The Decomposition Analysis of Tourism Water Footprint in Taiwan: Revealing Decision-Relevant Information

Abstract

Tourism water consumption reflects the dynamics between the visitation volume, economic structure, and water use technology of a destination. This paper presents a structural decomposition analysis that attributes changes of Taiwan’s tourism water footprint into the demand factors of total consumption and purchasing patterns, and production factors of the industry input structure and water use technology. From 2006 to 2011, Taiwan experienced a 48% growth in visitor expenditures and a 74% surge in its water footprint. Diseconomies of scale were observed, with a 1% increase in consumption leading to a 1.5% increase in the tourism water footprint. A strong preference by visitors for water-intensive goods and services and a changing economic structure requiring more water input for tourism establishments and supply chain members contributed to this worrisome pattern. The water requirements received only a minimal offset effect with technological improvements. Decoupling tourism water consumption from economic output is currently unattainable.

Keywords

tourism water footprint structural decomposition analysis environmentally extended input–output model Taiwan

Introduction

Tourism is heavily water-dependent, and the quantity and quality of water affect multiple facets of tourism sustainability. As a resource, water is used for cleaning, hygiene, and maintenance. As an asset, coastal tourism and marine destinations rely on water quality for scenery and to support recreational activities. Although water directly used for tourism is approximately less than 1% of all water used globally, this percentage varies greatly across countries and seasons (Gössling et al. 2012). The direct tourism-related water use of island destinations, such as Mauritius, Cyprus, Malta or Barbados, and coastal areas or mass-tourism destinations, accounted for more than 10% of the national water supply. In addition, domestic household water use is estimated to be, on average, 160 L per capita per day globally, while an international tourist consumes approximately 80–2,000 L per day (Gössling et al. 2012), adding higher water stress to the community when expanding tourist numbers is a high economic priority.

The distribution of freshwater supply worldwide has a similar pattern as tourism consumption, as both are characterized by uneven distribution through seasons and geographic regions, and highly vulnerable to natural hazards, such as floods and droughts between regions. A country will face chronic water scarcity/stress when the water supply drops below 1000 m³ per capita per year for renewable water, or absolute scarcity when it falls below 500 m³ (UNESCO 2016). The estimated population in water-stressed river basins was approximately 1.6 billion in 1995 and is expected to increase to a range between 4.9 and 6.9 billion in 2050, depending on the population growth rate and climate change scenarios (Bates et al. 2008). Currently, many areas that face high levels of water stress annually overlap with destinations that embrace a high visitation rate, such as Northern China, Middle Asia, and the Mediterranean rim countries (United Nations 2015; LaVanchy and Taylor 2015; Essex, Kent, and Newnham 2004; Cole 2012). In particular, changing precipitation patterns, extreme hydrological disasters, and the salinization of freshwater have exacerbated water shortage problems.

The impact of tourism water consumption is regional and time specific to the travel seasons, where the local production sectors and residents immediately feel the social and economic burden once the local aquifer cannot satisfy water demand. Additionally, water inequality is especially critical in developing countries, due to the relative differences of access to water for stakeholders with different levels of power (Cole 2012). Significant social conflicts between the tourism industry and the local community over water availability and distribution have been reported, particularly for issues related to depleted groundwater for farming, increased health problems related to untreated wastewater from lodging, and lost control over water accessibility rights (Tourism Concern 2012). In addition to the social challenges, the job and water sectors present a dynamic nexus in which approximately 42% of the global workforce is employed in eight water- and natural resource-dependent industries, including tourism (UNESCO 2016). This water-dependent job pattern demands constant attention from policy makers to maximize the contribution of water to economic development and job creation, imposing a trade-off between water sustainability and employment targets.

Considering the immediate social and economic impact associated with tourism water consumption, it is essential to address both tourism economic development and water use to evaluate whether anthropogenic consumption will be increasingly or decreasingly reliant on this natural resource during different economic stages. Based on the theory of the Environmental Kuznets Curve (EKC), human demand for natural resources or the production of environmental pollution will first deteriorate and then improve along the development path of economic output, resulting in an inverted U-shaped curve (Grossman and Krueger 1991). The EKC hypothesis specifies that when the wealth of a nation increases at an early stage, such as in preindustrial and agrarian economies, the sheer scale of environmental pollution will increase, reach its peak at a certain income level in industrial economies, and then begin to decrease along the later stage of wealth development in service economies.

In the tourism context, an EKC evolution path for the associated water consumption can also be developed theoretically. In the beginning, tourism water intensity may increase when demand rises, meaning more water is required to deliver one unit of tourism service. This may result from increasing consumption in scales, changing preferences for high-end products and luxury services, and the growing popularity of gardens, swimming pools, or golf courses (Hof and Schmitt 2011). The rise of tourism receipts after the turning point will begin to drive the improvement of tourism water efficiency, toward more sustainable and green practices, thus reducing the tourism water intensity per visitor. The possible drivers for such a desired condition are (1) tourism industries’ embracing of the green paradigm as a competitive advantage for strengthening their branding and market share, and (2) the local government enforcing stringent environmental regulations on water use due to public pressure for better control after the environmental carrying capacity is breached (Lenzen et al. 2006).

Currently, the literature presents only a snapshot of the relationship between travel volume and water consumption, typically for a one-year period (Hadjikakou et al. 2015; Patterson and McDonald 2004; Cazcarro, Hoekstra, and Sánchez Chóliz 2014; Cazcarro, Duarte, and Sánchez Chóliz 2016). The interplay between national tourism demand and spatiotemporal changes in water consumption has not been analyzed. Knowledge regarding how quickly demand drives water consumption and the compensating effect of supply is strongly needed. With this information, the path toward tourism water efficiency over time can then be identified. Therefore, the purpose of this study is first to present the decomposition framework based on an environmentally extended input–output model (EEIO). This process involves assessing the spatiotemporal distribution of direct water consumption, indirect water consumption, and virtual water requirements. Structural decomposition analysis (SDA) is then used to identify the relative roles and contributions of tourism demand, economic structure, and technology for water use. An empirical case on the tourism water footprint from 2006 to 2011 is presented for Taiwan. This analysis demonstrates the proposed analytical framework and offers knowledge regarding the progress of water technology developments from the industry analysis and consumption trends in the demand pattern. The results highlight the areas where a water management strategy should focus and puts future tourism water consumption in perspective.

Literature

The early literature regarding tourism water consumption focuses primarily on offering quantitative insights from a micro approach, providing direct water use figures at the enterprise level (Charara et al. 2011; Filimonau et al. 2011; Rodríguez-Díaz et al. 2011; Tortella and Tirado 2011; Bohdanowicz and Martinac 2007). The main purpose of such an analysis is to profile a baseline figure regarding the average water use per unit service and to identify policy and operational factors that may facilitate efficient use of resources. Strong attention has been drawn to accommodations and golf course facilities, which are identified as key water-consuming establishments among all travel-related service providers.

At the macro-level, a few recent studies have begun to examine the water footprint (WF) of tourism at the national level. The water footprint concept encompasses three components, including the tourism industries’ direct water consumption (on-site) for their daily business needs, indirect water consumption by domestic suppliers to provide intermediate goods and services to tourism industries, and embedded water through imports by international trade (also referred to as virtual water) with water resources utilized in foreign countries during the production process (A. Hoekstra et al. 2012). This type of study is used to establish tourism’s baseline for total water use and to benchmark the efficiency performance of tourism against other sectors in the economy. The reported cases include Cyprus (Hadjikakou et al. 2015), New Zealand (Patterson and McDonald 2004), and Spain (Cazcarro, Hoekstra, and Sánchez Chóliz 2014; Cazcarro, Duarte, and Sánchez Chóliz 2016).

A consistent finding across these macro-level research is the significance of the indirect water footprint for agglomerated tourism services, which can range from 3 to 10 times more than that for direct water use, depending on the type of products and services consumed on a trip (Y. Y. Sun and Pratt 2014a; Hadjikakou et al. 2015). This proves that measuring only direct water use overlooks hidden costs within the supply chain, and substantially underestimates the magnitude of the “local” and “global” water demand of tourism services. In addition, increasing attention is placed on the role of embedded water footprints through imports. For arid regions, adopting imports alleviates the local pressure on water withdrawal and enhances the quantity and quality of tourism services (Hadjikakou, Chenoweth, and Miller 2013). However, relying on imports also signals a trade-off between retaining the domestic tourism GDP and mitigating local water stress.

In general, these studies present a snapshot of the relationship between travel volume and water consumption. The lack of continuous evaluation leaves certain perspectives unaddressed. First, the evolution of the tourism water footprint over time for a given destination has not been examined. Single-year studies, although insightful, do not illustrate the spatiotemporal distribution of water for the full range of travel services. Direct, indirect, and virtual water footprints across sectors need to be assessed to visualize the distribution of the water supply by the domestic and foreign sources. Sectors that move toward a water-intensive status need to be highlighted and addressed.

Second, the literature has rarely evaluated the influence of the individual impetus behind the changing WF of tourism. Water use is determined by the underlying factors of tourism demand, economic structure, and technology (A. Hoekstra et al. 2012; Cazcarro, Duarte, and Sánchez-Chóliz 2013). Tourism demand relates to the total number of visitors and their consumption patterns at a destination. The tourism WF is not only a function of the volume of visitors but is also influenced by their choices of lodging, dining preferences, recreational activities, length of stay, and type of souvenirs purchased (Hadjikakou, Chenoweth, and Miller 2013; Y. Sun and Pratt 2014a; Gössling et al. 2012). Given that the demand for global tourism is expected to grow at a rate of 3%–4% annually, combined with a change in preference for luxury amenities (UNWTO 2014; Scott and Gössling 2015), it is important to understand the extent of tourism water consumption that is driven by this demand-side factor.

In contrast, the supply-side components of the economic structure and water-use technology may provide a possible route to alleviate the demand for water. The economic structure refers to the production process of local business sectors and the type and quantity of intermediate inputs that firms purchase from domestic and foreign suppliers. Sectors that move toward lower linkages with other suppliers incur fewer intersectoral transactions, lowering their embedded water content. In this context, a larger proportion of sales will be converted to value added in terms of employee incomes, business profits, and governmental taxes (a definition of GDP), yielding a smaller water footprint per dollar GDP. In addition to the economic structure, water-use technology provides the best hope for delivering water-saving effects without compromising service quality, which is strongly promoted by installing water-saving and recycling equipment during each stage of the product lifecycle to contribute to the reduction of water use volume (Styles, Schoenberger, and Galvez-Martos 2015).

Considering the significance of the economic contribution of tourism to many destinations and countries, it is essential to gather information on the interplay between total water consumption and tourism economic output over time. Currently, to our knowledge, there are no previous discussions or empirical evidence that attempt to evaluate (1) the temporal changes of the national tourism water footprint and (2) the contribution of supply and demand factors to changes in the national tourism WF. To address this research gap, a decomposition analysis is presented in the next section to discuss whether “economic good” and “environmental bad” can be decoupled.

Method

Estimating National Tourism WF

Combing the tourism satellite account (TSA) and the environmentally extended input–output (EEIO) model provides an adequate tool to assess the complete picture of the national tourism water consumption and determine its direct and indirect effects, domestically and internationally (Hadjikakou, Chenoweth, and Miller 2013; Hadjikakou et al. 2015; Cazcarro, Duarte, and Sánchez Chóliz 2016). TSA provides comprehensive and consistent visitor consumption data (UNSD-EUROSTAT-OECD-WTO 2008; World Tourism Organization 2010), which is used to proxy the total quantities of tourism services and goods. In the TSA, tourism expenditure is measured and reported by products based on the Central Product Classification (CPC) five-digit system and is differentiated by three visitor groups—domestic tourists, inbound tourists, and outbound tourists—for those spending incurred in the domestic territory.

With the tourism expenditure data supplied by TSA on hand, the EEIO model then begins to calibrate the direct and indirect transactions based on the local economic account and trading patterns. EEIO is employed here because of its transparent methodology to calibrate the macro-level economic and environmental impacts, its ability to give detailed results by industry for policy formulation, and its comparability across regions, countries, and different tourism applications (Collins, Jones, and Munday 2009; Minx et al. 2009). This method is also well applied to a broad range of carbon and water footprint applications; see the reviews in Minx et al. (2009), Feng et al. (2011), and Chenoweth, Hadjikakou, and Zoumides (2014).

Supplemented with the water use intensity data by sectors, the EEIO traces monetary transactions from visitors to direct tourism providers and then to suppliers where it calibrates the total water consumption required to support this monetary flow. Combining the TSA and the EEIO model produces estimates that are compatible with the Systems of National Accounts (SNA) (Y. Sun 2014; Hadjikakou et al. 2015; United Nations 1993). This allows a direct comparison between the tourism output against other sectors in the economy on both economic and environmental performance, facilitating the discussion regarding whether tourism development should be a priority. Furthermore, the specification of visitor consumption from TSA can be easily separated based on inbound tourism versus domestic tourism to contrast the water use patterns for different visitor segments.

Decomposing National Tourism WF

Structural decomposition analysis (SDA) is a method used to trace the contribution of underlying factors to changes in the observed indicator. Different from other decomposition analysis approaches, SDA is especially suitable for analyzing IO results because of its ability to address the indirect water effect from domestic suppliers and the virtual water effect (Ang 2004; R. Hoekstra and van den Bergh 2003; Rose and Casler 1996). The preferred formula adopted in this framework is the “additive decomposition of the absolute indicator” method proposed by J. Sun (1998). The J. Sun (1998) formula is credited for its parsimony, and the additive nature of the formula allows different tourism WFs to be allocated to each determinant factor (R. Hoekstra and van den Bergh 2003). This facilitates a direct interpretation of the results because the percentage of contribution by each factor adds up to 100%.

To analyze the tourism WF, four determinant factors are proposed and examined in this study. The first two factors are demand driven, whereas the other two factors concern the supply structure.

The final demand effect captures the amount of tourism water changes associated with the fluctuations in the total expenditure injection. This factor reflects the volume change of tourists and their aggregated consumption in the region.

The distribution effect gauges the amount of tourism water use that is introduced by changing expenditure patterns among tourists. Visitors may, for example, consume more on dining than transportation on the trip at time t+1 than at time t.

The intensity effect measures tourism water changes due to the improvement of water use per dollar output, a proxy for industrial production technology in water utilization. Better technology equates to a lower water usage per dollar output.

The Leontief effect considers the modification of the input structure and the propensity to incorporate imports and the associated influences of these factors on tourism water consumption. This reflects the context in which industries may modify their production process, replace domestic purchases with imports, or improve their value-added components.

Formula

Tourism direct water effect

The EEIO calibration and decomposition formulas for the direct and indirect environmental effects are specified as below. The direct water effect is a straightforward multiplication of three factors: the aggregated visitor consumption ( Y ); the distribution percentage ( D ), which allocates the total visitor consumption to the individual service items; and the corresponding water intensity ratio by sector ( W ) (Eq.1). The decomposition analysis first calculates the changes in direct water consumption between two periods, and then evaluates the share of consumption by each underlying component.

Direct water = W D Y

Decomposition formula:

\begin{array}{l} \begin{array}{l} Difference in direct water use between two \\ time periods \end{array} \\ \begin{array}{l} = {Direct effect}_{t + 1} - {Direct effect}_{t} \\ = D_{dis} + D_{fd} + D_{int} \end{array} \end{array}

Final demand effect, $\begin{array}{l} D_{f d} = W D Δ Y + 1 / 2 Δ W D Δ Y \\ + 1 / 2 W Δ D Δ Y + 1 / 3 Δ W Δ D Δ Y \end{array}$

Distribution effect, $\begin{array}{l} D_{dis} = W Δ D Y + 1 / 2 Δ W Δ D Y \\ + 1 / 2 W Δ D Δ Y + 1 / 3 Δ W Δ D Δ Y \end{array}$

Intensity effect, $\begin{array}{l} D_{int} = Δ W D Y + 1 / 2 Δ W Δ D Y \\ + 1 / 2 Δ W D Δ Y + 1 / 3 Δ W Δ D Δ Y \end{array}$

where

Y is a vector of aggregated visitor consumption,

D is a vector of the distribution percentage allocating visitor consumption to individual services,

W is a vector of water use coefficients,

∆Y is a vector of final demand differences between times t and t+1,

∆D is a vector of spending distribution differences between times t and t+1, and

∆W is a vector of water coefficient differences between times t and t+1.

Tourism domestic indirect water effect

The indirect water consumption of domestic suppliers is calculated by feeding the visitor consumption into the EEIO model, where the interdependence of sectors (the Leontief inverse matrix, B ) is utilized to trace the transactions of industries in the supply chain. The indirect water effect is calculated as the total water effect in the domestic territory minus the direct water effect. The decomposition analysis will be performed twice to analyze the two components in equation (3) separately. The associated influences of the intensity effect, structure effect, final demand effect, and Leontief effect are calculated first for component 1 ( WBDY ) in equation (3), and then the associated influences of component 2 ( WDY ) are subtracted to derive the overall result.

\begin{array}{l} Domestic indirect water \\ = W {(I - A_{d})}^{- 1} D Y - W D Y \\ = W B D Y - W D Y \end{array}

Decomposition Formula:

\begin{array}{l} \begin{array}{l} Difference of indirect water use \\ = {indirect effect}_{t + 1} - {indirect effect}_{t} \end{array} \\ {= D}_{fd} {+ D}_{dis} {+ D}_{int} {+ D}_{ltf} \end{array}

For WBDY, the decomposition formula is listed below as:

Final demand effect,

\begin{array}{l} D_{fd} = W B D Δ Y + 1 / 2 (\begin{array}{l} Δ W B D Δ Y + W Δ B D Δ Y \\ + W B Δ D Δ Y \end{array}) + \\ 1 / 3 (Δ W Δ B D Δ Y + Δ W B Δ D Δ Y + W Δ B Δ D Δ Y) \\ + 1 / 4 Δ W Δ B Δ D Δ Y \end{array}

Distribution effect,

\begin{array}{l} D_{dis} = W B Δ D Y + 1 / 2 (\begin{array}{l} Δ W B Δ D Y + W Δ B Δ D Y \\ + W B Δ D Δ Y \end{array}) + \\ 1 / 3 (Δ W Δ B Δ D Y + Δ W B Δ D Δ Y + W Δ B Δ D Δ Y) \\ + 1 / 4 Δ W Δ B Δ D Δ Y \end{array}

Intensity effect,

\begin{array}{l} D_{int} = Δ W B D Y + 1 / 2 (\begin{array}{l} Δ W Δ B D Y + Δ W B Δ D Y \\ + Δ W B D Δ Y \end{array}) \\ + 1 / 3 (Δ W Δ B Δ D Y + Δ W Δ B D Δ Y + Δ W B Δ D Δ Y) \\ + 1 / 4 Δ W Δ B Δ D Δ Y \end{array}

Leontief effect,

\begin{array}{l} D_{ltf} = W Δ B D Y + 1 / 2 (\begin{array}{l} Δ W Δ B D Y + W Δ B Δ D Y \\ + W Δ B D Δ Y \end{array}) + \\ 1 / 3 (Δ W Δ B Δ D Y + Δ W Δ B D Δ Y + W Δ B Δ D Δ Y) \\ + 1 / 4 Δ W Δ B Δ D Δ Y \end{array}

where

I is the identity matrix,

A_d is the matrix of technology coefficients for domestic transactions,

B is the Leontief inverse matrix, and

∆B is the difference in the Leontief inverse matrix for domestic transactions between times t and t+1.

Tourism imported water effect

To consider the tourism virtual water consumed through imports, two routes of water effects can be considered. One estimates the water contents of imports that are directly purchased by tourists, such as a US-made Coca Cola consumed by tourists in Japan, whereas the second component traces the embedded water of intermediate inputs used by the tourism industries directly and indirectly. This could range from the Spain-grown tomatoes used by restaurants in the United Kingdom to electronics made in China and used by travel agencies in Brazil. Two approximations are adopted in this study to calculate the imported water effect. The first is to use the domestic production assumption (DPA), which assumes that the imports are produced based on the same technology as domestic goods (Druckman et al. 2008; Lenzen, Pade, and Munksgaard 2010; Wood and Dey 2009). In addition to the consideration of data availability, using the DPA allows the destination country to directly evaluate how much additional water would be needed if all imports were produced domestically. The second approximation assumes that all visitor purchases are 100% domestically produced. This consideration arises because visitors’ preferences and expenditures for imports are generally minimal. The error in assuming that there is no direct consumption of imports is expected to be small.

The embedded water footprint of imports is computed as follows (Druckman et al. 2008):

\begin{array}{l} Virtual water \\ = W_{f} {(I - A_{f})}^{- 1} A_{m} {(I - A_{d})}^{- 1} D Y_{d} \\ + W_{f} {(I - A_{d})}^{- 1} D Y_{m} \end{array}

Adopting the two assumptions mentioned above, we replace the foreign production structure ( A_f and W_f ) with domestic technology ( A_d and W ). In addition, Y_m , the visitor consumption of foreign products, is assumed to be zero. The formula is then reduced to:

Virtual water \approx W {(I - A_{d})}^{- 1} A_{m} {(1 - A_{d})}^{- 1} D Y = W M D Y

Decomposition Formula:

\begin{array}{l} \begin{array}{l} Difference in virtual water use \\ = {virtual water}_{t + 1} - {virtual water}_{t} \end{array} \\ {= D}_{fd} {+ D}_{dis} {+ D}_{int} {+ D}_{ltf} \end{array}

Final demand effect,

\begin{array}{l} D_{fd} = W M D Δ Y + 1 / 2 (\begin{array}{l} Δ W M D Δ Y + W Δ M D Δ Y \\ + W M Δ D Δ Y \end{array}) \\ + 1 / 3 (Δ W Δ M D Δ Y + Δ W M Δ D Δ Y + W Δ M Δ D Δ Y) \\ + 1 / 4 Δ W Δ M Δ D Δ Y \end{array}

Distribution effect,

\begin{array}{l} D_{dis} = W M Δ D Y + 1 / 2 (\begin{array}{l} Δ W M Δ D Y + W Δ M Δ D Y \\ + W M Δ D Δ Y \end{array}) \\ + 1 / 3 (Δ W Δ M Δ D Y + Δ W M Δ D Δ Y + W Δ M Δ D Δ Y) \\ + 1 / 4 Δ W Δ M Δ D Δ Y \end{array}

Intensity effect,

\begin{array}{l} D_{int} = Δ W M D Y + 1 / 2 (\begin{array}{l} Δ W Δ M D Y + Δ W M Δ D Y \\ + Δ W M D Δ Y \end{array}) \\ + 1 / 3 (Δ W Δ M Δ D Y + Δ W Δ M D Δ Y + Δ W M Δ D Δ Y) \\ + 1 / 4 Δ W Δ M Δ D Δ Y \end{array}

Leontief effect,

\begin{array}{l} D_{ltf} = W Δ M D Y + 1 / 2 (\begin{array}{l} Δ W Δ M D Y + W Δ M Δ D Y \\ + W Δ M D Δ Y \end{array}) \\ + 1 / 3 (Δ W Δ M Δ D Y + Δ W Δ M D Δ Y + W Δ M Δ D Δ Y) \\ + 1 / 4 Δ W Δ M Δ D Δ Y \end{array}

where

Y_d is the aggregated visitor consumption of domestic products,

Y_m is the aggregated visitor consumption of imported products,

W_f is the water intensity matrix for foreign producing countries,

A _m is the import use matrix for the predefined country,

A_f is the matrix of technology coefficients of country f, and

M is the product matrix of (I – A^d )^–1A_m(1 – A_d)^–1.

The Case Study of Taiwan

Two salient factors, tourism development and national supply-and-demand conditions for water, are critical to the overall pattern of tourism water consumption. Taiwan, an island destination, has seen strong tourism growth over the past decade. Inbound arrivals increased from 3.5 million in 2006 to 10.4 million in 2015, corresponding to an annual growth rate of 12% (Taiwan Tourism Bureau 2016). The strong bloom of arrivals, in part, is due to the emerging Mainland China market, where the rapid expansion of this segment led to Taiwan being ranked as the second-fastest growing area among the top 50 tourism destinations worldwide in 2014 (World Tourism Organization 2015). Total tourism receipts on domestic and inbound travel amounted to US$25.6 billion in 2015, with an annual growth rate of 7% for the decade.

In terms of the availability of water resources, Taiwan is a region with sufficient water supply at the country level. The baseline water stress, drought severity, and interannual water variability are deemed to be very low (World Resource Institute 2016). Seasonal variability and floods are two factors that may expose certain parts of the country to medium to high levels of water stress. Based on the Aqueduct Water Risk Atlas (World Resource Institute 2016), Taiwan performs relatively well on indicators of water quantity, water variability, water quality, public awareness of water issues, access to water, and ecosystem vulnerability.

Taiwan also has an extensive water supply network. In 2014, 92% of the population and businesses were connected to the public water pipeline, with 3.19 billion cubic meters (m³) of tap water consumed (Taiwan Water Corporation 2016). This corresponds to 180 m³ of tap water use per capita per year, which is higher than the amounts used in China (32 m³), India (52 m³), France (106 m³), or most other developed countries (IWA 2014). One important reason for the high level of freshwater consumption in Taiwan is its low pricing strategy and long-term governmental subsidies. Water prices charged to the public have not been adjusted for the past 20 years, and the price is set below the break-even point for water provision in Taiwan. From 2006 to 2014, the per unit revenue loss of the water supply ranged from 0.05 to 0.98 $NT/m³, corresponding to 2%–8% of unit sales (Taiwan Water Corporation 2016). Because of ongoing operation deficiencies since 1974, the Taiwan Water Cooperation, a governmental agency, reported a debt of US$3.47 billion, with the debt ratio reaching 60% of their total assets in 2014. The deficit is completely compensated by tax dollars. In addition, the vicious cycle of insufficient sales and funding has resulted in a 20% water loss through leakage in recent years—a significant factor of inefficiency and waste.

The low-cost water provision in Taiwan is especially significant when we compare this indicator across countries. The International Water Association (IWA) provides an extensive comparison regarding the supply, consumption, and cost of freshwater across 34 major countries. The IWA report (2014) indicated that the annual water cycle charge for 100 m³ is approximately $63 in Taiwan, with this indicator ranging from $12 to $605 for major cities globally. Water affordability, calculated as the percentage of water cost to the GDP per capita, equated to 0.16% for Taiwan in 2012, much lower than the relative costs (0.2%–6%) reported in developed countries in Europe, North America, or even other Asian regions. This places Taiwan as the country, next to the United States, with the second highest water use intensity per capita, approximately 300 L per capita per day, among the 34 countries covered by IWA. The cross-country comparison clearly indicates that Taiwan is a low water cost region with an excessive consumption pattern for this natural resource.

Taiwanese EEIO Model

To construct a water-based environmentally extended input–output model for tourism services, the parameters of visitor consumption data, national IO table, and water use coefficients are needed. Visitor consumption data are used to quantify the tourism services volumes of different industries. This information is obtained through the Taiwan TSA, which includes consumption for domestic travel, inbound travel, and tourism expenditures by residents within the national boundary for their outbound travel (Taiwan Tourism Bureau 2007, 2013). This provides the complete scope of tourism consumption within the territory of Taiwan, forming a sound foundation of data to feed into the EEIO model. TSA expenditures are categorized as lodging, food and beverages, recreational activities, aviation, land transportation, car-rental services, travel agency services, and 10 shopping categories.¹ This rich data set allows us to perform item-specific WF calculations. In particular, the breakdown of shopping expenses into the categories of food items, clothing, medicine, electronics, and handicrafts greatly improves the calculation accuracy. As emphasized by Whittlesea and Owen (2012) and Y. Y. Sun and Pratt (2014a), shopping expenses need to be itemized to distinguish the water contents by product category. This consideration is important because of the substantial differences in water intensity among souvenirs, for example, pastries versus handicrafts. A proportion of shopping expenses is first allocated to the “retailing and wholesaling” sectors based on the retailing margin and then assigning the rest to the corresponding manufacturing sectors based on the 10 types of shopping items. Therefore, the water footprint of shopping expenses is an agglomerated figure, reflecting both the water use pattern of retail locations and the sectors that manufacture them.

The second required parameter is a national input–output table with industry- specific water use coefficients. An EEIO model of 47 sectors is constructed for Taiwan for the years 2006 and 2011. Only a five-year model is presented in this study due to limited official data. Unlike the extensive carbon emissions statistics, Taiwan, like many other regions, has just begun to step up their efforts to measure and report water usage by detailed sector, especially for services, which are key to measuring the tourism WF. In this model, the water use coefficient is measured as water consumption per dollar output by industry. Water consumption includes “groundwater or runoff” and “freshwater supplied by the water utility agency” (Water Resources Agency 2007a, 2007b, 2007c; 2012a, 2012b, 2012c). Specifically, we have assigned water usage that is associated with “evapotranspiration and leakage during water transportation processing” to the water-supply sector.

Results

Taiwan Tourism Water Footprint

Based on the Taiwanese TSA, the total tourism consumption in 2006 and 2011, including inbound tourism expenditures, domestic tourism expenditures, and domestic spending associated with outbound travel, was NT$560 billion and NT$831 billion (2006 prices), respectively. This corresponds to an annual 10.4% or 5-year 48% growth rate. The Taiwanese tourism WF calculated using the proposed framework increased from 1,243 million m³ to 2,160 million m³ during this period, a 14.8% annual or 74% increase (Figure 1). The net increase of 902 million ton of water use is composed of direct tourism consumption (2%), indirect water consumption (29%), and virtual water (69%).

Figure 1.

Taiwanese tourism water footprint, 2006 and 2011.

Comparing both economic and environmental tourism factors, a 1% increase in visitor consumption will lead to 1.33% increase in direct water use and 1.5% increase in the total WF. Overall, the tourism water intensity, measured as the total WF per dollar spent, deteriorated from 2.18 to 2.55 kg/NT$, corresponding to a 17% change.

Composition of water footprint

For Taiwan, water directly consumed by tourism businesses contributes to less than 2% of the total tourism WF, whereas domestic suppliers and imports make up the dominant share, 45% and 54%, respectively. Of the three types of water consumption, virtual water experienced the largest hike, increasing from 513 million m³ to 1,139 million m³, a 122% growth rate from 2006 to 2011. In contrast, direct water and domestic indirect water consumption were reported to increase by 65% and 38%, respectively, in the same period. The fast expansion of virtual water, and the subtle increase of domestic indirect water consumption signal that the economic structure of Taiwan experienced a shift, where products with high water content were gradually imported, instead of being produced domestically. As a result, 40% of the tourism WF in 2006 depended on foreign production, whereas this ratio increased to 53% in 2011. In other words, approximately half of the tourism water pressure currently rests on foreign suppliers.

Water footprint by sector

Tourism is a form of agglomerated consumption. The individual inspection of service items helps to identify the key water users. In terms of direct water usage, tourist demands for shopping and accommodation services are the major factors, each contributing approximately 35% of the direct water consumption (Table 1). For indirect water use, shopping and dining are the dominant contributors to the water footprint of the domestic and international supply chains, accounting for more than 90% of the total impact from upstream. Agricultural products in the dining and shopping categories have substantially raised their WF figures because agricultural and meat products require tremendous water use during the irrigation and farming processes (Bates et al. 2008). The strong positioning of agriculture products and dining services as major tourism attractiveness in Taiwan have lured visitors to engage with extensive food-related activities and have promoted the purchase of agriculture products, such as Chinese tea, pineapple pastry, and dry fruits (Hsieh and Chang 2006). From 2006 to 2011, tourism demand for lodging and shopping saw a strong increase, with 69% and 94% growth rates in sales, respectively. Consequently, this has allowed the lodging sector to tilt the tourism industry’s demand for direct water consumption onsite, whereas the spill-over effect of shopping has resulted in a large surge of virtual water. By absolute number, the net increase in the tourism water footprint (902 mil m³) in Taiwan is composed mainly of imported water for shopping (43%), imported water for dining (24%), domestic indirect water for shopping (17%) and domestic indirect water for dining (11%).

Table 1.

Taiwanese Tourism Consumption and Water Footprint by Service Category, 2006 and 2011.

Tourism service categories	Visitor Spending (NT$ Billion)			Direct Water Footprint (MM m³)			Domestic Indirect Footprint (MM m³)			Imported Water Footprint (MM m³)			Total Water Footprint (MM m³)			Water Use Efficiency (L/NT$)
Tourism service categories	2006	2011	% change	2006	2011	% change	2006	2011	% change	2006	2011	% change	2006	2011	% change	2006	2011	% change
1. Shopping	105	203	94	8.3	14.1	70	230.1	379.3	65	276.2	660.4	141	515	1,054	105	4.92	5.20	6
2. Dining	126	172	37	3.5	4.9	41	400.4	501.4	25	199.0	417.3	111	603	924	53	4.80	5.39	12
3. Air transport	129	169	31	2.0	1.5	−22	12.9	15.2	12	20.8	25.8	28	36	43	19	0.28	0.25	−9
4. Land transport	76	105	39	1.2	1.5	19	6.0	6.8	14	5.8	8.8	63	13	17	32	0.17	0.16	−5
5. Lodging	44	74	69	6.4	13.8	116	24.6	26.5	30	5.6	10.7	279	37	51	39	0.83	0.69	−17
6. Entertainment	55	66	20	1.6	2.2	32	7.9	12.2	51	4.4	13.6	310	14	28	101	0.25	0.42	67
7. Car rental	9	23	142	0.1	0.3	105	0.4	0.9	105	0.4	1.1	210	1	2	133	0.10	0.10	−4
8. Travel service	17	21	21	0.3	0.4	8	1.0	2.3	94	0.7	1.0	62	2	4	74	0.12	0.17	43
Total	560	831	48	23.5	38.6	65	683.3	944.5	39	513.0	1138.7	125	1,220	2,122	74	2.18	2.55	17

Water use efficiencies

Water use efficiency, measured as the total water footprint per dollar output, presents a clear link between economic output and resource requirements. For the 8 tourism services reported in Table 1, lodging, air transportation, land transportation, and car-rental services experienced efficiency improvements of 17%, 9%, 5%, and 4%, respectively. For the rest of the services, stagnation or deterioration in water use is reported. In particular, the two dominant players, shopping and dining, reported lower water efficiencies, with deteriorations of 6% and 12%, respectively. More than 5 L of water is needed to support one dollar of output for both services, and this level of water intensity is approximately 10–15 times more than their counterparts. As a result, their deterioration in the WF leads to overall poor performance for agglomerated tourism services, with a drop of 17%.

To explore water use per dollar output, a further disaggregation is presented to demonstrate the water use conditions on site by tourism establishments versus those of their suppliers. Two indicators of water efficiency are calculated. Direct water efficiency measures the water use at tourism establishments, such as hotels, restaurants and retailing sectors, given their sales volume, whereas indirect water efficiency represents the water consumption of suppliers. It is noted that the water footprint of shopping is a composite figure, reflecting both the water use pattern of retail as well as the corresponding manufacturing sectors.

The percentage change in the two efficiencies is plotted in Figure 2. A positive value represents an improvement in efficiency or vice versa. The horizontal axis represents the water use context of tourism-characteristic industries. Transportation, shopping, and travel services all report a water efficiency gain, whereas lodging, entertainment, and restaurants experienced poor performances. In particular, for the lodging sector, the direct water use increased from 0.146 kg/NT$ to 0.187 kg/NT$, a 28% surge. Because the lodging sector accounts for a major share of the direct tourism water footprint, its poor performance on water savings leads to a deterioration of direct water efficiency (–11%) for overall tourism consumption.

Figure 2.

Changes in the direct and indirect water use efficiencies of eight tourism services.

The vertical axis of Figure 2, on the other hand, addresses the nature of tourism suppliers. The transportation and lodging supply chains experienced better efficiency gains in water use, whereas shopping, dining, travel service, and entertainment suppliers required more water use per dollar output. The dominant indirect water demand categories, shopping and dining, reported 6% and 12% efficiency losses in their supply chains, respectively. Similar to the pattern mentioned above, the poor performances of key players have a dominant effect, leading to a deterioration of indirect water efficiency (–17%) for overall tourism consumption (marked by the square in Figure 2).

Decomposition of the Tourism Water Footprint

From 2006 to 2011, the Taiwanese tourism WF increased from 1,243 million m³ to 2,160 million m³, which is a difference of approximately 900 million m³. The decompositions analysis traces the contribution of four underlying factors and explains the relative contribution to the net increase in tourism water consumption. The first column of Figure 3 represents the water use difference (902 million m³), which is the sum of the final demand effect (637 million m³, second column), distribution effect (265 million m³, third column), intensity effect (–225 million m³, fourth column), and Leontief effect (226 million m³, fifth column). Each column is marked by three colors to indicate their respective influences on direct water use, domestic indirect water use, and imported water.

Figure 3.

Decomposition results of Taiwan’s tourism water footprint by four factors.

The overall pattern of Figure 3 indicates that two demand factors, visitor consumption and purchasing patterns, and one supply factor, the economic structure, positively contributed to net tourism water consumption. The final demand effect has the largest influence, increasing 70% of the net water use from the base year, with the changing consumption pattern (distribution effect) contributing approximately 265 million m³ (30%). The supply-side factor changes in the input structure (Leontief effect) also indicated that the economy has shifted toward a water-intensive nature among the suppliers, with interindustry transactions requiring an additional 226 million m³ of water. Technological improvement (the intensity effect) is the only factor that has helped decrease tourism-related water use, saving 225 million m³. In other words, if industries used the production technology at the base year of 2006 to support the tourism demand of 2011, the old technology would raise the tourism water footprint in 2011 up by an additional 16% compared to the reported figures.

The decomposition results further confirm two important patterns from the supply side. First, the intensity effect, a proxy for technology improvement, was found to be more significant among domestic suppliers (saving 124 million m³) than foreign companies (saving 100 million m³) and tourism businesses (saving 1 million m³). This indicates either that the domestic production process has been improved or water-saving equipment has been extensively adopted, allowing domestic suppliers to produce the same amount of goods and services with fewer water resources. The tourism sectors experienced the lowest degree of technological improvement, saving only 1 million m³ in water consumption through water-saving equipment or procedures.

Second, Taiwan’s economic structure for serving the tourism demand has experienced adjustments, measured through the Leontief effect. Water consumption has gradually shifted pressure from domestic suppliers to foreign production lines, with 45 million m³ (approximately 5% of the net water increase) saved for Taiwan. The transfer of domestic transactions to foreign trade patterns reflects the increasing amount of imports of agriculture and forest products over the years. Both products are highly water intensive, alleviating the water demand from domestic production.

Limitations

There are two major limitations of the results, which are associated with the data sources and model assumptions. Because of limited data availability, the observed increases in the tourism WF are derived only from a five-year data source under a special context where the governmental policy in Taiwan induced a dramatic volume increase of inbound tourist arrivals (Cang, Sun, and Li 2017). This high-volume injection of visitors corresponds to the “development” stage in the concept of the destination life cycle proposed by Butler (2006), where the region embraces a high concentration of market segments (Mainland Chinese visitors), fast visitor growth, and rapid expansion of tourism facilities and services in lodging, dining, retailing, and land transportation. It is highly possible that this situation presents unique spatiotemporal changes to the tourism WF. Whether this observed pattern can be applied to other destinations or other time points in Taiwan awaits further analysis.

Second, our results reflect only the changes in the tourism WF associated with direct tourist monetary consumption. No explicit impacts are provided to address services associated with vacation accommodation on own account, tourism social transfers in kind, or tourism investment. In other words, the observed results demonstrate only a partial resource demand regarding the overall tourism development in Taiwan.

The operation of the current framework is also bounded by its model assumptions. This model assumes homogeneity and proportionality when converting visitor consumption data to water requirements (Becken and Patterson 2006). Homogeneity assumes that tourism products and services are produced based on the same water intensity as the national averages without differentiating the input mix that may be offered specifically to tourists (e.g., tourists may prefer Western cuisines with more imported ingredients than the local households). Proportionality, on the other hand, assumes that the more that is spent on one item/service, the more water is needed. The linearity between monetary injection and water consumption may not hold in reality, as travel consumption can be influenced by seasonal price fluctuations and quality issues that are not relevant to water-related amenities. Finally, it should be noted that the standard limitations of the input–output model also apply, please see Miller and Blair (2009) and Smeral (2006).

Discussion and Conclusion

The decomposition analysis of tourism WF is important to illustrate the spatiotemporal change in the demand of this natural resource—converting massive quantities of data into simple supply and demand factors. This study contributes to the literature by first providing a water decomposition analysis formula for the tourism context by specifying four underlying forces: visitor consumption, purchasing patterns, industry input structure, and water use technology. This analytical framework provides a tool to portray the relative contribution of each underlying factor with respect to the fluctuations of water demand. Such information is useful to address whether tourism economic output and water consumption can be decoupled and whether technological improvements in water intake can offset the aggregate demand for water from increasing travel volumes.

Based on the example of Taiwan, a destinaiton that has experienced an average 10% annual growth in visitor consumption from 2006 to 2011, diseconomies of scale are reported as more water inputs are required per dollar tourism spending received. A 1% increase in visitor consumption leads to an estimated 1.33% increase in direct water use and a 1.5% increase in the overall water footprint. This provides quantitative evidence supporting the general expectation that global tourism water consumption will increase over time (Gössling 2015; Gössling et al. 2012). In terms of water use efficiency (water consumption/dollar output), tourism industries and their suppliers reported a deterioration of 11% and 17%, respectively. Decoupling tourism water consumption from economic output is currently unattainable for this island destination.

This macro-level model also allows us to benchmark the environmental performance of tourism against the national average to reflect the current status of our travel industry and its reliance on natural resources. For Taiwan, the direct water use efficiency² of tourism was 0.042 L/NT$ and 0.046 L/NT$ in 2006 and 2011, respectively, and the national average for freshwater use per dollar output was 0.053 and 0.044 L/NT$³ (Taiwan Water Corporation 2016). Although the current tourism water intensity is comparable to the national average, a pessimistic pattern emerges from the fact that over the five-year period, the water use efficiency of tourism dropped by 11%, whereas the national average water use efficiency improved by 18%. Water-saving policies seem to be effective from a nationwide perspective, which highlights the inferior environmental performance of local tourism firms.

The decomposition analysis offers some insights regarding this unsustainable trend of development. The increase in tourism consumption contributed to approximately 70% of the net water demand, whereas strong tourist preferences for shopping, dining, and lodging over other services caused another 30%. The supply factors of technological improvement and production structure, on the other hand, have effects that cancel each other and have no influence on the net tourism water demand.

The empirical study of Taiwan reveals two specific phenomena that deserve further discussion. The first phenomenon involves the changes in visitor preferences for individual items and how it relates to water withdrawal. We found that spending propensity on specific services can easily modify the overall water consumption map, consistent with the findings from other studies (Cazcarro, Hoekstra, and Sánchez Chóliz 2014; Hadjikakou, Chenoweth, and Miller 2013; Hadjikakou et al. 2015). In particular, when the market experiences a new mix of visitor segments with distinct demographics and trip characteristics, the aggregate demand for certain types of tourism services adjusts accordingly. For example, Asian segments, especially the Chinese population, are famous for their shopping sprees (Y. Y. Sun and Pratt 2014b; Cripps 2013), elevating the indirect water footprint of food-related souvenirs, whereas North American and European segments are found to indulge more in high-end lodging and entertainment services (Taiwan Tourism Bureau 2016), which causes more immediate water stress on-site. The changing composition of visitor segments over time reflects the changes in preferences for individual items, which is influential to the water intensities that are reported here. In other words, we should be concerned not only with the total amount that visitors spend in the local region but also with the items they purchase.

While most of the literature focuses on the relationship between water consumption and trip behavior, few studies have addressed how production linkages with other sectors, that is, the ripple effect, will influence the tourism water footprint. In the context of Taiwan, we observed that food products are emphasized as key ingredients to fostering certain services in tourism establishments. For example, based on the Taiwan input–output table, the lodging sector doubled their expenses for purchasing “agriculture products,” whereas the food and beverage sector included more “drinking and tobacco products” in their service package. Overall, the Taiwanese tourism sectors, especially lodging, dining, and shopping, tend to provide more water-related services and undergo a process of input substitution with a higher proportion of purchases on water-intensive ingredients (such as agricultural products). This transformation leads to a surge in the net tourism water consumption.

Lessons for the Tourism Industry

Tourism firms are found to be slow at adopting water-saving and recycling equipment, providing only a minimal offset effect to the strong water consumption from the demand side, which is similar to findings in Barbados (Charara et al. 2011). The deterioration of water efficiency (liter water/sales) in the accommodation (–28%) and entertainment sectors (–10%) during a tourism boom period confirms that tourists are accommodated through increasing water-related facilities or services because of their preferences for comfort and luxury (Gössling 2015). Solely relying on water-saving equipment or water-conscious consumers is insufficient to compensate for additional water demand because industries are experiencing a fundamental change of adding more water-intensive amenities onsite, such as spas, swimming pools, in-room Jacuzzis, or sports and health centers. If information regarding water use is required to be disclosed in the future (such as water footprint labeling), tourism firms will face a trade-off between attracting environmentally conscious consumers and hosting travelers attracted to luxury and water amenities.

Reflecting the emergence of global value chains, tourism services were also found to have an increasing reliance on foreign suppliers, especially for agriculture and forestry products in the case of Taiwan. This reliance alleviates domestic water stress, especially during drought or monsoon periods, and allows firms to benefit from product specialization, cost efficiency, and flexibility in new offerings or volume expansion. While this trend seems to be unstoppable, engaging with international suppliers also exposes local firms to a higher level of product and financial risk. Possible supply disruption, supply delays, and price fluctuations are expected as a result from fluctuating exchange rates or natural disasters and social unrest in the production countries (Chopra and Meindl 2015; Long 2003). Reliance on imported materials also raises concerns over the carbon footprint of tourism, as overseas transportation is energy-intensive, substantially increasing the food miles of dining and food-related souvenirs. Considering that tourism services are embedded with a higher propensity for virtual water than average goods consumed by local households (Hadjikakou et al. 2015), the ability to cope with a complex supply chain relationship is strongly needed for tourism firms.

Policy focus

Taiwan is considered as a region with a sufficient water supply (World Resource Institute 2016), and this resource currently provides adequate support for tourism functions in terms of their service quantity and quality. However, policy intervention to increase water use efficiency is still strongly encouraged. The tourism water consumption costs in Taiwan are currently dispersed to the general public because of the “below the break-even point” pricing strategy, which means the water supply company is unable to overcome the serious water leakage problem. As a result, greater tourist consumption implies more freshwater use with additional tax dollar injections and water leakage. The low-price policy simultaneously dampens the incentive for firms to engage in water-saving facilities or educate consumers to change their behaviors.

The fundamental policy recommendation is to implement the carrot-and-stick water management mechanism. The stick policy is to lift governmental control and allow the water price to reflect both operational costs and the funding needed to fix the water leakage problem. While this increases the operational cost of firms, once the leakage problem is resolved, the water-delivery agency will experience an immediate 20% efficiency gain. Solely relying on the water-pricing strategy, however, is less likely to result in significant water savings among tourism establishments and their suppliers, because of the low cost of water relative to overall business operation costs (Gössling et al. 2012; Cazcarro, Hoekstra, and Sánchez Chóliz 2014; Y. Y. Sun and Wong 2014). A tax-credit system that encourage firms to adopt water-saving devices (the technology improvement effect) is also strongly needed. A well-functioning policy would then produce savings of approximately 25% in tourism water use over five years.

Future research

The usefulness of decomposition analysis will be greatly enhanced once the sophistication of the model can be further improved. We feel a greater spatial, temporal and sectoral resolution of the tourism water use will serve this purpose. Coordinating water consumption patterns with the regional and monthly water supply helps highlight potential water-stressed areas and seasonal periods. This hidden information is especially insightful when a nation, on average, has sufficient water quantity while regions suffer a high level of water scarcity. In addition, because water consumption intensity varies by standards of service (e.g., resorts, starred hotels, B&Bs, or hostels), existing macro-level models rarely have the capacity to mimic the diversified array of services used at destinations, but to assume that all visitors consume homogeneous products under a given sector. Further disaggregation of key water-intensive tourism sectors would be helpful.

With the increasing integration of international trade, tourism services and products are expected to consist of a higher level of virtual water. In addition to considering the type and quantity of imported inputs, future research is also needed to identify countries from which virtual water is derived. In this article, we adopt the one-country IO model with treatments of domestic production assumption (DPA) on imports. A further step is to adopt the multiregional input–output (MRIO) model, which can trace imports to the country of production. Calibrating virtual water by country of origin yields two distinct advantages—one is to perform the uncertainty analysis to countries exposed to high-risk climate change with a greater variation of annual rainfall. Establishing a supply chain relationship with these countries may impose high climate-related risks. Second, such an assessment may be vital to avoid importing water-intensive inputs from water-scare areas, subsequently reducing our role in aggravating the water equality issues in that region.

The last recommended research direction is to provide empirical evidence regarding individual firms’ responses to the national water pricing strategy, in terms of adjustments to their prices, output levels, value added or input substitution between domestic and foreign suppliers. Water pricing is perceived to be the most effective instrument to drive sustainable water use, and a comprehensive water pricing strategy no longer merely reflects the basic financial costs but also considers environmental and resource costs (Hrovatin and Bailey 2001; Riegels et al. 2013; Ruijs, Zimmermann, and van den Berg 2008). A large increase in water price is likely to induce a significant but unequal impact on tourism establishments. While most of the water-pricing literature focused on irrigation and household water consumption, more theoretical and empirical examinations of tourism establishments are needed to provide an explicit assessment of costs and benefits for different stakeholders.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support from the Taiwan Ministry of Science and Technology under MOST 103-2410-H-006 -092 -MY2 is gratefully acknowledged. We also thank three anonymous reviewers for their helpful comments on the previous version of this paper.

Notes

ORCID iD

Ya-Yen Sun

Author Biographies

Ya-Yen Sun’s research interests is on the economic and environmental impact evaluation of tourism development. Related research includes the tourism carbon footprint, economic development and resource consumption, and the interaction among travel behaviors and transport mode selection.

Ching-Mai Hsu’s research interests is on the economic and environmental impact evaluation of tourism development. Related research includes the tourism carbon footprint, economic development and resource consumption, and the interaction among travel behaviors and transport mode selection.

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