tourism gdp contribution in thailand

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Tourism industry in Thailand - statistics & facts

Domestic tourism, the outlook of tourism industry in thailand post covid-19 pandemic, key insights.

Detailed statistics

Foreign tourist arrivals Thailand 2015-2023

Value of tourism GDP Thailand 2017-2021

Value of tourism expenditures Thailand 2017-2021

Editor’s Picks Current statistics on this topic

Current statistics on this topic.

Travel, Tourism & Hospitality

Share of tourism to GDP value Thailand 2017-2020

Leisure Travel

Number of international visitors Thailand 2023, by region of origin

Related topics

Recommended.

• Demographics of Thailand
• Tourism worldwide
• Tourism and hotel industry in Singapore
• Tourism in Malaysia

Recommended statistics

• Premium Statistic Value of tourism GDP Thailand 2017-2021
• Basic Statistic Number of quarterly foreign visitors Thailand Q1 2021-Q4 2022
• Premium Statistic Number of local tourists Thailand 2023
• Premium Statistic Size of employment in tourism sector Thailand 2017-2021

Total value of the tourism's contribution to the Gross Domestic Product in Thailand from 2017 to 2021 (in billion Thai baht)

Number of quarterly foreign visitors Thailand Q1 2021-Q4 2022

Number of international tourists in Thailand from 1st quarter 2021 to 4th quarter 2022 (in 1,000s)

Number of local tourists Thailand 2023

Number of domestic visitors in Thailand from January to December 2023 (in millions)

Size of employment in tourism sector Thailand 2017-2021

Number of employees in the tourism sector in Thailand from 2017 to 2021 (in millions)

Economic impact

• Premium Statistic Direct contribution of tourism to gross domestic product Thailand 2017-2021
• Premium Statistic Indirect contribution of tourism to gross domestic product Thailand 2017-2021
• Premium Statistic Value of tourism tax Thailand 2017-2021
• Premium Statistic International tourist arrivals revenue Thailand 2023
• Premium Statistic Local tourists revenue Thailand 2023

Direct contribution of tourism to gross domestic product Thailand 2017-2021

Direct contribution of the tourism industry to the gross domestic product (GDP) in Thailand from 2017 to 2021 (in billion Thai baht)

Indirect contribution of tourism to gross domestic product Thailand 2017-2021

Indirect contribution of the tourism industry to the gross domestic product (GDP) in Thailand from 2017 to 2021 (in billion Thai baht)

Value of tourism tax Thailand 2017-2021

Value of tourism tax in Thailand from 2017 to 2021 (in billion Thai baht)

International tourist arrivals revenue Thailand 2023

Tourism revenue incurred from international tourist arrivals in Thailand from January to December 2023 (in billion Thai baht)

Local tourists revenue Thailand 2023

Tourism revenue incurred from domestic visitors in Thailand from January to December 2023 (in billion Thai baht)

• Premium Statistic Number of international visitors Thailand 2023, by region of origin
• Premium Statistic Number of foreign visitors Thailand 2023, by region
• Premium Statistic Revenue generated from foreign visitors Thailand 2023, by region
• Premium Statistic Number of traveling locals Thailand 2023, by region
• Premium Statistic Revenue generated from traveling locals Thailand 2023, by region

Number of foreign tourists in Thailand in 2023, by region of origin (in 1,000s)

Number of foreign visitors Thailand 2023, by region

Total number of foreign visitors in Thailand in 2023, by region (in millions)

Revenue generated from foreign visitors Thailand 2023, by region

Total revenue generated from foreign visitors in Thailand in 2023, by region (in billion Thai baht)

Number of traveling locals Thailand 2023, by region

Total number of Thai travelers within the regions of Thailand in 2023, by region (in millions)

Revenue generated from traveling locals Thailand 2023, by region

Total revenue generated from Thai travelers within the regions of Thailand in 2023, by region (in billion Thai baht)

Accommodation

• Premium Statistic Share of foreign visitors to total guests of accommodations Thailand 2015-2023
• Premium Statistic Share of foreign visitors to total guests of accommodations Thailand 2023, by region
• Premium Statistic Advance reservation rate of accommodations Thailand 2015-2023
• Premium Statistic ADR of accommodations Thailand 2015-2023
• Premium Statistic Popular accommodations for Thais when travelling Thailand Q2 2021

Share of foreign visitors to total guests of accommodations Thailand 2015-2023

Proportion of international tourists to total guests of the accommodations in Thailand from 2015 to 2023

Share of foreign visitors to total guests of accommodations Thailand 2023, by region

Proportion of international tourists to total guests of the accommodations in Thailand in 2023, by region

Advance reservation rate of accommodations Thailand 2015-2023

Share of 3-month advanced reservations on the total accommodation occupancy rate in Thailand from 2015 to 2023

ADR of accommodations Thailand 2015-2023

Average daily rate (ADR) of accommodations in Thailand from 2015 to 2023 (in thousand Thai baht)

Popular accommodations for Thais when travelling Thailand Q2 2021

Accommodations that Thais planned to stay at when travelling in Thailand in 2nd quarter 2021

Tourist perspectives

• Premium Statistic Ideal travel destinations in Asia among Thais 2023
• Premium Statistic Online travel agency usage Thailand 2023
• Premium Statistic Most used online travel agencies Thailand 2023
• Premium Statistic Most common duration before a trip to purchase travel tickets on OTAs Thailand 2023
• Premium Statistic Reasons for using online travel agencies for purchasing tickets Thailand 2023
• Premium Statistic Reasons for not using online travel agencies for purchasing tickets Thailand 2023

Ideal travel destinations in Asia among Thais 2023

Leading travel destinations in Asia among tourists in Thailand as of January 2023

Online travel agency usage Thailand 2023

Online travel agency usage in Thailand as of June 2023

Most used online travel agencies Thailand 2023

Leading online travel agencies in Thailand as of June 2023

Most common duration before a trip to purchase travel tickets on OTAs Thailand 2023

Most common duration before a trip to purchase travel tickets on online travel agencies (OTAs) in Thailand as of June 2023

Reasons for using online travel agencies for purchasing tickets Thailand 2023

Reasons for using online travel agencies for purchasing tickets or services in Thailand as of June 2023

Reasons for not using online travel agencies for purchasing tickets Thailand 2023

Reasons for not using online travel agencies for purchasing tickets or services in Thailand as of June 2023

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Thailand GDP expands 4.5% in Q3, buoyed by tourism

Private spending also helps economy mimic pre-COVID era but inflation weighs

BANGKOK -- A reinvigorated tourism sector not only helped Thailand's gross domestic product to a year-on-year 4.5% expansion for the third quarter but also gave a big boost to personal consumption, the National Economic and Social Development Council (NESDC) announced on Monday.

The economic lift came despite inflation taking a toll.

Thailand shifts to European, U.S. tourists to rely less on Chinese

Thailand, singapore tourists jump in line as japan opens borders, thailand asks business to power down as leaders arrive for apec, malaysia's q3 gdp grew 14.2%, highest in over a year, thai banks' interest rate hikes spark economic concerns, japan gdp shrank annualized 1.2% in q3 amid weak yen, inflation, thai shrimp output plunges by half from peak as disease spreads, indonesia bags pledges for $71bn from g-20 presidency: jokowi, indonesia, philippines hike key rates in bid to tame inflation, latest on economy, china's consumer prices rise for 3rd month, signaling demand recovery, china suffers 56% drop in foreign investments on weak domestic demand, china central bank vows to support economic recovery, sponsored content, about sponsored content this content was commissioned by nikkei's global business bureau..

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Asia Travel Re:Set

Issue #70 - Thailand, Tourism & "Contribution to GDP"

How dependent is thailand's struggling economy on tourism.

Welcome to issue 70 of Asia Travel Re:Set…

Thailand is South East Asia’s most-visited country.

It counts the region’s 2nd-largest economy (after Indonesia), and its 4th-largest population (after Indonesia, the Philippines and Vietnam) of approx 74 million.

Thailand, largely due to its own particular brand of political drama, is also the most consistent travel headline maker.

This week, the Land of Smiles announced v.04 of its pandemic-era tourism reboot.

So, today, it’s time to talk about Thailand.

Thanks for being on board,

The Sunday Itinerary

- this week’s top 5.

Updates from South East Asia, Malaysia, Australia, Thailand, Vietnam

- Thailand, Tourism & “Contribution to GDP”

How dependent is Thailand’s struggling economy on tourism?

This week’s The South East Asia Travel Show generated the most single-day downloads since Hannah Pearson and I began the show 2 years ago. Especially strong listener interest came from the UK, US, Australia and Brazil. Part II of our 2022 travel wish list countdown includes governments start communicating with common sense, not in proxy terms; domestic tourism stimulus packages assist players across the value chain; and the tourism industry is attractive again for fresh grads. Plus, should tourism firms publish zero emissions targets? And when will travel be fun again?

Listen here: 🎧  Website 🎧  Spotify 🎧  Apple

Or wherever you grab your pods!

This Week’s Top 5

We’ve heard this several times before, but South East Asian Tourism Ministers reconfirm a commitment to open an ASEAN Travel Corridor . Hmmm.

Malaysia introduces a 3-tier isolation period for returning nationals and inbound arrivals. Vaccine boosted visitors now need to home-isolate for 5 days .

Backpackers are welcome again in Australia , as the government waives the visa fee for Working Holiday visitors . Arrival testing requirements are also eased .

Thailand will restore its currently suspended Test & Go quarantine-free visitor scheme , with amended rules. Applications will begin on 1 February.

Is Vietnam edging closer to reviving travel? The government is considering removing inbound restrictions from 1 May .

Thank you for reading Asia Travel Re:Set. This post is public so feel free to share it.

Thailand, Tourism & “Contribution to GDP”

Image from Tourism Authority of Thailand

“Thailand’s economic recovery in the period ahead remains highly uncertain and would depend largely on the recovery in foreign tourist arrivals. This is because income from spending by foreign tourists accounts for 11% of GDP and tourism-related businesses account for 20% of total employment in Thailand.”

That paragraph was written by the Bank of Thailand in a March 2021 report.

The statistics can be interpreted in two ways, depending on your standpoint.

Some might argue that 11% does not necessarily correlate with the oft-quoted statement that Thailand is “heavily dependent on tourism.”

Alternatively, 11% is a high figure for one of the the 30 largest economies in the world.

Equally important is that tourism, including a robust domestic travel sector, accounts for one-fifth of employment. The pandemic shutdown of inbound tourism saw many people lose their jobs. They likely drifted into other sectors, perhaps permanently.

Quantifying Thai Tourism & GDP

The Bank of Thailand and the Thailand Development Research Institute (TDRI - which advises the Thai government) now place the economic value (or direct contribution to GDP) of tourism at 11-12%.

That figure, though, is fairly meaningless without quantifying the size of the Thai economy.

According to the World Bank, Thailand’s Gross Domestic Product (GDP) in 2020 totalled USD501.64 billion . That places it among the world’s 30 biggest economies.

GDP in 2020 was significantly down from USD544.26 billion in 2019 - but a huge jump from USD281.7 billion in 2010.

Economic growth in 2021 is estimated to have been around 1% - perhaps less.

Will Tourism Reduce in Value?

In 2022, the Thai economy is “expected to grow 3.5%-4.5% ,” The Bangkok Post reports this week citing Finance Minister, Arkhom Termpittayapaisith.

Hitting that ambitious target without a solid contribution from tourism previously would have been unthinkable.

It might still be.

Thailand is losing a sizeable chunk of economic value without a functioning tourism sector. Hence, from 1 February, it will try for a 4th* time during COVID-19 to resuscitate inbound arrivals.

However, an ongoing travel stasis in Asia Pacific and structural change in Thailand’s economy - mostly caused by a coming together of various pandemic impacts - could see tourism ultimately become less valuable to Thailand.

Key Growth Drivers

Thailand’s target range of 3.5%-4.5% GDP growth in 2022 will rely on exports, a recovery of domestic demand and investment, particularly in infrastructure and manufacturing, which would combine to generate much-needed jobs.

Job creation is vital.

Thailand’s unemployment rate doubled from 2020 to 2021. An especially large number of jobs were lost in the travel sector. Some might never return.

A Spotlight on Exports

Manufactured exports account for over 50% of Thailand’s GDP, and grew fast in 2021. Economic rebalancing in major purchasing markets, such as the US, EU, Japan and China, is driving export demand.

Consequently, Thai exports recorded a 15% year-on-year upswing in 2021, says the TDRI - representing a near 10-year high. This might moderate, however, in 2022.

Even so, Thailand lags its competitors - being the 10th largest exporter in East Asia.

Interviewed this week by Channel News Asia , Dr Kirida Bhaopichitr, Economics Research Director at the TDRI, says export growth is being bolstered by Thailand being one of the world’s largest automobile accessories and parts producers.

In addition, hard disk drive sales are thriving as more people work from home. Food products are in high demand, as are pet foods. Thailand is one of the world’s top 3 pet food producers.

A weakened Thai Baht and the US-China trade war (forcing US importers to diversify their source markets) are supporting the export renaissance.

Digital Diversification

And then there is the internet economy, which is enjoying era-defining growth across South East Asia.

The value of Thailand’s internet sector is projected to increase from USD30 billion in 2021 to USD57 billion in 2025 , according to the e-Conomy SEA 2021 report by Google, Temasek and Bain & Co.

In 2019, it was valued at just USD19 billion.

New Taxes - Including Tourism

Another 2022 driver could be fiscal policy - and Thailand is moving into tax-revenue raising mode.

The government announced plans to collect a new THB300 Tourism Tax from April.

The Financial Times reports this week that it is “considering imposing a a flat 15% withholding tax on all cryptocurrency trades,” plus a tax on stock trades.

Turning to taxation is being considered by cash-strapped governments across South East Asia, and further new tax tweaks in Thailand are possible this year.

A Long Road Back for Thai Tourism

So where does this leave the tourism sector?

Dr Kirida Bhaopichitr of the Thailand Development Research Institute, says the country will target a top line of “5 to 6 million visitors” in 2022.

The subdued recovery is influenced by the absence of Chinese tourists, which Dr Kirida does not expect to return “until the end of this year.” Thailand welcomed 10.99 million Chinese visitors in 2019.

She adds, though, that Thai tourism is geographically imbalanced.

While tourism benefits more people, through its extended value chain, than capital-intensive industries like manufacturing, the benefits are poorly distributed, because:

“Most visitors [to Thailand] tend to visit 6 or 7 major destinations.”

She adds that it “will probably be another three years from now” to get back to the 39.8 million visitor arrivals in 2019 .

That might be considered an optimistic forecast, especially as annual inbound growth was showing signs of sluggishness before the pandemic.

According to Bank of Thailand, the country welcomed 156,930 foreign tourists from July 2021 (when the Phuket Sandbox began) through November . This included 91,260 in November, when the quarantine-free Test & Go entry scheme was in effect.

Rebuilding demand will clearly be challenging.

Indeed, the World Bank’s Thailand Economic Monitor notes in its latest issue:

“Tourism is expected to contribute 2% to GDP growth in 2022, and 4% in 2023.”

* Thailand launched its Special Tourist Visa in October 2020. The Phuket Sandbox scheme commenced in July 2021, and the (currently suspended) Test & Go programme began in November 2021. Test & Go II is scheduled to start on 1 February .

Thanks for reading Asia Travel Re:Set! Subscribe for free to receive new posts and support my work.

A bonus extra edition of The South East Asia Travel Show this week as I chat to talented Lombok-based travel documentary maker Josh Edwards about capturing the island’s stunning diversity on camera. Josh also talks about environmental preservation, coral reef restoration and beach clean-up projects in Lombok, plus the upcoming Indonesian Moto GP at the new Mandalika race circuit.

And, that’s a wrap for Issue 70.

Until next Sunday, find me on  Twitter ,  LinkedIn and the  Asia Travel Re:Set  website.

On Friday, The South East Asia Travel Show will round up the regional travel outlook for the Lunar New Year holiday period.

Have a great week,

Ready for more?

Super Songkran Success: A triumphant tale of Thai tourism’s economic, global, and social impact

 Traveloka’s support of Thailand’s travel industry continues as the travel platform’s domestic search traffic grew by 60% from 8-21 April compared to the same period in March.  This year Thailand’s iconic Songkran Festival 2024 was extended nationwide from 1 – 21 April following UNESCO’s recent recognition of the festival as an ‘Intangible Cultural Heritage of Humanity’.

UNESCO’s recognition highlights the profound significance of Songkran, emphasizing its role in fostering community cooperation, family bonds, and the spirit of forgiveness. As stated on UNESCO’s website, Songkran symbolizes as a time-honored tradition for cleansing away misfortunes, praying for prosperity, and honoring ancestors—an embodiment of Thailand’s cultural resilience and heritage.

This journey from ancient water festival to UNESCO cultural heritage recognition is a testament to the enduring strength of Thai traditions and testament to Thailand’s growing global ‘soft power’.

Traveloka fully supported the Songkran Festival extension, understanding its pivotal role as a driving force behind Thailand’s thriving tourism scene. This iconic celebration not only adds a splash of excitement but also amplifies the Kingdom’s reputation as a must-visit destination in Asia, promising travelers unforgettable, immersive cultural adventures.

As this year festival unfolded, cities around Thailand were transformed as Songkran in modern times is primarily about huge fun. Serving as vibrant testament to Thailand’s cultural allure, Songkran has played a pivotal role in positioning the country as top-notch destination for travelers worldwide. Ament to Thailand’s Songkran has helped promote Thailand as a cultural destination for travelers worldwide. Its global appeal transcends national borders, attracting international visitors from around the globe who gather for the event.

The 21-day extravaganza that celebrates the Thai New Year was an economic juggernaut, creating employment in vital sectors including hospitality, transportation, retail, and entertainment.

The Economic, Tourism, and Sports Division said this year the extended Songkran festival generated 140.335 billion baht over the 21-day period from April 1-21, surpassing pre-event estimates. This was distributed across various travel segments including accommodation (42.069 billion baht), food and beverage (33.069 billion baht), souvenirs (26.839 billion baht), travel (15.505 billion baht), entertainment (14,758 billion baht), tourism services (5,865 million baht), and others (2,230 million baht).

According to Traveloka, domestic search traffic spiked from April 8 – 21 by over 60% compared to the same period in March. Additionally, it shared top its 5 domestic destinations during Songkran 2024 to gauge the travel platform’s economic and social contribution to Thailand’s travel industry:

Bangkok – A hub of electrifying street parties and cultural experiences.

Chiang Mai – Known for its vibrant parades, traditional rituals, and local celebrations.

Hat Yai – A nighttime water war downtown with foam parties and concerts.

Phuket – A blend of beachside revelry and cultural festivities.

Udon Thani – Immersing visitors in historical elegance and regional ceremonies.

This year, travelers were drawn by the allure of vibrant street parties, captivating parades, and an electrifying atmosphere that illustrates the rich cultural contribution of Thai soft power. The Tourism Authority of Thailand revealed a major success of ‘Maha Songkran World Water Festival 2024’. Over 780,000 people attended, generating more than 2,880 million baht from 11–15 April. Travelers experienced festive activities including fantastic parades, EDM concerts, Thailand’s tallest dancing fountain show, and stunning performances by over 1,200 drones in choreographed patterns.

The Songkran Festival’s economic, global, and social impacts in 2024 were multifaceted and far-reaching, showcasing its significance as both a cultural celebration and a catalyst for tourism, cultural exchange, community cohesion, and social development in Thailand.

Traveloka leveraged the reach and influence of its platform to spotlight the Songkran Festival as the cornerstone of Thailand’s annual tourism calendar.  It will continue to seek to capture the imagination of travelers worldwide and inspire them to participate in future Thai events through engaging content, immersive experiences, and strategic promotions. All are invited to explore Traveloka’s portfolio of rich cultural experiences for upcoming festivals around Thailand for 2024 and beyond.

Thailand extends visa waiver for Indian and Taiwanese tourists till 1 November

IndiGo adds daily direct flights between Delhi-Phuket

Sustainable pathways with the ‘JW Garden’ at JW Marriott Khao Lak, Thailand

Meliá Hotels International adds two new hotels in Thailand

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Thailand Factsheet

Discover the total economic contribution that the Travel & Tourism sector brings to Thailand and the world in this data-rich, two-page factsheet.

Discover the direct and total economic contribution that the Travel & Tourism sector brings to the Thailand’s economies in this comprehensive report.

Discover the direct and total economic contribution that the Travel & Tourism sector brings to Thailand in this comprehensive report.

Create an account for free or login to download

Over the next few weeks we will be releasing the newest Economic Impact Research factsheets for a wide range of economies and regions. If the factsheet you're interested in is not yet available,  sign up to be notified via the form on this page .

Factsheet details

This factsheet highlights the importance of Travel & Tourism to Thailand across many metrics, and features details such as:

• Contribution of the sector to overall GDP and employment
• Comparisons between 2019 and 2023
• Forecasts for 2024 and 2034
• International and domestic visitor spending
• Proportion of leisure vs business spending
• Top 5 inbound and outbound markets

This latest report reveals the importance of Travel & Tourism to the Thailand in granular detail across many metrics. The report’s features include:

• Absolute and relative contributions of Travel & Tourism to GDP and employment, international and domestic spending
• Data on leisure and business spending, capital investment, government spending and outbound spending
• Charts comparing data across every year from 2014 to 2024
• Detailed data tables for the years 2018-2023 plus forecasts for 2024 and the decade to 2034

Purchase of this report also provides access to two supporting papers: Methodology and Data Sources and Estimation Techniques.

This latest report reveals the importance of Travel & Tourism to Thailand in granular detail across many metrics. The report’s features include:

This factsheet highlights the importance of T&T to this city across many metrics, and features details such as:

• Contribution of the sector to overall GDP and employment in the city
• Comparisons between 2019, 2020 and 2021, plus 2022 forecast
• Proportion of the T&T at city level towards overall T&T contribution at a country level
• Top 5 inbound source markets

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Thailand's economy rebounds in early 2023 as tourism surges

Executive Director and Asia-Pacific Chief Economist, S&P Global Market Intelligence

Thailand has shown a gradual economic recovery from the COVID-19 pandemic during 2022, helped by rising international tourism arrivals. Real GDP growth rose from 1.5% in 2021 to 2.6% in 2022, with growth momentum expected to improve further in 2023.

The latest S&P Global Thailand Manufacturing PMI survey results for April 2023 showed a strong upturn in manufacturing output and new orders. Due to the importance of international tourism for the Thai economy, the strong rebound in international tourism inflows evident in early 2023 signals that the tourism economy will be a key growth driver in 2023.

Thailand's economic recovery from the pandemic

The Thai economy has shown an upturn in economic growth momentum in early 2023, with first quarter GDP growth up by 2.7% year-on-year (y/y), compared with 1.4% y/y growth in the fourth quarter of 2022.

The strong first quarter growth rate was underpinned by rapid growth in private consumption, which rose by 5.4% y/y, helped by surging international tourism arrivals. Expenditure on services rose by 11.1% y/y due to buoyant spending in hotels and restaurants. However private investment grew at a modest pace of just 2.6% y/y, while public investment grew by 4.7% y/y.

Thailand's growth rate in 2022 was quite moderate in comparison with other large ASEAN economies such as Malaysia, Vietnam and Philippines, which posted very high growth rates as they rebounded from the pandemic. Thailand recorded real GDP growth of 2.6% in 2022, representing a relatively modest pace of economic recovery from the recessionary conditions caused by the COVID-19 pandemic.

A key driver for improving economic growth in 2022 was the recovery of private consumption, which grew by 6.3% compared with just 0.6% y/y growth in 2021. Private investment growth also improved from a pace of 3.0% in 2021 to 5.1% in 2022. However public investment contracted by 4.9% in 2022, while government consumption was flat.

Strong growth in private consumption and investment as well as rising energy import prices helped to boost import growth, which rose by 15.3% in 2022, while exports rose by just 5.5%, measured in USD terms. Consequently, the trade balance narrowed from USD 32.4 billion in 2021 to USD 10.8 billion in 2022.

Due to the important contribution of international tourism to Thailand's GDP, a key factor that constrained the rate of recovery of the Thai economy in 2022 was the slow pace of reopening of international tourism, although this gathered momentum in the second half of 2022.

The S&P Global Thailand Manufacturing PMI surged to 60.4 in April from 53.1 in March, signalling a rapid improvement in overall business conditions and the strongest performance in any month since the survey started in December 2015. The month-on-month increase in the headline PMI, at 7.3 points, was also by far the biggest on record (the next-largest upward movement was 4.8 points in May 2020).

A key factor driving the improvement in manufacturing operating conditions was a marked expansion in new orders during April. The rate of growth was the fastest on record and largely driven by domestic demand.

Despite rising demand for raw materials and components in April, manufacturers reported shorter suppliers' delivery times for the first time since April 2022. This reflected a wider recovery in regional and global supply chains. Price pressures eased in April, with input prices having increased at the slowest rate in three months.

Thailand's headline CPI inflation rate eased to 2.7% y/y in April 2023 compared with 5.0% y/y in January 2023 and 7.9% y/y in August 2022. The Monetary Policy Committee (MPC) of the Bank of Thailand decided to raise the policy rate by 0.25% from 1.25% to 1.50% at their Monetary Policy meeting on 25 January 2023, with a further 0.25% rate hike implemented on 29 March 2023. This follows three 25bp rate hikes by the MPC in 2022, In 2022, the Monetary Policy Committee (MPC) decided to increase the policy rate three times by 25 basis points each in August, September and November. The MPC assessed that headline inflation will likely return to the target range by mid-2023, with average CPI inflation projected to decline to 2.9% in 2023 and 2.4% in 2024.

Recovery of international tourism sector

International tourism was a key part of Thailand's GDP prior to the COVID-19 pandemic, contributing an estimated 11.5% of GDP in 2019. However, foreign tourism visits collapsed after April 2020 as many international borders worldwide were closed, including Thailand's own restrictions on foreign visitors.

As COVID-19 border restrictions were gradually relaxed in Thailand and also in many of Thailand's largest tourism source countries during 2022, international tourism showed a significant improvement during the second half of the year. The number of international tourist arrivals reached 11.15 million in 2022, compared with just 430,000 in 2021. However, the total number of visits was still far below the 2019 peak of 39.8 million, indicating considerable scope for further rapid growth in the tourism sector during 2023.

International tourism arrivals in the first quarter of 2023 surged to 6.5 million visitors, which was more than half the total number of international tourist visits in 2022. Total tourism receipts in the first quarter for both domestic and international tourism spending was estimated at 499 billion baht, up by 127% y/y. The Tourism Authority of Thailand has increased its estimated target for international tourism visits in 2023 to 25 million, which is more than double the total number of international tourism arrivals in 2022.

Thailand economic outlook

Despite the upturn in private consumption and international tourism arrivals in 2022, the overall pace of economic expansion was relatively moderate, at just 2.6%. Easing of pandemic-related travel restrictions during 2022 has also allowed a gradual reopening of domestic and international tourism travel, which gathered momentum in the second half of 2022.

With more normal conditions expected for international tourism travel in 2023, this should provide a significant boost to the economy. Due to the importance of tourism inflows from mainland China prior to the pandemic, the reopening of mainland China's international borders will be an important factor contributing to the further recovery of Thailand's tourism market.

Helped by the continued recovery of the international tourism sector, some upturn in GDP growth to a pace of around 3.4% is expected in 2023.

Over the next decade Thailand's economy is forecast to continue to grow at a steady pace, with total GDP increasing from USD 500 billion in 2022 to USD 860 billion in 2032. A key driver will be rapid growth in private consumption spending, buoyed by rapidly rising urban household incomes.

The international tourism sector will continue to be a dynamic part of Thailand economy, buoyed by rapidly rising tourism arrivals the populous Asian emerging markets, notably mainland China, India and Indonesia.

By 2036, Thailand is forecast to become one of the Asia-Pacific region's one trillion-dollar economies, joining mainland China, Japan, India, South Korea, Australia, Taiwan, Philippines and Indonesia in this grouping of the largest economies in APAC. The substantial expansion in the size of Thailand's economy is also expected to drive rapidly rising per capita GDP, from USD 6,900 in 2022 to USD 11,900 by 2032. This will help to underpin the growth of Thailand's domestic consumer market, supporting the expansion of the manufacturing and service sector industries.

However, rising per capita GDP levels will also put pressures on Thailand's competitiveness in certain segments of its manufacturing export industry. Therefore, an important policy priority for nation will be to continue to transform manufacturing export industries towards higher value-added processing in advanced manufacturing industries.

One of the key economic and social challenges facing Thailand is its rapidly ageing population, which will result in a rising burden of health care and social welfare costs over the next two decades. This will be a drag on Thailand's long-term potential growth rate, making investment in technology and innovation increasingly important to mitigate the economic impact of demographic ageing.

Rajiv Biswas, Asia Pacific Chief Economist, S&P Global Market Intelligence

[email protected]

© 2023, S&P Global Inc. All rights reserved. Reproduction in whole or in part without permission is prohibited.

Purchasing Managers' Index™ (PMI ® ) data are compiled by S&P Global for more than 40 economies worldwide. The monthly data are derived from surveys of senior executives at private sector companies, and are available only via subscription. The PMI dataset features a headline number, which indicates the overall health of an economy, and sub-indices, which provide insights into other key economic drivers such as GDP, inflation, exports, capacity utilization, employment and inventories. The PMI data are used by financial and corporate professionals to better understand where economies and markets are headed, and to uncover opportunities.

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Sandy beach erosion: impacts and adaptation strategies in Thailand

• Open access
• Published: 08 May 2024

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• Hiripong Thepsiriamnuay 1 &
• Nathsuda Pumijumnong 1  

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This paper focuses on sandy beach erosion as a result of rising sea levels, including adaptation option analysis in Thailand. Twenty-seven beaches in the provinces of Rayong, Nakhon Si Thammarat, and Trang were selected as study areas with high rates of erosion. The major scientific challenge entailed determining the relevance and contribution of rising sea levels (including storms) to beach erosion. The SimCLIM/CoastCLIM model was utilized to forecast changes in sea level and shoreline during the period of 1940–2100. A cost–benefit analysis (CBA) was conducted to evaluate adaptation options in terms of economics. The main outcome was a relevant contribution to knowledge on sea-level rise (including storm alteration) and its effects on sandy beach erosion, in terms of loss of sandy beach area and population migration. The results indicate that Mae Phim, Ban Ko Fai, and Pak Meng beaches are all threatened by erosion, and 8.02 and 23.26% of the erosion are attributed to storms and sea-level rise. Future scenarios (in 2100) showed 124.38 cm rise in 2100 sea level (compared to the 1995 baseline) leading to 507.90 m of eroded beaches, 2.15 km 2 of sand loss and 873 people migrating. The results of CBA show that scenario 2 (beach nourishment) is not proper for application as an adaptation option on sandy beaches with high amounts of erosion (Ban Ko Fai and Pak Meng beach), while the option of beach nourishment alone is likely to be applied on Mae Phim beach due to its lower rate of erosion.

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1 Introduction

Sandy beaches may be affected by coastal erosion, decreasing their value for tourism and recreation.

In international context, the regional review of Asian Development Bank ( 2009 ) showed that there are four adaptation options on coastal erosion; mangrove conservation and plantation, strengthening existing dike and seawall, better design and standard for construction and provision of information and awareness. A comparative study and review of Krainara ( 2014 ); Walter ( 2018 ) showed that several countries tend to utilize adaptation options in terms of nature conservation/plantation and soft structure than hard structure. For instance, beach nourishment with some engineering structures in United States of America including conservation of coastal natural process in the Netherlands and France. Moreover, the option of revegetation of coastal forests and trees (as bioshields) combined with some types of soft and hard structures including ecosystem-based adaptation with proper institutional design/arrangement will be one of the adaptation options that are increasingly favored (also in sandy coast/ beach). On the other side, in term of impacts estimation, Addo et al. ( 2011 ) projected the expected impacts of sea level rise in three communities in the Dansoman coastal area of Accra, Ghana using the SimCLIM model based on global scenarios, the Commonwealth Scientific and Industrial Research Organization General Circulation Models (CSIRO_MK2_GS GCM), the modified Bruun rule, and aerial photographs taken in 2005. The results show that sea level will rise approximately 60.27 cm (SRES B2) to 79.71 cm (SRES A1FI) in 2100 compared to the period 1970–1990. The Dansoman coastline could recede about 202 m in 2100 relative to 1970–1990. Moreover, 650,000 people, 926 buildings, and a total area of about 0.80 km of land are vulnerable to permanent inundation in 2100.

Thailand has approximately 320,000 square kilometers of maritime zone, 2800 km of shoreline (including the Gulf of Thailand and the Andaman Sea), and 23 coastal provinces (Aquatic Resources Research Institute-Chulalongkorn University, 2011 ). Furthermore, it has a number of renowned and attractive beaches, such as Mae Phim beach in Rayong province, Patong beach in Phuket province, Pattaya beach in Chonburi province, SaiRee beach in Chumphon province, Railay beach in Krabi province, and Pak Meng beach in Trang Province. Consequently, if these valuable beaches are ruined by coastal erosion, it will affect tourism and the economic system in Thailand (National Research Council of Thailand, 2012 ; Tourism Authority of Thailand, 2013 ).

Adaptation is one of the options/strategies to confront the future effects of climate change (also sea-level rise [SLR]-induced beach erosion). It aims to build resilience in several sectors and communities without causing them other problems. In the case of adaptation to sea level rise and beach erosion, three possible options are (1) retreat: people migrating due to coastal erosion; (2) accommodation: impact avoidance by heightening buildings or planting flood/salinity tolerant crops; and (3) protection: seawall/dike construction/strengthening or beach nourishment (Mclean et al., 2001 ; Nicholls, 2003 ). Thailand has adaptation strategies for coastal erosion in several areas: Samut Prakarn province, Pak Phanang district, and HuaSai district of Nakhon Si Tammarat province and some provinces along the coast of the Andaman Sea (Boonma & Saelim, 2011 ; Ritphring et al., 2021 ; Roy et al., 2023 ). Nonetheless, the beach erosion situation in Thailand has still not been alleviated.

Previous studies in Thailand have focused mostly on erosion in coastal provinces (local scale) such as Surat Thani, Nakorn Si Thammarat, Krabi, and Phuket (Saengsupavanich et al., 2009 ; Snidvongs et al., 2008 ; Thampanya et al., 2006 ). However, few studies have been conducted on national-scale coastal erosion, and to the best of our knowledge, there is no national estimation of the sandy beach erosion caused by rising sea levels (Department of Mineral Resources, 2001 , 2002 ). Thus, this paper aims to fill this information gap, using the coastal erosion model SimCLIM/CoastCLIM as a tool for estimation.

The main objectives of this study were (1) to forecast the rate of sandy beach erosion and (2) to estimate the impact of sand loss and forced migration due to global/regional SLR at the Thai national level for the period 1940–2100. Moreover, this research also attempts to present an adaptation option resulting from the economic evaluation approach: a cost–benefit analysis (CBA) (with/without adaptation strategies) under various climate change scenarios. The results will contribute to national-scale data on sandy beach erosion in terms of the rate and potential impacts of SLR for future Thai scenarios. The CBA results could aid various stakeholders and local communities as they are forced to adapt to beach erosion in Thailand.

This paper is classified and organized as follows: Sect.  1 introduces the objectives and study areas, Sect.  2 describes the methodology, and Sect.  3 presents the results. Section  4 discusses the findings, notes the limitations, and makes recommendations for further study. Section  5 presents the conclusions. Section  6 presents the limitations of study.

2 The study area

Regarding the study areas, three critical provinces and 27 sandy beaches were selected as the study areas from a total of 18 coastal provinces and 152 sandy beaches in Thailand (Department of Marine & Coastal Resource, 2013d ). Rayong is located between 12 and 13° N and 101 and 102° E on the eastern coast of the Gulf of Thailand, while Nakhon Si Thammarat and Trang are located at 8–10° N, 99.15–100.05° E and 7–8° N, 99.20–99.90° E on the western coast of the Gulf and the Andaman Sea coast, respectively ( 2007b ; Eastern Province Cluster Office of Strategy Management, 2007 ; Land Development Department, 2007a ). The locations of the study areas are shown in Fig.  1 . In regard to the 27 sandy beaches, 11 are located in Rayong: Pla, Phayun, Namrin, Suchada, Laem Charoen, Mae Ramphueng, Sai Kaew, Phe, Suan Son, Pak Khlong Klaeng, and Mae Phim. Nakhon Si Thammarat contributed 11 beaches: Kanom, Thung Sai, Sichon, Hin Ngam, Piti, Baan Roh, Pothong, Sai Kaeo, Tha Soong Bon, Ban Ko Fai, and Chan Chaeng. The remaining five beaches are located in Trang: Leam Makham, Hua Hin, Khlong Son, Pak Meng, and Chao Mai (Department of Marine & Coastal Resource, 2013d ). These three provinces were listed in the 18 critical/vulnerable areas where erosion rates exceeded 5 m/yr, as mentioned by Kraipanon ( 2010 ).

Location of the study areas and estimated retreat of coastline (m) in 2100 of the 27 sandy beaches in Rayong ( a ), Nakhon Si Thammarat ( b ) and Trang ( c ) (in the counterclockwise direction)—critical beaches showing in purple and red colors (Data: revised from Department of Marine & Coastal Resource, 2013e , 2013f )

In Thailand, critical areas are defined by the coastal erosion rate. Coastal areas with severe erosion rates—more than 5 m/yr—are classed as critical areas. In addition, risky areas have moderate erosion rates between 1 and 5 m/yr (Duriyapong & Nakhapakorn, 2011 ; Department of Marine and Coastal Resource 2013 h; Prukpitikul et al., 2014 ). From the recent data (2003–2011), Rayong, Nakhon Si Thammarat, and Trang all had high impacts from erosion at 21.75, 53.46, and 43.7 km of eroded shorelines, respectively (Department of Marine & Coastal Resource, 2011 ). Furthermore, the three provinces are renowned for beach tourism, with several beautiful and famous beaches such as Mae Ramphueng, Sai Kaew, and Mae Phim in Rayong Province; Hin Ngam, Sai Kaeo, and Kanom in Nakhon Si Thammarat Province; and Pak Meng and Chao Mai in Trang Province (National Research Council of Thailand, 2012 ; Tourism Authority of Thailand, 2013 ).

3 Methodology

3.1 the simclim/coastclim model.

The input parameters for SimCLIM/CoastCLIM analysis (Table  1 ) consisted of six site parameters and two storm parameters. Site parameters were shoreline response time (τ), closure distance (l), depth of material exchange (d), dune height (h), residual movement (RM), and vertical land movement (VLM). Storm parameters were storm surge cut mean (SSCM) and storm surge cut standard deviation (SSCSD).

Shoreline response time refers to the responsiveness of the coastal system to SLR in a given year and influences the annual change in the shoreline.

Closure distance is the distance offshore at which the process of sediment exchange ceases and the sediment is lost.

Depth of material exchange is the water depth at the closure distance.

Dune height is the frontal dune/berm/beach height.

Storm parameters represent random storm characteristics, including storminess (frequency and intensity). These factors determine the erosion potential of the shoreline due to storms in terms of the means and standard deviations of impacts (meters of erosion). Users can select and add values in storm surge cut mean (SSCM) and storm surge cut standard deviation (SSCSD) flexibly, as representative of the mean and standard deviation of erosion potential in any given year.

For the SimCLIM/CoastCLIM model, actual storm erosion was assumed to be 10% of the value selected in the potential one. For analysis with CoastCLIM, all parameters mentioned in Eqs. ( 1 ) and ( 2 ) were used as input parameters, including another two parameters: residual movement and vertical land movement. Residual movement is the long-term variation in shoreline position (erosion and accretion), which influences trends of sediment supply and transport. Vertical land movement is the change in relative sea level that excludes climate change–related components (e.g., land subsidence or uplift) (CLIMsystems, 2013 ; Warrick, 1998 ).

Closure distance and depth of material exchange were obtained from observation data from the Aquatic Resources Research Institute and Chulalongkorn University ( 2006 ). Dune height and residual movement were obtained from observation data from the Department of Mineral Resources ( 2001 , 2002 ). Vertical land movement data were collected from SLR with VLM for cities’ data from CLIMsystems ( 1995 ) and observation data from Trisirisatayawong et al. ( 2011 ). The VLM values from CLIMsystems data were generated from direct continuous observations of the Global Positioning System—GPS (the SONEL program)—and from trend analysis of tidal observations (the PSMSL program). Due to the lack of observations and secondary data, the default/initial values (1940 values) of the model were applied to shoreline response time and storm parameters.

Sand loss was calculated using Eq. ( 3 ), while forced migration was calculated in terms of the area of sand loss multiplied by the average population density per segment. The segment length data were collected from observations and secondary data from the Department of Mineral Resources ( 2001 , 2002 ), Nakhon Si Thammarat Provincial Governor’s Office ( 2009 ), Department of Marine and Coastal Resource ( 2013a , 2013b , 2013c , 2013d , 2013e ), Rayong Provincial Governor’s Office ( 2014 ), and Trang Provincial Governor’s Office ( 2014 ). The erosion factor of a sandy beach (assuming the value from 1940 remained constant over time) was taken as 1 according to Vafeidis et al. ( 2004 ) and Hinkel et al. ( 2013 ). The populations of the three study areas were obtained from the Bureau of Registration Administration, Department of Provincial Administration (1994). The areas of the three study regions were obtained from Earthpower ( 2002 ), the Nakhon Si Thammarat Provincial Governor’s Office ( 2009 ), the Rayong Provincial Governor’s Office ( 2014 ), and the Trang Provincial Governor’s Office ( 2014 ).

This study utilized the coastal impact model (CoastCLIM) of the Simulator of Climate Change Risks and Adaptation Initiatives model (SimCLIM 2013 version 3.3) to forecast sandy beach erosion due to sea-level rise. CoastCLIM operates following the modified Bruun Rule and focuses on change in the equilibrium shoreline position of a beach-and-dune system due to variation in sea level. The equilibrium shoreline position is readjusted and reestablished landward and eroded when the sea level rises, as shown in Eq. ( 1 ). The Bruun Rule was modified by adding the time lag of the shoreline response and the variation in the occurrence of severe stormy seasons, as shown in Eq. ( 2 ). The modified Bruun Rule attempts to overcome its two main drawbacks, the inability to estimate the change of yearly actual shoreline position and the failure to consider storm parameters (Warrick, 1998 ).

where C eq is the equilibrium change in shoreline position, z is the rise in sea level, l is the closure distance, h is the dune/berm height at the site, d is the depth of material exchange at the closure distance (l/(d + h) thus gives slope), t is the time (yr), C is the shoreline position relative to t = 0, τ is the shoreline response time, and S is a stochastically generated storm erosion factor.

Sea-level rise and coastline retreat were projected by employing the SimCLIM Model on the basis of two forcing scenarios (RCP2.6 and RCP8.5) for the period 1940–2100. The preselected presumptions were a high climate sensitivity and an averaging of the 24 GCMs. The output scenarios comprise VLM. RCP 2.6 and RCP 8.5 stand for the best and worst case scenarios for analysis in this paper. RCP 2.6 (as the best-case scenario) represents the ‘peak and decline’ pathway of radiative forcing and GHG atmospheric concentration, which peak at approximately 3 Wm −2 and 475–490 ppm CO 2 -eq in 2050 and decline to 2.6 Wm −2 in 2100. RCP 8.5 (as the worst-case scenario) shows a ‘rising’ pathway of the two parameters, which leads to approximately 8.5 Wm −2 and 1,313–1,370 ppm CO 2 -eq in 2100 (Intergovernmental Panel on Climate Change, 2014 ; International Institute for Applied Systems Analysis, 2015 ; Moss et al., 2010 ; Yin et al., 2013 ). These scenarios could be applied as extreme situations (in terms of high and low extreme future climates) for various climate-related analyses. Countries, including Thailand, can use the ‘extreme scenarios’ as input for climate modeling, atmospheric chemistry modeling, and thread and impact analysis for future climate-related planning (International Institute for Applied Systems Analysis, 2009 ). Nevertheless, it has to be noted that the sea-level rise rate in the 1940–1995 period was fixed to be constant referred to input-historical rate whilst the RCP scenarios and VLM were applied to the rate at 1995 onward. In regards to the model fundamental philosophy and algorithm, the 1940–1995 periods was determined as “warming-up period” for the model estimation in purpose of model calibration as well as errors detection and correction (Warrick, 1998 ).

Nevertheless, SimCLIM/CoastCLIM was unable to estimate sand loss (SL) and forced migration (FM). Thus, SL was calculated from the following equation, revised from Hinkel et al. ( 2013 ), and FM was calculated in terms of area of sand loss multiplied by the average population density per segment.

where SL is sand loss, s is segment length; C is the erosion rate, and E f is the erosion factor. E f stands for the factor used for estimating the proportion of s that is composed of sandy beaches and could therefore be inferred to represent sand supply.

In this paper, the root mean square error (RMSE) and the mean absolute error (MAE) were used together as statistical metrics for the model evaluation and validation process. The combination of these metrics provided a more complete picture for the assessment of model prediction errors. While RMSE is properly used to describe a normal distribution of errors, MAE is suitable for uniformly distributed errors. However, both these metrics have been beneficially used for model performance measurements in the areas of meteorological, climatic, and environmental data analysis (Akpinar et al., 2013 ; Chai & Draxler, 2014 ; Chen et al., 2012 ). The RMSE and MAE were calculated using the following equations:

where e i is the model estimation error of n samples (e i , i = 1,2,…,n), equal to the difference between the observed value (o i ) and the estimated or predicted value (p i ). Moreover, sensitivity analysis was introduced to assess the uncertainty and variation of the two main input parameters (SLR and storminess), which influenced the results (see details in the Discussion section).

3.2 The CBA method

The secondary data used for CBA were collected from several organizations. Two field studies were conducted for data gathering during (1) March 30th to April 2nd, 2016, and (2) June 16th to June 18th, 2016. The cost of adaptation under various scenarios was collected from Prince of Songkla University ( 2008 ), Department of Van Rijn ( 2010 ), Marine and Coastal Resource ( 2013 ), along with field study data. Benefits of each adaptation option/measure which consist of decrease in sand loss, decrease in forced migration, and increase in tourism revenue were calculated using the following equations:

where: DSL = Decrease in sand loss (baht).SLA = Sand loss area (km 2 ). LP = Land price as Market price in 2014 (baht).z = The segment length (km). R = Coastal erosion rate (m per year). Ef = Erosion factor.

Remarks: Market prices of land in 2014 were used for calculations since the research was conducted in year 2014.

where: DFPM = Decrease in forced people migration (baht).N OM = Number of migrants (people). MCPM = Migration cost per migrant (baht per person). PE TS = Population employed in (beach) tourism sector (people). AOB = Area of beach (km 2 ). TRC = Transportation cost (baht per person).H RC = House/infrastructure rebuilding cost (baht per person). LIC = Loss of income per capita as opportunity cost of migrating (baht per person).

where: ITR = Increase in tourism revenue (baht). NOTA = Number of tourist arrivals (thousand people). EPT = Expenditure per tourist (baht per person). FED = Food and drink expenditure (baht per person). ACE = Accommodation expenditure (baht per person).TRE = Transportation expenditure (baht per person).REE = Recreation expenditure (baht per person).SOE = Souvenir expenditure (baht per person).

Number of tourist arrivals (thousand people) was calculated using the equation below (revised from Hamilton et al., 2004 ):

where: ln Ai = Number of tourist arrivals (thousand people).A rea = Land area of sandy beach (km 2 ). T = Annual average temperature (°).C oast = Length of coastline (km). Y = Per capita income. i  = Indexes destination beach.

The three scenarios mentioned in Table  2 for the three study areas were analyzed with the economic evaluation approach (cost–benefit analysis: CBA), including the Do-Nothing Scenario (Scenario 0; Baseline Scenario), by using the four steps of CBA (revised from Tubpun, 1998 ).

The four steps of CBA to analyze the costs and benefits of each option (revised from Tubpun, 1998 ) consist of the following: (1) Study and identify the boundary and objective of each option; (2) identify and measure the cost and benefit of each option in each year; (3) evaluate the costs and benefits in monetary terms; and (4) compare the costs and benefits in terms of net present value (NPV), benefit–cost ratio (B/C ratio), and internal rate of return (IRR).

The total yearly benefits for each adaptation option/measure were summations of decrease in sand loss, decrease in forced people migration and increase in tourism revenue. The calculation was based on Eqs. ( 6 – 9 ) which were basely estimated by difference of erosion-rate avoidance per year (assumed to be constant) for each option/measure. Furthermore, the fundamental theoretic idea behind the benefit and cost calculations was referred to below equation (Costa et al., 2009 ).

where: Tb = Total theoretic benefit of adaptation option to sea-level rise. Ai = Avoided impacts. = I BAU – I ADP . I = Sum of sea flood, river flood, salinity intrusion, land loss and people migration costs. I BAU  = Costs under business-as-usual scenario. I ADP  = Costs under adaptation scenario. Ac = Cost of adaptation. = A ADP –M.M = Cost of maintenance option under business-as-usual scenario. A ADP  = Cost of adaptation under adaptation scenario.

The fundamental assumptions of the CBA in this study were determined as follows:

The number of tourists increases with population and income, and tourists prefer holidays at a temperature of 25 °C. There is no beach tourism if the warmest day of the month is below 15 °C; 65% of tourists spend their holidays in coastal areas (sandy beaches in a strip 1 km length); 25% of their expenditure is profit (Hinkel et al., 2013 ).

Other variables in Equation ( 8 ) were assumed to be constant over the time of analysis (temperature, length of coastline, and per capita income) in order to investigate the effects of beach erosion on beach-related tourism (through change in sandy beach area).

The boundary of analysis is the subdistrict (of each study area), and the main objectives of each adaptation option are to alleviate beach erosion problems and not to affect tourism activities (through the beach landscape).

The project lifetime is 22 years—initiated in 2014. It includes 1 year (2014) for preparation, 1 year (2015) for construction, and 20 years (2016–2035) for operation. In case of beach nourishment without integration of other adaptation options/measures, sand will be refilled every 5 years (Department of Marine & Coastal Resource, 2013a , 2013b , 2013c , 2013d , 2013e ).

4.1 Historical trends

Empirical studies with regard to sea level change and coastal erosion are represented by the observation data of the Hydrology Section Engineering Bureau, Marine Department () and the Department of Marine and Coastal Resource (2008; 2009; 2013 g,h). The observed SLRs of Rayong (1978–1995), Nakhon Si Thammarat (1986–1995), and Trang (1968–1992) were −0.37 to 0.12, −0.28 to 0.55, and 0.11 to 0.39 cm/yr, respectively. The observed coastal erosions of Rayong (1952–1995), Nakhon Si Thammarat (1952–1995), and Trang (1974–1995) were 1.40–5, 1.74–8, and 2.50–3.50 m/yr. The average values of SimCLIM/CoastCLIM estimation in the 1940–1995 period for the three provinces were 0.12, 0.14, and 0.13 cm/yr for SLR and 3.61, 5.33, and 5.27 m/yr for erosion, respectively. For the same period of observation data, the estimated values of SLR and beach retreat were 0.13–0.14, 0.14–0.15, and 0.14 cm/yr and 0.63–7.65, 1.50–1.48, and 2.95–7.33 m/yr, respectively. Moreover, the estimated values of sand loss for the three provinces in the period 1940–1995 were 0.03, 0.04, and 0.02 km 2 /yr, while around 18, 15, and 3 people/yr were forced to migrate (Table  3 ).

To validate/calibrate the model for future projections of SLR and sandy beach erosion, RMSE and MAE were introduced using Eqs. ( 4 ) and ( 5 ) along with the historical data mentioned above. RMSE and MAE represent the difference between the actual observed values and the estimated values; they also describe the accuracy of the model prediction. In this paper, the RMSE and MAE of sea-level rise were 0.17 to 0.33 and 0.14 to 0.30 cm/yr, while the values for sandy beach erosion were 1.06 to 1.94 and 0.75 to 1.90 m/yr. In the ideal case, these two values should be closer to 0, indicating a higher accuracy of model prediction. Thus, the accuracy of SimCLIM/CoastCLIM prediction was satisfactory and reliable compared to the high accuracy level (0.5–2 of the referred unit) mentioned by Marghany ( 2013 ) and Murdukhayeva et al. ( 2013 ).

5 Future projections

The estimated values for future scenarios resulting from analysis with the SimCLIM/CoastCLIM model are exhibited separately in two parts: (1) sea-level rise and sandy beach erosion and (2) sand loss and forced migration.

5.1 Sea-level rise and sandy beach erosion

In the best-case scenario (RCP2.6), with a peak-and-decline pathway of radiative forcing and GHG atmospheric concentration, global temperature will rise 1.79 °C by 2100 compared to 1995 levels (SimCLIM/CoastCLIM data). This will result in an increase in sea level (accompanied by local VLM, as shown in Table  1 ) of the three study areas. The estimated values of the 106-year sea-level rise are 0.13–83.48 cm (compared to 1995 levels), as shown in Fig.  2 . The future scenarios show that sea level will rise 18.01, 38.70, 61.66, and 83.48 cm in 2025, 2050, 2075, and 2100, respectively (Table  4 ). At this rate of SLR (accompanied by the stochastic storminess shown in Table  1 ), the current shoreline (sandy beach) in long-term estimation (the 1995–2100 period) will be eroded, resulting in landward migration. The estimated values of the three study areas range from 1.09 to 675.58 m (Fig.  2 ). The sandy beach erosion situation (provincial average values) in future scenarios tend to become worse, and the shoreline is estimated to retreat 107.30, 216.41, 324.91, and 436.11 m by 2025, 2050, 2075, and 2100, respectively.

Estimated sea-level rise (cm/yr) ( a ) and retreat of coastline (m/yr) ( b ) for the best-case scenario (RCP2.6) of the study areas during the 1995–2100 period (Note: RA, NA and TR stand for Rayong, Nakhon Si Thammarat and Trang)

In the worst-case scenario (RCP8.5), with continuous increase of the two parameters, global temperature will increase by 5.4 °C by 2100 compared to the 1995 level (the model data). Sea level also shows an increase in the three study areas during the 106-year period, but with higher magnitude. Figure  3 shows that the estimated value of 106-year SLR could reach 124.38 cm by 2100 (compared to 1995 levels). The future scenarios suggest that the sea level will rise 19.15, 43.25, 78.70, and 124.38 cm by 2025, 2050, 2075, and 2100, respectively (Table  4 ).

Estimated sea-level rise (cm/yr) ( a ) and retreat of coastline (m/yr) ( b ) for the worst-case scenario (RCP8.5) of the study areas during the 1995–2100 period

At this rate of SLR, the sandy beach of the three study areas in the long-term estimation will be highly eroded at 747.05 m by 2100 (Fig.  3 ). The sandy beach erosion situation (provincial-average value) in future scenarios tends to become worse, and the shoreline shows retreat of 108.27, 224.10, 353.13, and 507.90 m by 2025, 2050, 2075, and 2100, respectively.

Regardless of the scenario, Rayong is the least risky province in terms of sandy beach erosion, at 16.72% below the average value of the three study areas, while Trang is the most affected, at 17.62% above the average value in terms of the change in sandy beach position by 2100. According to Figs. 2 and 3 , six sandy beaches have the lowest risk of erosion—Sai Kaew, Thung Sai, Sichon, Hin Ngam, Piti, and Chao Mai—while Mae Phim, Ban Ko Fai, Chan Chaeng, and Pak Meng are the most highly affected. Furthermore, Ban Ko Fai, Chan Chaeng and Pak Meng are defined as critical areas with the highest erosion, exceeding 900 m by 2100, or equivalent to 5–6 m/yr (Fig.  1 ).

5.2 Sand loss and forced migration

Sand loss and forced migration are the major impacts from sandy beach erosion. In regard to rapid short-term erosion from stochastic storminess, loss of coastal land and population could follow in an ad-hoc manner. Thus, the researcher applied a constant value of beach length and population density in the base year (or the nearest year) as the baseline to calculate changes in these two impacts.

In the best-case scenario (RCP2.6), with 675.58 m of erosion of sandy beach positions, the value of change in the land area of the three study areas over the period of 1995–2100 varied from 0.01 to −5.20 km 2 (Fig.  4 ). This led to a change in the coastal population for the same period of between 1 and 2431 people. A simulation of the future impacts showed that the sandy coastal area (provincial average value) will be reduced by 0.46, 0.92, 1.38, and 1.85 km 2 by 2025, 2050, 2075, and 2100, respectively (Table  5 ). The estimated values of the reduction in coastal population were 200, 402, 591, and 778 people over the same period.

Estimated sand loss (km 2 /yr) ( a ) and forced people migration (people/yr) ( b ) for the best-case scenario (RCP2.6) of the study areas during the 1940–2100 period

In the worst-case scenario (RCP8.5), with 747.05 m erosion of sandy beaches, the value of change in the land area by 2100 (Fig.  5 ) could reach 5.61 km 2 compared to 1995 levels. In the 161-year period, the estimated value of reduction in the coastal population of the three study areas was higher, reaching 2,753 people by 2100. On the provincial scale, the sandy coastal area will be reduced (average values) by 0.46, 0.95, 1.50, and 2.15 km 2 by 2025, 2050, 2075, and 2100, respectively. This will reduce human settlement by 202, 412, 628, and 873 people over the same period.

Estimated sand loss (km 2 /yr) ( a ) and forced people migration (people/yr) ( b ) for the worst-case scenario (RCP8.5) of the study areas during the 1940–2100 period

In 2100, for both the RCP2.6 and the RCP8.5 scenario, Nakhon Si Thammarat was the least affected province, at 11.53% below the average values of land variation, while Trang showed the highest impact at 22.22% above average values (Table  5 ). Trang had the least impact on the coastal population, at 67.59% below the average values, while Rayong was the most affected province at 73.04% above the average values in terms of reduction in population by 2100 (Table  5 ). For the beach-scale analysis (Figs. 4 and 5 ), Sai Kaew, Pothong, and Leam Makham were the lowest-risk beaches, while Mae Ramphueng, Kanom, and Pak Meng were three beaches with the highest erosion. Furthermore, people living near Pla, Pothong, and Leam Makham had the lowest probability of migration, while several households in Phayun, Sichon, and Pak Meng faced the highest risk of displacement.

5.3 The adaptation option

Regarding the SimCLIM/CoastCLIM results, Mae Phim (in Kram subdistrict of Rayong province), Ban Ko Fai (in Khanap Nak subdistrict of Nakorn Si Thammarat province) and Pak Meng (in Mai Fat subdistrict of Trang province) were selected as the study areas for beach-scale analysis of adaptation options using the CBA method. The researcher gathered various kinds of data from field studies during March to June 2016, such as current adaptation options, erosion situation and rate, tourism activities, and transportation and livelihoods of native people, including some data for the CBA. These data affirmed that these beaches were better suited than others as the study area for this analysis. In Rayong province, Mae Phim beach was selected over another potential choice (Mae Ram Phueng beach) since it has more evidence of erosion and some adaptation options, including higher tourism activities (Fig.  6 ). Moreover, in Nakhon Si Thammarat province, Ban Ko Fai was selected over Chancheng beach due to its high erosion rate and various evidence of erosion although it has no tourism activities in the present time (Fig.  7 ). Lastly, in Trang province, Pak Meng beach was selected because it has high tourism activities and more evidences of erosion than Chao Mai beach (Fig.  8 ).

Tourism activities ( a ) and evidences of erosion/adaptation options ( b ) in Mae Phim beach

Evidences of erosion ( a ) and adaptation options ( b ) in Ban Ko Fai beach

Evidences of erosion/adaptation options ( a ) and tourism activities ( b ) in Pak Meng beach

The values shown in the below tables were calculated using two discount rates: social rate of time preference (SRTP) and social opportunity cost rate (SOCR) (for details, see Tubpun, 1998 ). In practical ways of application, SRTP and SOCR are represented by the interest rate of government bonds and the minimum loan rate (MLR) after tax and inflation rate deduction in 2014. The values applied in this research were 3% and 6% for government bonds and minimum loan rate (MLR), respectively (Bank of Thailand, 2014 , 2016 ). Moreover, the erosion rate in years 1 and 2 refer to the rate in scenario 0 (Do-Nothing Scenario). Benefits are the different values of each adaptation option in scenario 0.

Table 6 shows that on Mae Phim beach (Kram subdistrict, Rayong province), scenario 1—current adaptation option with stone sea wall—and scenario 3—potential adaptation option with artificial reef—should probably be selected for implementation. The stone sea wall option has the highest NPV (518.31 and 354.88 million baht for 3 and 6% discount rates, respectively), and the artificial reef option has highest benefits per unit of costs (7.10 and 5.47 for 3 and 6% discount rates). In the area of Ban Ko Fai beach (Khanap Nak subdistrict, Nakorn Si Thammarat province) , scenario 3—potential adaptation option: hard-structural option (artificial reef)—could be promoted because it has the highest NPV (470.27 and 325.98 million baht), B/C ratio (7.22 and 5.57), and IRR (48.52%). However, scenario 2 with the beach nourishment option should be avoided: It not only has negative values of NPV (−113.32 and −88.45 million baht) but also has a B/C ratio less than 1 (around 0.5), representing that the cost of the option is more than its benefit. In the last study area, Pak Meng beach (Mai Fat subdistrict, Trang province), the wave attenuation dome accompanied by beach nourishment option (scenario 1) is proper for the initiation of adaptation activities. The high values of NPV (around 109.93–185.74 million baht) and the high ratio of benefits to costs (2.00–2.59) are the key factors for selection/decision. Besides, the negative values of NPV (−232.95) and IRR (−55.35%) affirm that the option of beach nourishment alone (scenario 2) without integrating other measures/options is not suitable for a high–erosion rate area such as Pak Meng beach (or Ban Ko Fai beach).

6 Discussion

The main outcome of the study using the SimCLIM/CoastCLIM model was a relevant contribution to knowledge on sea-level rise and its effect on sandy beach erosion. Storm alteration was also considered.

In terms of impacts, the loss of sandy beach area and population migration were calculated with simple equations and assumed to be linear functions in terms of impacts from sandy beach erosion. The economic loss due to these two impacts was not considered; this may be overestimated, particularly in terms of migration numbers. Further studies should utilize more sophisticated formulations, using only the population statistics of the affected sectors (e.g., tourism and beach-related activities), and investigate the interaction between socioeconomic development and local factors (e.g., freshwater inflow). Nevertheless, in the overall analytical concept, the study attempted to conduct a new scientific achievement in terms of distinguishing the factors relevant to sandy beach erosion. The SimCLIM/CoastCLIM model can be potentially applied in this aspect. Users can customize the input factors (e.g., sea-level rise, and storminess) that affect the beach system, and investigate the result of each contribution factor separately. However, only a limited range of influential factors were used in the model; human-induced factors and natural erosion were not included. In addition, the main assumptions of the model are about the way to generate a climate scenario for a climate variable under a certain emission scenarios from multiple GCMs and potential storm erosion. In term of multiple GCMs, combining results from multiple models is based on the fundamental assumption that errors tend to cancel if the models are independent, and thus uncertainty should decrease as the number of models increases. In regards to storm erosion, for the SimCLIM/CoastCLIM model, actual storm erosion was assumed to be 10% of the value selected in the potential one (CLIMsystems, 2013 , 2014 ). Thus, future works or analysis should endeavor to focus on the others especially the human-induced factors or human activities.

In comparison with evidence-base data, relative sea-level change results of SimCLIM/CoastCLIM in Rayong and Trang (0.30 and 0.31 cm/yr) are likely consistent with observation data of Hydrology Sect. Engineering Bureau Marine Department ( 2014a , 2014b , 2014c ) in the 1968–2014 period but seem to be different in case of Nakhon Si Thammarat (0.32 cm/yr). The underlying reasons are: (1) vertical land movement inclusion in the model analysis but not for the tidal stations and (2) the limitation (of the model) in translating/downscaling SLR scenarios (in GCMs) from global to local scale (CLIMsystems, 2013 ). The estimated value of retreat of coastline rate in Trang (5.37 m/yr) is quite similar to observed value of Department of Marine and Coastal Resources ( 2008 ; 2013 g,h) in the 59-year period. Nevertheless, retreat of coastline values of Rayong and Nakhon Si Thammarat (3.62 and 5.51 m/yr) are slightly underestimated comparison to the observation data. This possibly caused by: (1) inability of the model to estimate ‘total erosion’ and (2) limited data of depth of material exchange of Thailand in base year (CLIMsystems, 2013 ). The sand loss value of Trang (0.03 km 2 /yr) and Nakhon Si Thammarat (0.04 km 2 /yr) reinforces the conclusion in the study of Thampanya et al. ( 2006 ) which reported that estimated net change of coastal area in the 30-year period (1967–1998) should be accounted for 0.01–0.32 and 0.03–0.06 km 2 /yr for the east and west coast of Southern Thailand. Moreover, the estimated value of sand loss in Rayong (0.032 km 2 /yr) is also quite consistent with observation data of Department of Marine and Coastal Resource ( 2009 ) which ranges about 0.01–0.02 km 2 /yr in the 2002–2008 period. Unfortunately, there is no direct measurement/research on change in human settlement caused by sandy beach erosion in Thailand. Few studies (e.g., Saengsupavanich et al., 2009 ) have been attempted to measure the impacts but only in term of infrastructure loss (e.g., house, road).

With regard to the sensitivity analysis, the contributions of the major factors in the model to future shoreline retreat and loss of coastal land were analyzed. Residual movement (RM) showed the highest contribution, ranging from 70.02 to 75.44%, while 6.76–8.02% and 20.31–23.26% were attributed to storm and sea-level rise parameters, respectively. RM data for long-term change of coastline position (multi-century) should also be added to SimCLIM/CoastCLIM. Unfortunately, the availability of these data in Thailand on a local scale (appropriate for use as input) is limited, and the longest period of observation is only 35 years (1967–2003). Table 1 shows the RM values of Rayong, Nakhon Si Thammarat, and Trang at −1.5 to −3.5, −2 to −5.3, and −1 to −5 m/yr over that period (Department of Mineral Resources, 2001 , 2002 ). The large values of RM may contribute to increased future changes in shoreline at around 70%, as previously mentioned. Thus, further studies should be aware of this issue and seek longer periods for this value to reduce the large influence of RM. Furthermore, the uncertainty and variation of the two main factors, SLR and storms, in the model were assessed using a sensitivity analysis approach. Based on the empirical data from the Hydrology Section Engineering Bureau, Marine Department () during 1995–2014, the variation of sea-level rise was about 4%. However, there were no observation data or studies concerning the issue of storms in Thailand. Thus, the researcher applied the same value of uncertainty for the storm parameter. After analysis, the results showed that when the storm parameter varied within ± 4%, shoreline retreat changed by approximately + 0.17 to + 0.21 and −0.17 to −0.21%, respectively. In addition, the same value of variation in sea-level rise influenced beach erosion to change by + 0.11 to + 0.18 and −0.11 to −0.18%. The RMSE and MAE values for sandy beach erosion in the period 1952–2010 were 2.58 to 2.59 and 1.52 to 1.53 m/yr with ± 4% uncertainty for both SLR and storm parameters. These values compare well with those mentioned previously by Marghany ( 2013 ) and Murdukhayeva et al. ( 2013 ).

In this study, the Bruun Rule was applied in SimCLIM/CoastCLIM to estimate the change of shoreline/erosion in sandy beaches due to SLR (as mentioned in Sect. 2). The Bruun Rule has two important limitations: Firstly, the Bruun Rule omits consideration of alongshore sediment transport, which influences the sediment budget and the erosion/accretion rate (Hinkel et al., 2013 ). There is also a lack of total/complete erosion analysis due to all factors. The Bruun Rule merely considers a portion of the factors that affect a beach-and-dune system: only sea-level rise and storm characteristics. Other factors, such as the variation of sediment budgets due to coastal protections on rivers (e.g., dams), types of coastal vegetation (as shoreline protection), and land use in coastal areas, are not included (Warrick, 1998 ). Moreover, the model also has uncertainties in projections/estimations/simulations for future scenarios due to the uncertainty in translating GCMs and RCPs to the local scale (CLIMsystems, 2013 ). A further limitation was the scarcity and unavailability of input parameters for SimCLIM/CoastCLIM analysis in the base year (1940). Several input parameters required expert advice and proved difficult to acquire, particularly shoreline response time (τ), storm parameters (SSCM and SSCSD), closure distance (l), and depth of material exchange (d) (Warrick, 1998 ). Both τ and storm parameters required historical data of storm frequency and intensity. Several studies estimated the value of τ as ranging from 3 to 15 years (Addo et al., 2011 ; Leatherman, 1984 ), while the values of SSCM and SSCSD were estimated at 4.5–10, and 1.57–5 m of erosion (Addo et al., 2011 ; Department of Mineral Resources, 2002 ). Furthermore, difficulties were encountered in the estimation of l and d due to lack of available data and in several empirical formulations, as mentioned by Ranasinghe and Stive ( 2009 ). Several studies have estimated the values of l and d as varying between 4.19 and 10 and between 595 and 1000 m, respectively (Addo et al., 2011 ; Batten et al., 2007 ; Farrel, 2007 ).

Furthermore, the “partial analysis” of sandy beach erosion (only sea-level rise and storm patterns) conducted in the study possibly neglected the origin of problem. Major effects from human interventions were not accounted and analyzed. Although the CBA exhibited the cost-effectiveness of different adaptation options/measures but it didn’t show the “whole picture” of beach erosion and solutions. Process-based models could alternatively solve the problems. Shoreline development modelling package (UNIBEST-CL+) which can estimate erosion using bathymetry data accompanied with main anthropogenic interventions factors (effect of harbour jetties and river dams) as well as satellite images analysis should be deployed. Google Earth Engine (GEE) platform could be conducted as a mean of efficient global scale shoreline detection. In addition, a sand nourishment impact model (Ntool) together with current situation of waves (Delft3D-WAVE) should be implemented for detailed simulations and analysis of repeated sand nourishments over time as well as erosion and SLR impacts on the shoreline position (Giardino et al., 2018 ; Luijendijk et al., 2018 ; Stronkhorst et al., 2018 ).

The NPV, B/C ratio, and IRR values at a 6% discount rate for the three beaches were recalculated with sensitivity analysis in terms of ± 20% variations in cost and benefit. On Mae Phim beach, with variation in + 20% of costs and −20% of benefits (the worst case), scenarios 1 and 3 also showed the potential for implementation due to their high NPV (244.74 million baht) and B/C ratio (3.65). Moreover, on Ban Ko Fai beach, scenario 3 with the reef is still also recommended due to its high value of NPV (232.23 million baht), B/C ratio (3.71), and IRR (32.24%) in the worst case. On the other hand, this leads to a negative value of NPV in scenarios 1 (−73.13 million baht) and 2 (−140.77 million baht). Furthermore, on Pak Meng beach, scenario 1 should still also be promoted due to its high values of NPV (43.80 million baht) and B/C ratio (1.33). Besides, scenarios 2 and 3 tend to be avoided due to negative values of NPV: −268.88 and −9.01 million baht, respectively.

Since a sandy beach is the transition zone between land and sea and a dynamic system that changes over time (a complex-adaptive system), the introduction of hard structural options for adaptation (a jetty, groin, breakwater, or seawall) could affect the natural balance in the system of sediment transport. In the sandy beach system, there is a natural balancing process of sediment transport during monsoon and normal seasons. Erosion occurs in the monsoon season (with severe winds and waves), and accretion occurs in the normal season (with low levels of winds and waves). The eroded beach areas could be restored in a few years by this natural process and returned to dynamic equilibrium, as shown on Samila beach, Songkhla province (Faculty of Economics, Prince of Songkhla University, 2011 ). However, hard structural options could interrupt this process and change the directions of wave and alongshore sediment transport. These structures cause deposition of sediment on the updrift side and lead to erosion on the near (downdrift) side (Faculty of Economics, Prince Edward Island Department of Environment, Labour & Justice, 2011 ; Prince of Songkhla University, 2011 ; Ritphring et al., 2021 ; Roy et al., 2023 ). There is evidence of hard structure problems along the coast of the Gulf of Thailand. Hard-structural adaptation options and/or strategies in sandy beach areas should be applied with high awareness of the impacts mentioned before and with consideration of the natural system and the process of sediment transport. Moreover, the structures also block the inland migration of coastal wetlands, leading to loss of wetland areas and ecosystems.

7 Conclusions

This paper simulated the possible impacts of sea-level rise in terms of sandy beach erosion. These impacts included loss of land area and the number of people forced to migrate. Several input parameters were added into the SimCLIM/CoastCLIM program to generate variations in sea level and shoreline, while the two major impacts were calculated using fundamental equations.

The results show that the historical trend of sea-level rise was between 0.12 and 0.14 cm/yr, and sandy beach erosion was 3.61–5.33 m/yr from 1940 to 1995. During the same period, the historical sand loss was between 0.02 and 0.04 km 2 /yr, and 3–18 people/yr were forced to migrate. Future projections indicate that the sea level will rise by 122.33, 124.38, and 121.26 cm in Rayong, Nakhon Si Thammarat, and Trang, respectively by 2100 compared with the 1950 level. Sandy beaches will be eroded by between 348.31 and 507.90 m. Sand loss values varied from 1.49 to 2.15 km 2 . Moreover, 172–873 people will be forced to migrate because of erosion. Many sandy beaches will be affected by this rate of erosion, including Mae Phim, Pla, Phayun, Namrin, Sichon, Ban Ko Fai, Kanom, Pak Meng, Leam Makham, and Hua Hin. Moreover, high–population density areas, including industrial provinces like Rayong, will be more affected in terms of change in coastal population, despite having lower rates of erosion and sand loss. The interaction between economic activities and population migration due to sand loss must be considered, e.g. land-use regulation/zoning in areas at risk of flood or erosion and/or substantial financial compensated cost for existing property owners.

In the aspect of adaptation options, the CBA method was applied. In summary, scenario 2 is not proper for application as an adaptation option on a sandy beach with a high amount of erosion (Ban Ko Fai and Pak Meng beaches), while other options are likely suitable, such as a sea wall, a wave attenuation dome with beach nourishment, or an artificial reef. On Mae Phim beach, the option of beach nourishment alone (scenario 2) is likely to be applied due to its lower rate of erosion. However, a stone sea wall would be most proper, as it would have the highest NPV (518.31 and 354.88 million baht). Results from sensitivity analysis with variations in cost and benefit of about 20% do not change the results. Nevertheless, scenarios 2 and 3 tend to be avoided due to the previously mentioned negative values of NPV.

8 Limitations of study

The scarcity of data also hindered the CBA’s performance. The limited number of data available for evaluating benefits such as long-term erosion rate in the study areas affected the reliability of the results (for sand loss). Moreover, the erosion rate was assumed to be constant over time due to the lack of data. This could affect the amount of sand loss and forced migration, meaning they are not the best estimates. The uncertainty in future income growth and tourist behavior could affect the results. Nonetheless, the “partial analysis” of sandy beach erosion (sea-level rise and storm patterns) conducted in the study possibly abandoned the human -interventions effects. Although the CBA exhibited the cost-effectiveness of various adaptation alternatives but it did not express the “holistic framework” of SLR-impact analysis under similar coastal systems. Further studies should apply more complex and related models that can analyze the impacts of beach erosion or sea-level rise on tourism flow directly. Moreover, the erosion rate of a specific beach on a local scale can be estimated by applying MEPBAY (Model for Equilibrium Platform of BAY beaches).

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research was financially supported by the Thailand Research Fund (TRF) through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/0197/2556). The observation data obtained from Hydrology Section Engineering Bureau, Marine Department, and Department of Marine and Coastal Resource are gratefully acknowledged. We thank the anonymous reviewers for their carefully reading and many insightful comments and suggestion.

Open access funding provided by Mahidol University.

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Thepsiriamnuay, H., Pumijumnong, N. Sandy beach erosion: impacts and adaptation strategies in Thailand. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04763-7

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