Tourism and Water Demand Dynamics on the island of Cyprus: An Application of Cointegration and Error Correction Modelling Techniques

Eleni S. Menegatou

Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Spatial, Transport and Environmental Economics at Vrije Universiteit Amsterdam

Thesis Supervisor Department of Spatial Economics Vrije Universiteit Amsterdam Amsterdam, Netherlands

Tourism & Water Demand Dynamics

Abstract

A

better understanding of residential and tourist water use in a mass tourist destination with extreme water scarcity is imperative. Severe water shortage has often stimulated considerable debate among local economists, water utility managers, regulators, consumer interest groups and policymakers. Although many studies have surveyed water management through demand and supply-oriented policies, little effort has been made to synthesize the water use patterns between short-run and long-run paths. Based on cointegration and error correction techniques, we build a model for investigating the response of water demand dynamics to changes in socioeconomic and climatic factors in Cyprus. Secondly, we examine the water use patterns by tourists in three major tourist destinations of the island, Larnaca, Limassol and Nicosia. To verify the outcomes generated from the national model, price and population elasticities are compared with the regional estimates. Drawing on the results from a timeseries analysis on Cypriot annual data from 1981 to 2011, this study emphasises the need for appropriate water pricing schemes and long-term planning so as to move towards sustainable water resource management. Keywords: water demand, cointegration, error correction model, unit roots, water scarcity, tourism, demand-side management

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â€œ

Chuck Pahlaniuk

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Tourism & Water Demand Dynamics

Acknowledgements

I owe my deep and most sincere gratitude to Richard Tol, my supervisor, as he exceeded his role and academic obligations by providing me extensive guidance with understanding and patience. It has been an honour and an enormous privilege to have had the opportunity to expand my knowledge and research skills through the avenues of his scientific legitimacy. His teaching thoroughly instilled with passion for the world of environmental economics and constituted an exciting transformative potential of this study. As such, the path of him will always be a constant reminder and a source of inspiration to pace myself on my future endeavours. I am also deeply grateful to all my Professors in the Department of Spatial Economics who have been a direct source of knowledge spillovers, encouragement and support for me as I hurdle all the obstacles in the completion of this MSc programme in order to walk myself into these exemplary coordinates. Their ideals and concepts will have a remarkable influence on my entire career. My warm thanks are due to all the experts in Cyprus for all the information they provided and for their kindness, and especially to Dr. Theodoros Zachariades, M.Sc. Iacovos Iacovides, and Mr Takis Papas. Special thanks go to Alexandros for proofreading my thesis and Francisco for honing my knowledge on the advanced time-series regression techniques. Finally, I would like to take the opportunity to thank my friend Katerina for her invaluable support and empathic encouragement, my personal heroes and parents Stefanos and Dionysia, my brother Vangelis and my sister-in-law Mary - including extended family of close friends, Nikos, Kelly, Crystallenia, Angelos, Jimmako, Maria, for being by my side in every possible way.

This thesis is lovingly dedicated to my two-year-old nephew, Stefanos.

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1. Introduction

When the wells run dry, we will know the value of water. Benjamin Franklin

Water is considered as a scarce, economic good. Tourism is considered as the engine for economic growth of some economies and major employment multiplier. The bond between tourism and water is unequivocal and unbreakable. The quality and abundance of water supply is a vital element of sustainability and viability of tourist destinations, and consequently a determining factor of the tourist cycle model (Essex et al., 2004, RicoAmorros et al., 2009). Water is an integral part of tourist amenities, accommodation activities, and indirectly, agriculture and infrastructure development (Gössling et al. 2011). As a matter of fact, with the number of international tourists expected to surpass the milestone of 1 billion in 2012 (UNWTO, 2012), the tourism model also imposes negative environmental externalities by deteriorating the quality and availability of water resources at unsustainable rate, causing problems of overexploitation, groundwater salinization, land subsidence, degradation of water ecosystems, lowering of the groundwater table and water pollution.1 Water resources become increasingly valuable in dryland, coastal areas and small islands with propensity to consecutive drought episodes (Black and King, 2009; Eurostat, 2009; Ecologic, 2007; Gikas and Tchobanoglous, 2009, Gössling, 2001; Pigram, 2001). Characteristically, as tourist activity is concentrated in time and space with warmer climates during periods of high irradiation, the regional and temporal massive water overuse poses a real problem with respect to water resource management (Aguiló et al., 2005). The seasonal influx of tourists outnumbers the local population and therefore, the water use is beyond the normal requirements of the infrastructure (Essex, 2004). That is to say, the availability of water resources can be intensified by the natural predisposition of cyclical droughts, demographic stresses, climate change and environmental pollution (Perry, 2006). “Limited water availability, poor water quality or media portrayal of a water crisis can consequently do great harm to the image of tourist destinations” (Hall, 2010; Hall & Stoffels, 2006 quoted in Gössling et al, 2011). These characteristics make the island of Cyprus suitable for our empirical case study. Particularly, poorly regulated tourism and weak water governance undermine access to secure and adequate water supply, provoking deeper social contradictions. The services provided by the hotel industry enable water use to the guests, but have also led to a 1

Drinking water quality will continue to deteriorate from untreated sewage system and pesticides which are extensively used to maintain gardens and golf courses.

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Tourism & Water Demand Dynamics

significant ecological footprint, hampered the long-term viability of any tourist industry and exacerbated water scarcity problems to agriculture and households. In the last decade, there is an increasing international recognition of the need to reconcile tourism businesses with management of water resources in an accountable, sustainable and equitable manner. The public institutions call for demand-led policies, water conservation initiatives and integrated water management, instead of the expansion of water supply infrastructures with high social, economic and environmental cost. To this end, there are at least two prerequisites. First, estimating and understanding the patterns in water demand for tourists and residents is necessary in order to manage and expand water systems in more sustainable and efficient way. In the same line, hotel managers need to have sufficient knowledge and financial means to adopt a state-of-the-art environmentally responsible behaviour and all the vibrant practices that scientists and policy makers suggest (Bohdanowicz & Martinac, 2005). This thesis attempts to empirically quantify short-run and long-run effects of several water demand determinants; such as the number of users, climatic conditions and price changes. The usefulness of cointegration and error correction techniques contributes towards finding new evidence of the factors that influence water consumption at a mass tourist destination with marked seasonal pattern. This type of analysis is used to assess the speed of adjustment towards water demand and supply imbalances and to analyze the dynamics of the relationships. The structure is organised as follows. Section 2 lists the previous studies on cointegration and error correction mechanism. A review of the chronicle of tourism development in the Republic of Cyprus and its water peculiarities are described in section 3. The main body of econometric methodology and data description is presented in section 4, while results are discussed in Section 5. Finally, section 6 concludes, addressing several policy implications for policy-makers, water utility authorities, regulators, consumer interest groups and tourism managers.

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2. Review of Literature 2.1 Water demand modelling

As the semiarid regions continue to grow in area, the literature in environmental economics has extensively focused on orchestrating water consumption patterns and developing useful planning tools. Most of applied research, however, differs in methodological approach, time, and spatial elements, which makes comparisons more complex. Because data availability is a problem on the subject, time-series techniques2 are broadly used by the existing studies in order to estimate residential water demand and provide information to the markets (Sewell and Roueche, 1974; Hansen and Narayanan, 1981; Martin et al., 1984; Chicoine and Ramamurthy, 1986; Martınez-Espiñeira and Nauges, 2001; Martınez-Espiñeira, 2007; Ben Zaied and Binet, 2011). To our knowledge, Martınez-Espiñeira (2007) is pioneering in the study of cointegration and error correction mechanism on residential water demand. Such a view is also supported by Ben Zaied and Binet (2011) who distinguish between short- and long-run responses of municipal water use to price changes. Proper water pricing is a key element of study in the literature, while other authors investigate demographic effects on water use in terms of household size to detect economies of scales (Höglund, 1999; Arbués et al., 2000), or the impact of users’ age (Lyman, 1992; Nauges and Thomas, 2000) or different cultural backgrounds (Griffin and Chang, 1990). Water price

Lately, in contrast to the traditional emphasis on supply-side measures, there has been growing recognition of the need to institutionalize demand-side policies for more efficient water management. Appropriate water pricing is the main instrument to control demand. Public institutions are being forced to ensure that pricing structure is designated to offer environmental efﬁciency, ﬁnancial stability, public acceptability, and transparency (OECD, 1987, p. 14, 1999). Water demand is inelastic since water has no substitutes for basic needs (Chicoine and Ramamurthy, 1986; Arbués et al., 2000). Bills on water represent a small proportion of users’ income, and thus consumers do not carefully study the tariff structure (Billings and Agthe, 1980). Using a panel data model with observations from French municipalities, Nauges and Thomas (2003) estimate short and long-run price elasticities equal to -0.26 and -0.40, indicating a higher price elasticity of demand in the long-run than the short-run. Similarly, Martinez-Espineira (2007) uses times series data from Seville in Spain and obtains a long-run price elasticity of -0.5 from a cointegration model and a shortrun price elasticity of -0.1 from an error correction specification. Marginal price of water is included in his model specification. Ben Zaied and Binet (2011) estimate long-run price elasticity equal to -0.39 (for the upper block) by using average price. Most of the studies use marginal price corresponding to the block of consumer, while Taylor (1975) and Nordin 2

“The development of models goes beyond the normal activities of statistics institutes, although they do use statistics; but they can be a useful short-term tool for use in decision-making, defining tourism policies and strategies, and assessing the impact of future tourism investment projects” (Eurostat, 2009).

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Tourism & Water Demand Dynamics

(1976) include a difference variable.3 Only the study by Hewitt and Hanemann (1995) obtains price elasticities in an elastic range (-1.57 to -1.63), using marginal prices applied on a maximum likelihood model. Another price speciﬁcation applied by many researches is the average price (or both average and marginal price, Shin (1985)). A thorough literature review implemented by Arbués et al. (2003) illustrates that the choice of variable does not substantially affect results, but it is considered that demand is more responsive to average price. Polycharou & Zachariadis (2012) estimate the average price elasticity for Cyprus and find it equal to -0.25 and the marginal price elasticity to -0.45.4 Therefore, water demand for three major urban areas of Cyprus is inelastic, but not insensitive; that is, the higher the water tariffs, the lower the residential water consumption. Climatic variables

Climatic variables were seen to play different roles in the context of water use. Stevens et al. (1992) used temperature in tandem with annual rainfall. Beattie (1979) included precipitation during the growing season. Average monthly temperatures and summer rain have also been used by Billings (1987) and later by Grifﬁn and Chang (1990). MartiınezEspiñeira (2002) suggests that the number of rainy days should be taken into account and not the amount of precipitation as the only occurrence of precipitation, regardless its amount, affects consumption due to psychological reasons.

2.2 Tourism Development and Water Resources

While tourism has significantly increased water demand in residential areas, there is little literature in the field. The lack of official statistics and detailed data availability, the general belief that the international tourism accounts mostly less than 1% of national water, may plausibly attribute why tourism have not emerged as a focus of scientific interest. However, the findings of an international review by Gössling et al. (2011) suggest that water use by tourism should not been overlooked in the national discussions. That is, the proportion of tourism water demand differs extremely at regional scale, ranging from less than 5% of domestic water use to 20% and 40% in Cyprus and Mauritius, respectively.5 3

It is the difference between the total bill and the price that the consumer would have paid if marginal price changed. 4 The literature suggests that irrespective of whether the elasticities are different from zero, demand management matters. Empirical evidence from several studies suggests that that the short- and long-run elasticity lies in the range of -0.2 to -0.7 (Arbués et al. (2003); Dalhuisen et al. (2003); Olmstead and Stavins (2008); Schleich and Hillenbrand (2009), and Worthington and Hoffman (2008)). 5

To this extent, many authors have highlighted the importance for further scientific research into tourism water

consumption. Particularly, the assessment of the factors that deteriorate water-stressed tourist destinations is crucial for guiding future policies on the environmental performance of tourism industry (Tortella & Tirado, 2011; Eurostat, 2009; Gossling et al., 2011; Gossling, 2001; Ecologic, 2007; Hof & Schmitt, 2011).

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The range of estimates for tourist water consumption fluctuates substantially according to the geographical location, hotel comfort standards, different settlement densities (hotels, campsites, resort hotels, apartments, bed and breakfast guesthouses, residential homes), seasonality, land use patterns and tourist activity during the accommodation (e.g. golf, swimming). Figure 1 depicts the sharp geographic variations in water consumption as estimated in existing literature. Water consumption per tourist per day ranges between 84 and 2000 litres (L) (Gössling et al, 2011). For instance, water use per day per tourist in Spain can be more than double (400 L per day) than that of the average local demand (UNEP, 2005). Similar estimates have been found in an assessment study of Cyprus’s water resources and water demand conducted in 2000 (Savvides, 2001). In tropical islands, water demand by tourists is rather higher with an average amount of 685 L. This leads to an average daily consumption 15 times the local water demand (Zanzibar, Tanzania - Gössling, 2001).

Tunisia (2009) Australia (1993) Coastal Normandy, France (1995) Cyprus (2001) Zanzibar, Tanzania (2001) Las Vegas, USA (2007) Benidorm, Spain (2009) Sharm El Sheikh, Egypt (2009) 0 200 Tourism water consumption (Litre per tourist per day)

400

600

800

1000

Figure 2.1. Water consumption by tourist with respect to geographic variations (Gössling, 2001; WDD-FAO, 2001; Lamei et al., 2009; Ricos-Amoros et al., 2009; Gössling et al., 2012; Australian Institute of Hotel Engineers,1993 quoted in Bohdanowicz and Martinac 2007; Davies and Cahill, 2000 quoted in Bohdanowicz & Martinac 2007); Langumier & Ricou, 1995)

Rico-Amoros et al. (2009) show that similar tourist land use patterns and different accommodation densities (type and age of accommodation) can have a significant impact on individual tourist water consumption (escalating from 140 L per day to more than 600 L). Higher consumption is observed in single houses, representing the impact of quality tourism. Ecologic (2007) finds higher water consumption at high standard hotels and holiday houses (394 L per overnight stay) than campsites (174 L per overnight stay). The consumption grows exponentially subject to the predominant type of tourist settlement, and consequently the category. Due to water-intensive facilities, such as spas and irrigated landscape grounds, higher standard hotels tend to use higher amount of water (Bohdanowicz and Martinac, 2007). Essentially, an analysis conducted among 349 European accommodation centres reports that five-star hotels appear to use most water (594 L per overnight stay) compared to

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Tourism & Water Demand Dynamics

the average hotel consumption rates (Hamele and Eckardt, 2006).6 In line with other studies, Plan Blue estimates an average consumption level between 500 and 700 L at luxury hotels in the Mediterranean. In contrast to footnote 6, swimming pools increase freshwater use by 15% (or 140 L per tourist per day) and laundry7 accounts for 10% of water used in guesthouses and 5% of the water used in hotels. The volume of irrigated gardens in hotels has been quantified by 50% of total use (456 L per tourist per day) (Gössling, 2001). As previously noted, the results by Rico-Amoros et al., (2009) find larger water consumption figures observed in single houses with gardened areas and swimming pools (seasonal occupation), which varies from 865 L per household per day to a sharp peak of 2068 L per household per day during the summer. The absence of gardens and swimming pools yields two to three times lower mean use per household per day during the period of maximum water consumption. Based on the above-discussed findings, Hof & Schmitt (2011) distinguish higher water consumption levels per capita in quality tourism rather than mass tourism, after comparing summer water consumption in holiday homes, mass tourism, and residential urban areas in Mallorca, Balearic Islands. In fact, the authors consider that garden irrigation, accounting for 70% of total consumption in summer, is the main cause of high volumes of water use. Chan et al. (2009) show that hotels in Hong Kong have reduced their water consumption from 572.5 up to 452 L per day, by investing in water saving appliances and practices. Similarly, a modelling study based on hotel’s physical characteristics, degree of seasonality and management system variables estimates a 13.6% annual water reduction when adopting water saving initiatives (Tortella and Tirado, 2011). The use of drought-tolerant plants in gardened areas along with the employment of alternative ways of irrigation, such as treated wastewater or grey water, can substantially reduce hotel water consumption (Gossling, 2011).8 Such measures are especially important at golf courses that can dramatically alter water availability. The problems are more serious as agricultural sector compete tourism industry and urban uses for water during the dry season. Water supply of tourism is a great priority given its high contribution to a country’s GDP (Eurostat, 2009). Contrary to expectation, 6

The presence of swimming pools increases the water use by 60L per day. The Iberotel Sarigeme Park Hotel survey in Turkey highlights higher water use produced by kitchen and laundry units (30%) and followed by swimming pools (25%) (Antakyali et al, 2008). 8 The study by Stefano (WWF, 2004) reveals that a golf course needs around 1 million cubic metres per year, 7

that is, the water consumption of a city with 12.000 inhabitants. In Cyprus, controversies have been stirred up among the stakeholders due to the proposed strategy for further sustainable growth of “sun and beach” models and the transformation of the island into a competitive golf park. Boukas et al. (2012) discuss that the construction of 15 golf courses could be panacea for stimulating economic development from the recent economic recession. On the other hand, golf tourism develops agricultural, environmental and real estate contradictions; a golf course needs between 10,000 and 15,000 m3 of water per hectare and year on an island where water resources are relatively scarce.

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(Auernheimer and Gonzalez, 2002) pinpoint a higher value added to water by tourism sector which is 60 times per cubic meter that of the agricultural use. Additionally, Antakayali et al. (2008) find higher water use per guest reported in low occupancy periods. The above discussion indicates the preparation and maintenance cost of the hotels even in periods with low occupancy rate (swimming pools filling, landscape maintenance (Eurostat, 2009)). In general, a prominent area of discussions concerns the direct and indirect and the consumptive and non-consumptive role of water in tourism development, along with the sustainability nexus, as a derivative of water management. However, not only is the empirical evidence limited in most of the case studies, but also there is no study that mingles these approaches together in one location.

3. A case study in Mediterranean: the Island of Cyprus 3.1 Tourism

Cyprus is the third largest island in the Mediterranean basin with a Greek-Cypriot population of approximately 862 thousand9 people (CyStat, 2012) and a member of European Union since 2004 with sustained economic growth in the last three decades, and per capita GDP of 20.000 Euros in 2009. Being the crossroad of Europe, Africa and Asia, its development as neuralgic port of culture and innovative ideas, ultimately seeking to liberal trading relations has resulted in a major trading post, a prominent international business consultation and service centre. The Republic of Cyprus has considered tourism as an important expedient for an accelerated endogenous national growth since the beginning of 1960 (CTO, 2005). The central strategic positioning and good geomorphology of the country coupled with its cultural heritage, warm climate and natural resources abundance facilitate the encouragement of economic diversification with the development of package tourism during the post-colonial era. In order to attract demand and generate tourist flows from Northern Europe mainly, fine tuned investments were made along the coastal areas. These investments acted as a self-accelerating growth engine. The rise in tourism and its growth was rapid during the period between the 1960s and 1970s. As an illustration, while the tourist arrivals amounted to 25.000 in 1960, a considerable increase of 900% (or 225.000 visitors) was noted between 1960 and 1973, a period during which international tourism rose by only 175% (Witt, 1991)10. 9

It corresponds to a population density of about 80 inhabitants per square kilometer. Notwithstanding, in July 1974, at the height of the tourist season, the Turkish invasion and occupation of a third of the island (37.3% of the land surface) brought the upward trend of the tourism interruption to an abrupt halt and led free areas of the Republic to deep political instability along with economic debacle. The impact of invasion on tourism was vast and grave. The tourist arrivals declined to 47.000 in 1975 with the loss of much infrastructural capital and the tourist gaze damage. Namely, the main air gateway, the Nicosia International 10

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Tourism & Water Demand Dynamics

Figure 3.1. Map and global location: The island of Cyprus is the third largest in Mediterranean and is located at the North-eastern corner of the basin, 75 km south of Turkey, 105 km west of Syria, 380 km north of Egypt, and 380 km east of Rhodes, Greece with an area of 9.251 square kilometres.

Hasty and unforesighted decisions were made in times when the survival of the island was the first priority. The mass tourism model revitalized the economy quickly; the government, through the Cyprus Tourism Organization (CT0) rapidly reactivated tourism and the island became a competitive destination again in the 1980s. The rebound showed 448% increase in 1990 compared to the previous decade. After this robust growth rates in the 1990s, a rather impressive tourist development was noted in 2001. More than 2.6 million tourists visited the country and 2.2 billion Euros were added to the national economy. The tourism GDP share between 1990 and 2005 of 15-20% reflects this development (Planning Bureau, 2007). However, a continuous and significant decrease was recorded over the next decade with the lowest level in 2009. This might be attributed to the global tourism crisis (Hall, 2010) coupled with the islandâ€™s water deficient supply in 2008. Lately, revenues of around 1.5 billion Euros were generated in 2009 by the tourism industry, providing 38.000 jobs (CyStat, 2009).

The countryâ€™s tourist arrivals rates have been lagging considerably behind neighbouring countries, such as Turkey and Egypt. The Cypriot heliocentric tourism model is relatively more expensive compared to similar Mediterranean competitors. The reliance on the

Airport, and 45% of the restaurants, 65% of the bed capacity (13.000 beds) and 96% of the beds (or 5.000 beds) under construction were located in the North part of the island (Famagusta and Kyrenia).

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markets of the UK and the Scandinavia has left the sector with little support. What is more, the lack of tourism campaigns, the saturation of the demand for a sun-and-sand holiday and the questionable quality imply negative forecasts. It is true that the unstable political climate both indoor and in the gulf of Middle East have deterred tourists. Other factors that have threatened the fragile future of the industry is a dominated ‘laissez-faire' policy and the lack of basic tourist infrastructure. That is to say, no tourism development planning or studies that examine the tourism development in harmony with the environmental impact can be quoted. Lately, Cyprus is in the midst of a serious financial crisis. Domestic and international tourism can be seen as a potential solution to the crisis. In the simplistic terms of tourist destination life-cycle, noticeable improvements needed to be initiated so that the national economy could move from the ‘stagnation stage’ of Butler’s resort life-cycle model (Butler, 1980) towards the rejuvenation of international tourism.11 Tourism officials seem to have reacted to the common baselines of “quality of life” through territorial planning and “tourism sustainability”. The construction of marinas, golf courses, convention centre and enhancement of hospitality operations aspire to excel a quantitative and qualitative tourism for demanding customers, and therefore to create a competitive advantage for Cyprus.

3.2 Water Shortage

Quality tourism establishments rely on large land areas and high volumes of water (RioAmoros et al., 2009). With the reservoirs virtually empty, quality tourism strategy has raised contradictions between the Cypriot stakeholders given the negative impact related to environmental and sustainability aspects (Boukas et al., 2011). Supply-side strategies have led to depletion of reserves and salt intrusion through groundwater extraction and energyintensive and costly methods of desalination of sea water. The island’s water scarcity has been worsened by climate change, political instability and shortcomings in regulation, parallel with the predominant illusion that water was abundant for any economic sector and use. The Water Exploitation Index is an indication of water stress problems, illustrating the pressure that water demand puts on the water resources (figure 8.1 presented at the annex). Here, it comes with no surprise that Cyprus suffers from the highest water exploitation in Europe; the average annual renewable water resources amounted to 460 per capita in 2009 (Charalambous et al, 2011). Falkenmark et al. (1989) mention that ‘Countries with less than 500 per capita per year experience absolute water scarcity, based on the requirements for agriculture, households, energy, industry and environment’.

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Tourism & Water Demand Dynamics

Figure 3.2. Water availability and Tourist arrivals in 2008

Due to mild winters, hot and dry summers, Precipitation (mm) and highly irregular rainwater supplies, the 350000 60 Tourist Arrivals island suffers from recurring and 300000 50 prolonged droughts every two-to-three 250000 40 years. The average annual rainfall of about 200000 30 500mm (or 60% of all precipitation), 150000 which consists the main source of 20 100000 replenishment of water resources, falls 10 50000 from November to March. The natural 0 0 recharge of the aquifers is expected to decline by 7-25% in mid-21st century, according to World Bank Climate Change Portal. As an example, in figure 3.2 precipitation and tourist arrivals are illustrated for the driest hydrological year (2008). 70

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

400000

In a word, water availability is restricted in the summer months, when tourist arrivals peak and water demand increases (Eurostat, 2009; Gössling, 2001; WWF, 2004). The average temperature reaches values of 32oC during summer period and the evaporation effect accounts for 2000mm per year, and therefore this nexus substantially reduces the contribution of the rainfall to the ground and man-made dams. The most severe and recent drought of 2008 (average rainfall of 272mm) necessitated the shipment of 8 million cubic meters of water to the island from Greece in ships at a huge expense.12 Huge investments have been made in water supply infrastructure since 1960, but yet recurrent water crises prove that an uninterrupted supply has not been fulfilled. Under the policy ‘Not a drop to the sea’, Cyprus became the country with the highest degree of dam development in the world according to the International Commission on Large Dams (WDD 2009) as engineering constructed dams at all potential key catchments. Notwithstanding, the construction of over 100 dams and reservoirs with a total water storage capacity of about 330 million m3 past13 have been designed in a wetter past and thus, have not lived up to the expectations these projects raised (Klohn, 2002; Zachariadis, 2010a; WDD, 2009). On top of that, the complete elimination of the natural recharge to the aquifers downstream has also been attributed to the cut-off effect by the dams. Apparently, this has brought the realization of investing in reuse activities, desalination and improved water storage capabilities in order to provide greater water security towards a sustainable future. Seawater desalination has ensured a steady supply of drinking water to both domestic consumers and tourists. By 2013, five desalination plants will be operating on a large scale basis to cope up with the successive geographical and diachronic natural hydrological 12

At a cost of € 50 million, with € 7.6 million support provided by the European Solidarity fund (Christofias, 2009). 13 A ratio of fifty large dams for every 10.000 square kilometers.

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decline and the overdependence on dams (Zachariadis, 2010b).14 However, desalination jeopardizes sustainability perspectives and allows imprudent water use based on the illusion of water abundance; it represents a costly solution with energy-intensive process and environmental impacts, whereas water security and availability highly rely on oil imports.

Table 3.1. Projected annual water demand (million m3) for the main sectors (2000â€“2020) in CY 2000 Sector of demand/year Agriculture Domestic â€“ Inhabitants â€“ Tourism Industry Environment Total (million m3)

mcm 180

2005 % 77

2010 %

Mcm 182

69

mcm 182

2020 % 63

Mcm 182

% 58

54 14 3 13

23

58 18 5 14

20 5 1 5

63 23 6 16

22 8 2 6

74 31 7 20

23 10 2 6

266

100

277

100

290

100

315

100

Data source: Savvides et al., WDD-FAO, 2001

Moreover, the scarcity problem has acted as a constraint on the development of agriculture and tourism. Strictly speaking, close to 70% (or 183 million m3) of all water resources of Cyprus are consumed by irrigated agriculture, directly contributing only 5% to the national wealth. Considering that the municipal, tourist and industrial sectors use the remaining 30% (or 70 million m3) of total use of which only 5% goes to the backbone of the economy, the backbone of the economy, the tourism sector - which contributes 30% of the GNP, a close scrutiny of water allocation policy appears to be imperative. In table 3.1 the sectoral competition for current and projected water demand (WDD-FAO, 2001)15 is presented; data came from the only national survey found in the literature to assess the water allocation.

14

For instance, desalination plants supplied 48 million cubic meters of drinking water in 2011, while 24 mcm were provided by dams and only 6,7 mcm by boreholes. 15 The water allocation to agriculture remains constant throughout years, contrary to the common suggestion by the literature for importing goods from water-abundant countries, rather than producing them domestically and exacerbating water scarcity. As a matter of fact, the cropping pattern in Cyprus has been determined by the market trends rather than its competitive advantage (i.e. the cultivation of water-intensive crops such as bananas rather than grapes).

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Tourism & Water Demand Dynamics

4. Data and econometric methods In an effort to understand the pattern of dynamic short-run responses to changes in a measure of water demand and its determinants, the cointegration technique and a multiequation Vector Error Correction Models (VECM) are employed in this study. More specifically, these techniques are built to investigate the causal relationships and indicate the interaction among water consumption, its users, economic and climatic factors in the time path. To assess the short and long-run dynamics of water demand in a single country study, annual data that spans from 1981 through 2011 is used. The time series data pertains information on domestic water consumption (expressed in million , the sum of permanent residents and tourist arrivals (in thousands - hereinafter population), the price of drinking water (Euros), precipitation (mm) and temperature (째C). The table 4.1 presents the sources of our data collection. All series were sourced from the Statistical Service of Cyprus and the Water Development Department of Cyprus. Price corresponds to the average price of water which has been converted from Municipal Water Board data on residential, commercial and industrial charges imposed on water consumption. Table 4.1. Data Sources of time series data National water use

Source

Dependent variable

Statistical Service of Cyprus, Industrial Staitistics 2011 p. 76

Domestic Water Demand Independent variable Population

Statistical Service of Cyprus

Price

Water Framework Directive* & MWB

Precipitation

Water Developement Department

Temperature

Water Developement Department

Regional water use & tourism

Water Demand Larnaca

Water Board of Larnaca

Water Demand Limassol

Water Board of Limassol

Water Demand Nicosia

Water Board of Nicosia

Tourist Arrivals in Accommodation. Est.

Cyprus Tourism Organisation

Tourist Arrivals in Accommodation. Est.

Cyprus Tourism Organisation

Tourist Arrivals in Accommodation. Est

Cyprus Tourism Organisation

* Reporting Sheets on Economics - Republic of Cyprus, 2010 16

Figure 4.1. Time series plot of the logged variables

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Prior to empirical research, all data were expressed in their natural logarithms. The use of logarithms helps to reduce variance and interpret estimated coefficients in terms of elasticities. Therefore, the estimated coefficients constitute constant elasticities of the dependent variable with respect to the independent variables. In addition to estimating the water demand pattern by tourists and comparing the elasticities generated, information was collected from each of the three Municipal Water Boards (MWB).16 Each MWB charges consumers for different periods: the Nicosia MWB issues bills every 2 months, the Limassol MWB every 4 months, and Larnacaâ€™s Board every 3 months. Tourism information was provided by the Tourism Organisation of Cyprus (CTO). As a starting point for analysing the data, all variables used in this study are plotted on time series graphs. Graphical plots shed light on the properties of the data, identify whether or not data is stationary and succour in the selection of the correct assumption when estimating the vector error correction model. Figure 4.1 above suggests that four variables i.e. water demand, population, price and temperature seem to be trending upwards, while precipitation does not indicates a clear trend as it fluctuates over time. Thus, it can be inferred that most of the series seem to be exhibiting a time varying mean and variance, indicating nonstationarity. Non-stationary time series may contain unit roots. Nonetheless, a graphical method does not provide an accurate presentation of the data regarding unit root evidence. Thus, it is crucially important to perform unit root tests so as confirm the plot assumptions.

4.1 Tests for order of integration

Several preliminary diagnostic steps are essential prior to estimating a model in econometrics. Time series need to be stationary, meaning that their variables have a constant distribution over time. Nonstationarity of the time series poses threats to conventional regression analysis (Hamilton, 1994). Non-stationary series have no long-run mean and variance dependent on time and going to infinity as time approaches infinity (Enders, 2004). To check the characteristics and behaviour of the data, stationarity properties must be evaluated by means of unit root tests. The variables are presented both in levels and in differences. If unit root tests show that data are non-stationary, then the series can be investigated for the existence of potential long-run (cointegrating) relationships. If the variables are cointegrated, this eliminates the possibility of spurious results, which are normally persistent in non-stationary series (Granger and Newbold, 1974). A spurious regression yields high and t-statistics appear to be very significant, though in reality there 16

Information is not publicly available. Data was collected via email. Telephone follow-ups were also conducted. MWB Larnaca (corresponding person: Takis Papas, tpapas@lwb.org.cy) MWB Nicosia (corresponding person: Yiannos Andreou, y.andreou@wbn.org.cy) MWB Limassol (corresponding person: Phryne Potamou, phryne@wbl.com.cy)

18

is no significant or meaningful economic relationship. Granger and Newbold revealed through simulation that the confidence in the estimated parameters is overestimated when of the regression is greater than its Durbin-Watson d value. Following the convention for time series methodology, the order of integration of the individual series has been tested prior to the cointegration analysis and estimation of the Error-Correction Model (ECM). In this study, to examine the dynamic structure of the timeseries described above, the Augmented Dickey Fuller (ADF) and the Phillips-Perron (PP) tests are employed. It has already been noted in Figure 4.1 that some series are trending and are thus nonstationary. The unit root problem can be solved, or stationarity can be achieved, by differencing the series (Wei, 2006). Time series integrated of order zero are stationary in levels, while time series are said to be integrated of order d, I(d>0), after differencing d times to attain stationarity (difference stationary series). Trend stationary series implies that nonstationary time series are not driven by a unit root process. However, they can be transformed into stationary, I(0) by removing the deterministic trend (detrending). The trending behaviour is to investigate by means of unit root tests. Hence, two models are considered for detecting stationarity; one with a constant term and the second with a constant and deterministic trend term: (1) (2) The difference between the two equations concerns the presence of the deterministic elements and , but the parameter of interest is γ; if , the contains a unit root, i.e. the time series is non-stationary. Additionally, the t-value does not have an asymptotic normal distribution. The methodology of Augmented Dickey – Fuller (ADF) is the same for all the models, but the appropriate critical values for depend on sample size and the form of the regression (Dickey and Fuller, 1979). The estimated t-statistics are compared against the tabulated ADF critical values in order to determine whether or not to reject the null hypothesis of = 0. That is, if the t-ratio is less than the critical value, the null hypothesis of a unit root is accepted. Then, the first difference of the series is evaluated and if the null hypothesis is rejected, the series is considered to be stationary. Therefore, the assumption that the series is integrated of order one I(1) is valid. Critical values for this tstatistic are given in Mackinnon (1991).17 It should be mentioned that the ADF test is based on an assumption that the errors of term t are uncorrelated (iid) and have constant variance. To correct autoregressive heteroscedasticity of the error terms and serial correlation problems (Phillips, 1987), the PP unit root test should be undertaken (Bartlett window technique). In both the PP and ADF unit root tests, the null hypothesis of a unit root against the alternative hypothesis of stationarity is examined with identical critical values. If the variables studied are found to have a unit root, the Johansen test for cointegration is applied in order to test for long-run linkages among the series. 17

In this case, the null hypothesis is γ=0, γ= =0, =γ= =0. For 31 observations, the critical values are 4.38, -3.50 and -3.18 at the 1%, 5% and 10% significance levels, respectively.

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4.2 Cointegration & Vector Error Correction Model

Pioneered by Engle and Granger (1987), and evolved by Johansen (1988), cointegration is a long-term concept which epitomizes the momentous insight for equilibrium theories.18 Engle and Granger argued that if a linear combination of integrated variables is stationary, then such variables are said to be cointegrated. Phrased differently, if a number of nonstationary series, I(1) appear to move together over time, this suggests that an equilibrium relationship exists in the system. Any deviation from the equilibrium is temporary and series will return to the equilibrium level in the long-run horizon. Having indentified that, the analysis and interpretation of nonstationary variables become feasible. Nonetheless, the approach of Engle and Granger has been criticised for suffering from a number of weaknesses, including lack of power in unit root tests on finite samples, simultaneous equation bias and inability to test the actual hypotheses concerning the cointegrating relationship (Brooks, 2008). A more dynamic approach proposed by Johansen (1988) has received considerable appeal on recent studies for several reasons. Concretely, the Johansen representation is superior given that it does not necessitate a distinction between endogenous and exogenous variables; it contains better asymptotic properties and permits the identification of both I(0) and I(1) variables absorbing much of the pre-testing problems. More importantly, unlike Engle-Granger approach which assumes only one cointegrating vector, Johansen method allows testing for the presence of multiple cointegrating vectors (if any). Dwelling upon the aforementioned advantages, Johansen cointegration test in a multivariate framework is employed in this study. This mechanism strongly relies on the relationship between the rank of a matrix and its characteristic roots. Essentially, Johansen's methodology is a multivariate generalization of the Dickey-Fuller test (Enders, 2004). The starting point of Johansenâ€™s process is a vector autoregressive process (VAR) of order p: (3) where

= the (nx1) vector of variables that are I(1) = (nxn) matrices of coefficients = (nx1) vector of error terms

Rearranging the equation yields a representation of vector error correction model (VECM): (4) where

18

,

and

The majority of studies of recent years are found in macroeconomics, and mainly in energy economics.

20

The impact matrix Π defines the extent to which the system is cointegrated. Johansen's method determines the VAR subject to Π= β α΄. Therefore, the rank of Π defines the number of cointegration vectors of the system. The several values of r number of cointegrating vectors are estimated from the rank r of the matrix Π, using the maximum likelihood estimator under the assumption iid N (0, ). Thus, it can be rewritten as: (5) where α is an n x r matrix of error correction term or speed of adjustment parameters, while β is a matrix of cointegrating vectors. The vectors can be “interpreted as a long-term coeﬃcient matrix, since in equilibrium, all the will be zero, and setting the error terms, to their expected value of zero will leave Π = 0” (Brooks, 2008). Τhe examination of matrix Π can emerge in the following basic cases. Firstly, if r(Π) = 0, the matrix is null, and thus no linear combination of the different variables. Secondly, if r(Π) = n, the matrix has full rank and consequently, the process is stationary. If r(Π ) = 1, there is one cointegrating vector in the system and is the error correction term. For 1<r(Π)<n, the system determines k multiple linear combinations (Verbeek, 2008). To evaluate the number of cointegrating vectors, two test statistics are conducted, the trace test and the maximum eigenvalue test statistic: (5) (6) where λ denotes the estimated value of characteristic roots determined from the estimated Π matrix and T is the number of observations. The trace test concerns a joint test where the null hypothesis defines that the number of cointegrating vectors is less than or equal to r, against the alternative that there are more than r, that is k. The maximum eigenvalue tests the null hypothesis that the number of cointegrating vectors is r against the alternative of r+1 cointegrating vectors (Brooks, 2008). In a multivariate test of cointegration, we expect to find at least one cointegrating vector, that is, r(Π ) = 1. Thus, the null of no cointegration can be rejected, if the rank of the matrix exceeds or is equal to one. Once cointegrating relations are established in the system of variables, a Vector Error Correction Model or Equilibrium Correction Model provides evidence on short-run versus long-run impacts. The representation of VECM implies that all the series must be nonstationary, integrated of the same order, and move together in a long-run path. “A principal feature of cointegrated variables is that their time paths are influenced by the extent of any deviation from the long-run equilibrium” (Enders, 2004). Armed with this evidence, VECM seeks to assess the short-term dynamics influenced by temporary deviations from a long-run relationship. As proven mathematically in the model (5), error correction and cointegration are equivalent representations. Consequently, if the variables under consideration share a 21

Tourism & Water Demand Dynamics

common stochastic trend, then they are cointegrated and a VECM can be developed. The VECM determines the movements of at least some of the variables to the magnitude of the disequilibrium, if the system is to return to equilibrium. The model shows that there are two systematic effects on the changes of the dependent variable. The first effect is the impact of vector Γ, which describes the short-term dynamics. The second effect is the impact of β which describes the long-term relationship. Broadly, describes the speed of adjustment back to equilibrium. Small values of α, close to 1, indicate that policy makers remove a large percentage of disequilibrium in each period. Larger values, close to 0, indicate that adjustment is slow. Extremely small values, less than 2, imply an overshooting of equilibrium. Positive values determine diversion from the long-run equilibrium. Τherefore, the larger α is, the faster the series will respond to deviations from the long-run equilibrium that is denoted by the second part of the equation. The system responds quickly to shocks, showing strong relationship among the series. The smaller α is, more time is needed for the variables to move towards the equilibrium, when a shock disrupts the long-run relationship. Moreover, α identifies the exogeneity of the series. If one of α parameters in the system turns to be zero, then it is weakly exogenous and unable to react to discrepancies in the longrun relationship (Enders, 2004).

5. Results and Discussion 5.1 Unit Root tests

To verify robustness and whether the preliminary condition for cointegration is fulfilled, the order of integration of all relevant series is investigated, using ADF and PP test. The critical values for both unit root procedures have the same distributions.19 The number of lags was chosen selecting the Schwarz Information Criterion (SBIC), following the findings by Lutkepohl (2005), which demonstrate more consistent estimates of the true lag order when SBIC used. However, our results were also robust to alternative methods such as the AIC (Akaike Information Criterion). Due to the small sample size, the residuals become white noise after a lag length of one, expect for precipitation. Both the level and first difference of each series were tested and a summary of the outcomes is presented below. Table 5.1 illustrates test statistics of two regressions, one with an intercept only and one with an intercept and a linear time trend due to the clear upward trend in the data series. In the first case, the results indicate that we cannot reject the null hypothesis of a unit root at 1% significance level in all series. That is, water demand, price and temperature appear to be I(1). Nonetheless, there are two exceptions: population and precipitation, for which the hypotheses are rejected at 5% and 1% significance level respectively, are stationary. In the 19

Critical values are reproduced in Hamilton, 1994

22

next step, by taking the first differences, the first lag is subtracted from the original series. The ADF test shows that the unit root hypothesis is rejected at a confidence level of 99% for all of the non-stationary series. The PP test confirms this evidence. Unit roots are also present when we include a linear trend, with the not very surprising exception of precipitation residuals which validate the stationary status I(0) of precipitation as above. Therefore, having indentified its stationary nature, precipitation is left out of the model as it cannot explain the long-run relationships between I(1) variables. The ADF results clearly show that all the series are non-stationary in levels, but stationary in first differences. Population is sensitive with the inclusion of the trend, revealing here that is I(1) at 1% significant level. The PP test confirms, but temperature series results are ambivalent. Specifically, temperature turns out to be stationary at 5% significance level, but non-stationary when accepting 1% significance level. Consequently, temperature should be considered with caution in the co-integration relationship and error-correction model, as it might be I(0,1) and lead to misleading conclusions. Table 5.1. Stationarity test results for variables ADF Intercept

Intercept & Trend

Water ΔWater Population Δpopulation Price ΔPrice Precipitation ΔPrecipitation Temperature ΔTemperature Water ΔWater Population Δpopulation Price ΔPrice Precipitation ΔPrecipitation Temperature ΔTemperature

-0.024 (1) -4.499 (0)* -3.198 (1)** -5.755 (0)* -0.831 (1) -5.846 (0)* -5.293 (0)* -5.896 (1) -2.249 (1) -5.583 (1)* -2.293 (1) -4.435 (0)* -1.036 (1) -7.195 (0)* -2.928 (1) -5.740 (0)* -5.186 (0)* -5.777 (1) -3.038 (1) -5.473 (0)*

PP -0.194 (1) -4.499 (0)* -3.464 (1)** -5.755 (0)* -0.756 (1) -5.864 (0)* -5.293 (0)* -7.750 (1) -2.489 (1) -6.814 (1)* -2.171 (1) -4.435 (0)* -1.387 (1) -7.195 (0)* -3.050 (1) -5.740 (0)* -5.186 (0)* -7.990 (1) -3.675 (1)** -6.680 (0)*

Result I(1) I(0) I(1) I(0) I(1) I(1) I(1) I(1) I(0) I(1,0)

*, **, *** indicates significance at the one, five and ten per cent level, respectively. Optimal lag length selection is expressed in the parenthesis and is determined by the Schwarz Bayesian Criterion (SBIC).

23

Tourism & Water Demand Dynamics

On the whole, the data series fit well according to the second regression equation. The tests suggest that data series have a unit root in their log level form, and thus assumed to be integrated of order one, after being differenced once. This indicates that water consumption, population, price of drinking water and temperature series fulfil a necessary precondition to apply cointegration framework. Hence, these variables can be subjected to cointegration analysis.

5.2 The Johansen Cointegration Analysis

As integration of order one is established for the variables under investigation, the next step is to determine whether a long-run relationship exists. To do so, cointegration between common factors is examined by the standard time series tests such as the Johansen maximum likelihood test. The cointegration test in the multivariate framework proposed by Johansen is applied to the underlying series. The two likelihood ratio tests, the trace and the maximum eigenvalue test are performed in order to investigate the rank of the joint cointegration matrix. The lag length is selected to minimize the AIC, producing economically meaningful results. If the four variables are cointegrated in pairs, the rank of cointegration matrix r(Π) = k is expected to correspond to k = 3. A matrix of full rank, meaning that r(Π ) = k = 4 , is not expected as this would imply that the original series are stationary, which was rejected by the ADF tests in paragraph 5.1. If the matrix proves to have a rank r(Π ) = k = 1 , there is one cointegration vector and only one pair is cointegrated. The results of the multivariate test are depicted in Table 5.2. Table 5.2. Johansen multivariate cointegration results - trace test and maximum eigenvalue test Null Hypothesis tests r=0 r≤1 r≤2 tests r=0 r=1 r=2

Alternative Hypothesis

r>0 r>1 r>2 r=1 r=2 r=3

value 72.236 33.359* 11.415** value 38.877 21.438* 7.925**

5% Critical value

1% Critical value

47.21 29.68 15.41

54.46 35.65 20.04

Reject Hο

27.07 20.97 14.07

32.24 25.52 18.63

Reject Hο

Decision

Do not reject Hο* Do not reject Hο**

Do not reject Hο*

Do not reject Hο** Unrestricted multivariate cointegration rank test (trace test and maximum eigenvalue) as proposed by Johansen (1988) using critical values introduced by MacKinnon, Haug and Michelis (1999). The number of lags was defined according to the AIC method. *, ** indicates significance at the 1% and 5% level respectively.

The null hypothesis determines that the variables are not cointegrated, so that π = 0. Notably, the trace test statistic focuses on whether the water demand, population, price and temperature are jointly cointegrated. That is, we examine the null hypothesis that the variables are not cointegrated (r = 0) against the alternative of one or more cointegrating 24

vectors. Since the (0) value of 72.236 exceeds both 5% and 1% critical value of statistic, the null hypothesis of no cointegrating vectors against the alternative of one or more cointegrating vectors is clearly rejected. Next, we use the (1) statistic to test the null of r â‰¤ 1 against the alternative of two or three cointegrating vectors. With a value of 33.359, we cannot reject the null hypothesis at 1% significance level. However, 33.359 does exceed the 5% critical value of 29.68. Hence, the trace test indicates that the rank of the cointegration matrix r(Î ) is one at a 99% confidence level meaning that there is one cointegration relation and two at a 95% confidence level. The statistic confirms that. Namely, we can strongly reject the null hypothesis of no cointegrating vectors (r = 0) against the specific alternative r =1, given that the value of 38.877 exceeds the 5% critical value of 27.07. In contrast, because the maximum eigenvalue (21.438) at r = 1 against the specific alternative r = 2 is less than its critical value of 25.52, we fail to reject the null hypothesis at the 1%, but it can be rejected at 5%, significance level. Similarly, we accept r = 2 as our estimate of the number of cointegrating equations between the variables on a level of 95%. This basically confirms the existence of cointegrating vectors as the findings of the trace test coincide with those of maximum eigenvalue test. Essentially, it can be stated that the multivariate testing procedure leads to two cointegration relations among the four series at 5%, and one cointegrating equation at the stricter 1% significance level. The result does not exhibit much sensitivity with regard to the chosen lag length. The appropriate lag length when minimizing the AIC is 4. Multicollinearity

The VECM satisfies the assumptions of the classical normal linear regression model (CNLRM). Detecting for multicollinearity, the Variance Inflation Factor (VIF) values indicate that the models independent variables display collinearity problems. With a value greater than 5 (i.e. 5.92), it is apparent that population and price are collinear. Thus, the overall measure of goodness of fit is very high, but the t-ratio of population tends to be statistically insignificant. The auxiliary regression of population on price helps to designate that population indeed is related with price, but it is also used as remedial measure. In order to eliminate collinearity, the residuals generated from the auxiliary regression replace the population series in the following steps. Repeating the test for multicollinearity, the VIF values confirm now that there is no major concern for multicollinearity in our model. A detailed analysis of correlation is established when performing the diagnostic checks in the section below.

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Tourism & Water Demand Dynamics

5.3 Vector Error Correction Model

Given prior evidence of cointegration, the VECM is estimated to determine the true cointegrating vector for the purpose of normalization. By building a model in ďŹ rst differences which incorporates an error correction mechanism, the estimation of a spurious regression due to stochastic or deterministic trends in the data is not a possibility. Both the long-run and the short-run dynamics of the model are specified and reported in table 5.2. These include lagged values of differences of the underlying variables. The most important parameter when evaluating the VECM is the speed of adjustment, also called error correction term. The vector error correction model can be specified as the following:

where

is the error correction term of the model so that (Î˛â€™s represent the long-run parameters).

Error Correction Term

According to the theory, at least one of the error correction terms in the equations must be non-zero, otherwise the long-run relationship does not appear and there is no error correction or cointegration representation in the model. The results reveal that all the underlying error coefficients parameters in the table 5.3 are significant, which suggests that the specified equation is the true cointegrating relationship in the vector. The significance and the sign matter more here than the actual magnitude of the coefficients. The predicted adjustment parameter in response to a shock for water demand is statistically significant from zero at 10% level (t-ratio of 1.81), but not with the expected negative sign. Indeed, this identifies that water demand has a true cointegrating equation in the cointegrating vector. Apart from significant, the speed of adjustment should also be negative in order to signify that adjustments are made towards restoring long-run equilibrium. However, the positive sign of water demand suggests that the system diverges from the long-run equilibrium path by 16%, following a shock to the model. Since, short-run adjustment of consumption to population, price and temperature do not occur, our model is unstable. To draw a clear picture about the dynamics of the entire system, we further discuss the estimated error correction terms of the other equations. The error correction term in the equation for price is highly significant, but with positive value (0.254 or 25.4%). The absence of short-run price adjustment also implies movement away from the equilibrium.

26

Table 5.3. Estimating the VECM (a) Long-run multipliers Water

Pop

Price

Temp

Constant

1.000

2.511 (4.53)

-1.533 (-8.80)

11.869 (1.94)

-45.691

ΔWater

Δpop

ΔPrice

ΔΤemp

0.160 (1.81) -0.115 (-0.35) -0.596 (-1.68)

-0.195 (-2.32) 0.182 (0.58) 0.928 (2.74)

0.254 (2.44) -0.745 (-1.93) -1.070 (-2.55)

-0.027 (-1.68) 0.083 (1.41) 0. 103 (1.61)

Δ(Water (-3))

-0.932 (-1.73)

0.647 (1.26)

-0.888 (-1.40)

-0.029 (-0.30)

Δ(Pop (-1))

-0.183 (-0.57) -1.135 (-2.00) -0.126 (-0.34) 0.058 (0.20) -0.496 (-1.69) 0.043 (0.14) 0.374 (0.28) 0.395 (0.32) 1.754 (1.71) 0.034 (0.62) 0.563 16.78

0.778 (2.54) 0.713 (1.32) 0.017 (0.05) 0.229 (0.84) 0.214 (0.77) -0.017 (-0.06) 0.455 (0.36) -1.093 (-0.94) -0.479 (-0.49) 0.043 (0.82) 0.551 15.94

-1.099 (-2.89) -0.784 (-1.17) 0.180 (0.41) -0.449 (-1.34) -0.121 (-0.35) 0 .128 (0.36) -0.353 (-0.22) 1.189 (0.82) 0.163 (0.13) 0.013 (0.21) 0.677 27.27

0. 217 (3.73) 0.017 (0.17) -0.044 (-0.65) 0. 080 (1.56) 0.017 (0.32) -0.085 (-1.55) -0.298 (-1.24) -0.203 (-0.91) -0.085 (-0.46) 0.017 (1.74) 0.756 40.28

(b) Short-run dynamics Error correction term (α) Δ(Water (-1)) Δ(Water (-2))

Δ(Pop (-2)) Δ(Pop (-3)) Δ(Price (-1)) Δ(Price (-2)) Δ(Price (-3)) Δ(Temp (-1)) Δ(Temp (-2)) Δ(Temp (-3)) Intercept

F-statistic t-statistic in the parenthesis

The deviation from the equilibrium path for population is 0.195 (t-value of -2.32) and negatively signed. This indicates that about 19.5% of shock (short-run disequilibria) will be adjusted within the next period. Thus, the external pressure gaps between population and its equilibrium are eliminated in approximately one year later in Cyprus. Population is 27

Tourism & Water Demand Dynamics

endogenous and its values are determined inside the model. Other equation that is adjusted, but not rapidly, is temperature which appears to have correct sign, but its value is large. The coefficient of 0.026 is significant at 10% level with t-ratio -1.68, indicating slow speed of adjustment of removing the disequilibrium. More precisely, it indicates that only 2.6% of the disequilibrium in the system from the previous periodâ€™s shock converges back to the long-run equilibrium in the current period.

Short-run dynamics

The short-run dynamics of the water demand are subsequently modelled by means of a VECM. None of lagged difference terms of the series, the first lag of variables is found to have a significant impact on the short-run water demand. In constrast, population and price on the second lag are significantly diferrent from zero at 5% and 10% level respectively. The coefficient of population shows a negative short-term influence to the water demand of 1.13%. This indicates a short-run disequlibrium between water demand and population which reflects the deficient water supply and the short-term imbalances in supply and demand. The third lagged difference is also insignificant. The short-run effect of price on water demand is illustrated to be negative and inelastic (-0.495) at the 10% significance level. These results are in line with the dynamic estimates by Martinez-Espineira (2007) who found a short-run price elesticity equal to -0.159. The third lagged temperature is significant at 10%, which indicates that the impact on the water demand is not as significant as in the long-run case. The insignificant estimates can be explained by the short time period which is unable to capture the short-run discrepancies.

Long-run equilibrium

The long-run parameters as estimated in the normalised cointegrating equation by the Johansen maximum likelihood test variables are also reported in table 5.3. As shown, all variables are statistically different from zero and have the anticipated sign. Population has a positive and statistically significant long-run impact in the water demand model. The result is plausible since an increase in population means greater demand for water by 2.5%. =

+ 2.5

- 1.53

+ 11.89

- 45.69

(8)

The long-run price elasticity is estimated at -1.53 which is significantly larger than unity; if water authorities increase the price by 1%, the water demand will decrease by 1.53%. This possibly reflects the impact of water-pricing policy by the Municipal Water Boards as an instrument to control demand in an environmentally sustainable way. This demand-side policy seems to be effective in the long-run horizon as the water demand turns to be price elastic. Our review of the empirical literature advocated that only the study by Hewitt and Hanemann (1995) found elastic demand with respect to short-run sensitivity. Temperature yields a positive long-run relationship, but surprisingly the size of the coefficient is 28

relatively high. That is, a 1% increase in the temperature will cause an 11.9% increase in water demand. The t-ratio 6.11 is significant on a 95% confidence level. In a nutshell, equation (8) verifies the results of section 5.2; the existence of one cointegrating vector in the data is validated and therefore, water demand is positively related to population and temperature and negatively related to price in the long-run. Comparing the short and longrun price elasticities ( and , our findings are in line with existing literature. That is, long-run elasticities are higher than their short-run counterparts (Nauges and Thomas, 2003; Martinez-Espineira, 2007; Ben Zaied and Binet, 2011). The rationale behind the imbalance is that consumers need time to change their water consumption habits after a change in price. This also incorporates information acquisition costs as users change their consumption behaviour only when observing some effect on their bill (Ben Zaied and Binet, 2011). According to Nauges and Thomas (2003), the dynamic effects of short-run and long-run price elasticities are crucial for water utilities and public policy so as to appropriately evaluate the impact of volumetric water pricing.

Postestimation misspecification testing

Considering that the parameter in adjustment relies on the stationarity of the cointegrating equations, we further check for the specification of the model. Firstly, we predict the cointegrating equations. The graph (fig.5.1) illustrates the behavior of our cointegrating equations. There are some shocks which has influenced our predictions as stated above. Though, despite the large shocks, the cointegrating relation is stationary. Fig. 5.1. Cointegrating relationship used as the error correction term of the dynamic model

-1

0

1

2

Predicted cointegrated equation

3

Diagnostic tests have been conducted for normality and autocorrelation to ensure that residuals of the VECM are well behaved. If residuals are serially correlated and have non constant error variance, then the estimated parameters above might be biased. As reported in table 5.3 four statistics were computed: a skewness statistic, a kurtosis statistic, and the Jarqueâ€“ Bera statistic for normally distribured and a 1980 1990 2000 2010 yrs LM test for autocorrelation in the residuals. For each lag, the autocorrelation LM test has a null hypothesis that there is no serial correlation. The results show that the null hypothesis of no serial correlation fails to be rejected for any of the orders tested. Therefore, this test finds no evidence of model misspecification.

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Table 5.5 Postestimation: mispecification tests

Furthermore, the normality test (Jarque-Bera), tests the null hypothesis that the disturbances are derived from a multivariate normal distribution. The single-equation and overall Jarque-Bera statistics do not reject the null of normality. None of the errors are either skewed or kurtotic. Ergo, the misspeciďŹ cation tests performed on the VECM produce satisfactory results.

5.4 Tourism & Water Demand

The section presents the descriptive statistics of three important urban areas which also constitute main tourist destinations. Seasonal data on water tourism consumption employed to identify water use patterns by tourists and compare the regional estimates with the findings derived from the national water demand model. Essentially, in the water demand model above, we assume that residents use the same water amount per person per day as tourists do. These data allows testing that particular assumption. That is, by modelling separately for regional and national data total, we can compare the coefficients to investigate the differences between water use per person. To start with, two coastline areas (Limassol and Larnaca) and one region located at the hinterland (Nicosia) are the study cases used to shed light on the regional and seasonal variability of water demand in the hotel sector. Table 5.4 presents a summary of descriptive statistics for hotels and tourist accommodation in these particular regions. Sample means, maximums, minimums and standard deviations are reported from seasonal and time series components. As shown, quarterly water consumption by tourist accommodation and hotels ranges from 21.279 to 30.191 in Limassol (quarterly), 45.484 to 77.771 in Larnaca (4 month period), while the hotels in Nicosia average from 7.090 to 19.228 (bimonthly). In the region of Larnaca, there is a strong seasonal component. The maximum hotel water consumption is 125.242 during the peak season and 66.608 in the low season. To further compare the values obtained in our survey with those observed in previous studies, we also computed water consumption in litres per tourist arrival per day. Our estimates for Limassol and Larnaca reveal that the annual water consumption per tourist per day is 450 L and 671 L per tourist per day, respectively. More importantly, in the same line with the literature, hotels consume double water per day during the summer period, indicating the high seasonality pattern of water use. To better understand the difference between residential and tourism water use, Polycharou & Zachariadis (2012) recently found that quarterly water consumption per household in the same areas ranges from 30.3 to 57.3 with a mean value of 40.8 . 30

Table 5.6 Descriptive statistics in water consumption by tourist

Limassol Water consumption(m³)

Log of water consumption(m³)

Water consumption by tourist per day (l)

Larnaca Water consumption(m³)

Log of water consumption(m³)

Water consumption by tourist per day (l)

Nicosia Water consumption(m³)

Log of water consumption(m³)

Quarter 1 2 3 Total

Mean 21.279 22.065 30.191 73.536

Stand. Dev 4.242 3.661 5.198 13.103

Min 15.528 15.099 21.606 52.233

Max 28.495 26.220 37.833 92.548

1

4,32

0,09

4,19

4,45

2

4,34

0,08

4,18

4,42

3 Total

4,47 13,1

0,08 0,2

4,33 12,7

4,58 13,5

1

48,9

37,7

12,9

104,1

2

69,4

54,7

9,3

125,7

3

95,8

65,7

26,4

220,2

Total

214,2

158,1

48,6

450

45.484 55.808 77.771 73.469

10.619 12.696 23.276 21.020

33.807 24.355 28.266 32.904

4,66 4,75 4,89 4,87 19,16

4,03 4,10 4,37 4,32 16,82

4,53 4,39 4,45 4,52 17,88

66.608 74.105 125.242 116.456 382.411 4,82 4,87 5,10 5,07 19,86

1

89,4

26,3

28,5

120,4

2 3 4 Total

89,0 109,0 211,1 498,5

11,3 36,9 30,8 105,4

78,2 17,0 176,2 299,8

112,4 153,0 285,7 671,5

2 3 4 5 6 Total

7090 8.045 8.154 16.109 18.129 19.228 76.755

6.259 1.366 1.282 1.386 3.088 893 14.277

14.179 14.304 16.305 16.109 18.129 16.881 95.907

30.778 18.503 19.569 20.102 26.838 19.228 135.018

1 2 3 4 5 6 Total

4,15 4,21 4,21 4,21 4,26 4,28 25,32

0,12 0,04 0,03 0,03 0,06 0,02 0,31

4,15 4,16 4,21 4,21 4,26 4,23 25,21

4,49 4,27 4,29 4,30 4,43 4,28 26,06

4-month period 1 2 3 4 Total 1 2 3 4 Total

2-month period 1

31

Tourism & Water Demand Dynamics

This clearly reflects the impact of tourism sector on stressed water recourses of the island. In general, holidaymakers tend to use more water than the amount they normally use at home. It is a â€œpleasure approach to the showerâ€?, as it has been accurately quoted by Eurostat (2009), referring to the extra shower before and after using the swimming pool that can increase water consumption in the tourism destinations. Seasonal variation in the estimates is intense. Tourists that visit Limassol consume from 12 L to 220 L with a mean value of 71 L, while in Larnaca, it is ranged from 17 L to 285 L per tourist per day. The number of guest per room would be more appropriate in this case. Moreover, water use is influenced by the category of the hotel. From a more detailed analysis of the data, we observe that the average demand of a three-star hotel (namely, Holiday Inn) is 8 per day, whereas five-star (Hilton and Cyprus Tourism Development Public Company Ltd) ones reach values of 83 and 156 per day, respectively. The collective amount of water remains constant, regardless of the occupancy rate that hotels have (watering of gardens that must be kept attractive, daily cleaning of rooms, filling of swimming pools, kitchen and laundry). At this point, on the grounds of our empirical model above, we provide a comparison of price and population elasticities. The annual price and population elasticities from the three municipal water boards are displayed in table 5.2. The seasonal data spans from 2000 to 2010 and were converted to annual data in order to compute elasticities. The short-run price elasticity of demand for Larnaca, Limassol and Nicosia is estimated to be 0.584, 0.363 and 0.235 (in absolute values), respectively. This is in line with the elasticity estimate in national water demand model at -0.495. Therefore, all estimated water demand estimates are inelastic to their price. Table 5.7. Comparing the elasticities Elasticities Price Population | Tourists

National Model

Larnaca

-0.495 -1.135

0 .584 1.850

Limassol

Nicosia

-0.363 -1.498

-0.235 0.979

On the other hand, population elasticity is found to be -1.13 in the national water model and equal to 1.85, -1.49 and 0.97 when derived from the regional and tourist data. The latter coefficients tell us that if the tourist arrivals rate increases by 1%, the water consumption goes up by 1.13%., 1.85%, 1.49% and 0.97%. All the estimates are elastic. The only exception is Nicosiaâ€˜s estimate which is inelastic (with propensity to be elastic). It should be noted that these elasticities should be consider with caution due to limited amount of data in this particular water board. Larnaca estimate indicates the higher sensitivity of the quantity demanded to tourist influx. The findings are very important because they essentially reveal that tourists consume 1.5 times more water than the residents. This information in tandem with the evidence for inelastic demand should become a principal concern of water authorities and interest groups for reorientation of water management policy, structures and practices. 32

6. Conclusions and Policy Implications This study set out to analyze the impact of factors which synthesizes water demand function. Quantifying annual data from Cyprus, the econometric investigation suggests that the behaviour of water demand is related to population, price and temperature. Further knowledge into water demand determinants through cointegration test indicated that the series appear to move parallel in a long-run relationship. Once the cointegrating relationship was identified, the error-correction terms were extracted to open up further channels of short-run and long-run relations. In the cointegration equation, the coefficients of population and temperature have positive long-run relation with water demand, while price of demand is negatively signed and elastic. The significant results of this analysis suggests the application of long-run non-price management policies such as public education campaigns and the subsidization of programmes aimed at the adoption of water efficient technologies adoption. The speed of adjustment was found to be significant, but with a large disequilibrium effect. As predicted by the theory, the short-run dynamics indicated that price elasticity is negative and inelastic, and considerably much lower than the long-run elastic counterpart. This was also confirmed by the regional control estimates. It is apparent that the percentage reduction in the quantity of water demanded is less than proportionate to the percentage increase in price. In light of this, with reservoirs at critically low levels, high temperature and repeated drought episode waves caused by climate change, water demand-side management is considered to be the only short-term solution. Water managers and local planning authorities therefore can engage more productively with their stakeholders and move towards the application of appropriate pricing in conjunction with non-pricing instruments. The implementation of water-saving equipments, educational programs, consumer awareness campaigns, restrictions on the watering of gardens constitutes best ways to prevent excessive water demand. Notwithstanding, a common problem in the learning alliance approach and demand-led water management innovation is that they need a major source of national funding. In times of restricted financial programs appropriate water pricing policies emerge as the most efficient policy instrument. It is generally acknowledged that the existing water supply projects and planning have been designed in a wetter past and thus, have been lagging behind in regards to current and future needs and development. The existing studies have examined the residential water demand, without considering tourist arrival impact. As shown, the tourists outnumber permanent the residents in the island, and more importantly, water use by hotels is much higher than the residential. Accordingly, given that desalination plants and effluent recycling plant are already taking place, the assessment of the impact of water pricing policies on the behaviour of tourists and residents is imperative. To this extent, there is a dire need for empirical work

33

Tourism & Water Demand Dynamics

in this area. Longer time series or panel data with regional and seasonal observations can lead to accurate conclusions for progressive policymaking. The tourism policy and management of water resources has become a crucial challenge of local authorities and international institutions. Policy makers and hotel managers should systematically monitor the costs and neutralize the negative externalities. This can ensure the viability and sustainability of tourist industry and water resources. After an era of policies directed towards increasing water supplies infrastructure, the exploitation of nonconventional water resources such as water recycling and reuse can in turn reduce water supply. Such technologies should be in line with the allocation of water use from agriculture to urban use. In a sense, the water saving investments can enhance hotelâ€™s image, the quality of services and drive consumer loyalty. Other possible ways for sustainable water use are to prohibit the size, the filling or emptying and refilling of swimming pools, the installation of water efficient devices in guestrooms and kitchens and the water-wise plant selection with low to moderate water needs. Whatâ€™s more, staff training programmes and motivation through feedback and reward success are important so as they understand how to make prudent use of water and how to maintain equipment for optimum energy-efficiency. It should be noted that this report is limited in accounting the impact of seasonality on water demand dynamics due to lack of data availability. Our research goes a step further by modelling both residents and tourists and testing the hypothesis if tourists consume the same amount of water as residents. In an effort to provide best-practice estimates of price and population elasticities, this is the first study applying cointegration and error correction model technique in Cyprus, whereas using Johansen procedure on water demand modelling. Therefore, the econometric approach followed is proposed for further research and insights; the empirical evidence and the comprehensive look of this approach can be proved useful tool in effective water pricing policymaking which coupled with appropriate investments, awareness, climatic change adoption and institutional strengthening are promising pathways for sustainable water resources management.

34

7. References Aguiló E, Alegre J, Sard M (2005) The persistence of the sun and sand tourism model. Tourism Manage 26:219–231 Antakyali, D., Krampe, J., & Steinmetz, H. (2008). Practical application of wastewater reuse in tourist resorts. Water Science and Technology, 57, 2051-2057 Arbués, F., Garcıa-Valiñas, M. ., Martınez-Espiñeira, R., 2003. Estimation of residential water demand: a state-of-the-art review. The Journal of Socio-Economics, 32(1), 81-102. Arbués, F., Barberán, R.,Villanúa, I., 2000.Water price impact on residentialwater demand in the city of Zaragoza. A dynamic panel data approach. 40th European Congress of the European Regional Studies Association (ERSA) in Barcelona, Spain Auernheimer, C., González, G., 2002. Repercussions of the national hydrological plan on the Spanish Mediterranean coast. Agriculture and Urbanisation in the Mediterranean Region-Enabling Policies for Sustainable Development, Rabat, Morocco, April 2002. Binet, M., & Za, Y. Ben., 2011. Cointegration analysis of residential water demand in Tunisia, Working Paper, (November, 2011). Butler, R., 1980. The concept of a tourism area cycle of evolution: Implications for management of resources. Canadian Geographer 24(1), 5–16. Boukas N., Boustras G. & Sinka A., 2011, Golf tourism in Cyprus, Controversies in Tourism, London: CABI. Billings, R.B., 1987. Alternative demand model estimations for block rate pricing.Water Resources Bulletin 23 (2), 341–345. Bohdanowicz, P., Martinac, I., 2007. Determinants and benchmarking of resource consumption in hotels e case study of Hilton International and Scandic in Europe. Energy and Buildings 39, 82-95. Black, M., King, J. (2009). The atlas of water, mapping the world’s most critical resource. London: Earthscan Brooks, C., 2008, Introductory Econometrics for Finance, The ICMA Centre, of Reading, Cambridge University Press

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Granger, C.W.J. and P. Newbold. 1974. Spurious Regression in Econometrics, Journal of Econometrics, 2,111-120. Gössling, S., 2001. The consequences of tourism for sustainable water use on a tropical island: Zanzibar, Tanzania. Journal of environmental management, 61(2), 179–91. Gössling, Stefan, Peeters, P., Hall, C. M., Ceron, J.-P., Dubois, G., Lehmann, L. V., & Scott, D., 2011. Tourism and water use: Supply, demand, and security. An international review. Tourism Management, 33(1), 1–15. Griffin, R.C., Chang, C., 1990. Pretest analysis of water demand in thirty communities.Water Resources Research 26 (10), 2251–2255. Hall,C.M.,&Stoffels, M.(2006). Lake tourism in NewZealand: sustainable management issues. In C. M. Hall, T. Härkönen (Eds.), Lake tourism: An integrated approach to lacustrine tourism systems (pp. 182-206). Clevedon: Channelview Press. Hall, C. M., Timothy, D., & Duval, D. (2004). Security and tourism: towards a new understanding? Journal of Travel and Tourism Marketing, 15, 1e18 Hamele, H., Eckardt, S., 2006. Environmental Initiatives by European Tourism Businesses. Instruments, Indicators and Practical Examples. ECOTRANS, Germany. Hamilton J.D., 1994. Time Series Analysis. New Jersey:Princeton University Press. Hansen, R.D. & Narayanan, R., 1981. A monthly time series model of municipal water demand. Journal of the American Water Resources Association Vol 17,4, p 578-85 Hof, A., & Schmitt, T., 2011. Urban and tourist land use patterns and water consumption: Evidence from Mallorca, Balearic Islands. Land Use Policy, 28(4) Höglund, L., 1999. Household demand for water in Sweden with implications of a potential tax on water use. Water Resources Research 35 (12), 3853–3863. Klohn, W. (2002) ‘Re-assessment of the Water Resources and Demand of the Island of Cyprus – Synthesis Report’, Cyprus Water Development Department UN Food and Agriculture Organisation, Nicosia. Lamei, A., 2009. A technical economic model for integrated water resources management in tourism dependent arid coastal regions; the case of Sharm El Sheikh, Egypt. AK Leiden: CRC Press/Balkema.

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Lyman, R.A., 1992. Peak and off-peak residential water demand. Water Resources Research 28 (9), 2159–2167. Luetkrpohl H., 2005, Structural vector autoregressive analaysis for cointegrated variables, AStA Advances in Statistical Analysis, Springer, vol. (90), p. 75-88 Maddock, R., Castaño, E., 1991. The welfare impact of rising block pricing: electricity in Colombia. Energy Journal 12 (4), 65–77. Martin, R.C.,Wilder, R.P., 1992. Residential demand for water and the pricing of municipal water services. Public Finance Quarterly 20 (1), 93–102. Martiınez-Espiñeira, R., Nauges, C., 2001. Residential water demand: an empirical analysis using co-integration and error correction techniques. Paper presented at the 35th Meetings of the Canadian Economic Association, Montreal, June 1–3, 2001. Martiınez-Espiñeir, R., 2007, “An estimation of residential water demand using cointegration and error correction techniques”, Journal of Applied Economics, vol. x, n°1, 161-184. Martiınez-Espiñeira, R., 2002, Residential water demand in the Northwest of Spain. Environmental and Resource Economics 21 (2), 161–187. Eurostat, 2009. Medstat II: Water and Tourism pilot study. Eurostat, European Commission Nauges, C., Thomas, A., 2000. Privately-operated water utilities, municipal price negotiation, and estimation of residential water demand: the case of France. Land Economics 76 (1), 68–85. Nauges, C., Thomas, A., 2003. Long-run study of residential water consumption, Environmental and resource Economics, vol. 26, pp. 25-43. Nordin, J.A., 1976. A proposed modification on Taylor’s demand–supply analysis: comment. The Bell Journal of Economics 7 (2), 719–721. OECD. Household Water Pricing in OECD Countries, OECD, Paris. (1999). Perry A. 2006. Will predicted climate change compromise the sustainability of Mediterranean tourism. Journal of Sustainable Tourism, 14 (4): 367-375. Pfaff B., 2006. Analysis of integrated and cointegrated time series with R. London: Springer

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Phillips P. and Perron P., 1988, Testing for a unit root in time series regression, Biometrika (1988), 75, 2, 335-46 Pigram, J.J., 2001. Water resources management in island environments: the challenge of tourism development. Tourism (Zagreb) 49 (3), 267-274. Planning Bureau. 2007. Strategic Development Plan 2007-2013. Ministry of Finance, Nicosia, Cyprus. Polycharou, A., & Zachariadis, T., 2012. An Econometric Analysis of Residential Water Demand in Cyprus. Water Resources Management, 27(1), 309-317. UNEP (2005). Tourism expansion: Increasing threats, or conservation opportunities. Environment Alert Bulletin 6, United Nations Environment Programme, April Rico-Amoros, A. M., Olcina-Cantos, J., & Sauri, D., 2009. Tourist land use patterns and water demand: Evidence from the Western Mediterranean. Land Use Policy, 26(2), 493–501. doi:10.1016/j.landusepol.2008.07.002 Sewell, W.R.D. and Roueche, L. (1974) Peak Load Pricing and Urban Water Management, Natural Resources Journal, 13 (3) Shin, J.S., 1985. Perception of price when information is costly: evidence from residential electricity demand. Review of Economics and Statistics 67 (4), 591–598. Stevens, T.H., Miller, J., Willis, C., 1992. Effect of price structure on residential water demand. Water Resources Bulletin 28 (4), 681–685. Taylor, L.D., 1975. The demand for electricity: a survey. The Bell Journal of Economics 6 (1), 74–110. Taylor, L.D., Blattenberger, G.R., Rennhack, R.K., 1981. Residential energy demand in the United States. Tech. Savvides, L., Dörflinger, G. and Alexandrou, K., 2001. Re-assessment of the Water Resources and Demand of the Island of Cyprus – The Assessment of Water Demand of Cyprus, Cyprus Water Development Department & UN Food and Agriculture Organisation, Nicosia. Verbeek, Marno. 2008. A guide to modern econometrics. Chichester: John Wiley & Sons. Water Framework Directive, 2010. Reporting Sheets on Economics - Republic of Cyprus

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40

8. Annex Figure 8.1. Water Exploitation Index measured in 1990 and 2009 Total abstraction per Year Long term renewable resource 0%

10%

20%

30%

40%

50%

60%

70%

Cyprus Belgium Spain

WEI-2009

Italy Malta

WEI-90

Turkey Germany Poland France Portugal Estonia Greece England/Wales Czech Republic Netherlands Lithuania Macedonia Bulgaria Hungary Switzerland Austria Denmark Luxembourg Slovenia Romania Finland Ireland Sweden Slovakia Latvia Iceland Norway

Data: European Environmental Agency

(i). Non-stressed countries <10% (ii). Low stress 10 to < 20 (iii). Stressed 20% to < 40% (iv). Severe water stress â‰Ľ 40%

41

Tourism & Water Demand Dynamics

Figure 8.2: Tourist Distribution in Cyprus from 2000-2010 400000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

350000 300000 250000 200000 150000 100000 50000 0 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

Regardless of the miraculous development efforts that have been made to this end, the pressure on water resources by tourism, agriculture and standard of living continues to aggravate the water issue and poses a complex challenge. Tourism water use is included in the domestic water use, while the tariff changes depend on the amount of consumption. As shown in figure 8.3, the increasing trend in domestic water demand has probably been caused by the increase in the number of collective tourist accommodation establishments (e.g. a 50% rise of accommodation establishments caused a rise of 21% of domestic water use in 2001 as compared to 2000).

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

Volume of water (mcm)

90,0 80,0 70,0 60,0 50,0 40,0 30,0 20,0 10,0 0,0

20000000 18000000 16000000 14000000 12000000 10000000 8000000 6000000 4000000 2000000 0

1991

Nights spent

Figure 8.3: Water demand and Number of nights spent in tourist accommodation

Number of nights spent in collective tourist accomodation

The trend of domestic water demand continues to increase, even though the total number of nights spent in collective accommodation establishments decreases - overall 3% (figure 8.4). This depicts the increase of standard of living, the lavish water intensive tourism facilities and last but not least, the fact that hotels stay open even when their occupancy rates fall.

42

90,0 80,0 70,0 60,0 50,0 40,0 30,0 20,0 10,0 0,0

Establishments

1000 900 800 700 600 500 400 300 200 100 0

Number of collective tourist accommodation establishments

Volume of water (mcm)

Figure 8.4: Water demand and Number of collective tourist accommodation establishments

Domestic water

As a final step, we present the changes of tariff structure in Limassol, Larnaca and Nicosia with reference years of 1992, 2002, 2007, 2008 and 2009. The charges of the tariff structure and the changes compared to the previous reference year are displayed. Briefly, in 1992, the Water board of Limassol imposed the largest tariff increases on the commercial and industrial block tariff structure, which reached at 140% for the first block and 30% for the second one. Similarly, high increases were recorded on all the tariff structure in 2002 at 58% and 61% level. Afterwards, the price changes did not exceed 10%-11% overall. The Water Board of Larnaca substantially increased the prices at the residential consumption (36%-47%), while more conservative changes were imposed on the commercial and industrial block structure (25%-37.5%). Last but not least, water side measures were applied in Nicosia in 2002. However, the increases ranged from 22% to 27% for the commercial tariff block and from 11% to 33%. Since 2007, the changes did not exceed 10%-11% overall. It is worth mentioning that despite the small increases, Municipal Water Board of Nicosia has imposed the highest tariffs, while Limassol seems to have adopted a more conservative policy.

43

Regions - Years by tariff change

Commercial Tariff Structure (€) Commercial Tariff variation compared Tariff Structure to previous time period 1o

2o

3o

1o

2o

3o

Residential Tariff Structure (€) Residential Tariff Structure 1o

2o

3o

4o

5o

6o

Tariff variation compared to previous time period 7o

1o

2o

3o

4o

5o

6o

7o

Larnaca (m³) 1986-91

0,51

0,68

1992-01 2002-06

0,68 0,85

0,94 1,16

33,30% 24,90%

2007 2008 2009

0,94 1,03 1,13

1,28 1,4 1,54

10,10% 9,60% 9,70%

0,19

0,48

0,68

0,94

1,2

37,50% 23,60%

0,28 0,32

0,6 0,74

0,94 1,16

1,28 1,59

1,54 1,91

46,90% 17,30%

25,00% 22,80%

37,50% 23,60%

36,40% 23,90%

28,60% 24,40%

10,20% 9,40% 10,00%

0,36 0,39 0,42

0,81 0,89 0,97

1,28 1,4 1,54

1,75 1,92 2,11

2,1 2,31 2,54

11,10% 8,30% 7,70%

10,20% 9,90% 9,00%

10,20% 9,40% 10,00%

10,10% 9,70% 9,90%

9,70% 10,00% 10,00%

20,00% 66,70% 10,00%

10,00% 63,60% 11,10%

46,70% 59,10% 8,60%

60,00% 400,00% 10,00%

0,09 0,21 0,32 0,36

0,17 0,31 0,5 0,55

140,00% 58,30% 10,50%

80,00% 61,10% 10,30%

0,09 0,1 0,17 0,19

0,17 0,19 0,31 0,34

0,26 0,38 0,6 0,65

Limassol (m³) 0,43 0,68 3,42 3,76

2008

0,4

0,61

11,50%

11,60%

0,21

0,37

0,72

4,14

11,70%

8,30%

10,90%

10,10%

2009

0,44

0,67

10,00%

9,80%

0,23

0,41

0,79

9,50%

10,80%

9,70%

9,90%

1991-01

0,77

1,11

1,28

0,51

0,51

0,77

4,55 Nicosia (m³) 0,77 1,2 1,54

2002-06

0,94

1,37

1,62

22,20%

23,10%

26,70%

0,6

0,68

0,86

0,94

1,45

1,88

0,167

16,70%

33,30%

11,10%

22,20%

21,40%

22,20%

27,30%

2007

1,03

1,5

1,78

9,50%

9,60%

9,60%

0,65

0,75

0,94

1,03

1,59

2,07

0,086

8,60%

9,60%

9,90%

9,50%

9,40%

10,00%

9,90%

2008

1,13

1,65

1,96

9,70%

10,00%

10,10%

0,72

0,83

1,03

1,13

1,75

2,28

0,108

10,80%

10,70%

9,60%

9,70%

10,10%

10,10%

9,90%

2009

1,24

1,82

2,16

9,70%

10,30%

10,20%

0,79

0,91

1,13

1,24

1,93

2,51

0,097

9,70%

9,60%

9,70%

9,70%

10,30%

10,10%

10,00%

1986-91 1992-01 2002-06 2007

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