The data for India on 16 SDGs was extracted using Scopus keyword search. The dataset has been normalized to make it into a standard scale without disturbing the range. As mentioned earlier, Mini-Max scaling is used to normalize the data The complete nature of the SDGs indicates that a large number of potential publications across the 16 SDGs have to be considered by policymakers and an outline has to be proposed to illustrate SDG interactions. In this paper, we have done a systematic data-driven analysis of interactions between all SDG indicators. Statistically, we have tried to classify all 16 SDGs and their existing interactions with each other and classified them as synergies and trade-offs. In the present study, the progress in one SDG can be a goal favours to the progress in other SDGs and we can see a highly positive correlation among many SDGs.
Table 3. Correlation Matrix
We have used nonparametric Spearman’s Rank Correlation (𝜌) in the present study as the data is not normally distributed. To extract all possible combinations of the unique indicator data pairs for each SDG and to get the monotonic relationships, we have used Spearman’s Rank Correlation. Spearman’s Correlation Coefficient (𝜌) provides a measure to evaluate the strength of an association between two variables. Spearman’s analysis can capture the nonlinear correlation between the variables and is less sensitive to outliers. Spearman’s analysis is widely used to identify general relations beyond the linear correlation between two variables in various disciplines (Spearman, C.1904).
Table 4. Highly correlated variables (with threshold value 0.80 and above)
Since Python has excellent support for statistical analysis, we built a correlation matrix using the Python programming language. Correlation analysis was carried out with the 16 Sustainable Development Goal datasets. We could find a high correlation among many SDGs. From our dataset of publication from India, the highly correlated variable in the SDG list keeping a threshold value of 0.8 and above is put in Table 4. Particularly, SDG1 (No poverty) with SDG5 (Gender Equality), SDG2 (Zero Hunger) with SDG6 (Clean Water and Sanitation), SDG13 (Climate Action), and SDG15 (Life on Land), SDG6 with SDG13 (Climate Action) and SDG 15 (Life on Land), SDG7 (Affordable and Clean Energy) with SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production), SDG 10 (Reduced Inequality) with SDG 16 (Peace and Justice Strong Institutions), SDG 11 (Sustainable Cities and Communities) with SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action) with SDG 14(Life Below Water) and SDG 15 (Life on Land), and SDG 14(Life Below Water) with SDG 15 (Life on Land) show synergetic relations with 𝜌 values greater than 0.8.
The SDGs which are highly correlated in the dataset are sharing the same document in both the related SDGs. A careful study of these highly correlated variables will help to plan clear strategies for a university. An institution that is enthusiastic to participate in the ranking process and wanted to strategize properly their future activities and get ready for world ranking can follow these studies as an example. Some publications are not mapped in any of the 16 SDGs whereas some are in the real sense related to one or the other SDGs. We should have a new strategy on each topic and can relate the same topic with highly correlated and moderately correlated SDGs. We have taken the data with a threshold value range from 0.5 to 0.79 and extracted the moderately correlated variables as shown in Table 5.
Table 5. Moderately correlated variables (threshold value 0.5 to 0.79)
Looking at the moderately correlated variables, it will be easy for any researcher and university to plan its publications in the related SDGs, so that with a limited number of publications you will be able to qualify in participating in different subject area rankings. The highly published area SDG 3 (Good Health and Well Being) is moderately correlated with SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG12 (Responsible Consumption and Production), SDG13 (Climate Action), SDG14 (Life Below Water) and SDG15 (Life on Land). It clearly shows that the need of the hour has come to an end for a better strategic evaluation of SDGs. It needs to have a study on the relationship of SDG publications and work on strengthening the SDG partnership more. If the institution can produce more paper in the same direction, by focusing on SDG3 and strengthen the relationship with all the 7 SDGs by using the correct keywords, we can achieve a better rank in the Times Higher Education Impact Ranking. The relationship between all 16 SDGs is worth studying and implementing.
In the same way, if we could relate all the possible SDGs while working on a research topic, writing for the research funds, we could qualify in different SDGs with the same number of publications. This strategy can be followed by any university in qualifying to participate in all 17 Impact Rankings of Times Higher Education. This study also highlights the existence of negative correlations between many SDGs and this is a matter to be considered seriously. Progress in one indicator must give an improvement in another indicator then we can expect drastic changes in the overall data mapping. In the present study, SDG01 (No Poverty) is negatively correlated with most of the SDGs like SDG 02,03,06,07,11,12,13,14 and 15. If a university having a well-planned strategy in achieving the SDGs, it is very much necessary to study the collaboration of each SDG with one another and couple the publications and promote them within their domain of influence. As we considered Indian publications for identifying the correlation between SDGs, a notable difference is that SDG01(No Poverty) is negatively correlated with many related SDGs such as SDG02 (Zero Hunger), SDG03 (Good Health and Well Being), SDG12 (Responsible Consumption and Production), SDG13 (Climate Action), SDG14 (Life Below Water) and SDG15 (Life on Land). These SDGs are somehow favourably related to each other when we do a proper study on SDG01. A university’s research and innovation always have a key role in helping the society where it belongs by addressing these challenges. Our analysis reveals that a well-planned strategic approach to SDG mapping will address almost all challenges in the society which in turn will help universities to address THE Impact Ranking. Our study highlights the existence of typically more interactions within and among the SDGs. This specifies a strong groundwork for the successful implementation of the SDG indicators in future research. The evidence calls for a deeper investigation and demands advanced strategic planning. For this, all related research work needs to act as a system of interacting cogwheels that together move with different SDGs. Therefore, policies promoting cross-sectoral and supportive relations between SDGs has to be instigated and it will play a crucial role in the understanding of the SDG mapping at the researcher level.