Integrated Assessment of
Climate Change Impacts
Report on Methodology and
Workshop held at the ANU 3-4 July 2005
16 June 2006
M.F.Hutchinson1, S.Dovers1, R.Letcher2, J.Lindesay3, F.P.Mills1,
J.Sharples1
1
Centre for Resource and Environmental Studies
2 Integrated Catchment Assessment and Management Centre
3 School of Resources, Environment & Society
1
Executive Summary ................................................................................................................ 4
1 Climate change .............................................................................................................. 10
1.1
Climate changes in the historical record ............................................................... 10
1.2
Future changes in Australian Climate ................................................................... 11
1.3
Abrupt nonlinear climate changes ......................................................................... 13
2 Potential impacts of climate change .............................................................................. 14
2.1
Water resources ..................................................................................................... 14
2.2
Biodiversity ........................................................................................................... 15
2.3
Agriculture............................................................................................................. 16
2.4
Human health ........................................................................................................ 17
2.5
Forestry .................................................................................................................. 18
2.6
Marine ecosystems ................................................................................................ 19
2.7
Urban centres ......................................................................................................... 20
2.8
Tourism ................................................................................................................. 20
2.9
Cumulative Impacts ............................................................................................... 21
3 Integrated Assessment ................................................................................................... 22
3.1
What is IA? ............................................................................................................ 22
3.2
Why is IA needed? ................................................................................................ 24
3.3
What does IA require? ........................................................................................... 25
3.4
Connections to policy ............................................................................................ 26
4 Integrated assessment of climate change ....................................................................... 28
4.1
Frameworks for integrated assessment of climate change .................................... 28
4.1.1
A policy framework ....................................................................................... 29
4.1.2
A risk assessment framework ........................................................................ 30
4.1.3
The Millennium Ecosystem Assessment framework .................................... 31
4.1.4
A research framework ................................................................................... 32
4.1.5
Comparison of conceptual frameworks ......................................................... 33
4.2
Design issues for an integrated assessment ........................................................... 33
4.2.1
Problem focus ................................................................................................ 33
4.2.2
Project teams and personalities ..................................................................... 33
4.2.3
Communication ............................................................................................. 33
4.2.4
Role of Government and Links with Policy .................................................. 34
4.2.5
Scales ............................................................................................................. 34
4.2.6
Participation................................................................................................... 34
4.2.7
Iterative approaches ....................................................................................... 35
4.2.8
Quality control ............................................................................................... 35
5 Methods to support Integrated Assessment ................................................................... 36
5.1
Roles of models in IA ............................................................................................ 36
5.1.1
Design issues for integrated assessment models ........................................... 38
5.1.2
Modelling Approaches .................................................................................. 41
5.2
Participation........................................................................................................... 44
5.3
Risk Assessment .................................................................................................... 46
6 Selecting methods for use in an integrated assessment ................................................. 48
6.1
Criteria for selecting methods for IA..................................................................... 48
6.1.1
Is the method credible with the scientific community, policy community
and/or the general community? ..................................................................................... 48
6.1.2
Can the method answer key questions underlying the case study or meet the
case study objectives? ................................................................................................... 48
6.1.3
Can the method fit into an appropriate participatory process? ...................... 48
6.1.4
How easily can the method communicate uncertainty? ................................ 49
6.1.5
Cost – how expensive is it to develop, maintain and extend? ....................... 49
6.1.6
Can it be used in training, to build capacity or for social learning? .............. 49
6.1.7
Is it useful for educating a new breed of interdisciplinary scientist? ............ 49
6.1.8
Can the method or results/lessons from the method be transferred to other
case studies/ problems/areas and more broadly? ........................................................... 49
6.1.9
Can it handle multiple and/or conflicting issues? ......................................... 50
6.1.10
Can it be used in a complementary manner with other methods? ................. 50
6.2
A process for Integrated Assessment .................................................................... 50
7 Case Study Selection ..................................................................................................... 52
7.1
General context...................................................................................................... 52
7.2
Key attributes of future work in Integrated Assessment ....................................... 53
7.2.1
Representativeness: drivers and impacts ....................................................... 53
7.2.2
Representativeness: sectors, values and places ............................................. 53
7.2.3
Methodological development ........................................................................ 53
7.2.4
Data availability and institutional capacity ................................................... 54
7.2.5
Utilisation of past and current work .............................................................. 54
7.2.6
Policy and public relevance ........................................................................... 54
7.2.7
International significance and connection ..................................................... 55
8 Matching Criteria & Methods to Assessments .............................................................. 57
8.1
Scope of an Integrated Assessment ....................................................................... 57
8.2
Research approaches & reasons for an IA ............................................................. 58
8.3
Methodologies and methods for integration .......................................................... 58
8.4
Products/outputs and communication ................................................................... 59
8.5
Capacity building .................................................................................................. 59
9 References ..................................................................................................................... 61
Appendix 1. Policy and Integrated Assessment .................................................................... 69
A1.1 Connection to policy............................................................................................. 69
A1.2. Integrated policy assessment ............................................................................... 72
A1.3. Scale and integrated assessment .......................................................................... 74
Appendix 2. Workshop Program and Participants ................................................................ 77
3
EXECUTIVE SUMMARY
The area of integrated assessment of the impacts of climate change, with particular
emphasis on assessing vulnerability and adaptivity of key natural and human systems, is one
of growing international significance. It is now recognised that this “bottom-up” approach
has much to offer in making meaningful assessments of projected climate change, with real
policy impact. Detailed knowledge of impacted systems is just as important as knowledge of
the dynamics of possible future climates in assessing effective adaptation options. It is also
important for informing decisions by managers and policymakers. The approach goes
beyond traditional “top-down” scenario-based approaches, although such approaches
typically form one of several starting points for integrated assessment.
Integrated assessment for climate change is a method to assess potential impacts of climate
change and vulnerability of impacted systems in order to identify and implement effective
adaptation options. It must take account of the cross-sector nature of the impacts of climate
change, since all sectors impacted have significant interactions with other sectors. Ideally it
should be designed so that scientific and engineering knowledge of the impacted sectors can
have real influence on decision making and policy. This means that integrated assessment is
in part a communication process that needs to be continued over a lengthy period.
Bearing in mind that no one institution has comprehensive knowledge in this multi-faceted
area, ways forward need to be informed by appropriate expertise. We therefore proposed
and organised a two day workshop at the ANU on 3-4 July 2005. The aim of the Workshop
was to bring together Australian experts in impacts of climate change and integrated
assessment with key experts in the international integrated climate change assessment
community, in order to inform and facilitate further progress. Since effective integrated
assessment depends on long term cross-sectoral communication between a wide range of
stakeholders, as described above, the Workshop can be seen as an aspect of integrated
assessment in itself. The Workshop was funded by the Australian Greenhouse Office with
additional funding provided by the ARC Network for Earth System Science. The synthesis
of the outputs of this Workshop, combined with an assessment of the current literature,
forms the main body of this Report.
The program and list of attendees for the Workshop are provided in Appendix 2. The
workshop was attended by over 40 invited international and national experts and members
of key state and federal government agencies, with the explicit aim to assess approaches to
integrated assessment of climate change impacts and adaptation options. The intention was
to combine up to date international expertise in this area with Australian expertise in key
sectors including agriculture, natural ecosystems, water resources, human health and marine
ecosystems. This was combined with Australian expertise in risk management. One of the
aims of the Workshop was to strike a balance in the contributors between established
experts and young researchers with emerging expertise. Inclusion of young researchers was
an explicit goal of the additional funding provided by the ARC Network for Earth System
Science.
It was generally agreed that the Workshop was successful in informing attendees and in
raising general awareness in the area. On the other hand it was also agreed that much needs
to be done. It is intended that this report provide a resource document to inform further
progress on integrated assessments of climate change impacts and vulnerability in the
4
Australian context. The key outcomes of the Workshop and our review of the associated
literature are described below according to the main sections of this report.
Climate change
The global mean temperature of the Earth has increased by about 0.6°C since 1900 and the
Intergovernmental Panel on Climate Change has concluded that “most of the observed
(global) warming over the last 50 years is likely to have been due to the increase in
greenhouse gas concentrations”. Australia’s climate is also changing as part of the global
trend, with average temperatures over Australia between 1910-2004 increasing by 0.9°C.
Trends in rainfall are less clear, as Australian rainfall has exhibited substantial variation
over both time and space. Over the past century all-Australia annual mean rainfall has
increased on average with strongest increases over the central, northern and western
portions of the continent and decreases in the southwest region of Western Australia. The
observed decrease in winter rainfall over southwest Western Australia is most likely due to
the accumulated effect of several factors including enhanced greenhouse gas emissions.
CSIRO projections for Australia using up to 13 climate models driven by the IPCC emission
scenarios indicate that by 2030 annual average temperatures will be 0.4 to 2.0°C higher over
most of Australia, with slightly less warming in some coastal areas and Tasmania, and a
potential for greater warming in the north-west. By 2070, annual average temperatures are
projected to increase by 1.0 to 6.0°C over most of Australia with spatial variation similar to
those for 2030.
Projected annual average rainfall changes tend towards a decrease in the southwest and in
parts of the southeast and Queensland. The projected ranges for the tropical north represent
little change from current conditions. Overall, drier conditions are anticipated for most of
Australia over the next century. However, this overall decrease is expected to be
accompanied by an increase in heavy rainfall. This is a result of the shift in the frequency
distribution of daily rainfall toward fewer light rainfall events and more heavy events.
Interannual variability in ENSO leads to major floods and droughts in Australia. This
variability is expected to continue under enhanced greenhouse conditions, though possibly
with greater hydrological extremes as a result of more intense rainfall in La Niña years and
more intense drought during El Niño years.
Mean sea-level is expected to continue to rise into the future due to the thermal expansion of
sea water, melting of glaciers and polar ice-sheets. Simulations suggest that mean sea-level
will rise, relative to the 1990 level, 3-17 cm by 2030 and 7-52 cm by 2070.
The dramatic decline in rainfall in southwest Western Australia and the anomalous drought
in the eastern states may already serve as examples of abrupt climate change in Australia.
Abrupt changes in climate are less foreseeable, provide less time to adapt and have far
greater economic and environmental impacts than gradual warming.
Potential impacts of climate change
An assessment of potential impacts across the sectors shows a high degree of interaction.
Water resources are a critical component of most sectors, particularly agriculture,
biodiversity, human health and urban centres. The existing problems of increasing demand
for water resources are likely to be exacerbated by climate change. Biodiversity is also seen
to be highly cross-sectoral, with a wide range of types land ownership and interactions with
5
many policy areas. These include interactions with agricultural and forestry systems, with
the increasing recognition of the role of farmers in conserving biodiversity. Human health,
urban centres and agriculture all have important interactions relating to climate extremes
and water supply. Tourism and marine ecosystems are also strongly linked.
Common needs arise from these sectors. These include the need to address uncertainty, with
respect to the current climate as well as for projected future climates. This highlights the
role of risk management and adaptive management to minimise the adverse impacts of
climate variability and change. There are also commonly perceived needs for increased
awareness and understanding of potential climate change impacts, for key supporting data
and monitoring, for building human capacity and to address land use change. These raise
issues for policy across a range of scales.
Integrated assessment
Effective environmental management requires the consideration and understanding of
complex interactions between various economic, social and environmental outcomes.
Models or assessments that integrate considerations and understanding developed across a
broad range of fields is required to understand and evaluate the trade-offs required to inform
decision making. Integrated assessment can be defined as ‘the interdisciplinary process of
integrating knowledge from various disciplines and stakeholder groups in order to evaluate
a problem situation from different perspectives and provide support for its solution.
Integrated assessment should support policy and decision processes and should help identify
desirable and possible options. It depends on integrating knowledge about a problem
domain and on understanding of policy and decision making process.
Farrell and Jaeger (2006) define effective integrated assessment as one having a significant
influence on the associated issue domain. This can be satisfied by a wide range of outcomes
such as formulation and evaluation of policy options, improvements in scientific knowledge,
prevention or delay of environmentally harmful actions, establishment of environmental
regulations and enhanced prestige. They identify three key requirements to achieve
effectiveness - salience or relevance (the assessment should address an issue in which users
are interested and be relevant to action that users can actually take); credibility – the
authoritative technical community must find the assessment acceptable; and legitimacy –
(the users must believe that the process respects the rules and norms of relevant institutions
and that the interests of the users have been acknowledged and taken into account).
Integrated assessment for climate change allows for cross sector assessment of the impacts
of climate change, and consideration of the connections between different sections of the
community and environment and the way in which this affects their vulnerability to climate
change impacts. This assessment can be used to identify the extent and nature of risks facing
these community and environmental sectors in light of climate change as well as potential
adaptation or management options that can be used to ameliorate potential negative impacts.
Methods must be developed to serve the needs of other disciplinary components
incorporated in the analysis as well as being robust and defensible in a disciplinary sense.
Methods should be relatively simple while retaining appropriate levels of accuracy and
sensitivity to key assumptions. Simplicity has the additional advantage that assumptions
underlying the assessment can be more easily communicated and discussed with a wide
range of stakeholders.
6
Integrated assessment of climate change
Four conceptual frameworks for integrated assessments were identified as particularly
relevant for assessing the impacts of climate change: a framework for environmental and
sustainability policy, a framework for assessing the risks of climate change, the framework
devised by the Millenium Ecosystem Assessment and a framework for research integration.
This is not an exhaustive list but they illustrate the key components and requirements and
provide a good basis for designing the framework for future integrated assessments of
climate change
There are several design issues relating to integrated assessments. Problem focusing is a key
step in IA and needs to occur before methods or approaches are selected. It needs to include
a substantial stakeholder collaboration component to ensure that problem focus and impacts
of concern have been adequately identified. The parties involved must be able to respect and
acknowledge the contribution from other disciplinary components. This should enhance
participants’ understanding of the interactions between system components and provide
direction for research to fill critical gaps. Since IA aims to influence both science and policy
government plays an important role in the success of IA. The timeframes of science and
policy often conflict. Building trust in the community and ensuring successful public
participation is a process that relies on longer-term relationships. IA must aim to be flexible
to a changing policy and community environment, while allowing for the needs of rigorous
scientific assessment. This is one of the greatest challenges of IA.
A common feature of many successful IA exercises is the use of an iterative approach to the
assessment. Complexity can be added over time as the conceptual framework is enhanced
and changed. In this way, useful outputs are staged over the life of the assessment, the
assessment involves learning by all participants on the nature of the impacts, trust can be
built and the assessment remains flexible to a changing community and policy environment.
Quality control and maintenance of rigorous scientific or policy standards requires
documentation of assumptions and decisions relating to participation, review of science and
assessment approaches by experts and reporting and communication of results and
assumptions in plain English formats.
Methods to support integrated assessment
Models are a common approach applied as part of many integrated assessments. A model is
a description of a complex process or set of processes. It may be purely qualitative, as in
the case of a linguistic model, but more commonly is quantitative and represents
assumptions relating to system behaviour using a set of equations and parameter values.
There is a wide variety of quantitative modelling approaches that are commonly applied in
IA. Models are generally built to satisfy one or more of five main purposes – prediction,
forecasting, management and decision-making, social learning and development of system
understanding or experimentation.
Public participation can be defined as direct involvement of the public in decision-making,
and thus, in developing the tools used to inform decision makers. There are several reasons
for organizing public participation. These include the possibility of more informed and
creative decision making, more public acceptance and ownership of the decisions, more
open and integrated government, enhancing democracy and social learning, the ultimate
objective, to manage issues.
7
Risk assessment has been used as an integrating framework in many sectors including health
and the environment. The basic notion is that risk is defined as the probablility of an
outcome times the severity of its consequence, leading to the potential quantification of risk.
However, useful forms of qualitative risk assessment also exist, and may be more
appropriate where believable probability distributions cannot be assigned to the range of
possible outcomes. The essential notion of risk assessment can be extended to positive as
well as adverse impacts and the characterisation of uncertainty. The role of risk assessment
offers a way of organising knowledge about the world, particularly those aspects that are
difficult to define in precise terms.
Selecting methods for use in integrated assessment
There are several criteria by which potential methods may be judged and selected. These
include credibility in the stakeholder community, ability to meet case study objectives,
appropriateness for participatory processes, ability to communicate uncertainty, expense of
maintenance and further development, capacity for training and social learning, ability to be
transferred to other studies, ability to handle multiple and conflicting issues, and
complementarity with other methods.
Case study selection
The complexity of relevant natural and human systems (encompassing multiple natural
processes, environments, human uses and values) combine to make it impossible and indeed
undesirable to undertake an integrated assessment of all possible impacts and issues relating
to climate change across all relevant scales. It is possible to choose well targeted IA projects
to reflect key spatial or sectoral elements that are likely to be subject to significant effects
from climate change. These need to be chosen carefully and to build on existing studies to
ensure that the integration is the main focus of the assessment. Key criteria for case study
selection and project design include representativeness of drivers and impacts,
representativeness of sectors, values and places, promotion of methodological development,
data availability and institutional capacity, ability to use past work, policy and public
relevance and finally international significance.
Matching criteria and methods to assessments
Scoping of a case study should fall into two phases. The first is a broad assessment of the
need for and general parameters of an IA in the particular region/sector. The second phase
involves consultation and collaboration with stakeholders to determine the focus of the
study, its resourcing, the interests of potential users of information from the study, and to
assess what aspects of interest it will be viable to include in the study (taking into account
constraints such as data availability, timeframes and resourcing). For a regional study a
broad scope is likely to be most useful, including as many aspects as possible (e.g. land use,
ecosystem services, settlement, water issues, health).
Selecting appropriate methodologies and methods is an important component of planning an
IA. Considerations in making that selection include developing a conceptual framework,
incorporating integrative methodologies, using software and other tools that promote
participation and discourse, integrating policy and planning processes and frameworks into
the methodology and using geographic information systems to integrate data and distributed
model outputs.
8
Acknowledgement
The authors gratefully acknowledge the contributors and attendees at the Workshop at the
Australian National University in July 2005. Their contributions have been instrumental in
compiling this report.
9
1
CLIMATE CHANGE
1.1 Climate changes in the historical record
The global mean temperature of the Earth has increased by about 0.6°C since 1900. In 2001,
the Intergovernmental Panel on Climate Change (IPCC 2001)) concluded that “most of the
observed (global) warming over the last 50 years is likely to have been due to the increase
in greenhouse gas concentrations” (IPCC, 2001). There has been an increase in heatwaves,
fewer frosts, warming of the lower atmosphere and upper ocean, retreat of sea-ice and
glaciers, a sea-level rise of 10-20 cm and increased heavy rainfall in many regions. Many
species of plants and animals have changed their location or the timing of their seasonal
responses in ways that provide further evidence of global warming.
As reported by Hennessy (2005), temperature records indicate that Australia’s climate is
also changing as part of the global trend, with average temperatures over Australia between
1910-2004 increasing by 0.9°C (Nicholls and Collins 2005), and with most of this increase
occurring after 1950. Minimum temperatures have increased more than maximum
temperatures in most regions. The frequency of extreme hot events (e.g. hot days and
nights) has generally increased since the mid-1950s, and the frequency of extreme cold
events (eg. cold days and nights) has generally decreased (Hennessy et al., 2004a). The
warming trend over the last 50 years in Australia cannot be explained by the natural
variability of climate and most of this warming is likely due to the increased concentration
of greenhouse gases in the atmosphere (Karoly, 2001).
Trends in rainfall are less clear, as Australian rainfall has exhibited substantial variation
over both time and space. Over the past century all-Australia annual mean rainfall has
increased on average with strongest increases over the central, northern and western
portions of the continent (Collins and Della-Marta 1999; Hennessy et al., 1999; Smith
2004). These increases contrast with the well-known decreases that have occurred in the
southwest region of Western Australia. Since 1950, there has been an increase in rainfall
over the north-western parts of the continent while the southern and eastern parts of the
continent have experienced decreases in rainfall. Natural variability is likely the major cause
behind changes in rainfall, though the observed decrease in winter rainfall over southwest
Western Australia is most likely due to the accumulated effect of several factors including
enhanced greenhouse gas emissions (Smith, 2004; Timbal, 2004).
Changes in the evaporative demand of the atmosphere over Australia have recently been
analysed. Roderick and Farquhar (2004) reported that over the last 30 years pan evaporation
has experienced a continental-scale decrease. This is in keeping with similar studies
conducted in the northern hemisphere (Peterson et al., 1995; Chattopadhyay and Hulme,
1997; Thomas, 2000; Moonen et al., 2002). On a more regional basis, however, the
northwest of the continent has experienced decreases in pan evaporation while the eastern
portion of the continent has experienced mild pan evaporation increases (Sharples et al., in
prep.). The overall decrease in pan evaporation is generally thought to be due to a decrease
in solar radiation caused by increased cloud and/or aerosol concentration (Peterson et al.,
1995); Roderick and Farquhar, 2002, 2004; Linacre, 2004; Liu et al., 2004), although
rainfall-evaporation complementarity seems to better account for regional trends and finer
scale temporal behaviour (Brutsaert and Parlange, 1998; Thomas, 2000; Golubev et al.,
2001; Sharples et al., in prep.). Unfortunately, the pan evaporation network in Australia
prior to 1970 does not support a detailed analysis. Hence the pan evaporation data record is
10
too short to conclude that the observed changes in evaporation are a consequence of
anthropogenic climate change.
Observed changes in other facets of the Australian climate include:

Extreme daily rainfall observations shows a significant decrease in both the intensity of
extreme rainfall events and the number of extremely wet days in the far southwest of
Australia and an increase in the proportion of rainfall falling on extremely wet days in
the northeast (Haylock and Nicholls, 2000). An exception to this rule is southwest
Western Australia, where there has been a marked decline in mean rainfall and a 15%
decrease in heavy rainfall intensity during winter (Hennessy et al., 2004a).

The frequency of tropical cyclones in the Australian region has decreased since 1967,
along with an increase in cyclone intensity (Nicholls et al., 1998); (Hennessy et al.,
2004a). The trend is gradual and largely follows the downward trend in the Southern
Oscillation Index, since fewer cyclones occur in the Australian region during El Niño
years (Kuleshov, 2003).

Nicholls (2004) indicates that Australian droughts have become warmer over the second
half of the 20th century and that the severity and impacts of drought are enhanced by
increased evaporation and evapotranspiration associated with higher temperatures. The
recent 2002 El Niño drought was the worst on record with average Australian rainfall
between March and November the lowest ever during this period (Karoly et al., 2003).

Between 1950-2000 global-average sea-level has risen by 1.8 ± 0.3 mm per year
(Church et al., 2004).
1.2 Future changes in Australian Climate
This section is based on Hennessy (2005). Computer models of the climate system, based on
representations of the dynamics of the atmosphere, oceans, biosphere and polar regions are
the best tools available for simulating climate variability and change. A detailed description
of these models and their reliability can be found in IPCC (2001). To estimate what can be
expected of climate change into the future, these computer models are used in conjunction
with greenhouse gas and aerosol emission scenarios. The emission scenarios are not
predictions of what will actually happen but allow analysis of the likely impacts of assumed
human activities, economic growth and technological change. Climate change projections
for the Australian region are based on emission scenarios developed by the IPCC that are
described in the Special Report on Emission Scenarios (IPCC, 2000). These scenarios
assume “business as usual” without explicit policies to limit greenhouse gas emissions,
although some scenarios include other environmental policies that indirectly affect
greenhouse gases, for example, policies to reduce air pollution. Finding appropriate
methods to incorporate more general socio-economic factors into climate change projections
poses one of the main challenges in the integrated assessment of climate change impacts.
Climate projections are presented as ranges rather than as single values. The ranges
incorporate quantifiable uncertainties associated with future emission scenarios, global
climate sensitivity, and model-to-model differences in the regional patterns of climate
change. Global climate sensitivity is defined as the simulated global warming for a
11
doubling of carbon dioxide concentration. Regional climate projections on a 25km grid for
Australia, based on the IPCC (2000) emission scenarios can be obtained from the OzClim
model (CSIRO 2005).
CSIRO projections for Australia using up to 13 climate models driven by the IPCC(2000)
emission scenarios indicate that by 2030 annual average temperatures will be 0.4 to 2.0°C
higher over most of Australia, with slightly less warming in some coastal areas and
Tasmania, and the potential for greater warming in the north-west (CSIRO, 2001). By 2070,
annual average temperatures are increased by 1.0 to 6.0°C over most of Australia with
spatial variation similar to those for 2030. The range of warming is greatest in spring and
least in winter. In the northwest, the greatest potential warming occurs in summer.
Projected annual average rainfall changes tend towards a decrease in the southwest and in
parts of the southeast and Queensland. In some other areas, including much of eastern
Australia, projected ranges suggest large changes in rainfall, though it is less clear whether
they will tend towards a decrease or an increase. The projected ranges for the tropical north
represent little change from current conditions. Previous studies generally agree that
Australian rainfall will decrease on average in most regions (CSIRO, 1996; Hulme and
Sheard, 1999). Exceptions to this trend are southern Victoria and Tasmania in winter and
eastern Australia in summer where rainfall is not expected to change significantly from
current conditions.
Overall, drier conditions are anticipated for most of Australia over the next century.
However, this overall decrease is expected to be accompanied by an increase in heavy
rainfall. This is a result of the shift in the frequency distribution of daily rainfall toward
fewer light rainfall events and more heavy events (McCarthy et al., 2001).
Model simulations incorporating enhanced greenhouse conditions suggest that extreme
rainfall will increase in mid-latitudes, where average rainfall increases, or decreases slightly
(IPCC, 2001). In addition to changes in intensity, mid-latitude storms may also change their
frequency and location in response to changes in the westerlies and the Southern Oscillation
(McCarthy, et al., 2001). Potential increases in the intensity of 1-in-20 year daily rainfall
events have been projected for parts of South Australia (McInnes et al., 2003), for some
NSW regions (Hennessy et al., 1998) and for Victoria (Whetton et al., 2002). Studies
focussing on Queensland suggest increases of up to 30% by 2040 in the southeast (Abbs
2004), and an increase in 1-in-20 year daily rainfall intensity of 25% in northern Queensland
(Walsh et al. 2001). Decreases in extreme rainfall are likely in the Sydney region (Hennessy
et al., 2004b).
The locations of tropical cyclone genesis in the Australian region are correlated with ENSO
(Evans and Allan, 1992; Basher and Zheng, 1995) so any change in the mean state of the
equatorial Pacific may affect the incidence of tropical cyclones in particular locations. IPCC
projections suggest that by 2070 tropical cyclone frequency may change in some regions,
peak wind speeds may increase by 5-10% and peak rainfall intensities may rise by 20-30%
(IPCC, 2001).
Interannual variability in ENSO leads to major floods and droughts in Australia. This
variability is expected to continue under enhanced greenhouse conditions, though possibly
with greater hydrological extremes as a result of more intense rainfall, and hence flooding,
in La Niña years and more intense drought resulting from higher rates of evaporation during
12
El Niño years (Walsh et al., 2001). A more El Niño-like mean state over the tropical Pacific
would imply greater drought frequency, as does the drying trend found over the MurryDarling basin in recent simulations (Kothavala, 1999; Arnell 1999; Walsh et al., 2001). The
incidence of wildfire in Australia is also expected to increase as the continent becomes more
drought-prone (Beer and Williams 1995; Pittock et al., 1999; Williams et al., 2001).
Simulations of wind-speed show a tendency for annual-average wind-speed to increase by
up to 3% by the year 2030 and up to 12% by 2070. Increases in seasonal wind-speed tend to
be quite widespread in summer and spring, with the largest increases in summer-average
wind-speed expected along the northern and western coasts. There is, however, a tendency
for decreases in summer-average wind-speed in southeast and northern Queensland and
northeast NSW. Autumn-average wind-speed is expected to increase between the latitudes
25-30°S, with a tendency for decreases to the north and south of this band. In winter windspeed is expected to decrease between 30-35°S with a tendency for increase to the north and
south of this band.
Simulations suggest that in the future annual average humidity will tend to decrease over
most of the continent. Seasonally, summer and autumn humidity are expected to decrease by
as much as 3% by 2030 and up to 9% by 2070. However, increases of up to 1.5% and 4% in
2030 and 2070, respectively, are possible in parts of NSW, Southern Queensland, western
Northern Territory and central Western Australia. More significant and widespread
decreases in humidity are expected in winter and spring, reflecting the tendency for
decreased rainfall in these seasons.
Projections for solar radiation are limited by the unavailability of pertinent data and should
therefore be viewed with caution. Annual-average radiation is expected to decrease in the
western-half of Australia with the possibility of increases or decreases in the east. Decreases
in solar radiation are strongest in summer and cover most of the western and southern parts
of the continent. In autumn, decreases in radiation will affect most of Australia while in
winter and spring, increases in solar radiation are expected in the south and east, with
decreases in the northwest.
Mean sea-level is expected to continue to rise into the future due to the thermal expansion of
sea water, melting of glaciers and polar ice-sheets (IPCC, 2001). Local and regional
variations in sea-level rises are likely to occur as a result of land-sea movements and
changes to ocean currents and climate forcing. Simulations suggest that mean sea-level will
rise, relative to the 1990 level, 3-17 cm by 2030 and 7-52 cm by 2070.
For more detailed climate projections for individual States and Territories see (CSIRO
2005).
1.3 Abrupt nonlinear climate changes
Nonlinearities inherent in the functioning of the Earth’s climatic system provide the
potential for abrupt, extreme or irreversible changes in climate. Examples of such changes
that have occurred in the past include the rapid shifts in temperature in the North Atlantic
during the last ice age, the formation of the Antarctic ozone hole, the mid-Holocene shift of
North African ecosystems from savanna to desert and destabilisation of soil carbon under
global warming. The impacts of such events are neither local nor isolated. For example, the
13
shutdown of the North Atlantic thermohaline circulation would have significant
consequences for Australia.
The dramatic decline in rainfall in southwest Western Australia and the anomalous drought
in the eastern states may already serve as examples of such changes in Australia. Abrupt
changes in climate are less foreseeable, provide less time to adapt and thus would have far
greater economic and environmental impacts than gradual warming (Mastrandea and
Schneider, 2001).
2
POTENTIAL IMPACTS OF CLIMATE CHANGE
A comprehensive guide to the potential impacts of climate change has been presented by
Pittock (2003). An assessment of these impacts across the sectors summarised below shows
a high degree of interaction. Water resources are a critical component of most sectors,
particularly agriculture, biodiversity, human health and urban centres. This well illustrates
the role of the water cycle as the great global integrator (White 2005). The existing
problems of increasing demand for water resources are likely to be exacerbated by climate
change. Biodiversity is also seen to be highly cross-sectoral, with a wide range of types land
ownership and interactions with many policy areas (Williams 2005). These include
interactions with agricultural and forestry systems with the increasing recognition of the role
of farmers in conserving biodiversity (Williams 2005; Chesson 2005). Human health, urban
centres and agriculture all have important interactions relating to climate extremes and
water supply (McMichael 2005, Troy 2005, Crimp et al. 2005). Tourism and marine
ecosystems are also strongly linked (Marshall 2005).
Not surprisingly, common needs arise from these sectors. These include the need to address
uncertainty, with respect to the current climate as well as for projected future climates. This
highlights the role of risk management and adaptive management to minimise the adverse
impacts of climate variability and change. There are also commonly perceived needs for
increased awareness and understanding of potential climate change impacts, for key
supporting data, for building human capacity and to address land use change. These raise
issues for policy across a range of scales. It is the role of integrated assessment to address
these cross-sectoral issues.
2.1 Water resources
Growing demands for food and fibre due to expansion of the human population have
increased pressures on freshwater and land resources and their dependent ecosystems. In
Australia, water resources are already highly vulnerable with intense competition for water
supply between agriculture, power generation, urban areas and environmental flows.
Projected climate changes will adversely affect the availability of water in many areas with
consequent impacts on environmental flows, agriculture and other industries. The extreme
variability of the Australian climate, the vulnerability of its ancient landscape and the
hysteretic nature of some aquatic and land systems suggest that climate change may have far
reaching consequences on the quantity and quality of fresh water. The continued impact of
the January 2003 bushfires on fresh water quality and availability in the Canberra region is
but one recent example. This example also highlights the significant impacts jointprobability events can have, in this case the bushfire followed by the intense rainfall
14
constituted a 1-in-400 year event. Catastrophic events such as bushfires also put additional
demands on water availability as burnt forests regrow.
Water quality is also at risk from climate change as many of the microbial processes that
take place within water are temperature dependent. This can then lead to infection of
groundwater systems with bacteria such as ecoli. Estuarine fisheries such as oyster farms in
the Hawkesbury River are also at risk due to climate change, particularly through its effect
on drought. Occurrence of ‘Queensland unknown’ (QX) disease in the Hawkesbury has
been linked to drought incidence. Drought is only part of the problem. Land-use changes are
also important.
The National Water Initiative (NWI) recognises that there are significant knowledge and
capacity building needs for its implementation. These include understanding changes to
water availability and the interaction between surface and groundwater as a result of climate
and land use change. Understanding of the ecological outcomes from environmental flows
and the catchment processes that impact on water quality is also needed. The NWI
recognises that these knowledge gaps are multi-disciplinary, involving the interaction of
scientific, social and economic aspects of water, and extend beyond the capacity of any one
research institution. The consequences of nonlinear changes in freshwater supply systems
are mostly ignored in present water research institution in Australia (White 2005).
2.2 Biodiversity
Climate change has been identified as a major threat to biodiversity and has the potential to
invalidate traditional assumptions about biodiversity management. Even though there is a
lack of long-term data sets and active monitoring programs in Australia, which makes it
difficult to quantify the biological impacts of climate change, there is evidence of thickening
of vegetation in eucalypt woodlands as a result of increased supply of carbon dioxide and
the increased establishment of snow gums in sub-alpine meadows. In the past, natural
climate change has caused large-scale shifts in the geographic ranges of species, the
composition of biological communities and extinctions of species. Natural systems are
expected to respond to anthropogenic climate change in a similar way, but the effect will be
more severe because of the extremely rapid rate of the projected change. Moreover,
destruction of habitat due to human activities will prevent many species from colonising
new habitat when their old habitat becomes unsuitable. The combined effects of climate
change and habitat destruction would threaten many more species than either factor alone
(Peters 1990).
The impacts of climate change will be further confounded by interactions and responses of
invasive species and fire regimes, especially in regions where vegetation and other habitats
are fragmented. Some biological responses to climate change will be nonlinear and may
involve time lags.
Dealing with the pervasive uncertainties associated with climate change and its impact on
biodiversity would include assessing climate change impacts in relation to a variety of other
pressures and forcing factors, including mitigation. For example, mitigation strategies such
as the cessation of broad-scale clearing can have a range of impacts on biodiversity. A
greater understanding is particularly needed about the potential impacts of climate change
and management interventions at the regional level where natural resource management
efforts are focused in Australia.
15
The cross-sectoral nature of biodiversity adds another layer of complexity to adapting to
climate change. Natural systems are owned and managed by governments (e.g. in state
forests and national parks), non-government organisations (e.g. the growing number of
groups buying land for conservation), the agricultural sector (much of the biodiversity in
Australia is found on private land) and by indigenous land managers (whose land often has
limited agricultural value). Additionally, expenditure or economic activity across a wide
range of commercial sectors and policy portfolios affects or is affected by biodiversity.
Policies and programs on regional development, trade, urban planning and taxation can have
an impact on biodiversity. Integration of biodiversity, and the potential impact of climate
change, will be required across a range of domestic government policies, international
agreements and the private sector (Williams 2005).
2.3 Agriculture
The effect of climate change on the distribution, frequency and severity of drought is a
major concern for agricultural industries in Australia. Coupled with the projected changes in
rainfall and the rise in temperature and evaporation there is a high probability that
Australian farmers will need to operate in a drier climate with possibly declining standards
of water quality. Predicting the impacts of these changes is complicated, however, because
the environmental changes interact: increased carbon dioxide boosts plant productivity and
changes water use efficiency, while other changes in climate could offset or even enhance
these benefits, depending on the circumstances (BRS, 2004).
The diversification of on-farm production and the use of seasonal climate forecasting have
served to mitigate the impact of climatic variability on production to some degree. However,
longer term climatic variations on decadal and multi-decadal timescales have yet to be
considered in a fully integrated way. There is a growing realisation that longer term climate
variations (including both natural and anthropomorphic drivers) contribute to the overall
vulnerability of an enterprise. The assessment and management of these aspects of climate
risk remains limited due to the complexity and multi-dimensional nature of the drivers in
question (Crimp et al. 2005).
In southern Australia changes in winter and spring rainfall are likely to increase moisture
stress on wheat crops, even in the face of some carbon dioxide fertilisation (CSIRO 2001).
The positive response of wheat to increased carbon dioxide levels may also be offset by
lower grain protein content. The projected decreases in frost incidence and severity are
likely to result in a reduction of frost damaged fruit, though temperate fruits need winter
chilling to ensure normal bud-burst and fruit set. In northern Australia, where consensus
model projections show little change in simulated summer rainfall (the main growing season
for pastures in that area), there may be positive impacts on plant production.
Since the late 1980s studies have been conducted on potential impacts of climate change on
Australia’s grazing industry and individual components of the climate scenario, such as the
impact on frequency of droughts in extensive grazing industries, impacts on carrying
capacity and heat stress. This poses a problem in developing adaptation responses in the
grazing industry due to the limited commonality of most sensitivity studies. A systematic
approach is required to develop more comprehensive adaptation strategies for the grazing
industry. These should link regional production to location and regional land use so that the
16
climatic impacts on the grazing industry can be calculate and synthesised into a
comprehensive impact analysis (Crimp et al. 2005).
Under enhanced greenhouse conditions there are likely to be direct impacts on agricultural
industries including changes in the productivity of agricultural lands, effects on individuals
and farm businesses including mental health problems, effects on the quantity and quality of
water and changes in the capacity of agricultural lands to support biodiversity conservation.
Indirect, or flow-on effects of climate change on agriculture include impacts on other
biophysical systems through changes in energy use, the human and social capital available
to the industry and contributions of the industry to local and regional communities as well as
the nation as a whole (Chesson 2005).
2.4 Human health
Projected climate changes are likely to have significant effects on human health in
Australia. Expert consensus suggest that the changes in climate variability that will
accompany climate change, especially the frequency, intensity and location of extreme
events, will have much greater health, social and economic impacts than underlying changes
in mean conditions. Future modelling of population health risk will need to take account of
this dimension (McMichael 2005).
Increased thermal stress due to the higher incidence of heatwaves, coupled with an ageing
population, is expected to result in an average of several hundred more deaths annually in
all major cities. Unlike other countries such as the United Kingdom, increases in heatrelated mortality in Australia are unlikely to be offset much by decreases in cold-related
mortality. Increased atmospheric warming is also likely to result in a southward extension of
the transmission zones of vector-borne diseases such as dengue fever, Ross River virus and
malaria (Martens and McMichael 2001). Warming is also expected to increase the viability
of malarial parasites (McMichael 1997). The risk of diarrhoeal disease is also expected to
increase under enhanced greenhouse conditions. This increase will predominantly occur in
summer months and especially in remote and rural communities. Indigenous communities
are particularly prone with research suggesting a 15% increase in diarrhoeal hospitalisations
in Aboriginal children living around Alice Springs by 2030 (McMichael 2003).
Other potential health impacts of climate change include those associated with more severe
inland flooding, increased incidence of depression, suicide and other mental health problems
associated with drought (Butler et al. 2005), increased frequency of food-borne diseases
such as salmonellosis (D'Souza et al. 2004), a higher incidence of skin cancer due to ozone
depletion and general health effects due to shortages of food and water.
The process of integrated assessment of population health, as currently practised, is usually
inherently conservative in that it assumes future smooth changes in average conditions and
in the extent of climatic variability. Abrupt changes and the consequences of passing critical
thresholds are much less easy to foresee and model. This has been well illustrated by the
integrated assessment of climate change impacts on cereal grain yields. This has been based
on physiological models of how temperature and soil moisture affect plant growth. These
models have not been able to take account of a change in pattern of outbreaks of plant pests
and diseases.
17
A more sophisticated approach to the assessment of climate change impacts on human
health would incorporate information about ongoing trends in other determinants of health
outcomes believed to be reasonably extrapolatable (e.g. demographic trends in age
structures), likely future contextual conditions (e.g. uptake of air-conditioning by 2050;
advent of relevant vaccines and likely consequent population immunity level), and
deliberate adaptive changes (e.g. mosquito control programs, heatwave warning systems,
flood protection measures).
Until now there has been minimal attention paid to considering how people and health
systems might respond to climate change, or interact to reduce exposure and enhance
adaptive capacity. The estimation of future health impacts will be improved by an
understanding of the opportunities for (and the natural limits of) adaptive responses. This
work will need to be informed by an investigation of how people have responded to and
managed their vulnerability to past and present climate stresses. Adaptation can be separated
into two categories – planned (activities conducted by health or other government bodies),
and autonomous (individual responses to changing climate). Effective adaptation will need
to consider the effects of the multiple interacting stresses that influence individual and social
adaptive capacity (such as social and economic status, geographic location), in addition to
the driver of climate change itself (McMichael 2005).
2.5 Forestry
Anthropogenic climate change has the potential to impact on forestry in both positive and
negative ways. Greenhouse conditions imply a warmer climate and a more CO2-rich
atmosphere that can actually enhance plant growth and generally increased yields. On the
other hand, model predictions suggest below expected yields of forestry products due to
poor distribution of rainfall and temperature. Climate change is also likely to affect the
timing of harvesting regimes. Sawlogs and peeler logs have a minimum small-end diameter
limit and are harvested soon after the minimum limits have been reached. Climate change
may affect the time it takes to achieve these limits thus altering the product yield if timing is
retained or requiring longer or shorter rotations and cutting cycles (Brack and Richards,
2002; Richards and Brack, 2004ab).
Climate change also has the potential to affect the viability of certain pest and disease
problems leading to catastrophic defoliation or death of forest species. Shugart et al. (2003)
found for the United States that species generally migrate polewards or to higher latitudes in
response to increased temperatures, but that species mix may change and rates of migration
will depend on seed dispersal, the spread of insects and disease and the role of wildlife and
human intervention.
The frequency and severity of drought is projected to increase in southern Australia. Hanson
and Weltzin (2000) argue that drought leads to a net reduction in primary productivity,
increased mortality of seedlings and saplings, and increased susceptibility to insects and
disease. Moreover, drought induced reductions in decomposition rates may cause a build-up
of organic material on the forest floor, with ramifications for fire regimes and nutrient
recycling. Increased drought conditions in Australia are likely to be associated with
increased occurrence of fire (Cary 2002). Altered fire regimes are likely to have a
significant impact on forestry.
18
Howden and Gorman (1999) review the impact of projected global change on Australian
temperate forests. Productivity of exotic softwood and native hardwood plantations is likely
to be increased by carbon dioxide fertilisation, although the amount of increase is limited by
various acclimation processes and environmental feedbacks through nutrient cycling. Where
trees are not water limited, warming may expand the growing season in southern Australia,
but increased fire hazard and pests may negate some gains. Reduced rainfall in recent
scenarios would have an adverse effect on productivity and increase fire risk. Increased
rainfall intensity would exacerbate soil erosion and pollution of streams during forestry
operations (Cary 2002).
2.6 Marine ecosystems
Marine ecosystems are among the most vulnerable to climate change and many are already
showing the signs of impacts that can be attributed to changes in environmental factors that
are consistent with projected climate change. Climate change is expected to result in the
warming of sea temperatures, changes in ocean currents and altered ocean chemistry. Mass
mortalities due to coral bleaching have been reported with increasing frequency from
around the world over the last decades. The Great Barrier Reef has had widespread
bleaching in 1998 and 2002, although it has suffered until now relatively low levels of coral
death. These temperature events are expected to have a wide range of flow-on effects
throughout the reef ecosystem and dependent human communities (Marshall 2005).
Lowered seawater salinity as a result of flooding of major rivers in early 1998 are also
believed to have been a major factor in exacerbating the effects of inshore coral bleaching
(Berkelmans and Oliver 1999).
Many marine species are highly sensitive to small changes in average sea temperature. Over
a prolonged period, changes in average sea temperature of 1-2°C can impact on the growth
rates and patterns of reproduction of certain marine species. Coral bleaching results when
increases in average sea temperature lead to a break down in the symbiotic relationship
between coral and algae living within the coral tissue. Species such as sea turtles, for which
temperature plays a crucial role in determining the sex of young, provide another clear
example of how climate change can affect marine creatures. Changes in sea surface
temperature also correlate with the demise of kelp forests off the east coast of Tasmania.
Temperature changes can also affect the balance between predators and prey, the
susceptibility of organisms to disease, nutrient cycling and other energy flows, with followon effects to fisheries and other species that might not be vulnerable to sea temperature
changes themselves.
The location and timing of ocean currents are an important factor in marine ecosystems.
Currents carry the young of an enormous diversity of marine species and thus play a key
role in their dispersal and the maintenance of populations. Currents are also a major
influence in nutrient transport, bringing nutrient-rich waters to the surface through
upwelling. Climate change is expected to alter ocean circulation patterns, leading to changes
that interrupt the life cycle of many species and impact on local populations. Fisheries are
likely to be affected by changes in the extent and locations of nutrient upwelling. Changes
in ENSO, which influences recruitment of some fish species and the incidence of toxic algal
blooms, are also likely to have an affect on fisheries. Overall, climate change is expected to
lead to changes in productivity of some fisheries, though these have not yet been well
documented.
19
Changes in ocean chemistry can also be expected under enhanced greenhouse conditions,
with a decrease in the availability of carbonate ions a particular concern. Carbonate ions are
essential in the creation of skeletons for many key marine species, such as planktonic
organisms, which are thought to play important roles in ocean-atmosphere interactions
through cloud formation. Changes in ocean chemistry are thus likely to diminish the critical
processes necessary to maintain functioning coral reef ecosystems.
Impact monitoring programs designed to detect changes in marine ecosystems tat might be
attributed to climate change are few and not well coordinated. Many impacts are expected to
manifest gradually (such as shifts in the range of species), and are difficult to detect due to
background natural variation and the effects of other stressors. Other impacts, such as coral
bleaching, are more dramatic but are still difficult to measure due to their unpredictability.
The coral bleaching response program of the Great Barrier Reef Marine Park Authority
(GBRMPA) is complemented by detailed data on critical environmental variables.
GBRMPA is collaborating with Australian and overseas authorities to examine the likely
effects of future climate change on reef ecosystems and dependent human communities.
2.7 Urban centres
Climate change has already effected planning and policy in major urban centres and
projected changes in rainfall and temperature are likely to cause further problems for urban
areas (Troy 2005). Over the last few years, cities in eastern Australia have seen their potable
water supplies diminished significantly and as a consequence harsh water restrictions have
been imposed. While cutting back on garden watering might make a short term saving in
some cities, the irony is that such measures actually increase urban micro-climate
temperatures that in turn result in more households using air conditioning. The associated
increase in energy consumption then directly increases production of greenhouse gases.
Energy consumption is also likely to increase in cities under enhanced greenhouse
conditions due to increased temperatures alone.
Population pressure has caused the reshaping of cities towards an increase in the density of
infrastructure and housing. This again leads to an increase in energy consumption including
that embodied in the buildings and that due to enhanced heat-island effects. Positive
feedbacks through the use of cooling systems ensue. The current conventions and attitudes
to water use and the existing water delivery infrastructure have become impediments to
attempts to reshape consumption and bring it more into line with environmental capacity to
meet demand. New approaches are needed to reshape demand of both water and energy of
Australian cities (Troy 2005).
Urban infrastructure is also potentially at greater risk of extreme weather events such as
hail-producing thunderstorms, lightning strikes and windstorms. Cities in coastal areas are
particularly vulnerable to sea level rises and increased risk of storm surges. If current trends
of increasing migration to coastal settlements and building of infrastructure near beaches
continue, the above-mentioned risks will become more profound. Follow-on effects are
likely to impact sectors such as emergency management and the insurance industry.
Building local resilience as part of the recovery from emergency situations is likely to place
demands on social capital.
2.8 Tourism
20
Tourism is an important industry for many regional centres, as well as for Australia as a
whole, and can be impacted by projected climate changes in a number of ways. Many
northern Australian coastal resorts rely heavily on the attractiveness of the coral reefs, most
notably those associated with the Great Barrier Reef, but also others in Western Australia.
The appearance, and ultimately the function of coral reefs are threatened by global warming
through more frequent coral bleaching events, which can lead to death of corals and their
replacement by algae and weed-based ecosystems that are far less attractive. Damage to
coral reefs due to increases in the intensity of tropical cyclones is likely to exacerbate this
problem (Pittock, 2003). Infrastructure associated with coastal resorts is also susceptible to
the effects of climate change. The impacts of sea level rise and more severe storm surges on
coastal resorts are discussed in McInnes, et al. (2000).
Higher temperatures will accentuate algal blooms. Algal blooms and shoreline erosion
related to sea level rise are also factors impacting upon the tourism industry in other
locations such as the Gippsland Lakes in Victoria (Gippsland Coastal Board, 2002; 2003),
for example.
Several studies have considered tourist preferences in relation to climate, particularly
temperature and thermal comfort. These studies suggest that, subject to some regional
variations, increasing thermal indices and physiological discomfort, and the possibility of
increased risk of tropical cyclones might reduce tourism in some tropical destinations in
Australia, while warmer conditions may make some cooler destinations more appealing
(Pittock, 2003).
The ski industry is also likely to be affected by climate change, with significant reductions
in natural snow depth and snow cover duration. The immediate response of the ski tourism
industry might be to increase artificial snow-making although such a response is likely to
become less viable in the long term (Hennessy, et al., 2003).
2.9 Cumulative Impacts
In many of the above categories of impacts, it may be that crucial vulnerabilities will be
exposed where multiple impacts occur cumulatively, whether different impacts at one time
or cumulation of impacts over time. As Australia is a developed country with highly
evolved institutional, policy and informational systems, many impacts will, despite costs
incurred, not overwhelm coping capacities (although larger-than-expected events and
impacts may). However, a combination of climate-related impacts may challenge coping
capacities – for example, drought and related fire, or storm surge in combination with a hail
event, may impact on multiple areas and assets at a given time. This presents a challenge to
integrated assessment methods, but integrated assessment should by definition have greater
purchase on such possible scenarios than narrower assessment techniques.
21
3
INTEGRATED ASSESSMENT
3.1 What is IA?
Effective environmental management requires the consideration and understanding of
complex interactions between various economic, social and environmental outcomes. Any
management option or decision (including ‘do nothing’) will lead to both costs and benefits
being incurred by different groups and potentially positive and negative impacts on the
environmental system. These costs and benefits may occur over very different time scales,
with many costs associated with environmental damage not being seen for years and in
some cases decades, while some economic and social costs may be more immediate. There
is an increasing awareness of the complexity of evaluating these types of trade-offs to
inform decision-making. In general, models or assessments that integrate considerations
and understanding developed across a broad range of fields are required to understand and
evaluate these trade-offs.
Integrated assessment (IA) can be defined as ‘the interdisciplinary process of integrating
knowledge from various disciplines and stakeholder groups in order to evaluate a problem
situation from different perspectives and provide support for its solution:
 IA should support policy and decision processes
 IA should help identify desirable and possible options.
Hence IA builds on two major methodological pillars:
 Approaches to integrating knowledge about a problem domain
 Understanding of policy and decision making process.’ (Pahl-Wostl, 2004)
Integrated assessment provides a vehicle for addressing all key issues affecting the
sustainability of a system by combining the knowledge and understanding from different
research areas, such as economics, psychology, ecology and hydrology. A better
understanding of the complex interactions occurring within the system must include the
needs and concerns of communities and industries, as well as the environment.
According to Farrell and Jaeger (2006) IA is part of a long term communication process and
needs to be cognisant of the associated issue domain. This includes the principal actors
within the sectors - with a range of interests, resources and beliefs, who seek strategies to
advance their interests in the face of climate change; institutional settings - that regulate
interactions within the sectors and with the world outside; behaviours – the decisions,
policies and agreements that emerge from these interactions; and finally the impacts of these
behaviours on the world - such as enhanced resilience to climate change and improvements
in environmental quality.
Farrell and Jaeger define effectiveness as having a significant influence on the issue domain.
This can include a wide range of outcomes such as formulation and evaluation of policy
options, improvements in scientific knowledge, prevention or delay of environmentally
harmful actions, establishment of environmental regulations and enhanced prestige. Farrell
and Jaeger identify three key requirements to achieve effectiveness:

Salience or relevance – the assessment needs active participation from the organisations
impacted. The assessment must address an issue in which users are interested and be
22


relevant to action that users can actually take. The assessment process must be able to
adapt to changes required by the user community.
Credibility – the authoritative technical community must find the assessment
acceptable.
Legitimacy – political acceptability and fairness. The users must believe that the
process respects the rules and norms of relevant institutions and that the interests of the
users have been acknowledged and taken into account.
IA for climate change has been defined as ‘an interdisciplinary process of combining,
interpreting and communicating knowledge from diverse scientific disciplines in such a way
that the whole cause–effect chain of a problem can be evaluated from a synoptic perspective
with two characteristics: (i) it should have added value compared to single disciplinary
assessment; and (ii) it should provide useful information to decision makers (Rotmans and
Dowlatabadi, 1997)’ (quoted from van der Sluijs, 2002). IA for climate change allows for
cross sector assessment of the impacts of climate change, and consideration of the
connections between different sections of the community and environment and the way in
which this affects their vulnerability to climate change impacts. This assessment can be
used to identify the extent and nature of risks facing these community and environmental
sectors in light of climate change as well as potential adaptation or management options that
can be used to ameliorate potential negative impacts.
Integrated assessments have a number of common features (adapted from Jakeman and
Letcher, 2003).
1. IA is a problem-focussed activity, needs driven; and likely project based. IA is
rarely theoretical. The approach emphasises the importance of learning by case
studies and through applications (frequently project based research). In terms of
climate change, this means that IA is likely to focus on specific geographic or
sectoral issues with an emphasis on learning from the experiences of this assessment
for alternative situations. An IA must also be scoped to issues and impacts of
interest to policy makers and the general community, rather than those of purely
scientific interest.
2. IA links policy to research. IA emphasises the importance of close links between
research and policy. Research questions need to be defined in conjunction with
policy makers to ensure that the focus of research activity is aimed at delivering
information required to inform better policy. Researchers must also be sensitive to
the timeframes of policy makers and the need to provide the best available science in
the time available, rather than attempting to hold policy back until the science (or
data) is considered to be ‘good enough’. IA and policy relating to such complex
issues thus needs to embrace the concept of an iterative and adaptive approach to
both science and policy development to allow for the potential conflict between
scientific and policy timeframes.
3. IA must focus on key elements, and while aiming to be inclusive of a broad view of
impacts and connectivities, must be managed carefully to ensure that the assessment
is feasible. Processes and impacts that are important but for which there is limited
information should be included in the assessment in some form to acknowledge their
importance. Exclusion of these processes can lead to the perception that these issues
are of little interest or importance in the assessment.
23
3.2 Why is IA needed?
In general there has been a trend towards taking account of many different values ecological, cultural, social and economic - in decision making processes (Palmer, 1992;
Syme et al., 1994). Ewing et al. (1997) state that this has been a consequence of the
increasing dissatisfaction that decision makers feel with 'the outcomes resulting from
'narrowly-focussed, incremental, and disjointed' environmental management'. They
maintain that '[i]t is now well-recognised that earlier approaches to environmental
management usually failed to deal with the many interconnections and complexities within
and between, the physical and human environment'. Born and Sonzogni (1995) echo this
view of past approaches to environmental management when considering water resources
stating that '[o]ver the years, much of water resources management has been of limited
purpose, focused on only a portion of the watershed, and implemented incrementally'.
Integrated assessment is a response to the change in policy and research agendas from
compartmentalised treatment of distinct sectors and issues, to the broader and more
integrated agenda of sustainable development (or ecologically sustainable development
(ESD) in Australian policy and law – eg. EPBC Act 1999). In particular, ESD seeks to bring
environmental, social and economic considerations together, and recognises the
interdependence of resource sectors, economic management, and human development. This
demands integrated approaches in research, in policy support methods, and in policy making
processes. Climate change is a major ESD issue, and IA has a clear role in informing policy
considerations, but IA and related approaches are similarly developing in areas such as
catchment management and landscape-wide policy responses to biodiversity loss. It is
advisable to ensure that evolving IA methodological developments in such areas are crossreferenced so as to maximise learning.
Mirroring this integration is the need for integration in modelling and assessment of natural
resource and environmental systems to provide the answers required by integrated system
managers. For example, Park and Seaton (1996) stress the importance of linking scientific
research to policy, and see the need for an integrated approach, particularly with the social
sciences, for making this come about. Geurts and Joldersma (2001) state that 'policy
analysts that use traditional formal modeling techniques have limited impact on policy
makers regarding complex policy problems'. They argue that 'these kinds of problems
require the combination of scientific insights with subjective knowledge resources and
improved communication between various parties involved in the policy problem'. Villa
and Costanza (2000) argue that different modelling approaches need to be integrated into
higher-level simulation models because of the 'increasing complexity and multidisciplinarity
of environmental research and management problems, the spatial and cultural delocalization
of research groups, and the increasing recognition of the need for a multiplicity of scales to
be considered at the same time'.
From a Government perspective IA encourages the use of a cross-portfolio or whole-ofgovernment approach to managing climate change issues. While climate impacts are of
obvious interest to the Australian Greenhouse Office, climate change is likely to have
impacts on a range of interests such as tourism, water availability, agriculture, health,
biodiversity (see Section 1). These will impact on urban and rural communities and
regional economies. Possible adaptations or options to ameliorate these impacts will have
implications for sectors such as mining, transport and energy. Clearly effectively dealing
24
with the impacts of climate change will require considerable cooperation between
Government agencies, across Federal, State and Local government jurisdictions. This will
require development of a shared terminology and understanding of the issues as well as the
actions required. IA is one method for developing such an understanding.
3.3 What does IA require?
The features of IA place certain requirements and restrictions on methods used as part of an
integrated assessment of climate change. Methods must be developed to serve the needs of
other disciplinary components incorporated in the analysis as well as being robust and
defensible in a disciplinary sense. Methods must remain relatively simple while retaining
appropriate levels of accuracy and sensitivity to key assumptions. Simplicity has the
additional advantage that assumptions underlying the assessment can be more easily
communicated and discussed with a wide range of stakeholders, a key requirement of IA.
Given the importance of including key issues where very limited information is available,
assessments often contain components that are based on assumptions or expert or local
knowledge.
Some of the following considerations should commonly arise in integrated assessments of
natural resources management issues:
 Climate variability and episodes – these often have a profound effect on outcomes.
Variability can affect the returns of an investment in production as well as the response
of an ecosystem while episodes such as floods can have an inordinate effect on outputs.
Both raise issues of appropriate time periods and time steps over which to assess
scenarios.
 Representations of process complexity – once the basic processes and causal relations
are decided upon, often there is still much scope for selecting the level of underlying
detail including the spatial and temporal discretisation of process representation in an
IA. Data paucity, especially of system behaviour, should limit the assessment
complexity.
 Beyond business-as-usual scenarios – the nature of environmental or social decline may
mean substantial changes to the current situation are required. Other public and private
investments, policy incentives and institutional arrangements will be needed to change
resource activities.
 Consideration of long leads and time lags – the timeframes for returns on investments
and for ecosystems to respond to changes affect both the period and the temporal
resolution over assessments are made.
 Narrowing assessment objectives – in addition to simplifying types of models, scales,
system boundaries etc., it is critical to keep the level of integration of issues and
disciplines manageable in any integrated assessment exercise.
 Uncertainty – it is desirable to reduce and, where possible, characterise uncertainty; the
latter needs methodological attention by IA researchers.
 System representation – there is a need to balance the extent of the capacity to
characterise feedbacks and interactions with keeping assessment components and
linkages effective but efficient.
 Participation – refers to the inclusion of interest groups, multiple government portfolios
or general community members in the assessment process and is a common approach
used in integrated assessment. Participation aims at encouraging an environment of
‘learning by doing’ within the assessment. This learning is multidirectional –
participation is not solely about educating community, but also acknowledges the
25


importance of the knowledge they bring to an assessment. Participation also aims at
increasing the ownership of communities of the climate change issue as well as of the
decisions required to mitigate impacts. It seeks to increase adoption and acceptance of
final decisions by including a broad representation of government and community
interests in their development.
Government coordination – by its nature IA generally involves a range of issues that
cross various levels of Government interest and responsibility. This requires both crossportfolio cooperation as well as cooperation between Federal, State and Local
Governments. This means that an IA must operate in an environment or changing
government priorities and will rely on a good degree of cooperation between
government interests. Researchers must be sensitive to these needs and must be flexible
in their approach to allow for the challenges this creates. Governments must commit to
the demanding task of this cooperation, committing time and other resources to ensure
its success.
Teams and communication – IA requires the use of experts from specific fields as well
as generalists capable of thinking across a broad range of issues. Teams which
undertake IA will represent a range of scientific, community and government interests.
One of the challenges of IA is communication within these teams. This involves the
development of a shared language and understanding of the problem. It also requires
team members develop trust and respect for each other skills and expertise. This can be
very time consuming and also means that the choice of team members is key to the
success of IA.
3.4 Connections to policy
Integrated assessment is essentially a science focused on the aims and needs of policy and
policy makers. Appendix 1 provides a detailed discussion of the connections between IA
and policy. This section provides a brief summary of key points in this discussion.
A comprehensive approach to policy making includes four key phases: problem framing;
policy framing; policy implementation; and policy monitoring and evaluation. This
approach is described in more detail in Section 4 (as a potential IA framework). IA has a
clear role to play in the problem framing component of policy development and to a lesser
extent can play a role in policy monitoring and evaluation. Thus it is possible to embed IA
directly in the policy development and implementation process.
Four different types of policy learning can also be identified: instrumental learning;
government learning; social learning and political learning. It is likely to IA adds
principally to the process of social learning, which explicitly seeks to redefine problems and
goals, considering how useful constructions of policies and goals are. It is also possible that
IA will be used for instrumental learning, where learning is focused on the critique of
instruments in achieving goals. It is also possible that IA will lead to political learning in
some situations, where political actors learn about the most effective ways to engage with
and influence political and policy processes. Government learning is an unlikely outcome
from IA, given that it is focused on understanding how well administrative arrangements
and processes have allowed policy implementation.
Finally IA has the potential to aid in interagency and cross-sectoral integration of policy
initiatives. While this is one of the benefits of an IA approach, it must be said that
26
achieving such integration will still be very difficult and will require substantial resources
and commitment across agencies for this to be potential to be achieved.
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4
INTEGRATED ASSESSMENT OF CLIMATE CHANGE
As discussed in Section 3, the key distinguishing elements of Integrated Assessment are
cross-sectoral integration and a high degree of involvement by “stakeholders.” The degree
of integration, the type of integration, and the level of involvement by “stakeholders” in the
assessment process must be determined for each assessment. This section provides an
overview of some of the conceptual frameworks that have been devised to describe
integrated assessment processes relevant to climate change some of the approaches or
paradigms used for these assessments, and some of the most common tools (e.g. models)
used.
4.1 Frameworks for integrated assessment of climate change
Four conceptual frameworks for integrated assessments were identified as particularly
relevant for assessing the impacts of climate change: (1) a framework for environmental and
sustainability policy (Dovers, 2005), (2) a framework for assessing the risks of climate
change (Jones, 2001), (3) the framework devised by the Millenium Ecosystem Assessment
(MEA, 2005) and (4) a framework for research integration (Brinsmead, 2005). This is not
an exhaustive list of the frameworks that are relevant to integrated assessment of climate
change but they illustrate the key components and requirements and provide a good basis
for designing the framework for future integrated assessments of climate change. A brief
overview of each follows.
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4.1.1 A policy framework
The process of creating public policy is integrative and ‘stakeholder’ participation is a key
feature. Consequently, the policy making process provides one important conceptual
framework for integrated assessment of climate change. A summary of the four steps of the
policy making process is shown in Figure 1.
I. PROBLEM FRAMING
1. Discussion and identification of relevant social goals
2. Identification and monitoring of topicality (public concern)
3. Monitoring of natural and human systems and their interactions
4. Identification of problematic environmental or human change or
degradation
5. Isolation of proximate and underlying causes of change or
degradation
6. Assessment of risk, uncertainty and ignorance
7. Assessment of existing policy and institutional settings
8. Definition (framing and scaling) of policy problems
II. POLICY FRAMING
9. Development of guiding policy principles
10. Construction of general policy statement (avowal of intent)
11. Definition of measurable policy goals
III. POLICY IMPLEMENTATION
12. Selection of policy instruments/options
13. Planning of implementation
14. Planning of communication, education, information strategies
15. Provision of statutory, institutional and resourcing requirements
16. Establishment of enforcement/compliance mechanisms
17. Establishment of policy monitoring mechanisms
GENERAL
ELEMENTS:
In policy
process:
- coordination &
integration
- public
participation
- description &
communication
- transparency &
accountability
In institutional
arrangements:
- persistence
- purposefulness
- informationrichness &
sensitivity
- inclusiveness
- flexibility
IV. POLICY MONITORING AND EVALUATION
18. Ongoing policy monitoring & routine data capture
19. Mandated evaluation and review process
20. Extension, adaptation or cessation of policy and/or goals
Figure 1.
Detail of framework for analysis and prescription of environmental and
sustainability policy (adapted from Dovers (2005))
_____________________________________________________________________
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4.1.2 A risk assessment framework
Risk assessment and management is another common integrative process that can
incorporate ‘stakeholder’ participation, so it offers a second framework for integrated
assessment of climate change. A summary of the risk assessment process as it may be
applied to climate change is shown in Figure 2 from Jones (2001). IPCC is the
Intergovernmental Panel for Climate Change and FCCC is the United Nations Framework
Convention for Climate Change. This framework was developed on the basis that the
impact of climate change can be defined via thresholds which demark significant changes in
biophysical or behavioural states (Jones, 2001). It was adapted from the risk assessment
framework for impact assessment to incorporate “stakeholder” participation at most stages
of the risk assessment and management process (Jones, 2001).
Planned
Adaptation
Autonomous
Adaptation
F
C
C
C
I
P
C
C
Stakeholders
Risk
Analysis
Key Climate
Variables
Scenarios
Sensitivity
Analysis
Thresholds
Figure 2.
Risk assessment framework for assessing climate change impacts (from
Jones, 2001)
This framework shows stakeholders as central to the IA process, with stakeholders feeding
into all steps of the assessment. The IPCC is a scientific body. This framework shows this
scientific group as identifying key climate variables in conjunction with stakeholders. From
these variables scenarios are identified, sensitivity analysis is undertaken and thresholds are
identified. These then feed into a risk analysis. The potential for autonomous adaptation is
assessed, and planned adaptation recommended. These planned adaptations are fed back as
policy to the FPCC, the United Nations policy body on climate change.
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4.1.3 The Millennium Ecosystem Assessment framework
The conceptual framework for the Millennium Ecosystem Assessment, shown in Figure 3,
places human well-being as its central focus while recognizing that biodiversity and
ecosystems have intrinsic value and all three are considered when people make decisions
concerning ecosystems (MEA, 2003). The framework assumes people and ecosystems
interact dynamically and recognizes that each also interacts with external factors
independently of the other (MEA, 2005). The framework also recognizes that a multi-scale
approach is necessary (MEA, 2005).
GLOBAL
REGIONAL
LOCAL
Human well-being and poverty reduction
• Basic material for a good life
• Health
• Good social relations
Indirect drivers of change
• Demographic
• Economic (eg. Globalisation, trade, market
and policy framework)
• Security
• Sociopolitical (eg. Governance, institutional
and legal framework)
• Freedom of choice
• Science and technology
• Cultural and religious (eg. beliefs, consumption
choices)
Drivers of change
Ecosystem services
• Changes in local land use and cover
• Provisioning (eg. food, water, fiber, and
fuel)
• Species introduction or removal
• Regulating (eg. climate regulation, water
and disease)
• External inputs (eg. fertiliser use, pest control,
and irrigation)
• Cultural (eg. spiritual, aesthetic,
recreation, and education)
• Harvest and resource consumption
• Technology adaptation and use
• Climate change
• Supporting (eg. primary production,
• Natural, physical and biological drivers (eg.
evolution, volcanoes)
soil formation)
Strategies and interventions
LIFE ON EARTH - BIODIVERSITY
short term
long term
Figure 3.
Integration framework of the Millennium Ecosystem Assessment (from
MEA, 2003)
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4.1.4 A research framework
The final framework presented is a framework for integrated assessment as a research
concern. Figure 4 shows integrated assessment as the integration of three main components:
an integrated description of the problem; an integrated set of adaptation of mitigation
options and an integrated evaluation of options. It emphasises that this integration process
must occur within its own socio-political environment. This includes social, political and
institutional environments. Brinsmead (2005) also emphasises that due to finite resources
an ‘iterative bootstrapping’ process must be applied, which uses existing understanding and
incorporates this as part of the assessment so that a more detailed and elaborate
understanding may be developed over time. He emphasises that it is not important whether
a process is top-down or bottom-up but whether the resultant description represents relevant
real world features, and whether it does so sufficiently accurately for its purpose.
Figure 4.
Research framework for Integrated Assessment of Climate Change,
adapted from Brinsmead (2005)
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4.1.5 Comparison of conceptual frameworks
While all the conceptual frameworks put some emphasis stakeholder engagement they are
quite different in the emphasis they place on research and science versus policy
development. The risk assessment framework, for example, focuses early attention on the
IPCC whose role is to assess scientific, technical and socio-economic information on
climate change impacts. Steps outlined are largely scientific or research based leading to
consideration of planned adaptations to climate change. These adaptations then link to the
FCCC, a main policy instrument of the United Nations on climate change. Policy is largely
limited to this final link in the assessment and is not considered explicitly early in the
assessment process. In comparison the policy framework considers integrated assessment to
centre around the policy development process. In this framework research plays a role in
problem framing and monitoring but is not the primary component of the assessment.
Policy framing and implementation are shown to play much more substantial roles. Choice
of an appropriate framework will depend on the emphasis that is placed on policy versus
research and thus the audience for the work. In undertaking any particular integrated
assessment, development of a specific framework to be used in that application may be
necessary to ensure that the expectations of the policy, science and general communities are
in line. The research framework focuses largely on the components of problem description,
identification of adaptations and evaluation of mitigation options. It acknowledges that this
integrated assessment process must be undertaken within a socio-political environment,
including a stakeholder participation setting but puts very little emphasis on describing links
between the assessment process, stakeholders and policy.
4.2 Design issues for an integrated assessment
This section summarises a number of issues relating to the design of integrated assessments.
It is based largely on Letcher and Jakeman (2005), Farell (2005), Farrell and Jaeger (2006)
and Rotmans (2002).
4.2.1 Problem focus
Problem focusing is a key step in IA and needs to occur before methods or approaches are
selected. It needs to include a substantial stakeholder collaboration component to ensure
that problem focus and impacts of concern have been adequately identified. The problem
should be specific enough to get started but general enough to allow reorientation of the
project outcomes as the problem evolves over the life of the assessment.
4.2.2 Project teams and personalities
The ultimate success and lessons leant through an IA will depend critically on the
personalities and aims of those involved in the project. One key requirement of IA is that
the parties involved are able to respect and acknowledge the contribution from other
disciplinary components. Integration should not be about simply linking different
components models or methods. It should not only enhance participants’ understanding of
the interactions between system components, but should also provide direction for research
to fill critical gaps among the disciplinary components of the project.
4.2.3 Communication
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The communication required within the research team and between researchers and
stakeholders is important but extremely time- and energy-consuming. A significant
component of any IA project is communication between these groups. This means that IA
or ISM is not always an appropriate technique for considering management problems.
Where a problem is relatively simple or has a very short time frame, the time necessary to
manage this communication properly means that a simpler, less comprehensive approach
should be used. In general a good rule to live by is that if you don’t intend to pay due
attention to stakeholder views then you shouldn’t ask for them in the first place. A project
that claims to be participatory but that does not allow appropriate time and resources for
building trust between researchers and stakeholders risks alienating, as well as
disenfranchising, stakeholder groups and making future management efforts more difficult.
4.2.4 Role of Government and Links with Policy
IA is science for policy – it aims to influence both science and policy directions through
close collaboration between scientists, community and government. As such it is clear that
government plays an important role in the success of IA. IA must often contend with
restructures of government departments and changes in emphasis or direction of policy.
The timeframes of science and policy often conflict – science usually requires long time
frames to undertake comprehensive assessments. In addition, building trust in the
community and ensuring successful public participation is also a process that relies on
longer-term relationships and trust being built. In contrast, policy timeframes are often
rapid. IA must be developed to be flexible to a changing policy and community
environment, while allowing for the needs of rigorous scientific assessment. This is one of
the greatest challenges of IA.
4.2.5 Scales
If integrated assessment is to engage multiple disciplines and inform policy processes, IA
projects should take into account, respectively, the embedded scales in disciplinary
traditions, and scales of policy responsibility and governance, and not only considerations of
scales informed by natural system functions and modelling techniques. On the first,
disciplines across the natural and social sciences and humanities have very different scales,
spatial and temporal, and cognisance of cross-scale processes, embedded in their theory and
methods, and explicit recognition and exploration of these is necessary in the development
of a specific IA approach (see further Appendix A). On the second, IA needs to negotiate a
balance between scientific rigour and policy relevance in selected scales, at minimum to
ensure that model and other information outputs are congruent with policy responsibilities,
locations of responsible authority and cross
4.2.6 Participation
Public participation is a key component of most IA exercises. There are many choices to be
made in designing a participatory approach. There are different ways of approaching
participation including a variety of methods which can be used to facilitate participation. In
addition for any IA there are many types of stakeholders ranging from industry or interest
groups to individual members of the general public. An IA may engage with different
groups or individuals in different ways for different reasons. These aspects of participation
are discussed in more detail in Section 5.
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4.2.7 Iterative approaches
IA focuses on learning by doing. This learning is on the part of scientists, community and
policy makers. A common feature of many successful IA exercises is the use of an iterative
approach to the assessment. Frequently learning is enhanced by developing a common
shared understanding, or conceptual framework, for the connections between processes,
impacts and values and starting with simple assessment approaches for each of these
components. Complexity can be added to these components over time, the conceptual
framework can be enhanced and changed. Decisions to add these types of complexity over
time should be made on a needs basis, that is, complexity is added where experience shows
the framework or assessment to lack sufficient detail to be useful. In this way, useful
outputs are staged over the life of the assessment, the assessment involves learning by all
participants on the nature of the impacts, trust can be built by experiencing the response of
the project team to concerns over lack of detail and the assessment remains flexible to a
changing community and policy environment.
4.2.8 Quality control
An important concern in the development of IA is quality control and maintenance of
rigorous scientific or policy standards. This can mean documentation of assumptions and
decisions relating to participation, review of science and assessment approaches by experts
and reporting and communication of results and assumptions in plain English formats
capable of being assessed by members of the general public. An approach to quality control
needs to be developed early in the life of the project and properly resourced throughout the
project.
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5
METHODS TO SUPPORT INTEGRATED ASSESSMENT
This section outlines some common methods used in Integrated Assessments. These
methods comprise commonly only a component of any assessment. Assessments normally
require a combination of many different approaches, including models, risk assessment,
stakeholder participation and qualitative research approaches.
5.1 Roles of models in IA
Models are a common approach applied as part of many integrated assessments. A model is
a description of a complex process or set of processes. It may be purely qualitative, as in
the case of a linguistic model, but more commonly is quantitative and represents
assumptions relating to system behaviour using a set of equations and parameter values.
There is a wide variety of quantitative modelling approaches that are commonly applied in
IA. This section summarises the most common of these approaches. It is derived heavily
from Letcher and Weidemann (2004) and Jakeman et al. (2005).
When choosing the type of modelling approach to be used it is important to consider two
main issues: what is the purpose of the model; and, what types of data are available and
what requirements are there on the scales and formats of model outputs? This section
focuses on the purposes of model building. The next section discusses issues of scales and
data.
Models are generally built to satisfy one or more of five main purposes:
1. Prediction
2. Forecasting
3. Management and decision-making
4. Social learning
5. Development of system understanding or experimentation.
These purposes place different requirements on the model structure, scales and accuracy.
Prediction
Prediction involves estimating the value (quantitative or qualitative) of a system output in a
specified time period given knowledge of the system inputs in the same time period.
Models are often developed to predict the effect of a change in system drivers or inputs on
the system outputs. For example, a model may predict a change in the probability of an
algal bloom occurring in a dam given that there is going to be an increase in the level of
nutrients delivered to the dam. Predictive models may be very simple (often empirical) or
may be more complex. In many cases increased complexity of a model does not lead to
improved predictive performance, so many successful predictive models have relatively
simple structures that are well grounded in observations. Predictive models are generally
required to have some level of accuracy in reproducing historic observations of system
outputs from observed inputs. For integrative models, validating the predictive accuracy of
these models is often difficult due to a lack of appropriate data for validation.
36
Forecasting
Forecasting refers to predicting the value of a system output in future time periods, without
knowledge of the values of system inputs in those periods. For example, a model may use
observed rainfall today to forecast the chance of rainfall tomorrow. Time series methods are
very commonly used for forecasting problems. The accuracy of forecasting models is
commonly tested considering the difference between ‘forecast’ values and historic
observations.
Management and decision-making
Models are frequently developed for management and decision-making purposes. The term
‘decision support system’ is often applied to models that have been developed to aid
decision-making. These models may be simulation based (ie. developed to answer ‘what if’
type questions) or optimisation based (developed to provide the ‘best’ option under a given
objective subject to constraints). Tools such as multi-criteria analysis are essentially
optimisation-based models developed to provide the optimal outcome under multiple
objectives. Usually these tools also provide information about the sensitivity of the optimal
outcome to assumptions, such as weights placed on different objectives. Management and
decision-making models are usually required to be able to accurately differentiate between
decisions or management options. This usually requires the model to give accurate
estimates of the magnitude and direction of changes in system outputs in response to
changes in system drivers.
Social learning
The use of models for social learning is an increasingly important development area in
integrated assessment. This is consistent with the multiple values and interests represented
in the problem of and response to climate change issues (for an overview of the theory and
practice of social learning, see Keen et al 2005). Participatory models building and
application may utilise a range of tools, including IA, qualitative systems approaches (eg
causal loop diagrams), more detailed qualitative models, and a variety of participatory
deliberative methods (eg. inclusive multicriteria methods). One area of rapid development is
the use of agent-based models (Parker et al. 2002; Srbljinovic and Skunca 2003, Brown et
al. 2004). In this case, models are developed to allow individuals (not the model builder) to
learn and experiment so as to inform their understanding of the way in which the system
may work and the way their individual actions may interact with the actions of others to
create system outcomes. Models developed for the purpose of social learning generally
have a large emphasis on the importance of social interactions between individuals or
groups and may include representations of many less well-known or understood processes.
The emphasis of accuracy in models developed for social learning tends to fall more on the
plausibility of interactions and outcomes than the predictive accuracy of the model. Uses of
IA models for social learning is closely tied to issues of public participation, and similarly
qualifications and care should be exercised with respect to transparency and clarity of roles
and expectations (see section 5.2 below).
Developing system understanding/experimentation
Models are frequently developed to summarise and integrate available knowledge or
understanding of system components in order to improve understanding of the entire system
and the way it may react to changes in system drivers. Models that are developed to
improve system understanding or for experimenting on a system may include components
that are less certain (to test the potential effect of the assumed structure on the system) than
37
those used for prediction, forecasting or decision-making. These models tend to be
‘research’ models, accessible to the model builder and other researchers, as opposed to
social learning models that are generally developed with a large non-technical audience in
mind. As with social learning models, model accuracy tends to be considered in terms of
plausibility and possible implications for the system rather than history matching.
5.1.1 Design issues for integrated assessment models
There are a number of design issues that must be considered which strongly influence the
best choice of modelling approach for any application. The key issues are outlined below.
This section relies heavily on Letcher and Weidemann (2004).
5.1.1.1 Types of data
There are two main types of data able to be used to construct a model: quantitative data and
qualitative data. Quantitative data includes time series data, spatial, or survey data. This
data refers to the measurable characteristics or fluxes in a system. Qualitative data or
information includes expert opinion, stakeholder information or some types of information
derived from surveys and interviews. Almost all model development relies on both
quantitative and qualitative information. For example even purely quantitative models rely
on theory or knowledge about systems interactions in the development of their underlying
conceptual frameworks. However, some modelling approaches allow qualitative
information and data to be explicitly incorporated in not just the system conceptualisation
but also the calibration and parameterisation of the model. In this report the distinction
between a model’s ability to use quantitative or qualitative data refers to whether or not the
approach allows explicit incorporation of this data in model parameterisation, rather than
during model conceptualisation. Of particular importance in IA, where multiple values and
stakeholders and analytical approaches will interact, is for clear understanding of the
characteristics and limits of different data streams being incorporated into models, and for
clear connection along the continuum between highly qualitative conceptual models and
more detailed quantitative ones.
5.1.1.2 Treatment of space
There are essentially four different approaches to treating space in a model.
1. Non-spatial models do not make reference to space. For example regional and national
economic impacts arising from a change in the management of a system (eg. modelled
using a choice modelling approach) may not refer to any particular spatial scale.
2. Lumped spatial models provide a single set of outputs (and calculate internal states) for
the entire area modelled. For example the impact of a change in nutrient delivery to a
lake may be modelled using a simple function as a total change in biomass for the entire
lake system. In this case the lake system is not disaggregated into smaller units and the
interactions between parts of the lake system are not considered.
3. “Region”-based spatial models provide outputs (and calculate internal states) for
homogenous sub-areas of the total area modelled. These sub-areas are defined as
38
homogenous in a key characteristic(s) relevant to the model eg. homogenous soil types
or similar production systems. For example the lake system may be disaggregated into
areas within 1-2m of the shore line, the creek leading into the lake and the deeper lake
systems. Interactions between these three ‘regions’ are then considered by the model.
The model is also able to output impacts for each of these regions.
4. Grid or element-based spatial models provide outputs (and calculate internal states)
on a uniform or non-uniform grid basis. Neighbouring grid cells may have the same
characteristics but will still be modelled separately, as opposed to homogenous region
based spatial models where these areas would be lumped together. For example when
considering the impact of land use changes on terrestrial ecosystems the landscape may
be divided into a uniform grid, where the descriptors of that grid cell are based on either
a single measurement or an average of measurements in that cell (eg. landcover, species
distribution, soils). These cells may then be modelled either independently or as a
connected series of cells (ie. each cell affects the outcomes in neighbouring cells)
depending on the way in which the model has been conceptualised.
For integrated models the entire model may not operate using a single approach. For
example, a grid based lake hydrodynamic model may be used to feed a single spatially
averaged output to an economic or ecological model. The spatial approach of the integrated
model is generally at most as disaggregated as the least spatially distributed model in the
integrated system. Disaggregation of models to different spatial scales can lead to many
difficulties in integrated models, as the spatial scales of interest in one component model
may be quite different to those of a model from a different discipline. This is discussed in
more detail when individual integration approaches are overviewed (section 4).
5.1.1.3 Treatment of time (temporal scales)
There are three main approaches to dealing with time in models.
1. Non-temporal models are those that do not make reference to time. For example, key
ecological attributes of a landscape may be considered to be patch size and connectivity.
These may be modelled for different scenarios from a static land use or management
decision using appropriate ecological indicators. This is essentially a simple model of
ecological impact of land use change that has no reference to time.
2. Lumped temporal models generally provide outputs over a single time period, such as
average annual outputs. For example many nutrient and sediment models output an
average annual load, rather than an annual or daily time series. By definition a model
that is developed for forecasting purposes cannot be lumped temporally.
3. Dynamic models provide outputs for each time-step over a period. For example a
model may calculate the change in the system condition each day, month or year. This
approach is usually taken when the response of the system to a time varying input (such
as rainfall) is required.
As with their treatment of spatial scales, integrated models do not necessarily have to
integrate components working at the same temporal scale. The outputs of one component
may be aggregated or disaggregated before being input into another component model. The
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main consideration here is that the choice of aggregation or disaggregation method is
generally subjective and may affect the model outputs. Any such effect needs to be
considered when interpreting model results, and if the effect is too great the model may
need to be modified to remove this problem (for example component models may need to be
redesigned to work at a different scale).
5.1.1.4 Treatment of Uncertainty
Uncertainty is an important consideration in developing any model, but is particularly
important, and usually difficult to deal with, in the case of integrated models. Uncertainty
in models may be derived from uncertainties in system understanding (i.e. what processes
should be included, how do different processes interact), from uncertainties in data and
measurements used to parameterise the model or from uncertainty in the base line inputs or
conditions used for model runs (eg. world prices for a crop may change – does this change
the model recommendation?).
Some integration approaches are able to explicitly deal with uncertainty in data,
measurements or base line conditions. Other approaches require comprehensive testing of
the model to allow this understanding to be developed. The level of testing required to
develop this understanding is rarely carried out however, largely due to time and other
resource constraints given the complexity of such a task for even relatively simple
integrated models. For example the sensitivity of a model to changes in one or even two
parameters at a time may be tested but analysis rarely involves more complicated
combinations of parameter changes. The results from such a complex testing regime can
also be quite difficult to interpret. Very few approaches explicitly consider uncertainty
introduced by the system conceptualisation or model framework.
Finally model uncertainty must be considered in the context of the purposes of the model.
For example the variation of a system output from the observed value may be very
important for forecasting models, but may be much less critical for decision-making or
management models. In this case the user may be more concerned with being able to
accurately distinguish between the magnitude of impacts from two alternative management
options (or scenarios).
As well as explicit handling of uncertainty in model construction, problem definition and
participatory aspects of IA require recognition of added dimensions of uncertainty. First,
constructions of uncertainty other than scientific ones are important in how society and
policy systems define problems and consider responses. Wynne (1992) presents a widely
used typology including tractable risk, uncertainty, indeterminancy and ignorance, while
Smithson (1989) offers a detailed categorisation of types of uncertainty and their sources,
under the primary separation of error (to be ignorant) and irrelevance (to ignore).
Significantly, Smithson emphasises important political and social dimensions of uncertainty.
Second, discussions of uncertainty should have reference to the two main expressions of
uncertainty and risk in Australian policy: the Precautionary Principle (stated in over 120
Australian statutes, see Peel 2005; Fisher et al 2006) and Australian Standard/New Zealand
Standard 4360: Risk Management (Standards Australia 2004a).
A particular challenge in the case of climate change, given the possibility of abrupt change,
is identification and handling of residual uncertainty, that remaining after either
identification of tractable or easily identifiable risks in an assessment procedure, or after
40
treatment of identified risks in a practical management context. (See also section 5.3 Risk
Assessment below.)
5.1.1.5 External vs. Internal Optimisation
There are two main approaches to considering management interventions or decisions in
models. The first of these is scenario-based, where the model is developed to consider the
impacts of implementing options (often referred to as ‘what if?’ approaches). This type of
approach is developed to allow the user to explore the results of various actions and the
effects and trade-offs these involve.
The second approach is optimisation, where the model explicitly determines the best
intervention or decision according to a specified objective (maximise net returns, minimise
environmental costs) subject to various constraints. In this case the model user is generally
presented with a single ‘best’ option or intervention. The objective function may be defined
as a weighted combination of multiple outcomes.
5.1.1.6 Appropriate Software
Software development must be undertaken with a clear picture of the target audience, the
specific issues and the uses. While a sophisticated, object-oriented based software platform
may be both useful and desirable in some circumstances, in other cases a spreadsheet-based
model may be more useful for extending project ideas and science. Having different
software products aimed at different audiences can also be a useful outcome of a project. On
the other hand, software development should not be the primary objective of the work
undertaken. The software is a tool to enhance communication and interaction between
different disciplinary teams. It should be a focus of the project primarily in so far as it
encourages communication of ideas and enhanced understanding of the integrated nature of
the problem.
5.1.2 Modelling Approaches
Given the different definitions of what constitutes integration and the varied purposes of
developing integrative models, various approaches to developing integrated models have
been developed. This section provides a classification for previous integrated models before
providing a brief overview of applications of each approach. It concludes with a framework
for choosing the appropriate approach to integration given the requirements placed on the
model (taken from Letcher and Weidemann, 2004; Letcher and Jakeman, 2005). A
summary of each of the approaches, the types of model applications for which they are
appropriate and the way in which they deal with the model considerations outlined here is
given in Table 1.
5.1.2.1 System dynamics
System dynamics (e.g. Deaton and Winebrake, 1999) is a modelling approach that
investigates and manages complex feedback systems (eg. aquatic food webs). Many authors
41
consider it to be a philosophy of model development rather than a type of model. Nodes in
the conceptual framework generally represent state variables, while the links or arrows
between nodes represent functions transforming one state variable to the next. The
conceptual frameworks for system dynamics models often contain feedback loops. These
loops may be very complex, and/or only be supported by perceived ‘plausible’ connections.
Thus, system dynamics models are most commonly used to improve systems understanding
and to compare simulation responses rather than decision-making and policy. However, in
theory the latter could be achieved. Examples of the use of a systems dynamics approach to
model integration in climate change research can be found in Simonovic and Davies (2006),
Fiddaman (2002) and Fiddaman (1997).
5.1.2.2 Bayesian networks
Bayesian networks consist of a series of nodes and links that conceptualise a system.
Feedback loops cannot be included in this approach. They are fundamentally a decisionmaking tool. The nodes in the system are variables. The links are defined by conditional
probability distributions, thus providing a measure of certainty in the causal relationship
between each node. In this way this integrated approach differs from others that use
deterministic, rather than probabilistic, methods to determine the relationship among
variables (Borsuk et al., 2004). The implicit ability to account for uncertainty means that
Bayesian networks are able to make use of ‘soft’ sources of data, such as expert opinion,
where observed data is not available (Sadoddin et al., 2003). Bayesian Decision Networks
also include decision variables, which allow for management options to be implemented,
and utility variables, which reflect the benefit or cost of a particular decision. Examples of
the use of Bayesian Networks to consider the impacts of climate change can be found in
Koivusalo et al. (2005) and Ticehurst et al. (in press).
5.1.2.3 Metamodels
Metamodels are essentially a simplification of the processes within more complex models.
Data-mining techniques such as regression are often used to develop metamodels. Bouzaher
et al. (1993) suggest that metamodels are useful to approximate and aid in the interpretation
of simulation models. The mere size of the output from complex models can make them
difficult to view and interpret. Metamodels can provide look-up tables, or simpler functions
to represent the information found in the more detailed models. In integrated modelling
metamodels can be used to completely replace a complex model, or complex components of
a model. In the latter, the metamodels can be coupled into an integrated system. Examples
of the use of met-models in integration for climate change can be found in Martens (1998)
and van Kooten et al. (2004).
5.1.2.4 Coupled component models
Coupling component models involves combining models, typically from different
disciplines, to arrive at an integrated outcome. Conceptually each node in the framework
represents a model of a particular issue. The links between models pass the generated data.
The links maybe be manually linked external to the original models, or may be more tightly
linked where the component models share inputs and outputs (e.g. Merritt et al., 2004;
42
Letcher et al., 2004). Coupled component models are generally able to incorporate feedback
loops.
Coupled component models can account for non-trivial temporal and spatial discretisation.
This is particularly relevant in climate change assessment where complex processes must
often be integrated over large, and varying, spatial and temporal scales. Examples of the
use of coupled complex models as a methods of integration in climate change research can
be found in Krol et al. (in press), Nijkamp et al. (2005) and Pasquer et al. (2005).
5.1.2.5 Agent-based models
An agent- or actor-based model is essentially a type of coupled component model. It focuses
on the interactions between agents (individuals) in a system (e.g. Brown et al., 2004), where
agents adapt to changes to their environment. When two or more agents exist at the same
time, share resources and communicate with each other, it is called a multi-agent system.
Agent-based models are efficient at identifying large-scale outcomes resulting from often
simple, local interactions between individuals. For this reason, and because they tend to be
very hypothetical, agent-based models are usually applied in social and ecological science.
An example of the use of an agent-based model used for assessing climate change can be
found in Janssen and de Vries (1998).
5.1.2.6 Expert Systems
An expert system is a type of qualitative model where prior knowledge is encoded into a
knowledge base and then logic used to infer conclusions (Davis, 1995). The knowledge base
determines the success of the system (Forsyth, 1984). Given a problem, the expert system
simulates the problem-solving task(s) (Kidd, 1987). The conceptual diagram for an expert
system refers to questions about the nature of the system directed at the user. The response
to these questions then dictates the route down which the procedure looks for a solution.
Examples of the use of expert systems for assessing the impacts of climate change can be
found in Hood et al. (in press) and Huang et al. (2005).
The strengths and weaknesses of these approaches for different applications are summarised
in Table 1.
Table 1.
Appropriate use of integrated modelling techniques (from Letcher and
Jakeman, 2005)
System
dynamics
Bayesian
Networks
Meta
Modelling
Coupled
Complex
Models
What is your reason for modeling/type of application?
Predictive
X
X
X
Forecasting
X
X
Decision-making
X
X
X
X
System
X
X
understanding
Social learning
X
X
What types of data do you have available/want to use to populate your model?
Qualitative and
X
quantitative data
Agent
based
Models
Expert
Systems
X
X
X
X
X
X
X
X
43
Quantitative data
X
X
X
X
only
Do you want your model to focus more on a complex description of specific processes in the system or
have a greater breadth of coverage of interactions in your system?
Depth of specific
X
processes
Breadth of system
X
Compromise
X
X
Both
X
X
Do you want your model to provide explicit information about uncertainty caused by model
assumptions?
Yes
X
X
No
X
X
X
X
Are you interested in investigating the interactions between individuals and their impact on the system,
or only the aggregated effect of human behaviour?
Interactions
X
between individuals
Aggregated effects
X
X
X
X
X
5.2 Participation
Public participation can be defined as direct involvement of the public in decision-making,
and thus, in developing the tools used to inform decision makers. Clearly it can occur at
various levels. Mostert (in press) describes six levels of public participation: information
supply; consultation; co-thinking; co-designing; co-decision making; and self-control.
He proposes several reasons for organizing public participation. These include the
possibility of:
 more informed and creative decision making
 more public acceptance and ownership of the decisions
 more open and integrated government
 enhancing democracy
 social learning, the ultimate objective, to manage issues
Mostert states that it is important that public participation is organized well to avoid limited
and unrepresentative response from the public, disillusionment, distrust, less public
acceptance, more implementation problems, less social learning, and complication of future
participatory processes. He stresses the need for sensitive processes, taking into account the
culture (e.g. natural and socioeconomic conditions, ideology) and subculture (e.g.
environmentalists, industrialists, managers). He argues that if environmental management is
to be participatory, research supporting environmental management should also be
participatory. Not only should the public have access to research results, presented in an
understandable way, but it should also have a say in what is researched and how, and
participate in the research process itself.
Integrated assessment and ‘independent’ experts can provide an important and useful
mechanism for raising the level and quality of public participation in environmental
management. Involving communities in model development or other non-model based
assessments can not only add to the validity of the final results but can also create an
opportunity for constructive interaction between stakeholders. This allows them a less
threatening focus for developing a shared system understanding than interactions focused on
resolution of specific environmental conflicts. An integrated assessment can capture a
shared understanding of system processes and can allow people to manage disagreements
44
about system assumptions. Delivery of models through software or development of a
decision-support system can permit the model developed to be reused in order to make
management decisions after the end of the research project. Conflict over management
options can often be resolved as conflict over key system assumptions. In these cases
conflict may be managed by identifying areas of disagreement or gaps in knowledge, and by
improving system understanding through targeted data collection or system observation.
Such attempts at resolution of conflict tend to be positively received by stakeholders. They
feel that their concerns are being addressed by the process.
There are numerous methods available for public participation. Good summaries of these
methods and their strengths and weaknesses are given in World Bank (1996) and Mayoux
(2006). It is important to note that not all methods are suitable for all situations. Methods
need to be tailored for the situation or issue at hand. Often a combination of methods is
required throughout the life of an assessment.
World Bank (1996) define four different categories of participation approaches: workshopbased methods; community-based methods; methods for stakeholder consultation; and
methods for social analysis. Table 2 summarises the methods discussed by World Bank
(1996).
A further set of participatory methods under rapid development and increasing
implementation in policy are those gathered under the general term ‘deliberative designs’
(for an overview see Munton 2003). These involve structured processes of participation,
featuring various means of informing representative groups of stakeholders or the general
community about the issue at hand, and deliberative processes of recommending responses
to that issue or decision problem. Such approaches include citizen’s juries, consensus
conferences, deliberative polling, inclusive (non-deterministic) multi-criteria methods, and
planning cells.
Key factors to any participatory approach being incorporated effectively (and equitably) into
an IA process include: clarity as to roles and responsibilities; definition of the purpose of
participation; transparency of methods (ie. not ‘black box’ models); careful definition of
‘stakes’ and of the relevant community; and, if the participation is linked to policy or
management processes, sufficient longevity of engagement (ie. not simply as a swiftly
curtailed data input). Sensitivity to the volunteer and limited nature of stakeholder inputs to
research processes needs also to be maintained.
Table 2.
Participation approaches adapted from World Bank (1996)
Method
Description
Collaborative Decision Making: Workshop-based Methods
AppreciationA workshop-based technique where stakeholders produce a visual
Influenceinfluence diagram of the project or issue considering social,
Control (AIC)
political and cultural factors along with technical or economic
aspects. Appreciation is developed through listening, influence
through dialogue and control through action.
ObjectivesWorkshops focus on development of a project planning matrix.
Oriented Project Stakeholders are asked to set priorities and plan for
Planning (ZOPP) implementation and monitoring. Builds stakeholder team
commitment and capacity.
45
Collaborative Decision Making: Community-Based Methods
Participatory
A family of methods and approaches that emphasise local
Rural Appraisal
knowledge and enable local people to do their own appraisal,
analysis and planning. Uses group animation and exercises to
facilitate information sharing, analysis and action among
stakeholders.
SARAR
Aims to build on local knowledge and strengthen local capacity to
assess, prioritise, plan, create, organise and evaluate. Allows for
training of local trainers and facilitators. Encourages participants
to learn from local experience rather than external expertise and
provides a multi-sectoral, multilevel approach to team building.
Methods for Stakeholder Consultation
Beneficiary
A systematic investigation of the perceptions of stakeholders to
Analysis (BA)
ensure their concerns are heard in and incorporated into project
and policy formulation. Involves lengthier, repeated and more
meaningful interaction among stakeholders.
Methods for Social Analysis (SA)
Social Impact
A systematic investigation of the social processes and factors
Assessment
affecting impacts and results. Aims to: identify key stakeholders
(SIA)
and establish appropriate framework for their participation; ensure
project objectives and incentives for change are appropriate and
acceptable to stakeholders; assess social impacts and risks; and,
minimise of mitigate adverse negative impacts.
Gender Analysis Focuses on understanding and documenting differences in gender
(GA)
roles, activities, needs and opportunities in a given context.
Involves disaggregation of quantitative data by gender and
highlights different roles and learned behaviour of men and
women based on gender attributes which vary across culture,
class, ethnicity, income, education and time.
5.3 Risk Assessment
Risk assessment has been used as an integrating framework in many sectors including health
and the environment (Jakeman et al., 2005). The basic notion is that risk is defined as the
probablility of an outcome times the severity of its consequence, leading to the potential
quantification of risk in a number of ways. However, relevant and useful forms of
qualitative risk assessment also exist, and may be more appropriate where believable
probability distributions cannot be assigned to the range of possible outcomes. The essential
notion of risk assessment can be extended to positive as well as adverse impacts and the
characterisation of uncertainty. According to Jasanoff (1993) the role of risk assessment is
to ‘offer a principled way of organising what we know about the world, particularly about
its weak spots and creaky joints.”
Kammen and Hassenzahl (1999) present much of the central theory and methods including
order of magnitude estimation, cause-effect calculations, exposure assessment, fault-tree
analysis, and managing and estimating uncertainty. In Australia, an increasing number of
policy sectors are approaching risk in a manner consistent with the risk management
standard (Standards Australia 2004a), including areas as diverse as finance, product design,
chemical engineering, emergency management and environmental management. The
Standard has been developed considerable since its first edition in 1995, and is supported by
46
application guidelines (Standards Australia 2004b). Wild River and Healy (2006) provide an
overview of the standard, and detail on implementing risk assessment consistent with it in
environmental management. The standard emphasises a systematic and integrated approach
to risk management, including the following core elements:
 Establish the context;
 Identify risks;
 Analyse risks;
 Evaluate risks;
 Treat risks;
 Communication and consultation (throughout process); and
 Monitor and review (throughout process).
The Standard is flexible, allowing for a variety of specific techniques (qualitative and
quantitative, strategic and more specific) to be employed within this framework. IA
processes dealing explicitly with risk should refer to and ideally maintain consistency with
the Standard, representing as it does an accepted, overarching framework for dealing with
risk.
Risk assessment has been used as an integrating framework in many sectors including health
and the environment (Jakeman et al., 2005). The basic notion is that risk is defined as the
probablility of an outcome times the severity of its consequence, leading to the potential
quantification of risk in a number of ways. This notion can also be extended to positive as
well as adverse impacts and the characterisation of uncertainty. According to Jasanoff
(1993) the role of risk assessment is to ‘offer a principled way of organising what we know
about the world, particularly about its weak spots and creaky joints.” Kammen and
Hassenzahl (1999) present much of the central theory and methods including order of
magnitude estimation, cause-effect calculations, exposure assessment, fault-tree analysis,
and managing and estimating uncertainty.
47
6
SELECTING METHODS FOR USE IN AN INTEGRATED ASSESSMENT
The previous sections outline a number of the methods available for use in integrated
assessments. The choice of methods to be used in an integrated assessment depends
primarily on the question or problem focus. Without defining an application area (such as a
sector, group of sectors, region etc) on which the assessment is to focus it is not possible to
recommend a specific method. However once an application area has been defined it is
possible to propose several criteria by which potential methods may be judged and selected.
This section provides an outline of these criteria and then gives an overview of an approach
to integrated assessment that may be applied.
6.1 Criteria for selecting methods for IA
Once an application area has been chosen and the problems and impacts associated with this
have been well-defined a method (or set of methods) may be chosen for the considering to
the following criteria.
6.1.1 Is the method credible with the scientific community, policy community
and/or the general community?
In order for an integrated assessment to have impact on policy makers and decisions
affecting a problem, it must have a degree of credibility with three separate audiences: the
scientific community; the policy or decision-making community; and the general
community including stakeholders. An assessment that is credible with one of these
communities may lack credibility with another audience. For example scientific credibility
is arguably achieved by publication of the results and methods of the assessment in the peer
reviewed literature. While this may improve the credibility of the assessment with
government or community members, their experience of the assessment and interactions
with the members of the IA team is much more likely to colour their judgment of the
credibility (or lack thereof) of the assessment. Thus the process of IA must attempt to appeal
to the judgment of these three audiences and not attempt to satisfy only one if it is to
achieve its aims.
6.1.2 Can the method answer key questions underlying the case study or meet the
case study objectives?
Most obviously the methods chosen must be capable of addressing the problems underlying
the case study or application area. They must be capable of assessing the types of impacts
focused on by the assessment while maintaining credibility and other criteria. This means
that methods should not be chosen until after an application focus has been selected and
well-defined. This is essentially a warning against the ‘have model will travel’ approach to
scientific assessment. IA is often referred to as a problem in search of a method rather than
a method in search of a problem.
6.1.3 Can the method fit into an appropriate participatory process?
Some problems require a large degree of collaboration with stakeholders. However some
methods do not lend themselves to use within a collaborative process due to their
complexity, their large computational requirements or their lack of credibility with the
general public or policy and decision-making audiences. Decisions must be made as to the
48
degree of public participation that is desirable or required and the ways in which the public
might be engaged. After this, methods can be chosen that complement this participation
strategy rather than conflict with the goals of the IA and the participation.
6.1.4 How easily can the method communicate uncertainty?
Some methods (such as Bayesian networks) explicitly communicate uncertainty in a fairly
straightforward and understandable way. Other methods do not easily allow for uncertainty
to be estimated let alone communicated. Where uncertainty is a key consideration then a
method that allows for it to be explicitly incorporated and easily communicated is desirable.
6.1.5 Cost – how expensive is it to develop, maintain and extend?
This criteria is often the most important in terms of achieving the outcomes desired by the
funder and project team. Integrated Assessment is expensive in terms of money, time and
resources (including the good will of both stakeholders and researchers). Before setting off
to undertake an Integrated Assessment, the question of whether IA is the best approach to
tackle the problem should be considered, and, if it is considered to be the best approach,
then sufficient time and resources must be budgeted for the IA activity. Once this budget is
set, it is then important to choose methods that fall within the scope of the time and
resources committed. Generally very inclusive and collaborative participatory approaches
and the development of very complex models can be considered to be the most expensive of
IA activities. Underestimating the cost of IA activities can lead to expensive failures that
alienate the community towards future assessments.
6.1.6 Can it be used in training, to build capacity or for social learning?
Some methods lend themselves to building capacity in researchers, decision makers or
technical staff as well as within the community more broadly to understand a problem from
multiple perspectives and to apply a systems approach. These methods may be a better
choice where capacity building or social learning are goals of the IA exercise.
6.1.7 Is it useful for educating a new breed of interdisciplinary scientist?
Sometimes a method or an Integrated Assessment might have its greatest influence through
training new scientists, decision makers and technicians in interdisciplinary, systems based
approaches. A method might be very useful for this type of education role and could be
selected even though this might not be the primary purpose of the assessment.
6.1.8 Can the method or results/lessons from the method be transferred to other
case studies/ problems/areas and more broadly?
Most useful integrated assessment has a strong applied focus. However, clients and other
funding bodies generally want to fund research that develops methods, approaches or results
that are able to be applied more broadly. It is important when developing and applying
methods in an assessment to consider whether those methods or the recommendations
arising from the research can be applied to other areas, sectors or problems. Most of the
emphasis that practitioners of IA have placed on the benefits of IA has been the learning
experience of participants rather than explicit results or products (such as models) arising
49
from the assessment (see for example Jakeman and Letcher 2003; Janssen and Goldworthy
1996).
6.1.9 Can it handle multiple and/or conflicting issues?
The main purpose of IA is to address problems in which trade-offs are a major issue. Where
a problem has a simple solution that is of benefit to all stakeholders and does not involve
interactions with any other environmental or social systems, IA is not usually beneficial.
Thus most problems in IA involve multiple and usually conflicting issues and impacts.
Methods selected must be capable of addressing these issues.
6.1.10 Can it be used in a complementary manner with other methods?
Some methods lend themselves to use in conjunction with other methods. For example a
Bayesian Network approach can be used to integrate information from complex numerical
models, surveys and expert elicitation. Other approaches such as coupled complex models
may be difficult to integrate with other methods unless they are used in conjunction with
such a complementary method.
6.2 A process for Integrated Assessment
These criteria highlight the importance of using an appropriate process for Integrated
Assessment. The success of an IA exercise will generally depend less on the methods
selected than on the process in which they are embedded. This section briefly outlines one
process that has been developed and applied in a number of very diverse IA exercises in
Australia (see Jakeman and Letcher 2003; Letcher and Jakeman 2003; Letcher et al. 2004;
Merritt et al. 2004; Newham et al. 2004; Merritt et al. 2005 for details). The process is
described in Table 3.
Table 3.
IA process where model development is one key outcome (from Letcher
and Jakeman, 2005)
Step
1. Clearly identify the aims and
objectives of the integrated assessment,
including stakeholders and potential
audiences for the results and any other
products of the IA
2. Build an understanding of the
constraints and issues in the case study as
well as possible targets and measures of
system performance
3. Develop an initial conceptual
framework, identifying key drivers,
including management and development
options as well as state and utility
variables and their interactions
4. Workshop the initial conceptual
framework and general scenarios with
stakeholders to get feedback on missing
scenarios, links between system
components and impacts that should be
considered in the assessment.
Comment
Reviewing existing information on the case study area including
management reports, previous studies and other ‘grey literature’
information.
Generally developed in-house using information sourced from
reports and other information available on the issue in discussion
with a few key stakeholders.
A very broad consultation with many different stakeholder groups
seeking feedback across social, economic and environmental
issues. This should include a discussion of the specific scenarios
to be considered and the types of impacts of greatest concern.
50
5. Revise the initial framework using
stakeholder feedback obtained in step 3.
6. Identify existing data and information
available to populate the assessment.
7. Identify and fill key knowledge or
information gaps
8. Populate the assessment with data and
other information
9. Review the conceptual framework and
assessment model with stakeholders
11. Revise the assessment, results and
conclusions in the face of stakeholder
feedback.
12. Distribute the assessment to relevant
stakeholders or other user groups with
appropriate training or information on
its use
This step produces a working version of the conceptual framework
for assessment to be focused on. Specific scenarios and key
impacts to be considered should be identified.
Reviewing the working conceptual framework to identify
processes, links and impacts for which no or very limited
information exists. A workplan to fill gaps with information is
then constructed. Feedback to the community on the limitation of
data used and factors not able to be included should be provided to
inform expectation of the assessment capabilities.
Very time consuming and is usually the primary focus of
traditional model building or assessment practice (usually the
prime focus in budgets as well!). This may include development
of quantitative models, or qualitative integration such as reports
and analysis of information. If a model is to be developed it
should generally be coded in such a way as to allow the end-users
of these results access to the model, scenarios and results.
The assessment and results are demonstrated to stakeholders.
Feedback is sought on the accuracy and validity of the results and
conclusions from the assessment.
Results, models and/or conclusions are generally distributed
through workshops run with people identified, by the client and/or
through the project activities, as key users, or through reports
which have had some iteration with stakeholders to ensure they
are adequate and understandable.
51
7
CASE STUDY SELECTION
This section describes some of the issues which need to be considered in selecting case
studies of climate change in which integrated assessment may be applied.
7.1 General context
Clarifying the purpose and context of IA is a first step in scoping further work. Integrated
assessment of climate change impacts has four underlying purposes: (i) to improve
characterisation of the phenomena which drive climate change impacts; (ii) to better define
the range of possible impacts on different components of interdependent natural and human
systems (issues, sectors, values); (iii) to identify, test and improve approaches and methods;
and (iv) to identify effective options to address the impacts of climate change. The second
aim is the most apparent and widely accepted, however all four are interrelated and should
be considered together. The fourth purpose is the ultimate aim of Government in IA of
climate change.
These four general purposes, and the complexity of relevant natural and human systems
(encompassing multiple natural processes, environments, human uses and values) combine
to make it impossible and indeed undesirable to undertake an integrated assessment of all
possible impacts and issues relating to climate change across all relevant scales. Simply, the
number of variables and parameters, and the time and effort involved in such a
comprehensive assessment, stands at odds with the purposes of undertaking IA and the time
frames within which research, policy agencies and the community would wish to gain
insights. Moreover, such a comprehensive integrated assessment would need to be spatiallybound to approach any degree of thoroughness, and it is doubtful that more than one or two
such studies could be undertaken, meaning that the outputs may be of limited transferability
or operational usefulness across regions, populations, environments and sectors. What is
possible and desirable is a few well targeted IA projects which are chosen to reflect either
spatial or sectoral elements of key importance which are likely to be subject to significant
effects from climate change. These needs to be chosen carefully to take advantage of the
opportunities presented by IA for learning both by government and researchers as well as by
the general population. They should build as much as possible on existing studies to ensure
that the integration is the focus of the assessment, rather than building primary disciplinary
data sets or understanding.
This context defines the scoping task as one of designing a coordinated suite of programs
and projects rather than a small number of more complete assessments, ensuring – through
definition of these and connection between them and previous or existing work – that more
rather than fewer climate variables, impacts categories, impact contexts and societal values
are captured, through a range of approaches and methods. The following identifies major
considerations to be taken into account in framing, enabling and funding future IA work,
stated as key attributes of a suite of programs and projects. In the interests of both efficiency
and effectiveness, it is crucial that these attributes (or criteria) are maximised across projects
rather than projects being designed in isolation from others.
52
7.2 Key attributes of future work in Integrated Assessment
Attributes are organised under seven general categories, and serve as an initial definition of
criteria for case study and project design (see Table 4). Within and across these categories,
there are tensions between criteria, and it is emphasised again that the task is to maximise
the breadth and depth of future work across projects – that is, specific projects cannot
address all criteria or resolve all such tension, but a portfolio of connected work may do so.
As criteria, the following is informing rather that prescriptive in potential use – part of the
point of their identification is to make choices and the implication of choices explicit and
transparent.
7.2.1 Representativeness: drivers and impacts
Accounting for multiple climate change impact categories and multiple potentially impacted
sectors is the intent and rationale of IA. However, as noted above, it is unlikely to be
possible, practical or desirable to capture all or even most in the foreseeable future within a
small numbers of IA processes (unless at a broad scoping level). Nevertheless, a suite of
programs and projects can and should seek representativeness in including a wide range of
climate-driven phenomena (eg. rainfall, temperature, extreme events) and impact types
(infrastructure, disease distribution, agricultural production, biodiversity, etc). Sufficient
attention should be paid to both potential positive and negative impacts of climate change.
7.2.2
Representativeness: sectors, values and places
Similarly, IA by definition must deal with multiple sectors and values, yet capture of all or
even most in a single exercise would be impossible. Again, the widest range of sectors,
values and places can only be captured within a coordinated suite of IA work. Work should
cover the most apparent and well-known (eg. coastal settlements, sensitive environments
such as the Reef or Alpine areas, agriculture, health, large cities) but also ones less wellrecognised in discussions of impacts thus far (eg. fisheries, remote and Indigenous
communities, biodiversity in urban areas, small-scale tourism, etc).
Given the unlikelihood of a sufficiently large number of regional case studies being
undertaken to capture all sectors and values, achieving such representativeness would
require a mix of spatially-defined (ie. region, major city) and issue or sectoral-focused
projects (ie. health, agriculture, infrastructure).
Reflecting considerations discussed under (f) Policy relevance below, IA work should
include both ‘iconic’ environments and places (eg. the Reef, Kakadu National Park or
similar) as well as less well-known places which nonetheless collectively embody
significant local or wider values. IA should also consider differentiated vulnerabilities
within subsets of regional communities or within industry sectors.
7.2.3 Methodological development
IA is not a method per se, but rather a style of research and application that has an emerging
broad framework within which it selects methods appropriate to the problem being
investigated (see Section 4). Future work should be designed in such a way as to test and
drive improvement in specific methods and in the understanding of the IA process more
generally. Very few methods are not contested, especially across disciplinary divides and
the research-policy interface, so testing of methods will only be effective if the IA process is
open to allow extended peer review communities to operate. For example, different
modelling approaches to characterising natural processes (see Section 4.1) should be
employed in a comparative fashion. Likewise, different methods of assessing social and
53
economic impacts should be employed in a way that allows comparison of robustness from
both a scientific and policy-relevance perspective (eg. standard Social Impact Assessment,
vulnerability assessment techniques and deliberative approaches, or standard and extended
cost-benefit analyses). An explicit choice must be made whether to compare like methods in
unlike settings, or to utilise similar settings to contrast and compare methods – either choice
is valid but different in intent and implementation. Ideally various strategies can be linked
across projects in an informing manner.
An important part of methodological development is to improve the capacity of IA and
component methods to identify, differentiate and explore interactions between climaterelated and other factors (eg. demographic, cultural, natural, trade, policy-driven) that also
determine the vulnerability or adaptability of sectors and communities.
7.2.4 Data availability and institutional capacity
Future IA work should span regions and sectors that are both data-rich and institutionally
well-resourced and capable, and data-poor and lacking receptive or capable institutional
settings. This is for two reasons: to maximise methodological development; and to ensure
that not only currently topical or previously well-studied places and issues are explored.
Especially at regional scale, but also across sectors and issues, the institutional capacity and
thus ability to engage with an IA process varies significantly. Given the costs and time
required for major data gathering or consolidation, data-poor regions or sectors, these
should be regarded as opportunities to apply and test methods suited to such conditions (eg.
Bayesian Networks or expert systems) rather than as imperfect settings for other, more dataintensive methods (eg. coupled complex or agent-based models). The capability of methods
to function and produce useful outcomes in the face of uncertainty should also be an explicit
part of the research scoping and design.
7.2.5 Utilisation of past and current work
There has been some work in IA or equivalents in the past, and a number of current projects
and processes. Future work in IA should build on such work, rather than engage in discrete
projects. Regions and sectors not subject to IA or similar work may nonetheless have
existing knowledge or capacity resources that would enable more rapid advance in assessing
at least some impact categories – for example, rural demographic and socio-economic
assessment undertaken in relation to structural adjustment or water policy change could well
underpin some IA work. This may also be the case with vulnerability assessments
undertaken for emergency management or public health reasons.
This suggests that, before defining future work, close engagement with R&D providers and
funders, and relevant agencies, should occur to identify both possible partners and existing
preliminary or complementary work (such as LWA, MDBC,other industry based Research
and Development Corporations). If the regional scale is pursued for IA, then existing data
gathering, management and policy processes at that scale provide potential synergies (eg.
CMAs, NAP and NHT processes, etc).
7.2.6 Policy and public relevance
To ensure support for IA work (eg. funding, collaboration), successful application in
particular settings (eg. engagement of agencies and communities), and usefulness of
outcomes (ie. relevance to the mandates of policy makers), IA needs to connect with policy
problems. This relevance can be considered in three ways: the current mandate and agendas
of policy agencies; actual policy processes directly associated with either impacts or
54
impacted sectors; and community concerns and social values. These three may coincide, or
differ, and all are strongly determined by the scale of focus – different levels of government,
non-traditional spatial scales of management (eg. CMAs), community perspectives varying
with scale, socio-economic or cultural character of regions, etc. A key variable is the loci of
management or policy responsibility for coping with impacts, which may be located at one
or more locations, including state and federal government, local government or catchment
organisation, Indigenous council or local community, industry sector, land parcel, and so on.
Central to maximising relevance is the necessity of joint problem framing and research
design, engaging policy agencies, resource managers, etc as well as relevant stakeholder and
community perspectives. The process of problem framing in itself represents an area of
imperfect and fragmentary expertise and methodological development (eg. qualitative joint
modelling, deliberative scoping approaches), and thus should be considered an integral part
of the research and development process rather than a preliminary step undertaken in
isolation. Close engagement of agencies and stakeholders throughout the research process is
a feature of IA, without which relevance of either the problem or outcomes is unlikely. If IA
appears unlikely to inform the coping capacity of communities, industries or management
agencies, then support for IA is unlikely. Past extreme events may be useful as an initial
focus to engage some interests.
Given that IA is a process that encourages learning and that generally leads to changes in
the understanding of issues and possible adaptations over time, it is important that
adaptability in research design and focus should be maintained, both within extended
projects, and across projects over time. This also allows for risks which are currently
deemed to be very low probabilities to be incorporated in the assessment over time if it
becomes apparent that their risk was initially underestimated.
An important characteristic of useful and adaptive IA is the ability to produce outputs in
stages, allowing feedback to users and review and improvement of the research and
assessment process. Ensuring early outputs does require balancing timeliness with rigour
and trustworthiness, including clear mutual understanding of the status of results between
researchers and users. A staged approach beginning with inclusive problem definition and
scoping phases suits the development of such outputs.
7.2.7 International significance and connection
Climate change science, impact assessment, and more specifically IA represents a rapidly
advancing domain, with a number of key networks and research initiatives. It is important
that IA in Australia be connected to endeavours elsewhere in the world to enable use of best
available knowledge and to allow comparative methodological development. This may be
achieved by: involving key international figures in Australian research projects or vice
versa; establishing formal connections between Australian processes and those elsewhere;
encouraging presentation of Australian research designs and outcomes to international
audiences; or linking specific Australian and international projects.
Table 4.
Summary of attributes of further work in IA (criteria for case study and
sectoral focus selection
Attribute/criteria
Case studies
Issue, sector or human
(spatially defined)
value focus
Representativeness of drivers and
impacts:
- climate-related phenomena
55
- impact categories
Representativeness of sectors, values
and places:
- production sectors
- values (health, biodiversity,
etc)
- regions/communities
Methodological development:
- comparative potential
(methods/cases)
- models, other
- purchase re uncertainty
Data availability:
- rich/poor
Institutional capacity to support IA:
- strong/weak
Existing or current work:
- IA or equivalent
- Non-integrated, but relevant
Policy and public relevance:
- policy agencies, processes and
agendas
- public/community topicality
International connections (linked
processes and projects, comparable
studies, institutional links)
Note: this matrix is intended as a simple illustration of the use of the attributes in (i) seeking
to maximise coverage across programs and projects, and (ii) is requiring explicit recognition
of the aims and potentials of IA work.
56
8
MATCHING CRITERIA & METHODS TO ASSESSMENTS
This section summarises several issues which should be considered in the context of
identifying either regional case studies or case studies based around sectoral issues. These
issues are considered in light of defining a specific regional case study – North Queensland
(see Box 1),
8.1 Scope of an Integrated Assessment
The scope of an IA of climate change impacts could be broadly and generically defined as
being the minimum scope to meet the objectives (i.e. the scope is necessarily determined by
the objectives of the IA). It is therefore essential to identify the justifications for the IA, and
to define its objectives, before attempting to define the detailed scope of the study.
Box 1. Justifying an IA of Climate Change Impacts in north Queensland
The justifications for and objectives of a climate change impacts IA will be determined
largely by the characteristics of the particular sector or region, and the relevant climate
change impact issues there. Possible reasons for undertaking a climate change impacts IA in
the north Queensland region include the following:
 This World Heritage area has important cultural and economic values, connected to its
status as the single most biodiverse region in Australia; adaptation to climate change is
important to maintain those values.
 It is a well-defined economic region with a socio-economic fabric that is dependent on
its biodiversity and cultural values, including the iconic status of the Great Barrier Reef.
 The system is potentially vulnerable to climate change impacts, for example in terms of
heritage values (threats to the reef and the rainforest), and in the area of health (increases
in vector-borne diseases).
 The region is already under significant pressure caused by impacts from other processes,
including land-use change.
Objectives of such an IA case study would include:
 Making an argument for the necessity for mitigation action to reduce greenhouse gas
emissions. Given known high-probability stresses on a global scale (rising
temperatures, ocean acidification), the GBR and montane tropics will be vulnerable.
Their preservation requires mitigation action. In addition, the gap between adaptive
capacity and residual risk in the region is large.
 Contributing to understanding the costs of inaction on climate change mitigation
(including social, environmental and economic costs), and focus on the need to reduce
avoidable costs.
 Providing knowledge necessary for adaptation to local and state government agencies,
management authorities and individuals, in consultation with stakeholders. This could
include considering issues like the effects of population growth on the resiliance of the
overall system.
 Establishing baseline monitoring and ongoing data collection to allow assessment of the
effectiveness of actions taken.
 Contributing to the development of adaptive policy processes and frameworks.
Scoping should thus fall into two phases. The first is a broad assessment of the need for and
general parameters of an IA in the particular region/sector (which would also identify any
prior relevant work and the ongoing validity of any previously-identified priorities). This
stage should include discussion of the starting point for developing the IA; should it be the
stakeholders’ interests, or the characteristics of the system itself? The second phase involves
57
consultation and collaboration with stakeholders to determine the focus of the study, its
resourcing, the interests of potential users of information from the study, and to assess what
aspects of interest it will be viable to include in the study (taking into account constraints
such as data availability, timeframes and resourcing). For a regional study a broad scope is
likely to be most useful, including as many aspects as possible (e.g. land use, ecosystem
services, settlement, water issues, health).
8.2 Research approaches & reasons for an IA
Integrated assessments are likely to be effective where multiple interests require integrated
actions to address climate change impacts, because it can provide
consistency within and across sectors
a systems perspective across all parts of a region/system
additional explanatory power to that available from studies of separate parts of the system,
often derived by considering the interactions among parts of the system
a means of serving the overlapping needs of a range of stakeholders with interests in
different issues (e.g. the Millennium Ecosystem Assessment, a model that provides both
integration and the possibility of extracting specific reports for different stakeholders)
A range of tools and research approaches is available for IA, ranging from literature surveys
and data appraisals to a variety of modelling techniques. In general, it is probably most
effective to identify a small number of relatively high-level studies that require integration
to be successful, excluding studies that are not highly inter-related. It is important to then
recognise potential linkages, interactions, synergies and amplification within the study, and
to focus on the principal co-variants. An example of a strongly inter-related system
(characterised by people-environment linkages and interdependencies) is the rate of
recovery of the Great Barrier Reef (GBR) following high temperature events, which is
dependent on water quality, which in turn is related to land use.
Suggestions for specific approaches to IA centre on the need for staging, and include
 beginning with a review of existing work to build upon
 identifying components of the system that can be modelled (e.g. fauna, forests, reef,
catchments, sediment, settlement, land use)
 undertaking a GIS mapping exercise to define the spatial dimensions of current
knowledge and data, informed by prior definitions of the desirable scope and focus of
the IA
 looking at the impacts of climate change on iconic natural resources first, e.g. the GBR
(primary impacts), and then considering the resulting impacts on tourism, etc.
(secondary impacts)
8.3 Methodologies and methods for integration
A wide range of methodologies and methods can be applied to IA, including models. A
range of these were discussed in Section 5. However, it is important to recognise that
models can be difficult to work with and are not applicable in all cases. Selecting
appropriate methodologies and methods is an important component of planning an IA.
Considerations in making that selection include
 first developing a conceptual framework, e.g. the Millennium Ecosystem Assessment
(Figure 3) or the National Land and Water Resources Audit (http://www.nlwra.gov.au/)
 incorporating integrative methodologies to achieve a whole that is greater than the sum
of the parts
58



using software and other tools that promote participation and discourse amongst a wide
range of previously non-communicating parties
integrating policy and planning processes and frameworks into the methodology
using both GIS, to integrate data and distributed model outputs, and qualitative system
approaches
A discussion of the considerations that should be made in choosing an appropriate set of
methods for an IA was given in Section 6.
8.4 Products/outputs and communication
It is important that the products of an IA study should be useful to and usable by a range of
current and potential stakeholders and other groups. Some key elements of a report that
succeeds in effective information delivery are:
 an over-arching narrative storyline (such as that for a book or movie on the subject of
the IA)
 a well-written Executive Summary
 a small number of key synthesising diagrams
 highlighting of simple bottom-line messages
 clear recommendations for action
 a focus on solutions as well as problems
 tailoring (language, format) to the target audience/s
 delivery in multiple forms, including web, newsletters, formal publication, media, etc.
This can only be achieved if there is a strong communication and education plan built in to
the study; and if the communication teams understand the science as well as the target
groups. An important decision that will shape the nature of the communication strategy is
the desired end point that should be reached along the continuum from awareness to
behavioural change; this should be specified in the communication plan.
A significant barrier to effective communication is stakeholder fatigue. This can be
overcome using a number of strategies, including:
 ensuring that the material has an exciting story to tell, and has both relevance and
substance
 regular communication on short timelines on issues of substance, to maintain interest
 generating excitement by using ‘post-emergency’ (or dramatic event) opportunities (e.g.
storms, floods, etc) to raise issues
 using event recurrences, anniversaries and other public events to maintain visibility of
issues, at a range of levels
An effective way of engaging the local community is using credible local experts as
advocates; this also helps to maintain interest in and action on the issues addressed in an IA.
It is important to develop local capacity so that the outcomes of the IA can be implemented
and monitored, without ongoing dependence on the IA community.
8.5 Capacity building
A key to successful capacity building in IA is to involve state and local planning agencies
and policy makers as collaborators, rather than stakeholders, in the IA process. Planners
(social, physical, urban design, etc) are particularly important in this respect, as they
develop the systems within which action occurs to implement the existing regulatory
59
frameworks. There is a question, however, as to the extent to which it is the role of the IA
community to engage in capacity building, beyond the collaborative relationships mentioned
above.
60
9
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APPENDIX 1. POLICY AND INTEGRATED ASSESSMENT
It can be assumed that IA is intended, either directly or indirectly, to inform policy decisions
by governments and other members of the policy community. If so, this can be viewed as a
subset – and potentially a particularly complex one – of the broader issue of science-policy
linkages. Three broad issues arise from considering the connection of IA to policy: (i) how
IA as a research activity connects to policy processes; (ii) the prospects for integrated policy
assessment within policy systems; and (iii) issues of scale. This section notes key
considerations in relation to these three issues, and concludes by discussion some
implications for methodological development and selection and for case study selection.
A1.1 Connection to policy
The general issue of science-policy linkages is a fluid and problematic area, with significant
tensions over: the ‘independence’ of pure as opposed to applied science; the roles and
responsibilities of scientists (and researchers more generally) in relation to informing versus
formulating policy; the time scales over which thorough theoretical and methodological
development, let alone empirical investigation, occurs compared to those over which policy
decisions may need to be made; the comprehensibility of scientific methods and findings to
a policy audience; and the changing nature of research provider-funder relationships in
recent years.1
It seems apparent that these tensions – and others – are particular problematic in the case of
IA and climate change. The phenomenon of climate change, and especially its impacts at
meaningful scales, is pervaded by uncertainty yet highly topical politically. Moreover, the
very nature of integrated assessment involves multiple disciplines and policy sectors, in turn
bringing into both research and policy a larger number of different interests and knowledge
systems (the latter including formal disciplines) into collaborative interactions. And, any
policy interventions will only produce measurable, substantive outcomes in the longer term,
affected by multiple factors in interacting natural and human systems, making the
identification of cause-effect linkages a challenging prospect.
The precise nature and potential of research-policy connections will of course vary
profoundly across specific contexts. At the broadest level, some guidance can be provided
for such context-specific research design by exploring: (i) different parts of the policy
process, and thus which particular parts an IA exercise might seek to connect with; and (ii)
different froms of policy learning, assuming that the design and outcomes of an IA has, at
least in part, the intention of contributing to learning how to formulate and deliver more
effective policy interventions aimed at ameliorating or avoiding adverse climate change
impacts (or conversely, taking advantage of potentially beneficial changes).
On the first, Table 1 offers a detailed representation of the policy process, constructed
specifically for the environment and sustainability domain.2 (Note that such ‘models’ should
not be taken to convey the way in which policy is made, or even how it might best be made,
but rather identifies the elements of a comprehensive policy process.) IA would be most
1
For a review, see Nowotny, H. Scott, P. and Gibbons, M. (2001). Re-thinking science: knowledge and the
public in an age of uncertainty. London: Polity Press
2
This construction is from Dovers, S. 2005. Environment and sustainability policy: creation, implementation,
evaluation. Sydney: Federation Press. For an alternative, generalised model, see Bridgman, P. and Davis, G.
2004. The Australian policy handbook. Sydney: Allen and Unwin.
69
relevant in process of problem framing (I, 3-6, 8) and to a lesser extent policy monitoring
(IV, 18). Cognisance of general principles and imperatives (V) and of the roles of and
responsibilities for other elements would be necessary contextual knowledge for scientists
engaged in policy-oriented IA.
Table 1.
Framework for analysis and prescription of environmental and
sustainability policy
_______________________________________________________________
I. Problem framing:
1
2
3
4
5
6
7
8
Discussion and identification of relevant social goals
Identification and monitoring of topicality (public concern)
Monitoring of natural and human systems and their interactions
Identification of problematic environmental or human change or degradation
Isolation of proximate and underlying causes of change or degradation
Assessment of risk, uncertainty and ignorance
Assessment of existing policy and institutional settings
Definition (framing and scaling) of policy problems
II. Policy framing:
9
10
11
Development of guiding policy principles
Construction of general policy statement (avowal of intent)
Definition of measurable policy goals
III. Policy implementation:
12
13
14
15
16
17
Selection of policy instruments/options
Planning of implementation
Planning of communication, education, information strategies
Provision of statutory, institutional and resourcing requirements
Establishment of enforcement/compliance mechanisms
Establishment of policy monitoring mechanisms
IV. Policy monitoring and evaluation:
18
19
20
Ongoing policy monitoring & routine data capture
Mandated evaluation and review process
Extension, adaptation or cessation of policy and/or goals
_____________________________________________________________________
V. General elements, throughout policy and institutional systems:
In policy processes:
- policy coordination and integration (across and within policy fields)
- public participation and stakeholder involvement
- transparency, accountability and openness
- adequate communication mechanisms (multi-directional, democratically structured).
Institutional arrangements:
- persistence over time
- purposefulness via mandate and goals
- information-richness & -sensitivity, including gathering, use and ownership
- inclusiveness in policy formulation and implementation
- flexibility, through evaluation, experimentation and learning.
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_____________________________________________________________________
On the second issue, policy learning Table 2 indicates four broad forms of policy learning,
and the actors involved, and the outcomes of learning.3 Instrumental and government
learning operates with respect to existing constructions of policy problems and goals,
whereas social and political learning explicitly seek to redefine problems and goals. It is
likely that IA will contribute principally to social learning, in terms of informing the
definition of policy problems, given that climate change impacts are uncertain and social
and political consensus on policy problems and goals is yet to materialise. That raises the
sensitive issue of informing versus formulating policy problems, a tension which can be
minimised through clear recognition of the roles of research and policy actors in any
collaborative IA project.
Table 2.
Policy learning: forms and purposes
Form
Instrumental
learning
What is learned?
How well
instruments have
allowed the
achievement of
goals
Government
learning
How well
administrative
arrangements and
processes have
allowed policy
implementation
How useful are our
constructions of
policies and goals
Social
learning
Political
learning
How to most
effectively engage
with and influence
political and policy
processes
Who learns?
Members of the policy
network, especially
government officials
engaged in policy
formulation and
implementation
Members of the policy
network, especially senior
officials responsible for
design and maintenance of
policy process
The broader policy
community, including both
more and less closely
engaged actors within and
outside government
Policy actors wishing to (i)
change policy agendas and
outcomes or (ii) defend
current agendas and
outcomes
To what effect?
Better design and
implementation of policy
instruments to achieve
predetermined policy
goals
Better design of
administrative structures
and processes within the
bureaucratic systems (and
engaging outside that
system)
Reframed problems and
related goals, through
changed cause-effect
understanding or altered
social preferences
Change in: problem
definition; policy goals;
and/or membership of the
policy network.
Final broad guidance on negotiating the connections between IA and policy can be found in
the area of knowledge utilisation in public policy, again offering a simple yet well-informed
framework for considering how information (especially scientific research outcomes) is or
could be used in policy systems, beyond the simplistic and now dated assumption of linear
connection and uncritical uptake of scientific findings (instrumental use). Table 3 proposes a
simple taxonomy of the use of composite sustainability indicators in policy systems, and the
similarity of that field to the likely composite and complex nature of outcomes of IA
suggests its relevance to IA.4 Reinforcing the observation regarding policy learning, the
3
From Dovers (2005) op cit, drawing on the broader policy and institutional learning literature.
From Hezri, A.A. 2004. Sustainability indicator system and policy processes in Malaysia: a framework for
utlilisation and learning. Journal of Environmental Management. 73: 357-371.
4
71
outcomes of an IA might occasionally be used instrumentally (directly driving policy
change), but most commonly would be used conceptually, contributing to the formulation of
policy problems. While both researchers and policy officials engaged in IA would not
intend for the outcomes to be used politically, tactically or symbolically, the political
realities of the climate change debate instruct such individuals and agencies to understand
and expect such use of science.
Table 3.
A taxonomy of indicator use
Nature of response
Degree of rationality
High
Low
Positive
Instrumental use
Political use
- use for action
- support predetermined use
Ordinary
Conceptual use
- for enlightenment
Symbolic use
- ritualistic assurance
Negative
(not used)
Tactical use
- delaying tactic
- substitute for action
- deflect criticism
IA and similar research initiatives do not exist in isolation in either space or time, but are
inextricably linked to previous, concurrent and future research and applications. Often, a
small range of organisations will be involved, and the potential exists for mutual learning –
both methodological and policy-oriented – across participating groups. Recent work on the
evolution of and learning within large scale assessment processes may inform the progress
of IA in the Australian context, informed as it is by both empirical analysis of assessment
processes and the organisational learning literature, recognising and taking into account
structural, cultural, contextual and personal variables and identifying different forms of
learning that contribute to improved assessments and applications.5
A1.2. Integrated policy assessment
The imperative of policy integration stems from the foundational principle of sustainable
development – codified in over one hundred Australian statutes and in international treaties
as well as much policy – of integrating environmental, social and economic consideration in
policy.6 The logic of policy integration is that attention to environmental problems n
isolation – methodologically or in institutional and policy terms – fails to deal with the
systemic and indirect causes of environmental degradation, which requires attention to other
policy settings which create the incentives or disincentives for environmentally damaging
behaviour. It incorporates a number of aspects and approaches which can be briefly noted
here.
5
This draws specifically on Siebenhuner, B. (2002). How do scientific assessments learn? Part 1: conceptual
framework and case study of the IPPC. Environmental Science and Policy: 5: 411-420.
6
Generally, see Lenschow, A. (ed). Environmental policy integration. London: Earthscan; and Dovers (2005)
op cit, chapter 10..
72
The first aspect is two purposes or degrees of integration. Environmental policy integration
refers to be embedding of environmental considerations into policy analysis and formulation
in non-environmental sectors and portfolios. The other is a more overarching and complete
requirement or intent of full integration of environment, social and economic factors.
With respect to both these purposes or degrees, there are institutional or policy process
approaches, and methodological or decision-support options, respectively summarised in
Tables 4 and 5.
Table 4.
Institutional and policy process options for policy integration
General form of
integration
Major options (selected)
Policy processes
Inter-agency and
cross-sectoral (within
jurisdiction)
Table 5.

Overarching environment and/or sustainability policy, or policy
development defined by problems that traverse policy sectors
(eg. oceans, biodiversity, energy, etc).

Policy assessment processes: strategic environmental
assessment; sustainability assessment; regulatory impact
review; environmental risk assessment.

Legislative review for consistency with sustainability
principles.

Insertion of environment and sustainability consideration in
agency decision making through statutory expression of
sustainability principles.

Agency reporting on environment and/or sustainability (incl
triple bottom line accounting).

Connecting existing parts: cabinet review processes; ministerial
councils, inter-departmental committees or taskforces; joint
policy programs; information sharing; parliamentary
committees.

Merging wholes or parts: portfolio and agency re-organisation
(super ministries, mergers, etc).

Whole-of-government mechanisms: offices or commissioners
of environment or sustainability; councils for sustainability;
sustainability legislation.
Methods for policy integration (selected)
Category
Main examples
1. Economic/neoeconomic
Extended cost benefit analyses (incorporating values not measured
economically in traditional CBA); non-market valuation (eg. contingent
valuation, hedonic pricing, travel cost method); choice modeling; multicriteria analysis; natural resource accounting; agent-based modelling.
2. Integrated
assessment and
Various forms of bio-economic and related modeling, sometimes
including agent-based models and scenario modelling, including
73
modeling
integrated assessment of climate change impacts
3. Policy assessment
methods and
procedures
Strategic environmental assessment, sustainability assessment and
integrated policy assessment, extending assessment of proposals beyond
the project scale of environmental impact assessment to the assessment of
policies, plans and programs in non-environmental policy sectors.
4. Discursive
approaches
Planning cells, collaborative planning, citizens juries, consensus
conferences, and a range of inclusive approaches (see Chapter 9).
There is overlap in the integrative intent of IA and The relevance of institutional options for
policy integration. The relevance of the options in Table 4 will largely be more indirect and
contextual, and variable depending on the IA process, the policy environment, and the
relationship between these. IA in the context of this volume is one option listed under (2) in
Table 5, however various other methodological options may be incorporated into a broader
IA exercise. Awareness of the breadth of integrative methodologies would be a necessary
input to design of an IA, even if the broadening of IA to include, for example, social values
via discursive approaches, is unlikely. No methodological approach has attracted broad
support or is likely to be suitable under all or even many conditions, so a preparedness to
choose and utilise methods from a number of options is necessary.
It is clear that policy integration will never – and should not be expected to – produce single
metrics that allow composite measures of the social, environmental and economic
implications of a policy decision. The same impossibility would clearly be the case with IA
methods.
A1.3. Scale and integrated assessment
Elsewhere in this document (3.2, 4.3) the issue of scale is dealt with, and is a recurrent
theme in climate change research and IA. However, most discussion is about scale issues in
joint modelling between biophysical sciences and sometime with a few social sciences
(especially economics). If we accept IA as being, either actually or potentially, an
undertaking inclusive of a larger range of disciplines from the social and natural sciences
and the humanities, and as being closely related to policy processes, then the issue of scale
becomes more problematic, for two reasons. First, the spatial and temporal scales that
underpin theory and method in different disciplines vary widely and are often not explicit or
well-explained when viewed from outside that discipline – the issue of embedded scale.7
Second, the spatial and temporal scales over which legal, policy, institutional and political
actors and processes operate are rarely congruent with those over which climate change
operates or with which the disciplines dominant in climate impacts research are familiar
with.
Here, this issue can only be identified and briefly illustrated. As with many issues in
interdisciplinary and policy-oriented research, the first challenge is to make disciplinary
(theoretical and methodological) differences explicit and understood early in the research
design phase, at least allowing for the possibility of subsequent theoretical and
7
This term, and much of this discussion, is taken from Dovers, S. 2004. Embedded scales: interdisciplinary
and institutional issues. Millennium Assessment Conference: Bridging scales and epistemologies, Alexandria,
17-20 March 2004.
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methodological development to integrate perspectives and approaches. The following uses
some simple (and to a degree simplified) examples to illustrate differences in embedded
scale:
Discipline/sub-discipline:
Typical scales (spatial, temporal)
Neoclassical economics:
- spatial: individual, household, firm,
economy (jurisdictions), trade systems
- temporal: short term (months-years).
Economic history:
- spatial: national state, sector
- temporal: longer term (decades-centuries).
Ecology:
a) ecosystem theory
b) community ecologist
a) ecosystem; multiple, but longer term
b) community; multiple, but shorter term.
Law:
a) common
b) statute
a) legal tradition
b) jurisdiction, enactment/repeal.
Psychology:
- individual, days-years.
Meteorology:
- spatial: local-regional-global (but not
jurisdictional)
- temporal: days-years-centuries.
Sociology, anthropology:
- group, years-decadal.
Chemistry:
- non-spatial, instantaneous.
Although simplified and selective, the above illustrates the different embedded scales across
and even with disciplines. We may note, however, that some disciplines and research
traditions have – or at least had – a tradition of natural-social science interactions at multiple
scales, the most obvious one being geography (Barnett et al 2003). To expand on this, it is
necessary to understand the underlying logic of a particular scale – that is, the reasons why a
particular discipline, theory, method or application utilises a particular scale. The following
is again simplified, but illustrates the point.
Apparent scales (examples)
Spatial:
- individual, household, policy or industrial
sector, locale, bioregion, catchment, subnational, nation state, inter-governmental,
regional, global.
Underlying logics (examples)
- consumption, distribution of taxa, nutrient
fluxes, jurisdictions, administration, legal
competence, information availability, trade
flows, transport systems and other
infrastructure, international treaties and
agreements.
Temporal:
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- instantaneous, hours, days, weeks, months,
seasonal, annual, decadal, generational,
geological.
- chemical reactions, half-lives, life cycles,
flowering, agricultural production, human
longevity and fertility, political mandate, profit
reporting, tax cycles, memory, data relevance,
evolution.
All the reasons in the second column above may be highly relevant to either and integrated
assessment, or to the utilisation of the outcomes of an IA in the policy process.
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APPENDIX 2. WORKSHOP PROGRAM AND PARTICIPANTS
Workshop on Integrated Assessment of Climate Change Impacts
4-5 July at the Australian National University
Organised by
ANU Centre for Resource and Environmental Studies
Sponsored by
Australian Greenhouse Office, Dept of Environment and Heritage
and the ARC Network for Earth System Science
Aims
The area of integrated assessment of the impacts of climate change, with particular
emphasis on assessing vulnerability and potential adaptivity of key natural and human
systems in specified regions, is one of growing international significance. Integrated
assessments require a wide understanding of natural and human systems and their
interdependency, as well as consultation with stakeholders.
The purpose of the workshop is to bring together international and Australian experts in
integrated assessment methods and in climate change impacts, in the presence of key State
and Federal stakeholders, to address three main aims:
1. To identify the main methods for integrated assessment of the impacts of climate change.
Methods will include “top down” scenario driven approaches and “bottom up” vulnerability
based approaches.
2. To assess the strengths and weaknesses of these approaches for the assessment of climate
change impacts and adaptation options with reference to key Australian sectors.
3. To identify 3-4 regional case studies to test these approaches in the Australian context.
The workshop is for two days. The first day will address aims 1 and 2 with presentations of
integrated assessment methodology across sectors by key invited international and
Australian experts. The second day will address synthesis of the methodologies presented on
the first day and progress to identification of 3-4 regional case studies as required by aim 3.
Detailed synthesis of workshop outputs in the light of the current literature will be circulated
to the Australian Greenhouse Office and to workshop participants.
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Program
Sunday evening – 3 July
6.00
Welcome Drinks at University House
Day 1 - 4 July
Introduction
9.00 Workshop logistics – Mike Hutchinson, ANU CRES
9.10 Key questions to be addressed by workshop – Jo Mummery, AGO
Major climate change vulnerability/adaptation issues by sector
9.30 Agriculture – Jean Chesson, BRS
9.40 Water Resources – Ian White, ANU CRES
9.50 Biodiversity – Jann Williams, Latrobe University
10.00 Marine Ecosystems – Paul Marshall, GBRMPA
10.10 Urban systems/infrastructure – Patrick Troy, ANU CRES
10.20 Human Health – Tony McMichael, ANU NCEPH
10.30 Emergency management – John Handmer, RMIT
10.40 Morning Tea
International Overview of Integrated Assessment Methodology
11.10 Alex Farrell, Energy & Resources Group, University of California, Berkeley
12.10 Discussion
12.30 Lunch
Methods in Integrated Assessment
1.30 Climate change scenarios and impacts – Kevin Hennessy, CSIRO
1.50 Climate change risks and integrated assessment – Roger Jones, CSIRO
2.10 Integrating quantitative models – Alex Farrell, University of California, Berkeley
2.30 Incorporating population health impacts – Tony McMichael, ANU NCEPH
2.50 Agriculture/grazing – Steve Crimp, Qld Dept Natural Resources
3.10 Afternoon Tea
3.40 Context-sensitive integrated methodologies – Amanda Lynch, Monash University
4.00 A role for models in integrated assessment – Rebecca Letcher, ANU iCAM
4.30 Methodology for integrated assessment – Tom Brinsmead, University of Newcastle
4.40 Criteria for assessment of case studies on Day 2 – Stephen Dovers, ANU CRES.
5.15 Close
Workshop Dinner
6.30 for 7.00 Vivaldi’s ANU
Day 2 – 5 July
Clarification of methods and their strengths and weaknesses – discussion led by
Rebecca Letcher ANU CRES
10.30 Morning Tea
11.00 Scoping of case studies – discussion led by Stephen Dovers, ANU CRES
12.30 Lunch
1.30 Matching methods to case studies – discussion led by Will Steffen, AGO/BRS
3.00 Afternoon Tea
3.30 AGO perspective on workshop – John Higgins, AGO
4.00 Close
9.30
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Workshop Participants
Jim Allen
SA Dept of Environment & Heritage
Anne Bennett
WA Dept of Premier and Cabinet
Bryson Bates
CSIRO Land & Water
Tom Brinsmead
University of Newcastle
Jean Chesson
Bureau of Rural Science
Steve Crimp
Qld Dept Natural Resources
Dale Dominey-Howes
Macquarie University
JeanDouglass
Australian Greenhouse Office
Steve Dovers
ANU CRES
Alex Farrell
University of California, Berkeley
Paul Graham
CSIRO
John Handmer
RMIT
Kevin Hennessy
CSIRO Atmospheric Research
John Higgins
Australian Greenhouse Office
Jack Holden
Victorian Greenhouse Policy Unit
Mark Howden
CSIRO Sustainable Ecosystems
Lesley Hughes
Macquarie University
Mike Hutchinson
ANU CRES
Tony Jakeman
ANU ICAM/CRES
Roger Jones
CSIRO Atmospheric Research
Jenny Kesteven
ANU CRES
Janette Lindesay
ANU SRES
Rebecca Letcher
ANU ICAM/CRES
Amanda Lynch
Monash University
Paul Marshall
GBRMPA
Tony McMichael
ANU NCEPH
Frank Mills
ANU CRES/RSPSE
Jo Mummery
Australian Greenhouse Office
Neville Nicholls
Bureau of Meteorology
Tamara O’Shea
Qld EPA
Pascal Perez
ANU RSPAS
Neil Plummer
Bureau of Meteorology
Paul Purdon
NT Greenhouse Unit
Hugh Saddler
Energy Strategies
Jason Sharples
ANU CRES
Will Steffen
AGO/BRS
Ros Taplin
Macquarie University
Nigel Tapper
Monash University
Graham Turner
CSIRO Sustainable Ecosystems
Pat Troy
ANU CRES
IanWhite
ANU CRES
Jann Williams
Latrobe University
Oliver Woldring
NSW Greeenhouse Office
Andrew Zuch
Qld Dept of Natural Resources
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