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501 © IWA Publishing 2015 Journal of Water and Climate Change | 06.3 | 2015
Toward an innovative interdisciplinary method for
vulnerability assessments: the case of Taiwan
Chia-Chi Lee, Ching-Pin Tung, Tzu-Ming Liu, Jung-Hsuan Tsao,
Gin-Rong Liu, Yi-Chang Chiang and Kuo-Ching Huang
ABSTRACT
Humans live in complicated social-ecological systems within which we interact with our
surrounding environment. This interaction is of concern to various disciplines, which focus on
various system elements (factors), many of which are mutually interacting. Assessments of
vulnerability to climate change assist us in realizing the magnitude of the impact of various
climate change factors, allowing us to determine and adopt appropriate adaptation measures.
Nevertheless, previous impact-driven vulnerability assessments are either disciplinary or
multidisciplinary and cannot easily account for the interaction between different disciplines. This
paper proposes an interdisciplinary vulnerability assessment method (IVAM) to develop a
framework by which interdisciplinary vulnerabilities can be understood. In addition, IVAM
processes can promote the emergence of an interdisciplinary system, which could be used to
identify the scope of interdisciplinary influence of a particular policy, along with the critical
elements (factors) and government stakeholders of such policies. This research seeks to further
the policy goals of the national government of Taiwan vis-à-vis climate change, covering the joint
cooperation of experts from fields including environmental disaster management, public health,
food security, ecology, and water resource management. The specific advantage of IVAM,
however, is that this universal model is not limited to any of these specific disciplines.
doi: 10.2166/wcc.2014.256
Chia-Chi Lee (corresponding author)Center for Environmental Studies,National Central University, Taoyuan County,Chinese TaiwanE-mail: nelisonlee@outlook.com
Ching-Pin TungTzu-Ming LiuJung-Hsuan TsaoDepartment of Bioenvironmental Systems
Engineering,National Taiwan University, Taipei City,Chinese Taiwan
Gin-Rong LiuCenter for Space and Remote Sensing Research,National Central University, Taoyuan County,Chinese Taiwan
Yi-Chang ChiangDepartment of Architecture and Urban Design,Chinese Culture University, Taipei City,Chinese Taiwan
Kuo-Ching HuangGraduate Institute of Urban Planning,National Taipei University, New Taipei City,Chinese Taiwan
Key words | adaptation, interdisciplinary, Taiwan, vulnerability
INTRODUCTION
Adaptation has received increased attention in recent years,
gradually replacing mitigation as the focus of human
response to climate change as a general consensus has
emerged on mitigation-related issues through the use of
international conventions to reduce the emission of green-
house gases. However, humans are still exploring ways to
adapt to climate variation. Whereas previous discussions
in the climate change research community focused exclu-
sively on mitigation, nowadays both mitigation and
adaptation should be emphasized (UNFCCC ; Linham
& Nicholls ; Christiansen et al. ; Clements et al.
; Elliot et al. ).
Practicing adaptation measures depends on the
implementation of adaptation technology. ‘Hard’
technology (hardware) refers to material measures such as
dikes and ditches, while ‘soft’ technology (software) refers
to non-material measures such as information, policies,
and institutional arrangements. Lately, ‘organizational’ tech-
nology (orgware) was proposed by Clements et al. () and
Trærup et al. () as a form of soft technology. Organiz-
ational technology refers to ‘the institutional set-up and
coordination mechanisms required to support the
implementation of hardware and software’ (Vincent et al.
, p. 69). Regardless of the kind(s) of adaptation technol-
ogies adopted, adaptation generally uses and integrates
numerous existing technologies, as opposed to mitigation
which largely depends on the development of recent tech-
nologies (UNFCCC ; Trærup et al. ). Thus, it is
502 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
not surprising that many approaches to adaptation have
emerged, usually involving considerable numbers of stake-
holders, such as various sectors, government organizations
(UNFCCC ; Linham & Nicholls ; Christiansen
et al. ; Clements et al. ; Elliot et al. ), and even
NGOs and other social actors (Thomas & Twyman ).
These approaches require a tool which effectively supports
government policy-making in response to climate change.
Such a tool could identify points of vulnerability within
the complexity of real-world situations, allowing us to
determine correct adaptive action, pre-assess the outcome
of potential policies at the planning stage and help identify
government stakeholders to facilitate cooperation and
accountability.
Organized by Taiwan’s Ministry of Science and Tech-
nology (www.most.gov.tw/mp.aspx), the Taiwan integrated
research program on climate change adaptation technology
(TaiCCAT, http://taiccat.ncu.edu.tw) developed the interdis-
ciplinary vulnerability assessment method (IVAM) as an
innovative tool to support adaptation decision-making.
Based on a framework referred to as driving force-state-
response (DSR), IVAM started with the development of sub-
system mind maps from different disciplines. The system
dynamics model for each subsystem was completed by
ensuring causality between factors in each subsystem. The
subsystems were then connected to each other by shared
factors as well as causality between the factors of the var-
ious subsystems. The software for this interdisciplinary
system features a ‘uses tree’ function which allows the
user to pre-assess the extent of influence of a specific
policy on climate change and to identify that policy’s gov-
ernment stakeholders. Meanwhile, major state factors such
as ‘supplies of services’ and ‘demands for services’ could
be used to identify and estimate the interdisciplinary vulner-
ability index for each subsystem, including the effects of
factors from other subsystems. This allows the interdisci-
plinary system to determine points of vulnerability and
corresponding adaptive measures to be taken.
This article begins with a discussion of interdisciplinary/
transdisciplinary research and vulnerability assessment.
IVAM is then described, and a simple case study is pre-
sented to demonstrate IVAMs applicability. Finally, the
features and inadequacies of IVAM are analyzed.
LITERATURE REVIEW
Interdisciplinary/transdisciplinary research
Some researchers consider ‘interdisciplinary’ and ‘transdis-
ciplinary’ to be synonymous, but more and more
researchers have attempted to distinguish the terms. Tress
et al. (, ) suggested the common features of ‘inter-
disciplinary’ and ‘transdisciplinary’ included a crossing of
disciplinary boundaries, common goal setting among disci-
plines, integration of disciplines and development of
integrated knowledge and theory. However, only ‘trans-
disciplinary’ invited the perspectives of non-academic
participants trying to cross scientific/academic boundaries
to promote cooperation between science and society. The
IVAM approach described here was designed to serve the
academic community and only one category of non-
academic stakeholders (government agencies), so IVAM is
seen as being ‘interdisciplinary’ rather than ‘transdisciplin-
ary’. Still, ‘transdisciplinary’ approaches remain the
ultimate aim of TaiCCAT. The significance of transdisciplin-
ary research is discussed below.
In the recently publishedHandbook of Transdisciplinary
Research, Hirsch Hadorn et al. () argue that transdisci-
plinarity was developed as a form of research to solve
problems stemming from the conventional separation of
scientific knowledge from practical knowledge in the real
world. ‘Scientific knowledge is universal, explanatory,
demonstrated to be true by a standard method, teachable
and learnable’ (Hirsch Hadorn et al. , p. 20). Neverthe-
less, based on scientific knowledge, existing scientific
research methods are unable to address issues characterized
by high-knowledge uncertainty, intrinsic controversy, or
multiple stakeholders, such as issues related to climate
change. In this context, transdisciplinary research is
oriented toward problem solving. ‘It can grasp the complex-
ity of problems, take into account the diversity of life-world
and scientific perceptions of problems, link abstract and
case-specific knowledge, and develop knowledge and prac-
tices that promote what is perceived to be the common
good’ (Pohl & Hirsch Hadorn , pp. 431–432). Not sur-
prisingly, transdisciplinary research is also applicable in
the field of climate change research.
503 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
The epistemic and methodological foundations of trans-
disciplinary research are pragmatic (Häberli et al. ;
Zierhofer & Burger ; Hinkel ; Hirsch Hadorn et al.
). Hirsch Hadorn et al. () indicated that transdisci-
plinary research is necessarily associated with three types of
mutually affected knowledge: ‘systems knowledge’, ‘target
knowledge’, and ‘transformation knowledge’. Systems knowl-
edge addresses ‘questions about the genesis and possible
further development of a problem, and about interpretations
of the problem in the life-world’ (Pohl & Hirsch Hadorn
, p. 431). Target knowledge deals with ‘questions related
to determining and explaining the need for change, desired
goals, and better practices’ (Pohl & Hirsch Hadorn ,
p. 431). Transformation knowledge is concerned with ‘techni-
cal, social, legal, cultural, and other possible means of acting
that aim to transform existing practices and introduce desired
ones’ (Pohl & Hirsch Hadorn , p. 432). The actual pro-
cedures of transdisciplinary research iteratively rotate
among ‘problem identification and problem structuring’ (‘tak
[ing] into account the state of knowledge that exists in the rel-
evant disciplines and among actors in society to define the
problem, identify important aspects, and determine the
research questions and who should be involved’, Hirsch
Hadorn et al. , p. 35), ‘problem analysis’ (‘determin[ing]
what forms of thematic collaboration and organization are
adequate to take into account different interests and circum-
stances’, Hirsch Hadorn , p. 35), and ‘bringing the
results to fruition’ (‘embed[ding] the project into the social
and scientific contexts [and] test[ing] the expected impact’,
Hirsch Hadorn , pp. 35–37).
Vulnerability assessments
Over the last decade, vulnerability has emerged as one of
the most frequently applied and discussed concepts, yet it
also remains rather poorly defined (Adger ; Ionescu
et al. ; Hufschmidt ). In the field of climate
change, vulnerability assessments have become more feas-
ible due to the IPCC’s () definition of vulnerability,
comprising exposure, sensitivity, and adaptive capacity. A
considerable number of researchers have assessed vulner-
ability following the three above-mentioned components
(Hahn et al. ; Center for Environmental Systems
Research ; Commonwealth of Australia ).
Füssel & Klein () distinguished four different stages
of climate change vulnerability assessments: impact
assessments, first- and second-generation vulnerability
assessments, and adaptation policy assessments. There is a
tendency among the four stages toward ‘adaptation’ (from
mitigation), ‘normative approaches’ (from positive
approaches), ‘full consideration of climate variability, non-
climatic factors and adaptation, uncertainty, high inte-
gration of natural and social sciences’, and ‘a high degree
of stakeholder involvement’, where non-climatic factors
imply a wide range of environmental, economic, social,
demographic, technological, and political factors. In Füssel
and Klein’s view, the four assessment stages are not
mutually exclusive or necessarily sequential, and three
former stages need not to be flatly abandoned. The appropri-
ate assessment stage depends on the purpose of the
assessment. Beyond the three former impact-driven stages,
adaptation policy assessments aim to provide specific adap-
tation measures beneficial to policy-making. These measures
are formed by stakeholder involvement, emphasize the vul-
nerability of people living under climate variability along
with the formulation and evaluation of corresponding pol-
icies, and integrate existing adaptation measures. The
proposed IVAM approach is directed toward adaptation
policy assessment and is introduced in the next section.
IVAM
Previous vulnerability assessment methods approached the
problem from the perspective of individual disciplines. How-
ever, the impacts we confront in the life-world, e.g. climate
change, are typically cross-sectoral. Climate change touches
on many different mutually interacting disciplines, and all dis-
ciplines belong to a complicated human/social ecology
(Warner et al. ; Hilhorst ;Walker et al. ; Schröter
et al. ; Cash et al. ; Eakin & Luers ; Miller et al.
; Gotham & Campanella ) at a certain spatial and
societal scale (Adger ). A discipline-specific approach to
vulnerability assessment may neglect/ignore subtle or critical
effects of other disciplines, diminishing the reliability or val-
idity of the assessment. In addition, competing values from
various disciplines frequently result in trade-offs and spillover
effects when taking practical adaptation action. For example,
construction of dikes to withstand flooding increases the
504 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
vulnerability of the ecological environment (Junker et al.
), while increasing food production reduces available
water resources, and expanding aquifer use for drinking
water during drought may increase the risk of waterborne dis-
eases. To plan appropriate adaptation strategies, government
agencies need integrated vulnerability assessment tools.
IVAM was constructed in TaiCCAT to take an interdis-
ciplinary approach to vulnerability assessment and consists
of six steps: (1) constructing the mind maps of disciplinary
subsystems using the DSR framework, (2) constructing the
system dynamics models of disciplinary subsystems, (3) con-
necting subsystems via shared factors, (4) confirming the
causality between factors of different subsystems via
relationship matrices, (5) using the concept of information
flow to achieve information integration, and (6) conducting
interdisciplinary vulnerability assessments. All six steps
rotate as iterative communication processes (Figure 1).
The various constituent units of IVAM are described.
Constructing the mind maps of disciplinary subsystems
using the DSR framework
The disciplinary section is used to develop the subsystems of
distinct disciplines (i.e. disciplinary subsystems), including
the precise issue each subsystem would examine (i.e. the
subsystem’s purpose), all factors of each subsystem (i.e. sub-
system scope), and causal relationship between them (i.e.
subsystem constitution). A disciplinary subsystem begins
by determining the major state factors, supplies of services
and demands for services. A subsystem is sustainable only
Figure 1 | Interdisciplinary vulnerability assessment method (IVAM).
if the supply of a given service within a subsystem is greater
than or equal to the demand for that service. The factors
influencing service supply and demand are then determined
using the DSR framework.
TheDSR frameworkwas developed by theUnitedNations
commission for sustainable development in 1996 and is widely
used to promote sustainable development. ‘The term ‘driving
force’ represents human activities, processes, and patterns
that impact on sustainable development either positively or
negatively. State indicators provide a reading on the condition
of sustainable development, while response indicators rep-
resent societal actions aimed at moving toward sustainable
development’ (UN DESA/DSD ). The main advantage of
this framework is that it uses the interaction between the driv-
ing force (D), state (S), and response (R) indicators to clarify the
causal relationship of various factors within each subsystem.
How do the various influence factors impact the major state
factors, and service supply and demand? And how do the var-
ious influence factors impact each other? Such questions assist
us in completing the scope and constitution of a subsystem
assuring the subsystem’s completeness and interpretability.
Moreover, the DSR framework can facilitate the development
of sustainable strategies as response to avoid negative driving
forces while enhancing positive ones by evaluating sustainable
states, and eventually achieving subsystem sustainability.
The mind maps of disciplinary subsystems are con-
structed using the DSR framework. Mind maps are
imaging tools used to assist cognition. On the map a seman-
tic network, which represents semantic relations among
concepts related to the central topic, is gradually grown by
brainstorming, enriching our cognition, and knowledge of
the central topic. Mind maps are also experience based
and can be used to explore and solve problems; thus, similar
to the DSR framework, they are heuristic in that they encou-
rage the user to discover and learn things for him or herself.
These characteristic features of mind maps are also useful
for understanding the causality of factors, thus the mind
map and DSR framework are mutually complementary.
Constructing the system dynamics models of
disciplinary subsystems
Though, the mind map can flexibly assist in the identifi-
cation and solving of problems, advanced qualitative (i.e.
Table 1 | A sample relationship matrix. D1 and D2 are different disciplines. Factors f11, f12,
and f13 pertain to discipline D1 while factors f21, f22, and f23 pertain to discipline D2.
Blue and red arrows, respectively, represent positive and negative causal relation-
ships, and the direction of an arrow indicates the direction of causality. For
instance, in this table, f11 positively affects f21, and f22 negatively affects f12.
The rest may be deduced by analogy. N/A indicates that no explicitly causal
relationship was found between f13, and f22. Please refer to the online version
of this paper to see this table in colour: http://www.iwaponline.com/jwc/toc.htm.
Discipline D1
Factor f11 f12 f13
D2 f21
f22 N/A
f23
505 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
causal) analysis of disciplinary subsystems is accomplished
through system dynamics models. Pioneered by Jay Forres-
ter in the 1950s, system dynamics features stocks, flows,
and their compound feedback loops which could be used
to explain the complex and non-linear dynamics of systems.
System dynamics has been shown to be one of the most
effective ways to resolve system complexity and problems.
In the field of public policy, for instance, Schwaninger
et al. () have argued that system dynamics can be
applied to complex transdisciplinary issues.
The system dynamics software Vensim (www.vensim.com)
was used to construct the system dynamicsmodels for the dis-
ciplinary subsystems according to their corresponding mind
maps. Based on the input of experts in various disciplines
or academic research literature, positively or negatively
causal relationships between two factors were identified
for each subsystem, facilitating comprehension of their
qualitative relationship.
Connecting subsystems via shared factors
After the system dynamics models are built, subsystems are
integrated into a single interdisciplinary system. Connecting
subsystems via shared factors is an intuitive and simple
approach. However, semantic ambiguity may arise in terms
of a given factor is viewed in various disciplines, and this poten-
tial ambiguity must be clarified by semantic ascent (Hinkel
) (i.e. semantic clarification). In this study, factors from
different subsystems which share names and meanings are
regarded as identical, and subsystems are then connected by
linking identical factors. If factors from different subsystems
have different names but the same meaning, their names are
unified, and then connected via common factors in their corre-
sponding subsystems. If factors share a namebut have different
meanings, their names are altered to create distinction.
Confirming the causality between factors of different
subsystems via relationship matrices
Except where two or more factors share a common meaning,
for a connection to be established, causalitymust exist between
the various factors of the different subsystems. Causality was
confirmed by relationship matrices completed by experts.
Compared to the causality between factors in a single
subsystem, the causality in different subsystems is usually
beyond the scope of current academic research and requires
examination by experts from distinct disciplines. Assuming
that factors f11 and f21, respectively, belong to disciplines D1
and D2 (cf. Table 1), once the relationship determinations of
the experts in the two distinct disciplines converge, the causal-
ity between factors in different subsystems is concluded.
By connecting factors across different subsystems and
confirming the causality between the factors of different sub-
systems, all disciplinary subsystems were linked together as
an interdisciplinary system. This system not only allows for
the identification of factor causality, but for information
integration as described.
Using the concept of information flow to achieve
information integration
One of the key components of interdisciplinary vulnerability
assessments is information transfer between different disci-
plines. The concept of information flow has been
popularized in information management, business manage-
ment, logistics and organizational management to describe
and explain the processes of information transmission.
This article defines information flow as ‘the processes of
information transmission when assessing interdisciplinary
vulnerability’. Information flows facilitate understanding of
information transmission, ensuring the correctness and
accuracy of calculating factors by numerical models in var-
ious disciplines ‘beyond’ the single interdisciplinary system
set-up by system dynamics. In other words, for the sake of
convenience, factor calculations are obtained by numerical
506 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
models in various disciplines (disciplinary numerical
models) ‘rather than’ a single interdisciplinary system,
even if they can be accomplished by a system dynamics
model. It is very time-consuming and difficult to build an
interdisciplinary system dynamics model with all the func-
tions of numerical models in various disciplines. To grasp
the information flow between different factors, one should
note the factor-related information including input infor-
mation from other factor(s) (e.g. units, spatial, and
temporal scales) in calculating factor values as well as
output information from this factor (e.g. units, spatial, and
temporal scales), which is then used to calculate other
factor(s) (Figure 2). Downscaling or upscaling will be
necessary if two factors with a causal relationship have
different spatial or temporal scales. The degree to which
one factor affects the other in the same discipline will be
quantitatively estimated based on the existing disciplinary
numerical models. Furthermore, to calculate the quantitat-
ive results of one factor (fA) influenced by another factor
(fB) in a different discipline, researchers can adjust the
input value(s) in a specific disciplinary numerical model,
according to the influence of fB (e.g. via empirical formulas,
artificial neural networks, etc.). For example, if one would
like to know the impact of river discharge (water resources
discipline) on paddy rice productivity (food security disci-
pline), the input value(s) in the decision support system for
agrotechnology transfer model, which is used to simulate
agricultural crop growth, can be adjusted by the influence
of river discharge to obtain the paddy rice productivity.
Conducting interdisciplinary vulnerability assessments
To use the interdisciplinary system to assess interdisciplin-
ary vulnerability, the first step is to set up the vulnerability
Figure 2 | A sample information flow between different factors (f1, f2,..., f8). Arrows
represent positive or negative causal relationships. Estimating f1 requires the
quantitative degree to which f2, f3, and f4 affect f1 (i.e., input information for f1).
We also focus on the quantitative degree to which f1 affects f5 and f6 (output
information for f1) because this is the input information used for estimating f5and f6 as well.
index of each subsystem. As mentioned before, the values
of the two types of major state factors (i.e. supplies of ser-
vices and demands for services), respectively, represent the
amounts of a subsystem’s supply (i.e. carrying capacity)
and demand (i.e. loading). The vulnerability index value
expresses the demand divided by the supply for a given sub-
system. As demand exceeds supply, the subsystem becomes
increasingly vulnerable. If a subsystem’s vulnerability index
is greater than or equal to one, the subsystem is unsustain-
able. Assessing a subsystem’s vulnerability, index is
interdisciplinary in that it considers the interdisciplinary
influence of how a subsystem’s major state factors are
affected by factors inside and outside the subsystem.
Iterative communication processes
The UNFCCC () proposed ‘iterative steps in planned
adaptation to climate change’, consisting of four steps ‘infor-
mation awareness’, ‘planning design’, ‘implementation’, and
‘monitoring and evaluation’. Necessary information is col-
lected in the information awareness step. Technical
feasibility, national development goals, and policy criteria
(e.g. cost–benefit analysis, sustainability, cultural, and
social compatibility, etc.) are then considered in planning
responses to climate change. Following the planning
design step, systemic methods actively favored by formal/
informal institutions are chosen for implementation. Ulti-
mately, monitoring and evaluation reveal that adaptation
technology can be adjustable, amendable, and innovative.
The entire process is iterative.
Climate change research by the UNFCCC () and
transdisciplinary research by Hirsch Hadorn et al. ()
mention iterative modification. On the other hand, Després
et al. () have noted that communication is another key
element of transdisciplinary research to reach ‘communica-
tive rationality’ as defined by Jürgen Habermas (, ),
so IVAM stresses iterative communication processes to
enable rolling modification rather than instrumental ration-
ality. Given the highly experimental/exploratory nature of
IVAM, the execution of TaiCCAT entailed many uncertain-
ties such as objectives, operational approaches, and
expected results, requiring constant communication and
confirmation among participants. Second, team members
originating from different disciplines are ‘not’ equally
507 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
familiar with IVAM-related concepts (e.g. vulnerability,
interdisciplinarity/transdisciplinarity, supplies of services,
demands for services, etc.) or research tools (DSR frame-
work, mind maps, and system dynamics model). This lack
of mutual understanding can only be remedied through con-
tinuing dialog. In particular, every step in the IVAM process
demands the participation of team members from different
disciplines to ensure that all research outcomes can be inte-
grated into IVAM to assess interdisciplinary vulnerability.
CASE STUDY: TAICCAT EXPERIENCE
The number and total funding of research projects on cli-
mate change in Taiwan has been increasing recently. The
Ministry of Science and Technology, which is responsible
for academic research and development in Taiwan, pro-
vided financial support for 72 research projects on climate
change in 2011, a four-fold increase from 2007 (extracted
and estimated from https://nscnt12.nsc.gov.tw/was2/
award/AsAwardMultiQuery.aspx). Despite these efforts,
adaption research is still insufficient and characterized by
a deficiency in understanding the functions and methods
of adaption, a lack of interdisciplinary/transdisciplinary
and integrated adaption research, and most importantly,
adaptation policy assessments. TaiCCAT was launched in
2009 to build an integrated information platform, long-
term sustainability indicators and data validation mechan-
isms to promote technologies for adaptation to climate
change and support policy decision-making in Taiwan.
The ongoing research program takes an interdisciplinary/
transdisciplinary approach to environmental system analysis,
vulnerability assessment and adaptation governance, and
focuses on formulating policies and plans to help urban,
rural, alpine, coastal, river-basin, and offshore-island areas
cope with and respond to climate change. This essay pre-
sents a partial outcome of TaiCCAT, specifically IVAM,
whose objective is consistent with adaptation policy assess-
ments by Füssel & Klein (), offering the government an
innovative and supportive decision-making tool. According
to IVAM, we would first construct the interdisciplinary
system, and then, based on ‘adaptation strategy to climate
change in Taiwan’ (ASCCT), define the scope of adaptation
policies/issues and assess interdisciplinary vulnerability.
Constructing the interdisciplinary system
There are five disciplines and corresponding subsystem(s)
discussed in the interdisciplinary vulnerability assessment
in TaiCCAT: environmental disaster management (with
the flood protection and landslide prevention subsystems),
public health (with the public health subsystem), food secur-
ity (with the aquatic food safety and crop safety subsystems),
ecology (with the ecology subsystem), and water resource
management (with the water supply subsystem). Figure 3
shows the mind map of the water supply subsystem (see
above under Constructing the mind maps of disciplinary
subsystems using the DSR framework). The purpose of con-
structing Figure 3 is to discuss whether the water supply is
sufficient, issue-related factors, and the relationships
between those factors. After confirming the positive/nega-
tive relationships between factors, Figure 4 displays the
system dynamics model of the water supply subsystem (see
above under Constructing the system dynamics models of
disciplinary subsystems). All relationships between the var-
ious factors are shown in Table 2.
Through linking common factors in different subsystems
(see above under Connecting subsystems via shared factors)
and determining the causality between factors of different
subsystems via relationship matrices (see above under Con-
firming the causality between factors of different subsystems
via relationship matrices), all seven supply subsystems (i.e.
flood protection, landslide prevention, public health, aquatic
food safety, crop safety, ecology, and water supply) could be
integrated in an interdisciplinary system as shown in Figure 5.
Defining the scope of adaptation policies/issues
In the interdisciplinary system, the factor(s) which affect(s)
some specific factor and the factor(s) some specific factor
affects can be, respectively, determined by the causes tree
and uses tree functions of the Vensim software. The tree
function can be used to help identify the scope and stake-
holders of a given adaptation policy on climate change.
Analyzing the impact of a particular adaptation policy on
climate change involves three steps: (1) An adaptation
policy is added to the interdisciplinary system as a new
factor, (2) the new factor is linked to other factor(s),
Figure 4 | The water supply subsystem constructed using Vensim. Blue and red arrows, respectively, represent positive and negative causal relationships. Black text indicates ordinary
factors. Green text represents service supply, while the orange text represents service demand – both of which belong to major state factors. Please refer to the online version
of this paper to see this figure in colour: http://www.iwaponline.com/jwc/toc.htm.
Figure 3 | The mind map of the water supply subsystem. Black text indicates ordinary factors. Green text represents service supply, while the orange text represents service demand –
both of which belong to major state factors. Black lines represent positive or negative causal relationships between factors. Please refer to the online version of this paper to
see this figure in colour: http://www.iwaponline.com/jwc/toc.htm.
508 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
Table 2 | Factors in the water supply subsystem and their relationships
Ordinary factor Ra Ordinary factor Ra Major state factorb (variable) Ra Subsystem
Active storage capacity Daily amount of public water supply insurface water (SW1)
Water supplysubsystem
Diversion capacity
Domestic water quality
Land use
Rainfall
Revenue water ratio
River discharge
Water storage facilities
Leakage rate
Active storage capacity Daily amount of agricultural water supplyin surface water (SW2)Revenue water ratio Diversion capacity
Leakage rate
Domestic water quality
Land use
Rainfall
River discharge
Soil type
Active storage capacity Water storage facilities
Channel length
Channel water conveyancelosses
Flow release
Leakage rate
Land use Daily amount of public water supply ingroundwater (SW3)Pumping facilities
Rainfall
Soil type
Forest coverage
Water purification capacity Amount of alternative water resourcessupply (SW4)
Forest coverage Daily amount of agricultural water supplyin groundwater (SW5)Pumping facilities
Rainfall
Soil type
Desalination (SW6)
Air temperature Domestic water demand (DW1)
Daily domestic waterconsumption per person
Gross domestic product (GDP)
Population
(continued)
509 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
Table 2 | continued
Ordinary factor Ra Ordinary factor Ra Major state factorb (variable) Ra Subsystem
Discount water supply
Non-tap water withdrawal
Industrial wastewaterrecycling
Water purification capacity
Urban sewagerecycling
Air temperature Industrial water demand (DW2)
Area of the industrial zone
Economic output value
Gross domestic product (GDP)
Discount water supply
Non-tap water withdrawal
Water purification capacity
Air temperature Agricultural water demand (DW3)
Aquaculture water
Crop output value
Crop species
Irrigated area
Livestock water
Discount water supply
Non-tap water withdrawal
Rainfall
Return flow
aCausal relationship: positive ( ) or negative ( ).b‘Service supply’ (green text) or ‘service demand’ (orange text). Please refer to the online version of this paper to see this table in colour: http://www.iwaponline.com/jwc/toc.htm.
510 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
and (3) the uses tree is used to analyze the scope and stake-
holders of the new factor (adaptation policy).
The ASCCT was approved by the Executive Yuan
(Council for Economic Planning & Development ) in
June 2012, and serves as the future prime directive of Tai-
wan’s national government on climate change. For
example, the policy includes ‘activation of existing water
storage capacity’ as an adaptation policy for water
resource management. To determine the influence of this
action, ‘activation of existing water storage capacity’ is
added as a new factor to the interdisciplinary system
which finds that it has an apparently positive affect (i.e.
a positive causal relationship) on the factor ‘water storage
facilities’, thus a link is drawn to denote their relationship.
The uses tree of ‘water storage facilities’ clearly shows that
the adaptation policy ‘activation of existing water storage
capacity’ will affect ‘water storage facilities’ which, in
turn, will affect ‘daily amount of public water supply in
surface water’ as well as ‘daily amount of agricultural
water supply in surface water’. Many other factors and
causal relationships related to ‘activation of existing
water storage capacity’ are displayed by arrows in Figure 6,
which also depicts the policy coverage of ‘activation of
existing water storage capacity’. Before adopting this
policy, the positive or negative effect it may have on the
factor(s) could be jointly considered to determine whether
this policy should be adopted.
On the other hand, it is noteworthy that ‘daily amount of
agricultural water supply in surface water’ (belonging to the
water supply subsystem in the water resources discipline)
influences the ‘amount of agricultural water supply’ (belong-
ing to the crop safety subsystem in the food security
Figure 5 | The interdisciplinary system constructed using Vensim consists of seven subsystems: flood protection, landslide prevention, public health, aquatic food safety, crop safety,
ecology, and water supply. Though the arrowheads are too small to see, the blue and red arrows, respectively, represent positive and negative causal relationships. This
complicated system includes 232 factors (i.e. nodes) and their causality. Please refer to the online version of this paper to see this figure in colour: http://www.iwaponline.com/
jwc/toc.htm.
511 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
discipline), while the ‘daily amount of public water supply in
surfacewater’ (belonging to thewater supply subsystem in the
water resources discipline) influences the ‘daily amount of
surface water supply’ (belonging to the aquatic food safety
subsystem in the food security discipline), illustrating the
uptake of interdisciplinary effects. The cross-sectoral compe-
tent authorities (i.e. the government stakeholders) of the
‘activation of existing water storage capacity’ policy, specifi-
cally, the Water Resources Agency (for water resources,
www.wra.gov.tw) and the Council of Agriculture (for food
security, www.coa.gov.tw/show_index.php) are determined
to promote cross-sectoral cooperation and accountability.
The interdisciplinary system provides government with the
capacity to consider cross-sectoral effects in formulating
policy, which is necessary given the complexities of modern
society.
Assessing interdisciplinary vulnerability
All quantitative results in this article are obtained through the
information flow concept (see above under Using the concept
of information flow to achieve information integration).
Figure 6 | The uses tree of the ‘activation of existing water storage capacity’ adaptation policy in water resources. Triangles refer to the specific policy; ellipses indicate factors in the
interdisciplinary system (Figure 5); rectangles represent the subsystems. Blue shapes belong to the water supply subsystem while brown shapes belong to the crop safety
subsystem, and gold shapes belong to the aquatic food safety subsystem. Please refer to the online version of this paper to see this figure in colour: http://www.iwaponline.com/
jwc/toc.htm.
512 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
Vulnerability index was established by two types of major
state factors, i.e. service supplies and demands (see above
under Conducting interdisciplinary vulnerability assess-
ments). Equation (1) is the vulnerability index of the water
supply subsystem. The factors within the water supply subsys-
tem can be used to estimate the so-called ‘interdisciplinarity’
in IVAM results from DW1, DW2, DW3 and SW1, SW2,…, SW6
(see Table 2) alongwith the interdisciplinary effects belonging
to the factors of other disciplines (subsystems) (see Figure 5).
Equation (2) is the vulnerability index in thewater supply sub-
system under the adaptation policy ‘activation of existing
water storage capacity’ in ASCCT (cf. Figure 6). These indices
are used to determine whether ‘activation of existing water
storage capacity’ would be comprehensively beneficial to
the water supply subsystem. The magnitude (positive or nega-
tive) of the impact of every factor could also be calculated.
These kinds of information are provided as a reference for
decision makers to determine whether the policy should be
implemented.
VW ¼ DW1 þDW2 þDW3
SW1 þ SW2 þ SW3 þ SW4 þ SW5 þ SW6(1)
V 0W ¼ DW1 þDW2 þDW3 þ ΔW2 þ ΔW3
SW1 þ SW2 þ SW3 þ SW4 þ SW5 þ SW6(2)
513 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
Equation (3) is the vulnerability index in the crop safety
subsystem (m and n, respectively, depend on the numbers of
major state factors, service supply and service demand), and
Equation (4) is that in the aquatic food safety subsystem (p
and q, respectively, depend on the numbers of major state fac-
tors, service supply and service demand). Equations (5) and
(6) take the influence of ‘activation of existing water storage
capacity’ into consideration (Figure 6).
VC ¼ DC1 þDC2 þ . . .þDCm
SC1 þ SC2 þ . . .þ SCn(3)
VA ¼ DA1 þDA2 þ . . .þDAp
SA1 þ SA2 þ . . .þ SAq(4)
V 0C ¼ DC1 þDC2 þ . . .þDCm
SC1 þ SC2 þ . . .þ SCn þ ΔC2 þ ΔC3 þ ΔC4 þ ΔC5(5)
V 0A ¼ DA1 þDA2 þ . . .þDAp
SA1 þ SA2 þ . . .þ SAq þ ΔA3(6)
Moreover, in Figure 6, ‘activation of existingwater storage
capacity’, through ‘water storage facilities’, ‘daily amount of
agricultural water supply in surface water’ and ‘daily
amount of public water supply in surface water’, positively
impacts the water supply subsystem by increasing its adaptive
capacity (Vw0 <Vw). Similarly, adaptive capacity of the
aquatic food safety subsystemwill be enhanced through inter-
mediate factors ‘water storage facilities’, ‘daily amount of
public water supply in surfacewater’, ‘daily amount of surface
water supply’, ‘amount of aquaculture water supply’, and
‘amount of aquatic food supply in aquaculture’ (VA0 <VA).
This is a spillover effect.
DISCUSSION
Methodological advantages of IVAM
IVAM provides three major methodological advantages as
follows:
1. Interdisciplinary approach: past vulnerability assessments
have adopted either disciplinary or multidisciplinary
approaches. In the latter, every discipline shares a
common goal but lacks enough integration and interaction
with other disciplines so that all disciplines are
intrinsically independent of the others (Tress et al. ,
; Repko ). Examples include computing the Live-
lihood Vulnerability index by means of the composite
index approach by Hahn et al. (), and assessing vul-
nerability from the IPCC’s () prevalent definition by
combining exposure, sensitivity, and adaptive capacity
(Hahn et al. ; Center for Environmental Systems
Research ; Commonwealth of Australia ). These
kinds of vulnerability assessments pertain to multidiscipli-
narity, not interdisciplinarity. Almost all vulnerability
assessment approaches lack interdisciplinarity because it
is very difficult, challenging and resource-intensive to inte-
grate various disciplines as well as to develop integrated
knowledge and theory. Different from the forward-looking
and cross-sectoral CLIMSAVE IA Platform (http://86.120.
199.106/IAP/#/Introduction), which can be considered
an integrated assessment tool (Harrison et al. ),
IVAM provides an interdisciplinary vulnerability assess-
ment by calculating service supply and demand, which
includes the effects of other factors from the same and
different disciplines. Meanwhile, IVAM is suitable to inte-
grate most disciplines and is not limited to the five
disciplines examined herein.
2. Problem-solving orientation: interdisciplinary research
is characterized by its problem-solving orientation
(Häberli et al. ; Zierhofer & Burger ; Hinkel
; Hirsch Hadorn et al. ). Supported by the Min-
istry of Science and Technology, TaiCCAT has focused
on practicable necessities in the context of Taiwan,
mainly in terms of adaptation technologies. IVAM is
the practical outcome of TaiCCAT and will be submitted
to the Ministry of Science and Technology along with
the National Development Council (www.ndc.gov.tw),
the government agency responsible for promoting
national economic development and planning future
national development directions. Several principal inves-
tigators on TaiCCAT directly participated in formulating
the ASCCT. Their dual roles guarantee that the research
results of TaiCCAT, and thus IVAM, take a highly
realistic approach toward satisfying the government’s
demands.
3. Academic interdisciplinarity inspiration: one of the
approaches to connecting subsystems draws on the
relationshipmatrices determined by experts fromdifferent
514 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
disciplines. The causality between the factors of different
subsystems is still subject to considerable uncertainty.
For example, various experts may differ in their assess-
ment regarding the positivity or negativity of certain
causalities between factors of different subsystems, or the
degree of causality due to a lack of effective methods to
prove causality. These kinds of causality, confirmed by dis-
ciplinary experts, are not based on existing research
evidence, and thus offer a valuable interdisciplinary
research direction/gap for climate change adaptation.
Extension research of IVAM
This study only proposes the qualitative impact of ASCCT.
We are now trying to calculate the quantitative interdisci-
plinary vulnerability of the public health subsystem under
the influence of the flood protection subsystem, which
means the estimation of infection rate of some waterborne
Table 3 | IVAM performance according to Hirsch Hadorn et al. (2008)
Three necessary types of knowledge in transdisciplinary research(Hirsch Hadorn et al. )
I
Systems knowledge addresses ‘questions about the genesis andpossible further development of a problem, and aboutinterpretations of the problem in the life-world’ (Pohl & HirschHadorn , p. 431)
C
Target knowledge addresses ‘questions related to determining andexplaining the need for change, desired goals and better practices’(Pohl & Hirsch Hadorn : 431)
E
Transformation knowledge addresses ‘technical, social, legal,cultural, and other possible means of acting that aim to transformexisting practices and introduce desired ones’ (Pohl & HirschHadorn , p. 432)
U
Three phases of research in a transdisplinary research process(Hirsch Hadorn et al. )
I
Problem identification and problem structuring: ‘tak[ing] intoaccount the state of knowledge that exists in the relevantdisciplines and among actors in society to define the problem,identify important aspects, and determine the research questionsand who should be involved’ (Hirsch Hadorn et al. , p. 35)
O
Problem analysis: ‘determin[ing] what forms of thematic collaborationand organization are adequate to take into account differentinterests and circumstances’ (Hirsch Hadorn et al. , p. 35)
N
Bringing the results to fruition: ‘embed[ding] the project into thesocial and scientific contexts [and] test[ing] the expected impact’(Hirsch Hadorn et al. , p. 35)
N
disease due to different inundation depths (Tung et al.
). Aside from the quantitative results to completely
verify the application of IVAM, future research on IVAM
is needed as follows.
Filling gaps between IVAM and transdisciplinary research
Table 3 examines IVAM under the definition proposed by
Hirsch Hadorn et al. () for transdisciplinary research.
As for types of knowledge, IVAM covers systems knowledge,
target knowledge, and transformation knowledge. In terms
of research phases, IVAM provides partial ‘problem identifi-
cation and problem structuring’ but lacks ‘problem analysis’
and ‘bringing the results to fruition’. IVAM must be
improved to include more stakeholders; determine the
forms of thematic collaboration and organization; and
embed the transdisciplinary research project in social and
scientific contexts and test its expected impact. Of these
VAM performance
onstructing mind maps of disciplinary subsystems using the DSRframework, and then establishing the interdisciplinary system byconforming relationships between factors. Analyzing key issues ofsome specific policy through the interdisciplinary system
stimating the vulnerability index and the number of subsystemfactors to determine whether we should implement a particularpolicy
sing the outcomes of IVAM to replace those of disciplinaryvulnerability assessments
VAM performance
nly the academic stakeholders (TaiCCAT members) andgovernment stakeholders (e.g. the Water Resources Agency andthe Council of Agriculture in the case study) can participate in theproblem identification and problem structuring process
/A
/A
515 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
requirements, including more stakeholders is the most
important task because it is a prerequisite for the others. Fur-
thermore, the practical application of the results of IVAM in
the real world is of vital importance. IVAM will be sub-
mitted to the government of Taiwan as a useful assessment
tool, and embedding the outcomes of IVAM into Taiwan
society will verify the ability of IVAM to achieve ‘problem
analysis’ and ‘bringing the results to fruition’ leading to
real-social change.
Increasing stakeholder participation
A greater absolute number of factors in the interdisciplinary
system (e.g. 232 factors in Figure 5) will increase the degree
of difficulty in estimating a subsystem’s interdisciplinary vul-
nerability. Considerable causality between factors should be
qualitatively and quantitatively ascertained, requiring signifi-
cant research resources. In addition, the information flow,
which is related to the dynamics of the factor-related infor-
mation, has to be precisely accounted for to ensure the
reliability and validity of assessing a subsystem’s interdisci-
plinary vulnerability. These denote the disadvantages of
IVAM in some way, but we still have to pay to deal with
such complicated interdisciplinary systems to understand
the social-ecological ones we live in.
Not all researchers are confident of their ability to harness
these complicated systems. Warner et al. () claimed that
the disaster paradigm has been shifted from the technocratic,
behavioral, and vulnerable to complexity paradigms, which
emerged out of ‘a growing understanding of the complex inter-
relationships of ecology and society’ (Warner et al. ,
p. 10). Climate change, overloaded ecosystems, and
exhausted natural resources cause people to reflect on the
interactive and causal relationships between humans and
the environment. Humans play the dual role of victims and
producers of disasters. In Hilhorst’s () view of the com-
plexity paradigm, research on three different social
domains, including ‘the domain of science and disaster man-
agement, the domain of disaster governance and the domain
of local responses’ (Hilhorst , p. 57), is a type of therapy
for ‘system thinking’, which refers to a concept in which
elements of a system and the system itself are functionally
and predictably relevant and will damage human agency
and diversity. ‘The study of social domains allows us to
focus upon the everyday practices and movements of actors
who negotiate the conditions and effects of vulnerability
and disaster’ (Hilhorst , p. 52). In the complexity para-
digm, humans seek neither a hegemonic explanation nor a
perfect solution to complexity. Rather, we have to concentrate
on the contradictory and inconsistent nature of the different
disaster discourses of social actors (stakeholders), and how
the final disaster discourse is established. In a way, Warner
et al. () and Hilhorst () coincide with the well-
known concept of post-normal science suggested by Funto-
wicz & Ravetz (, ). For Funtowicz & Ravetz (,
), the extended peer community (i.e. enhanced stake-
holder participation) is indispensable to post-normal
science: given the high-decision stakes and system uncer-
tainty, such a community allows us to effectively critique the
rationality of many decision alternatives. Transdisciplinary
research responds in a similar way as Hirsch Hadorn et al.
() argue: ‘It has been argued that transdisciplinary
research is necessary when knowledge about a societally rel-
evant problem field is uncertain, when the concrete nature of
problems is disputed, and when there is a great deal at stake
for those concerned with the problems and involved in inves-
tigating them’ (Hirsch Hadorn et al. , p. 37). The
foregoing discussion acknowledges the uncertainties govern-
ing such complicated systems by present scientific methods,
and emphasizes the need to involve more stakeholders in
decision-making processes, thus increasing the satisfaction
of community members.
This research explores the complexity of interdisciplin-
ary systems which inevitably raises various types of
uncertainty which cannot be completely resolved. In
addition to the academic stakeholders (TaiCCAT members)
and government stakeholders (e.g. the Water Resources
Agency and the Council of Agriculture in the case study),
other stakeholders such as NGOs, private sectors and com-
munity members should be included when using IVAM
(Füssel & Klein ). Unlike interdisciplinarity, non-
academic stakeholder participation is the pivotal feature of
transdisciplinarity (Tress et al. , ; Repko ;
Hirsch Hadorn et al. ). With broad non-academic stake-
holder participation, IVAM will transcend the outdated
technocratic, expert control logic (Hilhorst ; Funtowicz
& Ravetz , ; Beck , ), as will the future
outcomes of TaiCCAT.
516 C.-C. Lee et al. | Toward an innovative interdisciplinary method for vulnerability assessments Journal of Water and Climate Change | 06.3 | 2015
ACKNOWLEDGEMENTS
This study is a partial outcome of the TaiCCAT (Technology
Development Project for Establishing Cross-Sectoral
Vulnerability Assessment and Resilience) project. The
authors gratefully acknowledge financial support from the
National Science Council (NSC 100-2621-M-002-036), the
predecessor of the Ministry of Science and Technology.
This study benefitted from the active collaboration of
many team members from various TaiCCAT projects
(disciplines) including Prof. Kwan-Tun Lee, Prof. Huey-Jen
Su, Prof. Huu-Sheng Lur, Prof. Ming-Hsu Li, Prof. Hsueh-
Jung Lu, Dr Ming-Huei Yao, Prof. Shih-Liang Chan, Prof.
Hsing-Juh Lin, Prof. Pei-Fen Lee, Associate Prof. Hwa-
Lung Yu, Dr Wen-Dar Guo, Dr Yi-Ying Chen, Mr Jung-
Feng Shih, Mr De-Jen Peng, Ms Fane-Ching Liao, Ms Mu-
Jean Chen, Mr Shang-Chen Ku, Mr Kuo-Chan Hung and
Mr Chin-Chang Lu.
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First received 18 April 2014; accepted in revised form 6 November 2014. Available online 16 December 2014
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