Planning for Climate Change or: How Wicked Problems Shape the New Paradigm of Swarm Planning
ABSTRACT: Is there a planning approach that can improve the quality of our urban and rural environments while helping us address climate change? If so, radical changes in the way we plan for our cities and landscapes are necessary. Multiple reasons why are distinguished:
• We are observing an increase in the frequency and intensity of extreme climate events;
• We may have crossed boundaries defining Earth systems’ ‘safe operating space’ for humanity, according to Rockström and colleagues [2009a; 2009b];
• We need to manage increasingly complex urban systems;
• We need to deal with the growing uncertainties and unpredictable impact of climate change;
• We tend to decrease the flexibility of our urban and rural systems while increasing flexibility is required;
• We tend to approach problems from a technical–rational scientific discourse while complex interactions and non-linearity are features of real life;
• We take a probabilistic approach in dealing with climate change, only developing adaptation strategies to highly probable threats within the central bandwidth of the probability bell-curve, leaving society unprepared for the more extreme climate events;
• Most of our planning systems endeavour to solve problems by achieving ‘a single ‘optimal’ end-state for an area, planning for the average and frequent instead of the extreme and unpredictable climate events. This planning approach by ‘calculated ignorance’ is likely to lead to an increase in the magnitude of natural disasters, whether they are climate induced or not.
Effective adaptation to climate change requires that planning learn from complex adaptive systems theory - enhancing societal flexibility to deal with uncertain futures, and shifting its focus from finding the ‘best’ solution towards of creating resilience to an uncertain future. A new planning concept needs to be put in practice, which we call Swarm Planning. This approach can address both the mitigation and adaptation needs side of climate change while increasing the flexibility of spatial systems in two ways: assisting change in spatial land use over time and catalysing the emergence of autonomous and more resilient development.
Recent extreme weather events, such as bushfires near Perth and in Victoria, floods in Brisbane and Victoria, a cyclone in Queensland, heat-waves in Europe and Russia and hurricanes in the Caribbean and the US can be seen as signals not only of a changing climate but also as a ‘climate of surprise’. Several research papers have underpinned this shift to more extreme risks and weather events caused by human actions and activities [Stott et al, 2004; Min et al, 2011; Schiermeier, 2011; Barriopedro et al, 2011]. The model presented by Rockström et al [2009a; 2009b] offers a powerful and visualised insight on the boundaries of the Earth system humanity needs to remain within. However, this model only depicts a biophysical system, which does not yet include the capacity for adaptive social interactions and spatial transformations. This paper focuses on the contribution that a new approach to spatial design and planning can offer to improving resilient future systems, capable of dealing with one planetary boundary: climate change.
Current approaches to climate adaptation rely heavily on ‘probability’ or ‘expert’ assessments of the likely type, extent and frequency of negative impacts. They seek to
maintain or incrementally adjust existing built systems to climate change. These assessments of adaptation needs rely heavily on climate impact models that define a set ‘bandwidth’ or ‘bell-curve’ of negative possibilities. Decisions in the spatial domain focus on the risks deemed ‘most likely’ (in the middle of the bell-curve). This approach ensures spatial configurations remain under-prepared for more extreme, unpredictable events. As long-term strategists [Kahn and Wiener, 1967] encountered in the mid 1900’s, a probability-based approach can prove counter-productive when a fundamentally new risk-landscape appears. New methods are needed to assess and transform the spatial order that can foster climate adaptation. These new approaches leave room for uncertainty and the emergence of novel adaptation in spatial design outcomes, mirroring our uncertainties over climate change.
To deal with increasingly extreme climate change related weather events, planning approaches must do two fundamental things: increase the flexibility in our urban and rural spatial systems to better deal with these extremes and reduce global warming in order to minimise the number of weather extremes, consequently requiring less spatial flexibility. Spatial design can help address both (mitigation and adaptation) sides of climate change. However, the character of the designs needs to be different from current design practice. Foley [1963; 1964] distinguishes two planning types, technocratic (Unitary) and ‘sociocratic’ (Adaptive) in which the former tends to fixate future spatial order and the latter offers larger flexibility and room for processes to emerge. Most of current planning practice belongs to the ‘Unitary’ school, planning for a fixed and desirable future. Because climate change is a wicked [Rittel and Webber, 1984] problem [VROM-raad, 2007; Commonwealth of Australia, 2007], which changes over time and for which the ‘one solution that solves all’ is not available, it is no surprise that current planning approaches are struggling to adequately address climate change.
An adjusted ‘Adaptive’ planning approach is required in which flexibility and dealing with uncertainties are central. In this approach the spatial system is seen as being a complex adaptive system, making it possible to make adjustments in the system over time, enhance emergent patterns or allow for self-organisation [Roggema et al, 2011]. Because different spatial elements are linked to different time-dimensions or rhythms, the existing layer theory [Frieling et al, 1998] is extended to five layers, each with their own pace of change [Roggema et al, 2011]. This approach, Swarm Planning [Roggema, 2005; 2008a; 2008b; 2009; Roggema and Van den Dobbelsteen 2007; 2008; Roggema w. De Plaa, 2009] defines the spatial elements linked with the five time-dimensional layers and turns them in a step-by-step methodology for ‘Adaptive’ spatial planning. The five step-by-steps are defined as follows: analyse the importance and intensity of networks in the area (1), articulate, based on this analysis, the strategic points of intervention (the so-called focal points) in the network (2), mark the area around these points as unplanned (allowing for emergence) (3), define the areas with highest potentials for producing resources, i.e. water, energy, food (4) and let intense occupation patterns emerge in core areas of high identity (5). This methodology is tested in several pilot designs, specifically aiming to deal with climate change. New landscapes, in the form of iconic designs are presented and are described in the paper: the Floodable Landscape where an eventual future flood is anticipated in the design, the Zero-Fossil Region where the design provides a spatial framework for a complete renewable energy supply and the Net Carbon Capture Landscape, in which adaptation and mitigation strategies are designed leading to capturing of more carbon than emitted.
These examples illustrate that the two sides of climate change, adaptation and mitigation, can be unified into integrated future visions. These visions function as the imaginable and
desirable future. Formulating and drawing this vision enhances the start of a spatial transformation, which is constantly adjusted over time if changes in the environment require so. This process, which can be described as a slow-pace transition [Roggema, 2011], offers the opportunity for inhabitants to anticipate and become more aware of future changes.
Keywords: swarm planning, climate adaptation, climate mitigation, spatial design, transition
Introduction
In just a few recent years many parts of the Globe have dealt with significant weather extremes. Cities and regions such as New Orleans, Paris, Victoria, Brisbane, Cairns, Moscow, Cancun, Pakistan, Perth have been affected by one or a series of extreme events including hurricanes, heat waves, floods, cyclones and bushfires. Whether or not these events have a causal link to global warming, they are consistent with the expectations of the IPCC in 2007 [IPCC, 2007]. However, elaborating on Chris Bergs opinion article in The Age [Berg, 2011]: according to data from the International Disaster Database the average amount of people dying as a result of an extreme weather event dropped from 240/million in 1920 to 3/million in the last decade [www.emdat.be]. The reduction in the risk of death from droughts and floods dropped by respectively 99,9% and 89% over the last century. It may be concluded that humankind is getting better at managing and surviving extreme weather events. Nevertheless, there is reason for concern as the number of extreme events, reported as disasters, increased steadily over the last century, with a slight drop over the last decade [www.emdat.be]. Despite the fact that the number of extremes seems to increase, humankind is improving in dealing with their immediate impacts. This skill in post-event adaptation lies in stark contrast to our failure to pre-emptively mitigate climate change. While not undermining the case for urgent mitigation efforts, humanities’ ability to respond creatively in the face of repeated crisis should give us reason to ask whether there is value in putting a greater focus on adaptation than currently exists. As former Dutch soccer player Johan Cruijff said: “getting better in what you’re good at makes you an excellent player, it gives you a unique advantage in the field”. Translated to the field of climate change adaptation we might take the approach that: we should improve the capacity for our communities to adapt to forthcoming weather events - because we’re already good at it! Particularly if adaptation pathways can be found that substantially reduce society’s carbon footprint.
What does this imply? We take the view that for adaptation to be successful in the long-term, our goal must be to adapt our social and spatial systems and buffer and assist ecosystems to adjust as required. Humankind is already good at these adaptations, but improvements in the way we adapt are necessary. All three systems are crucial and are interrelated to each other.
Much research is carried out in the field of eco- or biophysical- systems and to a lesser extent socio-ecological systems. So far, research in the spatial systems domain does not yet substantially include adaptation. This paper focuses on the way spatial systems can contribute to adaptation and explores the way spatial planning and design practices can be improved in order to incorporate climate adaptation. It firstly reviews the Earth systems boundaries as defined by Rockström and colleagues [2009a; 2009b], before emphasising the potential of spatial planning in increasing our capacity to adapt, introducing a new planning methodology, Swarm Planning, which will be further clarified through illumination of several pilot designs, which were developed in the Netherlands.
Safe operating space
The concept of a ‘safe operating space’ as presented by Rockström and colleagues [2009a; 2009b] is appealing and the visualisation (figure 1) makes the concept accessible for the larger public. Nine ‘planetary boundaries’ are defined, which are tightly coupled and may not to be crossed if humankind wants to stay within a safe operation space. In their article seven out of nine boundaries are quantitatively determined and three of them are already crossed. The nine boundaries are all biophysical or biogeochemical. The two boundaries that remain to be determined are related to global anthropogenic change. As the authors refer, their concept is the first attempt to get grip of Earth system boundaries and this is in itself a valuable effort. Some of the values they determine are first guesses. The focus on biophysical boundaries and the attempt to quantify all boundaries however leaves room for extension and improvement. Socio-political interactions as well as interventions in the spatial systems are yet to contribute to the concept. Because research on socio-ecological systems is well underway [Cork, 2010; Olsson et al, 2006; Walker et al, 2004; Wilkinson et al, 2009] this paper focuses on the potential added value spatial systems may encounter.
Fig 1. Safe operating space for humankind [Rockström et al, 2009a; 2009b]
Doomed to fail?
The concept of a safe operation space for humankind as explored before leans on the premise that boundaries can be determined in a quantitative way. This premise is based on the assumption that measurable, biophysical problems can be quantified. A number, representing a boundary or tipping point for humanity implies a fixed perspective of change over time where problems are seen as tame [Conklin, 2001]. It relates to current approaches relying heavily on ‘probability’ or ‘expert’ assessments of the likely type, extent and frequency of negative impacts. These assessments rely heavily on models that define a set ‘bandwidth’ or ‘bell-curve’ of negative possibilities. Decisions focus on the risks deemed ‘most likely’ (in the middle of the bell-curve). This approach ensures society remains under-prepared for more extreme, unpredictable events, such as appearing under climate change. As long-term strategists [Kahn and Wiener, 1967] encountered in the mid 1900’s, a probability-based approach can prove counter-productive when a fundamentally new risk-landscape appears. One of the determined boundaries, climate change, is seen as a wicked [Rittel and Webber, 1984] problem [VROM-raad, 2007; Commonwealth of Australia, 2007], appearing to us as such a fundamentally new risk landscape, potentially creating an environment of novel uncertainty in which the traditional ‘bell-curve’ approach to risk management is likely to
reduce societal vulnerability than improve it. In dealing with these unlikely but high impact, hitherto low-probability events a statistics based approach is doomed to fail. The ‘best guess’, which tends to be located at the centre of the curve where probability is highest, is farthest from the most risky events.
Therefore, we appraise Rockströms concept as only part of the entire picture. Even if it is possible to measure accurately the boundaries within which humankind needs to stay and determine mitigation measures accordingly, the fact that humankind is very well capable of adapting to changes and thus potentially pushing the boundaries outward is not taken into account. Interpreting Rockströms paper in a way that we will have difficulty adapting in an environment which is functionally different from the one humanity has flourished in,.the possibility of having pushed systems beyond boundaries of stability does require a new approach to adaptation that factors in non-linear system behaviour. These new approaches need to leave room for uncertainty in the outcomes, exactly the way climate change appears to us.
Can Spatial Planning help?
Current spatial planning is often practiced as a technical-rational exercise, in which a spatial problem is defined in a quantitative way. This tame approach to problem solving leads to solutions, which meet immediate demands, but are ultimately brittle – unable to deal with uncertainties or unknown futures. As an alternative to current planning practice both Friedmann [1973] and Foley [1963; 1964] suggest ‘adaptive planning’ (as the opposite of unitary (Foley) or ‘allocative planning’ (Friedmann)), aiming for a system change. Climate change adaptation can be seen as the reason for this system change. In adaptive planning approaches transformations of current systems are encouraged and new systems are allowed and stimulated to emerge. In this approach, the goal and fundamental operating values are defined from the start but in consistency with emergence in complex systems, the final outcome is unclear (at least at the beginning). Adaptive planning potentially overcomes the problems of probabilistic and quantitative approaches to planning by allowing for novel and low-probability variables and drivers of change, to become part of the spatial process.
More specifically, this planning approach takes a new ‘layered’ approach to change over time in a way that explicitly acknowledges the possibility for system change over time. However, it does not explicitly define the time dynamics, i.e. the rhythm of change for different spatial elements. The Layer-theory, developed in the Netherlands [Frieling et al, 1998] is supportive here. It clearly defines which spatial elements belong to which paces of change: the time
rhythm for the landscape (erosive processes and water systems) is centuries, for networks of transport, energy and ecology (100 years), and for human occupation a generation (20-50 years). While Layer theory emphasises the existence of different rhythms for different spatial elements over time, it still takes a view that those elements are relatively unchangeable or static. However, combining Adaptive planning and the Layer theory, opens the way to a new planning paradigm, capable of dealing with constant change and greater uncertainty.
Swarm Planning; new methodology of the ‘unplanning’
Swarm planning connects Adaptive planning, where the possibility of unknown change shapes future spatial systems - with the Layer theory, which defines the relative time-dimensions of specific spatial elements to change. It recognises spatial systems are complex adaptive systems, as already has been demonstrated for the Earth system [Lenton and van Oijen, 2002; Lovelock, 1988] and (using the Fractal City metaphor), for any part of this system [Batty, 2005] as well as for cities [Allen, 1996; Batty, 2005; Portugali, 2000]. Moreover, complex adaptive systems science is increasingly seen as a theory on which to base spatial [Innes and Booher, 2010; De Roo and Porter, 2007].
In Complexity Theory the world is considered as built up of numerous dynamic open systems developing into higher complexity [Waldrop, 1992; O’Sullivan et al. 2006] by the influx of energy and information. They look rather stable for a long time remaining in a situation of equilibrium called an attractor seemingly developing in a linear way. However caused by developments outside and usually with a peripheral character the equilibrium is continuously under pressure and as a consequence the systems tends to change its structure slightly to adapt to the pressure, so that it can remain within its current attractor. If these changes are prohibited by internal withdrawals there is the risk of dramatic and unpredictable changes bringing it into another attractor, which can be considered as an alternative state of form and operation. Any complex system, although seemingly unchanged, is likely to become unstable as a consequence of changes in its environment. Note that instability does not equal change. It only means a growing likelihood that a shift will occur in some direction at some moment. As internal adaptation of the system becomes more and more difficult the system goes from one state of order (attractor) through a chaotic situation into another state of order or attractor [Waldrop, 1992; Cohen & Stuart, 1994]. The change is rapid and chaotic, and its direction is unpredictable [Timmermans et al., 2006].
Originally Complexity Science stems from the natural and physical sciences studying non-linear dynamic processes [Prigogine and Stengers, 1993] and self-organisation [Kaufman,
1993]. With the growing power of computers artificial life and cellular automata as referred to by Lewin [1992] became important. Soon, computer simulations studied social systems [Axtell and Epstein, 1996]. A growing amount of studies on the behaviour of complex systems appeared in varying fields as network science [Barabasi, 2003], ecosystems [Scheffer et al., 2001] innovations in industry [Kash and Rycroft, 2002] and planning [Timmermans 2004, 2009; Roggema 2009].
In order to determine the elements or features of the spatial system crucial to developing a region capable of adjusting to change, characteristics of complex adaptive systems [Phelan, 1999; Merry and Kassavin, 1995; Eoyang and Conway, 1999; Homan, 2005; Gladwell, 2000], such as adaptive capacity, self-organisation, emergence, a large ‘pool’ of elements and the number of interactions are translated spatially [Roggema et al, 2011]. By doing so, the following spatial characteristics are defined:
1. Number and importance of networks: If a large ‘pool’ of elements is available and numerous interactions take place, the quantity and quality of connections (the connectivity) increases. Translated spatially, this infers that the functional quality and number of networks and network nodes is an important factor determining systems’ adaptive capacity;
2. Starting/focal points: Network interactions, if intense and numerous, can enable the development and propagation of system shifts and crossing of tipping points, starting processes of innovations throughout the networks from these starting points. The spatial location of these focal points determine where developments are likely to start, i.e. where novel adaptation will emerge;
3. Create ‘free’ space; Unfilled and unstructured space is required to allow innovations in functions and use of space to develop beyond emergence and self-organise. Where areas can be kept free from structures (physical or regulatory) that determine spatial function these spaces can be filled in at a later stage in response to unforeseen or unpredictable change. The spatial system is then better equipped to adjust;
4. Natural resources: Adaptive capacity is also increased if a balance between supply and demand in a certain area is arranged and if there is (over-) capacity to store supplies. The spatial dimension to allocate space for natural resources, producing clean water, energy and food is essential;
5. Mix of functions: The adaptive capacity can be increased through diversity, flexibility and heterogeneity. An elevated mix of functions in a certain area increases flexibility, diversity and heterogeneity, providing an increased adaptive capacity of the area;
6. Mosaic in city/landscape: besides providing a mix of functions mix, spatial diversity in the city and landscape enhances adaptive capacity. Reconfiguration of existing function into a mosaic of spatial diverse elements supports the potentials to adapt; 7. Change in land use, human activities: If tipping points are occurring, most likely in
focal points, developments emerge from those locations propagating through the system, this will lead to changes in land use and human behaviour and activities. These spatial characteristics each have their own time-rhythm, which allows them to be connected to a specific layer. In the approach described here, the original layer theory, consisting of three (static) layers is adjusted to five (figure 2). This allows planning to integrate dynamic features of complex adaptive systems. Mix, mosaic and change all related to occupation patterns, while the other spatial characteristics are connected to separate layers for each (figure 3). Besides connecting the spatial properties to the five layers, these new layers can be used as a planning method, allowing spatial designs to anticipate climate change and/or extreme weather events.
Fig 3. Spatial elements linked with five new layers
If these five layers are used in a step-by-step planning method, a design is constructed, which contains the properties of a complex adaptive system and thus demonstrates its characteristics, such as self-organisation, emergence and adaptive capacity.
The first step determines the most intense and most important networks. Bundles of networks which consist of one or more (inter-)national connections are declared important and represented on the map of the first layer (figure 4).
The second map shows the focal points or ‘nodes’ with potential to form a distributed system through interconnection with other nodes. These focal points are locations with a high potential for the emergence of novel innovations and adaptations. They are characterised by the co-location of multiple resources (energy, food, water, information) and/or the intersections between networks of resource distribution and exchange. The potential for innovation and emergence of new system attributes at these locations is particularly high due to:
• High rates of information flow – reducing the risk of external change occurring without detection.
• The co-location of multiple resource systems – increasing the capacity for symbiotic relationships to form and common needs to be aligned.
• The proximity of supply, processing, consumption and management systems – allowing for learning and information exchange across sector networks and between people with diverse interests, responsibilities and perspectives
• The increased chance of chaotic (unplanned) interactions – giving rise to step shifts in thinking
• Localisation – reducing the distance required (and therefore transaction costs) for resource distribution and information exchange. In particular, this can improve feedback on the impact of local decisions and improve understanding of local resource
opportunities.
The key here is not that the richness of a single resource gives rise to more profitable opportunities, but that the arrangement of those resources and resource systems is novel. At these points, the functional environment is highly heterogeneous with different arrangements of resources, people, infrastructure and commercial incentives in close proximity, and where multiple formal and informal opportunities for connectivity exist. This localised connectivity reduces transaction costs and facilitates the sharing, testing and development of new ideas – allowing for learning and self-organisation [Biggs, 2010]. Furthermore, heterogeneity reduces the risk of group-think and the possibility that a change in external conditions goes undetected – both of which can inhibit effective adaptation. These high intensity ‘nodes’ are where the chance for innovations and new concepts to emerge is highest and where agility to changes is easiest [Florida, 2005; Ridderstråle and Nordström, 2004]. However, the potential of these focal points is typically latent. They therefore require some form of catalyst to encourage innovation or to prompt a change in thinking – allowing local opportunities to be recognised and harnessed [Biggs, 2010; Ryan, 2010].
Importantly focal points can also act as a source for dispersing the innovations they cultivate. The same network and connectivity features that allow information exchange and receptivity to external ideas helps disseminate local ideas. More importantly, where adaptations change the function of systems within these nodes, the functioning of wider systems of information and resource flows (with which they have interaction) is also affected. This can have a profound impact – catalysing and propagating innovation outside those nodes. For example, forces for change exist where focal points:
• Become an exporter rather than an importer of resources or services – resulting from improved efficiencies as a result of new symbiotic relationships or greater understanding of local resources.
• Evolve to export different resources and services – due to the development of new functions and capabilities
• Alter the way relationships are formed with external organisations – due to the
development of different (more intense or open collaborative) relationships between local actors.
• Are better able to capitalise on or reduce risks from changing circumstances compared to other regions and systems they are connected to – setting an adaptation precedent.
The third layer represents free space. The space around focal points remains unplanned in order to not restrict unexpected developments. It gives the area the flexibility to adjust if circumstances change unforeseen.
In layer four the areas that are most suitable to store and supply resources are located. The space required to produce food and energy, to store surpluses of rainwater and to supply drinking water is arranged according the most suitable conditions in the landscape. In order to increase adaptability in the landscape these functions are not to be separated as monocultures, but appear as a mosaic of functions.
The final fifth layer represents emergent occupation patterns, based on the configuration derived from the first four layers. Specific parts of cities and landscapes, namely where intensity is high (in or near focal points) and where historic landmarks appear in the landscape causing identity to connect with, are the places where spontaneous innovations are most likely to emerge. A specific feature in the emergent occupation layer is the introduction of a coastal defence system, which provides safety under threat of storm surges and sea level rise.
1 Networks 2 Focal points
5 Occupation patterns
Fig. 4 Five layers determining an adaptive design for Groningen province [Roggema et al, 2011]
The methodology described here illustrates the development of a region preparing for future climate change by making use of the properties of complex adaptive systems. It allows emergent characteristics (innovations) while safeguarding essential values, such as natural resources. The design resembles the balance between securing and protecting the area for future disasters and the need to stay flexible in advance of future uncertainties. Both are required to anticipate future uncertainty in the face of climate change. However, the methodology emphasises a primary role for networks and the emergence of new characteristics. This implies agility and ‘unplanning’ in decisions regarding the spatial organisation of the area. The extent to which unplanning is practiced depends on the specific
spatial context, but will be connected to and spatially organised around the most important and intense nodes, the focal points. These are crucial in starting emergent developments and need to be flexible in adjusting developments if circumstances change. Therefore it is essential that the unplanned area around these point are ‘filled’ at a certain moment, they are ‘erased’ once the external change has been dealt with. Rephrasing this in an example: if there is a need for shelter due to a flood, heat stress or drought, these unplanned areas can be temporarily used as places to live. Once the stress factor is gone, the unplanned area can return to its unplanned (empty) status in the expectation of new climate stresses.
Best practices in Swarm Planning
The swarm planning methodology has been used and tested in several pilot designs. Three of them will be discussed and reviewed here: the Floodable Landscape, The Zero-Fossil region and the Net Carbon Capture Landscape.
Floodable Landscape
In the current discourse in dealing with coastal defences for sea level rise and storm surges, the safety level is increased through the strengthening and heightening of protecting structures, such as levees and dikes. Fast and accelerated sea level rise as predicted by Hansen and others [Hansen 2007; Hansen et al, 2007; 2008; Hamilton and Kaiser 2009; Lenton et al, 2008; Rahmstorf et al, 2007; Tin, 2008] give reason to concerns over the capability of defences to withstand extreme circumstances at all times. Eventually, even the strongest dike will breech. The consequence of this belief in defending the values behind an increasingly stronger dike is that once it breeches these values are highly vulnerable for the effects and a huge disaster destroys most of the values, such as properties, productive land or human life.
Given the uncertain pace of sea level rise and the moment a dike eventually will breech, the question can be raised if alternative designs may potentially be better equipped for decreasing the impact of sea level rise and storm surges. The Swarm Planning method is used here to increase preparedness and anticipate future changes.
The crucial factor in this design [Roggema, 2009] is the changed viewpoint from which the problem is approached. Instead of trying to increase the protecting level through strengthening structures, an advanced crucial intervention is proposed. Instead of keeping water out, it is let in at a very slow pace and in a very well predictable way. A hole in the coastal defence allows water to enter the hinterland and the level will rise as sea level rise
increases. If this intervention is related to the Swarm Planning methodology of the five distinct layers, the analysis of networks in the area (layer 1) determines the most crucial and also most vulnerable or weak node in the coastal defence. Here (the white dot in the maps, figure 5), the focal point (layer 2) is found from where developments emerge. The unplanned area (layer 3) is defined by the space needed for water at different levels of sea level rise (0,3 m, 0,6 m, 0,9 m and 1,2 m, see figure 5), also determining where it is safe to live and where adaptation is required. Natural resources (layer 4) are found outside the floodable area and emerging occupation patterns (layer 5) are located on the edge between the floodable landscape and higher and dryer places. This twilight zone will face wet circumstances only if sea level rises 1,2 metre, but the buildings are already built in a way that they can withstand or even profit from being in the middle of sea water, being made floating, amphibious or water-proof.
The advantages of this design are that impacts as result of a big disaster are prevented, because the water is already allowed in the hinterland and used as an ally and not as an enemy. Because of the fact that accurately can be predicted where the water will flow, people, buildings and organisation are very well capable of adapting at a very early stage. The water will bring gradually changes and benefits. At first, in the unplanned areas brackish conditions emerge, allowing ecological conditions to enrich. Secondly and at a later stage all new buildings face water in their environment, a real estate asset of great value. Probably the biggest advantage is that due to the slow pace of entering seawater a disaster never happens, but it is tamed to a gradually changing wet environment, which makes the area inherent safe.
Zero-Fossil Region
Current debate regarding greenhouse gas emissions focuses on emission reduction targets through carbon taxing, carbon trading schemes or energy saving. ‘Sustainable’ pathways are developed how to reach these targets, which are in the order of magnitude of 20% by 2020 at the max [European Parliament, 2009; Samenwerkingsverband Noord Nederand et al, 2007]. The consequence of this approach is that the targets in themselves begin to function as end-goals and are limiting thinking beyond them. The other element of these targets is that they can be perceived as abstract goals at global or national levels and implementation on a concrete scale is difficult to achieve or imagine. These targets tend to end in policy documents, which are assessed on a yearly basis and are reported in the political arena. However, in many parts of the world initiatives are taken to develop strategies that formulate ambitions that are more ambitious: zero emissions, low carbon, living without oil and others [Bakas and Creemers, 2010; Droege, 2010; OMA and ECF, 2010; Rifkin, 2002; Wright and Hearps, 2010]. Most of these strategies however do not transform their content into concrete spatial designs. One of the early examples to perform in such a way is the design for Groningen province in the Netherlands (figure 6), demonstrating the spatial possibilities to become a Zero-Fossil region [Roggema and Boneschansker, 2010].
Fig. 6 Design for Groningen without the use of fossil energy [Roggema and Boneschansker, 2010]
The design and the underlying calculations were collaboratively developed in an intense seminar week in which international scientists and policy-makers jointly worked together in the so-called INCREASE II-seminar to design the region in a way that no fossil resources were needed to operate the region. The key element in this proposal was the shift from calculating carbon emissions towards calculating the local renewable energy potentials and use them a starting point for the spatial design. The Swarm Planning method is used to analyse the networks (layer 1) and define those crucial points (layer 2) in the networks where sustainable energy options can be started best, such as the places where the highest level of geothermal energy is found in the soil, osmosis and wave plant can be situated best and
where an integrated solar, wind and greenhouse landscape can be started. Unplanned space (layer 3) is subsequently used to fill with energy production and combined with food and water supply. Besides these a large ecological reserve is located in the eastern part of the region, making use of historically available peat conditions (layer 4), more than enough to serve the area. In the last stage emergent occupation patterns were introduced, like small-scale cooperatives, which jointly produce energy, clean water and food for their own usage or integrated aquaculture and living areas, using the heat locally available (layer 5).
The advantages of this design are that it provides the area completely with zero carbon energy, in itself a valuable contribution. Moreover, the integration with water supply and food production allows the region to become independent from other energy or other supply delivering areas. The region contains the capacity to produce even more than needed to fulfil own demands and makes it possible to export food and energy. The biggest advantage however is the contribution the region is able to give to moderating global warming and the example it provides to be for other regions across the world.
Net Carbon Capture Landscape.
On a global level the debate between developed and developing countries about pursuing the same living standards by developing countries, with a probable contribution of greenhouse gas emissions by these countries as a result is a question of fundamental equity. Reverse equity is not discussed very often. Reverse equity can be defined as the bonus contribution developed countries can deliver in order to allow for developing countries to gain welfare in a sustainable way. This bonus contribution tops the current standard of many cities and regions in the developed world to become carbon neutral. Therefore the aim of the Net Carbon Capture design is to capture more Carbon than is emitted in the region.
The crucial shift in thinking here is the change of mindset from achieving neutrality towards a potentially unlimited carbon capture. In the design for the Peat Colonies, a part of the Northern Netherlands, this aim is spatially visualised [Broersma et al, 2011]. In the designs Climate Change Mitigation (providing energy without emitting carbon) and Adaptation (adjust the area spatially to the expected changes in climate) are unified.
The designs are conceived along the Swarm Planning methodology. The first layer of analysing the networks clarifies the richness and intensity of energy, water and ecological networks.. Based on this analysis the major strategic points (layer 2) can be determined. In this step a fundamental choice imposes itself: are these focal point used as the starting points of centrally organised production units or are they seen as the cores of self sufficient entities
encapsulated in the landscape. Depending the choice two entirely different spatial models are derived (figure 7): the Lonelycolony-model, in which decentralised and small-scale units provide themselves with the necessary energy, water and food or the Peatropolis-model in which the nodes are the starting point for extended development to produce and export electricity, biofuel, food and clean water. As the first model leans towards becoming autonomous and ‘neutral’ in the use of resources, the latter model implies to provide sustainable goods for other societies. This exemplifies the function of reversed equity in delivering carbon neutral products to societies who else would have used fossil resources. Both models include unplanned space (layer 3); the Lonelycolony-model just leaves room open for eventual needs of the small communities and the Peatropolis opens the surrounding areas to occupy for the production of surpluses of energy, water and food. The fourth layer of natural resources consist of the introduction of a robust ecological and water system, necessary to adapt to changing precipitation patterns and required storage of valuable fresh water, but it also provides the space to extend forests for biomass production. The emergent occupation patterns (layer 5) differ for each of the models: coherent, but small core villages or efficient production strips along waterways.
The key advantages of the design lie in the integration of adaptation and mitigation strategies. The local production of valuable resources, such as energy and water demands adjustments in the agricultural, water and ecological structures in the area; adjustments that are also required and valued in the light of necessary adaptations to upcoming climate change. In this sense is creating space for forests, agriculture, canals and lakes both a spatial mitigation and adaptation strategy. Moreover, especially the Peatropolis design model illustrates the potential developed countries have to achieve more than their equivalent part in reducing global warming and this can be seen as a relief for developing countries struggling with tightened tensions between achieving higher living standards and minimising their contributions to global warming.
Fig. 7 Two spatial models for the Net Carbon Capture Landscape: Lonelycolony (left) and Peatropolis (right) [Broersma et al, 2011]
Conclusion and discussion
The definition of a safe biophysical operating space for humankind and measurable boundaries gives a critical first insight into Earth system limits. The fact that, according to Rockström and colleagues we have already crossed three of those boundaries may be interpreted even more alarming if the draft paper of Hansen and Sato [2011] is taken into consideration. In this paper, paleoclimatic data suggest the Earth likely to cross a threshold, jumping out of the Holocene ‘comfort zone’, with only a one degree increase in global temperature (not two, as previously assumed). Attempts of humankind to ‘control’ the Earth system by measuring and predicting and staying within these thresholds, is clearly worthwhile. However, as we argued in this paper, defining our adaptations in such a finite way can lead us to ignore the complexity and unexpected future changes in social and spatial systems may well have adverse or accelerating effects.
Our current pattern of thinking limits necessary innovations and breakthroughs and only accelerates biophysical change. Current spatial planning and design practices tend to repeat
former practices, thus giving solutions for the problems of the past. This proves counter productive when new and more complex problems arise. Current approaches to problem solving tend to limit our thinking and focus to carbon objectives (often in a symptomatic way) instead of exploring the available potentials of renewables. Current approaches capture our solutions in well-defined silos where mitigation and adaptation are seen as separate strategies leading to different results. This paper has shown how the two can be deeply integrated through spatial design.
A turn around in our thinking is required. We need to end ‘problem-solving’ and instead set our minds on the future we desire. Seeing climate risks in terms of new patterns of weather serves as a starting point for imagining desired future. Floods are natural events (often critical for ecological regeneration), heat-waves should be treated as potential energy sources, and not as a symptom to be treated by air-conditioning. Climate phenomena perceived in this way can be an inspiration for enhanced landscape design, capable of shaping our future societies to improve the living environment and allowing natural systems greater capacity to adapt as well. Furthermore, this approach to sees no distinction between adaptation and mitigation. It is not a passive acceptance of change, nor does it avoid responsibility for addressing the cause of climate change.
In dealing with our Planets boundaries the technical rational approach alone is not sufficient. Enabling social processes to increase resilience in society must be actively researched and implemented. In particular opportunities in the spatial domain are underestimated and can play a larger role. However, in order to play this role, the current planning and spatial design approaches need to shift. Innovations in design theory are essential to fulfil these expectations. The current spatial planning system is constraint by a rusty socio-political environment, in which incentives for change are sidelined by long deliberations and political compromises. Furthermore, planning practice is unable to apply new approaches. It is stuck in the existing framework of well-worn habits and assumptions that reflect a time when todays’ complexities and uncertainties were unknown.
A new planning methodology, as presented in this paper, offers potential way to overcome historic constraints. Swarm Planning, is able to deliver designs that unify mitigation and adaptation to climate change through a positive approach that starts from a focus on opportunities, not the problems. This methodology leaves room for the unplanned, while giving direction and enabling emerging processes from intense nodes. The designs, which represent a desired future, function literally as attractors - enhancing the emergence of processes towards the desired future. Critically, the method provides the openness and
adjustability required to deal with fundamental uncertain and surprising circumstances while also preserving the core values in an area of focus.
The main assignment ahead of us lies in the implementation of the methodology in the chaos of day-to-day practice. There is a need to connect the methodology with current biophysical and socio-ecological research and link it with different planning contexts across the world to review how the methodology functions across different socio-political environments.
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