Combining qualitative and quantitative understanding for exploring cross-sectoral climate change impacts, adaptation and vulnerability in Europe
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- Harrison, P.A., Holman, I.P., Cojocaru, G. et al. Reg Environ Change (2013) 13: 761. doi:10.1007/s10113-012-0361-y
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Climate change will affect all sectors of society and the environment at all scales, ranging from the continental to the national and local. Decision-makers and other interested citizens need to be able to access reliable science-based information to help them respond to the risks of climate change impacts and assess opportunities for adaptation. Participatory integrated assessment (IA) tools combine knowledge from diverse scientific disciplines, take account of the value and importance of stakeholder ‘lay insight’ and facilitate a two-way iterative process of exploration of ‘what if’s’ to enable decision-makers to test ideas and improve their understanding of the complex issues surrounding adaptation to climate change. This paper describes the conceptual design of a participatory IA tool, the CLIMSAVE IA Platform, based on a professionally facilitated stakeholder engagement process. The CLIMSAVE (climate change integrated methodology for cross-sectoral adaptation and vulnerability in Europe) Platform is a user-friendly, interactive web-based tool that allows stakeholders to assess climate change impacts and vulnerabilities for a range of sectors, including agriculture, forests, biodiversity, coasts, water resources and urban development. The linking of models for the different sectors enables stakeholders to see how their interactions could affect European landscape change. The relationship between choice, uncertainty and constraints is a key cross-cutting theme in the conduct of past participatory IA. Integrating scenario development processes with an interactive modelling platform is shown to allow the exploration of future uncertainty as a structural feature of such complex problems, encouraging stakeholders to explore adaptation choices within real-world constraints of future resource availability and environmental and institutional capacities, rather than seeking the ‘right’ answers.
KeywordsClimate change impacts Adaptation Vulnerability Integrated Scenarios Cross-sectoral
In recent years, a consensus has emerged amongst a wide range of policy-makers and stakeholders that climate change is an increasingly important strategic, economic and political concern (Shackley and Deanwood 2002; Turnpenny et al. 2004; Holman et al. 2008; European Commission 2009). Decision-makers and other interested citizens now need reliable science-based information to help them respond to the risks of climate change impacts and assess opportunities for adaptation (Turnpenny et al. 2004). However, these impacts will be in addition to, or concurrent with, those associated with continuing socio-economic and political changes (Rounsevell and Metzger 2010). Our vulnerability to, and the potential impacts of, climate change therefore need to be evaluated in a holistic or integrated assessment of the effects of our changing future. Integrated assessment (IA), which is a structured process of dealing with complex issues using knowledge from various scientific disciplines and/or stakeholders such that integrated insights are made available to decision-makers (Rotmans 1998), provides an approach and a variety of tools and methods to develop the information resources required.
The first generation of IA models, developed in the 1970s and 1980s (see Hordijk and Kroeze 1997), focused on acid rain, which opened the way for applications linked to climate change (Van der Sluijs 2002). The first models focusing on climate change were developed during the early 1990s (e.g. Nordhaus 1994; Alcamo 1994). These models were eventually used to address questions related to the effectiveness of environmental policies at a global scale. More recent IA modelling has focused on its application at regional to local scales (Rotmans 2006) and has been accompanied by the introduction of participatory IA (PIA) methodologies (Van Asselt and Rijkens-Klomp 2002), which have become increasingly popular over the last decade (see Salter et al. 2010). However, despite recent advances, many IA-related projects continue to provide results or interpretations to stakeholders based on the outputs of particular simulations of an IA model. This is not sufficient to test the sensitivity of the human–environment system, to engender organisational or behavioural change or to enable knowledge creation as a learning process (Holman and Harman 2008). The focus has remained too much on a one-way flow of information from researchers to stakeholders, rather than a two-way iterative process of dialogue and exploration of ‘what if’s’. More interactive IA processes exist such as the story-and-simulation approach, where quantitative models and qualitative stakeholder products are linked, but these focus mostly on novel methods to conduct stakeholder workshops (e.g. Kok et al. 2011a; Sheppard et al. 2011). Very little attention has been paid to improving the way quantitative models are used. Most climate change IA models have unacceptably long run-times for allowing rapid simulation and interactive engagement with the IA. Alternatively, PIA platforms or interface-driven models (Salter et al. 2010) involving clear user interfaces, explicit recognition of uncertainty, and transparency in model performance and operation can take account of the value and importance of stakeholder ‘lay insight’ and promote dialogue between the research and stakeholder communities within a process of mutual learning and guidance (Turnpenny et al. 2004; Holman et al. 2008).
The EU CLIMSAVE project (www.climsave.eu) is developing a PIA platform that will allow users to explore and understand the interactions between climate change impacts in different sectors (agriculture, forests, biodiversity, coasts, water resources and urban development). This user-friendly web-based tool is being initially developed for Europe, but the software is also being tailored to the Scottish context, to test regional application of the approach. This paper describes the conceptual design of the CLIMSAVE Platform based on a professionally facilitated stakeholder engagement process which aims to ensure saliency and relevance of the platform. As part of this engagement process, a series of stakeholder workshops at the European and Scottish scales are providing information on the scenario storylines and the adaptation options to be included within the platform, as well as feedback on the interface design and functionality. The paper does not include detailed descriptions of all the individual model components of the CLIMSAVE Platform, for which reference is made to other reports and papers. Rather, the paper focuses on the holistic framework which underlies the Platform which has been designed to assist stakeholders in developing their capacity to understand the complex interactions between sectors in adapting to both climate and socio-economic change.
Climate change adaptation is increasingly on the policy agenda in Europe. The key policy document for climate adaptation at the EU level is the White Paper on ‘Adapting to climate change: Towards a European framework for action’ (European Commission 2009). This sets out to provide a framework to reduce the EU’s vulnerability to the impacts of climate change. The role of the EU is seen as supporting and strengthening actions taken at other levels of governance (national, regional and local) by establishing coordination and dissemination mechanisms for knowledge transfer to improve the effectiveness of adaptation, ensure solidarity amongst Member States and change policy in those sectors (such as agriculture and biodiversity) that are closely integrated through the single market and common policies (Pataki et al. 2011). A major initiative of the EU under the white paper has been to create a knowledge base for adaptation, the ‘European Climate Adaptation Platform (CLIMATE-ADAPT)’, that helps Member States to access and share information on expected climate change in Europe, the vulnerability of regions and sectors, national and transnational adaptation strategies, case studies of potential future adaptation options (including their costs and benefits) and tools that support adaptation planning. The CLIMSAVE IA Platform will form part of the tools provided by CLIMATE-ADAPT to support adaptation decision-making in Europe and a short movie introducing the functionality of the Platform was prepared for the launch of CLIMATE-ADAPT (http://climate-adapt.eea.europa.eu/climsave-tool).
In addition to the European IA Platform, a regional version of the platform is being developed to test the methodology at a lower scale. Scotland was chosen as the regional case study due to strong interest from stakeholders and because 2012 will be a key year in shaping Scottish adaptation policy. Adaptation policy in Scotland is devolved and the key legislation is the Climate Change Act (Scotland), which was passed in 2009. This sets greenhouse gas emissions targets, provides ministerial powers to create climate change duties on public bodies (Scottish Government 2011) and sets up the reporting infrastructure for measuring progress against mitigation and adaptation targets (Pataki et al. 2011). A Scottish Climate Change Adaptation Framework was published in 2009, with the intention to catalyse improvements with respect to adaptation and resilience (Scottish Government 2009). This was followed in 2010 with the publication of 12 sectoral action plans. The UK Climate Change Risk Assessment (CCRA), which is partly funded by the Scottish Government, has also produced a report on climate change risks in Scotland in 2012. The Climate Change Act (Scotland) requires the Scottish Government to draw up an Adaptation Programme to address the identified risks within this assessment. Furthermore, the Scottish Government has recently funded ClimateXChange (CXC),1 a collaborative initiative between sixteen of Scotland’s leading research and higher education institutions to deliver objective, independent, integrated and authoritative evidence to support the Government in relation to its activities on climate change mitigation, adaptation and the transition to a low carbon economy. The CLIMSAVE IA Platform will contribute to core CXC objectives by exploring potential impacts and adaptation strategies and identifying vulnerability hotspots. CXC has expressed an interest in further refining the IA Platform for Scotland as its work programme develops and would therefore be well placed to host the Scottish platform in the future. This would ensure that CXC and other users, for example, Scottish Natural Heritage, the Scottish Environmental Protection Agency (SEPA), the Forestry Commission and the Councils would have continued access beyond the lifespan of the CLIMSAVE research project. Alternatively, Scotland’s Environment Web (SEweb), a knowledge base of public agencies aiming to help with the sharing of environmental information, could form a suitable host for the Platform. CLIMSAVE is in discussion with CXC and the SEPA to ensure full accessibility to potential Scottish users.
Stakeholder selection and engagement
Design of the selection procedure
The importance of maximising the inclusion of a wide range of stakeholders’ perspectives requires a careful and well-structured selection procedure. Stakeholder selection in CLIMSAVE is complicated by the fact that highly specific input by stakeholders through intensive and direct interaction is demanded, and by the fact that only 20–30 stakeholders can participate in each workshop due to budget constraints. The selection of individual stakeholders thus needs to be made with special care. The following categories were included in the procedure: (i) Social structure—governments, civil society, businesses, research; (ii) Geographical specificity—four regions in Europe and two regions in Scotland; (iii) Topical diversity—six sectors, including urban, agriculture, forestry, water, coasts and biodiversity; (iv) Gender balance; and (v) Age—four age groups. The same criteria for stakeholder selection from these categories were used for both the European and Scottish workshops. The aim is to maintain the same group of participants throughout the cycle of three workshops. This detailed process ensures that the project takes a conscious and planned approach to stakeholder identification and selection for participatory workshops.
Implementation of the selection procedure
The method of structurally identifying stakeholders helped to ensure a complete representation of stakeholders that needed to be invited. For example, the first European workshop covered the selection criteria as follows2: (i) Social structure—governments (10), civil society (7), businesses (7), research (4); (ii) Geographical specificity—northern Europe (3), southern Europe (6), western Europe (12), eastern Europe (5); (iii) Topical diversity—six sectors, including urban (15), agriculture (12), forestry (12), water (12), coasts (10) and biodiversity (14); (iv) Gender balance—women (10) and men (16); and (v) Age—20 to 30 years (1), 30 to 65 years (23), over 65 years (2). It also helped to increase the number of positive replies. However, it did not guarantee that there was a complete coverage amongst the stakeholders that actually participated which was particularly challenging for Europe where it was notably difficult to secure attendance by European government representatives. The attendance rate, however, was still relatively good, especially for the regional workshops, and participation by those stakeholders that did attend was very active in both sets of workshops. The method helped to identify those stakeholders that should be invited and subsequently those who did not participate, which is facilitating efforts for subsequent workshops.
Stakeholder satisfaction following the workshops
At the time of writing, two workshops have been carried out at the European scale and two at the Scottish scale. Stakeholder engagement in both sets of workshops conducted to date was successful. This is reflected by an overall high level of satisfaction specified in the evaluation forms from each workshop, illustrated by remarks such as: ‘Excellent and very informative’; ‘Engaging and thought provoking’; ‘Very interesting process. Looking forward to how this develops’; ‘Process worked well according to the high diversity of participants’; and ‘I really enjoyed the experience. Curious to see the final products’.
Nevertheless, it has proven easier to recruit stakeholders and sustain their return to subsequent workshops in Scotland than in Europe. For stakeholders, it seems easier to identify with a region, such as Scotland, than with Europe partly because the attribution of European policy is seen as less direct. The link between climate change adaptation and stakeholder’s own work is also clearer for the Scottish stakeholders. In addition, it is also easier for the Scottish stakeholders to join the event, since it involves less travel time and thus less time investment. These insights will be further elaborated using a stakeholder questionnaire after the third and final workshop to ascertain stakeholder’s perceptions of the credibility and legitimacy of the workshop process.
Results from the workshops undertaken to date relating to the socio-economic scenarios that the stakeholder’s developed are described in ‘Socio-economic scenarios’ section, whilst stakeholder’s input on the interface design is encompassed in the list of design concepts and design functionality presented in ‘Design of the user interface’ section. In the final workshops, stakeholders will explore sets of strategic options and their consequences for climate change adaptation under the different scenarios by using the IA Platform (see ‘Socio-economic scenarios’ section). The final workshop will also conclude with overall lessons learned on both strategies for climate change adaptation under different scenarios and the social learning experience of the stakeholders involved.
Socio-economic stories (qualitative, explorative, integrated, stakeholder-driven);
Climate scenarios (quantitative, explorative, climate, expert-driven);
Socio-economic and climate scenarios within the IA Platform (quantitative, explorative, integrated, both expert and stakeholder-driven);
Adaptation options within the socio-economic stories and IA Platform (qualitative and quantitative, normative, integrated, both stakeholder and expert-driven).
The overall scenario development method in CLIMSAVE closely follows the so-called story-and-simulation (SAS) approach in which narrative stories are developed and linked to mathematical models in an iterative procedure (Kok et al. 2011a, c). Essential in the SAS approach is the notion that the socio-economic stories that form the context for the modelling efforts are developed by stakeholders. These stories will then largely determine some of the important drivers of future change (e.g. population and GDP growth) that form the quantitative input for the mathematical models within the IA Platform.
This also closely resembles the latest efforts of the climate change community (led by the IPCC WGII and WGIII) to develop a new set of scenarios for the fifth assessment report. There is almost a complete analogy between the SAS approach and the approach taken by the climate change community as well as a strong resemblance between the four categories listed above and their Shared Socio-economic Pathways; Climate models, IA models, and Shared Climate Policy Assumptions. Note, however, that the IPCC-driven scenarios are global and therefore rely on (IPCC) expert opinions rather than on a broad stakeholder involvement.
Crucial in the SAS approach is the development of qualitative (socio-economic) stories and quantitative models in an iterative manner. Socio-economic qualitative scenarios are developed over a series of three stakeholder workshops at each scale (see ‘Stakeholder selection and engagement’ section). Iteration will ensure a high level of consistency between the stakeholder-led qualitative socio-economic stories and the joint expert/stakeholder-led quantitative socio-economic scenarios, such that the expert-determined quantitative model outputs are representative of the stakeholders’ stories. As such, the scenarios as developed by stakeholders determine the scenarios that are incorporated into the IA Platform.
In addition to socio-economic scenarios, a range of climate change scenarios have also been prepared as inputs to the models within the IA Platform. The user interface to the European IA Platform allows the user to select a SRES emissions scenario (A1b, A2, B1 or B2), the climate sensitivity (low, medium or high, with medium being the default) and the global climate model (GCM) in order to explore the effects of climate change uncertainties on cross-sectoral impacts and vulnerabilities. In order to make the number of combinations manageable for the user, it was decided to include five GCMs within the IA Platform out of the 16 available from the IPCC-AR4 database (http://www.mad.zmaw.de/IPCC_DDC/html/SRES_AR4/index.html). Thus, a methodology was developed to objectively select a representative subset of GCMs incorporating the ‘best’ GCM (through an assessment of GCM quality, based on the fit between model and observed annual cycles of precipitation and temperature), the most ‘central’ GCM (the GCM whose climate change scenario is the closest to the mean scenario over all 16 GCMs), and three other GCMs that preserve as much uncertainty as possible due to between-GCM differences (based on the Euclidean distance in an 8-dimensional space consisting of seasonal changes of precipitation and temperature) (Dubrovsky et al. 2011). The final set of GCMs selected to include in the IA Platform was MPEH5 (‘best’), CSMK3 (‘central’), and HADGEM, GFCM21 and IPCM4 (the triplet of most diverse GCMs for Europe).
The Scottish IA Platform incorporates climate change scenarios based on the UKCP09 scenarios (Murphy et al. 2009) as these provide projections of climate change for the United Kingdom with which the Scottish stakeholders are familiar and which give greater spatial and temporal details for Scotland than the GCMs from the IPCC-AR4 database. The UKCP09 scenarios are probabilistic projections based on ensembles of climate model projections consisting of multiple variants of the UK Met Office climate model, as well as climate models from other centres. They are also available for three SRES emissions scenarios (A1FI, A1b and B1). In order to ensure an acceptable speed of operation of the platform as well as making the number of scenarios manageable, internally consistent scenarios were developed based on the 10th, 50th and 90th percentiles of average annual temperature and winter and summer half-year precipitation based on guidance from UKCIP (Roger Street, personal communication 2011).
The IA Platform
The CLIMSAVE IA Platform is an interactive exploratory web-based tool to enable a wide range of professional, academic and governmental stakeholders to improve their understanding surrounding impacts, adaptation responses and vulnerability under uncertain futures. The tool provides sectoral and cross-sectoral insights within a facilitating, rather than predictive or prescriptive, software environment to inform understanding of the complex issues surrounding adaptation to climate change. The power of the tool lies in its holistic framework (cross-sectoral, climate and socio-economic change) and it is intended to complement, rather than replace, the use of more detailed sectoral tools used by sectoral professionals and academics. As such, the IA Platform is not intended to provide detailed local predictions, but to assist stakeholders in developing their capacity to address regional/national/EU scale issues surrounding climate change. The Platform is also expected to be a valuable teaching tool which contributes to a better adapted Europe through assisting the intellectual development of future decision-makers. This vision of the use of the CLIMSAVE IA Platform is consistent with the recognition that the outputs from policy assessments are generally not carefully considered or used directly by decision-makers, but that their impact occurs in more subtle and nuanced ways such as by facilitating group learning amongst stakeholders and providing ‘ammunition’ that can be used to persuade opponents (Owens 2005).
The broad range of target users that are consistent with our vision has three main implications for the IA Platform design. Firstly, that web-based access and interaction are likely to be more practicable and effective than software requiring installation on user’s PCs. Secondly, high visibility within the web is needed to reach users, which will require both local hosting (i.e. on the CLIMSAVE website) and access through European (European Climate Adaptation Platform—Climate-ADAPT; www.climate-adapt.eea.europa.eu) and regional (e.g. ClimateXChange—CXC; www.climatexchange.org.uk or Scotland’s Environment Web—SEweb; www.environment.scotland.gov.uk) portals. And finally, that the use of the final IA Platform by target users in both a supervised environment (e.g. the third set of CLIMSAVE stakeholder workshops facilitated by CLIMSAVE team members) and through free access via the Internet requires that the IA Platform design is as user-friendly and intuitive as possible.
The CLIMSAVE IA Platform is based on a web Client/Server architecture that uses both server-based (i.e. remote) and client-based (i.e. the user’s PC) computing solutions on the web. The models and the underlying physical (soils, land use, etc.) and scenario (climate and socio-economic) datasets use server-based web technologies, as this avoids the need for input data to be transferred to the user’s PC (and hence the requirement for the user to sign data licenses) and maximises access speed. The web-based interface for stakeholders has been developed using a client-based computing solution based on Microsoft Silverlight technology (a Rich Internet Application framework) as this allows: (1) fast reply to the user actions; (2) the output data from (server-based) models to be sent synchronously and asynchronously to the client-based interface, as output data from faster meta-models can be displayed by the user whilst other models finish their run to give the impression of a real-time response; and (3) the opportunity to use map services (e.g. Google Earth, Bing Maps) to display spatial data.
Details of the ten meta-models included within the IA Platform
Regional urban growth (RUG) (Reginster and Rounsevell 2006)
Meta-crop yield (winter wheat and spring wheat, winter barley and spring barley, winter oil seed rape, potatoes, grain maize, sunflower, soybean, cotton, grass, olives)
Soil/climate clustering combined with artificial neural networks
CLIMEX (Sutherst et al. 2001)
Artificial neural networks
Artificial neural networks
Soil/climate clustering combined with artificial neural networks
WaterGAP meta-model (WGMM)
3-Dimensional surface response diagrams
Coastal fluvial flood meta-model (CFFlood)
Simplified process-based model
Artificial neural networks
LPJ-GUESS (Sitch et al. 2003)
SnowMAUS snow cover simulator (Trnka et al. 2010)
Artificial neural networks
Output sectoral and ecosystem service indicators produced by the IA Platform
Sectoral output indicators
Ecosystem service indicators
Area of artificial surfaces
Area of residential and non-residential areas
Crop yields (potential, nutrient-limited and nutrient and water-limited) for 10 crops
Food production delivered through the rural land use sector
Number of generations per season (6 species)
Ecoclimatic index (quality of the ecoclimatic niche for 6 species)
Wood yield in managed forests
Water storage in soils
Naturalness, tranquillity, isolation
Rural land use
Total crop production
Irrigation water demand
Intensively and extensively farmed, forested and abandoned land
Attractiveness of agricultural landscapes
Naturalised high and average monthly river flow
Water availability per capita
Real low, average and high flows
Total water use
Area at risk of flooding
Damages caused by flooding
People affected by flooding
People in flood risk zones
Areas of coastal grazing marsh, salt marsh, intertidal flats and inland marshes
Species sensitivity indices
Net primary production (by plant functional type and species)
Biomass (by plant functional type and species)
Wild food plants
Vegetation influence on local climate
Attenuation of runoff
Charismatic or iconic wildlife
Species for hunting
Attractiveness of forest landscapes
Areas protected for nature
Linking the meta-models
Defining the spatial resolution of the data to be transferred between meta-models;
Identifying and prioritising meta-model inputs and outputs, based on the relevance for adaptation and for stakeholders (Table 2);
Identifying points of potential data transfer between the meta-models;
Specifying the data dictionaries, which define the inputs and outputs, for each meta-model;
Standardising the data dictionaries across all of the meta-models so that data can be passed between meta-models.
The spatial scale of the Platform represents a compromise between the scale of available harmonised datasets, model run-time and spatial detail of the outputs. The higher the resolution at which it operates, the greater is the number of times that the meta-models have to run and hence the greater the overall run-time of the Platform. The European and Scottish IA Platforms therefore operate at resolutions of 10 arcmin × 10 arcmin (approximately 16 km x 16 km in Europe) and 5 km x 5 km, respectively, consistent with the available baseline climatologies.
Design of the user interface
The user should not need to go through an extensive or prolonged model set-up and the run-times should be as short as possible to prevent users getting bored and disengaging;
The layout of the user interface should allow the user to understand potential sectoral and cross-sectoral impacts, evaluate the effects of adaptation on these potential impacts and to assess the cost-effectiveness of different adaptation measures;
Tooltips should be used to provide on-screen user guidance;
The user should be able to vary model input parameters within numerical ranges, rather than through qualitative descriptors of magnitude, to increase the transparency of the model/scenario assumptions (Schneider 1997);
The user should be able to conduct sensitivity and uncertainty analyses, but guidance must be given to constrain ‘realistic’ ranges of values within scenarios and to account for uncertainty (Turnpenny et al. 2004);
The user should be able to view model outputs as conventional impact indicators and as indicators of ecosystem services;
The user should be able to view model outputs in a variety of forms, for example, maps, tables and graphs;
The user should be able to view outputs at a range of scales of aggregation and zoom in, zoom out and pan across mapped model outputs within appropriate limits; and
The user should be able to export model outputs for subsequent analysis.
Future application of the IA Platform
This paper describes the conceptual design of the CLIMSAVE IA Platform and its integration within an ongoing stakeholder engagement process which ensures consistent socio-economic scenarios and model assumptions. Given the iterative nature of the development process, we expect the user interface to undergo further modifications and refinements in response to progressive stakeholder feedback. However, the underlying meta-model structure and linkages will not alter greatly from that reported here. The tool will allow stakeholders to undertake rapid simulations of cross-sectoral impacts and to explore adaptation strategies for reducing climate change vulnerability.
We anticipate that users will come from a broad community. At one level, these may be the policy-makers at EU, national and regional levels who are the target audience for the European Climate Adaptation Platform (CLIMATE-ADAPT) and who are represented by the demographic which participated within the CLIMSAVE stakeholder workshops. At the other end of the spectrum, it is envisaged that the CLIMSAVE IA Platform will be extensively used as a teaching tool in a similar manner to the Regional Impact Simulator (Holman and Harman 2008; Holman et al. 2008). However, in both cases, it is anticipated that the IA Platform will primarily facilitate users in exploring the complex inter-sectoral issues associated with climate impacts, adaptation and vulnerability, that will ultimately lead to a better adapted Europe through enhancing the adaptive capacity of current and future decision-makers. In doing this, this section provides a brief overview of the types of analyses that could be undertaken using the final IA Platform.
Assessment of impacts and vulnerability
The sensitivity of the different sectors and ecosystem services to changes in both key climate and socio-economic variables can be assessed through altering a wide range of model inputs covering five categories: social, technological, economic, environmental and policy (Fig. 4). The upper and lower numerical limits on the slider bar for each model input have been determined through an assessment of the range of values over which each meta-model gives reliable outputs. This allows the user to gain confidence in the performance of the meta-models within the platform and to identify drivers of change which are particularly important for different sectors or cross-sectoral interactions. The IA Platform can then be used to investigate whether different climate and socio-economic scenarios have a negative or positive effect on different sectors or ecosystem services in two time slices (the 2020s and 2050s), including the evaluation of cross-sectoral benefits, conflicts and trade-offs (Fig. 5). Default values for the socio-economic inputs to the meta-models consistent with the scenario storylines developed within the stakeholder workshops have been defined. However, users have the flexibility to alter these values within a credible range that is consistent with the underlying socio-economic story (coloured green on the sliders shown in Fig. 5) or outside of this credible range (coloured yellow in Fig. 5) to investigate uncertainty or if they do not agree with the defined credible range or want to create their own socio-economic scenario. In this latter case, the outputs are labelled as a user-defined scenario rather than one of the predefined scenarios.
Vulnerability is computed as a function of the magnitude of the drivers or pressures of change (exposure; represented through the scenarios), the sensitivity of the system to these drivers (as given by the changes in the outputs from the linked meta-models) and the capacity of people to cope with these effects (represented by coping capacity). Coping capacity depends on the amount of capital (human, social, natural, manufactured and financial) that can be deployed quickly to cope with exposure to pressures and a coping capacity index is calculated from a range of indicators representing these five capitals (Omann et al. 2010; Tinch et al. 2011). If the value of the ecosystem service or sectoral indicator (in a specific time slice) is greater than a predefined tolerance level and there is insufficient coping capacity, then the potential impact is deemed unavoidable and vulnerability occurs. The tolerance level at which a potential impact is defined may represent physical limits (e.g. the height of a flood defence dyke) or mandated limits, for example, the concentration of nitrates allowed in drinking water. Vulnerability hotspots can be viewed on maps which allow a user to explore vulnerability for a single ecosystem service or sectoral indicator or for various combinations of indicators which represent aggregated vulnerability across multiple ecosystem services or sectors.
Assessment of adaptation options and their cost-effectiveness
Broad adaptation options (‘sliders’) in the CLIMSAVE IA Platform and examples of specific actions for their implementation
Broad adaptation options
Related specific actions
Spatial planning for urban sprawl: Planning policy to control urban expansion, and so protect land availability for food and biodiversity
Planning restrictions on greenfield developments
Minimum density requirements for new schemes
Measures to force housing stock into greater use, for example, tax on empty properties, tax on second homes, tax breaks or regulatory relaxation on letting parts of properties
Spatial planning for coastal development: Discouraging coastal development to reduce exposure to coastal flooding
Planning controls on development within coastal floodplain
Availability of flood insurance
Preference for rural living: Reflects people’s relative desire to live in rural areas with access to green space or urban areas with access to social facilities
Consumer demand for green infrastructure
Market plus policy measures
Influencing preferences (e.g. investments in promoting outdoor activities, health education)
Flood protection upgrade: Improving the standard of flood defences
Building/maintaining flood defences
Improving defence heights
Flood resilience measures: Changes to reduce the amount of damage caused by a flood
Improvements to housing stock
Early warning systems
Water technological change: Using technology to reduce industrial and domestic water demand
Technological improvements in white goods efficiency
Investments in leak reduction
Industrial process use efficiency
Water structural change: Promoting behavioural change to use less water
Education and training
Water demand prioritization: How water should be prioritised when demand is greater than availability (food, environment, domestic and industrial)
Abstraction management and regulatory control
Irrigation water cost: Changing irrigation water price to change water use efficiency and demand.
Irrigation efficiency: Changing the amount of water used to produce a fixed amount of food
Investment in more efficient irrigation methods
Yield improvement: Change in yields, due to plant breeding and agronomy (leading to increases) or environmental priorities (leading to decreases)
Conventional crop breeding
Increased agrochemical use
Switch to organic farming
Change in food imports: To encourage food self-sufficiency but reduce European land availability for biodiversity, or increase imports but make Europe more vulnerable to external crop failures
Trade policy to restrict imports
Domestic policy to encourage production
Change agricultural support—more set-aside; more abandonment
Change in bioenergy production: Represents more land allocated to agricultural bioenergy and biomass crops (and so less for food and nature) or vice versa
Policy—non-fossil fuel requirements (biodiesel, etc.)
Regional development programme support
Change in dietary preference for beef/lamb and chicken/pork: Reducing meat consumption in response to anticipated food shortages
Education/promotion of healthy lifestyles
Pricing policy, direct via tax on meat
Pricing policy, indirect via taxes on animal emissions
Changes to agricultural support payments
Reducing diffuse source pollution from agriculture: Changing agricultural practices to reduce water pollution
Fertiliser restrictions (e.g. nitrate vulnerable zones)
Set-aside: Represents the percentage of land removed from production for environmental benefits or to regulate production
Agri-environment options (e.g. buffer strip)
Forest management: Changing forest management practices—from intensive management for timber production with lower nature and recreation values, through to lower intensity management with good nature and recreation/cultural values and reasonable timber production
Changing forest management practices
Tree species: Planting tree species which are better suited to the changed climate
Planting new species
Wetland creation: Represents managed realignment where flood defences are moved inland to make space for creating coastal wetlands
Habitat creation options: Increasing the size of existing protected areas (PA), so as to improve the ability of species to cope with change; or increasing the number of PAs, so as to fill gaps in the PA network and to improve species’ movements across the landscape
Land purchase (voluntary)
Land purchase (compulsory)
Each adaptation option has certain requirements (e.g. costs, skills and/or technologies) that may not be available in all socio-economic scenarios. Further, these requirements are cumulative and so choice of some adaptation options may ‘use up’ the capacity needed to take further adaptation options. This is taken into account in the platform by using the indicators of the five capitals to limit the ‘credible’ range of adaptation options such that it is consistent with the socio-economic scenario under consideration. These constraints are indicative but not binding, in order to maintain maximum flexibility for platform users.
A separate cost-effectiveness screen in the IA Platform provides information on the least-cost alternative out of all the specific adaptation measures that could be associated with a user-defined desired level of adaptation (Table 3; Skourtos et al. 2011; DEFRA 2008). Synergistic and/or antagonistic effects between available measures and across sectors are taken into account in the calculation of cost-effectiveness either directly (i.e. when ancillary costs and benefits are quantifiable) or indirectly (i.e. on the basis of a suitable weighting scheme) (Klein et al. 2005). Expected effectiveness in addressing climate change impacts is assigned to individual adaptation investments on the basis of engineering data. A cost-effectiveness analysis algorithm computes the unitary, financial costs in achieving the specified target for each adaptation measure or combination of measures. Cost estimates are being collected and homogenized in a suitably structured database and are normalized (or ‘weighted’) in order to control for inflation and wealth effects where possible. This is then used to calculate the total implementation cost which depends on the extent of implementation for each combination of measures separately. Finally, the cost-effectiveness ratio is defined and used to rank all possible combinations so as to identify the least-cost combination. The user can vary the implementation time for each measure and the default values for discount rate, expected effectiveness and unit cost within the analysis. The analysis can also be rerun if desired using different methods for quantifying uncertainties in the cost estimates (variation analysis, fuzzy sets, Monte Carlo simulations, log-normal analysis and extreme cases analysis) (CCSP 2009) and the user can examine how these affect the ranking of the cost-effectiveness ratios (Skourtos et al. 2011).
Illustrative results for Europe
The strength of the CLIMSAVE IA Platform is the rapid interactivity that allows the user to quickly explore different climate and socio-economic scenarios, uncertainty in the scenario settings and inter-relationships between sectors. To illustrate this, the European IA Platform has been used to explore the effects of two of the CLIMSAVE socio-economic scenarios (‘We Are The World’ and ‘Should I Stay or Should I Go’—Fig. 2) within the 2050s on urbanisation, land use and total water use, using the HadGEM climate scenario under an A1 emissions scenario and with medium climate sensitivity.
The innovation in ‘We Are the World’ also extends to successfully reducing water demand, with significant increases in both irrigation efficiency and domestic and industrial water savings due to both technological improvements and behavioural change, which leads to a reduction in the number of river basins classed as having medium water stress (withdrawals-to-availability ratio of between 0.2 and 0.4; Flörke et al. 2011; Alcamo et al. 2007; Vörösmarty et al. 2000) and no river basins being severely water stressed (withdrawals-to-availability ratio of greater than 0.4). However, the failure of innovation solutions in ‘Should I Stay or Should I Go’, characterised by little change in irrigation efficiency and water savings due to behavioural change, and a reduction in domestic and industrial water savings due to technological deterioration lead to a fourfold increase in river basins with severe water stress under the selected climate scenario.
Discussion and conclusions
Participatory integrated assessment is a young field, which is contributing significantly to the understanding of complex human–environment systems. Kloprogge and van der Sluijs (2006) developed criteria for analysing PIA processes based on active or passive stakeholder involvement, bottom–up or top–down perspectives, and whether particular stages of the process were open or closed to participation. The active involvement of stakeholders through the stages of scenario development (Workshop 1), scenario quantification and exploration (Workshop 2 and web-based access to the IA Platform) and exploration of adaptation responses (Workshop 3 and web-based access to the IA Platform) within CLIMSAVE enables an integration of bottom–up or top–down perspectives and consistent socio-economic scenarios and model assumptions.
Overall, past experiences with operationalising the story-and-simulation approach to integrate qualitative and quantitative information have been positive. An important critical note, however, has been related to the manner in which quantitative model results are produced and communicated (see Kok et al. 2011b). The models that have been used are typically very complex with very long run-times. Models thus needed to be applied offline and were necessarily treated as a black box when communicated to stakeholders. This lowered the credibility of the results for stakeholders, particularly those that have no experience with mathematical models.
The fundamental concept underpinning the specification of the IA Platform is therefore to deliver rapid interactivity for the user to support PIA, for which the CLIMSAVE Platform utilises the World Wide Web. This technology provides a flexible and familiar interface to stakeholders, which should broaden accessibility and participation and increase impact in research communities. However, the CLIMSAVE IA Platform operates in an application area where trust and credibility are relevant issues, in that the modelling results produced by the Platform should be credible, whilst the modellers and users running and evaluating the model results should be trustworthy (Aumann 2011). A fundamental challenge for establishing credibility of models for the investigation of policy or adaptation responses to climate change is that the future impacts of the response have not yet occurred and thus the ability of the model to reproduce such future behaviour is uncertain (Aumann 2011).
The design of the CLIMSAVE IA Platform is based on addressing both of these issues. Firstly, trust is developed through (1) the iterative stakeholder engagement processes to ensure that there is stakeholder confirmation of the scenario-dependent model inputs; (2) clarity in the user’s selection of the model inputs and responses, whether they be through scenario selection and/or slider positions that have produced the model output results; and (3) the CLIMSAVE IA Platform is freely accessible through the web ensuring that other users can reproduce and confirm prior simulation results and model settings. Credibility aims to be engendered through the scientific credibility of the meta-models (as demonstrated by the validation of the meta-models; Holman and Harrison 2011) and by allowing the users to rapidly interact with the IA Platform to establish whether the model behaviour reproduces their mental model (Kolkman et al. 2005) for their intended use of the model. The very short run-time allows for model execution during a stakeholder workshop, enabling direct stakeholder–model interaction which is rarely achieved within PIA activities. By experimenting with the online version and using immediate results, it is hypothesised that whilst the meta-models will not be totally transparent to lay persons, they will be regarded as a ‘grey box’ such that the stakeholders will gain confidence in model assumptions and model behaviour.
To further support the scientific credibility of the meta-models and integrated assessment approach, uncertainties related to the development of the meta-models, error propagation in integrated systems, the climate and socio-economic scenarios, and the cost estimates are being investigated. A version of the IA Platform is being created that can be run in batch mode allowing the research team to undertake large multiple runs on a dedicated server. The resulting database containing thousands of simulations will enable comprehensive analyses of these different sources of uncertainty and can be used to identify whether different representations of the future lead to divergence or convergence of vulnerability outcomes.
The final version of the CLIMSAVE IA Platform will be available from the end of 2012 from CLIMATE-ADAPT and the CLIMSAVE website (www.climsave.eu). It is anticipated that project partners will continue to maintain the tool after the project lifetime, but long-term maintenance will depend on the availability of further resources or agreements with CLIMATE-ADAPT. The meta-models within the Platform can be updated fairly easily with improved versions as long as the current input/output exchange data format is preserved. It is also possible to replace current data with new data but this is not currently an automatic process because the databases within the Platform are in unique formats associated with the specific meta-modelling approaches (e.g. clusters, trained neural network data and look-up tables). Future work could focus on developing OpenMI compliant meta-models (a standard for interconnecting data and models) which could allow automatic interconnection with external models and data complying with the same standard.
In their review of participatory IA, Salter et al. (2010) identify the relationship between ‘choice, uncertainty and constraints’ as a key cross-cutting theme in the conduct of past PIA. The scenario development process used in CLIMSAVE has allowed the exploration of future uncertainty that can expand and change the mental models of users (Salter et al. 2010) and more strongly represent the importance of qualitative information. As a consequence of this recognition that uncertainty is a structural feature of such complex problems (Wack 1985), adaptation decisions cannot be made based on the ‘right’ answers, but rather become a question of which choices might work best in the face of very different possible futures. Participatory IA platforms such as CLIMSAVE should seek to explore choices which inform the integration of adaptation actions and policies across sectors, such that unintentional adaptation resulting from actions in one sector does not reduce the effectiveness of purposeful adaptation in another sector, and to identify robust adaptation strategies which are scenario-independent or no-regret strategies (i.e. adaptation responses which will be beneficial for all future scenarios) (Holman and Harman 2008). However, it is recognised that complex human–environmental problems such as climate change are not solely defined by uncertainty and choice but bounded by real-world constraints (Salter et al. 2010). Future scenarios, and associated adaptation choices, are inevitably limited by future resource availability and environmental and institutional capacities. Such constraints are recognised within the CLIMSAVE IA Platform, through the key scenario uncertainties and qualitative assessment of resource availability identified by the stakeholders (based on the five capitals) which are used to constrain adaptation choices.
Given the vision for the CLIMSAVE IA Platform described in ‘The IA Platform’ section to assist stakeholders in developing their capacity to address regional/national/EU scale issues surrounding climate change, pragmatic decisions have inevitably had to be made to achieve an appropriate balance between spatial and temporal scale and system run-time. Greater complexity inevitably leads to increased model run-times and increased risk of users’ disengaging with the Platform, thereby failing its vision. One of the key necessary limitations is the assumptions of independency of the three time slices (baseline, 2020s and 2050s), rather than time dependence, such that the implementation of the desired adaptation responses within a time slice is treated as the end-point within the modelling of that time slice, rather than treating adaptation as a feedback process with many different timescales of response, extending into the next time slice. However, this is partly addressed through allowing the user to vary the implementation time for each measure within the cost-effectiveness analysis. Nevertheless, despite such simplifications, the embedding of a thorough stakeholder engagement process; consideration of credibility and trust; and the realistic representation of choice, uncertainty and constraints within the CLIMSAVE IA Platform is intended to engender sufficient validity to ensure that it becomes a widely used tool within the European Climate Adaptation Platform (CLIMATE–ADAPT). As such, it will allow stakeholders to assess climate change impacts and adaptation strategies which are of interest to themselves, as well as exploring and understanding the interactions between different sectors, rather than viewing their own area in isolation. This will contribute to the development of a well-adapted Europe by building the capacity of decision-makers to understand cross-sectoral vulnerability to climate change and how it might be reduced by various cost-effective adaptation options.
This work was supported by the CLIMSAVE Project (Climate change integrated assessment methodology for cross-sectoral adaptation and vulnerability in Europe; www.climsave.eu) funded under the Seventh Framework Programme of the European Commission (Contract No. 244031). CLIMSAVE is an endorsed project of the Global Land Project of the IGBP. The authors would like to thank all CLIMSAVE partners for their contributions to many productive discussions related to the content of this paper. Specific thanks to Jill Jäger for comments on the manuscript. The authors are also grateful to all stakeholders who participated in the project workshops and kindly offered their valuable input and to the facilitation team of Prospex.
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