Willingness to Pay for Geothermal Power: A Contingent Valuation Study in Taiwan

Abstract

Geothermal energy provides a stable baseload renewable source that is less affected by weather variability compared with solar and wind power, and is therefore increasingly considered in national energy transition and net-zero strategies. Yet its environmental externalities and associated social benefits are not fully priced in existing electricity markets, raising the question of how much the public is willing to pay for geothermal-based generation. This study applies non-market valuation theory to estimate citizens’ additional annual electricity payment required to replace coal-fired generation with geothermal energy. A contingent valuation method (CVM) survey was conducted through face-to-face interviews, employing a closed-ended single-bounded dichotomous choice format with incentive compatibility. Stratified random sampling yielded 678 valid observations. The estimated mean willingness to pay (WTP) per person per year is USD 56.18 (NTD 1792) under the Probit model and USD 52.16 (NTD 1663) under the Logit model, representing approximately 0.2–0.3% of average annual income and 16–20% of the average annual electricity bill. Aggregated to the population level, total annual WTP amounts to USD 688 million (NTD 21,934 billion; Probit) and USD 638 million (NTD 20,355 billion; Logit). These estimates correspond to support for developing approximately 108–335 MW of geothermal capacity, sufficient to supply around 202,000–624,000 four-person households. The findings indicate substantial public support for geothermal power as part of Taiwan’s renewable energy transition, and provide empirical evidence relevant to regions with comparable geothermal potential.

1. Introduction

Accurately valuing renewable energy sources is critical for guiding strategic investment, informing effective policy design, and fostering public acceptance. This study presents the first empirical assessment of geothermal power generation value utilizing the WTP methodology. Beyond quantifying established social benefits, we investigate public perspectives on geothermal energy, providing insights into broader acceptance factors and potential economic value beyond conventional metrics.

Renewable energy sources, naturally replenished on a human timescale, encompass solar, wind, hydropower, geothermal, biomass, tidal, wave, and hydrogen energy. Currently, these sources contribute approximately 15% to global energy provision, with wind, solar, and hydropower representing the dominant contributions. Future investment and development are projected to yield several-fold increases, particularly in wind, solar, biomass, and geothermal energy. This growth is crucial for diversifying energy portfolios and achieving sustainable energy systems [1,2,3].

The share of renewable energy is projected to increase annually, with solar, wind, and hydropower currently dominating development. While these sources lead in current deployment, geothermal energy offers a viable baseload power option and is therefore expected to remain a key component of the global energy portfolio. Its potential as a reliable baseload resource is particularly relevant given the increasing need for grid stability as intermittent renewables gain market share [4].

Global geothermal power generation capacity has expanded significantly, growing from 1.3 GW in 1975 to 10,715 GW in 2010, and is projected to supply over 3% of global electricity demand by 2050 [5]. Recognized by the International Energy Agency (IEA) as a significant global renewable energy source, installed geothermal capacity reached 16.4 GW in 2021 [6], demonstrating the continued growth and potential of this resource.

Taiwan’s location within the seismically active circum-Pacific belt and its associated geological activity provide significant geothermal resources. Numerous heat sources and substantial geothermal gradients contribute to an estimated exploitable potential of 159.6 GW at depths of 4000 m [7]. This potential is concentrated in four key areas: Yilan, the Tatun Volcanic Group, the Hualien–Taitung geothermal area, and Lushan [8,9,10].

Geothermal power generation offers significant advantages in both energy conversion efficiency and carbon emissions compared to fossil fuel-fired power generation. Studies demonstrate that the carbon footprint of geothermal electricity generation is approximately 10% of that from conventional coal-fired plants [5]. Beyond its environmental benefits, geothermal development also contributes to economic growth by creating employment opportunities and fostering local economic development [11].

Despite its substantial benefits, the development of geothermal energy faces significant obstacles stemming from public concerns regarding potential environmental impacts, landscape alteration, and induced seismicity. Addressing these concerns and gauging public perception and acceptance are therefore vital for the successful implementation and sustained growth of the geothermal industry [12].

This study employs the CVM to determine the Taiwanese public WTP for geothermal power generation. WTP, a key indicator of consumer preference and market value [13], represents the maximum amount an individual is willing to pay for a specific good or service. Analyzing Taiwanese public WTP will enable us to evaluate public expectations regarding geothermal energy and the level of support they are willing to provide for the development of the geothermal industry. The research findings will provide a scientific basis for government policy decisions, including adjustments to feed-in tariffs, tax incentives, and geothermal energy education initiatives.

This study investigates the following objectives:

(1)

to quantify the WTP for geothermal power generation among Taiwanese citizens using the CVM;

(2)

to identify and analyze the factors influencing WTP, including demographic variables such as age, income, and education level;

(3)

to benchmark Taiwan’s WTP against international benchmarks;

(4)

to develop evidence-based policy recommendations to facilitate the expansion of the geothermal industry in Taiwan, based on the findings of the WTP analysis.

This research significantly advances the development of Taiwan’s geothermal industry and supports evidence-based policymaking regarding geothermal renewable energy. Specifically, this study quantifies public WTP for geothermal power in Taiwan, providing a robust empirical basis for policy formulation. Furthermore, it proposes practical and feasible policy recommendations to foster a sustainable geothermal industry and establishes a rigorous methodology for evaluating the expected economic value of geothermal power generation. These contributions will inform both industry development and effective government policy regarding geothermal resources.

2. Literature Review

Geothermal energy is a stable and renewable energy source with significant potential to reduce reliance on fossil fuels, mitigate carbon emissions, and contribute to sustainable development. Its utilization dates back millennia, with evidence of early applications by Native American communities for cooking and bathing, and similar practices documented among the ancient Romans and Chinese for bathing and therapeutic purposes [14]. While historically utilized for direct applications, the Industrial Revolution in the 19th century spurred a transition towards more technologically advanced approaches. In 1827, Larderello, Italy, pioneered the industrial use of geothermal steam, initially for boric acid extraction [15]. This innovation culminated in the construction of the world’s first geothermal power plant at Larderello in 1911, marking a significant milestone in the development of geothermal energy as a viable electricity source [16].

Currently, the predominant technologies for geothermal power generation are dry steam, flash steam, and binary cycle systems [17,18]. Enhanced Geothermal Systems (EGS) and Organic Rankine Cycle (ORC) are also widely implemented methods for harnessing this resource [19,20,21]. Emerging applications, including closed-loop geothermal power generation and Complex Energy Extraction from Geothermal Resources (CEEG), offer the potential to expand geothermal contribution to the energy mix. These advancements, while promising, require continued research and development to improve efficiency and reduce the levelized cost of energy [22,23].

Dry steam technology represents the oldest and most efficient method, directly utilizing geothermal steam exceeding 150 °C to drive turbine generators; however, its application is limited by the scarcity of readily available high-temperature resources. Currently, flash steam technology is the most widely implemented, suitable for reservoirs containing liquid-vapor mixtures. When fluid temperatures exceed 180 °C, a rapid decrease in pressure induces flash evaporation, separating steam for turbine-driven power generation. Binary cycle technology offers a viable alternative, particularly for lower-temperature geothermal resources. This approach utilizes a working fluid with a lower boiling point than water, transferring thermal energy via a heat exchanger to drive a turbine generator, typically exhibiting efficiencies ranging from 10% to 13%. EGS represents a promising avenue for expanding geothermal capacity by creating engineered reservoirs through the drilling of two deep wells. Cold water is injected into one well, circulated through hot dry rock formations to absorb heat, and then extracted as heated water and steam from the second well to power a turbine generator. Closed-loop systems further mitigate environmental risks and operational challenges by establishing a fully contained pipeline network where water is injected, heated, and recirculated, minimizing the risks of formation water contact, geological constraints, and scaling/corrosion issues [24].

2.1. International Development of Geothermal Energy

Globally, significant progress in geothermal energy development has been achieved by numerous countries, including the United States, Iceland, and New Zealand, each establishing unique technological approaches and policy frameworks. As of 2022, China accounted for approximately 90% of global direct geothermal utilization, with the European Union representing the majority of the remaining proportion. Leading countries in both direct geothermal utilization for heating/cooling and electricity generation include the United States, Sweden, Turkey, and Japan; Iceland, Sweden, Finland, and Norway demonstrate the highest per capita utilization rates [25]. Approximately 29 countries or regions worldwide currently generate electricity from geothermal sources, with the United States, Indonesia, the Philippines, Turkey, and New Zealand serving as the primary producers [18,25,26,27]. The technologies employed in these countries encompass flash steam and binary cycle systems, with prevalent geological conditions including volcanic belts and areas with frequent geological activity.

The various geothermal development technologies and their representative countries are listed in

Table 1. Geothermal Technology Adoption by Country: A Comparative Overview.

Generation Technology	Rationale for Adoption	Representative Countries	Country-Specific Rationale
Dry Steam Generation	Geological factors: Limited availability of dry steam resources, concentrated in volcanic regions. Efficiency: High efficiency suitable for direct utilization of high-temperature steam.	Italy	Early adopter with abundant dry steam resources and mature technology.
		United States	Possesses efficient dry steam generation facilities (e.g., Geysers Geothermal Field).
Flash Steam Generation	Geological factors: Suitable for aquifers with steam-water mixtures, requiring fluid temperatures above 180 °C. Prevalence: Most common geothermal generation method, with mature and reliable technology.	New Zealand	Multiple high-temperature geothermal fields well-suited for flash steam technology, with established expertise.
		Philippines, Iceland	Geothermal power is a primary energy source; widespread flash steam application positions them as leaders in Asian geothermal development.
Binary Cycle Generation	Cost considerations: Effectively utilizes low-temperature geothermal resources, suitable for low-to-medium temperature geothermal fields. Efficiency: Thermal efficiency between 10 and 13%, adaptable to diverse geothermal resources.	United States	Effective utilization of low-to-medium temperature geothermal resources with ongoing technological advancements.
		Germany	Policy support for renewable energy drives the development of binary cycle technology, enabling utilization of diverse geothermal resources.
Enhanced Geothermal Systems (EGS)	Geological factors: Enables development of low-permeability hot dry rock, expanding the range of exploitable geothermal resources. Policy support: Government support and investment in renewable energy.	United States	Possesses advanced technology and research capabilities for developing previously untapped geothermal resources.
		Australia	Government support for EGS facilitates technological development.
Organic Rankine Cycle (ORC)	Cost considerations: Suitable for low-temperature heat sources, improving generation efficiency and reducing operating costs. Environmental: Avoids contact with formation water, minimizing pollution risk.	United States, Turkey, New Zealand	Abundant geothermal resources.
		Germany, Austria	Promotion of environmental policies aligns with the Sustainable Development Goals facilitated by ORC technology.

[14,28,29,30,31,32,33].

2.2. Geothermal Energy Development in Taiwan

Taiwan possesses substantial geothermal resources, particularly concentrated in the Qingshui, Tucheng, and Tatun Volcanic Group areas. Over half of the island exhibits a high geothermal gradient—exceeding 40 °C per kilometer, a rate at least 10 °C higher than in many other countries—enabling the attainment of 200 °C thermal energy at depths of approximately 4000 m [34,35]. This favorable geological condition makes both deep and shallow geothermal development economically viable. Advancements in drilling technology are unlocking the potential of these resources, offering a pathway to enhance Taiwan’s energy independence, increase its proportion of renewable energy, and establish a reliable baseload power source [36].

Geothermal energy development in Taiwan centers on two primary applications: geothermal power generation and direct utilization. While power generation currently represents the dominant share of geothermal energy use, direct utilization offers significant potential for sustainable development and diversification of the energy sector. Direct applications span multiple sectors, including agriculture, where geothermal heating enhances crop yields; building climate control, providing energy-efficient heating and cooling solutions; and tourism, leveraging Taiwan’s abundant hot spring resources to attract visitors. These diverse applications demonstrate the potential of geothermal energy to contribute to both economic growth and environmental sustainability in Taiwan [37].

Taiwan possesses substantial geothermal resources, with the Central Geological Survey and Mineral Resources Center (formerly the Central Geological Survey), under the Ministry of Economic Affairs, having compiled data from 405 wells nationwide. A 2023 evaluation of this data estimates a geothermal power generation potential of up to 40 GW at depths of 3–6 km across ten key geothermal areas: Datun Mountain, Qingshui Tucheng, Lushan, Ruihui, Wulu Yanping, Jhiben Jinlun, Baolai, Guanziling, Miaoli, and Dongpu. Taiwan’s ambitious geothermal development strategy targets 20 MW of installed capacity by 2025, 200 MW by 2030, and 6 GW by 2050. As of early 2024, 24 geothermal sites are under development across nine areas, with five operational power plants currently supplying 7.29 MW to the grid [38,39].

2.3. Utilizing WTP to Evaluate the Value of Renewable Energy and Geothermal Energy

Accurately valuing renewable energy, particularly geothermal resources, is paramount for informed economic policy and strategic energy planning. Robust value estimation is fundamental to determining the economic viability of these sources and fostering a sustainable energy transition. Comprehensive value assessment empowers policymakers to identify the benefits and risks associated with diverse energy portfolios, enabling the development of targeted policies and investment strategies. In the context of escalating global and national commitments to carbon reduction, geothermal energy—uniquely positioned by its geological characteristics—offers a compelling opportunity as a stable baseload power source. This can effectively address the intermittency challenges of solar, wind, and other renewable energies, thereby strengthening energy security. Consequently, a thorough assessment of potential value is not merely a prerequisite, but a key driver for the advancement of renewable energy [40,41].

WTP is a crucial economic evaluation technique for comprehensively assessing the benefits of geothermal renewable energy development. Unlike traditional project evaluations limited to construction and operational costs, WTP captures the full value beneficiaries derive from project outcomes, a particularly important consideration for public goods like renewable energy where benefits extend beyond individual consumption. In geothermal projects, WTP enables the quantification of key benefits, including reduced carbon emissions, enhanced energy security through decreased reliance on volatile fuel markets, and local economic development [42,43]. Traditional cost–benefit analysis often struggles to adequately capture these non-market benefits, making WTP a vital evaluation tool [44]. Illustrative examples of WTP application include the first survey regarding renewable energy introduction in Myanmar [45], an assessment of public WTP for offshore wind power compared to nuclear power in Taiwan [46], and an analysis of preferences for community solar projects in South Korea to inform national energy policy [42]. The CVM offers a flexible framework for designing policy options and incorporating future benefits and abstract values like environmental protection. CVM provides concrete monetized data, serving as robust empirical evidence for government energy policy and resource allocation, effectively balancing economic benefits and social acceptance.

WTP economic analysis is increasingly recognized as a powerful tool for quantifying non-market environmental values and informing energy policy. Evidence from multiple studies demonstrates a consistent WTP for a premium for renewable energy; for example, Japanese households exhibit a median WTP of $17 [47], while residents of eastern Canadian provinces (New Brunswick, Nova Scotia, and Prince Edward Island) indicate a monthly premium of $5.85 for wind energy [48]. The CVM is a widely adopted WTP technique that employs questionnaire-based hypothetical scenarios to elicit public preferences regarding specific policies or environmental improvements. CVM effectively captures both use and non-use values, making it particularly well-suited for valuing public goods like renewable energy, which lack readily observable market prices [49]. Existing research demonstrates CVM’s capacity to gauge public support for geothermal resource protection and development; for instance, [50] successfully measured the social value of protecting high-temperature geothermal areas in Eldvörp and Hverahlíð, Iceland. Furthermore, CVM has been applied to assess the multifaceted benefits of geothermal energy—including carbon reduction, air quality improvement, and household heating—underscoring its potential to inform policy decisions [51]. By integrating environmental externalities into economic analysis through an ecosystem services lens, CVM provides a robust framework for evidence-based decision-making [52].

Taiwanese research comprehensively assesses the economic value of renewable energy integration into the power sector, encompassing technical, policy, management, and social considerations. Studies have addressed key aspects of wind power, including optimal site selection and cost–benefit analysis [53], and the economic viability of offshore wind investments [54]. Solar power research has focused on maximizing investment returns [55,56] and assessing the technical and economic feasibility of large-scale deployment [57]. Beyond these established technologies, research has explored innovative approaches such as biomass generation from chicken manure [58], the production of solid recovered fuel (SRF) from rice straw—offering a sustainable alternative energy source and promoting circular economy principles in agriculture [59]—and policies to incentivize small hydropower development [60]. Geothermal energy research has investigated the economic potential of industry investment [61] and optimized energy use efficiency through engineering parameter analysis [62,63]. This research portfolio comprehensively addresses mainstream renewable options, including the industrial value generated by offshore wind farms [64] and integrated life cycle sustainability assessments of solar energy, considering environmental, economic, and social impacts [65].

Assessing public support for renewable energy is crucial for its successful deployment, and WTP methods offer a valuable tool for quantifying its social value. A comprehensive meta-analysis by [66], encompassing 63 studies from 28 countries, revealed a global average WTP for renewable energy ranging from €113 to €124 per year. Furthermore, [67] demonstrated that consumer WTP is significantly higher for solar and wind energy compared to hydro and biomass, and that this preference strengthens with increasing levels of renewable energy integration into the energy mix. This positive correlation suggests that as renewable energy becomes more prevalent, public willingness to support its further development also increases.

In recent years, meta-analyses using the OLS method have found the average household WTP for renewable electricity to be between 48 USD and 96 USD per year [68]. This evidence strongly suggests WTP is a good technique to evaluate development value, even serving as a policymaking tool. Separately, systematic reviews highlight technical, socioeconomic, and governance barriers specific to geothermal district heating [69].

In Taiwan, academic research utilizing the CVM has explored public WTP for renewable energy. Specifically, Chen [70] estimated the total annual Taiwanese public WTP for mitigating global warming at approximately NT$106.8 billion. Chung [71] found that the average Taiwanese public WTP to avoid the impacts of global warming was approximately NT$1316.1, resulting in an estimated total national WTP of approximately NT$10.4 billion. These studies collectively demonstrate public support for renewable energy in Taiwan, with WTP significantly correlated with factors such as energy usage attitudes, energy conservation behaviors, education level, and income level.

2.4. Valuation of Emerging Renewable Energy and Its Connection to National Policy

Despite the significant potential of renewable energy technologies, their widespread adoption is currently constrained by high upfront costs, developing supply chains, and limited cost competitiveness relative to established fossil fuels. Consequently, proactive government policies are essential for accelerating deployment through economic incentives, risk mitigation, robust research and development funding, and a commitment to environmental sustainability [72]. Several nations demonstrate this commitment through various policy instruments. The United States, for example, utilizes tax credits, grants, and feed-in tariffs to support the deployment of renewable energy microgrids and energy storage systems [73]. Germany’s pioneering fixed feed-in tariff (FIT) system for solar photovoltaic power generation has been instrumental in fostering the diffusion and development of these technologies, with policy evolution intrinsically linked to technological innovation [74]. Critically, the effective design of price adjustment mechanisms within these FIT systems remains a key challenge for ensuring reliable electricity supply, as demonstrated by ongoing considerations in Japan [75]. For taking geothermal energy as a sustainable source also discussed in terms of environmental and social economics, emphasizing its potential, extraction technologies, geothermal power plant, difficulties, potential areas for further research, and suggestions for its broad use [76].

Taiwan has actively pursued the expansion of renewable energy capacity through a series of policy measures anchored by the Renewable Energy Development Act. This legislation establishes a target of 27GW of installed renewable energy capacity by 2025, supported by key economic incentives, notably feed-in tariffs. These policies are designed to stimulate investment in renewable energy projects and mitigate financial risks for developers [77].

Beyond employing financial rationale in subsidy policy design, understanding public valuation of renewable energy development—as quantified through WTP research—is crucial for ensuring policy effectiveness and public acceptance. This knowledge enables governments to align resource allocation with public expectations, thereby maximizing policy impact and fostering broader support. Specifically, WTP research facilitates the creation of policies that better reflect societal preferences, enhancing both economic efficiency and social equity [78].

A growing body of academic research internationally examines policy incentives to promote geothermal energy development. Recognizing its potential, governments have implemented various support mechanisms. The U.S. Department of Energy’s GeoPowering the West initiative [79] strategically invests in geothermal resource development through targeted funding and technical assistance. Similarly, the Philippines has fostered geothermal energy expansion with a comprehensive package of policy tools, including tax exemptions, expense reductions, and direct subsidies, to ensure a reliable energy supply [80]. Taiwan’s policy framework leverages both the Demonstration Incentive Program and preferential feed-in tariffs, creating a supportive environment for operators and driving market competitiveness [81].

Effective policy frameworks for geothermal power generation, both domestically and internationally, consistently prioritize comprehensiveness, long-term planning, robust risk management, and principles of fairness and transparency [82,83]. A critical component of successful policy development is understanding public WTP for geothermal energy. This valuation process provides policymakers with crucial empirical evidence of public acceptance and support, enabling them to refine policies, accurately address public needs, and enhance policy legitimacy and credibility [84,85,86].

2.5. Synthesis of the Literature and Research Gap

Across international and domestic literature, geothermal energy development is commonly examined through the lenses of resource availability, technological feasibility, and the policy frameworks required to support deployment. As outlined in Section 2.1 and Section 2.2, global utilization of geothermal energy has progressed substantially, whereas Taiwan’s development remains limited despite favorable geological conditions. Existing work attributes this gap to regulatory uncertainty, insufficient financial incentives, and limited public familiarity with geothermal technologies.

For studies assessing the economic value of renewable energy (as detailed in Section 2.3 and Section 2.4), the Contingent Valuation Method or Discrete Choice Approaches are commonly employed to gauge public support for emerging technologies. These studies consistently demonstrate that the public’s WTP is influenced by factors such as policy design, perceived environmental benefits, and risk perception. However, empirical research focusing specifically on geothermal energy, particularly in regions where its development is still nascent, remains considerably scarce.

Specifically, most valuation studies tend to focus on widely adopted renewable energy technologies or integrate geothermal energy within a broader portfolio of renewables, offering limited specific insight for markets like Taiwan. Crucially, there is a distinct lack of research that directly links evidence of public WTP to specific policy mechanisms capable of mitigating the institutional and financial barriers currently faced by geothermal development.

This study addresses these gaps by providing an empirical estimate of the public’s WTP for geothermal energy in Taiwan and by examining how such evidence may inform targeted policy interventions—including adjustments to feed-in tariffs, tax incentives, and geothermal energy education. Thus, the study contributes to the literature in two ways: it offers one of the few empirical WTP assessments devoted specifically to geothermal energy in a jurisdiction with limited deployment experience, and it bridges valuation research with policy design by identifying policy measures that align with public preferences in emerging renewable energy markets.

3. Methodology

The CVM provides a robust and comprehensive economic assessment of resources lacking established market prices by capturing both use and non-use values [87]. Its inherent flexibility allows for application across diverse policy contexts, notably in the design of equitable environmental damage compensation mechanisms [88]. Critically, CVM informs evidence-based policy formulation and supports rigorous cost–benefit analysis, enabling a more sustainable balance between resource allocation and environmental protection—a crucial consideration within the energy and economic landscape [89].

This study assessed public acceptance of geothermal energy as a renewable energy source and estimated WTP using the CVM. A predictive model was developed employing a standard single-bounded dichotomous choice elicitation model and a closed-ended main question format to predict WTP [90].

This study assessed public WTP for geothermal power generation using face-to-face surveys. Respondents were presented with a discrete choice question: would they be willing to pay a specified amount (Bid_i) to facilitate its wider adoption? Responses were limited to ‘yes’ or ‘no’, with an affirmative response indicating financial support for geothermal energy, and a negative response indicating a lack thereof. This binary-choice approach allowed for a direct elicitation of public preferences regarding the financial viability of geothermal energy development.

Respondents indicated their WTP by comparing the questionnaire bid amount (Bid) to their maximum willingness to pay ($\mathit{ln}WTP$). A positive response signified that Bid did not exceed $\mathit{ln}WTP$, while a negative response indicated the opposite. This determination reflects a binary outcome: acceptance of the bid if Bid ≤ $\mathit{ln}WTP$, and rejection if Bid > $\mathit{ln}WTP$. The probabilities of these outcomes occurring are defined as ${\varnothing }^Y$ and ${\varnothing }^N$, respectively.

$${\varnothing }^Y=\mathit{Pr}\left(Yes to Bid\right)=\mathit{Pr}\left\{Bid\le Max\left(\mathit{ln}WTP\right)\right\}=1-D\left(Bid;\theta \right)$$

(1)

$${\varnothing }^N=\mathit{Pr}\left(No to Bid\right)=\mathit{Pr}\left\{Bid>Max\left(\mathit{ln}WTP\right)\right\}=D\left(Bid;\theta \right)$$

(2)

where D(.) represents the cumulative distribution function of a specific probability distribution, and θ is the set of parameters estimated from the data.

Assuming there is a total of N respondents, and each respondent (i) faces a payment willingness of Bidi, the log-likelihood function obtained using the single-bounded dichotomous choice method can be written as:

$$\mathit{ln}L\left(\theta \right)= {\sum }_{i=1}^N\left\{d_i^Y\mathit{ln}{\varnothing }^Y\left({Bid}_i\right)+d_i^N\mathit{ln}{\varnothing }^N\left({Bid}_i\right)\right\}$$

(3)

If a respondent answers “yes”, then $d_i^Y=1$, otherwise $d_i^Y=0$.

If a respondent answers “no”, then $d_i^N=1$, otherwise $d_i^N=0$.

The WTP was calculated using the following formula, as referenced in the literature.

$$\mathrm{M}\mathrm{A}\mathrm{X}\mathit{ln}WTP=X^{\prime }\theta +\epsilon$$

(4)

We model WTP as a function of exogenous variables (X), including respondent income, age, and education, employing a natural logarithmic transformation consistent with established practice in the literature. The choice between Probit and Logit models hinges on the distributional assumption of the error term (ε): a normal distribution yields a Probit model, while a logistic distribution yields a Logit model.

This study leverages questionnaire data to analyze the parameters within Equation (3), enabling a robust welfare analysis of geothermal power generation in Taiwan. Consequently, we present the empirical model used to estimate Taiwanese public WTP as follows:

$$\mathit{ln}WTP=f\left(RE, GE, SE\right)$$

(5)

RE represents respondents’ awareness of renewable energy;

GE represents respondents’ awareness of geothermal power generation;

SE represents respondents’ socioeconomic background.

The specific variables for RE, GE, and SE will be detailed in Section 5.

4. Questionnaire Design, Implementation, and Analysis

Understanding public perceptions of geothermal power generation and its potential contribution to a circular economy is crucial for sustainable energy transitions. This study assesses WTP for increased geothermal electricity generation as a partial replacement for coal-fired power, employing the CVM in face-to-face interviews with a representative sample of the Taiwanese population [91]. To ensure contextual relevance and data accuracy, field reconnaissance was conducted in the Lushan and Jinshan geothermal areas of Taiwan prior to survey implementation. This allowed for the assessment of socio-economic conditions in adjacent rural communities and informed the refinement of the questionnaire and interview protocol, aligning the research with local realities.

4.1. Questionnaire Design and Implementation

This study employed a four-section questionnaire to investigate public perceptions and WTP for geothermal energy as a substitute for coal-fired power. The questionnaire began by assessing respondents’ awareness and preferences regarding electricity generation, with a specific focus on geothermal energy. Subsequently, preferences for clean, renewable energy sources were evaluated. Respondents were then presented with a scenario involving increased geothermal power generation to reduce reliance on coal, followed by a bidding game designed to elicit their willingness to pay. The third section explored perceptions of the circular economy. Finally, demographic data were collected to facilitate further analysis. This comprehensive approach allows for a nuanced understanding of public acceptance and economic valuation of geothermal energy within the broader energy landscape.

WTP was elicited using a closed-ended, single-bounded dichotomous choice format, where respondents indicated acceptance or rejection of specified bid amounts. Bid values of NT$50, NT$100, NT$300, and NT$600 were calibrated through pilot testing with a sample of 30 students at National Chung-Hsing University. This pilot study employed an open-ended questionnaire to assess instrument validity and flow, and the resulting data were used to refine the questionnaire and determine appropriate bid amounts for the closed-ended format.

Respondent socio-economic and demographic data were collected via questionnaire to assess potential influences on stated maximum WTP. Collected variables included respondent age, gender, education level, income, knowledge of environmental issues and geothermal development, household size, and household composition. These characteristics were hypothesized to impact stated preferences, reflecting the potential for socio-economic factors to shape energy-related economic decisions.

Survey data were collected between 18 October and 18 November 2022, encompassing all cities and counties in Taiwan. Sampling units were selected based on population data published by the National Statistics Office, Ministry of the Interior, in August 2022.

4.2. Overall Sample Characteristics

A total of 1068 questionnaire responses were collected for this study. To address budgetary and temporal constraints, the offshore islands of Kinmen, Penghu, and Lienchiang were excluded from the study (see

Table 2. A total of 1068 valid questionnaires were collected from the sampled administrative districts. Response rates differed across these districts.

Region	Classification	Population	Total Samples Assigned	Administrative Districts Selected (Count/District Name)		Completed Samples per District
Central Taiwan	Old Classification District	3,273,630	152	4	(1) Taipei City, Shilin District	38
					(2) Hsinchu City, East District	38
					(3) Hsinchu City, North District	38
					(4) Keelung City, Ren-ai District	38
	Old Classified County-Level City	4,692,428	219	6	(5) Taoyuan City, Taoyuan District	37
					(6) Taoyuan City, Zhongli District	37
					(7) New Taipei City, Xinzhuang District	37
					(8) New Taipei City, Zhonghe District	36
					(9) New Taipei City, Sanchong District	36
					(10) New Taipei City, Xindian District	36
	Old Classification Township	2,580,536	120	3	(11) Taoyuan City, Yangmei District	40
					(12) New Taipei City, Sanxia District	40
					(13) Yilan County, Dongshan Township	40
	Old Classification District	1,170,392	54	2	(14) Taichung City, Xitun District	27
					(15) Taichung City, West District	27
	Old Classified County-Level City	1,314,698	61	2	(16) Changhua County, Changhua City	31
					(17) Taichung City, Fengyuan District	30
	Old Classification Township	3,245,084	150	4	(18) Taichung City, Daya District	38
					(19) Taichung City, Wufeng District	38
					(20) Changhua County, Tanzi Township	37
					(21) Yunlin County, Sihu Township	37
Southern Taiwan	Old Classification District	2,507,307	116	3	(22) Tainan City, Anan District	39
					(23) Tainan City, East District	39
					(24) Tainan City, South District	38
	Old Classified County-Level City	1,000,712	46	1	(25) Kaohsiung City, Fengshan District	46
	Old Classification Township	2,723,723	126	3	(26) Tainan City, Rende District	42
					(27) Kaohsiung City, Qiaotou District	42
					(28) Tainan City, Liujia District	42
Eastern Taiwan	Old Classified County-Level City	202,878	9	1	(29) Hualien County, Hualien City	9
	Old Classification Township	328,970	15	1	(30) Hualien County, Yuli Township	15
Total		23,040,358	1068	30		1068

for details). A stratified random sampling method was implemented to ensure the representativeness of the sample, crucial for robust analysis in the context of energy and economic modeling.

Data were collected through face-to-face interviews conducted by trained interviewers at designated sampling locations. Interviewers were equipped with identification badges and carried questionnaires and small incentives. Eligible respondents were identified using systematic random sampling and randomly assigned to one of four questionnaire versions (A, B, C, or D; see

Table 3. Questionnaire (Type: A, B, C, D) Completion Summary.

Questionnaire Type		Frequency	Percentage	Valid Percentage	Cumulative Percentage
Valid Type	A	267	25%	25%	25%
	B	267	25%	25%	50%
	C	267	25%	25%	75%
	D	267	25%	25%	100%
Total		1068	100%	100%

for details). Responses were recorded sequentially according to the questionnaire instructions, ensuring consistency across the sample.

A total of 1068 questionnaires were collected for this study. Following the exclusion of 390 protest responses, a final sample of 678 valid questionnaires remained, yielding an effective response rate of 63.33%. As detailed in

Table 4. Willingness to Pay—Response Frequency.

WTP Amount	Number of Responses	Willingness to Pay
		YES	NO
50	199	193	6
		(96.98%)	(3.02%)
100	179	182	7
		(96.3%)	(3.7%)
300	165	154	11
		(93.33%)	(6.67%)
600	125	112	13
		(89.6%)	(10.4%)

, WTP decreased with increasing bid amounts, while unwillingness to pay increased. This pattern aligns with established findings in the literature regarding stated WTP and bid values, and supports the validity of our elicitation approach

Table 5. Demographic Data and Respondent Profile.

Item	Options (Statistics)	All Responses (1068)	Protest
			NO (678)	YES (390)
Gender	Male (%)	44.1	44.7	43.1
	Female (%)	55.9	55.3	56.9
Age	Mean	47.59	47.56	47.66
	Median	47	47	47.5
	Mode	32	32	35
	STD	16.31	16.42	16.14
	SKEWNESS	0.1486	0.1407	0.1641
Education	Illiterate	0.8	0.7	1
	Elementary School or Below/Literate	7.2	6.8	7.9
	Junior High School	9.5	9.3	9.7
	High School/Vocational High School	27	26.3	28.2
	Junior College/Associate Degree	11.9	12.1	11.5
	University/Bachelor’s Degree	35.8	36.6	34.4
	Graduate School and Above	7.9	8.3	7.2
Marital Status	Married	56.5	56.3	56.7
	Single	34.9	36.1	32.8
	Divorced	5.1	4.6	5.9
	Widowed	3.3	2.7	4.4
	Other	0.3	0.3	0.3
Household Size (FamN)	Mean	4.02	3.96	4.12
	Median	4	4	4
	Mode	4	4	4
	STD	1.8	1.78	1.82
	SKEWNESS	0.9282	0.9919	0.9272
Household Adult Members (AdultN)	Mean	3.41	3.35	3.5
	Median	3	3	3
	Mode	2	2	2
	STD	1.57	1.54	1.61
	SKEWNESS	0.8241	0.881	0.7306
Derived Income Value	Mean	33,389.51	33,148.97	33,807.69
	Median	30,000	30,000	30,000
	Mode	30,000	30,000	30,000
	STD	19,564.85	19,864.83	19,050.1
	SKEWNESS	1.4673	1.5257	1.3625
Environmental Group Participation (EnvGroup)	Never Participated	82.7	81	85.6
	Previously Participated, But Not Currently	12	13.7	9
	Yes	3.2	4	1.8
	Other	0	0	0
	Unknown/Uncertain	1.5	0.6	3.1

reports the demographic characteristics and respondent profiles for the full sample (1068) and compares non-protest (678) against protest (390) responses. The overall sample is slightly skewed toward female respondents (55.9%) and has an average age of approximately 48 years (median: 47).

Educational attainment is broadly distributed, though the majority of respondents have completed formal schooling beyond the compulsory level. Roughly 27% completed high school or vocational high school, and 35.8% hold a university or bachelor’s degree, while only a small proportion attained graduate-level education (7.9%). Illiteracy is rare (0.8%), and an additional 7.2% report only basic literacy. Comparing across groups, the non-protest group has a marginally higher share of university-educated individuals (36.5%) relative to the protest group (34.5%), whereas the protest group includes a slightly larger proportion of respondents with high school or lower educational attainment. Although modest in magnitude, these patterns hint at subtle associations between education and the likelihood of submitting a protest response.

Participation in environmental organizations further differentiates the two groups. A substantial majority of respondents have never participated in such organizations (82.7%), yet non-participation is more pronounced among protest respondents (85.6%) than among non-protest respondents (81.0%). Prior or current participation is correspondingly more common within the non-protest group. These findings suggest that protest responses in this contingent valuation setting are not necessarily driven by higher levels of environmental involvement, but may instead reflect differences in attitudes, information levels, or institutional trust.

Household structure exhibits broadly similar patterns across groups. The average household size is four persons, with protest respondents reporting slightly larger households (mean = 4.12) compared to non-protest respondents (mean = 3.96). A similar pattern emerges for adult household members: protest households include an average of 3.5 adults, whereas non-protest households include 3.35 adults. Although these differences are small, they may indicate distinct household decision-making environments that potentially shape responses to valuation questions.

A methodological consideration arises from the presence of a small number of illiterate respondents. While their proportion is low overall, it is slightly higher among protest respondents (1.0% versus 0.7%). Illiteracy may influence comprehension during face-to-face interviews, especially when valuation scenarios involve abstract concepts or hypothetical market mechanisms. Interviewers addressed this by reading questionnaire text verbatim upon request, but refrained from providing additional explanation or interpretation. This approach mitigates potential comprehension issues, but the observed pattern underscores the importance of ensuring adequate comprehension protocols when administering contingent valuation surveys.

While the initial exploration across demographic distribution offers an essential baseline, the considerably greater proportion of protest responses (390) relative to affirmative responses (678) necessitates further analysis. Establishing the unique characteristics and underlying attributes of those expressing such non-affirmative views is critical for a comprehensive interpretation of the research findings.

4.3. Analysis of Protest Responses

To empirically examine the characteristics of individuals exhibiting protest responses, the sample was divided into a Protest Group and a Non-Protest Group based on respondents’ WTP. Following standard practice in contingent valuation studies, respondents who refused to report a WTP—reflecting objection to the payment scenario, skepticism regarding the policy, or other forms of protest behavior—were classified as the Protest Group, whereas those providing a valid WTP were categorized as the Non-Protest Group. This operationalization aligns with the distinction between public and private forms of engagement described by Stern et al. [92] and the typology of political participation articulated by Verba and Nie [93].

To empirically examine the characteristics of protest respondents, we conducted a preliminary analysis based on two sets of variables: (1) demographic data and respondent profiles, and (2) a selection of variables potentially relevant for distinguishing protest behaviors. Independent-samples t-tests and regression analyses were then applied to these variables to identify patterns differentiating the Protest Group from the Non-Protest Group.

Independent-samples t-tests were conducted to compare mean differences across socio-demographic, attitudinal, and behavioral variables, thereby delineating the distinctive characteristics of the Protest Group relative to the Non-Protest Group. As summarized in

Table 6. Descriptive Statistics and Statistical Tests for Protest vs. Non-Protest Respondents (Continuous variables).

Variable	Protest (390)		Non-Protest (678)		Welch t (p-Value)	Mann–Whitney u (p-Value)
	Mean	SD	Mean	SD
Age (years)	47.66	16.139	47.558	16.421	0.098 (0.9217)	132,760.5 (0.9098)
Childen	0.623	1.026	0.605	0.949	0.289 (0.7725)	132,068.5 (0.9729)
FamN	4.123	1.818	3.959	1.783	1.432 (0.1524)	140,196.0 (0.0937) *
AdultN	3.5	1.611	3.354	1.543	1.448 (0.1479)	138,916.5 (0.1577)

Sig. (Significance levels, p-value Threshold): * p < 0.10.

and

Table 7. Descriptive Statistics and Statistical Tests for Protest vs. Non-Protest Respondents (Binary variables, Proportion Tests).

Variable	Baseline	Prop. (Protest, 390)	Prop. (Non-Protest, 678)	Z (p-Value)	Sig.
Gender	Male	0.569	0.553	0.511 (0.6092)
Env. Experence	No Participate	0.108	0.177	−3.040 (0.0024)	***
Recycle Experience	No Freq. Recycle	0.687	0.842	−5.943 (0.0000)	***
Education_2	Illiterate	0.079	0.068	0.708 (0.4789)
Education_3		0.097	0.093	0.243 (0.8082)
Education_4		0.282	0.263	0.692 (0.4890)
Education_5		0.115	0.121	−0.270 (0.7870)
Education_6		0.344	0.366	−0.729 (0.4663)
Education_7		0.072	0.083	−0.631 (0.5278)
Marriage_2	Married	0.328	0.361	−1.094 (0.2739)
Marriage_3		0.059	0.046	0.952 (0.3413)
Marriage_4		0.044	0.027	1.506 (0.1320)
Marriage_5		0.003	0.003	−0.115 (0.9087)
IncomeCat_2	Below 20,000	0.387	0.400	−0.403 (0.6868)
IncomeCat_3		0.223	0.174	1.959 (0.0501)	*
IncomeCat_4		0.049	0.052	−0.209 (0.8348)
IncomeCat_5		0.021	0.028	−0.753 (0.4515)
IncomeCat_6		0.010	0.012	−0.230 (0.8178)
EnvGroup_2	No Participate	0.090	0.137	−2.298 (0.0216)	**
EnvGroup_3		0.018	0.040	−1.961 (0.0499)	**
EnvGroup_5		0.031	0.006	3.221 (0.0013)	***
EnvGroup_999		0.005	0.007	−0.438 (0.6614)

Sig. (Significance levels, p-value Threshold): *** p < 0.01, ** p < 0.05, * p < 0.10.

, the Protest Group, representing approximately 36.5% of the sample, did not differ significantly from Non-Protest respondents in terms of basic demographics such as age, gender, number of children, income, or household composition.

In contrast, significant differences were observed in variables reflecting environmental attitude. Specifically, Protest respondents reported lower levels of participation in household recycling and greening practices. Differences were also observed across environmental-attitude subgroups, highlighting behavioral and attitudinal distinctions rather than demographic ones.

Logit and Probit regressions, presented in

Table 8. Summary of Logit and Probit Estimation Results.

Item *	Logit	Probit
Observations	1068	1068
Pseudo R2	0.2438	0.048
Log-Likelihood	−667.76	−667.64
LR test (p-value)	7.54 × 10−6	6.96 × 10−6
Convergence	No	Yes
Significant variables (p < 0.05)	Recycl_1 (−) ** IncomeCat_3 (+) ***	Recycl_1 (−) IncomeCat_3 (+)
Sign consistency between models	100%	100%
Observations	1068	1068

* Baseline groups are: Gender = Male, Education = Illiterate, Marriage = Married, IncomeCat = Below 20,000, Env = No Participate, Recycl = No Participate, EnvGroup = No Participate. ** Recycl_1 (−) indicates that frequent recyclers are less likely to belong to the protest group, supporting the attitudinal-driven hypothesis. *** IncomeCat_3 (+)indicates that respondents in the IncomeCat_3 category (middle income) are more likely to belong to the protest group.

, confirm these patterns. Coefficients for variables related to environmental attitude indicating that respondents with lower perceived environmental quality or lower participation in pro-environmental behaviors are more likely to exhibit protest responses. Basic demographics, such as age and gender, remain non-significant, reinforcing the notion that protest behavior is primarily attitudinal rather than demographic in nature.

The analysis detailed a considered examination of protest responses. Results indicate that protest respondents are distinguished by behavioral and attitudinal dispositions—including lower engagement in environmental saving-related behaviors—rather than demographic factors, suggesting their protest reflects objection to the payment vehicle, policy, or other non-valuation motivations. Given the potential for substantial bias in WTP estimations, subsequent analyses and WTP calculations rely exclusively on the non-protest subsample. This approach is both empirically justified by the observed differences between protest and non-protest respondents and methodologically necessary to ensure WTP estimates accurately reflect genuine preferences regarding geothermal energy.

5. Empirical Results

This study aims to empirically analyze the factors influencing public WTP for ‘increasing geothermal power to replace a small portion of coal-fired generation’. The empirical WTP model incorporates a set of demographics, attitudinal, and household characteristics that are theoretically relevant to respondents’ acceptance decisions in single-bounded dichotomous-choice formats. In addition to the bid price, the model includes socio-economic factors such as age, gender, household composition, environmental attitudes, and monthly household income. The inclusion of income reflects its central role in valuation theory: individuals’ willingness to pay is constrained by their budget conditions, and income is a standard determinant of preference heterogeneity in stated-preference studies. Therefore, the analytical model employed is expressed as follows:

$$\begin{array}{cc}\mathit{ln}{WTP}_i =& {\beta }_0+{\beta }_1{age}_i+{\beta }_2{Gender}_i+{\beta }_3{Env}_i+{\beta }_4{Clean}_i+{\beta }_5{EnSecur}_i\hfill \\ & \hspace{1em}\hspace{1em}+{\beta }_6{NPRoad}_i+{\beta }_7{EPEdge}_i+{\beta }_8{Childen}_i+{\beta }_9{OneT}_i\hfill \\ & \hspace{1em}\hspace{1em}+{\beta }_{10}{Recycl}_i+{\beta }_{11}{GreenHau}_i+{\beta }_{12}{HotSRec}_i+{\beta }_{13}{AnimalP}_i\hfill \\ & \hspace{1em}\hspace{1em}+{\beta }_{14}{Income}_i+{\epsilon }_i\hfill \end{array}$$

(6)

where i denotes the i-th respondent; $\mathit{ln}{WTP}_i$ represents the natural logarithm of the WTP for the i-th respondent; ${\beta }_0, {\beta }_1, \dots {\beta }_{14}$ represents the vector of coefficients to be estimated; and ${\epsilon }_i$ represents the random error term.

The questionnaire survey comprised four sections (see Supplementary Materials). Independent variables potentially influencing WTP were categorized as follows: first, respondent knowledge of electricity generation and geothermal power, captured by the dummy variable Clean, indicating awareness of geothermal energy as a key renewable resource; second, direct assessment of WTP for increased geothermal power as a substitute for coal-fired generation; third, respondent engagement with the circular economy, operationalized through the variable OneT, which measured use of single-use plastics in the three months prior to the survey; and fourth, respondent socio-economic characteristics, including age, gender, number of children, and membership in environmental organizations. Detailed definitions of each variable in the WTP equation are provided in

Table 9. Variable Names and Definitions.

Name *	Variable Definition	Options/Explanation
price	Offered Price
const	Constant
age	Age
Gender	Gender	Male = 1, Other = 2
Env	Participation in Environmental Groups	Previously participated in/or currently participates = 1, Otherwise = 0
Clean	Perception of Geothermal as Clean Energy	Believes this point is important/or very important = 1, Otherwise = 0
EnSecur	Perceived impact of geothermal energy on energy security. Geothermal energy is a domestically produced resource capable of providing 24/7 baseload power, independent of weather conditions.	Believes this point is important/or very important = 1, Otherwise = 0
NPRoad	Support for geothermal facilities along roadsides within national parks (excluding ecological protection zones and special scenic areas).	Agree/Strongly Agree = 1, Otherwise = 0
NPEdge	Support for geothermal facilities at the edge of national parks (excluding ecological protection zones and special scenic areas).	Agree/Strongly Agree = 1, Otherwise = 0
Childen	Number of Children
OneT	Frequency of single-use plastic consumption in the past three months (e.g., plastic bags, straws, beverage cups, takeout containers, online shopping packaging).	Never = 1, otherwise = 0
Recycl	Frequency of recycling. Average number of times resources are separated for recycling out of every 10 disposals.	Always/Often = 1, otherwise = 0
GreenHau	What are the potential applications of geothermal energy?	Selected greenhouse agriculture as a potential use of geothermal energy = 1, otherwise = 0
HotSRec	What are the potential positive impacts of geothermal power generation?	Selected use of geothermal power plant tailwater for hot spring recreation = 1, otherwise = 0 (multiple selections allowed)
AnimalP	What are the potential negative impacts of geothermal power generation?	Selected habitat loss as a potential negative impact = 1, otherwise = 0 (multiple selections allowed)
Income	What is your personal monthly income (including student allowance)?	Selected one option from: (1) Under 20,000 NTD (2) 20,001~40,000 NTD (3) 40,001~60,000 NTD (4) 60,001~80,000 NTD (5) 80,001~100,000 NTD (6) Over 100,000 NTD

* Source of Data: This research.

, with descriptive statistics presented in

Table 10. Descriptive Statistics of the Explanatory Variables.

Parameter *	Mean	Mode	Median	Skewness	Std. Dev.	Correlation Coefficients
price	226.179941	50.0	100.0	0.925326	201.738405	NaN
age	47.557522	32.0	47.0	0.140674	16.421482	−0.041420
Gender	1.553097	2.0	2.0	−0.214071	0.497540	−0.010559
Env	0.176991	0.0	0.0	1.696402	0.381943	−0.068851
Clean	0.859882	1.0	1.0	−2.078195	0.347366	0.001836
EnSecur	0.918879	1.0	1.0	−3.075286	0.273222	0.026528
NPRoad	0.681416	1.0	1.0	−0.780458	0.466271	−0.027404
NPEdge	0.722714	1.0	1.0	−0.997223	0.447989	−0.008632
Childen	0.604720	0.0	0.0	1.744529	0.949279	0.003982
OneT	0.060472	0.0	0.0	3.696129	0.238535	0.008491
Recycl	0.842183	1.0	1.0	−1.881354	0.364839	−0.009006
GreenHau	0.530973	1.0	1.0	−0.124408	0.499408	0.066345
HotSRec	0.504425	1.0	1.0	−0.017739	0.500350	−0.028587
AnimalP	0.420354	0.0	0.0	0.323420	0.493980	0.003538
Income	2.073746	2.0	2.0	1.224868	1.073594	0.038471

* Source of Data: This research.

.

5.1. Empirical Results and Analysis

Results from the single-bounded dichotomous choice elicitation model (

Table 11. Model Estimation Results for Single-Bounded Dichotomous Choice Elicitation Models.

Parameter	Probit	Logit
	Est. Coefficient(Est./s.e., p-Value)	Est. Coefficient (Est./s.e., p-Value)
price	−0.0013 *** (−3.032, 0.002)	−0.0026 *** (−3.124, 0.002)
Const	0.5695 (0.894, 0.371)	1.2398 (0.945, 0.345)
age	−0.0076 (−1.338, 0.181)	−0.0141 (−1.208, 0.227)
Gender	0.0474 (0.241, 0.809)	−0.0472 (−0.120, 0.905)
Env	0.6147 * (1.740, 0.082)	1.2471 (1.627, 0.104)
Clean	−0.2061 (−0.757, 0.449)	−0.4053 (−0.761, 0.447)
EnSecur	0.9842 *** (3.784, 0.000)	1.8118 *** (3.738, 0.000)
NPRoad	0.4935 * (1.869, 0.062)	0.8628 (1.552, 0.121)
NPEdge	−0.1384 (−0.519, 0.604)	−0.2496 (−0.450, 0.653)
Childen	0.4590 *** (2.776, 0.005)	0.8839 *** (2.629, 0.009)
OneT	−0.3788 (−1.226, 0.220)	−0.6314 (−1.042, 0.298)
Recycl	−0.0898 (−0.357, 0.721)	−0.1972 (−0.388, 0.698)
GreenHau	0.2863 (1.478, 0.140)	0.5443 (1.395, 0.163)
HotSRec	−0.0317 (−0.159, 0.873)	−0.0072 (−0.018, 0.986)
AnimalP	0.3505 * (1.695, 0.090)	0.7410 * (1.707, 0.088)
Income	0.2566 ** (2.234, 0.025)	0.4881 ** (1.967, 0.049)
Log Likelihood	−108.57	−109.48
Restricted log-likelihood	−143.58	−143.58
Log-Likelihood Ratio (LR)	70.02	68.5
Pseudo R2	0.2438	0.2375
Number of obs	678	678

Significance levels, p-value Threshold: *** p < 0.01, ** p < 0.05, * p < 0.10.

) demonstrate strong model fit. Both Probit and Logit models yielded statistically significant results (χ² test, p < 0.01), with Log-Likelihood Ratios (LR) of 70.02 and 68.2, respectively. These findings indicate robust model performance and support the validity of the elicitation approach.

Consistent with expectations, both Probit and Logit models yielded coefficients of the same sign, confirming a consistent directional influence of each variable on WTP. This consistency across models strengthens the robustness of our findings regarding the factors affecting WTP.

The estimated coefficients reveal the direction and magnitude of each variable’s effect on respondents’ WTP. Positive coefficients indicate a positive correlation with WTP, while negative coefficients indicate a negative correlation. Analysis of both Probit and Logit models reveals that price, EnSecur, Childen, NPRoad, AnimalP and Income all exhibit statistically significant positive correlations with WTP.

The Probit model revealed that Env, EnSecur, NPRoad, Childen, AnimalP and Income significantly predicted WTP, while the Logit model focused on EnSecur, Childen, AnimalP and Income. Although Env and NPRoad were significant in the Probit model but not the Logit model, their effects on WTP remained consistently positive.

Both the Probit and Logit estimations show that household income is positively and significantly associated with acceptance of the proposed bid for geothermal energy development (Probit: 0.2566, p < 0.05; Logit: 0.4881, p < 0.05). It indicates that respondents with higher income levels exhibit a greater probability of accepting the bid, holding all other variables constant.

Respondents demonstrated a positive WTP for geothermal energy under conditions aligning with environmental and conservation values. Specifically, WTP was positively correlated with membership in environmental organizations, parenthood, and the perception that geothermal development could be both environmentally sustainable and compatible with national park preservation.

Consistent with expectations, both models revealed a negative and statistically significant coefficient for the bid variable. This indicates that higher initial WTP bids presented in the questionnaire elicited lower reported WTP among respondents, suggesting a potential anchoring effect. This finding supports the established notion of anchoring bias in stated preference elicitation, a phenomenon potentially relevant to the valuation of energy-related goods and services.

5.2. Estimation of WTP

The WTP was estimated using a purely parametric estimation method via the Maximum Likelihood Estimation (MLE) of a discrete choice contingent valuation model. We employed the WTP space specification to directly estimate the mean WTP [94]. This approach utilized a log-linear model where the dependent variable is the logarithm of WTP, consistent with the estimated parametric functional forms (Probit and Logit).

The estimated coefficients (Formula (6)) are obtained directly in the WTP space and the coefficient represents the estimated percentage change in WTP resulting from a one-unit change in variables (Table 9).

The final WTP measure is consequently derived directly from this estimated distribution. The Mean WTP is calculated as the expected value of the WTP distribution derived directly from the estimated parametric model coefficients [95]. Given the log-linear specification, the Unconditional Mean WTP is obtained as the expected value of the exponentiated WTP function, providing the overall average WTP consistent with the estimated model parameters. The mean WTP results are:

Probit Model: 1792 (95% CI: 1747.4596, 1835.5470)

Logit Model: 1663 (95% CI: 1621.5397, 1705.3328)

We estimated the coefficients of a single-bounded dichotomous choice model to determine Taiwanese households’ mean WTP for a 1% shift from coal-fired to geothermal electricity generation. Using established methods, the estimated mean WTP was NTD 1792 (approximately USD 56.18, based on an average exchange rate of 31.89 during the survey period) under the Probit model and NTD 1663 (approximately USD 52.16) under the Logit model. The minimal difference between these estimates—less than 8%—reinforces the robustness of our findings, which are consistent with previous research in the literature.

A substantial portion of Taiwan’s adult population (63.4%; 678/1068 = 0.634) demonstrates a WTP of an additional USD 56.18 (Probit) or USD 52.16 (Logit) annually to support a 1% increase in the share of geothermal energy in the national electricity mix.

5.3. Multicollinearity Test

In the analytical model includes multiple demographic variables that may be empirically correlated—particularly income, age, and household characteristics—it is essential to assess whether collinearity among regressors could affect coefficient stability or inflate standard errors. To address this concern, multicollinearity was evaluated using the variance inflation factor (VIF), a standard diagnostic in discrete-choice WTP studies. As shown in

Table 12. Summary of Logit and Probit Estimation Results.

Variable	VIF
const	48.658431
price	1.015497
age	1.086049
Gender	1.059928
Env	1.015614
Clean	1.111419
EnSecur	1.147979
NPRoad	2.022215
NPEdge	1.997084
Childen	1.017716
OneT	1.046477
Recycl	1.049205
GreenHau	1.038588
Income	1.082170

, all VIF values fall well below conventional thresholds, with most variables exhibiting values close to 1. The VIF for income (1.082) indicates that it does not introduce harmful linear dependence with other covariates. Slightly higher values for NPRoad and NPEdge (approximately 2.0) remain within acceptable bounds and do not pose threats to inference.

6. Remark Discussion

Utilizing parameter estimates from Table 11, we estimate the mean annual WTP for increasing geothermal electricity generation by 1% (relative to coal-fired generation) to be USD 56.18 (Probit) or USD 52.16 (Logit) per adult per year. This indicates a quantifiable public value associated with transitioning to cleaner energy sources, representing the additional annual electricity cost individuals are willing to bear to support this shift and reduce reliance on coal-fired power.

A substantial majority of Taiwan’s adult population (63.4%, n = 678 of 1068 respondents) demonstrates a WTP of an average of USD 56.18 (or USD 52.16) annually to support a 1% increase in geothermal electricity generation, displacing coal-fired power. This represents an individual’s willingness to pay, rather than a household valuation, indicating a significant public acceptance of transitioning towards renewable energy sources despite potential cost implications.

Approximately 36.5% of adult respondents expressed reluctance or uncertainty regarding financial contributions towards a transition from coal to geothermal energy. This group encompassed individuals favoring alternative funding mechanisms—such as government or large electricity consumers—or opposing a 1% shift in Taiwan’s electricity generation mix. Others attributed financial responsibility to geothermal developers or environmental groups, cited insufficient information, or declined to state a preference. Based on 2022 demographic data, Taiwan’s adult population (aged 20+) numbered approximately 19.32 million. Assuming that non-respondents’ preferences align with those of respondents, and accounting for a 63.4% response rate, our findings suggest that approximately 12.24 million Taiwanese citizens would be willing to pay a premium to increase geothermal energy generation by 1%, thereby reducing reliance on coal-fired power.

National annual WTP was estimated using both Probit and Logit models. Results indicate a WTP of USD 688 million (NTD 21.934 billion) under the Probit model and USD 638 million (NTD 20.355 billion) under the Logit model, demonstrating model-specific variation in estimated values.

Leveraging US geothermal development costs (US$1800–5200 per kWh) [92] and the total annual WTP for geothermal electricity generation determined in this study, a geothermal power plant with a capacity of 108–335 MW could be realized, potentially supplying electricity to 202,000–624,000 four-person households annually and contributing to renewable energy diversification.

One finding of this study concerning the bidding choices design of the WTP instrument warrants clarification. The apparent underestimation of the true WTP value is clearly evidenced by the high positive response rate (89.6%, Table 4), even at the highest bid price. This outcome strongly suggests that the initial bid range, which was determined by a preliminary pilot study involving a small, non-representative sample of 30 students, was inadequate to span the full WTP distribution of the general public.

The formal survey respondents, in contrast, possess demographic characteristics that reflect a more realistic decision-making context: their average age is 47 years and they are generally situated as individuals with significant financial responsibility, typically supporting an average household of four members (Table 5). The student sample, by comparison, likely lacked the necessary financial salience and direct experience with household budgets and utility costs to anchor their hypothetical WTP responses realistically.

Furthermore, the WTP demonstrated by the final sample is reinforced by behavioral context: those who offered a positive WTP were more likely to report active participation in environmental protection-related activities (Table 5), which acts as a significant factor (Table 11, Env). This indicates that their valuation is based on a genuine environmental commitment rather than a casual response. Consequently, the lack of financial realism in the pilot phase resulted in a conservatively estimated WTP value for the general population, suggesting the actual mean WTP is likely higher than reported here.

Recognizing the inherent environmental and baseload advantages of geothermal energy, this study provides a strong rationale for increasing feed-in tariffs to fully internalize the resource and environmental benefits of geothermal power generation. This policy adjustment would not only reflect the full societal value of geothermal energy but also serve as a critical benchmark for Taiwan’s ambitious renewable energy transition and its commitment to achieving net-zero emissions. The findings offer valuable insight for policymakers navigating the economic and environmental considerations of a sustainable energy future.

Based on this study, we propose the following policy recommendations for geothermal development:

This study’s WTP results reveal societal preferences that can inform a strategic approach to geothermal development. Specifically, we recommend a policy framework encompassing: (1) financial incentives—such as targeted subsidies or risk mitigation—to lower barriers to private investment in geothermal exploration; (2) voluntary green tariff programs and differentiated subsidies to ensure equitable access to sustainable energy and protect vulnerable populations; (3) phased demonstration projects to assess both economic feasibility and public acceptance, coupled with rigorous sensitivity analysis to quantify uncertainties in project implementation; and (4) streamlined regulatory frameworks—integrating energy policy, fiscal instruments, land use planning, and environmental impact assessment—to facilitate geothermal development and translate public support into tangible outcomes. This integrated approach addresses key barriers to geothermal deployment while aligning with societal preferences as indicated by our WTP findings.

Building upon these recommendations, the findings also yield broader policy and managerial implications for geothermal energy development in Taiwan.

Policy Implications can be done by using this empirical WTP result to refine Taiwan’s geothermal energy strategy. First, the results underscore public support for transitioning away from coal-fired generation, suggesting that geothermal development should be positioned as a key component of the national energy transition roadmap. Accounting for this huge amount of WTP, this is worthwhile to strengthen a long-term geothermal deployment policy, clarifying regulatory responsibilities, and enhancing coordination across ministries responsible for energy, land use, and environmental oversight. Second, the heterogeneity in WTP across demographic groups highlights the need for equitable subsidy designs—particularly mechanisms that protect low-income or energy-vulnerable households while maintaining fairness in cost allocation. Third, because underlying potential framing effects and information provision influence public acceptance, future policy communication should emphasize transparency, risk disclosure, and balanced explanations of benefits and limitations to reduce potential resistance. Finally, WTP-based economic evidence can inform the prioritization of demonstration sites, the setting of differentiated tariffs, and the allocation of public funds to projects with higher social desirability. These recommendations are consistent with the IEA’s policy prescriptions—financial risk mitigation and demonstration projects—to mobilize geothermal investments [96], and with recent EU calls for a coordinated geothermal strategy [97].

On managerial implications, we propose practical guidance for developers, local governments, and project managers. The WTP estimates indicate that public acceptance is strongly tied to trust, perceived environmental stewardship, and clarity of benefits; therefore, developers should adopt communication strategies that explain project risks, expected community gains, and environmental safeguards in accessible terms. Early engagement with local stakeholders—through participatory planning, feedback loops, and transparent reporting—can reduce uncertainty and mitigate concerns that often lead to protest responses. Moreover, developers can use WTP information as a financial planning tool, virtual economic simulations, integrating community preferences into cost–benefit analysis, tariff design, and long-term revenue projections. This alignment between community expectations and project implementation not only strengthens social license to operate but also enhances the financial and operational robustness of geothermal investments.

7. Conclusions

7.1. Summary of Findings

This study assessed public WTP for a 1% shift from coal-fired to geothermal electricity generation in Taiwan. Results indicate that adult citizens aged 20 and over are willing to pay an average of USD 55.57 (NT$1772, Probit model) or USD 50.89 (NT$1623, Logit model) per person annually to support this transition. This suggests a potential funding base of approximately 12.24 million citizens, representing an aggregate WTP ranging from US$680 million (NT$19,866 billion) to US$623 million (NT$21,689 billion). This funding could theoretically support the development of 115–330 MW of geothermal capacity, sufficient to power an estimated 215,000 to 615,000 four-person households.

While this study’s WTP estimates indicate societal preference for geothermal development, they should not be interpreted as a direct measure of available financial resources. WTP estimation is inherently subject to limitations, including hypothetical bias, social desirability effects, and concerns regarding sample representativeness. Consequently, aggregating individual WTP values to estimate total funding potential may significantly overestimate actual investment.

Despite acknowledged methodological limitations, the WTP estimates derived from this study remain crucial for informing energy policy and assessing public support for geothermal development. These estimates are comparable to those observed in neighboring countries, indicating that Taiwanese public attitudes towards and WTP for renewable energy are substantially shaped by domestic factors. These include energy infrastructure, economic development levels, environmental awareness, and levels of public trust in governing institutions.

Based on the aforementioned analysis, this study recommends that the government utilize the WTP results as a reference for policy formulation, coupled with the following strategies:

First, mitigating investment risk through financial instruments—including targeted subsidies and risk insurance—is crucial to attract private capital. Second, ensuring social equity demands flexible subsidy schemes designed to protect vulnerable populations from potential disproportionate impacts of the energy transition. Third, demonstration projects are essential to validate the cost-effectiveness and technological maturity of geothermal power generation. Finally, accelerating development necessitates the integration of cross-sectoral policies encompassing energy, finance, land use, and environmental impact assessment.

Effective governance of geothermal development in Taiwan necessitates proactive engagement with local governments, community residents, and relevant stakeholders to build consensus and ensure alignment with societal needs and environmental sustainability. This study’s WTP results indicate significant potential public support for geothermal energy, representing a crucial asset for achieving Taiwan’s renewable energy transition and net-zero emission targets. Translating this social preference into robust policy and targeted implementation measures is therefore essential to unlock the full economic and environmental potential of Taiwan’s geothermal resources, fostering a sustainable energy future.

7.2. Limitation

This study is subject to several limitations that should be stated herein. First, the CVM approach inherently involves hypothetical and strategic biases, and respondents’ stated WTP may deviate from their actual behavior.

Second, although protest responses were identified and excluded following established procedures, the classification involves a degree of researcher judgment, and the removal of these cases may introduce estimation bias.

Third, the sample was drawn through random sampling and excluded residents of Taiwan’s outlying islands, and may therefore not fully represent the broader Taiwanese population, limiting the generalizability of the findings.

Fourth, the questionnaire design may have introduced a framing effect. Specifically, the valuation question was preceded by descriptive information emphasizing the advantages and policy relevance of geothermal energy. Such positive framing may have unintentionally primed respondents, potentially inflating stated support or WTP for geothermal development.

Fifth, the valuation scenario simplifies certain aspects of geothermal development, and respondents’ understanding of these technical and policy details may vary, potentially influencing their responses.

Finally, the empirical models rely on specific functional and distributional assumptions, and the results could differ under alternative model specifications or with additional explanatory variables such as income. These limitations do not invalidate the study’s findings but suggest that caution is warranted when generalizing the results or applying them to future geothermal development policies. Interpreting the results should consider these potential sources of bias and the specific context of the study.