Evidence map›Paper›PMID 42093957›Full record

ArticleF1000Research2026

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

Warach Madhyamapurush

Abstract read
In one paragraph

Article in F1000Research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Warach MadhyamapurushUniversity of Phayao, Mueang Phayao District, Phayao, Thailand.ORCID https://orcid.org/0000-0003-4110-0780

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increase in longevity tourism, driven by an aging populace and wellness travel, raises environmental issues, notably carbon emissions from travel. Thailand is becoming a key destination for this type of tourism, necessitating strategic frameworks to integrate low-carbon travel methods into its management. This research intends to evaluate the carbon footprint of longevity tourism travel patterns in Thailand and provide strategic management solutions to encourage low-carbon travel and improve long-term health and well-being results. A mixed-method approach was used to gather primary data via structured questionnaires and travel activity logs from 450 longevity tourists in selected Thai destinations, along with secondary data on emission factors from national energy and tourism databases. Carbon footprint assessment was conducted using an LCA-based carbon footprint analysis. Descriptive statistics and One-Way ANOVA in IBM SPSS (version 28) analyzed emissions variations across transportation modes, accommodation types, dietary choices, and activity patterns. Thematic analysis on qualitative responses highlighted common behavioral patterns and perceptions related to low-carbon travel practices. According to the LCA-based carbon footprint analysis, transportation (347 kg CO2e per capita) and lodging (160 kg CO2e) had the highest emissions, compared to lifestyle choices (42.5 kg CO2e) and strategic awareness factors (50 kg CO2e). Significant emission disparities among different travel modes, housing types, food patterns, and awareness levels were confirmed by one-way ANOVA results (p < 0.01). Longevity tourism in Thailand is made more sustainable by intentional low-carbon planning and optimized infrastructure, while extended stays and energy-efficient lodgings lower emissions.

Indexed as

CarbonCarbon FootprintLongevityTourismTravelHumansThailandCarboncarbonemissionslifestyle.Long-term tourismtransportation

Identifiers

PMID42093957
PMCPMC13139847

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.