Unveiling Adaptive Planning: Shaping Sustainable Futures

Adaptive management

Adaptive planning approaches have their origins in the adaptive management concept that was introduced and has been used since the late 1970s. Adaptive management was developed to deal with uncertainties that occur in complex systems (Holling 1978). In contrary to traditional management decisions that are often based on empirical studies, adaptive management treats decisions as experiments to promote a ‘‘learning by doing’’ process in a proactive way (Holling 1978; Kato and Ahern 2008).

The adaptive management approach expands traditional approaches by adding feedback loops where the effectiveness of planning decisions made and measures taken is monitored, generating new data to structure alternative or future decisions (Walters and Holling 1990; Ahern 2012).

Adaptative design and experimentation

The use of adaptive planning can be beneficial because of the ability to cope with uncertainties that are inherent in natural and social systems (Kato and Ahern 2008). In recent years, the adaptive management concept has been expanded to include design principles into planning processes (Ahern 2012; Ahern et al. 2014). Adaptive design implies intentional, often experimental changes with a multifocal view on environmental and societal needs (see Nassauer and Opdam 2008). The changes, or ‘‘designed experiments’’ often take place in a small spatial extent with the aim to test innovative approaches in a ‘‘safe to fail’’ environment (Ahern et al. 2014). Designed experiments are developed in transdisciplinary processes including scientists, planners, design professionals and other stakeholders (Felson and Pickett 2005) amongst whom the risk of failure of such approaches is recognized (Ahern 2011).

Adaptative design and Resilience

These principles of adaptive design fit especially well with the concept of resilience, which is defined by Walker and Salt (2006, p. 1) as the ‘‘ability of a system to absorb disturbance and still retain its basic function and structure’’. According to Ahern (2011), resilience capacity of a system can be achieved through multiple ways, amongst others being the multifunctionality of measures, and the adaptive planning approach. To be effective, ecological limits, as well as economic and social limits need to be considered when constructing designed experiments (Pickett et al. 2004).

Scenarios

When envisioning future states of ecosystems, scenarios are a commonly used tool in landscape planning for coping with projection uncertainty. Scientists agree that scenarios are best used when information about the future under different policies is poorly defined and the knowledge is, at best, precarious (Shearer 2005; Biggs et al. 2007; Foley 2010; Metzger et al. 2010).

Scenarios allow us to explore possible futures and illustrate how different policies can alter the landscape (Shearer 2005). Based on information of current and past conditions, scenarios are plausible stories about future states (Biggs et al. 2007). They can be divided into different types regarding their focus. These types are (i) predictive scenarios with a narrow focus on future developments answering the question ‘‘what will happen’’ (forecasting), (ii) explorative scenarios with a broader focus addressing potential impacts that can significantly alter future states (forecasting) and (iii) normative scenarios that start with a desired future state and explore pathways with conditions to achieve this future state (backcasting) (see Maier et al. 2016 for detail).

Well-developed scenarios can be seen as an awareness raising tool that can help challenge the views of individuals about how a possible future may look like (Carter and White 2012). Through this it is possible to improve the robustness of planning through the incorporation of increasing amounts of uncertainty on the range from predictive scenarios to unframed exploratory scenarios (Maier et al. 2016).

Participatory process

To enhance understanding and raise the acceptance of landscape planning results, it is possible that scenarios and management options are developed in a participatory process. When developing scenarios or response measures, for example, it is possible to involve local stakeholders in the development process. This may lead to improved consensus building, strengthen the communication and can ultimately lead to decisions being made that are more acceptable amongst the general public (Biggs et al. 2007; Beach and Clark 2015).

Participation can also present a way to cope with sources of uncertainties in landscape planning, especially on a local level where generic scientific knowledge needs to be reinterpreted to fit the local context (Beunen and Opdam 2011).

It should be noted that participatory systems have limitations as well and that participatory processes can also have a negative effect on the broad acceptance of landscape planning results, for example when the structure of the process and the time frame makes it unlikely for participants to attend every meeting (see Beach and Clark 2015). The aim in knowledge generation for landscape planning should be a balance between scientific research and participation, which is well perceived by the public, stakeholders, politicians and planners altogether. When considered in innovative planning approaches like adaptive design, ‘‘uninformed decisions’’ can act as a starting point for the generation of knowledge through designed experiments. In such cases uncertainty can be seen as a catalyst.

Integration of uncertainty in planning

There has been a lack of integration of uncertainty information and limitations of certain models into planning results. Pe’er et al. (2014) believe that one main reason for this dearth is the simplification of model outputs for decision makers. This may be attributed to the belief that there is a considerable mismatch between information outcomes that scientists and practitioners produce and the expertise that policy and decision makers have. Even if science were freely available, it might remain inaccessible because of the level of detail desired by scientists and practitioners, which often conflicts with the time constraints imposed by policy makers (McInerny et al. 2014; McManus et al. 2015).

Consequently, there is a call for simpler and more understandable decision supporting tools (Ruckelshaus et al. 2015). It is important to communicate how information for decision support has been generated and address any potential uncertainties. If this does not occur, there is a risk that information will be assumed to be part of reality even if this is virtually impossible. In conventional planning systems such uninformed decisions could result in long-term risks to both the environment and humans (Pe’er et al. 2014).

Lluis Sanvicens, 2024

References

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