Wish You Were Here? The Economic Impact of the Tourism Shutdown from Australia’s 2019-20 ‘Black Summer’ Bushfires

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 including World Heritage rainforests, picturesque beachside villages, winery townships and endemic wildlife. The globally unprecedented 2019-20 bushfires burned worst in some of these pristine tourist areas. 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, which is the most common method for disaster analysis; to this end, we also developed a framework for disaggregating the direct fire damages in different tourism sectors from which to quantify the impacts, because after the fires, the economy was affected by COVID-19. We calculated losses of AU2.8billionintotaloutput,2.8 billion in total output, 1.56 billion in final demand, $810 million in income and 7300 jobs. Our estimates suggest aviation shouldered the most losses in both consumption and wages/salaries, but that accommodation suffered the most employment losses. The comprehensive analysis highlighted impacts throughout the nation, which could be used for budgeting and rebuilding in community-and-industry hotspots that may be far from the burn scar.

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Economics of Disasters and Climate Change (2024) 8:107–127
https://doi.org/10.1007/s41885-024-00142-8
1 3
RESEARCH
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Lenzen1,4 · ArunimaMalik1,5
Received: 22 September 2023 / Accepted: 16 January 2024 / Published online: 30 January 2024
© The Author(s) 2024
Abstract
Tourism, including education-related travel, is one of Australia’s top exports and gener-
ates substantial economic stimulus from Australians travelling in their own country, attract-
ing visitors to diverse areas including World Heritage rainforests, picturesque beachside
villages, winery townships and endemic wildlife. The globally unprecedented 2019-20
bushfires burned worst in some of these pristine tourist areas. The fires resulted in tour-
ism 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 tour-
ism losses across the entire supply chain using input-output (IO) analysis, which is the
most common method for disaster analysis; to this end, we also developed a framework for
disaggregating the direct fire damages in different tourism sectors from which to quantify
the impacts, because after the fires, the economy was affected by COVID-19. We calcu-
lated losses of AU$2.8billion in total output, $1.56billion in final demand, $810million
in income and 7300 jobs. Our estimates suggest aviation shouldered the most losses in
both consumption and wages/salaries, but that accommodation suffered the most employ-
ment losses. The comprehensive analysis highlighted impacts throughout the nation, which
could be used for budgeting and rebuilding in community-and-industry hotspots that may
be far from the burn scar.
Keywords Bushfires· Wildfires· Tourism· Input-output (IO) analysis· Economic impact
MSC Classification 15B99 (Linear and multilinear algebra; matrix theory)
JEL Classification C67(Input-Output Models)· Z3(Tourism Economics)
* Vivienne Reiner
vivienne.reiner@sydney.edu.au
1 ISA, School ofPhysics A28, The University ofSydney, Sydney, NSW2006, Australia
2 Charles Perkins Centre, The University ofSydney, Sydney, NSW2006, Australia
3 Business School, The University ofQueensland, Brisbane, QLD4072, Australia
4 Hanse-Wissenschaftskolleg, Lehmkuhlenbusch 4, 27753Delmenhorst, Germany
5 Business School, The University ofSydney, Sydney, NSW2006, Australia
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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 hot-
test 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 m 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 and 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 (Austral-
ian Government Department of Industry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 Gov-
ernment Department of Industry & 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 bush-
fires 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 condi-
tions 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, b), experiencing the most
hazardous air quality in the world (Norman etal. 2021). Melbourne was also affected by
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hazardous air-quality levels and smog from the fires made its way across the globe (Rodri-
guez 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 an 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 Aus-
tralians to return to bushfire-affected areas and support local communities across the coun-
try. 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.
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 exacer-
bated by climate change (Gergis 2018). In particular, bushfires have been increasing their
share compared to other natural hazards; 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 tech-
nique 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, uncov-
ering hotspots in particular sectors and regions.
This paper is set out as follows: Brief overviews of IO analysis, as well as the IO dis-
aster 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 anal-
ysis 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
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product (GDP) and environmental impacts to a breakdown of employment impacts in spe-
cific 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 eco-
nomic activity, including pollution (Leontief 1936). With the methodology demonstrat-
ing 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 tra-
ditional 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 collabo-
rations (Lenzen etal. 2017a, b, 2013; Malik etal. 2019; Tukker and 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 regu-
larly 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 meas-
urement of countries’ economic, social and environmental indicators against the Sustain-
able 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 National
Accounts has facilitated international comparisons across significant economic activities,
so too has IO and its extended environmental, social and disaster analysis facilitated com-
parisons 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 Sect.4 of Lenzen etal.
(2014b).
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
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to adapt and mitigate against catastrophic disasters, is now considered urgent (Okuyama
and Santos 2014). An increasing focus on quantifying disasters, assisted by increased data
availability has led to the development and advancement of empirical research method-
ologies including econometric models, social accounting matrix (SAM), computable gen-
eral equilibrium (CGE) and IO disaster analysis as well as hybrid models (for example,
as proposed by Oosterhaven (2017). The relative strengths and applicability of various
approaches have been well-discussed in the disaster analysis literature; CGE and IO mod-
els are commonly used (Galbusera and Giannopoulos 2018; Steenge and Bočkarjova 2007;
Zhou and 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 com-
monly employed method for disaster impact analysis (Okuyama and 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 etal. 2020a), 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 trac-
ing the indirect, flow-on effects from large-scale disasters in an increasingly globalised
world (Steenge and 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 straight-
forward 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 compari-
son, 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, particu-
larly where a large disruption occurs in a short timeframe (less than a year), which typifies
natural hazards (Rose 2004). Oosterhaven (2022) details a new approach, which has been
referred to as IO/SU non-linear programming and allows for substitutability, as well as
price rises, which minimise business disruption; however, it may be less suitable for quan-
tifying the impacts of complex disasters where shortages are common, along with busi-
ness shutdowns. 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 dis-
aster model. Limitations of IO disaster analysis have included the fact that the rigid struc-
ture 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
several limitations. Steenge and Bočkarjova described a “basic equation” that can be rep-
resented as
∼
𝐱
=
(
𝐈−

𝛄
)𝐱
– where the potential maximum production of each sector of the
impacted economy
∼
𝐱
takes into account the event matrix of losses placed on the diagonal,
gamma hat (

𝛄
), of proportionate production losses that shinks the pre-disaster economy
𝐱
,
so that losses in total output are the difference between this new output level and the initial
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output (Bočkarjova etal. 2004; Steenge and Bočkarjova 2007). Total output is determined
according to standard IO anaysis:
𝐱=(𝐈−𝐀)−1𝐲
, where
𝐈
is an identity matrix of 1’s on the
diagonal and 0’s elsewhere; the
𝐈
matrix is the same dimensions as the direct requirements
matrix
𝐀
to which it relates, which is a “production recipe” of the selected sector group-
ings comprising the economy, with the inputs into each sector are a fraction adding up to
$1 (or other monetary unit as relevant) worth of production;
(𝐈−𝐀)−1
being the famous
Leontief inverse
𝐋
that includes the entire supply chain, and demand from end-consumers
𝐲
(also referred to as final demand or consumption) being equal to
(𝐈−𝐀)𝐱
. 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 (
𝐲
) values, this method assumes that very small, marginal inputs to
production, in the
𝐀
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
𝐀
are reduced to zero to ensure the post-event
final demand sectors in
𝐲
are either zero or positive.
An alternative to removing marginal inputs in
𝐀
was developed by Faturay etal. (2020a)
because small values may nonetheless be important in complex supply-chain interactions;
this approach uses optimisation coding in MATLAB to determine the maximum total out-
put losses. Given the disaster “basic equation”
∼
𝐱
=
(
𝐈−

𝛄
)𝐱
, with
𝐱=(𝐈−𝐀)−1𝐲
, the opti-
misation approach to determining the post-disaster economy enables changes in intermedi-
ate demand (
𝐀
), final demand
𝐲
and total output (
𝐱
) in order to meet the constraints of no
negative values in final demand (
𝐲
). 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-dis-
ruption” 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. (2020a) described above, because of
its application to numerous recent disasters.
Data Collection
Tourism Research Australia (TRA) published a range of data in the National Vis-
itor 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 expendi-
ture 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 tour-
ism 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 corona-
virus were included. In order to disaggregate losses by sector, the proportionate rep-
resentation of tourism- and tourism-related sectors from TRA’s 2018-19 State Tour-
ism Satellite Accounts (TSA) (Austrade & Tourism Research Australia 2020) was
applied to the 2019-20 losses (Austrade & Tourism Research Australia2021). The UN
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World Tourism Organization and Australian Bureau of Statistics definitions of tour-
ism 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 etal. 2020a; 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 Accom-
modation 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 (
𝛄
) of direct losses and capital damages.
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 quanti-
fied 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 (http:// 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 Statis-
tical Area level 2 (SA2) regions (Australian Bureau of Statistics 2011). A concord-
ance 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 etal. 2014a) was Australia’s first such collaborative cloud-based platform for
IO analysis. Such platforms overcome the time-consuming nature of MRIO compila-
tion 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 etal. 2020a), China (Wang 2017) and the United States (Faturay etal.
2020b), 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 Sus-
tainable Consumption and Production Hotspot Analysis Tool (Lenzen etal. 2022).
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Table 1 The gamma matrix (
𝛄
). The gamma (event) matrix comprises direct tourism losses from Austral-
ia’s 2019-20 bushfires as a proportion of total output (
𝐱
) 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. 2020a; Lenzen etal. 2019. Regional abbreviations: New South Wales (NSW), Victoria (VIC),
Queensland (QLD), South Australia (SA), Western Australia (WA), Tasmania (TAS), Australian Capital
Territory (ACT), Northern Territory (NT)
NSW VIC QLD SA WA TAS ACT NT
1 Grapes - wine 0 0 0 0 0 0 0 0
2 Apples, pears and 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
00000000
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 equip-
ment
00000000
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 ser-
vices;
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 busi-
ness services
00000000
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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 compi-
lation 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
(
𝐱
) and final demand (
𝐲
).
The change in final demand (
𝛥𝐲)
is used to calculate the employment and income impacts
of the disaster using the formula
𝐪𝐋𝐲
, where
𝐪
is the multiplier derived by dividing the desired
indicator by pre-disaster total output
𝐱
, and
𝐋
represents the entire supply chain
(𝐈−𝐀)−1
.
Income data is extracted from the first row of the value-added block of the MRIO (
𝐯
), while
the aggregated total employment data is contained in the 24th row of the employment satellite
account in the IELab. In both instances, the
𝐪
matrix is calculated using element-wise multi-
plication so that the intensity sectors/multipliers align with the final demand sectors, enabling
production layer decomposition (PLD) for a disaggregated view of impacts. The entire impact
𝐪𝐋𝐲
is unravelled at each production layer through the following decomposition:
where
𝐪𝐲
is the direct impact (first layer in the PLD),
𝐪𝐀𝐲
is the additional impact from
direct suppliers involving the
𝐀
matrix of direct requirements,
𝐪𝐀2𝐲
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.
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 tour-
ism expenditure losses from the National Visitor Surveys and International Visitor Sur-
veys that were carried out from 21 January to 15 March (personal communication, 15
& 17 June, 2022), which asked tourists about the extent to which their changed behav-
iour 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
𝐪𝐲 +𝐪𝐀𝐲 +𝐪𝐀
2
𝐲+𝐪𝐀
3
𝐲+…+ 𝐪𝐀
n
𝐲
Layer
1
Layer
2
Layer
3
Layer
4
… Layer n
Table 1 (continued)
NSW VIC QLD SA WA TAS ACT NT
36 Government administration and
defence
00000000
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
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losses. As well, direct bushfire damages data were only available for regions, not sectors,
apart from 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 $112.6mil-
lion in depreciated infrastructure (in other words, $1742million direct losses for the year,
including infrastructure). In terms of regional breakdown, these direct losses (including infra-
structure) 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 $2801.6million in total output, $1561.8million in consumption, $809.4mil-
lion in income, and employment losses of more than 7292 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 indica-
tors measured in this study. Figure 1 shows that almost half of the 7292 jobs lost were
experienced in NSW, the most populous State, which is also where the fires were worst.
Figure1shows that although most of the 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 1499 and 1430 respectively, and SA experienced about one sixth of NSW’s
losses, at 516, followed by WA at 479. Tasmania, despite not suffering direct tourism dam-
ages, lost 13 jobs because of supply-chain impacts resulting from the contraction in con-
sumption. The ACT lost substantially more jobs (110), closely followed by the NT (75
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.
The “Accommodation” and “Cafes, restaurants and take-away” study sectors, which
make up our aggregated hospitality grouping, together suffered the most losses in the
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consumption, income and employment indicators. However, disaggregating into our 39
individual sector groupings in this study, the aviation-led “Air, water and other trans-
port” sector was the worst-hit in consumption and income (but not in employment, where
“Accommodation” dominated), losing $292million and $159million respectively.
NSW shouldered the most losses, including 44% of both consumption and 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 ser-
vices” across consumption, income and employment (reducing by $148million, $73mil-
lion and 662 jobs respectively), although when considering “Accommodation” and “Cafes,
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 3171 full-time jobs. The North-
ern Territory (NT), although not suffering catastrophic fires, nonetheless lost 75 jobs from the short-term
shock. Nationwide, more than 7292 jobs were lost. Other regions: Victoria (VIC), Queensland (QLD),
South Australia (SA), Western Australia (WA), Tasmania (TAS), Australian Capital Territory (ACT)
Fig. 2 Losses in consumption, income and employment, across Australia’s eight States and Territories and
disaggregated by broad sector groupings, because of the tourism shutdown from the 2019-20 bushfires.
Hospitality and transport suffered the most losses. Regional abbreviations: New South Wales (NSW), Vic-
toria (VIC), Queensland (QLD), South Australia (SA), Western Australia (WA), Tasmania (TAS), Austral-
ian Capital Territory (ACT), Northern Territory (NT)
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restaurants and take-away” together as hospitality, the hospitality industry dominated
NSW consumption, income and employment losses. 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 depreci-
ated - 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. These include: VIC suf-
fered more losses than QLD in consumption but the reverse was the case for related income
and employment, with a key driver being greater losses in VIC’s consumption of educa-
tion and training ($37 million loss compared to $20 million). As well, WA experienced
more losses than SA in consumption ($118million compared to $102million) and income
($60 million compared to $52 million) but not in employment (479 jobs lost compared
to 516), where SA suffered particularly more losses in hospitality (229 jobs compared to
179. SA hospitality 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
impacted; for example, the NT had the smallest population in Australia in March 2020,
at 245,400 people, which was more than half the TAS population of 539,600 (Australian
Bureau of Statistics 2020), however, the NT lost more income, at $9.3million compared to
$1.4million. 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.
Figure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.
The least-impacted regions tended to have a longer tail of losses because of supply-
chain spillovers. In comparison, the most-impacted regions, the 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.
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 Aus-
tralian Capital Territory and Tasmania. Additional disaggregation for regions and sectors
and the underlying data are in Online Resource 1 SI 4.
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ACT
The dominance of losses in accommodation and the aviation-led transport sector in Aus-
tralia’s capital may in part be explained by the “fly-in-fly-out” economy(Fig.4).
The ACT experienced the biggest income impact in the transport industry, led by the
“Air, water and other transport” sector, which lost $3.4 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 substantially more
employment losses than other sectors, equivalent to 30 jobs.
TAS
Despite Tasmania not experiencing an overall loss in direct tourism expenditure, losses
from other regions flowed throughout Tasmania’s economy.
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
Fig. 3 Production layer decomposition (PLD) of regions (a) and broad sector groupings (b). Layer 1 rep-
resents 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. Regions: New South Wales (NSW); Victoria (VIC);
Queensland (QLD); South Australia (SA); Western Australia (WA); Tasmania (TAS); Australian Capital
Territory (ACT); Northern Territory (NT)
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accumulate in upstream layers of production, particularly in the services sectors. TAS’s
employment indicator is notable because although the manufacturing industry in aggregate
was substantially impacted in TAS, out of the 39 sectors in this study, the “Accommoda-
tion” hospitality sector suffered the most, experiencing 2 job losses 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 peo-
ple (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 Aus-
tralia 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
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 group-
ings. Although the number of upstream supply-chain interactions is theoretically infinite, results are illus-
trated for up to 10 orders of production, which comprise most of the losses
Fig. 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 equiva-
lent (FTE) employment, up to 10 orders of production
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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 statis-
tics include not only holidaymakers but also the large and growing area of air travel gen-
erally. 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2020).
With bushfires/wildfires increasing compared to other natural hazards (Handmer etal.
2018) and natural hazards generally projected to intensify 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, that take account of supply-chain spillovers. As our
research demonstrates, by including the entire supply chain, using IO analysis, total output
losses identified were a 61% increase on top of the direct damages.
Our highly disaggregated results enabled us to identify differences in impacts along sup-
ply chains, which was important because the impacts were unevenly distributed. For exam-
ple, in addition to the key industries of transport, hospitality and cultural and recreational
services, the education sector was significantly impacted. Therefore, 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 because of increas-
ing natural hazards in particular regions or perceptions that Australia may be unsafe. As
well, although we found that the hospitality industry was the most affected overall, with
“Accommodation” experiencing the most job losses, followed by “Cafes, restaurants and
take-away”, in terms of consumption and income, the aviation-led “Air, water and other
transport” sector suffered the most out of the 39 sectors that we studied, indicating that
different approaches may be taken 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 vulner-
able 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 ser-
vices”, largely because 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 (see
Online Resource 1 SI2.6 Summary of infrastructure damage). These infrastructure losses
are borne by the State governments and their repair, which would be paid by public fund-
ing, 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
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municipalities. Therefore, natural hazards may increase economic inequalities, with the
burden 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 destina-
tion could become permanently damaged in the longer term under global warming, with
fewer people travelling in Australia in our peak summer holiday season; similarly, peo-
ple may start to avoid other countries and regions that are increasingly in the media for
their wildfires and other natural hazards. 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 econo-
mies to worsening climate-fuelled natural hazards 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 cal-
culate 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 Austral-
ia’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 bush-
fires, in terms of reduced tourism expenditure during the peak holiday season, also trig-
gered a reduction in income of $809million and the shedding of 7292 full-time-equivalent
jobs nationwide. Hospitality and the emissions-intensive transport sector were the most
impacted, the latter of which is one of Australia’s top exports. In particular, the accommo-
dation sector within hospitality stood out as suffering the most job losses.
By incorporating the entire supply chain, using IO analysis, we calculated significant
spill-over costs, with total output losses being an increase of 61% on top of the direct dam-
ages identified. As well, invisible impacts of the fires were identified, such as in Tasma-
nia, which did not suffer a direct loss in tourism but nonetheless lost 13 jobs overall, with
its manufacturing sectors among the most impacted, because of tourism losses suffered in
other parts of the country.
This sort of comprehensive supply-chain analysis is particularly important not only in
Australia but also in other nations in the disaster-prone Asia Pacific, and other regions vul-
nerable to fires worldwide. In line with findings that fires are increasing disproportionately
because of global warming, countries that rely on nature-based tourism are among those
with the most to gain from climate-change mitigation but these countries must also prepare
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for the impacts to community-and-industry hotspots that will result from future fires, in an
era that has been referred to as the Pyrocene.
Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s41885- 024- 00142-8.
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.
Author Contributions V.R. and A.M. conceptualised the study; V.R. performed scenario analysis and
drafted the manuscript, which was initially reviewed by A.M. and M.L. Coding for the model was designed
by N.L.P and A.M. Y-Y.S advised on tourism calculations and all authors edited the manuscript.
Funding Open Access funding enabled and organized by CAUL and its Member Institutions This work was
financially supported by the Australian Research Council (ARC) through its Discovery 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 pro-
ject (NeCTAR) through its Industrial Ecology Virtual Laboratory VR201. NeCTAR are Australian Govern-
ment 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 Scholar-
ships. Manfred Lenzen was financially supported by the Hanse-Wissenschaftskolleg in Delmenhorst, Ger-
many through its HWK Fellowships.
Data Availability The datasets generated and analysed during the study are available from the corresponding
author on request.
Declarations
Artwork and Illustrations Tableau was used to create Fig.1 and MATLAB was used to create all other fig-
ures.
Competing Interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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