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This project will employ multi-region input-output analysis (Isard 1951; Leontief 1953), structural decomposition analysis and a comprehensive global database (Lenzen et al. 2012; Lenzen et al. 2013a; Lenzen et al. 2017) to establish the underlying trends and causal drivers of global tourism’s carbon footprint (Lenzen et al. 2018). The project will also establish detailed future projections, and will map out how decision-makers can improve over business-as-usual scenarios.

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.


Dr Arunima Malik, Professor Manfred Lenzen.

Research location

School of Physics

Program type



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 well understood for a number of isolated years, a comprehensive analysis of underlying trends and drivers does not exist. This project will for the first time establish a structural decomposition of global tourism’s carbon footprint into causal drivers.

The project will utilize multi-region input-output (MRIO) analysis (Isard 1951; Leontief 1953) and a comprehensive global database (Lenzen et al. 2012; Lenzen et al. 2013a; 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).

This project will also apply Structural Decomposition Analysis (SDA) to a time series of global MRIO tables. Structural decomposition analysis is one of the most popular tools in input–output- related research (Lenzen 2006). Rose and Casler 1996 provide an early review and account of the history of SDA. In essence, the aim of SDA is to decompose changes in one variable into a mutually exclusive and collectively exhaustive set of contributions to those changes by a number of determinant variables, or ‘drivers’ (see eg Lenzen et al. 2013b; Lan et al. 2016; Lenzen et al. 2016; Malik and Lan 2016; Malik et al. 2016). SDA is a generalisation of index decomposition analysis (Ang 2000; Hoekstra and van den Bergh 2002; 2003) to matrix variables as common in input–output analysis.

Applicant is responsible for obtaining a stipend if needed.

Additional information

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.

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Opportunity ID

The opportunity ID for this research opportunity is 2393

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