## 3 Methods
### Decision-Making Methods  
Figure 2 shows a summary of steps that were used to assess the technologies for EMPs.  
Once the metrics and required data are obtained, the second step in the assessment is the normalisation of the collected performance indicators data. Normalisation transforms the raw data into non-dimensional numerical values from 0 to 10, with 10 being the highest and 0 being the lowest.  
The general formula that is used to normalise datasets is presented in Equation (4). This equation is appropriate for raw dataset values that are beneficial when they are high; thus, resulting in a high normalised score. For example, higher cumulative avoided GHG emissions values correspond to higher normalised scores. On the other hand, when the  
Figure 2: The steps that were used to assess technologies for EMP.  
value of the indicator suggests that it is less beneficial when raw dataset value is high, Equation (5) is used instead:  
\[z_{i}=\frac{x_{i}-min(x)}{max(x)-min(x)}*Q \tag{4}\]  
\[z_{i}=Q-\left(\frac{x_{i}-min(x)}{x_{i}-min(x)}*Q\right) \tag{5}\]  
where zi is the \(i^{th}\) normalised value, \(x_{i}\) is the \(i^{th}\) value, \(min\) (\(x\)) is the minimum value, \(max\) (\(x\)) is the maximum value and \(Q\) is the maximum number of normalised values.  
The third step was the application of the analytic hierarchy process (AHP) [51] to calculate weights. The weights were calculated from pairwise matrices, which were established using [PERSON]'s rating scale, as shown in Table 2.  
[PERSON]'s rating scale is based on expert opinions on the perceived difference between the importance of each performance indicator. For the fourth step, the additive aggregation method was applied to the weights to calculate an aggregated score. Equation (6) is the general equation that is used to calculate the aggregated score [52]:  
\[SI_{i}=\left(\omega_{1}I_{1}+\omega_{2}I_{2}+\ldots+\omega_{m}I_{m}\right)= \sum_{i-1}^{m}\omega_{i}I_{i} \tag{6}\]  
where \(SI_{i}\) is the overall score of the \(i^{th}\) alternative, \(\omega_{i}\) is the importance (weight) of the criterion (i.e., indicators, metrics, scenarios) and \(I_{i}\) is the relative score of the criterion.  
In Step 4, the aggregated scores were dependent on the weights that were calculated for each of the performance indicators without considering the scenarios, such as base (or neutral), pro-economic, pro-environmental and pro-social. To analyse each of the scenarios, the fifth step was to use the AHP again to determine the weight of each 3 BL metric. The calculated weights of the 3 BL metrics were multiplied by the aggregated scores from Step 4 to obtain the scenario scores (Step 5). A spider diagram was generated to visualise the scores for each of the scenarios (Step 6). Finally, based on the aggregated scores, the technology share or portfolio was assessed to match the energy demand of an area with a goal to reduce GHG emissions to net-zero. The input data (i.e., electricity demand and current/projected GHG emissions) were needed to determine the percentage share of the technology that is required for the development. A decision could then be drawn from the result and the SDGs that are met by these priority technologies could be mapped.