An Analysis of Renewable Energy Sources for Developing a Sustainable and Low-Carbon Hydrogen Economy in China

Abstract

A significant effort is required to reduce China’s dependency on fossil fuels while also supporting worldwide efforts to reduce climate change and develop hydrogen energy systems. A hydrogen economy must include renewable energy sources (RESs), which can offer a clean and sustainable energy source for producing hydrogen. This study uses an integrated fuzzy AHP–fuzzy TOPSIS method to evaluate and rank renewable energy sources for developing a hydrogen economy in China. This is a novel approach because it can capture the uncertainty and vagueness in the decision-making process and provide a comprehensive and robust evaluation of the alternatives. Moreover, it considers multiple criteria and sub-criteria that reflect the environmental, economic, technical, social, and political aspects of RESs from the perspective of a hydrogen economy. This study identified five major criteria, fifteen sub-criteria, and six RES alternatives for hydrogen production. This integrated approach uses fuzzy AHP to evaluate and rank the criteria and sub-criteria and fuzzy TOPSIS to identify the most suitable and feasible RES. The results show that environmental, economic, and technical criteria are the most important criteria. Solar, wind, and hydropower are the top three RES alternatives that are most suitable and feasible. Furthermore, biomass, geothermal, and tidal energy were ranked lower, which might be due to the limitations and challenges in their adoption and performance in the context of the criteria and sub-criteria used for the analysis. This study’s findings add to the literature on guidelines to strategize for renewable energy adoption for the hydrogen economy in China.

1. Introduction

The growing concerns about climate change and the need to reduce greenhouse gas emissions have led to a global shift towards renewable energy sources [1]. Among these sources, hydrogen is considered a promising alternative to fossil fuels due to its high energy density and potential to be produced from renewable sources. China, as the world’s largest greenhouse gas emitter, has recognized the importance of developing a hydrogen economy to achieve its long-term sustainability goals. The country is currently heavily dependent on fossil fuels, which contribute to air pollution and public health problems and exacerbate global climate change. The energy sector is responsible for most greenhouse gas emissions, which contribute to climate change and its associated impacts, such as sea-level rise, extreme weather events, and biodiversity loss [2]. Therefore, the transformation of the energy sector is crucial for mitigating climate change and achieving the goals of the Paris Agreement. The trends of CO₂ emissions in China, East Asia and Pacific regions, and the world [3], shown in Figure 1, reveal that the Chinese economy has a long way to go to reach a carbon peak. This calls for the transition from non-renewable to renewable sources of energy. There is a vast potential in the Chinese economy to rely on renewable energy sources. Currently, the Chinese economy has multiple options for RESs. The energy mix of the Chinese economy over the years (Figure 2) shows that the Chinese economy is still relying heavily on non-renewable energy sources [4]. There may be multiple challenges to pave the way for the transition to renewable energy. To address these challenges, China has set ambitious targets to increase the share of renewable energy in its energy mix and to promote the development of a hydrogen economy. The development of a hydrogen economy in China is seen as a promising pathway to reduce the country’s dependence on fossil fuels and to contribute to global efforts to mitigate climate change.

Several renewable energy sources, including solar, wind, and hydropower, can be used to produce hydrogen, which has a wide range of uses in industry, transportation, and power generation [5]. Solar, wind, hydro, biomass, geothermal, and tidal energy are some of the most promising renewable energy sources for producing hydrogen. China has the most solar energy resources and the largest installed solar PV capacity globally. The world’s usage of wind energy is likewise growing significantly, with China supplying the vast bulk of it. Hydrogen energy is another significant renewable energy source in the nation because it has the largest hydropower capacity in the world. A significant RES that might be exploited in China to produce hydrogen is biomass energy. Geothermal energy is relatively underdeveloped in China, but the country has significant geothermal resources. Tidal energy is also a promising renewable energy source, with China having the world’s largest installed tidal power capacity. The Chinese government has set ambitious targets to increase the share of renewable energy in its total energy consumption. According to the National Renewable Energy Development Plan (2016–2020), China aims to increase its non-fossil fuel energy consumption to 15% by 2020 and to 20% by 2030 [2]. Additionally, the Chinese government has launched several initiatives to support the development of a hydrogen economy. For instance, the Made in China 2025 plan includes the development of a hydrogen energy industry as one of its key objectives. However, a comprehensive analysis is needed to evaluate the alternative renewable sources of energy in the context of the hydrogen economy in China while considering the criteria of availability, cost, reliability, sustainability, and technological maturity with relevant sub-criteria.

For this purpose, this study aims to evaluate renewable energy sources for developing a hydrogen economy in China using the fuzzy Analytical Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches. The fuzzy Analytical Hierarchy Process (AHP) and the fuzzy TOPSIS are two approaches for making multi-criteria decisions under uncertainty and imprecision. The following are some advantages of employing these methods: Fuzzy AHP can handle linguistic variables and fuzzy numbers to represent the decision-maker’s preferences. Fuzzy AHP may calculate the weights of criteria and alternatives based on pairwise comparisons. The fuzzy AHP approach allows for the consideration of multiple criteria and the use of expert opinions and stakeholder inputs to determine the criteria weights [6]. In this study, the fuzzy AHP would support pairwise comparisons of criteria and sub-criteria of the RESs.

Moreover, fuzzy TOPSIS may rank alternatives by comparing them to ideal and negative ideal solutions. Fuzzy TOPSIS is capable of avoiding rank reversal and producing consistent results. Furthermore, the fuzzy TOPSIS approach also provides a systematic method for ranking the RESs based on their overall performance. Fuzzy TOPSIS could be handy in prioritizing alternative renewable energy sources based on the weights produced in fuzzy AHP analysis. Two useful techniques for determining the optimum renewable energy sources for China’s development of a hydrogen economy are the fuzzy AHP and fuzzy TOPSIS techniques. The results may assist investors and authorities to prioritize investments in renewable energy with the greatest potential for hydrogen economy. This study could contribute to the global understanding of the potential of renewable energy sources for developing a hydrogen economy and the transition towards low-carbon and sustainable energy systems. The development of a hydrogen economy in China presents an opportunity to reduce the country’s dependence on fossil fuels, contribute to global efforts to mitigate climate change, and accelerate the transition towards a sustainable and low-carbon energy system. The evaluation of renewable energy sources using the fuzzy AHP and fuzzy TOPSIS approaches provides valuable insights into the potential of renewable energy sources for hydrogen production. It can guide policymakers and investors in making decisions about deploying renewable energy sources and developing hydrogen.

This paper is structured in five sections: Section 1 consists of the interlocution of the study, followed by the theoretical background in Section 2. Section 3 represents the research methodology. Section 4 comprises the results and discussions. Whereas the conclusions, implications, and prospects of the research are presented in Section 5.

2. Theoretical Background

The literature on developing a hydrogen economy and using renewable energy sources for hydrogen production in China is relatively limited. However, several studies have explored the potential of renewable energy sources for hydrogen production and their suitability. This literature review will discuss some of the key findings from these studies and highlight the gaps in existing literature.

Identifying and analyzing the criteria and sub-criteria for the development of renewable energy sources is a crucial aspect. Multiple studies in the literature have investigated the optimal renewable energy sources for electricity generation, using multi-criteria decision-making methods to analyze, rank, and identify the criteria, sub-criteria, and renewable energy sources for sustainable development. The relevant studies on developing RESs for sustainable energy systems are presented in

Table 1. Previous studies on the development of renewable energy sources.

Study Objective	Results	Case Study	Method	Ref.
Strategic renewable energy resources selection	The analysis indicates that the economic and socio-political criteria hold the most significance. Additionally, the findings demonstrate that wind energy has considerable potential for electricity generation in both Sindh and Baluchistan provinces. Solar and biomass energy were ranked second and third, respectively.	Pakistan	Fuzzy AHP	[7]
Evaluating renewable energy sources for adopting the hydrogen economy	Wind and solar energy are the most efficient sources for hydrogen production in Pakistan. Municipal solid waste and biomass can also be used as hydrogen economy feedstock.	Pakistan	AHP-DEA	[8]
Evaluating renewable energy alternatives	The findings indicated that wind energy was optimal for Turkey’s energy investments, followed by solar, biomass, geothermal, hydraulic, and hydrogen energy.	Turkey	Type-2 fuzzy TOPSIS	[9]
Evaluating renewable and nuclear resources for electricity generation	According to the model findings, hydro energy is the most favorable resource, followed by solar energy. Wind and nuclear energy are ranked third and fourth, respectively. Biomass energy is identified as the least attractive option.	Kazakhstan	AHP	[10]
Assessing the renewable power sources	Based on the results, solar photovoltaic (PV) energy is deemed the most favorable option in China.	China	Fuzzy cumulative prospect theory	[11]
Selection of renewable energy alternatives	In the study, hydropower emerged as the most favorable renewable energy source, with geothermal, solar, wind, and biomass energy following in that order.	Indonesia	Fuzzy AHP	[12]
Ranking of renewable energy sources	The ranking results demonstrate that hydro energy is the most suitable option in Taiwan, succeeded by solar, wind, biomass, and geothermal energy, respectively.	Taiwan	WSM, VIKOR, TOPSIS, and ELECTRE	[13]
Selection of a renewable energy project	Based on the results, the best alternative is the biomass plant option, specifically co-combustion in a conventional power plant, followed by the wind power and solar thermo-electric alternatives.	Spain	VIKOR-AHP	[14]
Selection of renewable energy sources	According to the results, solar energy is the most favorable resource, followed by biomass energy. On the other hand, hydropower and wind energy were ranked third and fourth, respectively.	Malaysia	AHP	[15]
Renewable energy technology selection	The results indicated that the solar–wind hybrid energy system is identified as the most suitable technology as it achieved the highest appraisal score.	Bangladesh	AHP-CODAS	[16]
Renewable energy plant location selection	As per the findings, a province situated on the coast of south-central Vietnam is identified as the optimal location for constructing a wind power plant in Vietnam.	Vietnam	Fuzzy AHP and TOPSIS	[17]
Development of RESs in light of the European Green Deal	The study explored varied development in increasing RESs in the member states.	The EU	Hellwig’s method and Ward’s method	[18]
Assessment of the extent to which the EU nations use renewable energy	A high level of RES utilization has been found in Sweden, Austria, Latvia, and Finland. Whereas, a low level of development was found in Cyprus, Luxembourg, and Malta (for 2004), and in Luxembourg and Malta (for 2019).	The EU countries	WASPAS method	[19]
Evaluation of the progress towards sustainable energy development in the EU	There is a need to consider energy justice and affordability in renewable energy policy development.	The EU	Pythagorean fuzzy-SWARA-TOPSIS	[20]
Evaluation of sustainable energy development	The social, economic, and environmental dimensions are essential for sustainable energy development.	Poland	SAW, TOPSIS, SMD, and WASPAS	[21]
Identification of factors determining energy policy in the EU countries	The factor related to energy security, environmental concerns, economic policies, and politics are potential determinants of renewal energy development.	The EU	The best subset regression and the LARS method	[22]

.

Although there have been studies evaluating renewable energy sources for hydrogen production, there is still a research gap in the specific context of China. While the country is making significant investments in renewable energy and hydrogen infrastructure, there needs to be more comprehensive evaluations of the potential of renewable energy sources for developing a hydrogen economy. Moreover, there is a lack of consensus on the most suitable renewable energy sources for hydrogen production, as different studies have produced different rankings and conclusions. Thus, this study aims to address this research gap by evaluating renewable energy sources for hydrogen production in China using the fuzzy AHP and fuzzy TOPSIS approaches. This study provides a comprehensive evaluation of renewable energy sources based on multiple criteria and sub-criteria and considers the specific context of China. Moreover, this study uses a subjective approach incorporating the preferences and inputs of stakeholders and experts, which could provide more nuanced insights into the potential of renewable energy sources for hydrogen production. By addressing this research gap, this study could contribute to the development of a hydrogen economy by providing policymakers and investors with a more comprehensive understanding of the potential of renewable energy sources for hydrogen production.

2.1. Identified Criteria and Sub-Criteria

In general, the research question, objectives, and literature review are taken into consideration while choosing the criteria to be employed in this study. Typically, researchers begin by determining the main subject of the research topic they want to address before choosing appropriate criteria. Based on their capacity to offer greater insight into the main criteria, sub-criteria are chosen. As a result, the relevance of the criteria and sub-criteria to the study question and objectives is taken into consideration. The significance, applicability, and practicality of each criterion are normally carefully taken into account during the selection process. This study identified several significant criteria and sub-criteria for renewable-based hydrogen production in China through an extensive literature review. Based on the literature review of studies summarized in Table 1, this study established five essential criteria and fifteen sub-criteria for evaluating sustainable hydrogen energy systems.

Table 2. The identified criteria and sub-criteria of this study.

Criteria	Sub-Criteria	Short Description	Ref.
Availability	Resource availability	It refers to the availability of renewable energy resources such as solar radiation, wind speed, water flow, and biomass availability.	[23]
	Land availability	It refers to the availability of suitable land for renewable energy development, such as areas with high solar radiation or wind speeds.	[24]
	Grid connection	It refers to the proximity of renewable energy sources to the grid and transmission infrastructure, affecting the feasibility and cost of connecting to the grid.	[25]
Cost	Capital cost	It refers to renewable energy equipment and infrastructure costs, such as solar panels or wind turbines.	[26]
	Operating cost	It refers to the costs of maintaining and operating renewable energy equipment and infrastructure.	[27]
	Levelized cost of hydrogen	It refers to the cost per unit of hydrogen produced, which can be affected by capital and operating costs, energy efficiency, and the price of renewable energy sources.	[28]
Reliability	Capacity factor	It refers to the ratio of actual energy output to the maximum possible output, which can affect the reliability and availability of renewable energy sources.	[29]
	Variability	It pertains to how much energy output varies as a result of weather, which can have an impact on the stability and predictability of renewable energy sources.	[30]
	Energy storage	It highlights the accessibility and potency of energy storage options, which can lessen the fluctuation and erratic nature of renewable energy sources.	[31]
Sustainability	Environmental impact	It refers to the effects of renewable energy sources on the environment, including greenhouse gas emissions, land use, and water consumption.	[32]
	Social impact	It refers to the impact of renewable energy sources on local communities, such as employment opportunities and community development.	[33]
	Economic impact	It refers to the contribution of renewable energy sources to economic development, energy security, and other economic factors.	[34]
Technological maturity	Technological readiness	It refers to the level of development and maturity of renewable energy technologies, which can affect their performance, cost, and availability.	[35]
	Commercial viability	It refers to the availability of financing and investment for renewable energy projects and the potential market demand for renewable energy sources.	[36]
	Innovation potential	It refers to the potential for technological innovation and development in renewable energy sources, which can drive performance, cost, and sustainability improvements.	[37]

presents these criteria and sub-criteria for assessing renewable energy sources in developing a hydrogen economy in China.

These criteria and sub-criteria can be used to evaluate and compare the performance of different renewable energy sources for producing hydrogen in China. The fuzzy AHP and fuzzy TOPSIS approach can assign weights to these criteria and sub-criteria and rank the renewable energy sources based on their overall performance.

2.2. Identified Renewable Energy Sources for Developing a Hydrogen Economy

Renewable energy sources are sources of energy that are replenished naturally and can be continuously used without depletion. These energy sources can help reduce greenhouse gas emissions and promote a sustainable energy system. Moreover, these renewable sources help produce low-carbon hydrogen in China. Some of the important renewable energy sources are provided in this study for further assessment, these include:

2.2.1. Solar Energy

Solar energy is one of the most promising RESs for hydrogen production. Solar energy can be converted into electricity through photovoltaic (PV) cells, which can then be used to power an electrolyzer to produce hydrogen [38]. Solar energy has several advantages for hydrogen production. Solar energy is a virtually unlimited resource, potentially producing large amounts of electricity and hydrogen. Moreover, China has abundant solar resources, with many regions experiencing more than 2000 h of sunshine per year [39]. Solar PV systems can be designed and installed modularly, allowing for flexible and scalable deployment. This makes solar PV systems well-suited for distributed hydrogen production, which could increase the accessibility of hydrogen fuel. Recent years have seen a sharp decline in the price of solar PV systems, making it more and more cost-competitive with fossil fuels. Additionally, the price of producing hydrogen from solar energy has been falling, making it a competitive choice for hydrogen production. Solar energy is a clean, renewable energy source that does not emit any damaging pollutants or greenhouse gases. As solar energy has a low carbon impact, producing hydrogen from it could help to clean up the air and slow down global warming.

2.2.2. Wind Energy

Another promising renewable energy source for China’s hydrogen manufacturing is wind energy. As of 2021, the nation had more than 280 GW of installed capacity, making it the greatest generator of wind energy in the world [40]. Wind power can be captured and used by wind turbines to power an electrolyzer that creates hydrogen. China has a lot of wind resources, especially in the north and west, which might be used to produce hydrogen. A seemingly limitless source that has the potential to generate significant amounts of electricity and hydrogen is wind energy. There are many different sizes of wind turbines that can be installed, from tiny turbines for home or commercial use to massive wind farms. Due to its adaptability, wind energy is a good choice for distributed hydrogen production.

2.2.3. Hydro Energy

China has the biggest hydropower capacity in the world and uses a significant amount of hydro energy as a renewable energy source [41]. Water flow in rivers, streams, or oceans can produce hydropower, which can subsequently be used to power an electrolyzer to create hydrogen. Since it is unaffected by weather or intermittency, hydropower is a dependable and predictable energy source. Because large-scale hydrogen generation requires a consistent and constant energy supply, hydropower is a good fit for this use. Hydropower is a financially viable option for producing hydrogen because its cost is typically lower than that of other renewable energy sources such as solar and wind [42]. Moreover, hydropower has become a more affordable option for producing hydrogen, making it a desirable hydrogen production choice. Since they can be utilized for flood control, irrigation, and water delivery, hydropower systems can also work in harmony with water management.

2.2.4. Biomass Energy

Any organic material that can be used as fuel and is sourced from plants or animals is referred to as biomass [43]. Because it may be made from a variety of materials, including agricultural waste, forestry byproducts, and energy crops, biomass is a resource that is broadly accessible. Producing biomass locally can increase energy security and decrease reliance on foreign energy sources [44]. A renewable energy source that can decrease greenhouse gas emissions and slow down climate change is biomass. Moreover, waste materials can be converted into biomass, minimizing waste sent to landfills and fostering a circular economy. Since biomass production can improve soil health and nutrient cycling, it can also work in harmony with agriculture. A sustainable and low-carbon energy system may be developed using biomass, a renewable energy source that has the ability to produce hydrogen in some circumstances.

2.2.5. Geothermal Energy

Geothermal energy is the term used to describe the heat produced by the earth’s core that can be used to generate electricity and heat [45]. Hydrogen may be produced using geothermal energy by heating an electrolyzer. Given that it is unaffected by weather or intermittency, geothermal energy is a dependable and consistent energy source. Geothermal energy is thus well suited for large-scale hydrogen synthesis, which necessitates a steady stream of energy. As they may be utilized to supply both power and heat for buildings, these energy source systems can also create synergies with district heating.

2.2.6. Tidal Energy

Tidal energy is energy that comes from the tides in seas or oceans and can be used to create electricity [46]. Hydrogen may be made from tidal energy by running an electrolyzer on electricity. Since tides have a defined rhythm, tidal energy is steady and predictable. Because large-scale hydrogen synthesis requires a consistent and constant energy supply, tidal energy is a good fit for this use.

3. Research Methodology

This research methodology evaluates the renewable energy sources for developing a hydrogen economy in China using the fuzzy AHP and fuzzy TOPSIS approach. This methodology is used to evaluate the multiple criteria, sub-criteria, and different renewable energy sources for producing hydrogen in China, and to identify the most suitable renewable energy sources for developing a hydrogen economy in the country. Figure 3 presents the research methodology of this study.

3.1. The Fuzzy AHP Method

Saaty proposed the AHP method in the 1970s [47]. The fuzzy AHP method has several advantages over the traditional AHP method that incorporates fuzzy logic to account for the uncertainty and imprecision in decision making. This study employs linguistic variables and triangular fuzzy numbers (TFNs) to evaluate the weights of the criteria and sub-criteria.

Table 3. Triangular Fuzzy Numbers Scale [48].

Code	Linguistic Variable	TFNs
1	Equally dominant	(1,1,1)
2	Equally to the average dominant	(1,2,3)
3	Averagely dominant	(2,3,4)
4	Averagely to strongly dominant	(3,4,5)
5	Strongly dominant	(4,5,6)
6	Strongly to very strongly dominant	(5,6,7)
7	Very strongly dominant	(6,7,8)
8	Very strongly to extremely dominant	(7,8,9)
9	Extremely dominant	(9,9,9)

provides the TFNs scale of this study.

The Gogus and Boucher proposed method was employed in this research to determine the inconsistency ratio of the fuzzy pairwise comparison matrix. The following steps were utilized for this computation [49].

Step 1. The process of converting a triangular fuzzy matrix into two separate independent matrices is demonstrated below:

$$X_i=(l_i,m_i,u_i)$$

(1)

The first triangular fuzzy matrix can be formed using a middle fuzzy triangular matrix. The equation for this construction is expressed as:

$$X_m=\left[x_{ijm}\right]$$

(2)

The second triangular fuzzy matrix can be created by using a geometric mean method to determine the upper and lower bounds of the triangular fuzzy numbers (TFNs). The equation for this approach is as follows:

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

(3)

Step 2. Construct the weight vector based on the Saaty approach and computation of lambda max.

Step 3. Construct the consistency index (CI) for each matrix:

$${CI}_m=\frac{{\lambda }_{max}^m-n}{n-1}$$

(4)

$${CI}_g=\frac{{\lambda }_{max}^g-n}{n-1}$$

(5)

Step 4. Calculate the consistency ratio (CR) of each matrix, the consistency index (CI) of each matrix is divided by its corresponding random index (RI).

$${CR}_m=\frac{{CI}_m}{{RI}_m}$$

(6)

$${CR}_g=\frac{{CI}_g}{{RI}_g}$$

(7)

To ensure the validity and symmetry of fuzzy matrices, it is required that both ${CR}_m$ and ${CR}_g$ values are below 0.10. If the range goes beyond 0.10, it will not yield significant outcomes and will be deemed as inconsistent or invalid. This study offers the RI scale, outlined in

Table 4. RI scale.

$\mathit{n}$	${\mathit{R}\mathit{I}}_{\mathit{m}}$	${\mathit{R}\mathit{I}}_{\mathit{g}}$
1	0	1
2	0	2
3	0.4890	0.1796
4	0.7937	0.2627
5	1.0720	0.3597
6	1.1996	0.3818
7	1.2874	0.4090
8	1.3410	0.4164
9	1.3793	0.4348
10	1.4095	0.4455

, which is distinct from Saaty’s RI scale and is proposed by Gogus and Boucher.

The fuzzy AHP method has been used to evaluate the various criteria and sub-criteria to rank suitable renewable energy sources for hydrogen production in China.

3.2. The Fuzzy TOPSIS

The fuzzy TOPSIS is widely used in various fields, including renewable energy. The TOPSIS method was proposed by Hwang and Yoon in 1981 and has since been applied to many decision-making problems [26]. The TFNs are utilized to assess the alternatives based on the sub-criteria of the research. The scale for TFNs can be found in

Table 5. TFNs scale [50].

No	Linguistic Variables	TFNs
1	Very unsatisfactory	(1,2,3)
2	Unsatisfactory	(2,3,4)
3	Medium unsatisfactory	(3,4,5)
4	Medium	(4,5,6)
5	Satisfactory	(5,6,7)
6	Medium satisfactory	(6,7,8)
7	Very satisfactory	(7,8,9)

.

The basic steps of the fuzzy TOPSIS technique are given below:

Step 1. Let $\tilde{K}=\left(k_1,k_2,k_3\right)$ and $\tilde{L}=\left(l_1,l_2,l_3\right)$ represent the two fuzzy numbers:

$$\tilde{K}+\tilde{L}=\left(k_1,k_2,k_3\right)+\left(l_1,l_2,l_3\right)=\left(k_1+l_1,k_2+l_2,k_3+l_3\right)$$

(8)

$$\tilde{K}\times \tilde{L}=\left(k_1,k_2,k_3\right)\times \left(l_1,l_2,l_3\right)=\left(k_1{l}_1,k_2{l}_2,k_3{l}_3\right)$$

(9)

Step 2. Let ${\tilde{K}}_i=(k_{i1},k_{i2},k_{i3})$ represent the TFNs for $i\in I$. Then, the normalized fuzzy number of each ${\tilde{K}}_i$ is signified as

$$\tilde{D}=[d_{ij}{]}_{m\times n}$$

(10)

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

To obtain a positive ideal solution, the normalization process can be demonstrated as follows:

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

(11)

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

To obtain a negative ideal solution, the normalization process can be demonstrated as follows:

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

(12)

where $l_{1j}^{-}=min l_{1ij}$.

Step 3. Construct the weighted normalized fuzzy decision matrix:

$$\tilde{V}={[v}_{ij}{]}_{m\times n}$$

(13)

$i=\mathrm{1,2},3,\dots ,m$ 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 solutions:

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

(14)

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

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

(15)

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

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

$$d\left(\tilde{K},\tilde{L}\right)=\sqrt{\frac{1}{3}[{(k}_1-l_1{)}^2+(k_2-l_2{)}^2+(k_3-l_3{)}^2]}$$

(16)

Step 5. Construct the closeness coefficient $\left(C{C}_i\right)$ using Equation (17):

$$C{C}_i=\frac{d_i^{-}}{d_i^{+}+d_i^{-}}$$

(17)

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

Step 6. Determine the most appropriate renewable energy source by arranging them in descending order of their $C{C}_i$ values.

In this context the fuzzy TOPSIS method has been used to evaluate the suitability of different renewable energy sources for hydrogen production in China.

3.3. Experts of This Study

This study’s experts contributed to the research on evaluating renewable energy sources for developing a hydrogen economy in China using the fuzzy AHP and fuzzy TOPSIS approach. In this study, we consulted with energy researchers and academics specializing in renewable energy and energy systems analysis. We also consulted with industry experts and practitioners who have experience in developing and implementing renewable energy projects in China. Moreover, we conferred with the environmental scientists and experts who have knowledge and expertise in environmental impact assessment and sustainability analysis. Thus, in this study we consulted with six experts to analyze the decision-making problem of this study. These experts provided valuable inputs and perspectives on the criteria, sub-criteria, and different renewable energy sources for the analysis, weights assigned to each criterion and sub-criterion, and the interpretation of the results.

4. Results and Discussion

This research utilized a methodology based on fuzzy AHP and fuzzy TOPSIS to evaluate the criteria and renewable energy sources for low-carbon hydrogen production in China. This study employed the fuzzy AHP approach to analyze and rank the five criteria and fifteen sub-criteria. The fuzzy TOPSIS technique was then employed to determine the most suitable renewable energy source for China’s sustainable and low-carbon hydrogen production.

4.1. Main Criteria Results Using Fuzzy AHP

The fuzzy AHP approach involves determining the weights of the criteria and sub-criteria for evaluating renewable energy sources for developing a hydrogen economy in China. The weights and rankings of the primary criteria for producing hydrogen using renewable resources are shown in

Table 6. The ranking order of criteria.

Criteria	Weight	Rank
Availability	0.254	1
Cost	0.219	3
Reliability	0.163	4
Sustainability	0.231	2
Technological maturity	0.132	5

.

The assigned weights indicate that availability, with a weight of 0.254, is the most important criterion. With a weight of 0.231, the sustainability criterion received the second-highest ranking. The cost criteria came in third, with a weight of 0.219. Reliability criteria came in next, with a weight of 0.163. The least significant factor, with a weight of 0.132, was technological maturity. The results show that all of the specified criteria are critically important for bringing about a viable hydrogen economy that is low-carbon and sustainable in the country.

4.2. Sub-Criteria Results Using Fuzzy AHP

The results indicate that renewable energy resources, land availability, and grid connection are crucial factors to consider when evaluating renewable energy sources for developing a hydrogen economy in China. The sub-criteria of resource availability have the highest weight (0.385) within the availability criterion, indicating that the availability of renewable energy resources, such as solar radiation, wind speed, water flow, and biomass availability, is a critical factor. Figure 4 displays the rankings of the sub-criteria associated with the availability criteria.

Within the cost criterion, the cost of renewable energy sources and the levelized cost of hydrogen production are essential considerations. The sub-criterion of capital costs has the highest weight of 0.404, indicating that the initial investment required for renewable energy equipment and infrastructure is an essential factor. Figure 5 shows the weights and rankings of sub-criteria relating to cost criteria.

Within the reliability criterion, the reliability and predictability of RESs for hydrogen production are important factors. The sub-criterion of the capacity factor has the highest weight of 0.435, indicating that the ratio of actual energy output to maximum possible output is a critical factor. Figure 6 depicts the weights and rankings of sub-criteria with respect to reliability criteria.

Within the sustainability criterion, the environmental, social, and economic impacts of renewable energy sources are important factors. The sub-criterion of environmental impact has the highest weight of 0.475, indicating that the environmental impact of renewable energy sources, such as greenhouse gas emissions, land use, and water use, is a crucial factor. Figure 7 presents the weights and rankings of sub-criteria associated with sustainability criteria.

Within the technological maturity criterion, the level of development and maturity of renewable energy technologies is a less important factor. The sub-criterion of technological readiness has the highest weight of 0.523, indicating that renewable energy technologies’ level of development and maturity is still an essential factor to consider. Figure 8 indicates the weights and rankings of sub-criteria with respect to technological maturity criteria.

4.3. Renewable Energy Source Results Using Fuzzy TOPSIS

The fuzzy TOPSIS approach has been used to rank the renewable energy sources based on their overall performance with respect to the criteria and sub-criteria.

Table 7. The rankings of alternatives (renewable energy sources).

Renewable Energy Source	${\mathit{d}}^{+}$	${\mathit{d}}^{-}$	${\mathit{C}\mathit{C}}_{\mathit{i}}$	Final Ranking
Solar energy	4.432	12.705	0.7134	1
Wind energy	5.358	11.936	0.6870	2
Hydropower	6.332	11.546	0.6412	3
Biomass energy	7.965	8.213	0.5232	4
Geothermal energy	8.787	8.893	0.5043	5
Tidal energy	9.543	8.190	0.4702	6

presents the rankings of six renewable energy sources for low-carbon hydrogen energy generation. The results suggest that solar energy has the highest overall performance when evaluated against the criteria and sub-criteria, indicating that it is a promising renewable energy source for producing hydrogen in China. Solar energy has several advantages, including abundant resource availability, relatively low environmental impact, and a mature technology that is increasingly cost-competitive. Wind energy and hydropower are ranked second and third, respectively. Wind energy has a high capacity factor, low environmental impact, and is a mature technology that is cost-competitive. Hydropower has a high capacity factor, relatively low environmental impact, and is a well-established technology in China. Moreover, the ranking places biomass energy, geothermal energy, and tidal energy considerably lower. Geothermal energy has constraints in resource availability and technological maturity, whereas biomass energy has restrictions on resource availability and environmental impact. Tidal energy has constraints in terms of its commercial feasibility and technological development.

The outcomes are based on the weights assigned to the criteria and sub-criteria using the fuzzy AHP technique. Taking into account the relative weights of the criteria and sub-criteria, the fuzzy TOPSIS approach provides a systematic way to evaluate the renewable energy sources based on their overall performance.

4.4. Sensitivity Analysis

In this study, we adopted the fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenj (VIKOR) method to examine and validate the robustness of the obtained results from fuzzy AHP and fuzzy TOPSIS methods by examining the impact of changing the decision information. Since experts’ feedback may not always be consistent due to various factors that influence the decision problem, it is essential to analyze the sensitivity of the ranking results of the alternatives. Therefore, this study evaluated the impact on the ranking results of the alternatives by changing the experts’ feedback through sensitivity analysis.

In this context, it is essential to measure the stability of prioritization of alternatives since even a slight variation in the weight assigned to criteria can lead to significant changes in the results. To confirm the changes in the final prioritization of criteria, the weight of the main criteria is modified in this study, and its impact on ranking values of renewable energy alternatives is examined. This is achieved by creating five different cases, where only one criterion is assigned a high weightage of 0.40 while the others remain constant at 0.15.

Table 8. Criteria weight employed for sensitivity analysis.

	Availability	Cost	Reliability	Sustainability	Technological Maturity
Case-1 Availability	0.40	0.15	0.15	0.15	0.15
Case-2 Cost	0.15	0.40	0.15	0.15	0.15
Case-3 Reliability	0.15	0.15	0.40	0.15	0.15
Case-4 Sustainability	0.15	0.15	0.15	0.40	0.15
Case-5 Technological maturity	0.15	0.15	0.15	0.15	0.40

shows the priority weights used in each case. The final rankings of renewable energy alternatives resulting from sensitivity analysis in each scenario is presented in

Table 9. Rankings of renewable energy sources with priority weight.

		Solar	Wind	Hydropower	Biomass	Geothermal	Tidal
Case-1 Availability	weight	0.541	0.521	0.495	0.454	0.430	0.413
	Rank	1	2	3	4	5	6
Case-2 Cost	weight	0.543	0.526	0.494	0.452	0.432	0.417
	Rank	1	2	3	4	5	6
Case-3 Reliability	weight	0.545	0.525	0.497	0.455	0.438	0.415
	Rank	1	2	3	4	5	6
Case-4 Sustainability	weight	0.540	0.523	0.495	0.452	0.439	0.415
	Rank	1	2	3	4	5	6
Case-5 Technological maturity	weight	0.542	0.523	0.491	0.450	0.436	0.419
	Rank	1	2	3	4	5	6

.

According to the findings, in all five cases the ranking remained unchanged. Thus, solar energy is identified as the most significant renewable energy alternative for hydrogen generation. This study ranks wind energy as the second most important renewable source for hydrogen energy generation, while hydropower is considered the third most important renewable source. Other renewable energy sources also remained on the same rank. The sensitivity analysis demonstrates that the results obtained through the fuzzy TOPSIS and fuzzy VIKOR methods are both reliable and robust, and there is no significant alteration in the outcomes by changing the weights. The results of this study hold substantial significance for policy and decision makers regarding the implementation of hydrogen economy in China.

4.5. Discussion

The findings of the fuzzy AHP and fuzzy TOPSIS methods to the analysis for the development of a hydrogen economy offer important new information on the best renewable energy sources for hydrogen production. Due to its vast resource availability, minimal environmental impact, and established technology that is becoming more and more cost-competitive, solar energy is considered to be the most suited renewable energy source for creating hydrogen. China is the world’s largest manufacturer and consumer of solar photovoltaic panels, and solar energy has the potential to significantly contribute to the country’s transition to a hydrogen economy.

Wind energy and hydropower are also ranked highly, indicating that they are suitable renewable energy sources for producing hydrogen in China. Wind energy has a high capacity factor, low environmental impact, and is a mature technology that is cost-competitive. Hydropower has a high capacity factor, relatively low environmental impact, and is a well-established technology in China. China has a significant, installed capacity of wind and hydropower, and these renewable energy sources could be leveraged to produce hydrogen in the country. Biomass energy has limitations in resource availability and environmental impact, while geothermal energy has limitations in resource availability and technological maturity. Tidal energy has limitations in terms of technological maturity and commercial viability. However, these renewable energy sources may still have potential for producing hydrogen in specific regions or under certain circumstances.

Previous studies have also evaluated renewable energy sources for hydrogen production, and the results are consistent with the findings of this study to some extent. For example, a study used a cumulative prospect approach to evaluate renewable energy sources and found that solar is the most suitable source [11]. Similarly, another study evaluated the potential of renewable energy sources using AHP-DEA for hydrogen production in Pakistan and found that wind and solar energy had the highest potential [8]. These results are consistent with the rankings obtained in this study, where solar energy was ranked as the most suitable renewable energy source, followed by wind energy and hydropower. The findings of this study are consistent with previous studies that suggest solar and wind energy are promising renewable energy sources for producing hydrogen [7,9,15]. These renewable energy sources have abundant resource availability, low environmental impact, and mature technologies that are increasingly cost-competitive. Hydropower is also a well-established renewable energy source in China, and it has the potential to contribute to the development of a hydrogen economy in the country. However, other renewable energy sources such as biomass, geothermal, and tidal energy may have limitations or challenges that need to be addressed to maximize their potential for hydrogen production.

The analysis of renewable energy sources for developing a hydrogen economy in China using the fuzzy AHP and fuzzy TOPSIS approach provides valuable insights into the most suitable renewable energy sources for producing hydrogen in the country.

5. Conclusions

Developing a hydrogen economy in China presents an opportunity to reduce the country’s dependence on fossil fuels and contribute to global efforts to mitigate climate change. Renewable energy sources are a critical component of a hydrogen economy, as they can provide a clean and sustainable energy source for hydrogen production. This study used the fuzzy AHP and fuzzy TOPSIS approaches to evaluate renewable energy sources for developing a hydrogen economy in China. The criteria and sub-criteria were identified based on the inputs and preferences of stakeholders and experts, and the weights were determined using the fuzzy AHP approach. The fuzzy TOPSIS approach was then used to rank the renewable energy sources based on their overall performance with respect to the criteria and sub-criteria. A major innovation and contribution of this study is the application of an integrated fuzzy AHP–fuzzy TOPSIS approach to rank renewable energy sources. It assesses renewable energy sources for China’s transition to a hydrogen economy. This approach may manage the ambiguity and uncertainty in the decision-making process and offer a thorough and reliable evaluation of the choices. Moreover, from the standpoint of the hydrogen economy, it considers several criteria and sub-criteria that represent the environmental, economic, technological, social, and political elements of renewable energy sources. This study also determines the most viable and practical renewable energy sources for producing hydrogen in China based on the research outcomes. This study can offer helpful insights and advice for policymakers interested in boosting renewable energy sources.

The analysis results suggest that solar energy is the most suitable renewable energy source for producing hydrogen in China, followed by wind energy and hydropower. The results of this study can inform policymakers and investors in developing a hydrogen economy, helping to prioritize investments in renewable energy sources with the highest potential for hydrogen production. The rankings of renewable energy sources could be used to guide the deployment of hydrogen production facilities and the development of hydrogen infrastructure. Moreover, based on the results of this study, the following policy recommendations can support the development of a hydrogen economy in China.

• The results suggest that solar and wind energy are the most suitable RESs for producing hydrogen in the country. Therefore, policymakers can prioritize investments in these renewable energy sources to support the development of a hydrogen economy.
• To facilitate the deployment of hydrogen-producing facilities, policymakers can encourage the construction of hydrogen refueling stations, storage facilities, and transportation infrastructure [51].
• The potential for technological progress in the fields of hydrogen production and renewable energy sources is enormous. To increase the effectiveness, affordability, and environmental performance of renewable energy sources and hydrogen generation technologies, policymakers could promote research and development in these fields.
• To foster the growth of a hydrogen economy, policymakers can encourage collaboration and knowledge exchange between business, academia, and the government. This could involve programs such as public–private partnerships, technology transfer, and cooperative research projects.
• Frameworks for supportive policies that encourage the development of a hydrogen economy can be created by policymakers in the form of feed-in tariffs, tax breaks, and regulatory assistance for the creation of hydrogen.
• A hydrogen economy can be developed responsibly and sustainably with the help of policymakers. This could involve actions such as stakeholder engagement, environmental impact studies, and social impact assessments to ensure that the advantages of the hydrogen economy are distributed fairly and that any negative effects be kept to a minimum.

These suggestions would assist the country in making the transition to a low-carbon, more sustainable energy system. Policymakers can be guided in their decisions about the deployment of renewable energy sources and the development of hydrogen infrastructure by evaluating renewable energy sources based on their overall performance.