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.

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 greenhouse gases. However, humans are still exploring ways to adapt to climate variation. Whereas previous discussions in the climate change research community focused exclusively on mitigation, nowadays both mitigation and adaptation should be emphasized (UNFCCC 2006; Linham & Nicholls 2010; Christiansen et al. 2011; Clements et al. 2011; Elliot et al. 2011).

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’ technology (orgware) was proposed by Clements et al. (2011) and Trærup et al. (2011) as a form of soft technology. Organizational technology refers to ‘the institutional set-up and coordination mechanisms required to support the implementation of hardware and software’ (Vincent et al. 2011, p. 69). Regardless of the kind(s) of adaptation technologies adopted, adaptation generally uses and integrates numerous existing technologies, as opposed to mitigation which largely depends on the development of recent technologies (UNFCCC 2006; Trærup et al. 2011). Thus, it is not surprising that many approaches to adaptation have emerged, usually involving considerable numbers of stakeholders, such as various sectors, government organizations (UNFCCC 2006; Linham & Nicholls 2010; Christiansen et al. 2011; Clements et al. 2011; Elliot et al. 2011), and even NGOs and other social actors (Thomas & Twyman 2005). 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 Technology (www.most.gov.tw/mp.aspx), the Taiwan integrated research program on climate change adaptation technology (TaiCCAT, http://taiccat.ncu.edu.tw) developed the interdisciplinary 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 subsystem 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 various 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 government stakeholders. Meanwhile, major state factors such as ‘supplies of services’ and ‘demands for services’ could be used to identify and estimate the interdisciplinary vulnerability index for each subsystem, including the effects of factors from other subsystems. This allows the interdisciplinary 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 presented to demonstrate IVAMs applicability. Finally, the features and inadequacies of IVAM are analyzed.

LITERATURE REVIEW

Interdisciplinary/transdisciplinary research

Some researchers consider ‘interdisciplinary’ and ‘transdisciplinary’ to be synonymous, but more and more researchers have attempted to distinguish the terms. Tress et al. (2004, 2006) suggested the common features of ‘interdisciplinary’ and ‘transdisciplinary’ included a crossing of disciplinary boundaries, common goal setting among disciplines, integration of disciplines and development of integrated knowledge and theory. However, only ‘transdisciplinary’ 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 ‘transdisciplinary’. Still, ‘transdisciplinary’ approaches remain the ultimate aim of TaiCCAT. The significance of transdisciplinary research is discussed below.

In the recently published Handbook of Transdisciplinary Research, Hirsch Hadorn et al. (2008) argue that transdisciplinarity 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. 2008, p. 20). Nevertheless, 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 complexity 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 practices that promote what is perceived to be the common good’ (Pohl & Hirsch Hadorn 2008, pp. 431–432). Not surprisingly, transdisciplinary research is also applicable in the field of climate change research.

The epistemic and methodological foundations of transdisciplinary research are pragmatic (Häberli et al. 2001; Zierhofer & Burger 2007; Hinkel 2008; Hirsch Hadorn et al. 2008). Hirsch Hadorn et al. (2008) indicated that transdisciplinary research is necessarily associated with three types of mutually affected knowledge: ‘systems knowledge’, ‘target knowledge’, and ‘transformation knowledge’. Systems knowledge 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 2008, p. 431). Target knowledge deals with ‘questions related to determining and explaining the need for change, desired goals, and better practices’ (Pohl & Hirsch Hadorn 2008, p. 431). Transformation knowledge is concerned with ‘technical, social, legal, cultural, and other possible means of acting that aim to transform existing practices and introduce desired ones’ (Pohl & Hirsch Hadorn 2008, p. 432). The actual procedures of transdisciplinary research iteratively rotate among ‘problem identification and problem structuring’ (‘tak[ing] into account the state of knowledge that exists in the relevant 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. 2008, p. 35), ‘problem analysis’ (‘determin[ing] what forms of thematic collaboration and organization are adequate to take into account different interests and circumstances’, Hirsch Hadorn 2008, 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 2008, 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 2006; Ionescu et al. 2009; Hufschmidt 2011). In the field of climate change, vulnerability assessments have become more feasible due to the IPCC's (2001) definition of vulnerability, comprising exposure, sensitivity, and adaptive capacity. A considerable number of researchers have assessed vulnerability following the three above-mentioned components (Hahn et al. 2009; Center for Environmental Systems Research 2010; Commonwealth of Australia 2010).

Füssel & Klein (2006) 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 integration 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 appropriate assessment stage depends on the purpose of the assessment. Beyond the three former impact-driven stages, adaptation policy assessments aim to provide specific adaptation measures beneficial to policy-making. These measures are formed by stakeholder involvement, emphasize the vulnerability of people living under climate variability along with the formulation and evaluation of corresponding policies, 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. However, 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 disciplines belong to a complicated human/social ecology (Warner et al. 2002; Hilhorst 2004; Walker et al. 2004; Schröter et al. 2005; Cash et al. 2006; Eakin & Luers 2006; Miller et al. 2010; Gotham & Campanella 2011) at a certain spatial and societal scale (Adger 2005). A discipline-specific approach to vulnerability assessment may neglect/ignore subtle or critical effects of other disciplines, diminishing the reliability or validity 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 vulnerability of the ecological environment (Junker et al. 2006), while increasing food production reduces available water resources, and expanding aquifer use for drinking water during drought may increase the risk of waterborne diseases. To plan appropriate adaptation strategies, government agencies need integrated vulnerability assessment tools.

IVAM was constructed in TaiCCAT to take an interdisciplinary 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) connecting 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.

Figure 1

Interdisciplinary vulnerability assessment method (IVAM).

Figure 1

Interdisciplinary vulnerability assessment method (IVAM).

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. subsystem 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 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.

The DSR framework was developed by the United Nations 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 represent societal actions aimed at moving toward sustainable development’ (UN DESA/DSD 2001). The main advantage of this framework is that it uses the interaction between the driving 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 various 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 constructed using the DSR framework. Mind maps are imaging tools used to assist cognition. On the map a semantic 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 encourage 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 identification and solving of problems, advanced qualitative (i.e. causal) analysis of disciplinary subsystems is accomplished through system dynamics models. Pioneered by Jay Forrester 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. (2008) 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 dynamics models for the disciplinary 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 potential ambiguity must be clarified by semantic ascent (Hinkel 2008) (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 corresponding subsystems. If factors share a name but 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, causality must 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 causality between factors in different subsystems is concluded.

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 relationships, 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    
Discipline  D1   
 Factor f11 f12 f13 
D2 f21    
 f22   N/A 
 f23    

By connecting factors across different subsystems and confirming the causality between the factors of different subsystems, 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 disciplines. The concept of information flow has been popularized in information management, business management, 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 various 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 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 functions of numerical models in various disciplines. To grasp the information flow between different factors, one should note the factor-related information including input information 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 quantitative 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 discipline), 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.

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 f5 and f6 as well.

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 f5 and f6 as well.

Conducting interdisciplinary vulnerability assessments

To use the interdisciplinary system to assess interdisciplinary vulnerability, the first step is to set up the vulnerability index of each subsystem. As mentioned before, the values of the two types of major state factors (i.e. supplies of services 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 subsystem. 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 unsustainable. 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 (2006) proposed ‘iterative steps in planned adaptation to climate change’, consisting of four steps ‘information awareness’, ‘planning design’, ‘implementation’, and ‘monitoring and evaluation’. Necessary information is collected 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. Ultimately, monitoring and evaluation reveal that adaptation technology can be adjustable, amendable, and innovative. The entire process is iterative.

Climate change research by the UNFCCC (2006) and transdisciplinary research by Hirsch Hadorn et al. (2008) mention iterative modification. On the other hand, Després et al. (2008) have noted that communication is another key element of transdisciplinary research to reach ‘communicative rationality’ as defined by Jürgen Habermas (1984, 1987), so IVAM stresses iterative communication processes to enable rolling modification rather than instrumental rationality. Given the highly experimental/exploratory nature of IVAM, the execution of TaiCCAT entailed many uncertainties 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 familiar with IVAM-related concepts (e.g. vulnerability, interdisciplinarity/transdisciplinarity, supplies of services, demands for services, etc.) or research tools (DSR framework, mind maps, and system dynamics model). This lack of mutual understanding can only be remedied through continuing 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 integrated into IVAM to assess interdisciplinary vulnerability.

CASE STUDY: TAICCAT EXPERIENCE

The number and total funding of research projects on climate change in Taiwan has been increasing recently. The Ministry of Science and Technology, which is responsible for academic research and development in Taiwan, provided 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 mechanisms 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 presents a partial outcome of TaiCCAT, specifically IVAM, whose objective is consistent with adaptation policy assessments by Füssel & Klein (2006), 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 security (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 constructing Figure 3 is to discuss whether the water supply is sufficient, issue-related factors, and the relationships between those factors. After confirming the positive/negative 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 various factors are shown in Table 2.

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 in surface water (SW1)  Water supply subsystem 
   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 supply in 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 conveyance losses   
   Flow release   
   Leakage rate   
   Land use  Daily amount of public water supply in groundwater (SW3)   
   Pumping facilities   
   Rainfall   
   Soil type   
   Forest coverage   
   Water purification capacity  Amount of alternative water resources supply (SW4)   
   Forest coverage  Daily amount of agricultural water supply in groundwater (SW5)   
   Pumping facilities   
   Rainfall   
   Soil type   
      Desalination (SW6)   
   Air temperature  Domestic water demand (DW1)   
   Daily domestic water consumption per person     
   Gross domestic product (GDP)     
   Population     
   Discount water supply     
   Non-tap water withdrawal     
Industrial wastewater recycling  Water purification capacity     
Urban sewage recycling      
   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    
Ordinary factor Ra Ordinary factor Ra Major state factorb (variable) Ra Subsystem 
   Active storage capacity  Daily amount of public water supply in surface water (SW1)  Water supply subsystem 
   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 supply in 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 conveyance losses   
   Flow release   
   Leakage rate   
   Land use  Daily amount of public water supply in groundwater (SW3)   
   Pumping facilities   
   Rainfall   
   Soil type   
   Forest coverage   
   Water purification capacity  Amount of alternative water resources supply (SW4)   
   Forest coverage  Daily amount of agricultural water supply in groundwater (SW5)   
   Pumping facilities   
   Rainfall   
   Soil type   
      Desalination (SW6)   
   Air temperature  Domestic water demand (DW1)   
   Daily domestic water consumption per person     
   Gross domestic product (GDP)     
   Population     
   Discount water supply     
   Non-tap water withdrawal     
Industrial wastewater recycling  Water purification capacity     
Urban sewage recycling      
   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.

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.

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.

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 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.

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 Confirming 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.

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.

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.

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 stakeholders 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), and (3) the uses tree is used to analyze the scope and stakeholders of the new factor (adaptation policy).

The ASCCT was approved by the Executive Yuan (Council for Economic Planning & Development 2012) in June 2012, and serves as the future prime directive of Taiwan'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.

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.

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.

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’ (belonging to the crop safety subsystem in the food security discipline), while the ‘daily amount of public water supply in surface water’ (belonging to the water 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 competent authorities (i.e. the government stakeholders) of the ‘activation of existing water storage capacity’ policy, specifically, 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). Vulnerability index was established by two types of major state factors, i.e. service supplies and demands (see above under Conducting interdisciplinary vulnerability assessments). Equation (1) is the vulnerability index of the water supply subsystem. The factors within the water supply subsystem can be used to estimate the so-called ‘interdisciplinarity’ in IVAM results from DW1, DW2, DW3 and SW1, SW2,…, SW6 (see Table 2) along with the interdisciplinary effects belonging to the factors of other disciplines (subsystems) (see Figure 5). Equation (2) is the vulnerability index in the water supply subsystem 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 negative) 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.  
formula
1
 
formula
2
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 factors, service supply and service demand). Equations (5) and (6) take the influence of ‘activation of existing water storage capacity’ into consideration (Figure 6).  
formula
3
 
formula
4
 
formula
5
 
formula
6
Moreover, in Figure 6, ‘activation of existing water 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 (Vw < Vw). Similarly, adaptive capacity of the aquatic food safety subsystem will be enhanced through intermediate factors ‘water storage facilities’, ‘daily amount of public water supply in surface water’, ‘daily amount of surface water supply’, ‘amount of aquaculture water supply’, and ‘amount of aquatic food supply in aquaculture’ (VA < 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. 2004, 2006; Repko 2008). Examples include computing the Livelihood Vulnerability index by means of the composite index approach by Hahn et al. (2009), and assessing vulnerability from the IPCC's (2001) prevalent definition by combining exposure, sensitivity, and adaptive capacity (Hahn et al. 2009; Center for Environmental Systems Research 2010; Commonwealth of Australia 2010). These kinds of vulnerability assessments pertain to multidisciplinarity, not interdisciplinarity. Almost all vulnerability assessment approaches lack interdisciplinarity because it is very difficult, challenging and resource-intensive to integrate 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. 2012), IVAM provides an interdisciplinary vulnerability assessment by calculating service supply and demand, which includes the effects of other factors from the same and different disciplines. Meanwhile, IVAM is suitable to integrate 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. 2001; Zierhofer & Burger 2007; Hinkel 2008; Hirsch Hadorn et al. 2008). Supported by the Ministry 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 investigators 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 relationship matrices determined by experts from different disciplines. The causality between the factors of different subsystems is still subject to considerable uncertainty. For example, various experts may differ in their assessment 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 disciplinary 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 interdisciplinary vulnerability of the public health subsystem under the influence of the flood protection subsystem, which means the estimation of infection rate of some waterborne disease due to different inundation depths (Tung et al. 2014). 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. (2008) 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 identification 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 requirements, including more stakeholders is the most important task because it is a prerequisite for the others. Furthermore, the practical application of the results of IVAM in the real world is of vital importance. IVAM will be submitted 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.

Table 3

IVAM performance according to Hirsch Hadorn et al. (2008) 

Three necessary types of knowledge in transdisciplinary research (Hirsch Hadorn et al. 2008IVAM performance 
Systems knowledge 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 2008, p. 431) Constructing mind maps of disciplinary subsystems using the DSR framework, and then establishing the interdisciplinary system by conforming relationships between factors. Analyzing key issues of some specific policy through the interdisciplinary system 
Target knowledge addresses ‘questions related to determining and explaining the need for change, desired goals and better practices’ (Pohl & Hirsch Hadorn 2008: 431) Estimating the vulnerability index and the number of subsystem factors to determine whether we should implement a particular policy 
Transformation knowledge addresses ‘technical, social, legal, cultural, and other possible means of acting that aim to transform existing practices and introduce desired ones’ (Pohl & Hirsch Hadorn 2008, p. 432) Using the outcomes of IVAM to replace those of disciplinary vulnerability assessments 
Three phases of research in a transdisplinary research process (Hirsch Hadorn et al. 2008IVAM performance 
Problem identification and problem structuring: ‘tak[ing] into account the state of knowledge that exists in the relevant 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. 2008, p. 35) Only the academic stakeholders (TaiCCAT members) and government stakeholders (e.g. the Water Resources Agency and the Council of Agriculture in the case study) can participate in the problem identification and problem structuring process 
Problem analysis: ‘determin[ing] what forms of thematic collaboration and organization are adequate to take into account different interests and circumstances’ (Hirsch Hadorn et al. 2008, p. 35) N/A 
Bringing the results to fruition: ‘embed[ding] the project into the social and scientific contexts [and] test[ing] the expected impact’ (Hirsch Hadorn et al. 2008, p. 35) N/A 
Three necessary types of knowledge in transdisciplinary research (Hirsch Hadorn et al. 2008IVAM performance 
Systems knowledge 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 2008, p. 431) Constructing mind maps of disciplinary subsystems using the DSR framework, and then establishing the interdisciplinary system by conforming relationships between factors. Analyzing key issues of some specific policy through the interdisciplinary system 
Target knowledge addresses ‘questions related to determining and explaining the need for change, desired goals and better practices’ (Pohl & Hirsch Hadorn 2008: 431) Estimating the vulnerability index and the number of subsystem factors to determine whether we should implement a particular policy 
Transformation knowledge addresses ‘technical, social, legal, cultural, and other possible means of acting that aim to transform existing practices and introduce desired ones’ (Pohl & Hirsch Hadorn 2008, p. 432) Using the outcomes of IVAM to replace those of disciplinary vulnerability assessments 
Three phases of research in a transdisplinary research process (Hirsch Hadorn et al. 2008IVAM performance 
Problem identification and problem structuring: ‘tak[ing] into account the state of knowledge that exists in the relevant 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. 2008, p. 35) Only the academic stakeholders (TaiCCAT members) and government stakeholders (e.g. the Water Resources Agency and the Council of Agriculture in the case study) can participate in the problem identification and problem structuring process 
Problem analysis: ‘determin[ing] what forms of thematic collaboration and organization are adequate to take into account different interests and circumstances’ (Hirsch Hadorn et al. 2008, p. 35) N/A 
Bringing the results to fruition: ‘embed[ding] the project into the social and scientific contexts [and] test[ing] the expected impact’ (Hirsch Hadorn et al. 2008, p. 35) N/A 

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 vulnerability. Considerable causality between factors should be qualitatively and quantitatively ascertained, requiring significant research resources. In addition, the information flow, which is related to the dynamics of the factor-related information, has to be precisely accounted for to ensure the reliability and validity of assessing a subsystem's interdisciplinary 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. (2002) 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 interrelationships of ecology and society’ (Warner et al. 2002, 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 (2004) view of the complexity paradigm, research on three different social domains, including ‘the domain of science and disaster management, the domain of disaster governance and the domain of local responses’ (Hilhorst 2004, 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 2004, p. 52). In the complexity paradigm, 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. (2002) and Hilhorst (2004) coincide with the well-known concept of post-normal science suggested by Funtowicz & Ravetz (1991, 1992). For Funtowicz & Ravetz (1991, 1992), the extended peer community (i.e. enhanced stakeholder participation) is indispensable to post-normal science: given the high-decision stakes and system uncertainty, 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. (2008) argue: ‘It has been argued that transdisciplinary research is necessary when knowledge about a societally relevant 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 investigating them’ (Hirsch Hadorn et al. 2008, p. 37). The foregoing discussion acknowledges the uncertainties governing 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 interdisciplinary 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 community members should be included when using IVAM (Füssel & Klein 2006). Unlike interdisciplinarity, non-academic stakeholder participation is the pivotal feature of transdisciplinarity (Tress et al. 2004, 2006; Repko 2008; Hirsch Hadorn et al. 2008). With broad non-academic stakeholder participation, IVAM will transcend the outdated technocratic, expert control logic (Hilhorst 2004; Funtowicz & Ravetz 1991, 1992; Beck 1992, 2000), as will the future outcomes of TaiCCAT.

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.

REFERENCES

REFERENCES
Adger
W. N.
2006
Vulnerability
.
Glob. Environ. Change
16
,
268
281
.
Adger
W. N.
Arnell
N. W.
Tompkins
E. L.
2005
Successful adaptation to climate change across scales
.
Glob. Environ. Change
15
,
77
86
.
(doi:10.1016/j.gloenvcha.2004.12.005).
Beck
U.
1992
Risk Society: Towards a New Modernity
.
Translated by Mark Ritter
.
Sage
,
London
.
Beck
U.
2000
Risk society revisited: theory, politics and research programmes
. In:
The Risk Society and Beyond: Critical Issues for Social Theory
, (
Adam
B.
Beck
U.
van Loon
J.
eds).
Sage
,
London
, pp.
211
239
.
Cash
D. W.
Adger
W. N.
Berkes
F.
Garden
P.
Lebel
L.
Olsson
P.
Pritchard
L.
Young
O.
2006
Scale and cross-scale dynamics: governance and information in a multilevel world
.
Ecol. Soc.
11
(
2
),
8
.
Center for Environmental Systems Research
2010
Background Document on Vulnerability Indicators for the Project Climate Adaptation – Modeling Water Scenarios and Sectoral Impacts
.
Contract N° DG ENV.D.2/SER/2009/0034
.
Christiansen
L.
Olhoff
A.
Trærup
S.
(eds).
2011
Technologies for Adaptation: Perspectives and Practical Experiences
.
UNEP Risø Centre
,
Roskilde
.
Clements
R.
Haggar
J.
Quezada
A.
Torres
J.
2011
Technologies for Climate Change Adaptation: AgriCulture Sector
, (
Zhu
X.
, ed.).
UNEP Risø Centre
,
Roskilde
.
Commonwealth of Australia
2010
Indicators of Community Vulnerability and Adaptive Capacity Across the Murray-Darling Basin – A Focus on Irrigation in Agriculture
.
ABARE-BRS client report for the Murray-Darling Basin Authority
.
Council for Economic Planning and Development
2012
Adaptation Strategy to Climate Change in Taiwan
.
Available from: www.cepd.gov.tw/encontent/dn.aspx?uid=12205. (Last accessed 22 Aug 2005).
Després
C.
Fortin
A.
Joerin
F.
Vachon
G.
Gatti
E.
Moretti
G. P.
2008
Retrofitting postwar suburbs: a collaborative design process
. In:
Handbook of Transdisciplinary Research
, (
Hirsch Hadorn
G.
Hoffmann-Riem
H.
Biber-Klemm
S.
Grossenbacher-Mansuy
W.
Joye
D.
Pohl
C.
Wiesmann
U.
Zemp
E.
, eds).
Springer
,
New York
, pp.
327
341
.
Eakin
H.
Luers
A. L.
2006
Assessing the vulnerability of social-environmental systems
.
Ann. Rev. Environ. Resour.
31
,
365
394
.
Elliott
M.
Armstrong
A.
Lobuglio
J.
Bartram
J.
2011
Technologies for Climate Change Adaptation: The Water Sector
, (
De Lopez
T.
, ed.).
UNEP Risø Centre
,
Roskilde
.
Funtowicz
S. O.
Ravetz
J. R.
1991
A new scientific methodology for global environmental issues
. In:
The Ecological Economics
, (
Costanza
R.
, ed.).
Columbia University Press
,
NY
, pp.
137
152
.
Funtowicz
S. O.
Ravetz
J. R.
1992
Three types of risk assessment and the emergence of post-normal science
. In:
Social Theories of Risk
, (
Krimsky
S.
Golding
D.
, eds).
Westport CT
,
Greenwood
, pp.
251
273
.
Gotham
K. F.
Campanella
R.
2011
Coupled vulnerability and resilience: the dynamics of cross-scale interactions in post-Katrina New Orleans
.
Ecol. Soc.
16
(
3
),
12
.
Häberli
R.
Bill
A.
Klein
J. T.
Scholz
R. W.
Welti
M.
2001
Synthesis
. In:
Transdisciplinarity: Joint Problem Solving Among Science, Technology, and Society – An Effective Way for Managing Complexity
(
Klein
J. T.
Grossenbacher-Mansuy
W.
Häberli
R.
Bill
A.
Scholz
R. W.
Welti
M.
, eds).
Birkhäuser
,
Basel
, pp.
6
21
.
Habermas
J.
1984
The Theory of Communicative Action
.
Volume 1
.
Reason and the Rationalisation of Society. Beacon
,
Boston
.
Habermas
J.
1987
The Theory of Communicative Action
.
Volume 2
.
Lifeworld and System. A Critique of Functionalist Reason. Beacon
,
Boston
.
Harrison
P. A.
Holman
I. P.
Cojocaru
G.
Kok
K.
Kontogianni
A.
Metzger
M. J.
Gramberger
M.
2012
Combining qualitative and quantitative understanding for exploring cross-sectoral climate change impacts, adaptation and vulnerability in Europe
.
Reg. Environ. Change
13
(
4
),
761
780
.
Hilhorst
D.
2004
Complexity and diversity: unlocking social domains of disaster response
. In:
Mapping Vulnerability: Disasters, Development and People
, (
Bankoff
G.
Frerks
G.
Hilhorst
D.
, eds).
Earthscan
,
London
, pp.
52
66
.
Hinkel
J.
2008
Transdisciplinary knowledge integration: Cases from integrated assessment and vulnerability assessment. FA-VAIA (Formal Approaches to Vulnerability Assessment that Informs Adaptation) working paper 7.
Hirsch Hadorn
G.
Biber-Klemm
S.
Grossenbacher-Mansuy
W.
Hoffmann-Riem
H.
Joye
D.
Pohl
C.
Wiesmann
U.
Zemp
E.
2008
The emergence of transdisciplinarity as a form of research
. In:
Handbook of Transdisciplinary Research
, (
Hirsch Hadorn
G.
Hoffmann-Riem
H.
Biber-Klemm
S.
Grossenbacher-Mansuy
W.
Joye
D.
Pohl
C.
Wiesmann
U.
Zemp
E.
, eds).
Springer
,
New York
, pp.
19
39
.
Ionescu
C.
Klein
R. J. T.
Hinkel
J.
Kavi Kumar
K. S.
Klein
R.
2009
Towards a formal framework of vulnerability to climate change
.
Environ. Model. Assess.
14
,
1
16
.
IPCC 2001 Climate Change
2001
Synthesis Report. A contribution of Working Groups I, II and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change
.
Cambridge University Press
,
Cambridge
.
Linham
M. M.
Nicholls
R. J.
2010
Technologies for Climate Change Adaptation: Coastal Erosion and Flooding
, (
Zhu
X.
, ed.).
UNEP Risø Centre
,
Roskilde
.
Miller
F.
Osbahr
H.
Boyd
E.
Thomalla
F.
Bharwani
S.
Ziervogel
G.
Walker
B.
Birkmann
J.
van der Leeuw
S.
Rockström
J.
Hinkel
J.
Downing
T.
Folke
C.
Nelson
D.
2010
Resilience and vulnerability: complementary or conflicting concepts?
Ecol. Soc.
15
(
3
),
11
.
Pohl
C.
Hirsch Hadorn
G.
2008
Core terms in transdisciplinary research
. In:
Handbook of Transdisciplinary Research
, (
Hirsch Hadorn
G.
Hoffmann-Riem
H.
Biber-Klemm
S.
Grossenbacher-Mansuy
W.
Joye
D.
Pohl
C.
Wiesmann
U.
Zemp
E.
, eds).
Springer
,
New York
, pp.
427
432
.
Repko
A. F.
2008
Interdisciplinary Research: Process and Theory
.
Sage
,
Los Angeles
.
Schröter
D.
Polsky
C.
Patt
A. G.
2005
Assessing vulnerabilities to the effects of global change: an eight step approach
.
Mitig. Adapt. Strat. Glob. Change
10
,
573
596
.
Schwaninger
M.
Ulli-Beer
S.
Kaufmann-Hayoz
R.
2008
Policy analysis and design in local public management: a system dynamics approach
. In:
Handbook of Transdisciplinary Research
, (
Hirsch Hadorn
G.
Hoffmann-Riem
H.
Biber-Klemm
S.
Grossenbacher-Mansuy
W.
Joye
D.
Pohl
C.
Wiesmann
U.
Zemp
E.
, eds).
Springer
,
New York
, pp.
205
221
.
Trærup
S.
Olhoff
A.
Christiansen
L.
2011
Editorial
. In:
Technologies for Adaptation: Perspectives and Practical Experiences
, (
Christiansen
L.
Olhoff
A.
Trærup
S.
eds).
UNEP Risø Centre
,
Roskilde
, pp.
VII
VXV
.
Tress
G.
Tress
B.
Fry
G.
2004
Clarifying integrative research concepts in landscape ecology
.
Landsc. Ecol.
20
,
479
493
.
Tress
B.
Tress
G.
Fry
G.
2006
Defining concepts and the process of knowledge production in integrative research
. In:
From Landscape Research to Landscape Planning: Aspects of Integration, Education and Application
(
Tress
B.
Tress
G.
Fry
G.
Opdam
P.
, eds).
Springer
,
New York
, pp.
13
26
.
Tung
C. P.
Lin
L. Y.
Su
H. J.
Lur
H. S.
Lin
H. J.
Li
M. H.
2014
Technology Development Project for Establishing Cross-Sectoral Vulnerability Assessment and Resilience (II)
.
Ministry of Science and Technology (MOST 103-2621-M002-002)
.
UN DESA/DSD
2001
Indicators of Sustainable Development: Framework and Methodologies
.
Background paper No. 3, Commission on Sustainable Development, Ninth Session, 16–27 April 2001
,
New York
.
UNFCCC
2006
Technologies for Adaptation to Climate Change
.
UNFCCC Secretariat
.
Vincent
K.
Cull
T.
Joubert
A.
2011
Technology needs for adaptation in southern Africa: does operationalisation of the UNFCCC and associated finance mechanisms prioritise hardware over software and orgware? In
:
Technologies for Adaptation: Perspectives and Practical Experiences
, (
Christiansen
L.
Olhoff
A.
Trærup
S.
, eds).
UNEP Risø Centre
,
Roskilde
, pp.
69
79
.
Walker
B.
Holling
C. S.
Carpenter
S. R.
Kinzig
A.
2004
Resilience, adaptability and transformability in social-ecological systems
.
Ecol. Soc.
9
(
2
),
5
.
Warner
J.
Waalewijn
P.
Hilhorst
D.
2002
Public participation in disaster-prone watersheds. time for multi-stakeholder platforms? In:
Paper for the Water and Climate Dialogue, Disaster Studies, Irrigation and Water Management Group
,
Wageningen University
.
Zierhofer
W.
Burger
P.
2007
Disentangling transdisciplinarity: an analysis of knowledge integration in problem-oriented research
.
Sci. Stud.
20
(
1
),
51
74
.