WP4
Foresight analysis
The overall aim of WP4 is to assess medium term future of rural areas in terms of economic, environmental social and institutional developments by considering sustainability conceptual framework and under alternative policy scenarios. A specific attention will be paid to uncertainty treatment – an appropriate methodology will be developed.
The objective of the WP4 will be achieved through the following four specific tasks:
4.1 Scenario building: developing a set of future socio-economic change storylines that are appropriate to EU agriculture and rural development policy. These storylines (scenarios) will be analysed by using a combination of quantitative models and qualitative methods. For this purpose, the (qualitative, quantitative) nature of key scenarios variables and performance indicators will be identified and methods of their use and assessment decided.
4.2 Quantitative approaches: forecasting most probable patterns from these storylines using a chain of models (ESIM-CAPRI-LEITAP/IMAGE-CLUE-s), probability density functions of key scenarios variables and a systematic sensitivity analysis approach for Europe. The factors concerning global developments will be dealt with in the Foresight analysis, since future development of rural areas both depend on bottom-up processes as on top-down processes. The bottom-up process usually can be linked with the “strengths and weaknesses” of regions, whereas developments on national and global level will be reflected in de “opportunities and threats”. The global developments and their possible effects on rural areas will be modelled by means of the model chain, in which especially LEITAP and IMAGE are global models which take developments on other continents (GDP growth, demographic developments, world commodity markets, land use) into account. In formulating and analyzing global developments information from other relevant sources will be used as much as possible (FAO-studies on world agriculture, OECD/FAO-outlooks, the SCENAR2020 project etc).
The work will include:
- Creating a model set-up
- Translation of scenarios (story lines) into model inputs
- Selection of appropriate indicators in the planet, profit and people dimensions
- Running of the models
- Calculation of indicators in people, planet and profit dimension
- Output in appropriate formats
4.3 Qualitative approach: SWOT analyses for rural regions: For that part of internal and external rural development factors which are either of qualitative nature or for which there is no model or reliable data available, the foresight exercise will have to be dealt the other way. This task is to develop a methodology for a (semi) qualitative way of the assessment of rural futures in respect to sustainability and provide such assessment for selected case studies. SWOT analysis will provide an important instrument for assessing which factors and in which combination will likely drive the future of rural regions. A particular concern in the assessment of rural future is the economic prospect of rural areas. It includes understanding of a number of conditions shaping entrepreneurship and conditions for the business which most of them will be regarded as rather of qualitative than quantitative nature. To analyse them the concept known as Porter’s diamond will be adopted and modified for rural regions and economies. Following Porter, we will look at the factor endowment and skills and the way they are deployed, the nature of demand, the organisation of the rural economy and the access to competitive supporting industries. Since it will require local knowledge and expertise we will likely be able to conduct it only for case studies. Whenever provided, the qualitative assessment given by Porter’s approach will integrated in the SWOT analysis. In the end SWOT tables will be linked with results of trend analysis and quantitative modelling and will be evaluated against the scenarios defined in the task 4.1. A semi-qualitative methodology which combines expert judgement (translated in ranking, scoring) with quantitative multi-criteria assessment will be developed. The problem of uncertainty will require to be reflected throughout this task ( e.g. by associating probability distribution to key variables or providing sensitivity analysis)
4.4 Analysis of case studies in at least six countries: Poland, Romania, UK, The Netherlands, Spain and Portugal
Case studies in at least six EU27 countries (UK, The Netherlands, Poland, Romania, Spain and Portugal) will test the scenarios and the predictions of the models, and will apply at a regional scale the methodology developed in 4.3 to assess the future of rural areas regarding sustainability. The six countries cover three main areas in Europe, i.e. Northern (UK and The Netherlands, with highly urbanized rural areas), Central (Poland and Romania) and Southern (Spain and Portugal). In each country case study regions will be selected by using the regional typologies designed in WP2. For example, case studies will be selected adopting the perspective of "rurality" (mainly-rural, intermediate-rural and mainly-urban regions), and the perspective of employment (regions with a relative high employment growth and with stagnating/declining employment). In this way, the selected case study regions will be representative for the EU27 in terms of perspectives adopted. Such an approach was also applied in the RUREMPLO and DORA projects. In two current EU projects – SENSOR and SEAMLESS – case study regions are also selected based on typologies of regions. Where possible, the selection of case study regions in FARO will be tuned to these projects in order to maximize the information on the case study regions. Furthermore, supplementary information for other EU countries could be provided by REDR (partner 9), which belongs to ELARD (European Leader Network, having as members Portugal, Spain, Greece, Italy, Ireland and North Ireland, Finland and France) and has established an agreement with PREPARE and ECOVAST.
D 4.1 Set of future policy-relevant rural development storylines
D 4.2 A set of conditional probabilistic model input variables
D 4.3 Description of model framework and indicators
D 4.4 Quantitative description of the rural development scenarios by describing the impact of a scenario on the indicators in the people, planet and profit dimensions
D 4.5 GIS dataset outputs for the selected indicators, creating maps of the hotspots of change and meta-hotspot maps
D 4.6 A set of conditional probabilistic indicator change futures and the corresponding GIS datasets (i.e. land use, hotspots and meta-hotspot maps)
D 4.7 A report describing the applied methodology (including translation from scenarios to model inputs, interpretation of the outputs) and the scenarios outcomes
D 4.8 A SWOT analysis of alternative rural futures
D 4.9 Report on case studies