Waste Generation Flows and Tourism Growth: A STIRPAT Model for Mallorca

Arbulú, Italo; Lozano, Jorge; Rey‐Maquieira, Javier

doi:10.1111/jiec.12420

Cited by 25 publications

(12 citation statements)

References 64 publications

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“…Hospitality food waste generation correlates well with tourism growth (Manomaivibool 2015). For example, in the case of Mallorca, the 1% rise in the number of tourist arrivals at a destination brings about the 1.25% increase in waste generation where food waste represents the major fraction (Arbulu et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Food waste management in hospitality operations: A critical review

2019

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Hospitality food waste represents a significant societal challenge. It is however underresearched with most studies approaching the issue from the perspective of sustainable agriculture and environmental, rather than hospitality, management. Given the specificity of hospitality operations, this is a major shortcoming which hampers understanding of the determinants of effective mitigation. This paper provides a critical, analytical account of the literature on hospitality food waste made from the viewpoint of hospitality managers. It reviews the challenges in classifying, quantifying and characterising hospitality food waste, discusses the opportunities and obstacles to its mitigation and, drawing on good business practice examples, derives a framework for managing food waste across the different areas of hospitality operations. The framework is underpinned by such determinants of effective mitigation as: core in-house competencies; training needs; initial investment costs; and potential monetary savings. The feasibility of its broader adoption by managers across the sector is discussed.

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Section: Introductionmentioning

confidence: 99%

Food waste management in hospitality operations: A critical review

2019

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“…What is more, the environmental impact of food consumption by tourists is not only limited to the food itself (so-called 'direct' impacts), but also includes numerous embedded (or 'indirect') impacts within the food supply chain, such as arable land, water, and energy (Gustavsson et al, 2011;Scialabba et al, 2013). For example, tourism growth may significantly increase arable land requirements for the food supply of tourist destinations (Gössling, 2002) while there is good correlation between an increase in tourist arrivals to a destination and food resource intensity, including food waste generation (Arbulu et al, 2017). Indeed, it is estimated that, annually, approximately 75 billion meals are consumed by tourists and these meals are usually cooked of high quality foodstuffs which require abundant land resources to produce (Rutty et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Tourist food consumption and its arable land requirements in a popular tourist destination

Filimonau

Wang

et al. 2020

Resources, Conservation and Recycling

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Determining the resource intensity and environmental impacts of tourist food consumption is important for the design of sustainable development strategies for tourist destinations. Yet, studies aiming to accurately quantify the environmental repercussions of tourist food consumption for specific destinations are rare and take limited account of temporal changes in food consumption patterns among tourists. This study contributes to knowledge by calculating the impact of temporal changes in tourist food consumption on arable land requirements (ALR) in Lhasa, Tibet. It finds an 8% per capital per meal increase in tourist food consumption within the 2013-2015 period which translates into over 50% increase in ALR. The study further pinpoints that 84% of the increased ALR is attributed to dietary changes in tourist food consumption, i.e. grown consumption of meat. Lastly, the study shows that, in 2015, nearly 62% of the arable land area of the Lhasa region was required to meet growing tourist demand for food.

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“…The STIRPAT model is an improved environmental stress analysis method based on the IPAT model, which allows multi-factors and nonlinear analysis of anthropogenic impact on the environment ( York et al, 2003a , 2003b ). This model has been widely used in carbon emissions ( Yang et al, 2018 ), water footprint ( Zhao et al, 2014 ), energy consumption ( Ji and Chen, 2017 ), solid waste ( Arbulu et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%