![Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Similar to Living Labs For Iwrm Rev2
Similar to Living Labs For Iwrm Rev2 (20)
Recently uploaded
Recently uploaded (20)
Living Labs For Iwrm Rev2
- 1. ___ Francisco Pérez-Trejo Senior Adviser, FAO A Living Labs-based Methodological Framework for Sustainable Water Resources Management Berlin, 9 September 2009
- 3. The IWRM process IWRM is a process, which promotes the coordinated development and management of water, land and related resources, in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems.* * Technical Advisory Committee of Global Water Partnership (GWP-TEC, 2000) Strategic and operational implications for effective IWRM:
- 4. Driving Forces of Development - Structural Change Regional Disparities Urban vs. Rural
- 5. Socio-economic disparities between rich and poor
- 6. Sustainability of Natural Resources Use in Agriculture
- 7. Impacts of land-use changes
- 8. Impacts of land-use changes
- 9. Decline of rural communities
- 12. Social Spaces for Research and Innovation (SSRI)
- 16. Future scenarios development Scenarios for exploring possible futures of complex systems, and systematically assessing the possible impacts of key driving forces on water development policies and programmes.
Editor's Notes
- Rural Otero de Sariegos_Castilla y Leon
- Characteristics of structural change: Continuous, medium to long-term Complex Non-linear Not controllable Multi-directional Results from multiple interacting driving forces in space and time
- In fact we are talking about innovation-driven development process.
- Rondinelli (1993) suggests that the policy process can be conceived as a social experiment, less a matter of prediction, more a matter of trial-and-error.
- One of the policy recommendations of the STRIVER project (Policy Brief 15) is to improve the policy and technologies; innovations to have impact on water use efficiency and livelihood assurance, however no specific means or achieving this is proposed, or was even tested. In order to make progress towards sustainable development we must contend with the fact that attitudes will tend to incorporate a divergent set of approaches to issues such as sustainability, productivity and marginal returns, all of which reflect various degrees of similarity and conflict between the value systems of land managers, industry, policy makers, individual citizens and farmers. Thus, from a socio-economic perspective, with water systems tied to government policy directives, the development and water management linkage is inescapably a political question. Addressing the policy dimension of IWRM and the linkage of policy to the expected outcomes of programmes and projects to policy is a formidable challenge. The policy agenda of the many governments and funding agencies may contain potential conflicts between development, environment and socio-economic objectives that can not be anticipated. These may include achieving infrastructure or financial targets, biodiversity conservation, watershed protection, poverty reduction programmes. While these policy objectives are not necessarily contradictory, the harmonization of policies that might include food security, natural resource management, energy self-sufficiency presents serious challenges to policy-makers, donor and target populations. Each of the livelihoods aspects (natural resources, social, financial, physical and human) cannot be seen in isolation as each of these is linked to other aspects of the livelihoods assets that need to be considered in order for people to break out of the poverty/ food insecurity trap. Many policies and programmes concerning natural resources sustainability do not make these links, focusing on the management of the resources to the exclusion of other issues. Most policies, even when they are aimed at improving livelihoods, tend to ignore the links that exist between the different aspects of livelihoods, but also the linkages that exist with what are considered “external factors” such as economic, trade, or investment policies. Given the limited scope and capability of the analytical tools, there are serious limitations in our ability to consider the complex linkages among all the aspects in the livelihoods approach analysis framework. Policy support programmes are usually based on macroeconomic dimensions of development, reducing the intrinsic complexity of the development process to macro economic variables that focus on trade liberalization and economic short-term dimensions of the problem. The Best Practices framework presented here focuses on best practices for policy that can stimulate and support sustainable agricultural development.
- Social Networks Mapping is a means for understanding the dynamics of the social agents, in order to explore mechanisms for scaling-up results and to ensure that the benefits of Living Labs innovations benefit all segments of the population (including people with low education who normally be excluded from the benefit of ICT innovations) A Methodological Approach for Complexity-based Impact Assessment for C@R The complexity-based conceptual framework we have developed as a basis for the impact assessment of Living Labs emphasizes the complex nature of rural development. It is in this line of thinking that we have conceived of a methodological approach based on scenario development and social networks mapping as the means for addressing this complexity in a systematic way for identifying critical policy issues that would need to be addressed in the policy recommendations relating scaling-up C@R results in systemic and sustainable way. Using Scenario Development in Complexity-based Impact Assessment Scenario development is a methodological approach for addressing complex development and policy issues used for exploring possible futures of complex systems, such as development in rural areas. Scenarios are an important tool for learning and informing decision-making in situations of high uncertainty. Used properly, they can help people think more systematically about the different circumstances they may encounter in the future and the implications of those future possibilities for decision making in the present. There is no standard methodology for scenario development. Many factors determine the way scenarios are constructed, the assumptions that are made. Much of this depends on the nature and culture of the team involved in each scenario development exercise. Scenarios planning starts by using existing knowledge to determine the processes that are responsible for structural change into two broad domains: (1) things we believe we know something about and (2) elements we consider uncertain or unknowable. The first component, know as trends, can be used to develop robust models of the future based on existing knowledge, recognizing that our world possesses considerable momentum and continuity. The second component, considered as uncertainties, involve driving forces like climate change which are not understood well enough to be able to say how these drivers will interact with other components of the system, and therefore make it very difficult to explore the impacts of any particular intervention or policy. One of the major sources of uncertainty for scenario development is the complex social dynamics of interacting stakeholders, including the participants of Living Lab activities, the population actually benefiting from the results of C@R, the local authorities, national government, other academic and research organizations and agencies, private sector companies, European policy makers, international stakeholders. Social networks mapping methodologies will be used to address this complex dynamics in order to improve the robustness of the impact assessment. The first step in the scenario development process is delimiting the scope of the system under consideration. This is done through iterations of defining the components and boundaries of the systems, and then identifying and characterizing the driving forces that are known to be driving the complex evolution of the system. The interactions among driving forces are then characterized based on evidence, (usually seen in the field and/or elicited from stakeholders) framed in a systems analysis structures into a set of diagnostic hypotheses. From these initial hypotheses, deductions are made as to what to expect as far as the future outcomes or results of the dynamic interaction among system components. These hypothesized outcomes are then subjected to tests and further observations. These tests or further observations are carried out to either confirm or modify these initial hypotheses. Occasionally new data may lead to a new hypotheses not considered in the initial set of interacting driving forces. The scenarios are validated by obtaining evidence strong enough to fit a hypothesis, and the scenarios can be used to explore the impacts of interventions on the evolution of the system.