This project will employ multi-region input-output analysis (Isard 1951; Leontief 1953) and a comprehensive global database (Lenzen et al. 2012a; Lenzen et al. 2013; Lenzen et al. 2017) to assess global tourism against a wide range of sustainability indicators. The footprint of global tourism is well understood in terms of greenhouse gas emissions (Lenzen et al. 2018), but comprehensive analyses in terms of other resource and environmental indicators (water, biodiversity, etc), social indicators (employment, inequality) or economic indicators (income) do not exist. This PhD project will therefore cover completely new ground.
The PhD candidate will be supervised by Dr Arunima Malik, Dr Ya-Yen Sun, and Prof Manfred Lenzen. The applicant will join the ISA Research Group at the School of Physics – The University of Sydney. ISA develops leading-edge research and applications for environmental and broader sustainability issues, bringing together expertise in environmental science, economics, technology, and social science.
On the back of a growth in tourist expenditure from 2.5 $tr in 2009 to 4.7 $tr in 2013, the carbon footprint of global tourism increased rapidly from 3.9 Gt CO2-e to 4.4 Gt CO2-e during the same period. More than half of this carbon footprint was caused in high-income country destinations, and by visitors from high-income countries.
Whilst global tourism’s carbon footprint is now well understood, comprehensive assessments of complementary footprints in terms of water, land, biodiversity, employment and other indicators do not exist. This project will for the first time establish such global environmental and social footprints for global tourism.
The project will utilize multi-region input-output (MRIO) analysis (Isard 1951; Leontief 1953) and a comprehensive global database (Lenzen et al. 2012a; Lenzen et al. 2013; Lenzen et al. 2017). Environmental and social footprint analyses have recently been carried out using a hybrid method (Bullard et al. 1978; Suh and Nakamura 2007), combining detailed bottom-up process information about the system under study with comprehensive top-down input-output data on the background economy (Minx et al. 2009; Wiedmann 2009). This choice of method holds a number of benefits. Most importantly, it circumvents the problem of systematic truncation errors due to setting of finite system boundaries (Suh et al. 2004) whilst at the same time guaranteeing complete coverage of upstream supply-chain contributions (Moskowitz and Rowe 1985). Here, “complete coverage” means that all upstream supply-chain contributions such as emissions embodied in anything that a “tourist” as per UNWTO definition consumes – food, accommodation, transport, fuel, and shopping – are included in the footprint measure. Second, input-output-assisted footprinting is supported by a long history of numerous applications (see for example Hoekstra 2010). Third, international standards on integrated physical and monetary accounting by the United Nations (UN 1999; UNSD 2014) mean that input-output-based footprint analyses can be undertaken with consistent scope on a number of complementary indicators, such as energy (Lan et al. 2016), biodiversity (Lenzen et al. 2012b), air pollution (Kanemoto et al. 2014), water (Feng et al. 2011), land (Moran et al. 2013), nitrogen (Oita et al. 2016), material flow (Wiedmann et al. 2015) and many social indicators (Alsamawi et al. 2014a; Alsamawi et al. 2014b; McBain and Alsamawi 2014; Gómez-Paredes et al. 2016; Alsamawi et al. 2017; Hui et al. 2017; Xiao et al. 2017a; Xiao et al. 2017b). Finally, a number of very detailed large-scale global multi-region input-output (MRIO) databases have recently become available (Tukker and Dietzenbacher 2013). As a result, carbon footprint analyses incorporating the global international trade network are now almost routinely carried out (Hertwich and Peters 2009). Auxiliary analyses and tools have been developed, touching on issues related to causal driver identification (Arto and Dietzenbacher 2014; Xu and Dietzenbacher 2014; Malik et al. 2016), aggregation bias (Su and Ang 2010; Su et al. 2010; Zhou et al. 2013; Steen-Olsen et al. 2014), sensitivity (Wilting 2012) and uncertainty (Lenzen et al. 2010), database comparisons (Arto et al. 2014; Inomata and Owen 2014; Moran and Wood 2014; Owen et al. 2014), and corporate reporting (Huang et al. 2009). Global carbon footprints have featured prominently in policy and the media (BBC News 2008; Peters and Hertwich 2008; BBC News 2009; Lenzen et al. 2010; Atkinson et al. 2011; Barrett et al. 2013; Wiedmann and Barrett 2013).
Additional supervisors: Dr Ya-Yen Sun
Applicant is responsible for obtaining a stipend if needed
HDR Inherent Requirements
In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:
- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)
You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.
The opportunity ID for this research opportunity is 2394