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Journal of Coastal Conservation

, Volume 23, Issue 4, pp 773–783 | Cite as

Lost value assessment of bathing beaches due to sea level rise: a case study of the Mediterranean coast of Israel

  • M. Bitan
  • D. ZvielyEmail author
Article

Abstract

Economic losses from public bathing beaches, due to the potential threat of sea level rise (SLR), require economic analysis in order to find appropriate remedies. Three of the main Mediterranean public bathing beaches of Israel were selected as a case study. To calculate the morphological impact of SLR, a modification of the Bruun Rule was used for the current study. This rule is a two-dimensional model that estimates the cross-shore landward and upward displacement of a beach in response to SLR. By using a benefits-transfer approach, consumer surpluses from other areas were adjusted to the Israeli beaches. The latest report of the Fifth Intergovernmental Panel on Climate Change (IPCC) predicts that by the end of the twenty-first century sea level will be 0.26 to 0.98 m higher than at present. Based on this assessment the value loss for each beach was calculated for SLR from 0.2 m to 1.0 m at 0.2 m intervals. It is likely that Dado beach (Haifa) will be severely damaged or even lost by 0.4 m SLR, while Tel Aviv Promenade and Ashdod beaches would be severely affected by 1.0 m SLR. Overall the annual losses of public benefits are estimated to be NIS 122 million ($31 million) and NIS 416 million ($104 million) for values of 0.2 m and 1.0 m SLR respectively.

Keywords

Climate change Coastal erosion Non-market benefits Consumer surplus Beach value 

Introduction

The overwhelming bulk of humanity is concentrated along or near coasts on just 10% of the Earth’s land surface (Hinrichsen 1998). For instance, a significant portion of the population of the USA lives within the coastal zone, with many buildings and facilities located at elevations less than 3 m above sea level. This trend is similar in other continents, where two-thirds of the world population lives near the coast. The coastal area is characterized by a very high population density, concentration of infrastructure and recreational facilities, and significant economic, cultural and socio-economic factors. The initial use of the coastal zone was for commerce and international shipping, but in the last few decades there has been extensive development of tourist and recreational activity, with special focus on bathing beaches. Tourism has become one of the major economic growth engines, and beaches as natural assets have become part of the cultural, economic and social aspects of human identity (Vellinga and Leatherman 1989; Dean and Dalrymple 2002; Hinkel et al. 2010; Alexandrakis 2014; Zviely et al. 2015).

The latest report of the Fifth Intergovernmental Panel on Climate Change (IPCC) predicts that by the end of the twenty-first century the global average sea level will be 0.26 to 0.98 m higher than at present (Church et al. 2013). Sea level rise will usually cause severe coastal erosion and inundation, job losses in the vicinity of the beaches, reduction of property and associated economic values, loss of extremely valuable land (recreational and tourist beaches), damage to urban infrastructure, and loss of cultural values (van der Weide et al. 2001; Özhan 2002; Houston 2008; Alexandrakis 2014).

Within this context, the aim of this paper is to assess the economic losses generated by the likely decrease in the services of public bathing beaches due to the potential threat of SLR. In order to analyze those economic losses, three main bathing beaches along the Mediterranean coast of Israel were selected as a case study.

The Mediterranean coast of Israel

Morphological and sedimentological setting

The Mediterranean coastline of Israel extends 195 km from the border of the Gaza Strip (near Zikim) in the south to the Lebanese border in the north (Fig. 1). It is generally a smooth coastline open to the west that gradually changes in orientation from northeast to almost north, with the exception of Haifa Bay, the Mount Carmel headland and a few small rocky promontories.
Fig. 1

The Mediterranean coast of Israel: Inset (a) Dado beach: The main bathing area of Haifa and its vicinity (Google Earth 20.12.2014); Inset (b) Tel Aviv Promenade beach: The central bathing area of Tel Aviv and its vicinity (Google Earth 5.8.2015); Inset (c) Ashdod beach: The central bathing area of Ashdod (Google Earth 7.9.2016)

Most Israeli beaches are straight, flat and sandy. Their width is in the range from 10 to 50 m.

Around river mouths, the beach width can reach 200 to 300 m (Lichter et al. 2010). In central Israel, between Bat Yam and Hadera, where the beach is backed by a coastal cliff, it is generally less than 30 m wide, and sometimes a few meters only (Almagor et al. 2000; Zviely and Klein 2004).

From a sedimentological perspective, the Israeli coast and its inner shelf (i.e. from the beach to about 30 m depth) can be divided into two main provinces. The Southern Province stretches 175 km from Ziqim to the Akko headland (northern Haifa Bay), and is considered to be the northern flank of the Nile littoral cell (Inman and Jenkins 1984; Zviely et al. 2007). This region is mainly composed of fine-sized Nile-derived quartz sand. The Northern Province (i.e. the western Galilee coast) is a small, isolated and rocky littoral cell, partly covered with locally carbonated coarse sand (Emery and Neev 1960; Pomerancblum 1966; Almagor et al. 2000).

Bathing beaches

As a warm country with almost 8 months of summer (average temperatures of 25.3 °C in the north of Israel and 28.4 °C in the south), bathing beaches have become a crucial factor in the leisure and recreational culture of some of the Israeli population. The official swimming season is determined by the Israeli Ministry of Interior, and it lasts 170 days from May 1st until October 31st each year. The authorized bathing beaches include lifeguards, first-aid services and other beach facilities. Off-season there is no lifeguard and bathing is the swimmer’s responsibility.

A typical Israeli beachgoer (i.e. a person who goes to the beach frequently) spends at least 1 day a week on the beach, especially at the weekend. That means about 20 visits during the swimming season (Mr. Atef Hiraldine - Bathing Beaches Department Manager, Ministry of Interior, pers. comm.). It should be noted that inhabitants younger than 15 years and older than 65 years as well as religious people (i.e. Jewish and Moslem orthodox), have not been taken into account in calculating the number of seasonal beach visitors (Table 1). In general young people in Israel (i.e. younger than 15 years old) are most likely under their parents’ guide, and do not evaluate the beach benefits independently, and religious people rarely use bathing beaches.
Table 1

Selected public bathing beaches and their characteristics. The physical dimensions of beaches were measured from Google Earth orthoimagery. Demographic data was obtained from Ministry of the Interior, Local Government Administration (2017), Central Bureau of Statistics (2017), and the website www.moin.gov.il/LOCALGOVERNMENT/public/BathingSites/Pages/defaultp.aspx

Beach name

City/Town

Length (m)

Width (m)

Area (103m2)

Marine structures

Urban hinterland population

(103)

Number of visitors **

per season

(103)

Number of visits ***

per season

(103)

Dado (Haifa)

Haifa, Nesher, Tirat Carmel

1100

25

28

Groin

322

114

2300

Tel Aviv Promenade

Tel Aviv, Ramat Gan, Givataim, Petah Tikva*

1864

65

121

Seven detached breakwaters, two groins and marina

759

476

10,120

Ashdod

Ashdod

2200

80

176

Marina, and Ashdod Port main breakwater

221

130

2600

Adjustment was made for selected beach length relative to whole municipality beaches. Tel Aviv Promenade includes 600,000 day visits of tourists

*Only half of the population of Petah Tikva

**Inhabitants 15–65 years old

***Swimming season is 170 days, with 20 daily visits per person/season

Demographic setting

According to the Israeli Central Bureau of Statistics (CBS), the State of Israel has a population of approximately 8.68 million inhabitants as of May 2017. About 50% of the population is concentrated in the coastal plain, which includes heavily populated cities, such as Tel Aviv (433,000), Haifa (279,000), Ashdod (220,000) and Netanya (210,000) (CBS 2016). The population densities of the urban zones range from 1840 inhabitants per km2 in the Haifa district to 7470 inhabitants/km2 in the Tel Aviv district, as opposed to 335 inhabitants per km2 nationwide (CBS 2016). The coast of Israel includes ports, marinas, industrial infrastructure and military facilities that leave only 55% of its total natural length for public use (i.e. 1.2 cm of sandy beach per inhabitant). The population estimate for 2050 is 12.3 million inhabitants (Bystrov and soffer 2013), which implies an increase of population density in the coastal areas.

Methodology

Selection of bathing beaches

To estimate the potential economic damage to bathing beaches in terms of lost value to the public due to SLR, three main urban sandy beaches in Israel were selected (Fig. 1, Table 1):
  1. A.

    Dado beach: The main bathing area of Haifa and its vicinity, and the busiest beach in northern Israel. This coastal section stretches 1100 m (Fig. 1: Inset A) south of the Carmel beach, and serves about 320,000 people in the swimming season (i.e. 2.3 million visiting days). The beach is straight, relatively narrow (average width about 25 m), and backed by a well developed promenade with beach facilities and restaurants (Google Earth; EDT Marine Construction 2016).

     
  2. B.

    Tel Aviv Promenade beach: The central bathing area of Tel Aviv and its vicinity. It is the busiest beach in Israel. This coastal section stretches 1900 m from the groin of Charles Clore Park to Tel Aviv Marina (Fig. 1: Inset B; Fig. 2). It includes eight authorized bathing beaches that serve about 760,000 people in the swimming season (i.e. 10 million visiting days).

    The Promenade beach is 65 m wide on average and is backed by a few restaurants, stairs and an elevated wide promenade. A series of seven detached breakwaters 150 to 200 m offshore and associated tombolos create a scalloped coastline (Google Earth; Lia-Marine 2017).

     
  3. C.
    Ashdod beach: The central bathing area of Ashdod and the busiest beach in southern Israel. This coastal section stretches 2200 m from Ashdod Marina to the Lachish River estuary (near Ashdod Port main breakwater) (Fig. 1: Inset C). It includes five authorized bathing beaches that serve about 220,000 people in the swimming season (i.e. 2.6 million visiting days). The beach is straight, relatively wide (an average width about 80 m), and backed by a promenade with beach facilities and restaurants (Google Earth; EDT Marine Construction 2017).
    Fig. 2

    Aerial photograph of Tel Aviv Promenade beach - the central bathing area of Tel Aviv and its vicinity (taken towards north, 29.6.2017)

     

Lost beach area calculation

To calculate the morphological impact of SLR, a modification of the Bruun Rule was used for the current study (Bruun 1988). This rule is a two-dimensional model that estimates the cross-shore landward and upward displacement of a beach in response to SLR (Bruun 1962). The Bruun Rule is widely applied for predicting coastal erosion due to SLR and its common use is for smooth and flat beaches (Langedijk 2008). Although the basic concept of the Bruun Rule (i.e. a rise in sea level will lead to coastal recession) is well-accepted among coastal scientists and engineers, the underlying process and its applicability are subject of discussion (Dubois 2002; Cooper and Pilkey 2004; Ranasinghe et al. 2012; Rosati et al. 2013; Dean and Houston 2016). Based on the fact that the restriction to two spatial dimensions hinders an estimation of 3D spatial morphodynamic changes, generalizations of the original Bruun concept have been developed during the last years (Deng et al. 2014). However, an important advantage of the 2D concept is that its application requires a fairly limited amount of data to obtain a first estimate of the relocation rate of the waterline due to SLR. It is apparently useful for producing a first approximation of such estimates along the flat and sandy bathing beaches of Israel.

The 1988 modification of the Bruun Rule (Bruun 1988) is expressed in the following equation:
$$ R=S\ L/B+h, $$
where R is the relocation rate of the waterline (and the entire profile) landward (m), S is the amount of SLR (m) based on the IPCC assessment (the value loss for each beach was calculated for SLR from 0.2 m to 1.0 m at 0.2 m intervals), L is the length of beach profile measured from shoreline to closure depth (m) based on local bathymetric charts (EDT Marine Construction 2016, 2017; Lia-Marine 2017), B is the berm height (m) (the upper part of the coastal profile and the maximum height of sand transport landward by waves; along the Israeli coast the berm does not exceed 2 m above zero of a geodetic height; see for example Shtienberg et al. 2014, Naaman 2015, and the bathymetric charts above), and h is the closure depth (m) (the depth down to which waves maintain a permanent shape of the coastal profile) (Fig. 3).
Fig. 3

The Bruun Rule (modified after Langedijk 2008)

The closure depth for each beach is calculated according to Birkemeier (1985):
$$ h=1.57\;{H}_e, $$
where He is the nearshore significant wave height (m) exceeded only for 12 hours per year. This quantity is evaluated based on high-quality wave measurements offshore the coast of Israel, collected during strong storms between 1993 and 2016. The calculated He for the Israeli coast is 5.11 to 5.81 m (CAMERI - Coastal and Marine Engineering Research Institute wave database; Kit and Kroszynski 2014) which means the closure depth (h) is between 8 and 9.1 m. In the current study 9 m was selected for the closure depth as it was adopted by Golik (1997), Zviely (2006), Zviely et al. (2009) and Kit and Kroszynski (2014).
The economic vulnerability of Dado (Haifa), Tel Aviv promenade and Ashdod beaches due to SLR per m2 can be expressed by:
$$ BV={P}_i\ {N}_i/{A}_i, $$
where BV is the beach value, Pi is the consumer surplus per visit, Ni is the number of visits for the season period, and Ai is the beach area (m2)
$$ A=l\ W, $$
where l is the beach length (m), and W is the beach width (m).

Benefit transfer method

As there is a difference between the values of beaches to the Israeli visitor, unified weight for criteria and parameters were defined to score each beach. In the current study, the following preferences have been taken into account: beach matrix (e.g. soft or coarse sand, shingles, and pebbles, rocky), beach width, wave protection measures, parking, promenade and entertainment attractions and facilities (Tables 2, 3). It should be noted that the visitor preference may change in other countries and may have different weights for the local customers, as the lost value calculation is quite sensitive to the beach score.
Table 2

Evaluation parameters and criteria

Criterion

Parameter

Beach morphology

Sandy smooth? Protection from waves? Wide?

Promenade

Yes/No

Access

Good transportation? Parking?

Entertainment attractions

Yes/No/Partial

Table 3

Beach scores

Beach name

City/Town

Marine structures along the beach

Parameters

Sandy beach / Marine structure /Beach width

Promenade

Transportation/ Parking

Beach facilities

Average score

Dado (Haifa)

Haifa, Nesher, Tirat Carmel

Groin

4

7

5

4

5

Tel Aviv Promenade

Tel Aviv, Ramat Gan, Givatayim, Petach Tikva

Seven detached breakwaters, two groins and marina

7

8

4

5

6

Ashdod

Ashdod

Marina, and Ashdod Port main breakwater

8

8

7

7

10

Maximum average score = 10. Minimum average score = 1

The benefit transfer method was used to find the economic value of the beaches for consumers. This approach is used to estimate economic values for ecosystem services by transferring available information from studies in other locations and/or contexts to the local study. The more data there are, the more accurate is the method (Deacon and Kolstad 2000; Joshua 2003).

Estimates were made by the Contingent Valuation Method (CVM or CV - estimating certain classes of Non-Market Values) and Travel Cost Method (TCM) (NOEP 2008; Joshua 2003). The CVM usually relies on a survey questionnaire, which represents values for environmental goods and services based upon hypothetical situations. With this approach people are asked what they were willing to pay for the use of the beach, even if it was free, or what they were willing to pay to prevent the beach from being lost. This is the basis for calculating the consumer surplus. The essence of the TCM is that people only visit an area if the expected benefits exceed the travelling costs. Proxies of the benefits received by the visitors are the “recreational values” (Bin et al. 2005; Whitehead et al. 2008; Parsons et al. 2013). Tables 4 and 5 describe some bathing beaches consumer surplus in USA and Europe respectively that had been taken into account for the current study. Adjustments made for the Israeli beaches include local inflation, currency exchange and GDP per capita (Tables 4, 5). The value of a daily visit for each beach was estimated by consumer surplus and beach score (Tables 3, 4, 5) multiplied by the number of visits, and divided by the beach area to give the value per square meter of the beach (Table 7).
Table 4

Estimation of bathing beaches consumer surplus in USA adjusted to the Israeli bathing beaches

Reference

Study area (State and beaches)

Study methodology

Estimate of consumer surplus /visit/day*

Inflation adjustment to year 2016**

Adjustment to NIS***

Estimate of consumer surplus/visit/day (NIS)****

Bell (1992)

Florida beaches

CVM

3.0

3.4

13.6

9.1

Bell (1986)

Florida beaches

CVM

3.1

3.8

14.4

9.6

Bell and Leeworthy (1986)

Florida

Saltwater Beach

CVM

2.4

2.8

11.2

7.5

Curtis and Shows (1982)

Florida

Delray Beach

CVM

4.5

5.3

21.2

10.0

Curtis and Shows (1984)

Florida

Jacksonvill Beach

CVM

6.9

8.1

32.5

21.4

Dornbusch and Co. and Applied Economics Systems (1987)

California:

Orange and Hunboltd beaches

TCM

11.9

14.6

58.3

39.0

Huang et al. (2007)

Maine

General Beach

CVM

3.6

4.0

16.0

10.7

New Hampshire coastal area

CVM

3.6

4.0

16.0

10.7

Kline and Swallow (1998)

Massachusetts

Gossberry Island

CVM

5.4

6.2

24.6

16.2

Leeworthy (1990)

California:

Hugh Taylor Beach; Coral Reef State Park;

Honeymoon Island SRA; Everglades National Park

CVM

1.7–8.0

1.9–9.1

7.8–36.6

5.2–24.5

Lew and Larson (2005)

California:

Three beaches in

San Diego

TCM

11.1

12.3

49.3

32.56

McConnell (1977)

Beaches in

Rhode island

CVM

1.4–6.4

1.6–7.3

6.4–29.2

4.2–19.5

Rhode island

CVM

5.7

6.7

26.6

17.8

McConnell (1992)

Massachusetts

New Bedford

TCM

2.3

2.57

10.28

6.8

Parsons et al. (1999)

Delaware

Ocean City

TCM

13.6

15.5

62.0

41.4

Maryland

Cape Henlopen,

TCM

5.6

6.4

25.6

16.8

Delaware

Ortley

TCM

5.9

6.7

26.9

17.7

Parsons et al. (2013)

New Jersey, Virginia, Delaware:

Rehoboth; Ocean City; Cape Henlopen; Chadwick; Normandy

TCM

5.0

5.18

20.72

13.8

Silberman and Klock (1988)

New Jersey

CVM

14.2

16.0

64.3

42.4

*USA beach references updated to year 2008 after NOEP (2008)

**Inflation in USA in the years 2008–2016 = 13.8%

***NIS 4.0 = $ 1.0

****Adjustment to GDP/per capita ratio between Israel ($36,575) / USA ($54,920) = 0.67

Table 5

Estimation of bathing beaches consumer surplus in Europe adjusted to the Israeli bathing beaches

Reference

Study area

Study methodology

Estimate of consumer surplus /visit/day

Inflation adjustment

o year 2016*

Adjustment to NIS**

Estimate of consumer surplus /visit/day (NIS)***

Blakemore et al. (2002)

George’s Bay, Malta

CVM

£0.64

0.83

4.16

6.20

Blakemore and Williams (2008)

Olu Deniz Beach, Turkey

CVM

£1.1

1.33

6.65

5.58

Martino and Amos (2015)

Tarquinia, Italy

TCM

€9.0

€9.01

36.0

35.3

Marzetti (2003)

Trieste, Italy

CVM

€5.24

€6.2

26.4

25.9

Pellestrina, Venice, Italy

CVM

€9.22

€11.0

46.5

45.5

*Inflation: Italy years 2003–2016 = 20.8%; U.K years 2008–2016 = 21.2%; UK years 2002–2016 = 30.2%

**NIS 4.2 = € 1.0; NIS 5.0 = £ 1.0 (exchange rate at the end of 2016)

***Adjustment to GDP/per capita ratio between: Israel ($36,575) / Malta = 1.50; Israel / Turkey = 1.8; Israel / Italy = 0.98

Results and discussion

The results show that Ashdod beach has the highest score due to a wide sandy backshore backed by a promenade and nearby parking. Dado beach in Haifa, however, has the lowest score due to a narrow and eroded backshore, although it has a wide promenade and plenty of parking spaces (Table 3).

The adjustment of the consumer surplus for different shores addressed in previous studies to the Israeli beaches and local currency (NIS) has been evaluated based on inflation rates from the particular research time to the end of 2016, currency exchange rates at the end of 2016, and the GDP/per capita ratio between Israel and the location of previous studies (Tables 4, 5).

The maximum surplus is NIS 45.5 and the minimum is NIS 3.2. In order to link the beach score and consumer surplus, a score of 1 is taken into account as equal to NIS 3.2, and a score of 10 is equal to NIS 45.5. Consumer surplus for each score between the two edges are calculated by logarithmic function (Table 6). It was noted above that the criteria and parameters in Table 2 are defined for the Israeli visitor preferences.
Table 6

Score interval of the selected beaches and the consumer surplus

Beach score *

1

2

3

4

5

6

7

8

9

10

Consumer surplus NIS

3.2

15.9

23.4

28.6

32.7

36.1

38.9

41.4

43.5

45.5

Consumer surplus for each score are calculated by logarithmic function

*According to Tables 2 and 3

The Promenade beach of Tel Aviv has the highest value per m2 because of the relatively high utilization (4 visitors per m2 per day) and its high score. Ashdod beach has somewhat lower value per m2 in spite of its quality because of its relatively low utilization value (2.7 visitors per m2 per day) (Table 7).
Table 7

Estimate of beach values per m2

  

(1)

(2)

(3)

(4)

(5) = (3)x(4)

(6) = (5)/(1)

Beach name

City/Town

Beach area (103m2) *

Beach score**

Beach visitor surplus NIS ***

Number of visits for bathing season (103) *

Total value of beach (106 NIS)

Beach value per m2 (NIS)

Dado

Haifa, Nesher, Tirat Carmel

28

5

32.7

2300

75

2678

Tel Aviv Promenade

Tel Aviv, Ramat Gan, Givatayim, Petach Tikva

121

6

36.1

10,200

368

3041

Ashdod

Ashdod

176

10

45.5

2600

118

670

*Following Table 1

**Following Table 3

***Following Tables 4 and 5

According to the Bruun rule’s application mentioned above, SLR has deviating effects on different beaches. Provided a SLR of only 0.4 m, Dado beach, if not being refilled or managed in some other way, will be most likely severely damaged or will even be lost. The Promenade beach of Tel Aviv would be reduced to only a small strip of sand if the SLR will reach 1.0 m. About half of Ashdod beach is expected to remain at 0.6 m SLR and would still retain of almost 25% of its area at 1.0 m SLR (Table 8).
Table 8

Beach area loss (103m2) at 0.2 m intervals of SLR

Beach name

City/Town

A – Beach area (103m2)

l - Beach length (m)

B (m)

h (m)

L (m)

Beach area loss (103m2) = R x Beach length

0.2 m

SLR

0.4 m

SLR

0.6 m

SLR

0.8 m

SLR

1.0 m

SLR

Dado

Haifa, Nesher, Tirat Carmel

28

1100

2

9

690

14

28

Not relevant

Tel Aviv Promenade

Tel Aviv, Ramat Gan, Givatayim, Petach Tikva

121

1864

2

9

640

22

43

65

87

108

Ashdod

Ashdod

176

2200

2

9

660

26

53

79

106

132

Table 9 presents the annual estimated economic values of area loss for the selected beaches. The value loss of Dado beach of Haifa would be NIS 75 million ($19 million) with 0.4 m SLR. The value loss of the Promenade beach of Tel Aviv would be NIS 130 million ($32.5 million) and NIS 328 million ($82 million) at 0.4 m and 1.0 m SLR respectively. The value loss of Ashdod beach would be only NIS 88 million ($22 million) at 1.0 m SLR.
Table 9

Estimate of vulnerability (in NIS million) at 0.2 intervals of SLR

City/Town

Beach name

Value (m2 NIS*)

Loss of customer surplus (106 NIS) **

0.2 (m) SLR

0.4 (m) SLR

0.6 (m) SLR

0.8 (m) SLR

1.0 (m) SLR

Haifa, Nesher, Tirat Carmel

Dado

2678

38

75

Not relevant

Tel Aviv, Ramat Gan, Givatayim, Petach Tikva

Tel Aviv Promenade

3041

67

130

197

264

328

Ashdod

Ashdod

670

17

35

53

71

88

Total

122

240

250

335

416

*Following Table 7

** Area loss following Table 8 multiply by value of m2 NIS and rounded to millions

The calculated loss of the beach value may be mitigated by some corrective measures. In this respect a rough estimation shows the economic value of beach refill, for example the cost of a 1.0 m3 of nourished sand is about $20 (Israel’s Ministry of Environmental Protection, unpublished information). Therefore the annual nourishment cost is $240,000, $360,000 and $440,000 for Dado beach (12,000 m3 sand), Promenade beach of Tel Aviv (18,000 m3 sand) and Ashdod beach (22,000 m3 sand), respectively. The estimation afore mentioned clearly shows that sand nourishment is a proper solution for the beach loss along the Mediterranean coast of Israel considering the regional relation of economic and environmental values.

Conclusions

The expected SLR in the twenty-first century will most likely affect consumer welfare and cause narrowing of the Mediterranean bathing beaches of Israel. The current study estimates that Dado beach (Haifa), if not being refilled or managed in some other way, will be severely damaged or even lost by 0.4 m SLR, while the promenade beach in Tel Aviv and Ashdod beach will lose about half of their area at 0.6 m SLR. However, if the sea level will rise even by 1.0 m, it is still expected that a narrow strip of the promenade beach in Tel Aviv and about 25% of the area of Ashdod beach will remain.

A rough estimate of the cumulative consumer surplus that may be lost annually because of degradation of the highly populated beaches above is NIS122 million ($31 million) and NIS 416 million ($104 million), with 0.2 m and 1.0 m SLR respectively. These values reflect the economic situation at the end of the year 2016. The estimated costs of beach nourishment thus seem to be much lower than the potential loss of this coastal environment.

Even though a direct comparison of the virtual costs of the estimated losses with real costs of activities needed for sustainable development is usually not justified, in the case of the three beaches in question the difference in the above estimates is roughly three orders of magnitude. In such occasions it is often reasonable and feasible for both the coastal community and for state authorities to develop and implement measures (e.g. regular beach refill) that would preventively mitigate the loss in value of the beaches and avoid substantial changes in the consumers’ behavior.

It should be noted that the timing of the different scenarios of SLR on the loss of the value of beaches was not examined in this study. We expect that improved estimates of SLR in the next decades will facilitate more accurate estimates of the potential losses and foster the efforts towards identification of the most effective treatment for preventing or reducing loss to bathing beach areas. Finally, we also note that even though the applied method is fairly general, the policy recommendations in seemingly similar occasions of potential loss of the value of some beaches are highly site- and country-specific. While loss of even a few small beaches in densely populated countries with a short coastline such as Israel could lead to sizeable changes in the perceived quality of life, such a loss may be acceptable in less populated countries with extensive shoreline.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Maritime Civilizations, The Leon H. Charney School for Marine SciencesUniversity of HaifaHaifaIsrael
  2. 2.School of Marine Sciences, Ruppin Academic CenterEmek-HeferIsrael
  3. 3.Leon Recanati Institute of Maritime StudiesUniversity of HaifaHaifaIsrael

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