Climate change is expected to impact the world´s water system, increasing the intensity and frequency of extreme events, such as heavy rainfalls and droughts. To cope with with the expected impacts, urban managers and engineers have begun to search for a
lternative ways to increase the resilience of urban settlements against increased risks of extreme events.
Located in Scandinavia, Copenhagen , the capital city of Denmark, is starting to put plans forward to built resilience against flood events, both from storm surges from the ocean, as from more intense precipitation. Hitherto, responses to increased flood risk in some affected cities have been based on enlarging the capacity of the existing sewer systems. However, such an approach is linked to economic and technical constraints, which may put its sustainability in question (Chocat et al., 2007). There has been a growing call for a shift from a hard infrastructure-based approach to urban stormwater management towards combining it with, and increasing the role of, green infrastructure-based approaches.
In theory, nature based approaches enable the creation of multiple synergies with a sustainable urban form through an enhanced green infrastructure within the city, improved conditions for urban agriculture, freshwater aquifers’ recharge, and facilitating a more inclusive decision-making process in urban management based on multiple stakeholder involvement (Holman-Dodds et al., 2003; Fryd et al., 2010; Jensen et al., forthcoming). Nature based solutions are therefore accompanied by the prospect of increased resilience against climate change and, more importantly, by a possibility of accelerating the transition of cities towards sustainability (van de Meene et al., 2011; Frantzeskaki et al., 2012).
In 2011, the city of Copenhagen published the Copenhagen Climate Adaptation Plan (CCAP) in 2011. The CCAP is underpinned by several key considerations: decisions and investments into adaptation are to be based on sound technical knowledge; adaptation is flexible; adaptation is in strong synergy with urban development goals in general; and adaptation should ideally result in an attractive city that is based on green growth (CCAP, 2011: 6). One of the initiatives identified as essential to adapting the city to climate change is the establishment of green infrastructure solutions such as SUDS to reduce the flood risk (CCAP, 2011: 7). Calculations show that unilateral adaptation based solely on investments into the expansion of sewer capacity will produce a negative societal gain for the city. However, barriers seem to arise due to the transdisciplinary and multi-institutional nature of the NBS implementation process (Mguni, 2015). For Mguni (2015) the fact that Copenhagen’s discursive elite within the water management regime is made up of different professional groups with differing ideas provides a good opportunity for a change in the sanctioned discourse around storm-water management from a sewer-based approach towards the consideration of alternative approaches. However, since the sanctioned discourse is a manifestation of power (i.e. agenda setting), changing it will involve actively empowering the discourse that supports transition towards NBS.
Environmental decision making, as well as urban planning, requires the integration of complex interactions between ecological, economic and social aspects. For that reason, controversies are common and often arise because of differences in views and perspectives of stakeholders about what is considered sound management strategy.This is equally true for evaluating the environmental impacts of a specific project, the environmental assessment of a programme, or the development of sustainability pathways. In this process, one has to take into account not only ‘‘the facts’’, but also values, asking what ought to be honoured, protected, sustained, or developed (Forester, 1999). This constellation requires the active participation of all relevant stakeholders and their early involvement in the process (Antunes, 2004). Due to the underlying multiple perspectives, participatory planning and group decision making have become increasingly more important in natural resource management.
To help address plurality of perspectives from multiple stakeholders, a number of planning tools have been proposed. These tools can generally be categorised into two classes, namely, hard systems approach, and soft systems approach. As their names imply, both approaches adopt a holistic or system-oriented view of resource management. Clayton 8c Radcliffe (1996) defined a hard systems approach as a method that starts with a basic acceptance of a well-defined objective and problem specification. This approach is deeply rooted in the traditional 'scientific management' concept, which essentially assumes that the problem addressed can be sufficiendy understood and the interrelationships among the system elements can be sufficiendy modelled. In contrast, Checkland (1989) defined soft systems methodology (SSM) as a learning system designed for complex human-dominated systems. In other words, SSM is fundamentally designed as a tool to understand the system and not to solve a 'problem' as is the case of formal hard system-based approach (Rosenhead 1989). SSM takes the view that a formal model is only one out of potentially many perspectives of the problem.
Causal loop' qualitative system dynamics enhances linear and 'laundry list'16 thinking by introducing circular causality and providing a medium by which people can externalise mental models and assumptions and enrich these by sharing them. Furthermore, it facilitates inference of modes of behaviour by assisting mental simulation of maps. By identifying policy links in maps, it allows focussed speculation of how to intervene to redesigned systems. By using archetypal structures, particularly total generic two loop structures, involving policies, boundaries and delays, it enables potential unintended consequences to be anticipated and hence increases the chances of plans being achieved. The methods bring much needed tools to the strategic areas of management and allow a wide range of managers to access the power of feedback thinking.
Qualitative system dynamics is finding a home in management development programmes, where it is bring- ing clarity and rigour to a particularly abstract field. At the same time, system dynamics is being used to provide senior and middle managers with models for strategy and operational policy development at many different levels of an organisation. A number of large companies have over twenty different system dynamics modelling projects in existence at one time (Wohlstenhome, 1999).
In this paper, we use participatory system dynamics (SD) modelling to address these evidential and procedural challenges. Participatory SD modelling involves a range of stakeholders in a collaborative learning process to develop a shared theory about the causes of trends over time in a complex system, and the policies that are likely to have a desired influence on observed trends (Beall and Ford, 2010; van den Belt, 2004; Vennix, 1999).
The aim of this research was therefore to use a collaborative learning process to build on the initial causal model for the integration of NBS to Copenhagen´s flood mitigation infrastructure. We aimed to test the generalizability of the causal model for cities in the groups described above, and to enhance understanding of the system across stakeholder groups. To that effect, the purpose of this paper is to present system dynamics approaches to policy modelling and simulation. In light of this, this research follows through the following objectives: 1. To identify the dynamic complexities and challenges that are often associated with complex dynamic systems; 2. To develop a taxonomic analysis of policy formulation problems so as to visualize their generic structures; and, 3. To make a causal loop analysis of the identified policy formulation problems based on their generic structures.
METHODS
Participatory system dynamics (SD) modelling
We used participatory SD modelling to elicit a qualitative causal model of the influences and outcomes of cycling. We based this research on the following SD modelling principles (Forrester, 1969, 1980; Sterman, 2006, 2000; Richardson, 2011).
1. Complex systems include many interacting variables that change over time
2. Interaction between variables is characterised by reinforcing (positive) and balancing (negative) feedback loops and non-linear
relationships
3. Patterns of interaction within feedback loops explain system behaviour over time
4. Complex systems are also characterised by the accumulation of “stocks”: variables with a measurable value at any point in time,
e.g. people, information, or material resources
5. Time is an important component of complex systems and relationships may change variables at different rates over time, creating
tensions between short- and long-term policy effects
While many SD modelling endeavours are undertaken by groups of researchers or technicians, participatory SD modelling explicitly includes a wide range of stakeholders, and is often focused on public policy problems. It has been successfully used to improve decision making in a variety of relevant disciplines, including urban planning, transport policy, road safety and public health. The
method has also been used to consider the outcomes of transport policies on air quality (Stave, 2002) and understand the costs and benefits of cycling policies (Macmillan et al., 2014). As with many SD modelling efforts, these examples aimed to provide insights about future dynamic effects of policy alternatives by relating them to the system structure, as opposed to providing point predictions about outcomes at a future time. In the context of urban cycling, participatory SD provides an opportunity to bring together disparate sources of evidence with the understanding of policy makers, practitioners, and advocacy groups. It can also potentially support policy makers who typically face major challenges in implementing change by enabling them to communicate more confidently about desired and expected outcomes across a range of domains of interest.
Saeed (1992) describes a useful generalisable method for an SDM process that uses repeated cycles, starting with desired outcomes, then moving through the following stages: understanding of problem trends related to these outcomes; qualitative representation of the system structure; development of a dynamic simulation model; scenario experimentation; and policy design. This paper describes the first part of such a process, namely the development of an initial shared qualitative system understanding of urban planning.