The Structural Equation Model of Factors Affecting Decision-Making on Low-Carbon Tourist Destinations

Abstract

Low-carbon tourism (LCT) has emerged as a pivotal approach to mitigate the environmental impact of tourism, particularly its contribution to greenhouse gas emissions. This study develops a structural equation model (SEM) to investigate the factors influencing decision-making in selecting low-carbon tourism destinations. The research integrates key variables including destination characteristics, marketing strategies, and tourist perceptions, grounded in the Theory of Planned Behavior (TPB) and consumer behavior theory. Data were collected from 405 tourists visiting Khaoyai National Park, one of Thailand’s most popular nature-based destinations, utilizing structured questionnaires and a robust sampling strategy. Findings reveal significant interrelationships among destination characteristics, marketing strategies, and tourists’ perceptions of LCT, which collectively shape decision-making processes. Enhanced perceptions of LCT are mediated by innovative marketing communications and sustainable destination features, emphasizing the role of eco-friendly practices in fostering responsible tourism. The study underscores the importance of aligning policy, business strategies, and tourist education to promote sustainable travel behaviors. These insights provide practical guidelines for stakeholders to advance low-carbon tourism initiatives while preserving the integrity of tourist experiences and environmental stewardship.

1. Introduction

Tourism is a vital driver of global economic growth, generating substantial revenue for governments, private sectors, and local communities. However, its rapid expansion significantly contributes to environmental degradation, particularly through greenhouse gas (GHG) emissions from travel and related activities [1,2]. As the tourism sector continues to grow, its carbon footprint is projected to rise proportionally, posing a serious challenge to sustainable development [3,4].

Low-carbon tourism (LCT) has emerged as a sustainable alternative aimed at mitigating the environmental impacts of tourism by reducing energy consumption and minimizing both direct and indirect GHG emissions. This approach seeks to balance environmental preservation with enriching tourist experiences by encouraging engagement with local communities, fostering environmental awareness, and supporting local economies. LCT aligns with the principles of sustainable tourism by integrating economic, social, and environmental objectives to achieve balanced and responsible growth [5].

Despite the growing body of research on sustainable tourism, significant gaps remain in understanding the determinants that influence tourists’ decisions to choose low-carbon destinations. Previous research has broadly examined consumer behavior towards sustainable tourism and environmental awareness, limited studies have focused on the decision-making process within the context of low-carbon destinations [6,7,8,9,10]. This gap is critical because identifying the drivers behind tourists’ choices is essential for developing targeted marketing and policy strategies to effectively promote low-carbon tourism [11].

Addressing this gap, the present study aims to develop a comprehensive structural equation model (SEM) to examine the key determinants that guide tourists in selecting low-carbon tourism destinations. Specifically, the study will explore how tourists’ perceptions toward low-carbon tourism, along with the role of marketing efforts and destination characteristics, impact their decision-making process. SEM enables the analysis of both direct and indirect effects, offering nuanced insights into how these factors interact and guide tourist behavior [12].

By identifying the key drivers of tourist decision-making, this study will provide critical insights for policymakers, tourism businesses, and local communities to design targeted marketing strategies and policies that effectively promote low-carbon tourism. The findings will contribute to advancing sustainable tourism development by encouraging eco-conscious travel choices and supporting environmental sustainability initiatives.

2. Literature Review

Low-carbon tourism refers to tourism practices designed to reduce the environmental impact, especially carbon emissions, associated with travel, accommodation, and other tourist activities. As a concept, it is grounded in the principles of sustainable tourism, which seek to mitigate the negative effects of tourism on natural ecosystems, reduce the carbon footprint of tourism activities, and promote energy efficiency [13]. The growing demand for low-carbon tourism is driven by increased environmental awareness and the need for action against climate change. Tourism accounts for approximately 8% of global carbon emissions, with transportation being the largest contributor, followed by accommodations and activities [14].

LCT seeks to implement strategies that minimize carbon emissions through various means, such as promoting alternative transportation options like cycling or public transit, encouraging eco-friendly accommodations that use renewable energy, and developing carbon offset programs. These initiatives are designed to offer tourists a more environmentally responsible travel experience without compromising on the quality of their trip [15]. Furthermore, LCT encourages tourists to support destinations that are actively working to reduce their carbon footprints, thus creating a market for sustainable tourism practices.

Low-carbon tourists are characterized by their commitment to environmentally sustainable practices throughout their travel experiences. Recent research identifies several critical attributes that define this group:

• High Environmental Awareness: Low-carbon tourists possess strong environmental knowledge and consciousness, which motivates them to engage in sustainable behaviors such as reducing energy consumption and supporting eco-friendly accommodations [16].
• Willingness to pay for Sustainability: These tourists are inclined to spend more on eco-friendly services and products, recognizing the long-term benefits of sustainable tourism practices [17].
• Social Responsibility and Ethical Consumption: Their travel decisions are strongly influenced by a sense of social responsibility and a commitment to ethical consumption, driving them to engage in activities that reduce their environmental impact [18].
• Preference for Sustainable Transportation: These tourists favor low-carbon transportation methods, such as public transit, cycling, and walking, over private vehicles to limit carbon emissions [19].
• Engagement in Nature-Based Activities: They often choose outdoor and nature-oriented tourism experiences that align with their environmental values and promote sustainable interactions with natural ecosystems [8].
• Risk Sensitivity and Adaptive Behavior: Heightened health and safety concerns, such as those during the COVID-19 pandemic, can further reinforce their dedication to adopting low-carbon travel behaviors [20].

This profile of low-carbon tourists underscores the growing demand for sustainable tourism options that align with environmental, social, and ethical considerations.

• Low-Carbon Tourist Destinations: Concept and Significance

A low-carbon tourist destination is a tourism site designed to minimize environmental impacts through sustainable practices that reduce carbon emissions while supporting economic and social development. These destinations integrate eco-friendly infrastructure, renewable energy, sustainable transport, and environmental conservation strategies to balance tourism growth with climate change mitigation [19].

Key features include the implementation of green transportation systems, energy-efficient accommodations, and local resource utilization to lower carbon footprints [17]. Active community engagement and environmental education are also essential, fostering sustainable behavior among tourists and ensuring equitable economic benefits for local communities [16].

The adoption of low-carbon strategies in tourism promotes environmental conservation, enhances tourist experiences, and supports economic sustainability, positioning these destinations as vital contributors to global climate action efforts [21].

• Low-Carbon Services in Tourism: Key Components and Significance

Low-carbon services in tourism encompass a range of sustainable practices and innovations aimed at minimizing carbon emissions throughout the tourism value chain. These services play a critical role in achieving environmentally responsible tourism by integrating eco-friendly operations across transportation, accommodation, dining, and recreational activities. The core objective is to balance tourism growth with environmental sustainability.

Key components of low-carbon tourism services include sustainable transportation systems, such as electric buses and cycling infrastructure, which significantly reduce carbon emissions associated with tourist mobility [19]. Energy-efficient accommodations that utilize renewable energy and waste reduction strategies contribute to lowering the environmental footprint of the hospitality sector [22]. Additionally, sustainable food and beverage services emphasize local sourcing and waste minimization to support eco-friendly consumption practices [23].

Moreover, the implementation of green certifications and standards incentivizes continuous environmental improvements in tourism enterprises [23]. Community engagement is also vital, fostering inclusive economic development while promoting sustainable tourism behaviors [24].

The significance of these services lies in their ability to drive environmental sustainability, economic competitiveness, and social responsibility in the tourism industry [25]. By reducing carbon emissions and enhancing the tourist experience, low-carbon services contribute to long-term sustainable development in global tourism.

• Implementation of Low-Carbon Tourism

The implementation of low-carbon tourism involves a multi-faceted approach that includes both policy measures and technological innovations. Many destinations have introduced policies that support low-carbon initiatives, such as promoting the use of renewable energy in tourism-related infrastructure, developing low-emission transport networks, and creating incentives for eco-friendly accommodations [5]. For example, urban tourism areas often encourage the use of electric buses and cycling paths to reduce the reliance on fossil-fueled vehicles, while hotels may implement energy-saving technologies, such as solar panels and efficient water usage systems [13].

The successful adoption of low-carbon tourism depends on the coordinated efforts of governments, businesses, and local communities in developing policies and practices that align with sustainability principles. These efforts can range from enforcing stricter regulations on carbon emissions for airlines and tour operators to providing incentives for hotels and restaurants that adopt energy-efficient practices. As governments and tourism boards begin to recognize the economic potential of sustainable tourism, there has been an increased push for the development of low-carbon tourism infrastructure in destinations worldwide [26].

Moreover, the marketing strategies surrounding low-carbon tourism play a crucial role in its adoption. Raising awareness among tourists about the environmental impact of their travel choices and promoting the benefits of low-carbon alternatives can significantly influence their decision-making processes [27]. Tour operators, hotels, and local governments have increasingly begun to highlight their environmental credentials in marketing campaigns, focusing on their efforts to reduce carbon emissions as a key selling point. This is particularly important as environmentally conscious consumers are more likely to choose destinations that align with their personal values regarding sustainability and climate action.

However, despite these advancements, there are several barriers to the broader implementation of LCT. One major challenge is the lack of consumer awareness and understanding of what low-carbon tourism entails and how individual travel behaviors contribute to carbon emissions. Additionally, the higher upfront costs associated with eco-friendly accommodations and low-carbon travel options may deter some tourists, especially those from lower-income brackets [28]. Overcoming these barriers requires not only better education and awareness campaigns but also more accessible and affordable low-carbon tourism options.

• Cost and Convenience as Barriers to Low-Carbon Tourism and Strategies for Overcoming Them

Cost and convenience play a crucial role in tourists’ decision-making processes, particularly when considering low-carbon tourism (LCT) options. While LCT promotes environmental sustainability, many travelers perceive it as expensive and less convenient compared to traditional tourism alternatives. Addressing these concerns is essential for increasing LCT adoption and making sustainable travel more accessible to a broader audience.

Cost-Related Barriers in Low-Carbon Tourism: One of the most significant deterrents to LCT adoption is the higher financial cost associated with eco-friendly accommodations, transportation, and activities [29]. Sustainable hotels often incorporate green building materials, renewable energy, and water conservation systems, leading to higher operational costs that are passed on to consumers [30]. Additionally, some LCT options—such as carbon-offset programs, eco-certifications, and organic food offerings—come at a premium price, deterring budget-conscious travelers [31].

Eco-friendly transportation presents another financial challenge. Low-carbon transport options such as high-speed trains, electric buses, and hybrid rental cars typically have higher upfront costs compared to conventional fossil-fueled transport [31]. In some cases, limited availability of green transport results in higher demand and increased ticket prices, making LCT less competitive with conventional travel options [32].

Convenience-Related Barriers in Low-Carbon Tourism: Apart from cost, convenience is another major barrier preventing widespread LCT adoption. Tourists often perceive sustainable travel options as requiring extra effort and lacking seamless accessibility [33]. Many low-carbon destinations have limited infrastructure, requiring longer travel times, multiple transport modes, and additional planning [34]. For instance, while high-speed rail may be an environmentally friendly option, it is not always available in all regions, making it less convenient than flights for long-distance travel [11].

Additionally, eco-friendly hotels and lodges may be located in remote areas with fewer amenities, making them less attractive for tourists who prioritize comfort and accessibility [30]. The limited availability of sustainable services, such as electric vehicle charging stations or organic food options, further reduces the convenience of low-carbon tourism compared to conventional travel choices [5].

• Strategies for Overcoming Cost and Convenience Barriers in Low-Carbon Tourism

To enhance LCT accessibility and attractiveness, targeted interventions must be implemented, including policy incentives, technological advancements, and enhanced infrastructure.

Financial Incentives and Subsidies: Governments and tourism boards should introduce subsidies, tax breaks, and grants for businesses that implement low-carbon tourism initiatives [35]. By reducing operational costs, sustainable businesses can offer more affordable pricing, making LCT competitive with conventional tourism. Additionally, financial incentives for travelers, such as carbon-offset discounts, eco-travel vouchers, and loyalty programs, can encourage tourists to choose LCT options [30].

Infrastructure Development and Accessibility Improvements: Enhancing low-carbon transport infrastructure can significantly improve convenience. Investments in high-speed rail networks, integrated public transport systems, and widespread electric vehicle charging stations can make LCT more seamless and attractive to travelers [34]. Furthermore, increasing the number of eco-friendly accommodations in urban areas, rather than limiting them to remote locations, can help make sustainable lodging more convenient and widely accessible [32].

Public Awareness and Education: Many tourists perceive LCT as costly and inconvenient due to a lack of awareness about its long-term benefits. Governments and businesses should implement marketing campaigns and digital platforms to inform travelers about cost-saving measures, such as energy-efficient hotels that reduce long-term expenses or multi-modal low-carbon transport solutions that optimize travel time [36]. Consumer education on carbon footprint reduction and the economic benefits of sustainable tourism can enhance the appeal of LCT [31].

Technological Innovations and Digital Solutions: Leveraging technology-driven solutions can help eliminate many convenience-related challenges associated with LCT. Smart tourism applications that provide real-time sustainable travel options, eco-certifications for accommodations, and carbon footprint calculators can enhance decision-making [33]. AI-powered route optimization tools can help tourists select the most convenient and sustainable transport methods [5]. Additionally, advancements in biofuels and electric aviation may further reduce the cost of low-carbon air travel in the future [30].

Addressing cost and convenience barriers is essential for promoting the adoption of low-carbon tourism. While financial constraints and infrastructure limitations pose challenges, strategic interventions, such as financial incentives, improved transport networks, consumer education, and technological innovations, can significantly enhance the affordability and convenience of LCT.

• Practical Cases and Effect Analysis of Low-Carbon Tourism

Low-carbon tourism has emerged as a critical strategy to mitigate the environmental impact of the tourism industry. Several countries and regions have implemented diverse initiatives to promote sustainability, demonstrating measurable effects on carbon footprint reduction and environmental conservation.

• Case Study 1: The Maldives—Sustainable Luxury Tourism

The Maldives, a nation heavily reliant on tourism, has incorporated eco-friendly initiatives within its luxury resort industry. Many resorts have adopted renewable energy sources, such as solar power, and implemented sustainable waste management systems, including desalination plants and composting programs [37]. Coral restoration projects, led by both private and governmental entities, have significantly improved marine biodiversity, ensuring the long-term viability of the country’s tourism sector [38].

• Case Study 2: Valencia, Spain—Urban Sustainable Tourism

Valencia has undergone extensive sustainability transformations, including pedestrianizing major areas, expanding bike-friendly infrastructure, and increasing urban green spaces. The city has significantly reduced its carbon emissions by integrating electric buses and promoting sustainable mobility [39]. As a result, Valencia was designated the European Green Capital for 2024, highlighting its leadership in urban low-carbon tourism [40].

• Case Study 3: Findhorn Ecovillage, Scotland—Community-Led Sustainability

Findhorn Ecovillage in Scotland serves as a model for sustainable tourism, with its low-carbon housing, renewable energy use, and organic food production. The village has successfully reduced its ecological footprint to less than half the UK average, demonstrating the feasibility of community-led low-carbon tourism initiatives [30]. Its education programs attract international visitors, fostering knowledge-sharing on sustainable living and tourism practices.

• Case Study 4: Bhutan—Carbon Neutral Tourism

Bhutan’s approach to low-carbon tourism is embedded in its national policy of Gross National Happiness (GNH), which prioritizes environmental conservation. The government enforces a ‘High Value, Low Impact’ tourism strategy, charging a Sustainable Development Fee (SDF) to international visitors. These funds support conservation efforts, offset carbon emissions, and promote eco-tourism projects [41]. Bhutan remains carbon neutral, as forests cover over 70% of its land, acting as a major carbon sink [42].

• Measures to Achieve Low-Carbon Goals in Tourism

Governments and tourism organizations have employed various strategies to reduce tourism-related carbon emissions, focusing on policy initiatives, infrastructure development, and technological innovations.

• Policy Initiatives: International frameworks such as the Glasgow Declaration on Climate Action in Tourism advocate for industry-wide carbon reduction commitments. Signatories pledge to measure, decarbonize, regenerate, and report sustainability efforts to align with the Paris Agreement [35]. Some nations, like Costa Rica, have integrated sustainable tourism policies, promoting eco-certification programs and incentives for green businesses [43].
• Infrastructure Development: Sustainable infrastructure investments, including public transportation enhancements and green-certified accommodations, are pivotal in reducing carbon footprints. Countries like Germany and the Netherlands have invested heavily in high-speed rail networks and cycling routes, promoting eco-friendly travel alternatives [44]. Similarly, destinations such as New Zealand are incentivizing hotels to adopt energy-efficient technologies through government-led initiatives [45].
• Technological Innovations: Technology plays a crucial role in achieving low-carbon tourism. Smart energy systems, carbon offset programs, and AI-driven sustainability tracking tools are increasingly integrated into tourism management [46]. For instance, Norway’s Hurtigruten cruise line has transitioned to hybrid-electric vessels, cutting CO₂ emissions by nearly 30% [47].
• Community Engagement and Education: Engaging local communities in sustainability efforts ensures the long-term success of low-carbon tourism. Japan’s rural ecotourism initiatives empower local communities to manage and protect their natural and cultural heritage while benefiting economically from responsible tourism [48]. Public awareness campaigns, like those in Australia’s Great Barrier Reef, educate tourists on minimizing their environmental impact [49].

The global transition towards low-carbon tourism is evident in various practical cases across different regions. By implementing sustainable policies, investing in eco-friendly infrastructure, leveraging technology, and engaging communities, nations can significantly reduce the carbon footprint of tourism while ensuring long-term environmental and economic benefits.

Drawing from the conceptual framework of research on the decision-making process for selecting low-carbon tourist destinations, this choice is influenced by a diverse range of theoretical concepts that shape tourists’ preferences for such destinations. The research draws on the Theory of Planned Behavior (TPB) and consumer behavior theory to investigate the factors influencing tourists’ decisions to select low-carbon destinations.

• Theory of Planned Behavior (TPB)

Rationale for Selection: The Theory of Planned Behavior (TPB) is particularly applicable in this context as it provides insights into how tourists’ attitudes, subjective norms, and perceived behavioral control shape their decision-making processes. Within this framework, tourists’ perceptions of low-carbon tourism emerge as key determinants influencing their choice of low-carbon destinations. The theory is valuable for explaining how attitudes toward environmental sustainability, along with social expectations, can drive a preference for low-carbon travel. Furthermore, it incorporates the perceived ease or difficulty of adopting low-carbon tourism practices, which plays a critical role in understanding tourists’ decisions [50].

Relevance to the Framework: The Theory of Planned Behavior (TPB) corresponds with essential elements of the framework, including tourists’ perceptions of low-carbon tourism and their choices in selecting destinations. By analyzing these cognitive processes, we can better understand why tourists are more likely to opt for low-carbon tourism options when they perceive social support and believe they have the capability to participate.

• Consumer Behavior Theory

Rationale for Selection: Consumer behavior theory examines how individual preferences, social dynamics, and external factors, including marketing strategies, shape decision-making processes. Within this framework, marketing strategies are pivotal in shaping tourists’ choices. By studying consumer behavior, this research investigates the effectiveness of marketing efforts aimed at promoting low-carbon tourism—particularly those that highlight sustainability and eco-friendly experiences—and their influence on tourists’ destination selections [51].

Relevance to the Framework: This theory is closely connected to the framework by integrating marketing strategies with tourists’ perceptions and their decision-making processes. By analyzing consumer behavior, it becomes evident how the promotion and communication of low-carbon tourism, whether through informational campaigns or marketing efforts, can substantially influence tourists’ perceptions of the appeal and value of low-carbon destinations.

• Scope and Variables Selection

This study aims to examine the factors influencing tourists’ decision-making in selecting low-carbon tourism destinations. The scope and selection of variables are grounded in theoretical frameworks and empirical evidence, supporting the inclusion of key factors including the tourism destination, perceptions of low-carbon tourism, marketing strategies, and decision-making processes related to low-carbon tourism. These components were systematically chosen based on relevant literature to ensure the model effectively captures the determinants of low-carbon tourism decision-making. The proposed structural equation model (SEM) integrates three endogenous latent variables and one exogenous latent variable to comprehensively analyze these relationships.

Tourism Destination (TD): These exogenous latent variable impacts decision-making in low-carbon tourism (LM), perceptions of low-carbon tourism (PL), and marketing strategies (MS). Tourism destination encompasses key elements such as attractions, tourism activities, amenities or facilities, services, and accessibility. Together, these factors play a critical role in shaping tourists’ choices and preferences for low-carbon tourism experiences.

Perception of Low-Carbon Tourism (PL): This endogenous variable encompasses aspects such as climate change awareness, favorable perceptions and preferences, concerns and challenges, as well as knowledge and understanding. The perception of low-carbon tourism is shaped by factors such as tourist destinations and marketing strategies, which, in turn, play a significant role in influencing decision-making related to low-carbon tourism.

Marketing Strategies (MS): This endogenous variable serves as a mediator in the relationship between tourist destinations and decision-making related to low-carbon tourism. Marketing strategies encompass elements such as innovative approaches to marketing communication, destination branding, the utilization of distribution channels, and the enhancement of tourism experiences.

Low-carbon tourism decision-making (LM) serves as the key endogenous variable within the model. It is directly shaped by factors such as the choice of tourist destinations, perceptions of low-carbon tourism, and the influence of marketing strategies. This variable represents the model’s outcome, reflecting tourists’ decisions related to low-carbon tourism practices.

Figure 1 shows the conceptual model of low-carbon tourism decision-making model, which illustrates the tourist’s decision-making for low-carbon tourism. The symbol “H” in the model represents the research hypotheses that explore relationships among key variables.

• Research hypotheses

The hypotheses in this study are derived from extensive literature examining the relationships between tourist destination attributes, marketing strategies, perceptions of low-carbon tourism, and tourists’ decision-making processes. The formulation of these hypotheses is grounded in the Theory of Planned Behavior (TPB) and empirical studies related to sustainable tourism and environmental behavior. A total of eight hypotheses are proposed.

Hypothesis 1 (H1).

Tourist destination factors have a positive relationship with marketing strategies.

Destinations that integrate sustainable practices into their offerings tend to develop more effective marketing strategies. Highlighting eco-friendly transport, accommodations, and activities appeals to environmentally conscious travelers [52,53]. This positive relationship enhances destination competitiveness through sustainable marketing efforts [54].

Hypothesis 2 (H2).

Tourist destination factors have a positive relationship with the perception of low-carbon tourism.

Sustainable features such as accessibility, green infrastructure, and eco-friendly activities positively influence tourists’ perceptions of low-carbon tourism [55]. Destinations that adopt carbon reduction initiatives and renewable energy attract tourists seeking sustainable travel experiences [56].

Hypothesis 3 (H3).

Marketing strategies have a positive relationship with the perception of low-carbon tourism.

Strategic marketing that emphasizes sustainability fosters positive perceptions of low-carbon tourism. Incorporating green branding and promoting eco-friendly services increase tourists’ awareness and engagement [57,58].

Hypothesis 4 (H4).

Perception of low-carbon tourism positively influences decision-making in low-carbon tourism.

Tourists with favorable perceptions of low-carbon tourism are more likely to choose sustainable travel options. Prior experiences, environmental awareness, and social influences (e.g., social media) strongly affect decision-making [59].

Hypothesis 5 (H5).

Tourist destination factors have a positive relationship with low-carbon tourism decision-making.

Environmentally responsible destination attributes (e.g., green transport, sustainable lodging) directly impact tourists’ decisions to engage in low-carbon tourism [54].

Hypothesis 6 (H6).

Marketing strategies have a positive relationship with low-carbon tourism decision-making.

Marketing strategies that effectively communicate sustainability initiatives encourage tourists to participate in low-carbon tourism [60].

Hypothesis 7 (H7).

Perception of low-carbon tourism mediates the relationship between tourist destination factors and low-carbon tourism decision-making.

Perceptions of sustainability serve as a mediator, strengthening the link between destination attributes and tourists’ decisions to engage in low-carbon tourism [60].

Hypothesis 8 (H8).

Perception of low-carbon tourism mediates the relationship between marketing strategies and low-carbon tourism decision-making.

Sustainable marketing strategies influence tourists’ perceptions, which in turn affect their decision-making regarding low-carbon tourism [60].

3. Materials and Methods

3.1. Research Design

In developing the research design, this study adopts a structural equation modeling (SEM) approach to comprehensively analyze the relationships between key variables influencing low-carbon tourism destination selection. SEM is well suited for this research as it enables the simultaneous examination of direct and indirect effects among multiple latent variables, including destination characteristics, marketing strategies, and tourist perceptions of low-carbon tourism. This model facilitates the understanding of complex interactions and mediating effects that may exist between these factors and tourists’ decision-making processes. Additionally, SEM allows for a nuanced analysis of how each factor uniquely contributes to the overall decision to choose low-carbon destinations, thereby providing insights that are valuable for both theoretical understanding and practical application in sustainable tourism development.

3.2. Potential Sources of Bias and Measurement Deviations

Despite the rigorous research design, several potential sources of deviation should be acknowledged to ensure transparency and reliability of findings:

• Sample Bias: The study focuses on domestic tourists, limiting generalizability to international travelers. Convenience sampling may also introduce self-selection bias [61]. Future research should include a more diverse sample.
• Measurement Bias: Self-reported survey data may be influenced by social desirability bias [62]. Anonymity and neutral wording were used to mitigate this. Likert-scale responses have interpretation limitations, which future studies could address with behavioral experiments.
• Common Method Bias (CMB)—All variables were measured using a single survey, which may introduce bias. Harman’s single-factor test was conducted, and question randomization helped reduce this risk [63].
• Non-Response Bias—Differences between respondents and non-respondents could exist. A post hoc analysis comparing early and late respondents found no significant differences [64].
• Temporal Limitations—Data were collected over a limited period, which may not account for seasonal variations. Future research should incorporate longitudinal studies [65].

By acknowledging these limitations, this study enhances transparency and provides a foundation for improving LCT decision-making models in future research.

3.3. Research Instrument

In this research, a questionnaire was employed as the primary data collection tool to investigate the factors influencing tourists’ decision-making on low-carbon tourism destinations. The use of a questionnaire is justified due to its efficiency in collecting quantitative data from a large sample, enabling comprehensive analysis of complex relationships between multiple variables [36]. This method aligns well with structural equation modeling (SEM), which requires large datasets for model validation and hypothesis testing [66]. By utilizing a questionnaire, the study can systematically measure tourists’ perceptions, attitudes, and behaviors towards low-carbon tourism, facilitating robust statistical analysis.

The development of the questionnaire was grounded in an extensive literature review on sustainable tourism, low-carbon tourism behaviors, and decision-making models. Key constructs were identified, including tourist destination, marketing strategies, tourist perceptions of low-carbon tourism, and decision-making factors [67,68]. These constructs were operationalized into measurable items adapted and refined from existing validated scales to ensure content validity. The questionnaire was structured to reflect theoretical frameworks related to low-carbon tourism decision-making, ensuring alignment with established research models.

The questionnaire comprised two main sections: Demographic information and factors influencing decision-making on low-carbon tourism. Demographic Information gathered general information about respondents, including gender, age, education, monthly income, travel frequency, and origin of respondents. Factors influencing decision-making contained Likert-scale items (ranging from 1 = Strongly Disagree to 5 = Strongly Agree) measuring perceptions of various factors influencing the choice of low-carbon tourism destinations. The factors included the following:

Tourist Destination: attractions, tourism activities, amenities and services, and accessibility

Marketing Strategies: innovative marketing communication, destination branding, distribution channels, and tourism experience

Tourist Perceptions: climate change awareness, positive perception and preferences, concern and challenge, and knowledge and understanding of low-carbon tourism

Decision-Making: trust, social influence, practicality and convenience, and health and wellbeing

To ensure the reliability and validity of the questionnaire, a pilot test was conducted with a sample of 40 respondents. The collected data underwent reliability analysis using Cronbach’s Alpha and construct validity testing through Factor Analysis [69]. Items with a factor loading below 0.4 were reviewed and removed to enhance the questionnaire’s reliability [70]. The seven items were excluded due to low factor loadings. Post-revision, the constructs demonstrated strong internal consistency, with Cronbach’s Alpha values exceeding the recommended threshold of 0.7. The refined questionnaire was thus validated for comprehensive data collection and SEM analysis.

This rigorous development and testing process ensured that the questionnaire effectively captured the multidimensional aspects of tourists’ decision-making regarding low-carbon tourism destinations, supporting the study’s objectives.

3.4. Data Collection and Study Sampling

This study targeted Thai tourists visiting Khao Yai National Park, located in Pak Chong District, Nakhon Ratchasima Province. Recognized as one of Thailand’s most popular nature-based destinations, the park attracts a substantial and diverse domestic tourist population, offering a representative sample for studying tourist behavior. Its status as a leading eco-tourism destination with active sustainable practices, making it an ideal site for investigating low-carbon tourism behavior. The park’s rich biodiversity, extensive natural attractions and variety of eco-friendly activities provide a suitable context for evaluating tourists’ perceptions and decision-making related to low-carbon tourism [67]. These attributes collectively position Khao Yai National Park as a strategic and suitable site for this research.

A sample of 405 Thai tourists was selected using a convenience sampling method due to its suitability for collecting data from accessible participants [71]. The data were collected through a structured questionnaire survey conducted from September to November 2024 at various popular areas within Khao Yai National Park.

The sample size of 405 respondents is appropriate for structural equation modeling (SEM) analysis, which requires a substantial sample to achieve accurate and reliable parameter estimates [12]. Scholars suggest a minimum ratio of 10 respondents per parameter for SEM, with larger samples enhancing the model’s robustness and generalizability [72]. Therefore, the sample size in this study is sufficient for capturing complex relationships among variables and providing statistically significant results.

To ensure the accuracy and completeness of the responses, field editing was carried out on-site immediately after data collection. This process involved reviewing the completed questionnaires for missing or inconsistent answers and providing clarifications when necessary [66]. This methodological approach was chosen to enhance data reliability and minimize potential errors in data entry and analysis.

3.5. Data Analysis

Ensuring data accuracy and validity during analysis is essential to confirm its suitability for research. The data validation process involves several steps to identify and address any ambiguous data before conducting statistical analysis [67]. Initially, missing data are reviewed, with methods such as mean substitution or deviation adjustments applied as needed [64]. SPSS software version 26 is employed for this phase due to its capability in handling multivariate data efficiently [73].

To detect outliers, Mahalanobis Distance is calculated to identify and address any significant deviations within the dataset [12]. This approach, executed via SPSS, is beneficial for robust multivariate analysis. Structural equation modeling is subsequently performed using AMOS, further refining and validating the research model to ensure accuracy and rigor in analyzing complex variable relationships [72].

A p-value of less than 0.001 was used as the threshold for outlier detection, with each outlier carefully examined to determine its legitimacy as a data point or error. This process led to the removal of 13 outliers from the initial sample of 418. This comprehensive data-cleaning process enhanced the reliability of the retained data, creating a strong foundation for subsequent analyses and results.

The study employed Confirmatory Factor Analysis (CFA) to validate the measurement model, ensuring that the constructs accurately reflected the collected data. Using the Maximum Likelihood Estimation (MLE) method, which is highly effective for analyzing multivariate data, the model fit was evaluated within the framework of structural equation modeling (SEM) [12]. Following the removal of outliers, the dataset was assessed for construct validity and reliability. Factor loadings above 0.7 were considered acceptable, supporting the validity of the measured constructs [73].

To further evaluate the reliability of the questionnaire, Cronbach’s Alpha was applied, with a threshold of 0.7 or higher required to confirm internal consistency [74]. This data-cleaning process was essential in minimizing potential errors or biases, thereby strengthening the accuracy of the findings generated through structural equation modeling, which examines relationships among the variables.

4. Results

4.1. Demographic Profile of Respondents

The study surveyed 405 domestic tourists visiting Khao Yai National Park, Thailand. The demographic profile of respondents, as detailed in

Table 1. Demographic profile of respondents.

Participants	Frequency (LSPS)	Percentage (%)
Gender
Male	207	51.11
Female	198	48.89
Age
≤20 years	43	10.62
21–30 years	152	37.53
31–40 years	175	43.21
41–50 years	26	6.42
51–60 years	1	0.25
>60 years	8	1.98
Education level
Below Bachelor’s Degree	131	32.35
Bachelor’s Degree	232	57.28
Above Bachelor’s Degree	42	10.37
Income (Monthly)
≤15,000 THB	123	30.37
15,001–30,000 THB	160	39.51
30,001–45,000 THB	64	15.80
45,001–60,000 THB	26	6.42
60,001–75,000 THB	11	2.72
>75,000 THB	21	5.19
Number of times visited Pak Chong District or Wang Nam Khiao District
First time	74	18.27
Second time	40	9.88
Third time	41	10.12
Fourth time	19	4.69
Fifth time	10	2.47
More than 5 times	221	54.57
Origin of Tourists
Bangkok and Metropolitan Region	189	46.67
Central Region excluding Bangkok and Metropolitan Region	84	20.74
Nakhon Ratchasima	74	18.27
Northeastern Region excluding Nakhon Ratchasima	19	4.69
Eastern Region	32	7.90
Northern Region	1	0.74
Southern Region	6	1.48

, indicates a balance gender distribution with 51.11% male and 48.89% female respondents. The largest age group was 31–40 years (43.21%), followed by 21–30 years (37.53%), indicating that young to middle-aged tourists are more likely to engage in low-carbon tourism (LCT). Educational attainment was predominantly at the bachelor’s degree level (57.28%), and the majority of respondents had a monthly income of 15,001–30,000 THB (39.51%). Over 54.57% of participants had visited the region more than five times, reflecting a high level of familiarity with the destination. The majority of respondents were from Bangkok and its vicinity (46.67%), followed by central region (excluding Bangkok and vicinity) (20.74%), and Nakhon Ratchasima (18.27%).

This demographic information highlights a balanced representation of genders, a predominance of middle-aged participants, and varying levels of education and income, with a strong familiarity with the target districts. The high concentration of tourists from Bangkok and its vicinity reflects the region’s significant influence on domestic travel trends. This demographic diversity provides comprehensive insights into preferences and decision-making patterns in low-carbon tourism.

4.2. Reliability and Validity Testing

The results from

Table 2. Convergent validity and discriminant validity.

Constructs	Items	Factor Loading	S.E.	CR	Cronbach’s Alpha	AVE	r2	MSV	ASV
Tourist Destination	Attraction	0.609	0.050	0.807	0.824	0.516	0.371	0.425	0.301
	Tourism Activities	0.614	0.061				0.378
	Amenities or Facilities and Services	0.818	0.061				0.668
	Accessibility	0.803	-				0.644
Marketing Strategies	Innovative Marketing Communication	0.716	0.042	0.845	0.842	0.581	0.513
	Destination Branding	0.914	-				0.835
	Distribution Channel	0.698	0.043				0.487
	Tourism Experience	0.698	0.043				0.487
Perception of Low-Carbon Tourism	Climate Change Awareness	0.648	0.063	0.799	0.818	0.501	0.421
	Positive Perception and Preferences	0.617	0.056				0.380
	Concern and Challenge	0.765	-				0.585
	Knowledge and Understanding	0.785	0.067				0.616
Low-Carbon Tourism Decision-Making	Trust and Credibility	0.705	0.059	0.827	0.815	0.546	0.497
	Social Influences	0.812	-				0.659
	Practicality and Convenience	0.775	0.060				0.601
	Health and Well-being	0.652	0.047				0.425

demonstrate the convergent and discriminant validity of the constructs used in the proposed structural equation model for low-carbon tourism decision-making. To ensure construct reliability and validity, Confirmatory Factor Analysis (CFA) was conducted. The Average Variance Extracted (AVE) values were all above the recommended 0.5 threshold, indicating good convergent validity. Composite reliability (CR) values exceeded 0.7, confirming strong internal consistency. Discriminant validity was established as the Maximum Shared Variance (MSV) was lower than the AVE for each construct, demonstrating that the constructs are distinct and meaningful.

Structural Equation for Path Coefficient

$$\eta =\beta \eta +\Gamma \xi +\zeta$$

$$\left[\begin{array}{c}{\mathrm{\eta }}_{PL}\\ {\mathrm{\eta }}_{MS}\\ {\mathrm{\eta }}_{LM}\end{array}\right]=\left[\begin{array}{ccc}0& 0.466& 0\\ 0& 0& 0\\ 0.434& 0.352& 0\end{array}\right]\left[\begin{array}{c}{\mathrm{\eta }}_{PL}\\ {\mathrm{\eta }}_{MS}\\ {\mathrm{\eta }}_{LM}\end{array}\right]+\left[\begin{array}{c}0.171\\ 0.652\\ -0.059\end{array}\right]\left[\begin{array}{c}{\mathrm{\xi }}_{TD}\end{array}\right]+\left[\begin{array}{c}0.650\\ 0.575\\ 0.560\end{array}\right]$$

$${\mathrm{\eta }}_{PL}=0.466{\mathrm{\eta }}_{MS}+0.171{\mathrm{\xi }}_{TD}+0.650$$

$${\mathrm{\eta }}_{MS}=0.652{\mathrm{\xi }}_{TD}+0.575$$

$${\mathrm{\eta }}_{LM}=0.434{\mathrm{\eta }}_{PL}+0.352{\mathrm{\eta }}_{MS}-0.059{\mathrm{\xi }}_{TD}+0.560$$

Structural Equation for Endogenous Variables

$$y={\Lambda }_y\eta +\epsilon$$

$$\left[\begin{array}{c}{\mathrm{y}}_{PL1}\\ {\mathrm{y}}_{PL2}\\ {\mathrm{y}}_{PL3}\\ {\mathrm{y}}_{PL4}\\ {\mathrm{y}}_{MS1}\\ {\mathrm{y}}_{MS2}\\ {\mathrm{y}}_{MS3}\\ {\mathrm{y}}_{MS4}\\ {\mathrm{y}}_{LM1}\\ {\mathrm{y}}_{LM2}\\ {\mathrm{y}}_{LM3}\\ {\mathrm{y}}_{LM4}\end{array}\right]=\left[\begin{array}{ccc}0.648& 0& 0\\ 0.617& 0& 0\\ 0.765& 0& 0\\ 0.785& 0& 0\\ 0& 0.716& 0\\ 0& 0.914& 0\\ 0& 0.698& 0\\ 0& 0.698& 0\\ 0& 0& 0.705\\ 0& 0& 0.812\\ 0& 0& 0.775\\ 0& 0& 0.652\end{array}\right]\left[\begin{array}{c}{\mathrm{\eta }}_{PL}\\ {\mathrm{\eta }}_{MS}\\ {\mathrm{\eta }}_{LM}\end{array}\right]+\left[\begin{array}{c}0.579\\ 0.620\\ 0.415\\ 0.384\\ 0.487\\ 0.165\\ 0.513\\ 0.513\\ 0.503\\ 0.341\\ 0.399\\ 0.575\end{array}\right]$$

Structural Equation for Exogenous Variables

$$x={\Lambda }_x\xi +\delta$$

$$\left[\begin{array}{c}{\mathrm{x}}_{TD1}\\ {\mathrm{x}}_{TD2}\\ {\mathrm{x}}_{TD3}\\ {\mathrm{x}}_{TD4}\end{array}\right]=\left[\begin{array}{c}0.609\\ 0.614\\ 0.818\\ 0.803\end{array}\right]\left[\begin{array}{c}{\mathrm{\xi }}_{TD}\end{array}\right]+\left[\begin{array}{c}0.629\\ 0.622\\ 0.332\\ 0.356\end{array}\right]$$

4.3. Structural Equation Modeling Analysis (SEM Analysis)

The structural equation model (SEM) was developed to examine the relationships between destination characteristics, marketing strategies, tourist perceptions, and decision-making in LCT. The model fit indices confirm that the SEM provides a good fit to the data (χ²/df = 1.220, RMSEA = 0.023, NFI = 0.965, GFI = 0.968). These values indicate that the model reliably captures the determinants of LCT decision-making.

• Path Analysis Results

The path analysis diagram for the proposed research model is presented in Figure 2. The findings of the path analysis reveal several key relationships:

Destination characteristics significantly influence marketing strategies (β = 0.652, p < 0.001) (H1), indicating that destinations that adopt sustainable features can leverage them effectively in their marketing campaigns.

Destination characteristics positively influence tourists’ perceptions of LCT (β = 0.171, p = 0.025) (H2), highlighting that eco-friendly infrastructure and policies enhance tourists’ environmental consciousness.

Marketing strategies significantly impact tourists’ perceptions of LCT (β = 0.466, p < 0.001) (H3), suggesting that well-executed sustainability campaigns shape tourist awareness and decision-making.

Tourists’ perceptions strongly influence their decision to engage in LCT (β = 0.434, p < 0.001) (H4), confirming the mediating role of attitudes toward sustainability in shaping behavior.

Marketing strategies directly affect LCT decision-making (β = 0.352, p < 0.001) (H5), reinforcing the need for targeted communication efforts to encourage sustainable travel.

Destination characteristics have a non-significant direct effect on LCT decision-making (β = −0.059, p = 0.396) (H6), suggesting that destination attributes alone are insufficient to drive LCT adoption.

4.3.1. Relationships of Causality Among Latent Variables

The results of the path analysis, detailed in

Table 3. Path analysis.

Hypothesis	Paths	Path Coefficient	p-Value	Relationship
H1	Tourist Destination -> Marketing Strategies	0.652 ***	<0.001	Supported
H2	Tourist Destination -> Perception of Low-Carbon Tourism	0.171 *	0.025	Supported
H3	Marketing Strategies -> Perception of Low-Carbon Tourism	0.466 ***	<0.001	Supported
H4	Perception of Low-carbon tourism -> Low-Carbon Tourism Decision-Making	0.434 ***	<0.001	Supported
H5	Tourist Destination -> Low-Carbon Tourism Decision-Making	−0.059	0.396	Not Supported
H6	Marketing Strategies -> Low-Carbon Tourism Decision-Making	0.352 ***	<0.001	Supported

Remark: * significant at 0.05, *** significant at 0.001 level.

, indicate that a path coefficient below 1 signifies that the causal variable exerts an influence on the outcome variable. The hypothesis testing outcomes confirm that the tourist destination (TD) factor significantly and positively affects the marketing strategies (MS) factor (H1), as evidenced by a standardized coefficient of β = 0.652 and a p-value of less than 0.001. Furthermore, the TD factor plays a vital role in shaping perceptions of low-carbon tourism (PL), supporting hypothesis H2 (β = 0.171, p = 0.025). Similarly, the MS factor has a significant positive impact on PL, validating hypothesis H3 (β = 0.466, p < 0.01).

The analysis also reveals that the PL factor significantly and positively influences low-carbon tourism decision-making (LM), as proposed in hypothesis H4 (β = 0.434, p < 0.001). However, the TD factor demonstrates a negative but non-significant impact on LM, as shown in hypothesis H5 (β = −0.059, p = 0.396). Lastly, the MS factor significantly contributes to LM, providing strong support for hypothesis H6 (β = 0.352, p < 0.001). These findings underscore the interconnected relationships among the factors influencing low-carbon tourism decision-making.

The coefficient estimates illustrated in Figure 2 were obtained through path analysis and structural equation modeling (SEM) conducted in this study. A detailed overview of the key coefficient estimates, along with their statistical significance, is provided in the document. These path coefficients represent the magnitude and direction of the relationships between latent constructs, including tourist destination attributes, marketing strategies, perceptions of low-carbon tourism, and decision-making related to low-carbon tourism.

4.3.2. Meditation Analysis

Table 4. Meditation analysis.

Hypothesis	Paths	Direct Effect	Indirect Effect	p-Value	Mediation	Relationship
	Tourist Destination -> Low-Carbon Tourism Decision-Making	−0.059		0.396		Not Supported
H7	Tourist Destinations -> Perception of Low-Carbon Tourism -> Low-carbon tourism Decision-Making		0.089 *	0.049	Full	Supported
	Marketing Strategies -> Low-Carbon Tourism Decision-Making	0.352 ***		<0.001		Supported
H8	Marketing Strategies -> Perception of Low-Carbon Tourism -> Low-Carbon Tourism Decision-Making		0.0196 ***	0.001	Partial	Supported

Remark: * significant at 0.05, *** significant at 0.001 level.

provides an analysis of mediation within the framework of structural equation modeling, focusing on the intermediary roles played by two independent variables through mediating factors. Further mediation analysis highlights the indirect influence of destination characteristics on decision-making through intermediary variables:

Tourists’ perceptions fully mediate the relationship between destination characteristics and LCT decision-making (Indirect effect = 0.089, p = 0.049) (H7), indicating that environmental awareness plays a key role in driving behavioral change.

Marketing strategies partially mediate the relationship between destination characteristics and LCT decision-making (Indirect effect = 0.196, p < 0.001) (H8), suggesting that promotional efforts bridge the gap between sustainable tourism offerings and consumer engagement.

• Key Takeaways

Marketing strategies and tourists’ perceptions play a stronger role in LCT adoption than destination attributes alone.

Convenience factors (e.g., transport accessibility, accommodation availability) significantly impact decision-making, more so than financial concerns.

Sustainability awareness campaigns and eco-certification programs can enhance LCT engagement by improving perceptions and trust in low-carbon initiatives.

Policy interventions should focus on infrastructure improvements and integrated marketing efforts to boost LCT adoption.

These results provide a comprehensive understanding of the decision-making process for low-carbon tourism, offering actionable insights for policymakers, businesses, and tourism stakeholders to enhance sustainable travel experiences and mitigate environmental impacts.

5. Discussion

The findings of this study provide critical insights into the factors influencing decision-making in low-carbon tourism (LCT), highlighting the interconnected roles of destination characteristics, marketing strategies, and tourist perceptions. The structural equation model (SEM) results confirm that marketing strategies and perceptions of LCT play a more substantial role in shaping tourists’ decisions than destination attributes alone. These findings contribute to the growing body of research emphasizing the importance of sustainability marketing and behavioral influences in tourism decision-making [36,72].

5.1. Key Theoretical Contributions

This study reinforces the Theory of Planned Behavior (TPB) by demonstrating that tourists’ perceptions of sustainability significantly mediate their choices of LCT destinations [42]. Attitudes toward sustainability, combined with social norms and perceived behavioral control, emerge as strong determinants of decision-making. Additionally, this study validates consumer behavior theory, showing that effective marketing strategies directly influence consumer perceptions and subsequent behavior toward LCT adoption [75]. These insights underscore the need for more strategic, perception-based interventions in LCT promotion.

5.2. Cost and Convenience as Barriers to Low-Carbon Tourism

Despite the growing awareness of low-carbon tourism (LCT) benefits, its adoption remains hindered primarily by practical and accessibility concerns, with financial barriers playing a secondary role [34]. The SEM results indicate that logistical challenges, such as transport connectivity, the availability of sustainable accommodations, and accessibility of real-time travel information, are key determinants of tourist decision-making. This finding aligns with previous research suggesting that low-carbon options must be as accessible and convenient as traditional tourism services to drive higher adoption rates [76].

Convenience-related barriers significantly impact the feasibility of LCT. The complexity of multi-modal transport integration makes planning sustainable travel more effort-intensive, discouraging tourists from choosing low-carbon alternatives [32]. Additionally, the limited availability of eco-friendly accommodations near major attractions restricts the practicality of LCT, leading travelers to opt for more accessible but less sustainable options [11]. The lack of a centralized system providing real-time, comprehensive information on sustainable travel routes and services further exacerbates the perceived difficulty of LCT adoption. Without seamless connectivity and easy access to sustainable tourism infrastructure, many tourists remain reluctant to shift toward low-carbon travel choices [32].

While cost-related barriers are present, they are secondary to convenience factors. Although eco-certified accommodations and sustainable transport options often come at a premium, tourists are more likely to be discouraged by the absence of convenient, readily available alternatives rather than cost alone [75]. Moreover, many travelers are unaware of the long-term financial benefits of LCT, such as savings from energy-efficient hotels or carbon offset programs [11]. Addressing these challenges requires strategic interventions that not only make LCT affordable but also practical and easily accessible.

5.3. Practical Implications for Low-Carbon Tourism Development

The successful implementation of low-carbon tourism (LCT) depends on the development of sustainable destinations that prioritize environmental conservation without compromising convenience and accessibility. One crucial aspect of this transformation is the investment in multi-modal transport networks, which would facilitate seamless low-carbon travel. The integration of high-speed rail, electric public transit, and shared mobility solutions can significantly enhance the ease of movement for tourists while reducing carbon emissions [34]. Additionally, the creation of sustainable tourism hubs that feature green-certified hotels, pedestrian-friendly zones, and eco-friendly amenities would contribute to making low-carbon tourism more accessible and practical [76]. To further strengthen the infrastructure supporting LCT, destinations must enhance their eco-tourism facilities by establishing electric vehicle charging stations, promoting the use of renewable energy-powered accommodations, and implementing smart waste management systems that ensure efficient resource utilization and minimal environmental impact [32].

Beyond infrastructure improvements, enhancing marketing and awareness campaigns is essential in encouraging tourists to embrace sustainable travel options. The use of AI-driven personalized marketing strategies can significantly increase awareness and engagement by tailoring information to individual travelers based on their preferences and behaviors [75]. Furthermore, the integration of storytelling and consumer-generated content in marketing strategies allows for a more engaging and experiential approach, emphasizing the immersive benefits of low-carbon tourism while fostering emotional connections with sustainability-conscious travel choices [42]. However, a critical challenge in sustainable tourism adoption lies in the credibility of eco-certifications, as many tourists remain skeptical about green claims made by service providers. To address this, leveraging blockchain technology can enhance transparency by providing verifiable and tamper-proof records of sustainability credentials, ensuring that eco-friendly certifications are authentic and reliable [30].

While awareness and accessibility play key roles in LCT development, financial constraints often deter tourists from opting for sustainable alternatives. To overcome cost barriers, targeted policy interventions and financial incentives must be introduced. Governments and tourism stakeholders can subsidize eco-tourism businesses, allowing them to reduce operational costs and offer sustainable services at more competitive prices [31]. Furthermore, the implementation of tax rebates and travel incentives for tourists who actively choose low-carbon options would encourage a shift toward sustainable travel behaviors by making such choices more economically viable [76]. Another effective mechanism is the establishment of carbon credit programs, where tourists can earn rewards for engaging in environmentally responsible activities, such as selecting eco-friendly accommodations, using sustainable transport, or participating in conservation efforts [77].

Incorporating smart low-carbon tourism ecosystems into destination planning can revolutionize the way sustainable tourism is managed and experienced. The development of digital travel assistants and integrated platforms would centralize information related to low-carbon travel, offering real-time guidance and seamless booking options that simplify decision-making for tourists [33]. Additionally, big data analytics can be employed to assess tourist behavior and optimize the planning of sustainable destinations, ensuring that resources are allocated efficiently while maintaining environmental integrity [32]. In fragile ecosystems, the use of smart sensors and real-time monitoring systems can play a vital role in managing tourist flows, preventing overcrowding, and mitigating potential damage to the environment [34]. These technologies create a dynamic and adaptive framework for managing LCT, ensuring that tourism growth does not come at the expense of ecological well-being.

By integrating sustainable infrastructure, advanced technology, targeted marketing, and financial incentives, low-carbon tourism can become a more practical, attractive, and widely adopted travel option. These measures will not only reduce the carbon footprint of the tourism industry but also contribute to long-term environmental conservation while enhancing the overall travel experience for sustainability-conscious tourists.

5.4. Comparison with Similar Studies and Innovation of This Study

This study offers a distinctive contribution to the field of low-carbon tourism (LCT) by integrating destination characteristics, marketing strategies, and tourist perceptions within a structural equation modeling (SEM) framework to assess their impact on tourists’ decision-making. This approach provides a nuanced understanding of the interplay between these factors, setting this research apart from previous studies.

5.5. Comparison with Similar Studies

Previous research has explored various dimensions of LCT, often focusing on specific factors influencing sustainable tourism practices:

Environmental Knowledge and Awareness: Studies have examined how environmental knowledge, awareness, and concern drive sustainable tourism behaviors. For instance, research utilizing the Stimulus-Organism-Response (SOR) model has highlighted the role of environmental awareness in promoting low-carbon travel intentions [78].

Evaluation Indicators and Performance Metrics: Some studies have concentrated on developing evaluation systems for low-carbon tourism destinations. An example includes constructing objective indicator systems based on economic, environmental, and social dimensions to assess the development level of low-carbon tourism cities [79].

Behavioral Intentions and Psychological Factors: Research has also delved into the psychological determinants of low-carbon travel intentions, employing SEM to explore relationships between latent psychological variables and travel behaviors [80].

5.6. Innovation and Uniqueness of This Study

While prior studies have provided valuable insights into individual aspects of LCT, this study’s innovation lies in its comprehensive approach:

• Integrated Framework: By combining destination attributes, marketing strategies, and tourist perceptions into a single SEM, this research captures the complex interdependencies among these factors, offering a holistic view of what influences tourists’ low-carbon destination choices.
• Emphasis on Marketing Strategies: The study underscores the significant impact of targeted marketing strategies on shaping tourist perceptions and behaviors toward LCT. This focus on the strategic promotion of low-carbon destinations addresses a gap in existing literature, which often emphasizes environmental or infrastructural factors over marketing influences [75].
• Policy and Practical Implications: The findings provide actionable recommendations for policymakers and tourism operators, advocating for integrated marketing and sustainable development efforts to effectively promote LCT. This practical orientation enhances the study’s relevance and applicability in real-world contexts [68].

6. Conclusions

This study advances the understanding of factors influencing tourists’ decision-making in selecting low-carbon tourism destinations by integrating key variables: tourist destination characteristics, marketing strategies, and perceptions of low-carbon tourism; within a structural equation model (SEM) framework. The findings confirm that marketing strategies and tourist perceptions are stronger determinants of LCT adoption than destination attributes alone [75]. The findings suggest that improving accessibility, convenience, and strategic sustainability marketing will drive higher adoption rates. Additionally, this study highlights that while financial barriers exist, practical barriers, such as lack of seamless infrastructure and clear information, are more critical deterrents [31].

From a policy perspective, governments and tourism enterprises should focus on expanding sustainable infrastructure, integrating digital solutions, and incentivizing tourists to adopt LCT. By aligning these efforts with consumer expectations and behavioral insights, stakeholders can increase the appeal and feasibility of LCT while supporting global sustainability goals [76].

7. Suggestions for Further Research

• Cross-Cultural Analysis of LCT Decision-Making: Future studies should examine how cultural differences influence perceptions and adoption of LCT, as sustainability preferences may vary across international markets.
• Longitudinal Study on Behavioral Change: Investigating whether tourists who adopt LCT sustain their eco-friendly behaviors in subsequent trips will provide insights into long-term behavioral shifts.
• Economic Feasibility Studies: Assessing how financial incentives, pricing strategies, and subsidies impact LCT adoption could offer valuable policy recommendations.
• Multi-Case Study Approach: Comparing LCT implementation across different geographic and economic contexts will help identify best practices and region-specific challenges.

By addressing these areas, future research can contribute to more effective LCT policies and business models, ensuring the long-term viability of sustainable tourism development.