In regions with higher policy narrative intensity, particularly those with a strong presence of state-owned enterprises (SOEs), government discourse provides long-term policy certainty and investment incentives, encouraging firms to prioritize renewable energy innovation. This alignment strengthens not only corporate engagement in clean technology development but also public–private collaboration in advancing the green transition.

Taken together, these findings contribute to a broader understanding of sustainability governance, demonstrating that policy narratives are not passive ideological rhetoric but active instruments of governance that shape policy execution, corporate strategy, and public engagement with clean energy transitions. While existing research has largely focused on economic incentives and technological progress, this study underscores the importance of institutional discourse in driving energy transition outcomes. Future research should explore how policy narratives interact with market forces and examine whether similar governance models can be observed in other state-led sustainability transitions. Understanding the interplay among policy discourse, institutional capacity, and technological development is critical to navigating the evolving landscape of global energy governance and sustainability transformation.

5.2. Policy Implications

The findings of this study provide important insights into how policy narratives shape energy governance and renewable energy innovation. While strong policy discourse can effectively mobilize state capacity, guide corporate strategies, and foster public engagement, its ultimate impact depends on how well it integrates with economic incentives, institutional frameworks, and market mechanisms. A successful energy transition strategy should not only utilize policy narratives to establish direction and commitment, but also ensure that these narratives are translated into practical, economically viable, and regionally adaptable policies. The following policy recommendations outline key areas where aligning policy discourse with real-world implementation can enhance the effectiveness of China’s green energy transformation.

(1) Strengthening Local Government Incentives for Green Transformation

Policy narratives significantly influence local government motivation to engage in green transformation initiatives. A strong sustainability discourse fosters a sense of national responsibility, encouraging regional governments to prioritize renewable energy projects. This has historically driven regions such as Jinggangshan and Yan’an to proactively develop photovoltaic poverty alleviation projects and wind power initiatives. However, an over-reliance on ideological mobilization without considering economic feasibility and market incentives can reduce policy effectiveness. To ensure sustainable and impactful local implementation, policy design should balance political commitment with market-driven mechanisms. This can be achieved through green finance models, tax incentives, and performance-based energy transition frameworks, aligning local government priorities with long-term national energy goals.

(2) Promoting Market-Driven Renewable Energy Innovation Beyond State-Led Models

Government discourse has traditionally emphasized technological self-reliance as a fundamental principle of national innovation strategy. While state-owned enterprises (SOEs) such as the Three Gorges Group and State Power Investment Corporation (SPIC) have led major breakthroughs in wind, solar, and energy storage technologies, an over-reliance on SOEs may hinder private sector participation and overall market competitiveness. A shift is needed from a state-dominated model to a more diversified and market-driven innovation ecosystem. Encouraging venture capital investment, university–industry collaboration, and international technology partnerships can inject new dynamism into green technology development. Additionally, enhancing intellectual property (IP) protections and offering targeted R&D subsidies for private enterprises will promote a more competitive and globally integrated renewable energy sector.

(3) Expanding Green Energy Access and Reducing Barriers to Green Consumption

Policy narratives shape not only government and corporate behavior but also public attitudes toward sustainability. While awareness of green energy has increased, economic barriers remain a major challenge for widespread adoption, particularly among low-income and rural communities. Instead of relying solely on policy-driven messaging, the government should introduce targeted financial instruments to lower the cost of green energy adoption. These could include green energy consumption subsidies, flexible electricity pricing structures, and installment-based financing for renewable energy products. Additionally, strengthening supply chain transparency and eco-labeling standards can increase consumer trust in clean energy technologies, encouraging a long-term societal shift toward green consumption.

(4) Overcoming Local and Market Bottlenecks to Accelerate Energy Transition

Despite China’s significant progress in renewable energy development, local policy bottlenecks and resource allocation imbalances continue to hinder implementation. In certain regions, local protectionism, administrative inefficiencies, and uneven access to financial resources have slowed policy execution, reducing the effectiveness of national-level strategies. To address these barriers, China should transition from a top-down administrative model toward a decentralized, market-oriented energy transformation framework. Shifting from government-mandated enforcement to incentive-based regulatory structures can allow market forces and regional specialization to drive renewable energy deployment. Additionally, capacity-building programs for local governments should be expanded to equip officials with technical expertise in energy project management, financial structuring, and carbon trading mechanisms, ensuring more effective, localized implementation of green transition policies.

China’s policy narratives provide a strong foundation for driving renewable energy transformation, but their success ultimately depends on how effectively they translate into actionable, market-compatible strategies. While state-driven policy discourse is essential for mobilizing large-scale efforts, an over-reliance on centralized directives may limit private sector engagement and create inefficiencies in local implementation. A balanced approach that integrates government leadership, market incentives, and public participation will enable a more effective and sustainable energy transition. Through precise policy design, multi-stakeholder collaboration, and strategic economic integration, China can solidify its position as a global leader in green energy development and sustainability governance.

(5) Recognizing the Role of Non-State Actors in Narrative-Driven Energy Governance.

While government institutions play a central role in shaping and executing renewable energy strategies, non-state actors—including private enterprises, non-governmental organizations (NGOs), and local communities—are increasingly influential in China’s energy transition. Policy narratives not only guide state behavior but also diffuse through corporate decision-making, civil society engagement, and grassroots innovation. For instance, renewable energy firms often incorporate national narrative themes into branding and R&D agendas, aligning technological development with state-defined sustainability goals. NGOs and academic institutions contribute to policy discourse by shaping public understanding, advocating for environmental justice, and piloting community-based renewable energy models. Therefore, future policies should explicitly consider the mobilization capacity and feedback mechanisms of these stakeholders, establishing inclusive governance structures that amplify bottom-up initiatives and foster more resilient energy transitions across regions.

5.3. Limitations and Future Research Directions

Despite the robustness of the empirical results, this study has several limitations that should be acknowledged. First, although endogeneity concerns have been addressed using instrumental variable estimation—specifically the number of prefecture-level cities and its interaction with time—this identification strategy, while theoretically grounded and statistically validated, may not fully eliminate the possibility of reverse causality. The selected instruments may only partially capture the complex institutional processes through which policy narratives evolve and exert influence. Future studies may consider employing alternative proxies, natural experiments, or difference-in-differences designs to strengthen causal inference.

Second, the study draws on multiple data sources, including official statistical yearbooks, policy documents, industry reports, and third-party databases. Although cross-checks were performed to minimize data distortions, some residual measurement errors or selection bias may persist. Future research should further improve data triangulation or incorporate validation from firm-level or grassroots-level data to enhance empirical credibility.