Data Analysis

Complex systems approaches offer a rich set of methodological analysis tools based on Hard Systems Methodology (HSM) and Soft Systems methodology (SSM). Each approach has its strengths and weaknesses and applicability is dependent on the type of complexity

104

being examined. In the following it is demonstrated that the combined use of soft systems methodology and hard systems methodology provides a suitable framework to elucidate complexity as part of the descriptive phase of the case study.

4.3.1 Hard Systems Methodology

Hard systems methodology, frequently referred to as general systems theory or system dynamics, identifies driving forces of problems, explores cause-and-effect relationships and the dynamic linkages between different systems components.

Hard systems methodology has been used extensively in the study of environmental and energy systems. It provides a powerful toolset to develop an understanding of complexities inherent in a multi-facetted problem. Concerned with technical factors it is a scientific approach to problem-solving. It uses computer simulations to quantitatively analyse the structure of the system under investigation.

Causal loop diagrams (CLD) explain the causal relationships that exist within a system. They create an understanding of the causes and effects of a problem and how different parts of a system interrelate. A CLD depicts a system as a collection of connected variables and feedback mechanisms created by these connections (Forrester, 1994). One or more of the variables represent the symptoms of the problem. The rest are part of a causal chain contributing to the problem under investigation (Haraldsson, 2000; Bala et al., 2017).

Arrows and signs to illustrate cause and effect relationships and directions of the relationship. The plus and minus signs at the arrow heads indicate the direction of the change. A positive sign means that both variables change in the same direction. Conversely, a negative sign describes an interaction where the increase of one variable results in the in the decrease of another variable. Diagram 4.3 below shows a simplified CLD of an energy system. Changes to the problem, the rise of atmospheric temperatures because of carbon intensive electricity generation, does not arise from individual variables. It is created by the link between supply, price and demand. A CLD helps to pinpoint locations of where the fundamental forces causing a problem are coming from. In this example, the price of electricity appears to the key variable to influence the level of carbon emissions from power generation.

105 Diagram 4.3 Example of a Causal Loop Diagram

Objective of system dynamic modelling is to establish how certain system variables interact over time. Interactions are dependent on the internal structure of the system. As

previously noted, system structure is a reflection of content related complexity stemming from path dependence, self-organisation, feedback mechanisms and the co-evolution of events. Adopting the metaphor of the iceberg, system dynamic modelling identifies and quantifies unobservable event patterns, which are linked through the system’s structure.

Stock and flow diagrams enhance the understanding of dynamic behaviour of complex systems. Stocks determine the current state of a problem (e.g. the amount of CO2

accumulated in the atmosphere). Stocks therefore often provide the basis for designing interventions. Changes to stocks occur via flows that are expressed in a unit of time (e.g.

the amount of CO2 emitted in a year). Key to solving a problem is to regulate flows into stocks (Sterman, 2000; Quelhas and McCalley, 2002). Diagram 4.4 depicts a basic stock and flow model. It illustrates how the price of electricity influences system behaviour and has mitigating or exacerbating effect on the problem of climate change. More specifically, the model demonstrates the extent to which the price level determines the flow of carbon from energy generation, which ultimately accumulates as a stock of CO2 in the atmosphere.

A stock and flow model studies the system in a quantitative way. Relationships between variables are described by equations that are executed during simulation runs. The real power of system dynamics is utilised through these simulations. In the example,

simulations in the stock and flow model (Diagram 4.4) provide the opportunity to test the potential of various price-based interventions to solve the problem of energy related carbon emissions. Simulations also help to better understand the effect a change in price has on other parts of the system (the economy for example). Diagram 4.5 shows the sample output of three system simulations testing the effect of different price levels on energy related CO2 accumulating in the atmosphere.

106

Diagram 4.4 Example of Basic Stock and Flow Diagram

Scenario modelling has been traditionally used to project different possible pathways into the future. It is increasingly applied as a learning tool to understand the long-term

consequences of interventions. Scenario modelling is closely linked to systems based analysis as it acknowledges the higher degree of complexity existent in systems, unlike the study of individual projects or technologies. Scenario modelling is also closely linked to the case study approach adopted by this research (Section 4.1). System simulation in a stock and flow model is merely a tool to operationalise the scenario approach and to capture the effect of a particular intervention scenario on pre-determined variables (i.e. ‘stocks’) within the system.

Diagram 4.5 Example of System Simulation Output

In context of this research system dynamic simulation intends to show how the

introduction of a price on carbon affects the stakeholder areas of concern. Variations in the

107 carbon price level are simulated to ascertain the effect of a price signal on changing usage behaviour. The scenarios are executed for a particular province or city which is

representative for each study region (‘case’). Interventions are simulated against socio-economic and energy sector related data specific to the case in order to reflect the effect of a specific carbon market design on the region. The simulation of scenarios is carried out in the systems modelling software package Vensim.

A systems dynamics model requires a number of inputs: Firstly, it requires the decision of which systems variables to include in the model. Secondly, it requires a description of the relationships between these variables, i.e. the knowledge of how the variables interact with each other as well as the data which is used to quantify relationships. Thirdly, it requires the selection of variables that are used to measure the effect of a market based intervention.

The system variables are implicitly defined by the stakeholders themselves. Variables are part of the causal chains which describe the areas of concern highlighted by stakeholder during the interviewing process. Insights gained from stakeholders and the review of literature are used to formulate the simulation equations, which describe the relationship between these variables. Socio-economic and environmental data from official sources supplemented by secondary data from relevant research provide the quantitative basis for the model. Hard Systems Methodology relies on tools associated with Soft Systems Methodology to provide the causal foundations for the system dynamic model.

4.3.2 Soft Systems Methodology

Soft Systems Methodology problematises the relationship between system dynamic models and the many realities that exist for those who are affected by a particular situation. Systems approaches recognise that the construction of a problem situation by different stakeholders varies dependent on personal interests, values and beliefs.

In order to elicit, integrate and incorporate the different stakeholder perceptions participatory instruments linked to stakeholder analysis are used to verify and complete the understanding of the problem and its potential causes. Insights gained from the interviews with experts and those directly affected by the electricity sector reform are translated into a ‘problem oriented causal diagram’ which forms the basis for intervention simulation in a systems dynamic model. Stakeholders are asked to identify areas they are most concerned about in context of a reform. As described in the previous section the effect of a particular intervention on these stakeholder areas of concern is measured through system dynamic simulations in stock-and-flow diagrams.

Hard systems methodology and soft systems methodology are often presented as two incompatible ways to operationalise the concept of Systems Thinking. Soft system approaches are regarded to be suitable to address the ambiguities involved in messy

108

‘human’ problems where different views are involved, rather than well-defined technical problems, which should be analysed by a hard systems approach (Checkland, 2000).

A number of studies concerned with complex problems involving both a ‘human’ and

‘technical’ aspects highlight that the two methodologies complement rather than exclude each other (Lane and Oliva, 1998; Mingers, 2000; Pit et al., 2016). Awareness of people’s mental models creates an understanding of the system’s structure. The internal structure manifested in the dynamic relationships between system variables, explains patterns and identifies ways to re-shape them.

4.3.3 Multi-Criteria Decision Analysis

One fundamental assumption in this study is that there is no ‘best’ solution that could be identified through the application of a scientific method such as system dynamic modelling in a stock and low model. In line with the complexity inherent in sustainable energy transitions the research recognises that solution design is subject to a high degree of uncertainty and ambiguity (Stirling, 2001, Stirling and Mayer, 2001; Foxon et al., 2008).

People’s preferences for a solution often contradict the rationality of human behaviour assumed by economic models. This was described earlier as the problem of ‘bounded rationality’. A discussion in wider society is needed to understand not only what the actual problem is but also what the most adequate and favourable solutions are.

Multi-Criteria Decision Analysis (MCDA) has proven to be a useful tool for the evaluation of solution designs in the presence of multiple and diverging stakeholder objectives for the sustainability reform. It has been applied in a number of case studies (Stagl, 2003; Kowalski et al., 2009). A key component of MCDA is the elicitation of stakeholder preferences (Marsh et al., 2017). During the empirical part of this study stakeholder preferences are captured in a close-ended questionnaire. Analytical Hierarchy Processing (AHP), a form of Multi-Criteria Decision Analysis (MCDA), is adopted to act as a participatory decision framework. AHP provides a toolset to determine regional preferences for a particular solution.

AHP involves several steps, which are executed in the spreadsheet application Excel.

Diagram 4.6 illustrates the sequence of steps required to complete the analysis. Firstly, questionnaire responses are coded numerically in order to be scored. The decision criteria are the sustainability criteria which were highlighted as important by stakeholders earlier in interviews. Preferences for each criterion are explored through a number of questions.

Secondly, an algorithm is applied to translate the survey responses for each criterion into a score along a pre-defined preference scale (1 to 10 for example). Thirdly, based on the score for each criterion relative weightings are calculated for each study participant. This figure indicates the relative importance an individual attaches to the sustainability objective. In the final step individual preference ratings are synthesised for each of the study regions.

109 Diagram 4.6 Derivation of Regional Preferences for Sustainability Objectives through

Analytical Hierarchy Processing

The second part of the analysis is concerned with ranking the intervention scenarios according to the aggregated regional preference ratings. AHP is applied to determine a ranking for each scenario in terms of regional preferences for a particular sustainability outcome. As shown in Diagram 4.7 the process involves two steps. Firstly, a rating is calculated to indicate the extent the outcome of a scenario meets a particular sustainability objective. Secondly, a scenario score is calculated based on aggregated stakeholder

preferences in each of the study regions. A score evaluates scenarios in light of the trade-offs that exist between the sustainability objectives.

110

Diagram 4.7 Derivation of Regional Preferences for Intervention Scenarios through Analytical Hierarchy Processing

Aggregated regional preference scores enables the researcher to identify the scenarios which were most accepted and the scenarios which were most contested in light of people’s priorities. The analysis is carried out for each regional case. To arrive at an overall score at national level regional scenario preferences are weighted according to population size.