7.1 Normalization of indicators
A big challenge in applying the conceptual framework of the Green Growth Index has been finding appropriate indicators to directly measure performance in different green growth dimensions. Desired data or data with high or moderate relevance represented 67 percent of the 36 indicators; the rest are considered “proxy variables” (Table 9). Although proxy variables are not a direct measure of the indicators, they capture underlying relationships between the green growth indicators and dimensions and include a sufficient number of countries to build a global index (Miola, Paccagnan, Papadimitriou, & Mandrici, 2015).
According to OECD & JRC (2008), correlation and sensitivity analyses can be used to check the accuracy of proxy variables. These analyses were done for the Green Growth Index (see Chapters 5.5 and 5.10), and results showed that the index is relatively robust despite the use of proxy variables. In addition, the GGPM team conducted a literature review to find empirical evidence on the relevance of not only the desired data but also proxy variables to green growth dimensions (Chapter 4.2 and Appendix 1). The proxy variables are expected to be replaced by desired data as data become available. Likewise, the GGPM team envisaged to include additional indicators for efficient and sustainable resource use as well as for green economic opportunities as data become available in order to provide a balance in the number of indicators across all dimensions. This will address the issue of implicitly assigning more weights to the indicators in dimensions with a lesser number of indicators (see Appendix 4).
7.2 Data availability
Availability of data is another important challenge that affects the relevance of the indicators. The GGPM team considered indicators to be of high relevance for the framework if they are not only conceptually relevant but also publicly available. The completeness or lack of the data influences scores of the Green Growth Index. For example, a country with complete data for all indicators for green economic opportunities will have lower scores if one of the four indicators have a value of zero, thus pulling values of other indicators down. In contrast, another country with incomplete data will have a higher score because the fourth indicator, which may also have a value of zero but missing and unknown, will be excluded by default. The lack of data thus causes some level of uncertainty in the results of the Green Growth Index. Allowing missing values is, however, necessary for two reasons: first, to allow substitutability of indicators that represent the same concept as represented by the indicator category; second, to maintain a larger number of countries until the last level of aggregation. Not allowing for substitutability at the first and second levels of aggregation will exclude countries with missing values. Table 10 provides information on data gaps for indicators in the Green Growth Index by region and their implications on the number of countries.
If there were no missing values, the index could be computed for about 207 countries globally. Due to data gaps, however, the current index has been computed only for 115 countries (Figure 3). The data gap is the largest for the indicators for green economic opportunities, with Oceania and Africa having as high as 83 percent and 61 percent missing values, respectively. There are no data gaps for the indicators for natural capital protection in any of the regions. Data gaps for each country are presented in Table A1.14 (Appendix 1).
7.3 Data availability
Sustainability targets provide critical information to benchmark the Green Growth Index. The scores depend on the reliability of these targets. A quarter of the targets for the index are currently based on mean values of the top five performing countries (Chapter 5.6.3), which allow countries to already reach the targets regardless of their performance on a given indicator. For example, the target for the indicator for green innovation, which is the share of export of environmental goods to total export, was based on the top five performing countries. The maximum value for this indicator is only 20 percent, hence limiting the space for increasing performance for green exports because the target is very low. Similarly, the maximum value for the indicator for green employment, which is the share of green employment in total manufacturing employment, is only 14 percent, allowing some countries to have a score of 100, although green employment has not significantly contributed to the economy. Moving forward, sustainability targets for the indicators not included in the SDG should have valid and sufficient bases. The producer or publisher of data will be requested to recommend targets for the indicator.
Finally, SDG targets are either explicit or implicit. Because implicit SDG targets leave room for interpretation, different targets were given to the same SDG indicator (Table 4). For the Green Growth Index, the GGPM team did not attempt to interpret the SDG targets but used available interpretation, such as that suggested by (OECD, 2019a, 2019b) and by SDSN Sachs et al. (2018, 2019). Whenever the suggestions on the targets diverge, the team adopted the SDSN targets because, as with the Green Growth Index, the SDSN methodology was developed based on the global context. In the future, alignment with the SDG targets will continue to be important to provide consistent policy recommendations to the countries.