Evaluating barriers and strategies to green energy innovations for sustainable development: developing resilient energy systems

Achieving sustainable development and reducing climate change require a shift to green energy sources. Yet, switching to green energy sources necessitates substantial research and development, as well as regulatory and policy adjustments. Additionally, a number of obstacles are impeding the development of green energy innovation. This study identified several key barriers and sub-barriers that obstruct the development of green energy innovation. Thus, this study identified multiple strategies to overcome those barriers. Therefore, this study uses the fuzzy Analytical Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods to assess and rank the barriers and strategies to building resilient energy systems in China. First, the fuzzy AHP method identifies the four barriers and sixteen sub-barriers, while the fuzzy TOPSIS method classifies six strategies for the green energy innovation system in China. According to AHP results, funding and policy constraints are the most crucial barriers to green energy innovation. The fuzzy TOPSIS findings show that providing incentives for green energy investment and strengthening policy implementation and enforcement are the most significant strategies for overcoming the barriers to green energy projects.

1 Introduction

The detrimental effects of human activity on the environment have made the need for sustainable development more important (Kutlu, 2020). Climate change is one of the most urgent environmental issues that the world is currently experiencing. It results from human activities like burning of fossil fuels, which releases glasshouse gases, most notably carbon dioxide (Sen and Ganguly, 2017; Ali et al., 2021). Climate change poses a grave threat to both the ecology and human well-being. It can have a harmful effect on food security, water resources, and biodiversity while increasing the frequency and severity of natural disasters like hurricanes, floods, and droughts (IRENA, 2017). It is crucial to make the switch to clean and renewable energy (RE) sources in order to reduce the effects of climate change and promote sustainable development (Ali et al., 2022). Wind, solar, hydropower, and geothermal energy are examples of green energy sources that have the potential to meet a sizeable portion of the world’s energy needs while causing the least amount of environmental harm (Vidadili et al., 2017). Innovations in green energy can also boost economic expansion and open up new work opportunities. Furthermore, the idea of sustainable development is predicated on the understanding that social advancement, environmental preservation, and economic expansion are linked and mutually supportive. Consequently, achieving a balance between social, environmental, and economic goals is necessary for sustainable development (Chel and Kaushik, 2018).

China is the largest carbon emitter in the world, contributing over 30% of all carbon emissions (Guo et al., 2018). The main cause of the country’s excessive carbon emissions is its reliance on coal as its main energy source. The Chinese government has established challenging goals for cutting carbon emissions and raising the proportion of RE in the energy mix in recognition of the significance of green energy (Li et al., 2021). Notwithstanding these initiatives, China’s transition to RE confronts a number of barriers. The limitations to technology are one of the key issues. In order to increase efficiency and lower prices, green energy technologies like solar and wind power still need to be researched and developed. Additionally, the intermittent nature of some RE sources, including wind and solar, necessitates the creation of energy storage devices to guarantee a steady energy supply. The absence of finance is another obstacle to green energy innovation in China. It can be difficult to get the substantial investment needed to develop and implement green energy solutions, especially for small and medium-sized businesses (Liu et al., 2019). The speed of invention can also be slowed down, and the adoption of new technology might be hampered by a lack of financing for research and development in green energy technologies. Another barrier to green energy innovation in China is policy restrictions. Although the Chinese government has set high goals for cutting carbon emissions and raising the proportion of RE in the energy mix, putting these plans into practice can be difficult (Dong et al., 2018). For instance, overcapacity and decreased profitability for RE companies have resulted from government subsidies for RE. Furthermore, the necessity for environmental conservation may occasionally be overshadowed by the government’s emphasis on economic expansion.

Therefore, the goal of this study is to evaluate the major barriers and sub-barriers to green energy for sustainable growth as well as to suggest solution strategies for China’s energy system development. The study uses the fuzzy analytical hierarchy process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which enable modeling of complicated problems to solve the decision-making problem. The fuzzy AHP method is appropriate for this study because it can take into consideration the criteria and sub-criteria for the evaluation and development of green energy innovation in China. Therefore, making the switch to green energy innovation is crucial to reducing the consequences of climate change and ensuring the long-term viability of the Earth. Thus, China is a key player in this shift as the world’s greatest carbon emitter.

The structure of the study is as follows. The literature review is presented in Section 2. The barriers and strategies are explained in Section 3. The research methodology is given in Section 4. The findings and analyses are presented in Section 5. The conclusion and policy implications are presented in Section 6.

2 Literature review

Due to the worldwide environmental catastrophe brought on by human activity, green energy innovation and sustainable development have become critical issues of discussion (Eckstein et al., 2019). The literature on green energy innovation and sustainable development is reviewed in this section, with a special emphasis on the difficulties and prospects in China.

2.1 Green energy innovation

Innovation in green energy refers to the creation and application of technologies and procedures that lessen or completely remove the damaging effects of human activity on the environment (Lai, 2020). An ample portion of the world’s energy needs might be met by green energy sources, including solar, wind, hydropower, and geothermal energy, while limiting harm to the environment. As it enables the switch from fossil fuels to clean and RE sources, green energy innovation is essential for sustainable development. Innovation in green energy, however, faces a number of difficulties, including technical restrictions, financial constraints, policy constraints, and a lack of public support (Eleftheriadis and Anagnostopoulou, 2015). Notwithstanding these difficulties, green energy innovation has a great deal of promise to boost economic expansion and generate new job possibilities. Green energy use can also help reduce the consequences of climate change and guarantee the long-term viability of our planet.

2.2 Sustainable development in China

Being the largest carbon emitter in the world, China is vital to the global fight against climate change. The Chinese government has set lofty goals for cutting carbon emissions and raising the proportion of RE in the energy mix because it understands the value of sustainable development. Nevertheless, the country faces a number of barriers in its quest to achieve sustainable development. The reliance on coal as the main energy source is one of the major issues. According to the Energy Information Administration (EIA), around 55% of China’s energy needs are met by coal, which also contributes significantly to the high carbon emissions (EIA, 2021), while around 19% of energy is generated from petroleum sources. Figure 1 presents the primary energy mix of China in 2021.

FIGURE 1

FIGURE 1. Total primary energy consumption in China.

The lack of funding for green energy projects and the study and development of green energy technologies is another issue (Mngumi et al., 2022). Due to the high prices and lengthy commitment necessary, small and medium-sized businesses, which are essential to green energy innovation, sometimes struggle to find funding. Constraints on policy are another barrier to sustainable growth (Jiang et al., 2020). Although the government has set high goals for cutting carbon emissions and raising the proportion of RE in the country’s energy mix, putting these plans into practice can be difficult. The government’s emphasis on economic expansion occasionally obscures the necessity of environmental conservation. Despite these barriers, the country has made great strides toward sustainable development. The country has made significant investments in RE, and its percentage in the energy mix has been continuously rising (Duan et al., 2018). The literature underlines China’s possibilities and difficulties, as well as the significance of green energy innovation for sustainable development. Although the country has made tremendous strides toward sustainability in recent years, there is still more work to be performed in order to meet its sustainability objectives.

In the previous studies, it was identified that the development of green energy is crucial since multiple authors discussed this issue using multi-criteria decision-making (MCDM) methods to analyze the particular decision-making problem (Pohekar and Ramachandran, 2004). Some of the relative studies on the development of green energy sources are compiled in the study, which identified the research gap to conduct this research. Table 1 shows the previous studies related to green energy sources for sustainable development.

TABLE 1

TABLE 1. Previous studies on renewable/green energy development.

A comprehensive assessment of green energy innovation for sustainable development using decision-making techniques like fuzzy AHP and fuzzy TOPSIS still needs to be performed, despite there being sufficient literature on the subject. Although various studies have identified these impediments, most of them have concentrated on a specific issue, such as technological constraints or governmental restrictions (Mateo, 2012). The interplay of the numerous elements that contribute to green energy innovation has rarely been thoroughly analyzed. A small number of studies have also suggested strategies for creating resilient energy systems that take into consideration the interconnection of the many deciding factors (Zhang and Gallagher, 2016).

Some research has evaluated sustainability challenges in other sectors, including energy planning and environmental management, using decision-making techniques like the fuzzy AHP and fuzzy TOPSIS methods. By employing these techniques to undertake a thorough assessment of the barriers and strategies to green energy innovation for sustainable development in China, our research attempts to close this research gap. In order to construct resilient energy systems that take into consideration the interdependence among the various decision criteria, the study will identify the main barriers to green energy innovation and suggest policy strategies to address these barriers. The analysis will add to the body of knowledge on sustainable development and offer insightful information about the potential and the problems associated with China’s transition to green energy sources.

3 Proposed barriers and policy strategies to green energy innovation

3.1 Barriers and sub-barriers

The study highlighted the barriers and sub-barriers to green energy innovation for sustainable development based on the existing literature. These barriers are linked and have an impact on each other. Table 2 shows the proposed barriers and sub-barriers of the study.

TABLE 2

TABLE 2. Proposed barriers and sub-barriers for green energy innovation.

3.2 Policy strategies to overcome the barriers

To overcome the barriers to green energy innovation for sustainable development in China, the study proposed various policy strategies. These strategies have been identified from previous studies and are considered to be very significant to overcome the barriers and develop green innovation for sustainable development in China. Table 3 shows the policy strategies of this study.

TABLE 3

TABLE 3. Proposed policy strategies.

These policy strategies can aid in eliminating barriers to the development of green energy innovations in China. The fuzzy AHP and fuzzy TOPSIS techniques can be used to evaluate these policy options and pinpoint the best possibilities for achieving our intended goals.

4 Research methodology

This research methodology uses the fuzzy AHP and fuzzy TOPSIS methods to evaluate the barriers to green energy innovation for sustainable development and to suggest policy strategies for building resilient energy systems in China. The fuzzy AHP technique is a useful tool for determining barriers and sub-barriers, while the fuzzy TOPSIS method is significant in ranking the best policy strategy to overcome green energy innovation barriers. Figure 2 displays the research methodology of the current study.

FIGURE 2

FIGURE 2. Research methodology.

4.1 Fuzzy AHP

One of the main techniques used in MCDM, which consists of a goal, a set of criteria, a set of sub-criteria, and alternatives, is the AHP method (Saaty, 1995). In this study, we used a fuzzy-based AHP technique to get more trustworthy and consistent outcomes. Fuzzy set theory reduces uncertainty in a fuzzy environment (Zadeh, 1978). Both quantitative and qualitative data can be utilized using the AHP technique. The complex choice problem can be broken down into smaller ones using the fuzzy AHP method. The pairwise comparison matrix was used in this study as triangular fuzzy numbers (TNFs) to evaluate barriers and sub-barriers to green energy innovations. The TFNs scale utilized in this study is displayed in Table 4.

TABLE 4

TABLE 4. Triangular fuzzy numbers scale.

In this study, we employed the following procedures to determine the fuzzy pairwise comparison matrix’s inconsistency ratio, as suggested by Gogus and Boucher (Gogus and Boucher, 1998):

Step 1. Transform a triangular fuzzy matrix into two independent matrices, which is presented as follows:

$X_i=\left(l_i,m_i,u_i\right).(1)$

Then, the middle fuzzy triangular matrix can be used to generate the first triangular fuzzy matrix; this equation is written as:

$X_m=\left[x_{ijm}\right].(2)$

Here, a geometric mean approach can be used to generate the second triangular fuzzy matrix for the upper and lower limits of the TFNs. As a result of this equation, we get:

$X_g=\left[\sqrt{x_{iju}{x}_{ijl}}\right].(3)$

Step 2. Create the weight vector based on the Saaty technique and lambda max computation.

step 3. Create the consistency index (CI) for each matrix:

${CI}_m=\frac{{\lambda }_{\mathit{max}}^m-n}{n-1},(4)$

${CI}_g=\frac{{\lambda }_{\mathit{max}}^g-n}{n-1}.(5)$

Step 4. Create each matrix’s consistency ratio (CR). Each matrix’s consistency index (CI) is divided by its random index for (RI).

${CR}_m=\frac{{CI}_m}{{RI}_m},(6)$

${CR}_g=\frac{{CI}_g}{{RI}_g}.(7)$

When both ${CR}_m$ and ${CR}_g$ are less than 0.10, the fuzzy matrices are deemed to be legitimate and symmetric. However, if the range is greater than 0.10, it will not produce findings that are meaningful and will be regarded as invalid or inconsistent.

4.2 Fuzzy TOPSIS

One of the fundamental MCDM methodologies is TOPSIS (Behzadian et al., 2012). For selecting the alternatives, this method provides the distance between the positive ideal and the negative ideal solution. However, this TOPSIS method is also combined with fuzzy set theory to create fuzzy TOPSIS, just like fuzzy AHP. As a result, linguistic variables and TFNs are used in this study to evaluate the weights of the alternatives (policy strategies). The alternatives are analyzed using TFNs in relation to the study’s sub-criterion (sub-barriers).

The basic steps of the fuzzy TOPSIS technique are given as follows (Cayir Ervural et al., 2018):

Step 1. Let ${\stackrel{\sim }{K}}_i=\left(k_{i1},k_{i2},k_{i3}\right)$ are the TFNs for $i\in I$. Then, the normalized fuzzy number of each ${\stackrel{\sim }{K}}_i$ is signified as

$\stackrel{\sim }{D}={\left[d_{ij}\right]}_{m\times n},(8)$

where $i=\mathrm{1,2,3},\dots ,m$ and $j=\mathrm{1,2,3},\dots ,n$.

For a positive ideal solution:

$d_{ij}=\left(\frac{k_{1ij}}{k_{3j}^{+}},\frac{k_{2ij}}{k_{3j}^{+}},\frac{k_{3ij}}{k_{3j}^{+}}\right),(9)$

where $k_{3j}^{+}=\max k_{3ij}$.

For a negative ideal solution:

$d_{ij}=\left(\frac{l_{1j}^{-}}{l_{3ij}},\frac{l_{1j}^{-}}{l_{2ij}},\frac{l_{1j}^{-}}{l_{1ij}}\right),(10)$

where $l_{1j}^{-}=\mathit{min}{l}_{1ij}$.

Step 3. Construct the weighted normalized fuzzy decision matrix:

$\stackrel{\sim }{V}={\left[v\right.}_{ij}{]}_{m\times n}.(11)$

$i=\mathrm{1,2,3},\dots ,m$ and $j=\mathrm{1,2,3},\dots ,n$,where $v_{ij}=d_{ij}\times w_j$.

Step 4. Recognize the distance between positive and negative ideal solution:

$d_i^{+}=\left(v_1^{+},v_2^{+},v_3^{+},\dots ,v_n^{*}\right),(12)$

where $V_j^{+}=\left(\mathrm{1,1,1}\right)$ and $j=\mathrm{1,2,3},\dots ,n$.

$d_i^{-}=\left(v_1^{-},v_2^{-},v_3^{-},\dots ,v_n^{-}\right),(13)$

where $V_j^{-}=\left(\mathrm{0,0,0}\right)$ and $j=\mathrm{1,2,3},\dots ,n$.

Here, the distance between $\stackrel{\sim }{K}=\left(k_1,k_2,k_3\right)$, $\stackrel{\sim }{N}=\left(n_1,n_2,n_3\right)$ is shown as

$d\left(\stackrel{\sim }{K},\stackrel{\sim }{L}\right)=\sqrt{\frac{1}{3}\left[{\left(k_1-l_1\right)}^2+{\left(k_2-l_2\right)}^2+{\left(k_3-l_3\right)}^2\right]}.(14)$

Step 5. Construct the closeness coefficient $\left(C{C}_i\right)$ using Eq. 15:

$C{C}_i=\frac{d_i^{-}}{d_i^{+}+d_i^{-}},(15)$

where $i=\mathrm{1,2,3},\dots ,m$, $d_i^{+}$ is the distance from positive ideal solution, and $d_i^{-}$ is the distance from a negative ideal solution.

Step 6. Identify the suitable green supplier based on descending order of $C{C}_i$ value.

For this study, five specialists in green energy innovation and sustainable development in China were consulted. The professionals we contacted were knowledgeable about numerous green energy technologies, their efficacy, and their potential for advancement and application in China. To ensure a thorough study of the barriers to green energy innovation for sustainable development in China, all of the chosen experts come from a variety of backgrounds, including academics, researchers, industry professionals, and policymakers. As a result, the study methodology entails evaluating the barriers and strategies to green energy innovation using the fuzzy AHP and fuzzy TOPSIS methods to create resilient energy systems.

5 Results and discussion

The current case study offers a thorough analysis of the potential and the problems associated with the switch to green energy sources, as well as the success of the suggested solutions in removing the barriers to green energy innovation. The study evaluated and ranked the barriers, sub-barriers, and policy strategies for the development of green energy using the fuzzy AHP and fuzzy TOPSIS techniques. This case study will have a great impact on sustainable development and green energy innovation in China and other countries.

5.1 Results of barriers

The study identified barriers using the fuzzy AHP method. As is seen, the funding and policy constraints have the highest weights, indicating their importance in the evaluation of green energy innovation development. The technological limitations and public support are the next important barriers in the evaluation process. The ranking of barriers is useful in identifying the most critical dimensions of the sustainable energy system and can be used to guide policy development and decision-making. Figure 3 shows the results of barriers to green energy innovation.

FIGURE 3

FIGURE 3. Final ranking of barriers for green energy innovation.

5.2 Results of sub-barriers

The study also presents the outcomes of sub-barriers related to each primary barrier. Within the category of technological limitations, it is found that the lack of energy storage technologies holds the top position as a significant sub-barrier in the transition towards a green energy innovation. High costs of some green energy technologies emerge as the second most crucial sub-barrier. Meanwhile, the intermittency of some RE sources and the low efficiency of some green energy technologies are deemed less critical sub-barriers that impede the development of green energy innovations. Table 5 illustrates the weights and rankings of these technological limitations sub-barriers.

TABLE 5

TABLE 5. Sub-barrier ranking (technological limitations).

Within the funding constraints category, the findings reveal that the sub-barrier of difficulty in securing financing for green energy projects has attained the utmost significance in green energy innovation development. Lack of funding for research and development in green energy technologies is ranked as the second most essential sub-barrier, followed by limited access to financing for small and medium-sized enterprises and limited availability of international financing for sustainable resilient energy systems in China. Table 6 showcases the weights and rankings of the funding constraints sub-barriers.

TABLE 6

TABLE 6. Sub-barrier ranking (funding constraints).

Within the policy constraints category, the results indicate that inconsistent policies and regulations are the most critical sub-barrier that obstructs the development of green energy innovation. The subsidies that lead to overcapacity and reduced profitability for RE companies are ranked as the second most important sub-barrier, followed by a lack of policy implementation and enforcement and a lack of clarity and transparency in policy implementation. Consequently, these sub-barriers are deemed highly significant for green energy innovation and addressing climate change. Table 7 depicts the weights and rankings of the policy constraints sub-barriers.

TABLE 7

TABLE 7. Sub-barrier ranking (policy constraints).

In the public support category, the results reveal that a lack of public awareness and understanding of green energy technologies is the most crucial sub-barrier. Perceived high costs are regarded as the second most important sub-barrier, while perceptions that green energy is more expensive than traditional energy sources and concerns about the reliability of green energy sources hold moderate to lower significance. Table 8 illustrates the weights and rankings of the public support sub-barriers.

TABLE 8

TABLE 8. Sub-barrier ranking (public support).

5.3 Results of policy strategies

The results of the analysis are typically based on a range of barriers that are considered important for achieving sustainable energy development. Thus, the results of the fuzzy TOPSIS analysis indicate that providing incentives for green energy investment was found to be the most important strategy to overcome the barriers to transitioning to a sustainable energy future and addressing climate change, followed by strengthening policy implementation and enforcement and developing and implementing new and more efficient green energy technologies. The fuzzy TOPSIS analysis is a sophisticated decision-making instrument that assists in determining and prioritizing crucial strategies for green energy innovation in China. The final ranking of different policy strategies for green energy innovation is presented in Table 9. Moreover, the ranking value based on CCi is presented in Figure 4.

TABLE 9

TABLE 9. Final ranking of multiple strategies.

FIGURE 4

FIGURE 4. Ranking of strategies based on the highest CCi value.

The analysis using the fuzzy TOPSIS method will provide valuable insights into the most effective strategy for promoting green energy innovation and achieving sustainable development in China.

5.4 Discussion

To attain sustainable development and mitigate climate change, there is a need to transition toward green energy sources. However, this shift necessitates significant expenditures for research and development, as well as significant regulatory and policy adjustments. Policy restrictions are a relatively less important barrier, according to the fuzzy AHP research, with inconsistent rules and regulations being the most important sub-barrier. To get around this barrier, the study suggested that uniform policies and rules be put in place to encourage green energy innovation and offer a secure investment environment for green energy projects (Mirjat et al., 2017). Increasing public knowledge and education about green energy technology is essential for overcoming the public support barrier. We have suggested a mix of encouraging the use of green energy and raising public consciousness and education to get around this barrier. In order to deal with the inconsistent nature of RE sources and increase the dependability of the energy system, the research has also recommended making investments in energy storage technology (Zhang et al., 2016). It is crucial to recognize the connections and interactions between China’s constraints on green energy innovation and sustainable development. For instance, a lack of financing for green energy technology research and development may restrict the creation of new technologies, which may restrict the possibility of cost savings and improved efficiency (Mngumi et al., 2022). Inconsistent laws and regulations can lead to uncertainty for investors and difficulty obtaining funding for green energy projects (Solangi et al., 2021). We have been able to simulate the intricate relationships between choice criteria and alternative options using the fuzzy AHP and fuzzy TOPSIS methods. These methods have allowed us to conduct a thorough investigation of the challenges and solutions for building resilient energy systems in China.

The findings could have a big impact on how China and other countries make uniform policies. The suggested actions can help policymakers make choices that will advance green energy innovation and help the nation reach its sustainability objectives (Kamran, 2018). More private investment in green energy projects and research and development can be attracted, in particular, by establishing a green energy fund, offering tax benefits, and funding energy storage technology. A stable investment environment for green energy projects and support for the expansion of the green energy industry can be achieved by establishing uniform laws and regulations and enhancing policy implementation and enforcement (Baležentis and Streimikiene, 2017; Ali et al., 2023). In addition, raising public knowledge and understanding of green energy technologies can encourage public acceptance of and growth in the use of RE sources. In the end, these actions can help China establish a more sustainable and resilient energy system and lessen the effects of climate change.

6 Conclusion and implications

This study used the fuzzy AHP and fuzzy TOPSIS methods to assess the barriers to green energy innovation and policy strategies for a sustainable energy system in China. Based on the findings, the most important barriers, sub-barriers, and strategies have been identified and weighted in the evaluation process of decision-making. According to the fuzzy AHP results, funding constraints are crucial barriers to green energy innovation in China. The study suggested the creation of a green energy fund and tax breaks to promote more private investment in green energy projects in order to eliminate this barrier. Policy constraints are the second most important barrier. The study also suggested that uniform policies and rules be put in place to encourage green energy innovation and offer a secure investment environment for green energy projects. To overcome the barriers of public support, we have also recommended combining the provision of incentives for the use of green energy with an increase in public awareness and education. The intermittent nature of RE sources must be addressed, and the dependability of the energy system must be increased by making investments in energy storage technology. The suggested policy actions can help policymakers make choices that will advance green energy innovation and help the country reach its sustainability objectives.

6.1 Policy implications

The findings have important policy implications for China’s efforts to advance green energy innovation and sustainable development. The suggested actions can help guide policy choices that can boost public support for RE sources, encourage more private investment in green energy projects, and create a stable investment climate for green energy innovation.

• By offering tax benefits for green energy investments, tax incentives can also promote increased private investment in green energy projects.

• The government can establish and enforce policies and rules that encourage green energy innovation and provide fair playing conditions for green energy projects. Uniform laws and rules help lessen investor uncertainty and create a secure environment for green energy innovation.

• The government can offer financial incentives to individuals and companies that employ RE sources like solar and wind power. Public support for RE sources can be increased by raising awareness of green energy technology and educating the public about it.

• The suggested actions can help build a more robust and sustainable energy system and lessen the effects of climate change. The nation can minimize its carbon footprint and help the world transition to a more sustainable energy system by encouraging green energy innovation and lowering its dependency on fossil fuels.

The proposed regulatory changes will have a big impact on China’s efforts to advance green energy innovation and sustainable development. The government can use these policies to increase public support for renewable energy sources, encourage private investment in green energy projects, and foster a secure investment environment for green energy innovation.

6.2 Limitations and future research

The study has various drawbacks that could be resolved in future studies. First, since the analysis was limited to China, it is possible that the conclusions cannot be applied to other regions. In order to conduct a more thorough study of the barriers to green energy innovation, future research could broaden the scope of the analysis to include particular regions or areas in that country. In addition, the proposed solutions to barriers to green energy innovation were based on the opinions of experts and might not accurately represent the desires and concerns of other stakeholders. In the future, surveys or focus groups with these stakeholders could be used to gather their perspectives and develop further strategies to remove the barriers. Finally, the proposed initiatives ignored the larger social and environmental effects of green energy development in favor of boosting green energy innovation in China. Future studies could conduct a thorough sustainability analysis of green energy development, taking into account the effects on the economy, society, and environment.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

Methodology: DW and JL. Formal analysis: YL. Investigation: DW. Data collection: YL. Writing—original draft preparation: DW, JL, and YL. Writing—review and editing: JL and YL. Funding acquisition: JL. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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