Necessity to Assess the Sustainability of Sensitive Ecosystems: A Comprehensive Review of Tourism Pressures and the Travel Cost Method
Abstract
:1. Introduction
2. Sustainable Tourism and Sensitive Ecosystems
2.1. Tourism in Protected Areas
2.2. Pressures Applied on Ecosystems Due to Unsustainable Tourism
- Low-priced transportation tickets and travelling opportunities;
- An abundance of bed and breakfast accommodation, as well as several other tourists’ facilities;
- Political phenomena, such as war or terrorist attacks, that disfavor certain destinations;
- The expansion of the Internet, through which information is easily obtained and experiences are shared on various platforms [28].
- Inclusive and sustainable economic growth: 8, 9, 10 and 17;
- Social inclusiveness, employment and poverty reduction:1, 3, 4, 5, 8 and 10;
- Resources regulation, environmental protection and climate change: 6,7, 8, 11, 12, 13, 14, and 15;
- Cultural heritage and diversity: 8, 11, and 12;
- Peace and security: 16.
3. State-of-the-Art Research
3.1. Non-Market Economic Valuation Methods
3.2. The Travel Cost Method
3.2.1. Limitations of the Travel Cost Method
- The number of trips to the area;
- Demographics;
- TC;
- Time spent travelling.
- Time spent at the destination;
- Other sites visited during the same trip;
- Visiting purpose;
- Overall satisfaction (usually referring to environmental quality).
- ○
- Travel duration: The time spent travelling to a destination can be seen as part of the trip’s expenditures by time-constrained individuals, whereas others may benefit from a scenery journey.
- ○
- Multi-purpose and multi-destination trip: When visiting the examined site is part of a trip that involves several other destinations, there are difficulties in calculating the TC of interest. When the overall TC is allocated to different destinations, the partial cost drastically decreases.
- ○
- Substitute destinations: The presence of other destinations, even of the same type as the one ultimately visited, actually adds value to the latter. So, given two different travelers covering the same distance, the one with access to substitutes could value the preferred destination more.
- ○
- Other expenditures: In addition to transportation costs, there are several others, such as parking fees, vehicle maintenance expenses, as well as food and accommodation costs.
3.2.2. The Opportunity Cost of Time
3.3. Global Experience
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Tourism Type | Traits | Origin |
---|---|---|
Sustainable tourism | Considers social, economic, and environmental needs of given community and visitors | World Tourism Organization |
Aims to improvement | ||
Ecotourism | Stresses the importance of knowledge and education for everyone involved in a given area (stakeholders and visitors) | The International Ecotourism Society, 2015 |
Volunteer tourism | Working holiday for social and environmental causes | Not explicitly determined |
Covers basic expenses | ||
Responsible tourism | Promotes activities that improve the economy and conserve the cultural heritage of a protected area | Cape Town Declaration, 2002, Goodwin |
Geotourism | Involves environment, agriculture, culture, local cuisine, etc. | National Geographic |
Aims to improve the geographical character of protected area |
Type | Method | Definition |
---|---|---|
Stated preference | Willingness to pay (WTP) | The total valuation is estimated by multiplying the average WTP by the total number of users. It is found that respondents tend to over-report their WTF under hypothetical questions. |
Contingent valuation (CV) | Users are presented with one or more hypothetical scenarios in which they are asked to express their WTP, for a specific improvement in environmental quality or conservation of a natural resource. The responses are then used to estimate the overall economic value of the environmental good or service. Suitable for sites with aesthetic features. | |
Choice experiment (CE) | Users choose between different alternatives presented to them, each with a different combination of attributes regarding environmental quality or conservation policies. | |
Revealed preference | Mitigation behaviour (MBV) | MBV is based on estimated costs related to avoiding or reducing exposure to environmental risks (e.g., a potential flood). |
Hedonic pricing (HPM) | HPM involves gathering data on price variations, pinpointing their origin, and estimating the impact of the environment on them. | |
TC method (TCM) | TCM is based on the assumption that the travel expenditures to a site are related to visitor’s valuation of it. Suitable for tourist destinations. | |
Dose–response (DR) | DR data are often utilized to estimate the impact of environmental quality (e.g., air pollution) on human health. | |
Replacement cost technique | Mostly used in damage assessment, this method estimates the costs of replacing lost or damaged services or goods. |
ITCM | ZTCM |
---|---|
Specifies individual’s traits | Uses average data per zone |
Produces more accurate demand function | Avoids outliers |
Requires significant sample | Deals with lack of data |
Assumes that an individual’s characteristics affect travelling decisions | Considers certain socioeconomic variables statistically insignificant |
Robust estimations require variation in visitation rate | Robust estimations require an adequate number of zones |
Year | Authors | Country | Type of Ecosystem | Valuation Method | Data | Consumer Surplus per Trip/Visitor (EUR) | Analysis | Annual Economic Value (EUR) |
---|---|---|---|---|---|---|---|---|
Non-European Countries | ||||||||
2018 | Riesti and Indah [42] | Indonesia | 6 beaches | ITCM | On-site survey | - | Log-linear model | 190 thousand |
2018 | Dong et al. [43] | Taiwan | Bay | ITCM | On-site survey | 234 | Truncated Poisson model, truncated binomial distribution model, on-site Poisson model | 221 million |
2020 | Mukhopadhyay et al. [6] | India | Coastal zones | ZTCM | On-site survey | - | Panel regression | 47 billion |
2020 | Kheyri et al. [45] | Iran | Lake | ZTCM | On-site survey | 74 | Linear regression | 1.7 million |
2018 | Lavee and Menachem [50] | Israel | Lake | ITCM + CVM | On-site, online, telephone surveys | 17 | Linear regression | 24.4 million |
2021 | Houngbeme et al. [11] | Benin | Beach | ITCM | On-site survey | 0.76 | Poisson model, negative binomial regression | - |
2018 | Zambrano-Monserrate et al. [10] | Ecuador | Beach | ITCM | On-site survey | 15 | Zero-truncated negative binomial regression | 18 million |
2020 | Menendez-Carbo et al. [44] | Ecuador | Urban park | ITCM | Online survey | 14 | Zero-truncated negative binomial regression | 271–334 million |
2019 | Pascoe [37] | Australia | Beach | ITCM | Online survey | 15.5 | Hurdle model | - |
2021 | Rolfe et al. [33] | Australia | 5 coastal towns | ITCM + choice experiment | On-site survey | 40 | Advanced logit models | - |
2018 | Heagney et al. [52] | Australia | Various | ITCM | Telephone survey | 28 | Random effects ordered logit model | 3 billion |
European Countries | ||||||||
2018 | Clara et al. [49] | Australia + Portugal | Lagoon | ITCM + CVM | On-site survey | 130 | Negative binomial model | - |
2021 | Soares [36] | Portugal | 2 beaches | ZTCM | On-site survey | 23–114 | Linear, linear-log, log-linear, log-log models | 3.6 million |
2018 | Pouso et al. [51] | Spain | 3 beaches | ITCM + partial cost–benefit analysis | On-site survey | 7 | Poisson model | 3.5 million |
2021 | Trovato et al. [9] | Italy | Coastal zone | ITCM + CVM | On-site survey | - | Linear regression | 3 million |
2022 | Sinclair et al. [3] | Italy | Various | CTCM | Geotagged photographs | 6–87 | Zero-truncated Poisson model | 0.3–174 million |
2019 | Latinopoulos [46] | Greece | Lake | Hybrid TC | On-site survey | 59 | Log-linear regression | 0.2 million |
2022 | Hynes et al. [48] | Ireland | Bay | ITCM + cost–benefit analysis | On-site survey | 11 | Negative binomial model | 0.6 million |
2022 | Deely et al. [38] | Ireland | Coastal zones | ITCM | On-site survey | 295 | Logit model, negative binomial model | - |
2022 | Kennerley et al. [4] | UK | Coastal zone | ITCM + CVM | On-site survey | - | Negative binomial regression | - |
2020 | Sinclair et al. [54] | Germany | Various | CTCM | Geotagged photographs | 17–35 | Log-log ordinary least squares regression | 1.67 billion |
2020 | Rousseau and Tejerizo Fuertes [47] | the Netherlands | Estuary | ITCM + choice experiment | Online survey | 108–197 | Log-linear regression | 22 million |
2019 | Lankia et al. [41] | Finland | Coastal zones | ITCM + CVM | Online, mailed paper survey | 16 | Poisson model, negative binomial regression | - |
2021 | Tienhaara et al. [39] | Finland | Lake | ITCM + CVM | On-site survey | 71 | Poisson model | - |
2022 | Juutinen et al. [40] | 4 Nordic Countries | Various | ITCM | On-site survey | 21–64 | Negative binomial models | 3.1–120.8 million |
2021 | Börger et al. [31] | 14 EU countries | Blue spaces | ITCM + CVM | Online survey | 41 | Multivariate Poisson lognormal regression | 631 billion |
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Skarakis, N.; Skiniti, G.; Tournaki, S.; Tsoutsos, T. Necessity to Assess the Sustainability of Sensitive Ecosystems: A Comprehensive Review of Tourism Pressures and the Travel Cost Method. Sustainability 2023, 15, 12064. https://doi.org/10.3390/su151512064
Skarakis N, Skiniti G, Tournaki S, Tsoutsos T. Necessity to Assess the Sustainability of Sensitive Ecosystems: A Comprehensive Review of Tourism Pressures and the Travel Cost Method. Sustainability. 2023; 15(15):12064. https://doi.org/10.3390/su151512064
Chicago/Turabian StyleSkarakis, Nikolaos, Georgia Skiniti, Stavroula Tournaki, and Theocharis Tsoutsos. 2023. "Necessity to Assess the Sustainability of Sensitive Ecosystems: A Comprehensive Review of Tourism Pressures and the Travel Cost Method" Sustainability 15, no. 15: 12064. https://doi.org/10.3390/su151512064
APA StyleSkarakis, N., Skiniti, G., Tournaki, S., & Tsoutsos, T. (2023). Necessity to Assess the Sustainability of Sensitive Ecosystems: A Comprehensive Review of Tourism Pressures and the Travel Cost Method. Sustainability, 15(15), 12064. https://doi.org/10.3390/su151512064