Assessment of Shallow Geothermal Development Potential Based on the Entropy Weight TOPSIS Method—A Case Study of Guizhou Province

Abstract

Shallow geothermal energy is a renewable and clean resource, yet its potential varies significantly across different regions of China. The disparity in resource conditions, technology development, and the level of regional understanding regarding its potential hinder efficient utilization. This study applies the entropy weight method, combined with the TOPSIS model, to assess the shallow geothermal utilization potential across various regions of the country, with a case study in Guizhou Province employed to evaluate the actual development potential. Additionally, SWOT analysis is employed to assess regional development and provide recommendations for the future growth of shallow geothermal energy in Guizhou. The findings indicate a high potential for shallow geothermal energy in East China, with significant resource potential and conceivable economic benefits in Guizhou Province. This research not only evaluates the development potential for shallow geothermal energy in different regions but also proposes strategic recommendations to enhance its effective development and contribute to the high-quality transformation of the energy industry.

1. Introduction

China’s total energy consumption will reach 5.97 billion tonnes of standard coal in 2024 [1,2]. The increase in energy consumption will cause an increase in greenhouse gas emissions, which will hinder sustainable development. Traditional fossil energy sources, such as coal, oil, and natural gas, are highly energy-consuming and polluting, and have a huge impact on the ecological environment [3,4]. In 2020, China proposed the goal of “carbon peaking and carbon neutrality” [5], and “low carbon” has become a new trend in the sustainable development of renewable and clean energy sources. The vigorous development of new energy sources may effectively replace fossil energy sources. These measures, by optimizing the energy structure and reducing the emission of pollutant gases, are a key means of promoting the green transformation of China’s energy development, helping to achieve the “dual-carbon” goal and actively responding to global climate change [6].

Shallow geothermal energy is a new type of cyclic renewable strategic resource. The term refers to the low-temperature geothermal resources embedded in shallow rock, soil bodies, and groundwater within a certain depth range below the earth’s surface, usually within 200 m, with a temperature lower than 25 °C, and with the potential for exploitation under the current technological and economic conditions [7]. Deep geothermal energy is mostly used for building heating and cooling. A ground source heat pump system, which extracts constant temperature energy for building heating and cooling via inputting a small amount of electric energy, is one of the main methods of utilizing shallow geothermal energy. China’s “14th Five-Year Plan” for a modern energy system emphasizes that China will actively promote geothermal energy for heating and cooling and carry out demonstrations of geothermal energy power generation in an orderly manner in areas with high-temperature geothermal resources [8]. As of the end of 2019, the floor area of shallow ground source heat pumps for energy supply in China had exceeded 8.58 × 10⁸ m² [9], with these pumps mainly used in non-carbonate rock formations in North China. Guizhou Province is rich in geothermal energy resources, especially shallow geothermal energy with high thermal conductivity, renewable capacity, and other unique advantages and characteristics [10]. The natural reserves of shallow geothermal energy (200 m or less) in Guizhou Province are estimated to be 92,300 × 10⁸ MJ, the recoverable resources are equivalent to 315 million tonnes of standard coal, and the available area for building heating (cooling) is 34.4 billion square meters [11]. However, the development of the shallow geothermal industry varies greatly in different regions due to the geological conditions, economic development level, energy policies, and other aspects. Some areas have already realized the large-scale development and efficient use of shallow geothermal energy via their abundant resources and advanced technology, while some regions are still in the initial stages and face many developmental bottlenecks [12]. This regional imbalance makes it necessary to assess the development potential of shallow geothermal energy in different regions.

The entropy weighting method is an objective weighting method that calculates the weights of indicators the between the data and can accurately reflect the importance degree of each indicator in the indicator system, reduce the evaluation error due to irrational weight setting, and produce results with strong credibility [13]. TOPSIS model is a method of analyzing the order of ideal values and judging the order of the evaluation criteria by calculating the positive and negative ideal values of the indicators [14]. The entropy weight method and the TOPSIS model are widely used in various societal and economic fields, but they are less developed and utilized in the field of shallow geothermal energy. Therefore, this study will analyze the development potential of shallow geothermal energy based on the entropy weight method and the TOPSIS model. The specific objectives of the study are as follows: (i) to calculate the weights of different technical, economic and environmental dimensions of the actual projects in different regions and Guizhou Province, based on the entropy weight method; (ii) to calculate the weights through the entropy weight method and assign the weights to the TOPSIS model to construct an effective evaluation system, so as to evaluate the development potential of shallow geothermal energy in different regions of the country and the economic benefits of the actual projects that already exist in Guizhou; (iii) to derive the ranking of the potential of shallow geothermal energy in different regions of the country under this evaluation system, to measure the economic benefits of different projects in Guizhou, and to utilize the SWOT analysis to propose appropriate policy recommendations for the development of the potential of shallow geothermal energy in different regions and in Guizhou Province in order to promote the development and utilization of shallow geothermal energy.

2. Literature Review

Currently, academic research on the development potential of shallow geothermal energy continues to grow and is analyzed and evaluated using different methodologies. In Madrid, in a study of 50 buildings using ground source heat pump systems, it was found that the use of geothermal systems for district heating and cooling resulted in a 64% savings in primary energy and a 76% reduction in CO₂ emissions [14]. D’Agostino, D., et al. compared buildings with ground-source heat pumps to buildings with predominantly gas-fired boilers and showed that buildings with ground-source heat pumps displayed very low primary energy demand, significant financial savings, and reduced CO₂ emissions in 2022 [15].

Many scholars at home and abroad use hierarchical analysis to analyze the suitability of shallow geothermal energy. Dong et al. used hierarchical analysis to measure the applicability range of the ground source heat pump system in 2022 [16]. In 2018, Zhang et al. used hierarchical analysis to determine the weight value of thermal conductivity, used thermal conductivity to calculate the heat exchange power, and measured the thermophysical parameter of the geotechnical layer to optimize the heat pump system and reduce costs [17]. In 2023, Liu et al. used hierarchical analysis to quantify the index of deep geothermal resource exploration and development, utilizing the quantitative zoning evaluation method to delineate the prospective target area for deep geothermal resource exploration and development on a regional scale [18]. Harish Puppala, in 2018, used fuzzy hierarchical analysis (fuzzy AHP) to study the strengths of each geothermal field and identify important locations in Indian geothermal fields that can be subjected to further developmental work and the commercial exploitation of geothermal energy [19]. In 2024, John Ngethe et al. studied the selection of optimal use options for geothermal resources in central and western Kenya using the hydrogeochemical analysis of hot spring waters and GIS-based AHP modeling [20]. However, the hierarchical analysis method is scored according to the experience of experts, followed by the determination of the weights; therefore, the judgment standard is somewhat subjective.

The entropy weighting method is an objective method of assigning weights which calculates the weights of indicators through the patterns between data and it can accurately reflect the importance of each indicator in the indicator system, reduce the evaluation errors caused by unreasonable weight settings, and produce results with strong credibility [13]. At present, scholars from various countries have conducted a significant amount of research on the entropy weight method, which has been applied in many fields. Some scholars have used the entropy weight method to assess a series of environmental problems, such as the impact of environmental pollution and the vulnerability of construction landslides [21]. The entropy weighting method has also been widely applied in economic research, including areas such as supplier selection and order allocation [22]. In the transport sector, it has been used to assess the environmental sustainability of freight transport and other applications [23].

The TOPSIS model, on the other hand, is a method of analyzing the ordering of ideals, which determines the order of the evaluation criteria by calculating the positive and negative ideal values of the indicators [14]. Currently, scholars from various countries are applying the TOPSIS model in several fields. In the supply chain, the TOPSIS model is mostly used to evaluate the risk management of a sustainable supply chain [24]. In the energy- and environment-related fields, it has been mostly used to assess regional climate change mitigation methods [25]. At the same time, TOPSIS methods are constantly being improved, e.g., policymakers continue to improve the behavioral tendencies of TOPSIS methods [26]. However, the TOPSIS method usually assigns weights to each criterion subjectively. The weight assignment may be influenced by the decision maker’s preference. This subjective choice may provide different solutions, depending on the weights chosen by the decision maker [27,28]. The advantage of the fuzzy comprehensive evaluation method is that it can better solve problems that are difficult to quantify or exhibit a high degree of fuzziness, but in some specific cases, there will be a super-fuzzy phenomenon, and the influence of subjective factors is strong. The method of approximating ideal solution ranking (TOPSIS) can solve the problem of limited sample information and can make full use of the original information [29,30].

While the entropy weight method is often used as the weight determination method of TOPSIS, the TOPSIS technique based on the combined value method can more accurately and objectively measure the current status of the comprehensive indexes through the introduction of entropy weights, avoiding the inconsistency of the evaluation results due to the establishment of different standards [31,32]. The combination of the two has been widely used in various fields of social economy, such as product design [33] and public blockchain evaluation [34], but is less developed and used in shallow geothermal energy fields. There are fewer existing studies on this topic. In 2012, Zhao et al. used the TOPSIS method and the suitability evaluation algorithm to evaluate only the coastal area of Nantong, China [14], and the results may not apply to some geologically complex and complicated areas.

AHP, as a knowledge-driven method, is based on the experience of experts. However, the experience of the expert can lead to subjective results. If the study area is large, it is almost impossible to expect the expert to acquire detailed knowledge about each location in the study area [14]. Subjective weight assignments may be influenced by the preferences of the decision maker, providing different solutions based on the weights chosen by the decision maker [28]. Pathan et al., in their 2022 study, used actual data to quantitatively state that the TOPSIS approach more accurately estimated flood risk coverage than did the AHP approach [35]. The entropy weighting and TOPSIS methods allow for the automatic calculation of weights and the ranking of suitable regions utilizing information entropy, which is more objective and ensures that the established evaluation system can be applied to other regions as well.

SWOT analysis is a common tool for strategic planning and is a form of brainstorming. Analyzing and locating the four areas of the organization’s resources and environment (strengths, weaknesses, opportunities, and threats) helps organizations to better understand their internal and external business environments when developing strategic plans and decisions [36]. Strengths and weaknesses are the internal (controllable) factors that support and hinder an organization’s achievement of its mission, respectively. Opportunities and threats are external factors—beyond the control of the organization—that either facilitate or hinder its ability to accomplish its mission [37]. The existing research in the field of geothermal energy mainly uses SWOT analysis to evaluate power plants based on renewable energy, including geothermal energy [38]. In addition, SWOT, the multi standard decision-making method, and game theory are combined to determine the best renewable energy development plan, including the use of geothermal energy [39]. Fewer methods have been singled out for geothermal energy applications.

This section describes the current status of research on shallow geothermal energy, entropy weight, and TOPSIS methods. The entropy weight–TOPSIS method has not yet been combined and applied in the research and practical application of shallow geothermal development potential, which is explored and discussed in this study in combination with the SWOT analysis method.

3. Methodology

This study focuses on the assessment of the development potential of shallow geothermal energy. The overall line of this study is shown in Figure 1. The research theme is set in the context of high energy consumption and the advantages of shallow geothermal energy and aims to assess the potential to promote its development and utilization. Through a comprehensive literature review, the current status of shallow geothermal energy research and the application of the entropy weighting method and the TOPSIS model are sorted out, and the innovative points of the research method are clarified. Then, the evaluation index systems of projects in different regions of China, as well as Guizhou Province, were constructed, respectively, with the former selecting 7 indexes and collecting heat flow data, air temperature, and electricity price, and the latter selecting 11 indexes of technical, economic, and environmental dimensions, collecting technical parameters and economic data of the different projects. The entropy weighting method was used to evaluate the data of the projects. The data are standardized using the entropy weight method, which calculates the weight of the indicators, the information entropy, and the entropy weight. The entropy weight and standardized data were combined to construct a normalization matrix, and the TOPSIS model was used to determine the positive and negative ideal solutions and calculate the distance and closeness. Based on the proximity degree, the potential of shallow geothermal energy in different regions is analyzed; the development of different areas is analyzed by SWOT analysis, and suggestions are made for the future development of shallow geothermal energy in Guizhou Province. Four projects in Guizhou were evaluated to identify the advantages and disadvantages of each. Additionally, the Guizhou Science and Technology Park project is selected for an economic benefits assessment, focusing on the analysis of its operational costs, as well as the potential energy savings and emission reduction benefits. Finally, the study’s findings are summarized, and relevant recommendations are provided.

3.1. Construction of Geothermal Evaluation Index System in Different Regions

Heat flow data comprise a necessary parameter for geothermal resource evaluation, oil and gas exploration, and other production activities [40]. In this study, we selected the data from the compilation of China’s terrestrial geothermal heat flow data (the second, third, fourth, and fifth editions) [41,42,43,44] that can provide the number of areas and obtained 2340 registered terrestrial heat flow records through screening and statistical data. The combined shallow geothermal energy–solar collector wall system can solve the problem of overheating indoors in summer, meet the thermal comfort requirements of the human body in winter, and is a passive cooling system with great potential for the development of passive solar room heating/cooling in the cold Xinjiang region [45]. In winter, shallow geothermal energy can be used as a new type of heating method, displaying the characteristics of cleanliness, continuity, and stability. Regions with lower average annual temperatures in winter have a higher demand for heating, and the development potential of shallow geothermal energy increases accordingly [46]. For the ground source heat pump system, there are seasonal differences, i.e., there is a need for cooling in summer, and there is an imbalanced need for heating in the winter. In the Guiyang area, for example, its ground source heat pump system presents a higher winter use frequency than that in the summer, and measures such as the flow of groundwater must be regulated to maintain the low-temperature balance of the natural mechanism, while still artificially monitoring and making necessary adjustments in due course to promptly supplement heat or adjust the air cooling [47].

The average temperatures in summer and winter are selected from 2022–2023 and are processed to obtain the temperature data of different regions (excluding the temperature data of Hong Kong, Macao, and Taiwan). Under different pricing mechanisms, electricity price has an impact on the investment and financing aspects of geothermal heating projects. Electricity price is an important factor affecting the development of shallow geothermal energy, and electricity price may affect the market competitiveness and development potential of shallow geothermal energy [46]. The process of precipitation infiltration is the process of water transformation and energy exchange. Precipitation in the process of infiltration, on the one hand, increases the water content of the soil, which is conducive to heat conduction; on the other hand, it can absorb or release part of the energy in the process of infiltration to achieve the exchange of heat with the soil around the underground heat exchanger—the source heat pump system—which increases the rate of heat exchange and then improves the efficiency of the heat pump system. At the same time, soil moisture content significantly affects the conductivity and thus, the efficiency, of geothermal energy applications [48]. Thus, this study selected the amount of precipitation as an indicator [49]. With the growth of the population and the acceleration of urbanization, the demand for energy in areas with high population density is increasing. As a clean and renewable energy source, shallow geothermal energy can provide a sustainable energy supply for densely populated areas [50]. At the same time, after expert consultation, the percentage of new shallow geothermal floor space can, to a certain extent, reflect the scale of technical variability and development potential between regions. Therefore, the following indicators were chosen to weigh the potential of developing shallow geothermal energy in each area: land-based heat flow data registry, average annual temperature in summer (°C), average annual temperature in winter (°C), precipitation (mm/year), electricity price (Yuan/kWh), population density (people/km²), and proportion of newly built shallow geothermal building area, as shown in

Table 1. Comprehensive evaluation system of shallow geothermal energy in different regions.

	Northeast China	North China	East China	South China	Southwest China	Northwest China	Central China	Indicator Trend	Source of Data
Land-based heat flow data registrations	159	539	538	144	242	582	136	+	[41,42,43,44]
Average summer temperature (°C)	22.2	23.5	28.3	28.5	22.4	20.9	28.2	+	[51]
Average winter temperature (°C)	−8.7	−1.9	7.9	17.2	6.4	−2.4	7.6	−	[51]
Precipitation (mm/year)	644.2	512.8	1260.3	1606.8	846.7	417.3	1260.3	+	[52]
Electricity price (CNY/kWh)	0.583	0.592	0.659	0.630	0.481	0.402	0.630	−	[53]
Population density (persons/km2)	121	285	536	429	225	31	400	+	[54]
Proportion of newly built shallow geothermal building area	0.004799171	0.010735447	0.002134362	0.002953675	0.003817887	0.002475544	0.004559984	+	[55,56]

.

3.2. Construction of a Geothermal Evaluation Index System for Projects in the Guizhou Region

The development and utilization of shallow geothermal energy can effectively promote the sustainable development of the region. In order to evaluate the development potential of the existing projects in the provincial areas in a more refined way, it is necessary to demonstrate the three categories of economic, social, and ecological objectives [57].

The study is conducted from the perspective of the costs and benefits of renewable energy by examining the economic costs and benefits. Energy is an important driver of economic growth [58]. Based on the previous theories and the situation of Guizhou Province, gross floor area C₁, heating and cooling area C₂, gas price C₃, heat generated per CNY C₄, and cost per kW of heat C₅ can be selected to explore the relationship between resources and economy. At the same time, experts and existing studies have pointed out that the cost per kilowatt of heat can be inversely proportional to social acceptance [59]. For the economic evaluation of the project, we also need to pay attention to its initial investment cost C₆ and payback period C₇ to evaluate the feasibility of investment [37]. The heat exchange power of the ground source heat pump in the study area is calculated and will be used as one of the influencing factors for evaluating the development and utilization of shallow geothermal energy [60]. The heat transfer performance of various ground source heat pump systems exhibits varying effectiveness in regards to both cooling and heating, with the heat transfer capacity being higher in summer than in winter. Therefore, at the technical level, the total summer cooling load at the ground source side C₈, the total winter heat load at the ground source side C₉, the maximum load C₁₀, and the extended meter heat transfer C₁₁ are selected as the evaluation indexes in this study [61]. Therefore, this study follows the principles of science, comprehensiveness, and accessibility, selects indicators from the three evaluation criteria of technology, economy, and environment, and builds a preliminary indicator system by combining the existing evaluation studies of geothermal energy potentials, energy planning documents, and reports related to the social responsibility of geothermal energy enterprises. Based on the initially formulated indicator system, this study builds nine second-level indicator layers from the technology, economy, and environment, as presented in Table 2.

Among them, the unit price of fuel and the cost per kW of heat are negative indicators, and the rest are positive indicators. High gas prices result in higher operating costs, the cost per kilowatt of heat reduces the willingness of residents to use it, and high investment and long payback periods reduce the willingness to invest in the project. Larger buildings and heating and cooling areas generate economies of scale and lead to higher economic reporting. Higher heat production per unit cost, load, and heat exchange per meter are evidence of high efficiency and adequate technology. Based on the analysis of the investment components indicated in the research results, the operating costs of the project can be calculated as a percentage of the fixed capital [62], To assess the effectiveness of the investment, the value of the total investment divided by the total load of heating and cooling was used instead of these data. In addition, considering the special geological landscape of Guizhou Province, this study will be based on the data from the Guizhou Geology and Mining Bureau and the characteristic data of existing projects to evaluate the development potential for shallow geothermal resources in Guizhou.

Table 2. Comprehensive evaluation system of shallow geothermal energy in Guizhou.

Target Level	Standard Floor	Indicator Layer	Index Trend	Source of Data
Development potential of shallow geothermal energy in Guizhou	Environmental dimension B1	Gross floor area C1/m2	+	[61]
		Heating and cooling area C2/m2	+	[61]
	Economic dimension B2	Gas price C3/CNY/m3	−	[58]
		Heat generated per CNY C4/kW/CNY	+	[58]
		Cost per kilowatt of heat C5/CNY/kW	−	[58]
		Initial investment cost C6/10,000 CNY	−	[46]
		Payback period C7	−	[46]
	Technical aspects B3	Total summer cooling load at ground source side C8/kW	+	[60]
		Total winter heat load at ground source side C9/kW	+	[60]
		Maximum load C10/kW	+	[60]
		Extended meter heat transfer C11/W/m	+	[60,63]

Table 2. Comprehensive evaluation system of shallow geothermal energy in Guizhou.

Target Level	Standard Floor	Indicator Layer	Index Trend	Source of Data
Development potential of shallow geothermal energy in Guizhou	Environmental dimension B1	Gross floor area C1/m2	+	[61]
		Heating and cooling area C2/m2	+	[61]
	Economic dimension B2	Gas price C3/CNY/m3	−	[58]
		Heat generated per CNY C4/kW/CNY	+	[58]
		Cost per kilowatt of heat C5/CNY/kW	−	[58]
		Initial investment cost C6/10,000 CNY	−	[46]
		Payback period C7	−	[46]
	Technical aspects B3	Total summer cooling load at ground source side C8/kW	+	[60]
		Total winter heat load at ground source side C9/kW	+	[60]
		Maximum load C10/kW	+	[60]
		Extended meter heat transfer C11/W/m	+	[60,63]

3.3. Introduction to Entropy Weights and TOPSIS Models

The entropy weight method is a comprehensive evaluation method for multiple objects and indicators. It is mainly based on the data information of the objective environment to reflect the degree of connection between the indicators and effectively avoid the influence of subjective factors. The entropy weight method determines the objective weight through the degree of variability of the indicators. The greater the degree of variability of an indicator, the greater the corresponding weight, and vice versa [64]. The specific calculation formula is as follows:

Step 1: Since the units of measurement of the indicators are not uniform, the data were normalized before calculating the weights.

Positive indicators:

$${\mathrm{x}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}-\mathrm{m}\mathrm{i}\mathrm{n}\left\{{\mathrm{x}}_{\mathrm{i}\mathrm{j}},\dots {\mathrm{x}}_{\mathrm{n}\mathrm{j}}\right\}}{\mathrm{m}\mathrm{a}\mathrm{x}\left\{{\mathrm{x}}_{\mathrm{i}\mathrm{j}},\dots {\mathrm{x}}_{\mathrm{n}\mathrm{j}}\right\}-\mathrm{m}\mathrm{i}\mathrm{n}\left\{{\mathrm{x}}_{\mathrm{i}\mathrm{j}},\dots {\mathrm{x}}_{\mathrm{n}\mathrm{j}}\right\}}}$$

(1)

Negative indicators:

$${\mathrm{x}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{\mathrm{m}\mathrm{a}\mathrm{x}\left\{{\mathrm{x}}_{\mathrm{i}\mathrm{j}},\dots {\mathrm{x}}_{\mathrm{n}\mathrm{j}}\right\}-{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{\mathrm{m}\mathrm{a}\mathrm{x}\left\{{\mathrm{x}}_{\mathrm{i}\mathrm{j}},\dots {\mathrm{x}}_{\mathrm{n}\mathrm{j}}\right\}-\mathrm{m}\mathrm{i}\mathrm{n}\left\{{\mathrm{x}}_{\mathrm{i}\mathrm{j}},\dots {\mathrm{x}}_{\mathrm{n}\mathrm{j}}\right\}}}$$

(2)

i represents different geothermal energy development projects, and j represents specific evaluation indicators.

Step 2: Calculation of indicator variability, i.e., the weight of the indicator value of program i under indicator j ${\mathrm{P}}_{\mathrm{i}\mathrm{j}}.$

$${\mathrm{P}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}}$$

(3)

i represents different geothermal energy development projects, and j represents specific evaluation indicators.

Step 3: Calculation of the information entropy, i.e., the information entropy value of the $\mathrm{j}\mathrm{t}\mathrm{h}$ indicator e_j.

$${\mathrm{e}}_{\mathrm{j}}=-\mathrm{k}\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{P}}_{\mathrm{i}\mathrm{j}}\ln {\mathrm{P}}_{\mathrm{i}\mathrm{j}}$$

(4)

i represents different geothermal energy development projects, and j represents specific evaluation indicators. $\mathrm{k}={\displaystyle \frac{1}{\ln \left(\mathrm{n}\right)}}$ satisfying e > 0

Step 4: Calculation of the weights, i.e., the information entropy redundancy of the jth indicator ${\mathrm{g}}_{\mathrm{j}}$.

$${\mathrm{g}}_{\mathrm{j}}=1-{\mathrm{e}}_{\mathrm{j}}$$

(5)

j represents the evaluation index; ${\mathrm{g}}_{\mathrm{j}}$ is the information entropy redundancy of index j, which is used to measure the relative importance of index j. The smaller the ${\mathrm{e}}_{\mathrm{j}}$, the larger the ${\mathrm{g}}_{\mathrm{j}}$, and the more important the index.

Step 5: Calculation of the weight of indicator j ${\mathrm{w}}_{\mathrm{j}}$.

$${\mathrm{w}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{g}}_{\mathrm{j}}}{\sum _{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{g}}_{\mathrm{j}}}}$$

(6)

j represents the evaluation indicator, and m is the total number of indicators. The formula obtains the weight of indicator j by normalizing all the indicators ${\mathrm{w}}_{\mathrm{j}}$.

Step 6: Calculation of the composite score.

$${\mathrm{S}}_{\mathrm{j}}=\sum _{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{w}}_{\mathrm{j}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}$$

(7)

i represents different geothermal energy development projects, and j represents specific evaluation indicators.

The TOPSIS model is a commonly used decision-making method, which ranks multiple evaluation indicators according to the posting progress, making full use of the information in the raw data and accurately reflecting the gaps between the indicators [13].

Step 1: Construction of the normalization matrix.

$$\mathrm{Z}=({\mathrm{z}}_{\mathrm{i}\mathrm{j}})={\mathrm{w}}_{\mathrm{j}}{\mathrm{p}}_{\mathrm{i}\mathrm{j}}$$

(8)

i represents evaluation indicators, and j represents different geothermal energy development projects. The formula utilizes the weights of the indicators obtained by the entropy weighting method ${\mathrm{w}}_{\mathrm{j}}$ s and the values of the indicators after some processing ${\mathrm{P}}_{\mathrm{i}\mathrm{j}}$.

Step 2: Determination of the positive and negative ideal solutions.

Positive ideal solution:

$${\mathrm{Z}}^{+}=\left\{{\mathrm{m}\mathrm{a}\mathrm{x}\mathrm{z}}_{\mathrm{i}\mathrm{j}}|\mathrm{i}=\mathrm{1,2},3,\dots \mathrm{m}\right\}$$

(9)

Negative ideal solution:

$${\mathrm{Z}}^{-}=\left\{{\mathrm{m}\mathrm{a}\mathrm{x}\mathrm{z}}_{\mathrm{i}\mathrm{j}}|\mathrm{i}=\mathrm{1,2},3,\dots \mathrm{m}\right\}$$

(10)

i represents he evaluation indicators, j represents different geothermal energy development projects, and n is the total number of projects.

Step 3: Calculation of the distance of the evaluation indicator from the positive and negative ideal solutions.

$${\mathrm{S}}_{\mathrm{j}}^{+}=\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{({\mathrm{Z}}_{\mathrm{i}}^{+}-{\mathrm{Z}}_{\mathrm{i}\mathrm{j}})}^2} (\mathrm{i}=1, 2, 3\dots \mathrm{n})$$

(11)

$${\mathrm{S}}_{\mathrm{j}}^{-}=\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{({\mathrm{Z}}_{\mathrm{i}}^{-}-{\mathrm{Z}}_{\mathrm{i}\mathrm{j}})}^2} (\mathrm{i}=1, 2, 3\dots \mathrm{n})$$

(12)

i represents evaluation indicators, m is the number of indicators (m = 9), and j represents different geothermal energy development projects.

Step 4: Calculation of the closeness

$${\mathrm{C}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{S}}_{\mathrm{j}}^{-}}{{\mathrm{S}}_{\mathrm{j}}^{+}+{\mathrm{S}}_{\mathrm{j}}^{-}}}$$

(13)

3.4. SWOT Analysis

A SWOT analysis seeks to identify the organization’s strengths and weaknesses, as well as the opportunities and threats in the environment. After identifying these factors, strategies are developed that can build on strengths, eliminate weaknesses, capitalize on opportunities, or address threats [31].

Strengths (S) refer to the internal factors or favorable conditions inherent to an enterprise or project, which enable it to achieve a competitive advantage and distinguish itself in the market. Weaknesses (W) are factors that exist within the enterprise or project that are unfavorable to its development. Opportunities (O) are factors in the external environment that are favorable to the development of the enterprise or project. Threats (T) are factors in the external environment that may adversely affect the enterprise or project. Based on the combination of different dimensions and specific circumstances, various strategies and recommendations are proposed.

3.5. Case Study of Guizhou Province

In this study, four geothermal energy development projects were screened in Guizhou Province, taking into account the characteristics of the geothermal energy resources in the province. The karst landscape covers most of the province, and Zunyi, Guiyang, and Renhuai, selected in this study, all belong to the karst landscape in terms of geologic conditions, with complex geological formations; however, Liupanshui City is a typical coal resource area, with rich underground deposits, and it belongs to a typical uplifted area; thus, the four projects selected can represent the overall geologic conditions of Guizhou, to a large extent [65,66], Some of the data characteristics are shown in

Table 3. Project data characteristics.

Project	Project Location	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11
A	Zunyi City, Guizhou Province	110,000	75,003	4.11	2.433	0.411	7600	0.56296	7200	6300	7300	53
B	Renhuai City, Guizhou Province	73,644	58,868	4.11	2.433	0.411	3438	0.32131	5200	5500	5500	50
C	Liupanshui City, Guizhou Province	67,413	569,433	4.73	2.114	0.473	5400	0.40329	6500	6890	4950	55.2
D	Guiyang City, Guizhou Province	105,000	85,000	4.17	2.398	0.417	3450	0.4589	3592	3926	3926	50

. Based on the indicator structure determined by the entropy weight TOPSIS method, the benefits of different projects in terms of technical, economic, and environmental indicators were analyzed. Among them, Project A is located in Zunyi City, Guizhou Province, and is home to the first air-conditioning system in Zunyi that uses shallow geothermal energy (buried pipe ground-source heat pump) for heating and cooling. Project B is located in Renhuai City, Guizhou Province, and it contains a huge amount of shallow geothermal energy; this project alone can supply 200,000 square meters of buildings with summer cooling, winter heating, and domestic hot water. Project C is located in Liupanshui City, Zhongshan District, which is currently the first relocation site for poverty alleviation in Guizhou Province to adopt a shallow geothermal energy system to solve the problem of mass heating requirments. Project D is located in Guiyang City, Guizhou Province, and is the first shallow geothermal energy development and utilization project in Guizhou to use ground source heat pump technology to provide cooling/heating for buildings.

4. Results

4.1. Entropy Weighted TOPSIS Model Measurement Results

When assessing the geothermal potential of different regions of the country, the results obtained based on the entropy weighting method reveal that the number of land-based heat flow data registrations exhibit the highest weighting of 20.09% (Figure 2), indicating that geothermal resources still exert the greatest influence on the geothermal potential between different regions.

According to the evaluation results in

Table 4. Progress of posting in different regions.

District (Not Necessarily a Formal Administrative Unit)	Closeness	Arranged in Order
Northeast China	0.27726861	7
North China	0.59237353	1
East China	0.49450098	2
South China	0.39092218	4
Southwest China	0.34761606	6
Northwest China	0.48099566	3
Central China	0.38431618	5

, the posting progress of different regions fluctuates significantly, and overall, it seems that there is a clear gap between the development potential of shallow geothermal energy in different regions, and the development potential of shallow geothermal energy is the largest in North China. The reason for this may be due to North China’s unique resource advantages and population density in determining the larger market demand; the new shallow geothermal building area accounted for a high proportion of the accumulated economies of scale and better technology, which can also stimulate a certain market demand. Meanwhile, due to the climate, the demand is higher in summer, and conditions such as precipitation and soil moisture also provide better development conditions.

When evaluating the projects in Guizhou Province, it can be found in the results calculated based on the entropy weighting method that the environmental level is weighted more for the heating and cooling area (21.82%), the economic level is weighted more for the payback period (7.05%), and the technological level is weighted more for the heat exchange of the extended meter (14.94%), which is shown in Figure 3.

If the evaluation criterion closeness is higher, it indicates that the project utilizes shallow geothermal energy more effectively; if the closeness is lower, it is less effective [65]. According to the evaluation results in Figure 4, the closeness of the four projects fluctuates less, and overall, all four projects are more perfect in the exploitation of shallow geothermal energy. Project C exhibits the best performance in the overall score, and its high development potential is supported by the advantages of the technical aspects (especially the extended meter heat exchange) and the heating and cooling area. Project D ranked the lowest, mainly due to its low technical indicators, which did not effectively improve its overall score, despite its large heating and cooling area. Projects A and B displayed similar overall scores, but the technical advantages of Project B gave it a slight edge in the overall potential evaluation.

It can be seen that all three levels have some influence on shallow geothermal energy. Economic growth is the way to achieve sustainable development, but the cost of energy consumption has been an obstacle to energy development and utilization. The development of shallow geothermal energy not only reduces the unit price of fuel but also produces heat of high value. At the same time, the technical aspect has a greater impact on the development of shallow geothermal energy because the technology determines the utilization rate of shallow geothermal energy and the degree of utilization, which needs to be taken into account in the practical application of this resource.

4.2. SWOT Analysis and Recommendations for Different Regions and Guizhou Province

As shown in

Table 5. SWOT analysis of shallow geothermal development potential in East China.

	Advantage (S)	Weaknesses (W)
	East China boasts 538 land heat flow registers, forming a solid geothermal base. Its high population density generates strong energy demand and vast market potential, enabling large-scale geothermal development.	In East China, the relatively high electricity price increases the operating costs of geothermal heating projects, weakens their market competitiveness, and limits the large-scale promotion of shallow geothermal energy.
Opportunities (O)	SO Strategy	WO Strategy
It has the highest overall development potential among the different regions and is the policy support from the central government; with the “dual carbon” target, society’s demand for clean energy will continue to grow.	With rich geothermal resources and high energy demand in East China, the company, backed by policies, expands shallow geothermal development. Through demonstration projects, it aims to capture market share and promote regional development. Aligning with the “dual-carbon” goal and local advantages, it will focus on shallow geothermal development to attract industrial clustering.	To tackle high electricity costs hindering East China’s shallow geothermal development, we will seek government subsidies, strengthen domestic and international tech cooperation, drive joint R&D with local partners, cultivate talent, and boost technological self-sufficiency.
Threat (W)	ST Strategy	WT Strategy
As the clean energy market grows, new energy technologies emerge, competing with shallow geothermal energy in some regions. Despite favorable overall resources in East China, complex local geology raises exploration and development challenges, costs, and risks.	Facing competition from other new energy sources, the weather-independent and stable advantages of shallow geothermal energy are leveraged to offer differentiated services, expanding market share. Resource and market strengths are utilized to boost investment in geological exploration and development technology, enhancing project reliability and stability.	To address cost risks from high electricity prices and complex geology, we have tightened cost management and optimized project design and construction to cut expenses. We have also ramped up tech innovation investment, sharing achievements and reducing R&D costs through cooperation to boost industry resilience and market edge.

and

Table 6. SWOT analysis of shallow geothermal development potential in Southwest China.

	Advantage (S)	Weaknesses (W)
	Southwest China has significant geothermal resources. The favorable electricity price reduces the operating costs of shallow geothermal heating projects, enhancing the market competitiveness of shallow geothermal energy.	The region lags in shallow geothermal energy development. Low population density shrinks the market, and the local climate offers no thermal advantage, weakening geothermal demand.
Opportunities (O)	SO Strategy	WO Strategy
Southwest China, rich in tourism resources, sees clean energy heating/cooling demand in scenic spots and towns. With strong provincial policies and the “dual-carbon” goal driving clean energy demand, it can seize the opportunity to accelerate energy transition.	With its geothermal resource base, policy support, and 242 land-based heat flow data registrations, Southwest China should leverage these strengths, develop a specialized shallow geothermal energy plan, integrate geothermal development with tourism, and expand the industrial and commercial shallow geothermal market using its electricity price cost advantage.	Utilize policy support to address development gaps, seeking more financial subsidies and tax incentives. Leverage policy advantages to actively introduce advanced domestic and foreign shallow geothermal development technologies and management experience, establishing cooperation mechanisms to reduce external dependencies. Strengthen publicity to enhance public awareness and acceptance, cultivating market demand.
Threats	ST Strategy	WT Strategy
To tackle weak tech and market competition, increase R&D investment, and encourage joint research. For complex geology and talent scarcity, establish a risk-prevention mechanism, strengthen surveys, and pre-assess risks. To expand the market, target surrounding areas, cooperate with neighbors, and export technologies and services.	Southwest China should capitalize on the weather-resilience and high stability of shallow geothermal energy, promoting it in areas with high energy-stability demands. Leveraging its geothermal resource advantages, it should increase investment in geological exploration and development technologies. For complex geology, conduct targeted research and employ advanced survey techniques. Also, utilize favorable electricity prices to optimize the energy cost structure of shallow geothermal projects.	To address weak technology and market competition, it boosts R&D investment and promotes joint research between enterprises and institutions. Facing complex geology and talent shortages, it sets up a risk control mechanism, beefs up geological surveys, and anticipates geological risks. To expand the limited market, it actively reaches out to surrounding areas, collaborates with neighboring provinces, and exports shallow geothermal technologies and services to widen the market reach and boost industry influence.

, by comparing the SWOT analysis of shallow geothermal development potential in East China and Southwest China, it can be observed that both Southwest China and East China have a good foundation of geothermal resources, but the geothermal resources in East China are better. Southwest China offers specific advantages in regards to electricity price, which can reduce the cost to a certain extent, but Southwest China still exhibits specific deficiencies in stimulating the demand for shallow geothermal energy. The topography of the two regions may pose challenges to the promotion of shallow geothermal energy. At the same time, Southwest China is rich in tourism resources, so it has a greater advantage in regards to expanding the shallow geothermal market and promoting energy transition. Because of the difference in the level of economic development between the two regions, East China has advantages concerning talent retention and technological innovation breakthroughs. Based on the strengths and weaknesses of the two regions and the opportunities that exist, this study also carries out a SWOT analysis of the future development potential for shallow geothermal energy in Guizhou Province and puts forward the corresponding opinions via the strengths and weaknesses of the two regions and the opportunities that exist, as shown in

Table 7. SWOT analysis of shallow geothermal development potential in Guizhou Province.

	Advantage (S)	Weaknesses (W)
	Guizhou has a solid foundation of shallow geothermal resources, with its electricity price offering a cost-effective edge. Boasting abundant tourism resources, it also holds significant market potential. Moreover, the province’s policies provide robust support, all of which contribute to favorable conditions for shallow geothermal energy development.	In Guizhou, low population density in some areas reduces demand, hindering large-scale development. Its mild climate reduces geothermal heating and cooling needs. Complex topography raises development difficulty and costs. Additionally, talent attraction and retention are challenging, with heavy reliance on external tech and talent and weak independent innovation.
Opportunities (O)	SO Strategy	WO Strategy
Driven by the “double carbon” goal, the demand for clean energy is rising. Guizhou can leverage this to speed up energy transformation. With neighboring provinces needing clean energy, Guizhou can expand into those markets. Moreover, as the nation focuses more on Southwest energy development, Guizhou stands to gain more resources and policy support.	Leverage Guizhou’s abundant geothermal resources and policy support to create a provincial shallow geothermal development plan with defined goals and routes. Integrate shallow geothermal development with tourism, capitalizing on the province’s rich tourist resources. Use the electricity price cost advantage to expand the shallow geothermal market, attracting enterprises across sectors. Seize the national support for Southwest energy development.	Actively strive for more financial subsidies and tax incentives to reduce development costs; take advantage of the policy to introduce advanced shallow geothermal development technology and management experience at home and abroad and cultivate local technical talents; increase the publicity and promotion of shallow geothermal energy; improve the awareness and acceptance of residents and enterprises; and cultivate local market demand.
Threat (T)	ST Strategy	WT Strategy
Competition in the clean energy market is fierce; complex terrain increases the difficulty and risk of shallow geothermal development, which may encounter technical difficulties and difficulties in attracting and retaining talent, leading to a shortage of talent.	Exploit the high stability of shallow geothermal energy to expand the market for Guizhou’s data centers. Conduct targeted geological research on Guizhou’s complex terrain, using advanced exploration tech to boost efficiency. Leverage electricity prices to optimize project energy costs and increase competitiveness. Strengthen cooperation with neighboring provinces to integrate resources and enhance the overall competitiveness of the regional shallow geothermal industry.	Boost R&D investment, and encourage enterprise–research institute collaborations to develop terrain-adaptive tech, reducing risks. Intensify geological surveys, pre-assess risks, and plan contingencies. Offer preferential policies to attract, retain, and cultivate talent. Strengthen cross-provincial cooperation and export tech and services to expand market reach and industry influence.

. According to Table 7, corresponding policy recommendations are made for Guizhou in terms of energy, planning, policy, demand, and development direction to help Guizhou province better utilize and develop shallow geothermal energy.

4.3. Detailed Comparison of Projects in Central and Southwest China

Based on the data availability, East China, which displays better development potential, was chosen for a comparative analysis with Southwest China, as shown in

Table 8. Comparison of detailed project indicators for Central and Southwest China.

Norm	Dongying District Project, East China	Guiyang City Project, Southwest China
Total building area m2	252,400	105,000
Gas price CNY/m3	4.28	4.17
Total heat load kW	20,598	3926
Project investment, CNY million	5100	3450
Coal saving t	8800	2520
Carbon dioxide emissions t	21,600	6405
Other business directions	Heating of agricultural facilities	not have

. Leveraging the experience of the Dongying District Project, the Guiyang City Project could explore opportunities for active collaboration with neighboring agricultural facilities to provide heat supply services. In addition to facility agriculture, the Guiyang City Project should further explore additional applications, such as those in industry and commerce and similarly, the feasibility of providing heating services for other sectors, customizing geothermal heating solutions for different sectors according to their requirements, replacing some of the traditional high energy-consuming heating methods and reducing the cost of production for enterprises. Compared with the Dongying District Project, the Guiyang City Project exhibits a relatively high investment per unit heat load. Therefore, the Guiyang City project should pay more attention to the reasonableness of the operation of the project during the subsequent construction and operation processes, conducting sufficient feasibility studies and cost-benefit analyses during the project planning stage and optimizing the project design scheme to avoid the unnecessary wastage of investments. The Guiyang City project originally exhibited a certain advantage over the Dongying District project in terms of operating costs due to lower gas costs, and it can further reduce operating costs. In addition to technological innovation, the Guiyang City project should also strengthen the energy savings and emission reduction publicity, improve the public’s awareness and recognition of shallow geothermal clean energy by optimizing the operation and management process, and highlight the advantages of shallow geothermal energy in regards to energy saving and emission reduction by engaging in publicity activities and constructing demonstration projects to guide more enterprises and residents to use shallow geothermal energy. It should also promote the development of Guiyang City’s clean energy industry in order to increase the demand for shallow geothermal energy.

4.4. Assessment of Economic Benefits

In this study, the entropy weight method and TOPSIS model comprised a comprehensive evaluation system for the development potential of shallow geothermal energy and initially assessed the development potential of shallow geothermal energy in Guizhou Province from the technical, economic, and environmental aspects. Specifically, the weights of the indicators were objectively calculated through the entropy weighting method, which clarified the degree of influence of the technical, economic, and environmental dimensions on the development potential and provided solid data support for the subsequent evaluation results. Based on this evaluation system, this study will further practically test the weights and impacts of the economic dimension in the model by evaluating the economic benefits of the Guizhou Science and Technology Park project in Guizhou Province.

The Science and Technology Park in Guizhou is selected as the research object, and the selected building uses a ground source heat pump system for cooling and heating. The total building area of this science and technology park is 105,333.80 m².

The project has been in operation since 2017. This study selects the 2019–2021 project operating data; the project operating costs mainly include electricity, water maintenance, and management costs, as shown in

Table 9. Study regarding the operating costs of ground source heat pumps in the Guizhou Science and Technology Park in Guizhou Province.

Ground Source Heat Pump Operating Costs in the Guizhou Science and Technology Park in Guizhou Province
Vintages	Operating Mode	Running Time (h)	Power Consumption (kW/h)	Electricity (CNY)	Water Consumption (tonnes)	Water Charges (yuan)	Maintenance Overheads (CNY)	Total Cost (CNY)	Air-Conditioned Area (m2)	Running Time (months)	Operating Unit Cost (CNY/month/m2)
2019	Refrigeration	760	1,132,932	1,146,225	776	3647.2	410,000	971,248.86	47,200	2.5333	2.5
	Heat production	1706	373,978.6	411,376						5.6866
2020	Refrigeration	3396.5	333,401	366,741	195	916.5	410,000	1,711,480.6	79,600	4.7173	1.95
	Heat production	4560	848,930	933,283						6.3333
2021	Refrigeration	2010	111,389	122,528	283	1330.1	580,000	1,241,556.7	79,600	2.8	2.51
	Heat production	2464.5	488,817	537,699						3.4229

. With the increase in the occupancy rate of the park and the increase in the area used for air conditioning, the running costs of the project are lower and gradually stabilize. According to the statistics, the energy consumption and acquisition of the ground source heat pump air-conditioning system in the park can reach 1:4:6, i.e., for every 1 kW of energy consumed, more than 4 kW of heat or more than 6 kW of cold can be obtained, the running cost is reduced by about 50%, and the amount of coal burned can be reduced by 2520 tonnes per year. The economic cost savings also bring environmental benefits, conducive to reducing carbon dioxide emissions by 6405 tonnes, reducing dust emissions by 1680 tonnes, and reducing sulfur dioxide emissions by 189 tonnes. The service life of the project is 50 years, and the specific payback period needs to be constant according to the area of the project. In short, the project is stable in operation and can produce good economic and environmental benefits.

The operational data of the project show that the ground source heat pump system results in significant effects regarding reducing operational costs, saving energy consumption, and reducing carbon emissions, which further confirms the importance of economic indicators in the assessment of the overall development potential. This linkage not only verifies the scientific and practical elements of the previous indicator system but also provides a quantitative economic basis for the practical application of shallow geothermal energy, thus strengthening the theoretical framework of the interaction between the three dimensions of technology, economy, and environment.

5. Conclusions

Against the background of accelerating globalization and the urgency of the global energy transition, this study provides an in-depth assessment of the development potential of shallow geothermal energy in different regions of China and contributes important Chinese experiences and strategies for global, sustainable energy development.

This study innovatively integrates the entropy weight method and the TOPSIS model to accurately assess the development potential of shallow geothermal energy in different regions of China. The results show that North China is the leading region in terms of development potential due to its rich geothermal resources and huge market demand, while other regions, including Guizhou Province, show different development dynamics due to differences in resource endowment, economic development level, technological conditions, and policy environment. This significant difference between regions echoes the common phenomenon of uneven global energy distribution, providing a typical case for the development of energy development strategies on a global scale, according to local conditions.

Guizhou Province, as the study object, is rich in shallow geothermal energy reserves, but its development faces many challenges, such as uneven population density, restricted climatic conditions, complex topography, and a shortage of technical personnel. The entropy-weighted TOPSIS model was used to evaluate several projects in Guizhou, clarifying the impact weights of different indicators and providing a scientific basis for project optimization. This case is a valuable reference for other regions with similar resource conditions and development difficulties as they explore their own shallow geothermal energy development paths. Through the empirical study of the economic and environmental benefits of the Guizhou Science and Technology Park project, it has been verified that shallow geothermal energy development not only brings significant economic benefits, such as lower operating costs, but also has outstanding environmental benefits, which can significantly reduce coal consumption and pollutant emissions. This is highly compatible with the global sustainable development goals and provides strong practical support for global energy transition, encouraging more countries and regions to increase their investment in the development of clean energy, such as shallow geothermal energy, and promoting the transformation of the global energy structure in green and low-carbon directions.

This study provides new research ideas and methods in the field of shallow geothermal energy development. In the future, with the acceleration of globalization and the increasing international energy cooperation and exchange, the research scope can be further expanded, and international cooperative research can be strengthened. By combining the characteristics of different countries and regions, we can improve the evaluation index system and explore in depth the interaction between shallow geothermal energy development and global climate change, energy market fluctuations, and other factors in order to provide more comprehensive and in-depth theoretical support and practical guidance for the sustainable development of global energy.