Available via license: CC BY 4.0
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Wish You Were Here? the Economic Impact of the
Tourism Shutdown from Australia’s 2019-20 ‘Black
Summer’ Bushres
Vivienne Reiner ( vivienne.reiner@sydney.edu.au )
The University of Sydney
Navoda Liyana Pathirana
The University of Sydney
Ya-Yen Sun
The University of Queensland
Manfred Lenzen
The University of Sydney
Arunima Malik
The University of Sydney
Research Article
Keywords: Bushres, wildres, tourism, input-output analysis, economic impact
Posted Date: September 28th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-3376778/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
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Additional Declarations: No competing interests reported.
1
WISH YOU WERE HERE? THE ECONOMIC IMPACT OF THE TOURISM SHUTDOWN FROM
AUSTRALIA’S 2019-20 ‘BLACK SUMMER’ BUSHFIRES
Vivienne Reiner1, Navoda Liyana Pathirana1,2, Ya-Yen Sun3, Manfred Lenzen1, Arunima Malik1,4
1. ISA, School of Physics A28, The University of Sydney, Sydney, New South Wales, 2006, Australia
2. Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, 2006, Australia
3. Business School, The University of Queensland, Brisbane, Queensland, 4072, Australia
4. Sydney Business School, The University of Sydney, Sydney, New South Wales, 2006, Australia
Correspondence to: vivienne.reiner@sydney.edu.au
ORCID IDs
Vivienne Reiner: 0000-0003-0964-160
Ya-Yen Sun: 0000-0001-6788-7644
Manfred Lenzen: 0000-0002-0828-5288
Arunima Malik: 0000-0002-4630-9869
ABSTRACT
Tourism, including education-related travel, is one of Australia’s top exports and generates substantial economic
stimulus from Australians travelling in their own country, attracting visitors to diverse areas from Queensland’s
Gold Coast Hinterland to the New South Wales Blue Mountains, Snowy Valleys and South Coast, Victoria’s
Lakes and High Country regions and South Australia’s Kangaroo Island and Adelaide Hills. The globally
unprecedented 2019-20 bushfires burned worst in these pristine tourist areas among others. The fires resulted in
tourism shutting down in many parts of the country over the peak tourist season leading up to Christmas and
into the New Year, and tourism dropped in many areas not physically affected by the fires. Our research
quantified the cost of the short-term shock from tourism losses across the entire supply chain using input-output
(IO) analysis. We calculated losses in Australian total output of AU$2.8 billion and $1.56 billion in final
demand, with this reduced consumption triggering losses in income of $828.65 million and almost 8,100 jobs.
Among the key sectors were hospitality, transport and cultural/recreational services, including National Parks
which are expected to be increasingly at risk from climate change.
KEYWORDS: Bushfires, wildfires, tourism, input-output analysis, economic impact
CLASSIFICATION CODES
MSC:
15B99 (Linear and multilinear algebra; matrix theory)
JEL
C67 (Input-Output Models)
Z3 (Tourism Economics)
ACKNOWLEDGEMENTS
Tourism Research Australia provided crucial survey and expenditure data, without which this study would not
have been possible. The authors thank Sebastian Juraszek for expertly managing our advanced computation
requirements.
This work was financially supported by the Australian Research Council (ARC) through its Projects
DP0985522, DP130101293, DP200103005, DP200102585, DE230101652, LP200100311 and IH190100009,
and the University of Sydney SOAR Funding. IELab infrastructure is supported by ARC infrastructure funding
through project LE160100066, and through the National eResearch Collaboration Tools and Resources project
(NeCTAR) through its Industrial Ecology Virtual Laboratory VR201. NeCTAR are Australian Government
projects conducted as part of the Super Science initiative and financed by the Education Investment Fund.
Manfred Lenzen was financially supported by the Hanse-Wissenschaftskolleg in Delmenhorst, Germany
through its HWK Fellowships.
2
INTRODUCTION
Australia’s 2019-20 bushfires were unprecedented globally, burning through more than one-fifth of its temperate
broadleaf and mixed-forest biome (Boer et al., 2020) over several months, and including every State and
Territory. Starting in what was then Australia’s hottest and driest year on record (Norman et al., 2021), in a
nation used to increasing extremes (Gergis, 2018), the fires killed or displaced an estimated three billion animals
(van Eeden et al., 2020) in addition to at least 33 people (Royal Commission into National Natural Disaster
Arrangements, 2020). At its peak, Australia’s worst fire, the Gospers Mountain mega-fire that formed from the
convergence of six fires, was one day away from spreading from bushland to the built-up Sydney suburb of
Hornsby (McDonald, 2020), where 20,000 homes lie within 100 metres of the bush (Hannam, 2016). The
ferocity of the fires has also raised questions about cumulative or irreversible damage, for example to rainforest
dating back to the Jurassic Period, including cultural heritage (Australian Government Department of
Agriculture, Water and Environment, 2020); animals (Murphy & van Leeuwen, 2021) such as the koala
becoming endangered in New South Wales (NSW), Queensland (QLD) and the Australian Capital Territory
(ACT) (Dalton, 2022); and potentially climate targets as a result of greenhouse gases emitted from the fires (van
der Velde et al., 2021). The fires resulted in about 830 million tonnes of carbon dioxide-equivalent emissions (
Australian Government Department of Industry, Science, Energy and Resources, 2020), which is one-and-a-half
times Australia’s annual emissions for the previous year to March 2019, of 538.9 Mt C02-e (Australian
Government Department of Industry, Science, Energy and Resources, 2019). The Australian government (2020)
noted that bushfires tend to result in a carbon sink in future years as a result of regrowth, however, concerns
have been raised regarding the extent of Australia’s 2019-2020 fires in terms of post-fire recovery (Boer et al.,
2020).
The bushfires (also called wildfires) started in mid-2019; in the nation’s north, what became known as the
Queensland bushfires between September to December 2020, marked the start of a catastrophic season. The
QLD fires affected a number of tourism areas, including the World Heritage Lamington National Park, where
the historic Binna Burra Lodge was destroyed, along with Parks and road infrastructure (Binna Burra, 2022;
Queensland Government, 2020). A State of Emergency had been declared briefly in November. In December the
fires intensified in NSW, VIC and South Australia (SA) in the lead-up to Christmas and in the early New Year
period, disrupting the peak tourist season, with many tourists stranded, evacuated or cancelling trips. A “Tourist
leave zone” was set up in NSW, extending 500 km from Batemans Bay along the coast to the VIC border in the
first week of January. The Rural Fire Service warned of upcoming weekend fires “the same or worse” than the
devastating New Year’s Eve fires and encouraged holiday-makers to get out of harm’s way and allow emergency
workers to focus on protecting the local communities (BBC, 2020). Tourism Australia paused its advertisement
Matesong, which focused on celebrity Kylie Minogue (Bourke, 2020) and an image of the spots of bushfires
over a month on a map of Australia was Tweeted by singer Rhianna and went viral, giving the impression that
the whole of the country was on fire simultaneously (Rannard, 2020). In Australia’s 4th-most visited island, SA’s
Kangaroo Island, catastrophic conditions resulted in evacuations and the death of two people before the fires
were brought under control in late January (Kangaroo Island Council, 2020). With conditions improving in some
areas such as the NSW Blue Mountains, Australia’s capital, the ACT became the worst-hit region in February
(Tourism Research Australia, 2020a, 2020b), experiencing the most hazardous air quality in the world (Norman
et al., 2021). Melbourne was also affected by hazardous air-quality levels and smog from the fires made its way
across the globe (Rodriguez, 2020), before the bushfire season ended in March 2020.
In some regions directly affected by fires, business takings were reduced by more than 70% in the important
summer holiday period; in addition to losses from tourism, residents also spent less because of safety- and
supply-chain disruptions (Indigo Shire Council, 2020; McIlwain, 2020). Research showed that at its peak,
awareness about the fires was close to 100% across all markets (Tourism Australia, 2020), and the government’s
export arm noted that tourism and education was its most-impacted industry (Austrade, 2020, p. 2).
The federal government provided a AU$76 million Rebuilding Australian Tourism package and among other
initiatives, Tourism Australia executed a successful "Holiday here this year” campaign after some of the worst of
the fires, in January, encouraging Australians to return to bushfire-affected areas and support local communities
across the country. In February an international campaign was launched, called "There’s still nothing like
Australia”, building on a pre-fire campaign, "There’s nothing like Australia”; however, all marketing was
stopped because of increasing coronavirus travel restrictions, and Australia comprehensively closed its
international borders on 20 March 2020.
3
What has been referred to as Australia’s “Black summer” could be a sign of things to come (Canadell et al.,
2021; Handmer et al., 2018; Norman et al., 2021; Van Oldenborgh et al., 2021) in a continent already subject to
heatwaves and drought, which is being exacerbated by climate change (Gergis, 2018). In particular, bushfires
have been increasing their share compared to other natural disasters; this trend has been noted in Australia as
well as globally (Handmer et al., 2018; Swiss Re, 2021)
This is, to our knowledge, the first supply-chain analysis of the cost of the 2019-20 bushfire tourism damages on
the Australian economy and is based on the popular technique input-output (IO) analysis. Drawing on surveys
by Tourism Research Australia about the direct impact of the bushfires, we calculate losses across the entire
supply chain, uncovering hotspots in particular sectors and regions.
This paper is set out as follows: Brief overviews of IO analysis, as well as the IO disaster analysis stream and its
application to this study are detailed in the Methods section. Key findings are set out in the Results, risks to
long-term tourism as well as future research recommendations are outlined in the Discussion, and we then
conclude.
METHODS
Overview of input-output analysis
This study draws on input-output (IO) analysis, a methodology that is globally standardised and enables impact
analysis along the entire supply chain. In comparison to production-based accounting, where impacts are
relegated to the producer or territory responsible, IO underpins consumption-based accounting because it traces
the impact of intermediate industry demand in addition to the impact of final demand from the consumption of a
good or service (Afionis et al., 2017). In this way, IO analysis facilitates comprehensive impact analysis such as
carbon footprints that include all scope-3 emissions. Furthermore, IO analysis facilitates researcher and policy
expert insights, through the ability to provide highly disaggregated information at the sectoral and regional
levels and across a wide range of indicators (Wiedmann, 2009); outputs range from headline figures on gross
domestic product (GDP) and environmental impacts to a breakdown of employment impacts in specific regions
or sectors.
IO analysis was developed by economist Wassily Leontief during the 1930s and 1940s to enable practitioners to
quantify the relationship between inputs into, and outputs of economic activity, including pollution (Leontief,
1936). With the methodology demonstrating the relationships between consumption and production and
enabling the identification of supply-chain hotspots (Leontief, 1966), IO analysis increased in popularity during
the 1970s oil shocks, winning Leontief a Nobel Prize (The Nobel Prize, 1973).
Because it is based on the economic structure of nations, Leontief’s prize-winning formula overcomes barriers
such as data collection from indirect suppliers faced in traditional life-cycle assessment (LCA). Traditional LCA
tends to be limited to direct (on-site) impacts or else typically does not extend beyond direct suppliers or
suppliers of direct suppliers. However, hybrid IO-LCA studies can make use of LCA’s bottom-up data while
benefitting from the calculation of higher orders of production via IO’s top-down approach. The structure of IO
analysis additionally lends itself to numerous applications related to economic activity (Wiedmann, 2009),
extending to environmental and social indicators and answering complex and novel research questions (for a
recent example, see Malik et al. (2022)).
Multi-region input-output (MRIO) models were expanded globally by research collaborations (Lenzen,
Geschke, Abd Rahman, et al., 2017; Lenzen, Geschke, Malik, et al., 2017; Lenzen et al., 2013; Malik et al.,
2019; Tukker & Dietzenbacher, 2013). These developments were supported by increased data availability and
improvements in high-performance computing that enables the analysis of billions of supply chains.
Global, multi-regional analysis, also referred to as GMRIO draws on data issued regularly by more than 100
statistical agencies around the world and is routinely employed by organisations such as the OECD, the
European Commission and the World Bank. Recently, IO analysis was used to build an open-access database on
behalf of the UN for the measurement of countries’ economic, social and environmental indicators against the
Sustainable Development Goals (SDGs), for standardised reporting and hotspot analysis (Lenzen et al., 2022).
IO analysis draws on input-output tables (IOTs) from countries’ statistical agencies as part of the almost
universal System of National Accounts 2008 framework (European Commission et al., 2009). IO analysis also
draws from international data providers such as Eurostat and sources such as UN Comtrade, the data from which
can be used to build inter-country supply-and-use-tables that form the basis of IOTs. Just as the System of
4
National Accounts has facilitated international comparisons across significant economic activities, so too has IO
and its extended environmental, social and disaster analysis facilitated comparisons of impacts across
companies, industries and regions; IO analysis is governed by standards set by the United Nations (UN Statistics
Division, 1999).
In this study, negative entries in the final-demand and value-added blocks of the pre-disaster MRIO tables were
removed by mirroring, as described in Section 4 of Lenzen, Moran, Geschke & Kanemoto (2014).
Disaster analysis sub-stream of IO
Research focusing on quantifying the impacts of disasters was not common until a string of disasters in the
decade from the 1990s, including the Kobe earthquake in 1995, the Indian Ocean earthquake and tsunami in
2004 and Hurricane Katrina in 2005 (Okuyama, 2007). The need for routine, effective and efficient
quantification of economic impacts, in order to adapt and mitigate against catastrophic disasters, is now
considered urgent (Okuyama & Santos, 2014). An increasing focus on quantifying disasters, assisted by
increased data availability has led to the development and advancement of empirical research methodologies
including econometric models, social accounting matrix (SAM), computable general equilibrium (CGE) and IO
disaster analysis as well as hybrid models. The relative strengths and applicability of various approaches have
been well-discussed in the disaster analysis literature; CGE and IO models are commonly used (Galbusera &
Giannopoulos, 2018; Steenge & Bočkarjova, 2007; Zhou & Chen, 2021), with IO analysis increasingly popular
in recent years (Bočkarjova et al., 2004; Li et al., 2013; Schulte in den Bäumen et al., 2015) to become the most
commonly employed method for disaster impact analysis (Okuyama & Santos, 2014). For example, IO analysis
has been used recently to quantify the impacts of cyclone Debbie in Australia (Lenzen et al., 2019), earthquakes
in Taiwan (Faturay, Sun, et al., 2020), COVID-19 (Lenzen et al., 2020) and the Venezuelan energy crisis (Li et
al., 2022).
With IO models previously established as the major tool of impact analysis that assesses more than one region
(Richardson, 1985), this methodology plays a crucial role in tracing the indirect, flow-on effects from large-
scale disasters in an increasingly globalised world (Steenge & Bočkarjova, 2007). IO post-disaster analysis
calculates impacts from direct damages data rather than relying on expected future trends as inputs to the model.
In addition to its universality, IO disaster assessment because of its relatively straightforward structure, has also
enabled integrative approaches where IO is incorporated with other models to enable them to assess higher-
order (indirect) effects, such as for CGE (Koks et al., 2016; Rose, 1995) and transportation networks (Okuyama,
2007).
In comparison, CGE’s approach in simulating future states is more flexible, which can be a weakness. In a meta-
analysis of CGE studies, Zhou and Chen (2021) concluded that variability in results because of practitioner
assumptions inherent in the modelling remain a challenge. An assumption that the market produces optimal
outcomes and the economy re-adjusts towards equilibrium over the longer term can provide an underestimation
of losses, particularly where a large disruption occurs in a short timeframe (less than a year), which typifies
natural hazards (Rose, 2004).
Recognising the imbalance that exists in the aftermath of a disaster and the new situation the economy faces as a
result of spillover impacts along supply chains, Steenge and Bočkarjova (2007) built on the concept of a basic
equation, using IO analysis, to describe the pre- and post-disaster economies, with the idea that governments can
use the model to determine market interventions and analyse alternate pathways to the desired post-catastrophe
equilibrium. We follow the approach by Steenge and Bočkarjova to determine the short-term post-disaster loss
for the economy, which has been built on in recent studies to improve the disaster model. Limitations of IO
disaster analysis have included the fact that the rigid structure requires constant prices and does not take account
of real-world behaviour such as substitution of goods and services; however, advances have gone some way to
addressing the question of substitutability. Steenge and Bočkarjova described a “basic equation” that can be
represented as
– where the potential maximum production of each sector of the impacted
economytakes into account the event matrix of losses placed on the diagonal, gamma hat (
), of
proportionate production losses that shinks the pre-disaster economy , so that losses in total output are the
difference between this new output level and the initial output (Bočkarjova et al., 2004b; Steenge & Bočkarjova,
2007). Total output is determined according to standard IO anaysis: , where is an identity
matrix of 1’s on the diagonal and 0’s elsewhere; the matrix is the same dimensions as the direct requirements
matrix to which it relates, which is a “production recipe” of the selected sector groupings comprising the
5
economy, with the inputs into each sector are a fraction adding up to $1 (or other monetary unit as relevant)
worth of production; being the famous Leontief inverse that includes the entire supply chain, and
demand from end-consumers (also referred to as final demand or consumption) being equal to .
A relaxation of Steenge’s approach is described by Schulte in den Bäumen et al. (2014). Developed to be used in
instances when the “unbound” constant production recipe approach results in negative final demand () values,
this method is based on the assumption that very small, marginal inputs to production, in the matrix, are
substitutable, meaning intermediate demand can shoulder some of the shock to ensure no negative final demand
in order to reflect better the real world. To achieve this, marginal inputs in are reduced to zero to ensure the
post-event final demand sectors in are either zero or positive.
An alternative to removing marginal inputs in was developed by Faturay et al. (2020) because small values
may nonetheless be important in complex supply-chain interactions; this approach uses optimisation coding in
MATLAB to determine the maximum total output losses. Given the disaster “basic equation”
,
with , the optimisation approach to determining the post-disaster economy enables changes in
intermediate demand (), final demand and total output () in order to meet the constraints of no negative
values in final demand (). This model was used in a comprehensive footprint of the first COVID-19 lockdown
(Lenzen et al., 2020) and more recently in projections of climate impacts on the Australian food system (Malik
et al., 2022). A new “minimum-disruption” approach to modelling the post-disaster transition has also been
proposed by Li et al. (2022), who apply priority weights to essential sectors to guard against the economic
shock.
In this novel study of the tourism impacts of Australia’s 2019-20 mega-fires, we use the optimisation approach
developed by Faturay et al. (2020) because of its application to numerous recent disasters.
Data collection
Tourism Research Australia (TRA) published a range of data in the National Visitor Survey (2020b) and
International Visitor Survey (2020a); in order to provide a more comprehensive understanding of the losses from
the bushfires, TRA provided, upon request, quarterly expenditure including day trips (personal communication,
31 August, 2022). Only data from January-March 2020 was used because expenditure only started falling in that
quarter. By the following quarter, the fires had been brought under control and it would not be possible to
determine if any bushfire tourism losses flowed through to the following quarter because non-essential travel
was banned because of the coronavirus pandemic. TRA also provided the underlying data from surveys that
sought to determine the proportion of tourists who were impacted by the bushfires (personal communication, 10
& 14 June, 2022). These percentages were applied to the March quarter losses to ensure no losses because of the
coronavirus were included. In order to disaggregate losses by sector, the proportionate representation of tourism-
and tourism-related sectors from TRA’s 2018-19 State Tourism Satellite Accounts (TSA) (Austrade & Tourism
Research Australia, 2019) was applied to the 2019-20 losses (Austrade & Tourism Research Australia, 2020).
The UN World Tourism Organization and Australian Bureau of Statistics definitions of tourism relate to
expenditure by visitors to an area where the visit is for less than a year; TSAs, which comprise expenditure on
tourism and related activities of nations, include business- and education-related travel, in other words, any
visits, as opposed to long-term moves. Online Resource 1 SI 2.7 provides further details about the calculations
of direct damages.
The event (gamma) matrix
Losses were divided across eight regions (each State and Territory) and 39 sectors, which included the most-
affected sectors as well as other key primary, secondary and tertiary sectors. The post-disaster losses were then
compared with the pre-disaster total output for the year, for the relevant sectors and regions in the MRIO and
converted into a proportion. A separate gamma matrix of capital damages was also compiled, with the treatment
of infrastructure, including depreciation, following the approach described in previous IO disaster studies
(Faturay, Sun, et al., 2020; Lenzen et al., 2019) (see Online Resource 1 SI 2.6 for details); added together, these
make up the final gamma matrix of proportionate losses for the economy. For example, 0.02 for South
Australian Accommodation means that income in the accommodation sector in SA reduced 2% for the year;
areas where no known direct damage occurred are 0. Table 1 shows the final gamma matrix () of direct losses
and capital damages.
6
Table 1
NSW
VIC
QLD
SA
WA
TAS
ACT
NT
1
Grapes - wine
0
0
0
0
0
0
0
0
2
Apples, pears & stone
fruit
0
0
0
0
0
0
0
0
3
Livestock
0
0
0
0
0
0
0
0
4
Other agriculture
0
0
0
0
0
0
0
0
5
Forestry
0
0
0
0
0
0
0
0
6
Fishing and seafood
0
0
0
0
0
0
0
0
7
Coal, oil and gas
0
0
0
0
0
0
0
0
8
Non-ferrous metal ores
0
0
0
0
0
0
0
0
9
Other mining
0
0
0
0
0
0
0
0
10
Wines
0
0
0
0
0
0
0
0
11
Other food
manufacturing
0
0
0
0
0
0
0
0
12
Textiles, clothing and
footwear
0
0
0
0
0
0
0
0
13
Wood and paper
manufacturing
0
0
0
0
0
0
0
0
14
Automotive petrol
0.0024
0.0012
0.0015
0.0064
0.0016
0
0.0040
0.0044
15
Chemicals, petroleum
and coal products nec
0
0
0
0
0
0
0
0
16
Non-metallic mineral
products
0
0
0
0
0
0
0
0
17
Metals, metal products
0
0
0
0
0
0
0
0
18
Machinery appliances
and equipment
0
0
0
0
0
0
0
0
19
Miscellaneous
manufacturing
0
0
0
0
0
0
0
0
20
Electricity supply, gas
and water
0
0
0
0
0
0
0
0
21
Residential building
construction
0
0
0
0
0
0
0
0
22
Other construction
0
0
0
0
0
0
0
0
23
Rail transport
0.0007
0.0011
0.0005
0.0015
0.0011
0
0.0051
0.0019
24
Taxi transport
0.0089
0.0051
0.0065
0.0055
0.0041
0
0.0064
0.0049
25
Other road transport
0.0006
0.0004
0.0017
0.0003
0.0003
0
0.0018
0.0006
26
Air, water and other
transport
0.0035
0.0024
0.0037
0.0047
0.0035
0
0.0057
0.0068
27
Transport equipment
rental
0.0150
0.0190
0.0123
0.0115
0.0118
0
0.0056
0.0062
28
Travel agency and
information centre
services
0.0095
0.0101
0.0098
0.0137
0.0085
0
0.0091
0.0026
29
Accommodation
0.0174
0.0125
0.0106
0.0217
0.0061
0
0.0178
0.0059
30
Cafes, restaurants and
take-away foods
0.0047
0.0027
0.0033
0.0028
0.0025
0
0.0022
0.0030
31
Wholesale and retail
trade
0.0008
0.0004
0.0006
0.0005
0.0004
0
0.0005
0.0007
32
Ownership of dwellings
0.0005
0.0004
0.0003
0.0005
0.0003
0
0.0001
0.0001
33
Cultural and
recreational services;
0.0082
0.0043
0.0047
0.0081
0.0045
0
0.0021
0.0032
34
Communication
services
0
0
0
0
0
0
0
0
35
Finance, property and
other business services
0
0
0
0
0
0
0
0
36
Government
administration and
defence
0
0
0
0
0
0
0
0
37
Education and training
0.0015
0.0010
0.0009
0.0007
0.0006
0
0.0008
0.0005
38
Health and community
services
0
0
0
0
0
0
0
0
39
Personal and other
services
0
0
0
0
0
0
0
0
7
The gamma matrix (). The gamma (event) matrix comprises direct tourism losses from Australia’s 2019-20 bushfires as a proportion of
total output () for the 2018-19 financial year, in particular sectors and regions. Losses were calculated for Australia’s States and Territories
against 39 sector groupings of this study, comprising Australia’s economy. Infrastructure losses were depreciated in the manner described by
Faturay et al., 2020 and Lenzen et al., 2019.
As can be seen in Table 1, no losses were recorded for Tasmania (TAS) because that State recorded increased
tourism expenditure (of $1.1 million). This study quantified the total impact from direct losses, across the supply
chain, but did not quantify the impact of gains (in Tasmania); in the same way, insurance paid out can be seen as
an economic stimulus, as can hospitalisations in providing money for the healthcare sector; these are also not
included in IO disaster-analysis calculations. In this way, a standardised approach is followed in quantifying the
short-term economic cost of the losses from each impacted sector.
MRIOs are based on countries’ input-output tables (IOTs) in the National Accounts so the aggregated sectors
were selected from 1284 sectors in the Australian Industrial Ecology Virtual Laboratory (www.ielab.info/),
which are based on the Input-Output Product Classification in the Australian National Accounts (Australian
Bureau of Statistics, 2009). The States and Territories were aggregated from the 2214 Statistical Area level 2
(SA2) regions (Australian Bureau of Statistics, 2011). A concordance matrix to aggregate the sectors and
regions was converted into a MRIO of the Australian economy in the IELab. The IELab developed at the
University of Sydney (Lenzen, Geschke, et al., 2014) was Australia’s first such collaborative cloud-based
platform for IO analysis. Such platforms overcome the time-consuming nature of MRIO compilation through an
open-source approach, with the source data updated regularly. IELabs have now been built for Indonesia
(Faturay et al., 2017), Japan (Wakiyama et al., 2020), Taiwan (Faturay, Sun, et al., 2020), China (Wang, 2017)
and the United States (Faturay, Vunnava, et al., 2020), and the Global MRIO Lab infrastructure was used to
build GLORIA on behalf of the UN International Resource Panel as the database for the open-access global
Sustainable Consumption and Production Hotspot Analysis Tool (Lenzen et al., 2022).
Indicators for impact analysis and unravelling supply chains through production layer decomposition
The satellite account attached to the tailored MRIO was selected during the MRIO compilation in the IELab,
with matrix calculations then carried out in MATLAB. In this study, the satellite account employment (in
addition to the related indicator, income) was selected as an indicator for impact analysis, to be quantified along
with the post-event total output () and final demand ().
The change in final demand (is used to calculate the employment and income impacts of the disaster using
the formula , where is the multiplier derived by dividing the desired indicator by pre-disaster total output
, and represents the entire supply chain . Income data is extracted from the first row of the value-
added block of the MRIO (), while the aggregated total employment data is contained in the 24th row of the
employment satellite account in the IELab. In both instances, the matrix is calculated using element-wise
multiplication so that the intensity sectors/multipliers align with the final demand sectors, enabling production
layer decomposition (PLD) for a disaggregated view of impacts. PLD is also carried out on consumption (as
well as total output). The entire impact is unravelled at each production layer through the following
decomposition:
Layer 1 Layer 2 Layer 3 Layer 4 + … + Layer n
where is the direct impact (first layer in the PLD), is the additional impact from direct suppliers
involving the matrix of direct requirements, is the impact from suppliers of the direct suppliers and so
on, to infinite (n) layers/orders of production i.e., direct suppliers as well as all indirect suppliers. In this study it
can be seen that there are material increases in the national disaster impacts until about the sixth or seventh
production layer (Fig. 3).
Limitations
Our results may be conservative because it is possible that tourists would have spent even less than anticipated
in the Tourism Research Australia surveys on which this bushfire research is based, had it not been for the first
COVID-19 lockdown, which then became the primary reason for slashed tourism expenditure. We calculated
bushfire-related tourism expenditure losses from the National Visitor Surveys and International Visitor Surveys
that were carried out from 21 January to 15 March (Tourism Research Australia, 2022), which asked tourists
8
about the extent to which their changed behaviour occurred because of the bushfires. Had it not been for the
pandemic, the bushfire impact on tourism may have measured more post-fire, without being mixed up with
losses from concurrent disasters, meaning we would have identified potentially higher bushfire losses. As well,
direct bushfire damages data were only available for regions, not sectors, with the exception of tourism-related
infrastructure. Although sectoral expenditure is available in the 2019-20 ABS Tourism Satellite Account, this
period included COVID-19; therefore assumptions had to be made about bushfire-specific direct sectoral losses,
based on the usual proportionate representation of tourism spend, which were then used to calculate total losses
including indirect impacts (see Online Resource 1 SI 2.7 for details about the calculation of the direct damages
data).
RESULTS
From the Tourism Research Australia quarterly data and bushfire surveys, we calculated direct losses in tourism
expenditure of $1629.9 million nationwide, in addition to $112.6m in depreciated infrastructure. In terms of
regional breakdown, these direct losses were: NSW - $760.5 million, VIC - $347 million, QLD - $343.7
million, SA - $113.8 million, Western Australia (WA) - $135.6 million, TAS - $0, ACT - $22.2 million and
Northern Territory (NT) $19.7 million. This triggered losses across the entire supply chain totalling $2801.559
million in total output, $1561.783 million in consumption, $828.645 million in income, and employment losses
of almost 8087 full-time equivalent (FTE), which impacted sectors and regions in different ways. Taking the
total output losses into account as a proportion of the pre-fire total output, in 2018-19, the losses were worst in
NSW, closely followed by SA, which experienced significant fires not only in the Adelaide Hills winery and
day-trip region near the State’s capital but also in Kangaroo Island, where almost half the island burned
(Kangaroo Island Council, 2020). Next in terms of proportionate losses was NT, despite the fact that this region
did not suffer catastrophic fire conditions.
Geographic distribution of the employment losses
The geographical spread of losses in employment are similar to losses in the other indicators measured in this
study. Fig. 1 shows that more than one third of the 8087 jobs lost were experienced in NSW, the most populous
State, which is also where the fires were worst.
Figure 1
Fig. 1 Losses in full-time equivalent (FTE) jobs across Australia resulting from the tourism losses because of the 2019-20 fires. The most
populous State, New South Wales (NSW), suffered about twice as many losses in employment as its neighbouring eastern States, equivalent
to 2817 full-time jobs. The Northern Territory (NT), although not suffering catastrophic fires, nonetheless lost 224 jobs from the short-term
shock. Nationwide, almost 8100 jobs were lost. Other regions: Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia
(WA), Tasmania (TAS), Australian Capital Territory (ACT).
9
Fig. 1 shows that although most of the most employment losses were concentrated around Australia’s east-coast
States that also suffered the most direct tourism losses, spillovers were experienced across the nation. QLD and
VIC suffered about half as much job losses as NSW, at 1498 and 1461 respectively, and SA experienced about
half as much again, at 768, followed by WA at 589. Tasmania, despite not suffering direct tourism damages, lost
376 jobs because of supply-chain impacts. The ACT lost a similar amount of jobs (355), closely followed by the
NT (224 jobs).
Sectoral and regional hotspots
As can be seen in Fig. 2 that compares losses in consumption (final demand) and related income and
employment, from an industry perspective, hospitality and transport were the worst hit overall, with the
recreation and educational sectors also significantly impacted.
Figure 2
Fig. 2 Losses in consumption, income and employment across Australia’s eight States and Territories and disaggregated by broad sector
groupings, as a result of the tourism shutdown from the 2019-20 bushfires. Hospitality and transport suffered the most losses. Regions: New
South Wales (NSW), Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia (WA), Tasmania (TAS), Australian
Capital Territory (ACT), Northern Territory (NT).
The “Accommodation” and “Cafes, restaurants and take-away” study sectors, which make up our aggregated
hospitality grouping, together suffered the most losses in both consumption and employment. The most income
losses, however, were experienced in transport industries, with the aviation-led “Air, water and other transport”
sector the worst-hit out of all the 39 sector groupings in this study, at $104 million lost income for the year,
followed by “Education and Training” at $95 million. From an employment perspective, the most losses were in
the “Accommodation” sector at 1334 full-time equivalent jobs and “Cafes, restaurants and take-away foods” at
1079 jobs, closely followed by “Cultural and recreational services”, which shed 913 jobs.
NSW shouldered the most losses, including 44% of consumption and 34% of income impacts; NSW differed
somewhat from the other regions in that the single most-impacted sector, out of the 39 sector groupings in this
study, was “Cultural and recreational services” in both consumption and income (reducing by $148.33 million
and $41.18 million respectively), although when considering “Accommodation” and “Cafes, restaurants and
take-away” together as hospitality, the hospitality industry dominated. The particularly large NSW losses in
“Cultural and recreational services” is in part a reflection of the destruction of more than $200 million in Crown
Lands and National Park infrastructure, (which was then depreciated - see Methods). These impacts including on
areas such as the World Heritage Blue Mountains National Park (where approximately 82% was burnt (
Australian Government Department of Agriculture, Water and the Environment, 2020)) would also have resulted
in flow-on impacts to the local community, as well as more broadly, in areas such as reduced consumption of
lodgings, restaurant meals and transport. Differences can also be seen between indicators for regions that
suffered similar total losses. For example, WA experienced more losses than SA in consumption ($118 million
compared to $102 million) and income (almost $1 million difference) but not in employment (589 jobs lost
compared to 768), where SA suffered particularly more losses in manufacturing sectors (142 jobs compared to
56); as well, SA experienced more job losses in hospitality (203 compared to 155), where losses included from
the Kangaroo Island destination the Southern Ocean Lodge, which was destroyed by the fires and for which the
rebuild was estimated at $50 million (Boisvert, 2022). From a per-capita perspective, the NT was particularly
10
impacted; for example, the NT had the smallest population in Australia in March 2020, at 245,400 people, which
was almost half the ACT’s population at 429,800; and more than half the TAS population of 539,600 (Australian
Bureau of Statistics, 2020), however, the NT lost almost as much income, at $30 million compared to $37.5
million and $37 million respectively. The underlying data on losses is in Online Resource 1.
Chain reactions along supply chains
In the following analysis, we discuss the impact on income and employment, which was triggered by the
contraction in consumption.
Fig. 3 shows that almost all the impacts were felt within about six or seven layers in the upstream supply chain,
after which the additional losses per layer particularly start to level off.
Figure 3
a
b
Fig. 3 Production layer decomposition (PLD) of regions (a) and aggregated sectors (b). Layer 1 represents the impact from the direct
tourism losses, layer 2 represents the impact from direct suppliers and so on, with the cumulative losses up to 10 layers depicted, which
represent most of the losses. Employment losses measured in full-time-equivalent (FTE) jobs. Regional abbreviations: New South Wales
(NSW); Victoria (VIC); Queensland (QLD); South Australia (SA); Western Australia (WA); Tasmania (TAS); Australian Capital Ter ritory
(ACT); Northern Territory (NT).
The least-impacted regions tended to have a longer tail of losses as a result of supply-chain spillovers. In
comparison, the most-impacted regions, the south-eastern States of NSW, VIC and QLD, experienced more
losses directly or within a few orders of production.
Similarly, key industries such as transport and hospitality experienced more losses earlier on, while “Other
services”, which includes sectors not directly impacted such as “Finance, property and other business services”,
continued to experience significant losses further along the supply chain.
11
Regional case studies
Losses in the most-impacted State, NSW, followed a similar trend to the national results so in this section we
highlight examples of divergence from the aggregated results, in the Australian Capital Territory and Tasmania.
Additional disaggregation for regions and sectors and the underlying data are in Online Resource 1 SI 4.
ACT
The dominance of losses in accommodation and the aviation-led transport sector in Australia's capital may in
part be explained by the “fly-in-fly-out” economy.
Figure 4
Fig. 4 Production layer decomposition of the impact of tourism losses from the 2019-20 bushfires in the Australian Capital Territory, for
income and full-time equivalent (FTE) employment in broad sector groupings. Although the number of upstream supply-chain interactions
is theoretically infinite, results are illustrated for up to 10 orders of production, which comprise most of the losses.
The ACT, in line with the national trend regarding income, experienced the biggest impact in the transport
industry, led by the “Air, water and other transport” sector, which lost $4.12 million. These losses are in part
because of the extreme bushfire smoke that the ACT’s capital Canberra experienced, which resulted in the
airport being closed for hours on one of the worst days (Foster, 2020). “Accommodation”, as part of the
hospitality industry, experienced proportionately more employment losses than in any other region, equivalent to
54 jobs.
TAS
Despite Tasmania not experiencing an overall loss in direct tourism expenditure, losses from other regions
flowed throughout Tasmania’s economy.
Figure 5
Production layer decomposition, across 10 broad sector groupings, of the impact of tourism losses from the 2019-20 bushfires in Tasmania.
The losses are illustrated in terms of income and full-time equivalent (FTE) employment, up to 10 orders of production.
12
As Fig. 5 shows, the losses from elsewhere in Australia flowed through Tasmania across the primary, secondary
and tertiary sectors, with significant losses continuing to accumulate in upstream layers of production,
particularly in the services sectors. Although losses were spread broadly across the employment sectors, the
“Accommodation” hospitality sector suffered the most losses, which is consistent with our results nationally,
with almost 29 jobs lost in that sector in TAS. From an income perspective, the most losses were in the transport
industry, led by the “Transport equipment rental” sector, which contracted by $3.3 million in TAS.
DISCUSSION
Tourism has been a critical driver of economic output and risks damage from climate change, particularly in
countries such as Australia that are vulnerable to disasters. Tourism spend in the year prior to the fires was more
than AU$120 billion, being responsible for the employment of 5% of Australians overall and 8.1% in rural
areas, or almost one in 12 people (Tourism Australia, n.d.), and the bushfires burned worst outside of major
cities because of tree cover. From a global perspective, Australia was one of the highest-yielding tourism
destinations in 2018-19, with international visitors spending $44.6 billion (Tourism Australia, n.d.). Education-
related travel services was Australia’s fourth-biggest export in 2018-19 and, when combined with personal
travel, was beaten only by iron ore and coal and was responsible for more export income than natural gas (
Australian Government Department of Foreign Affairs and Trade, 2020, p. 19); in fact, the federal export arm
Austrade reported that its two most-impacted industries from the bushfires were tourism and education (2020, p.
2). It should be noted that tourists are defined as any visitors who will stay for less than a year; standardised
tourism satellite accounts (TSA) of tourism and related activities of nations include business- and education-
related travel, meaning the official tourism statistics include not only holidaymakers but also the large and
growing area of air travel generally. In Australia prior to the bushfires, “Air, water and other transport” was the
main component of tourism travel, responsible for expenditure of $23.278 billion (compared to just $1.090
billion for rail transport, $1.252 billion for taxi transport, $1.854 billion for other road transport and $1.839 for
motor vehicle hiring (Austrade & Tourism Research Australia, 2019).
With bushfires/wildfires increasing compared to other natural disasters (Handmer et al., 2018) and expected to
intensify as projected for disasters generally under climate change (IPCC, 2022), it is important for countries
such as Australia to quantify the economic impact of disasters as part of routine practice, including supply-chain
spillovers. As our research demonstrates, by including the entire supply chain, using IO analysis, we calculated
total output losses of $2.8 billion, which is a 61% increase from the direct damages identified.
Our highly disaggregated results enabled us to identify differences in impacts along supply chains, which was
important because the impacts were unevenly distributed. For example, in addition to the key industries of
transport, hospitality and cultural and recreational services, the education sector was significantly impacted,
indicating that universities and other providers could consider planning for potential cuts to expenditure on their
services in the longer term if major shutdowns to tourism become more commonplace as a result of disasters in
particular regions or perceptions that Australia may be unsafe. As well, although we found that the hospitality
industry was the most affected overall, in terms of impacts on people’s income, the aviation-led “Air, water and
other transport” sector suffered the most, indicating that different approaches may be made by the government
depending on policy priorities, for example in terms of jobs or income. From a regional perspective, although
the States that were declared bushfire catastrophes also instigated State inquiries to help guide responses, we
found that some other regions suffered significant impacts compared to their pre-fire total output. As well,
supply-chain spillovers rendered some smaller regions more vulnerable to shouldering the impacts from a per-
capita perspective, and this was particularly the case for the remote NT, which has the smallest population.
These disaggregated results may help decision-makers in investigating certain hotspots, not only for re-building
post-fire but also in terms of preparing for the next disaster.
It is also worth considering the losses suffered by “Cultural and recreational services”, largely as a result of
extensive infrastructure destruction, and spillover impacts, in National Parks that are key drawcards for tourists,
for example to the World Heritage Blue Mountains in Greater Sydney. From infrastructure damage and
destruction before even accounting for supply-chain impacts, we identified $275 million in losses (although
these direct damages were then depreciation for the purposes of our impact analysis). These infrastructure losses
are borne by the State governments and their repair, which would be paid by public funding, is an investment in
tourism in addition to nature-based recreation generally. However, similar damages in poorer countries could be
much more difficult to rectify efficiently ahead of upcoming holiday seasons, particularly in developing nations
with weak local municipalities. Therefore disasters may increase economic inequalities, with the added burden
13
of climate adaptation and mitigation adding to the costs of governments already struggling under business-as-
usual.
Although the losses from the tourism damage that we calculated only represented a small fraction of the nation’s
economic output, Australia’s reputation as a pristine destination could become permanently damaged in the
longer term under global warming, with fewer people travelling in Australia in our peak summer holiday season;
similarly, people may start to avoid other countries and regions that are increasingly in the media for their
wildfires and other disasters. The aviation-led transport sector, which suffered among the most losses, may
expect a double-hit from future impacts of climate change, with carbon-intensive industries such as air travel set
to become more expensive in the low-carbon transition, making a trip “down under” less attractive. Our findings
have broader implications for other nature-based destinations particularly in the Asia Pacific, which is the most
disaster-prone and populous region (United Nations Office for the Coordination of Humanitarian Affairs, 2017).
Countries reliant on tourism and in particular remote nations, where aviation plays a major role, are advised not
only proactively to adapt their economies to worsening natural disasters but also to prepare for the impacts of
the necessary global transition to a low-carbon world. On the other hand, aviation globally has been increasing
because of growing affluence (Lenzen et al., 2018) and it is possible that in the near future this trend will
continue.
This is the first IO analysis applied to Australia’s 2019-20 bushfires, with a focus on the supply-chain impacts of
the reduction in tourism expenditure. Future IO studies could calculate the impact of other megafire-affected
areas such as forestry or estimate the economic impact of the fires overall, from this globally unprecedented
event.
CONCLUSION
Our novel research into the losses from the tourism shutdown resulting from Australia’s 2019-20 fires found that
flowing on from direct impacts of AU $1.7 billion, indirect impacts along supply chains resulted in $2.8 billion
in total output losses and $1.6 billion in reduced consumption. The short-term drop in consumption from the
unprecedented bushfires, in terms of reduced tourism expenditure during the peak holiday season, also triggered
a reduction in income of $830 million and the shedding of 8087 full-time-equivalent jobs nationwide.
Hospitality as well as the emissions-intensive transport sector were the most impacted, the latter of which is one
of Australia’s top exports.
Our results are an illustration of what can be expected in other nations, particularly in the Asia Pacific, which is
the most vulnerable to disasters. In line with findings that fires in particular are increasing because of global
warming, nations that rely on nature-based tourism are among those with the most to gain from climate-change
mitigation efforts but these countries must also prepare for the impacts to their communities and industries that
will result from future fires, in an era that has been referred to as the Pyrocene.
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STATEMENTS & DECLARATIONS
Funding
This work was financially supported by the Australian Research Council (ARC) through its Discovery Projects
DP0985522, DP130101293, DP200103005, DP200102585, LP200100311 and IH190100009, and the University
of Sydney SOAR Funding. IELab infrastructure is supported by ARC infrastructure funding through project
LE160100066, and through the National eResearch Collaboration Tools and Resources project (NeCTAR) through
its Industrial Ecology Virtual Laboratory VR201. NeCTAR are Australian Government projects conducted as part
of the Super Science initiative and financed by the Education Investment Fund. This research is also supported by
federal government Research Training Program Stipend Scholarships. Manfred Lenzen was financially supported
by the Hanse-Wissenschaftskolleg in Delmenhorst, Germany through its HWK Fellowships.
Competing interests
The authors have no relevant financial or non-financial interests to disclose.
Author contributions
Vivienne Reiner and Arunima Malik designed the study; Vivienne Reiner performed scenario analysis and
drafted the manuscript reviewed by Arunima Malik and Manfred Lenzen; Navoda Liyana Pathirana and
Arunima Malik wrote the coding for the model; Ya-Yen Sun advised on tourism calculations and all authors
edited the manuscript.
Data availability
The datasets generated and analysed during the study are available from the corresponding author on request.
Artwork and illustrations
Tableau was used to create Fig. 1 and MATLAB was used to create all other figures.