**4. Results**

### *4.1. Data Analysis of the Input and Output Factors*

The mere difference between the NIC and HIC is that NICs are those countries in which their economic development is said to be in between those classified to be as developing and highly developed. Substantial growth in their own gross domestic product (GDP) is a key indicator in transitioning from one classification to another. However, the authors choose to process the data combining NICs and HICs due to the small number of HICs in the world. For DEA to come up with a highly reliable result, there should be sufficient number of DMUs for comparative analysis. Table 3 below lists down the names of countries belonging to NICs and HICs with their respective DMU representation.


**Table 3.** Name of the countries in their respective DMU number and group.

The primary objective of data envelopment analysis is to calculate the e fficiency using multiple inputs and outputs. From the diverse combination of factors used by previous studies as presented in Table 1, the authors decided to use total renewable energy consumption (TREC), the labor force (LF) and total energy consumption (TEC) as input factors, while carbon dioxide emission (CO2) and gross domestic product (GDP) are the outputs. Table 4 below summarizes the descriptive statistical values and the coe fficient of correlation from the 2015 period with reference to the input and output factors.


**Table 4.** Descriptive statistics and Pearson correlation coe fficients of the year 2015.

Note: The scores of the correlation coe fficient are all positive values from 2013–2018. 2015 data is used to represent the other year periods.

### *4.2. GM (1,1) Grey Prediction Model Results*

Acquiring a positive correlation using the data from 2013 to 2018, indicates that the input and output factors used complied with the homogeneity and isotonicity requirement of DEA. For this reason, the data is suitable for Grey prediction to obtain the future factors for 2019 to 2022 periods. Table 5 provides the summary of gained mean absolute percentage error (*MAPE*) from 2013 to 2022.

The results above show that most of the *MAPE* from HIC and NIC are below 10%, aside from Russia which gained 12.82% for the GDP factor. This can be a result of a tremendous decline in their GDP output from the year 2015 to 2018. However, this score is still considerably "good" as far as a grey prediction is concerned. Since most of the average MAPE scores are less than 10%, this study can proceed to the next phase using the predicted values for 2019 to 2022.

Table 6 below displays no negative coe fficient using the projected data for the year 2021. This also indicates that the other projected data for the year 2019, 2020, and 2022 do not contain any negative coe fficients. Thus, the authors can use these data for further analysis using DEA.


**Table 5.** Summary of the average mean absolute percentage error (*MAPE*) of HICs and NICs.

**Source:** Calculated by the authors.

**Table 6.** Descriptive statistics and Pearson correlation coe fficients using the projected values for the year 2021.


Note: The scores of the correlation coe fficient are all positive values from 2019–2022. The data from 2021 is used to represent the other year periods.

#### *4.3. Results of the DEA Undesirable Model for the Period 2013–2018*

### 4.3.1. E fficiency Scores of HICs and NICs

Since the requirement for DEA has been met from the previous analysis, the DEA undesirable output model will be used to calculate the e fficiencies of NICs and HICs as well as their rankings according to their corresponding country category. Table 7 below shows how every country performed in terms of technical e fficiency as well as which country is on top for every year in the period.

As seen in the table, three countries—France, the United Kingdom, and the United States—are the most e fficient among the HICs which recorded a score of 1 in all year periods. Germany was able to follow through in 2018. While countries from NICs have lower e fficiency scores compare to HICs, one country—Indonesia—has shown improvement by obtaining a score of 1 starting from the year 2015 to 2018. South Africa was recorded to be the most e fficient NIC from 2013 to 2014.

Since the United Kingdom, the United States, and France have shown consistency in obtaining an efficiency score of 1, there is no need to include them in the line graph as shown in Figure 3 below. It can be observed that Russia has the lowest scores among the groups. Russia's lowest point score of 0.14 efficiency was during 2015, wherein most HIC countries (except the US, UK, and France) also experienced the same decline.


**Table 7.** Efficiency scores of countries and group rankings from period from 2013 to 2018.

**Source:** Calculated by the authors.

**Figure 3.** Graphical Presentation of other HICs Efficiency Scores (<1.0) from 2013–2018.

It can also be noticed that all of them have declining efficiencies from 2013 to 2015. After this period, it can be observed that most of these countries have increased their efficiencies from 2015 to 2017. However, Japan's score dropped again until 2018. Germany shows a huge increase in their technical efficiency, achieving a score of 1 at the end of 2018.

South Africa started in a high score during the 2013 to 2014 period as seen in Figure 4 below. Unfortunately, the country's efficiency dropped from 2015 and this trend continued until 2018. In contrast, Indonesia improved at the same time South Africa's score fell. Indonesia was able to maintain a score of 1.0 efficiency until 2018, making the country the most efficient among the NICs. India performed with the lowest efficiency. India scores only 0.09 in 2014 and reached its highest point of 0.15 in 2017. With almost the same performance with India, China placed second lowest in terms of efficiencies during the whole 2013 to 2018 period. Some other NICs performed very low, with efficiency scores below 0.5.

**Figure 4.** Graphical Presentation NICs Efficiency Scores (<1.0) from 2013–2018.

### 4.3.2. Average Efficiency Scores and Overall Ranking

Table 8 below arranges the HICs and NICs according to their average efficiency scores and ranking. The average efficiency scores were calculated from the values of efficiency scores per individual year 2013 to 2018 as shown in Table 7 from the previous section.


**Table 8.** Average Efficiency Scores and Overall Rankings of HICs and NICs.

**Source:** Calculated by the authors.

Since France, the UK and the US are consistently obtaining a 1.0 score for the whole year periods, their average scores are the highest among the others and therefore, the three countries ranked first while Germany, with a score of 0.711 is not too far from acquiring the highest score in the future. Russia remains the least efficient in the HIC group. With the Indonesia obtaining a score of 1.0 from 2015 to

2018, the country was able to take the lead in terms of efficiency among the NIC group with an average score of 0.765. Not too far from Indonesia's score is South Africa in second with 0.519 efficiency. With the rest of the countries from the NIC group obtaining an efficiency score below 0.5, India appeared to be the most inefficient, ranking 9th with a very low score of 0.124.

### *4.4. Projected E*ffi*ciency Scores for the Period 2019–2022*

The projected efficiency scores are calculated using the values obtained from the grey prediction method. These values are used as inputs and outputs for the computation of technical efficiencies using the undesirable output model of DEA. Table 9 shows the efficiency scores for the period of 2019 to 2022.


**Table 9.** Projected efficiency scores of countries and group rankings from period from 2019 to 2022.

**Source:** Calculated by the authors.

The result of the forecasted efficiencies shows that in the HIC group, France, Germany, United Kingdom, and the United States will continue topping the ranks, while Canada and Russia remain in the 7th and 8th rank, respectively. Japan and Italy can be seen switching their ranks in the last period of 2022 with the former going up from 6th to 5th.

Figure 5 does not include the countries that obtain an efficiency score of 1 through the whole period of 2019 to 2022 as they are understood to be highly efficient already. It can be observed that Italy and Japan display a quite positive trend, with a slight increase in efficiency, while Canada and Russia have a slightly negative trend.

**Figure 5.** Graphical Presentation of other HICs' Efficiency Scores (<1.0) from 2019–2022.

Efficiency scores of the NICs are mostly around of below a 0.5 level, except for Indonesia which garnered a score of 1.0 from 2019 to 2022. None of the countries show any remarkable positive trends. Instead, some countries like China, India, and Thailand are expected to display stable performance or very little improvement in efficiency. Mexico, Thailand, and Turkey will have declining efficiencies during the projected period, as seen in Figure 6.

**Figure 6.** Graphical Presentation of NICs' Efficiency Scores from 2019–2022.

As presented in Figure 7, the calculated average efficiencies of HICs during the past and projected periods are comparatively higher than the NICs. However due to the existence of the very low efficiency scores of Russia and Canada, the effect to the average efficiency scores of the HIC group reached the 0.7099 level for the past period and is expected to increase by 7.76% to reach the 0.765 projected efficiency level. A different scenario is expected from the NIC group which will exhibit a decline of 1.23% from a 0.325 score down to a projected average level of 0.321.

**Figure 7.** Comparative graph of HICs and NICs average efficiency scores for two different periods.
