Strategic Management of Low Carbon Travel in Longevity Tourism Evidence from Thailand

1.1 Research objective and contributions

The main aim of the research is to measure the effect of the longevity tourism travel trend in Thailand on the carbon emission. The following are some of its contributions:

1.2 Research organization

The research is divided into six parts: Section 1 addresses the issue of the increased longevity tourism in Thailand and the carbon emission challenges. Section 2 analyses past studies on environmentally friendly tourism. Section 3 explains a mixed-method method of research, combining surveys of 450 tourists and the different forms of analysis. Section 4 gives the results of carbon emission and behavior pattern. Section 5 provides implications on sustainable tourism management. The conclusions and useful recommendations on future research are provided in section 6.

2.1 Research gap

In the knowledge of the low-carbon travel practices and tourism carbon emission has some gaps. Some of them are the reliance on studies that focus to specific regions or countries, and this can restrict the extrapolation of findings to other tourist destinations or tourist settings (Naik et al., 2025). Moreover, certain methods simplify complicated tourism systems and do not consider how tourists react to carbon-oriented efforts (Gallego et al., 2025). The results cannot apply to other nations or situations because they are solely based on China (Tsoggerel et al., 2025).

This study identifies four key gaps in the current literature on longevity tourism. Firstly, there is a lack of studies conducting a per-capita carbon footprint analysis using a lifecycle assessment specifically for longevity tourism. Secondly, existing behavior-sensitive frameworks for low-carbon tourism have not been applied to longevity tourists. Thirdly, there is a gap in destination-level carbon accounting for the longevity tourism segment in Thailand. Lastly, there is a lack of integration of quantitative emission estimation with qualitative analysis of tourist behavior and perceptions in the context of longevity tourism in Asia. This study aims to address these gaps by providing an integrated, mixed-method, LCA-based carbon footprint assessment of longevity tourism in selected Thai destinations and proposing a behavior-sensitive strategic framework for low-carbon longevity tourism management.

3.1 Data collection

Data were collected across five purposively selected Thai destinations — Chiang Mai, Hua Hin, Pattaya, Bangkok, and Phuket — which collectively represent the principal geographic, infrastructural, and market-type clusters of longevity tourism in Thailand, encompassing urban medical-wellness hubs, coastal retirement enclaves, and international resort-wellness destinations.

Structured questionnaires and travel activity logs were used to collect primary data on the housing, transportation, food preferences, and other activities of the 450 longevity tourists in certain areas in Thai locations (Madhyamapurush, 2026). The carbon footprint research was supported using secondary data on emission factors obtained through the national energy and tourism databases. The use of both primary and secondary information provides the right solution to the monitoring of the travel trends and emission levels.

For inclusion in the study, a longevity tourist was operationally defined as an individual aged 45 years or above who was residing in Thailand for a minimum of 30 consecutive days and whose primary travel motivation was wellness enhancement, health-seeking, preventive healthcare, or retirement lifestyle, consistent with the conceptual definition.

3.1.1 Sampling Strategy and Recruitment

Participants were recruited using a purposive convenience sampling strategy at five longevity tourism destinations in Thailand selected to represent the geographic and infrastructural diversity of the longevity tourism market: Chiang Mai (northern wellness and cultural hub), Hua Hin (coastal retirement and wellness enclave), Pattaya (coastal long-stay zone), Bangkok (urban medical and wellness destination), and Phuket (international resort and wellness island). These sites were selected because they collectively account for the largest concentrations of long-stay wellness visitors in Thailand and offer a range of accommodation types, healthcare facilities, and lifestyle environments relevant to longevity tourists. Participant recruitment was conducted at wellness centers, health resorts, eco-certified hotels, international hospitals offering wellness programs, and expatriate community centers at each site over a ten-week data collection period.

To be included in the study, participants were required to meet the operational definition of longevity tourist: aged 45 years or above, currently staying in Thailand for a minimum of 30 consecutive days, and traveling primarily for wellness, health, preventive care, or retirement lifestyle purposes. Participants whose primary reason for travel was business, conventional leisure, or medical treatment only (without a wellness or lifestyle component) were excluded. Potential participants were first screened using a two-question eligibility filter administered verbally by the research team before questionnaire administration. A total of 510 individuals were approached; 450 met the inclusion criteria and provided completed, usable questionnaires, yielding a response rate of 88.2%.

3.2 Questionnaire design

A survey questionnaire was prepared to capture the data on the demographics of participants, the patterns of travel, and low-carbon behavior amid brand longevity tourism in Thailand depicted in Table 1. To measure the sustainability of the travel practices, the tool provided questions regarding accommodation, food, destination, mode of transport and knowledge of the renewable energy programmes. The questionnaire involved multiple-choice, open-ended and Likert-scale questions to understand the adoption of low-carbon activities and the impacts on the decisions of visitors.

Questions Q1 to Q8 each employed a five-point Likert-type response scale anchored from 1 (Strongly Disagree /Never) to 5 (Strongly Agree/Always). The questionnaire also included two open-ended qualitative questions — ‘What factors most influence your transportation choices during your stay?’ and ‘What changes in destination infrastructure or policy would most encourage you to adopt lower-carbon behaviors?’ — the responses to which were subject to thematic analysis. Prior to field deployment, the questionnaire underwent a two-stage validation process: (i) content validity was assessed through expert review by three academics with expertise in tourism sustainability and carbon footprint research, resulting in minor wording clarifications for Q3 and Q7; and (ii) a pilot test with 30 longevity tourists (excluded from the main analysis) was used to assess instrument reliability. Cronbach’s alpha values from the pilot indicated satisfactory internal consistency for each of the four construct domains: Low-Carbon Travel Behavior (α = .79), Sustainable Accommodation Choices (α = .76), Low-Carbon Lifestyle Adoption (α = .81), and Strategic Awareness and Support (α = .74). For the main analysis, composite scores for each domain were computed as the arithmetic mean of the two constituent Likert items, yielding a continuous 1–5 scale per domain.

3.3 Carbon footprint assessment

To assess the emissions of GHGs related to the longevity tourism travel patterns in Thailand, the research employs the carbon footprint analysis of LCA method. It measures the travel, housing, food, and tourism emissions based on primary trip data and on a set of predetermined emission parameters. The evaluation makes it possible to compare per-capita emissions when traveling a certain distance and performing a specific behavior. This solution help to identify the low-carbon travel solutions that could be effective without negatively affecting the well-being of visitors.

3.3.1 Lifecycle Assessment (LCA) for Carbon Footprint Evaluation and Low-Carbon Strategy in Longevity Tourism

This study uses a bottom-up LCA approach to calculate greenhouse gas emissions per capita for longevity tourism in Thailand. The analysis includes emissions from transportation, accommodation, dietary consumption, and recreational activities. Emission factors are sourced from reputable organizations, and key factors used in calculations are provided. Per-capita emissions are calculated for each domain, considering travel distances, accommodation stays, dietary choices, and activity-specific emissions. Assumptions and sensitivity analyses are conducted to assess the impact of variations in key factors on emission estimates.

3.4 Statistical analysis

Variations in carbon emissions across various travel patterns, dwelling types, means of transportation, and lifestyle activities of longevity tourists in Thailand were assessed using statistical analysis. The emission data was initially summarized using descriptive statistics in IBM SPSS (version 28), and any significant group differences were subsequently examined using One-Way ANOVA. The research employs a quantitative approach to identify major emission sources and assess low-carbon travel options. It includes thematic analysis of qualitative data from questionnaires and trip diaries, revealing behaviors and attitudes linked to low-carbon travel. This integrated analysis offers key insights into visitor behavior and travel patterns, contributing to the management of sustainable longevity tourism in Thailand.

The four key constructs — Low-Carbon Travel Behavior, Sustainable Accommodation Choices, Low-Carbon Lifestyle Adoption, and Strategic Awareness and Support — were operationalized at two levels for the analyses. At the behavioral level, each construct was represented by the per-capita carbon emission estimate (in kg CO₂e) derived from the LCA calculation applicable to that domain, as described in Section 3.3. At the attitudinal level, each construct was represented by the composite mean score of its two Likert questionnaire items (Q1–Q2, Q3–Q4, Q5–Q6, Q7–Q8 respectively). For the One-Way ANOVA analyses, participants were grouped by the discrete travel behavior sub-categories reported in their activity logs (e.g., air travel vs. private vehicle vs. public transport for the transportation domain; hotel/resort vs. serviced apartment vs. eco-certified hotel for the accommodation domain), and the dependent variable in each ANOVA was the per-capita kg CO₂e emission estimate for that domain. This approach directly links the questionnaire-derived sub-group classifications to LCA-derived emission outcomes, enabling statistically grounded comparison of emission levels across behavioral sub-groups.

Descriptive statistics were applied to the analysis of the travel patterns and carbon emissions of longevity tourists in Thailand and the patterns of transportation, accommodation, food, and activities were found. The information can help policy makers and tourism operators to determine the sources of high pollution and what low-carbon options are possible. The median offers a realistic view of typical travel behavior, while the mean indicates average emissions per traveller. Additionally, standard deviation highlights variances in hotel and transport preferences among travellers.

ANOVA is a statistical procedure used to compare means of a continuous response variable of an analysis across multiple groups characterised by discrete variables. It ascertains the statistical significance of the differences in the means of the groups by the comparison of the intergroup variance with the intragroup variance. When the population means are more than two, the F-statistic is the test statistic. This research uses one-way ANOVA, to determine whether the means of transportation, accommodation facilities, and activities of longevity tourists in Thailand have significant impacts in their carbon emission.

Before interpretation, the following ANOVA assumptions were verified for each model: (i) normality of the dependent variable within each group was assessed using Q-Q plots and Shapiro-Wilk tests; (ii) homogeneity of variance across groups was assessed using Levene’s test. In models where Levene’s test indicated significant heteroscedasticity (p < .05), Welch’s robust one-way ANOVA was substituted for the standard F-test, and Games-Howell post-hoc comparisons were applied in place of Tukey HSD.

The research employs the thematic analysis that systematically determines and rates recurrent themes in the qualitative answers of the longevity tourists and stakeholders. It seeks to identify some of the major themes in terms of travel patterns, hotel preference, activities, and low-carbon traveling attitudes. The approach adds to the existing knowledge of the impact of passenger-choice on carbon emissions, with a complementary role to quantitative data, and it can aid in the construction of low-carbon tourist policy in Thailand.

Thematic analysis was conducted on qualitative data obtained from two sources: (i) written responses to the two open-ended questionnaire items, and (ii) narrative entries from the travel activity logs in which participants described their in-destination behavior and motivations. Thematic analysis followed Braun and Clarke’s (2006) six-phase reflexive framework: familiarization with the data, generation of initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. The analysis was conducted inductively in the first instance, with no pre-specified coding framework, to allow themes to emerge from participants’ own language and reasoning.

Ethical considerations

The research described in this article was reviewed and approved by the University of Phayao Human Ethics Committee, Thailand. The approval reference number is HREC-HSS 2.2/175/68. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. Informed consent, in written form, was obtained from all individual participants included in the study.

4.1 Demographic distribution

The demographic distribution of the 450 responses is shown in Table 2, which reveals a balanced gender distribution with a small male predominance. Most longevity travellers are 55 years of age or older due to the senior-focused nature of long-term wellness travel. The fact that foreigners make up most visitors highlights Thailand’s allure as a long-term tourism destination. Long stays and wellness-focused travel choices are encouraged by the sample’s high level of education and middle-class to upper-class demographics. A significant portion of travellers stay longer than a month to assess carbon emissions and low-carbon travel activities. The demographics of longevity tourism respondents, including (a) gender distribution, (b) age group distribution, (c) nationality composition, (d) education level, and (e) duration of stay, are shown in Figure 2.

Figure 2. Demographic characteristics of longevity tourism respondents: (a) Gender, (b) Age Group, (c) Nationality, (d) Education Level, (e) Length of Stay.

4.2 LCA-based carbon footprint analysis

According to the LCA-based carbon footprint analysis, lifestyle decisions, activities, and strategic awareness have less of an impact on emissions in longevity tourism than transportation and lodging shown in Table 3 and Figure 3. The adoption of renewable energy, energy-efficient accommodation, and sustainable transportation are only a few of the important areas where low-carbon interventions can successfully lower the overall carbon footprint when emissions are categorized by major themes.

Figure 3. LCA-based comparison of carbon emission drivers in longevity tourism.

4.3 Descriptive statistics

The descriptive statistics in Table 4 indicates that Low-Carbon Travel Behavior and Sustainable Accommodation Choices significantly contribute to per-capita carbon emissions in longevity tourism. The focus on low-carbon lifestyle, knowledge and assistance, can help reverse the emissions, by reducing the effects of activities with low impact, the choice of food, and the awareness of renewable energy. These statistics highlight the main areas to follow the low-carbon strategies in Thai longevity tourism.

The high standard deviation for Low-Carbon Travel Behavior (SD = 182.5) reflects the wide dispersion in transport mode emissions — primarily the contrast between air travelers and public transport users — and underscores the importance of transport mode as the single largest and most variable source of per-capita emissions in longevity tourism.

4.4 One-Way ANOVA

According to the One-Way ANOVA findings in Table 5 and Figure 4, there are significant differences in the amount of carbon emissions among the essential travel factors, such as lodging, transport, food preference, and strategic awareness $\left(p<0.01\right).$ Public transportation, eco-certified housing, and plant-based diets generated the lowest emissions, while air travel and traditional hotels had the highest. This indicates opportunities for implementing low-carbon strategies in longevity tourism.

Figure 4. One-Way ANOVA–based comparison of carbon emissions in longevity tourism.

For Low-Carbon Travel Behavior (transport mode), the one-way ANOVA revealed a statistically significant effect (F(2, 447) = 28.45, p < .001, η² = .113), indicating a large effect; post-hoc comparisons (Tukey HSD) confirmed that all three transport mode groups differed significantly from one another (all pairwise p < .001). For Sustainable Accommodation Choices, a significant effect was found (F(2, 447) = 19.32, p < .001, η² = .079, moderate effect); eco-certified hotel guests emitted significantly less than both hotel/resort guests (p < .001) and serviced apartment guests (p = .003). For Low-Carbon Lifestyle Adoption, the ANOVA was significant (F(1, 448) = 12.87, p < .001, η² = .028, small-to-moderate effect); plant-based/local diet tourists emitted significantly less than standard diet tourists (p < .001). For Strategic Awareness and Support, the effect was also significant (F(1, 448) = 10.12, p = .006, η² = .022); high-awareness tourists emitted significantly less than low-awareness tourists (p = .006). Public transportation, eco-certified housing, and plant-based diets generated the lowest emissions, while air travel and traditional hotels had the highest. This indicates opportunities for implementing low-carbon strategies in longevity tourism.

4.5 Thematic analysis

The thematic analysis shows that the low-carbon travelling behavior such as the eco-friendly living that is reported in Table 6 and Figure 5 is being adopted by longevity travellers. The knowledge and availability of renewable energy in the region enhances travel satisfaction and destination loyalty. These insights underscore the significance of intentional low-carbon management to reduce emissions while maintaining Thailand’s appeal as a long-term vacation spot.

Figure 5. Thematic framework of low-carbon practices in longevity tourism in Thailand.

The following illustrative participant quotes provide direct evidence for each thematic finding:

Low-Carbon Travel Behavior (Sustainable Transport Preference): “I stopped renting cars completely after the first week. Walking and songthaew [local shared taxis] are actually faster in the old city, and I realized I wasn’t adding to the pollution that bothers my breathing anyway” (International participant, Chiang Mai, aged 67).

Sustainable Accommodation Choices (Energy-Efficient Lodging): “When I found the hotel had solar water heating and recycled rainwater for the gardens, it made me feel the extra cost was worth it. I knew my long stay wasn’t hurting the place” (International participant, Hua Hin, aged 71).

Low-Carbon Lifestyle Adoption (Local and Plant-Based Diets): “Thai food is naturally lighter and more plant-based than what I eat at home. I genuinely eat better here and I know it’s better for the environment too — so it’s not a sacrifice, it’s a pleasure” (Domestic participant, Bangkok, aged 58).

Strategic Awareness and Support (Policy and Infrastructure): “If the buses came more frequently and the cycle paths connected the wellness center to the beach, I would never need a taxi at all. The infrastructure just isn’t quite there yet, but I can see they are trying” (International participant, Phuket, aged 63).

Limitation and future work

This study has a few limitations that impact the generalizability of its findings. First, data were collected at five Thai destinations, which, while selected to represent the diversity of longevity tourism in Thailand, do not capture all destination types or regional contexts within the country. Findings should be considered most directly applicable to established longevity tourism hubs in Thailand with developed wellness infrastructure. Second, the study focuses exclusively on the longevity tourist segment as defined in this paper; the emission patterns and behavioral dynamics identified here may not be directly transferable to short-stay leisure tourists, backpackers, medical tourists without wellness components, or longevity tourists in other national contexts with different transport networks, energy grids, or dietary cultures. Third, as noted in the Discussion, the ANOVA analyses do not control for potential confounders such as length of stay, age, and nationality. Fourth, the reliance on self-reported travel activity data introduces the possibility of recall and social desirability bias in emission estimates. These limitations point to several priorities for future research: longitudinal studies tracking the same longevity tourists across multiple visits to assess whether low-carbon behaviors are sustained; comparative studies across multiple destination countries to establish cross-cultural generalizability; multivariate regression analyses controlling for confounders; and intervention-based studies testing the effectiveness of specific low-carbon infrastructure investments and behavioral nudges in longevity tourism contexts.