Data analysis

Analyses were designed based on the framework created by Mayer et al. (2004). The ability to explicitly recognise which activities are relevant in a particular policy analysis enables conscious choices to be determined for analysis styles and for subsequent selection of methods (Mayer et al. 2004). Based on the PhD project’s research questions, ‘research and analyse’, ‘design and recommend’, and ‘strategically advise’ have been identified as the three main activities which will be undertaken. Therefore the policy analysis is mostly appropriately approached through rational and client advisory styles. Quality of analysis will be assessed by its validity-reliability, usability-action orientation, and workability-feasibility.

This approach is recognised to serve as general guidance to specifically tailored policy analysis methods as will be described in the following sections.

Although analysis styles were identified using the framework developed by Mayer et al.

(2004), further considerations when performing policy analysis on sustainable livelihood related problems were addressed based on recommendations put forward by Shankland (2000). These considerations include working from existing policies towards recommendation as the best approach in this PhD study context. Inclusion of measures for implementation as an integral part of the policy process ensures analyses do not stop at formulation. To further avoid any broad brushing, disaggregation is needed between specific sectors analyses and cross-sectors analyses, between sustainable energy policy measures and the policy itself, and between impacts of measures to different stakeholder groups.

There are two analysis approaches used in this PhD study. The first is a ‘quantitative approach’ which consists of statistical analysis (SA), numerical modelling (NM), and cost and benefits analysis (CBA). The second is a ‘qualitative approach’ which consists of language, object, act analysis, network analysis, and stakeholder analysis. Both analysis approaches are explained in further details in the next sub sections.

Quantitative approach

Statistical analysis and numerical modelling

A quantitative approach through SA and NM was performed to construct a baseline of existing energy consumption patterns and resources available, as determinants in evaluating efficiency, future sizing, and impact of sustainable energy technologies. The baseline was used to analyse appropriate technology as guided by the energy resource-technology matching process for remote, rural societies (Ashworth 1982). One main limitation is the impracticality of performing a complete analysis for time-restricted projects such as this PhD work. Therefore, SA and NM for energy consumption and available resources were performed in selected groupings of communities based on current limited access to energy and potential roles in future pilot projects.

Cost benefit analysis

One of the key items in establishing business cases is an analysis of the costs of a project, which normally includes some considerations of costs and payback time. Cost benefit analysis (CBA) compares the total costs and total benefits associated with an initiative, namely those reflected in market prices (private cost or benefit) (Diakoulaki and Karangelis 2007).

It has been highlighted that the payback method is simple and best used as an initial screening tool (Levitan 2010). One of its weaknesses is that it only takes into consideration the cash flow as opposed to other investment opportunity, which Net Present Value (NPV), Internal Rate of Return (IRR), and Average Rate of Return (ARR) considers. Regardless of its limitations, the payback method is seen to be the most appropriate method for the purpose of achieving the objectives of this PhD research. The payback method was used to analyse the potential costs and benefits of creating an indigenous, appropriate sustainable energy technology manufacturing capability in Aceh.

Qualitative approach

A qualitative approach through interpretive policy analysis (language, object, act analysis) and actor analysis (network analysis and stakeholder analysis) is seen to complement the traditional quantitative approaches, in which the outcome leads to the justification for

interventions. These methods were chosen based on the necessity of analysing policies in a society where formulation takes place based on professional judgement and conducted in the midst of social and political tensions. This raises the issue of multiple stakeholders’

motives and interpretations thereby requiring a method that can relate actor analysis to policy problems (Van der Lei 2009).

Language, object, and act analyses

The steps involved in conducting the interpretive policy analysis for this research follows the sequence as explained by Yanow (2000):

1. Identification of language, objects, acts, as significant carriers of meaning for a given policy issue;

2. Identification of interpretation groups relevant to analysed issue;

3. Identification of specific meanings communicated through thought, speech, and act;

4. Identification of conflicting points and their causes for differences (affective, cognitive, moral)

5. Intervention:

a. Presentation of different interpretations’ implications

b. Presentation of reasoning behind that different interpretation.

Words, symbolic objects, and acts of policy-relevant actors together with policy texts are the data collected for language, object, and act analyses, thereby engaging groups of people sharing understandings of policy ideas and language is the first steps in interpretive policy analysis. It is followed by steps to discover how members of each group (e.g. tribe, profession, and organisation) categorise the social world (Yanow 2000). Document review, conversational interviews, and participatory observation are interactive methods to generate rich data, with many researcher-analysts preferring not to record (on tape or notes) at the time of interaction with subjects (as subjects may say more and freely express their views, especially controversial ones, when not being recorded). Instead, examples are given for encouragement of training oneself to be able to follow conversations with jotted notes, to be summarised with the researcher’s understanding afterwards (Yanow 2000). Whichever analytical method chosen (as described in more detail in the following sub sections), the process’ success depends on the immersion of the researcher-analysts in the details of her data.

Network and stakeholder analyses

Actor analysis for this research was performed based on the method defined by Hermans and Thissen (2009) and Van der Lei (2009) as a method that allows studies of characteristics of multiple actors. There are two types of actor analysis methods: one that describes multi-actor decision-making and one that describes structural multi-actor relationships. Van der Lei (2009) categorises the actor analysis methods further into two groups as represented on Figure 2-1: network analysis and stakeholder analysis.

In network analysis (known also as social network analysis), graphs (nodes and lines) are used to describe relationships between actors. The relationships may represent different things (e.g. value, friendship, kinship, etc.), making network analysis a multi-dimensional approach (Kenis and Schneider 1991; Scott 2000; Van der Lei 2009). For research purposes, usually one type of relationship is chosen for an analysis (Van der Lei 2009).

Stakeholder identification is usually done in simple, descriptive, grids. It is used to map different actors based on their interests, resources, and/or power (Bryson 2004; MacArthur 1997; Van der Lei 2009). The technique equips the researcher-analyst to obtain an idea of stakeholders under study at a snapshot view. Although lacking in depth, it provides a robust overview of the overall situation (Van der Lei 2009).

Figure 2-1: Groups of actor analysis methods Reproduced from Van der Lei (2009)

Actor Analysis Methods

Methods of multi-actor decision making

Structural actor analysis methods

Top down Bottom up Structural actor analysis

methods that f ocus on the actors

Structural actor analysis methods that f ocus on the relationships between actors

e.g. game theory, metagames, conf lict analysis,

and transactional analysis