Adopted by the world leaders at the United Nations Sustainable Development Summit in September 2015, the 17 Sustainable Development Goals (SDGs) came into force on 1 January 2016, embarking on a new journey over the next 15 years to the common future we want.
Successful implementation of the SDGs requires an effective framework for monitoring and reporting on the progress towards sustainable development at the global, regional, national and local levels, and on the accountability of relevant actors. Sound indicators are the mainstay of such a monitoring framework which can help turn the SDG targets into management needs, identify national priorities and address them by allocating resources efficiently, foster cross-sectoral collaboration and enhance coherence in strategies to pursue the SDGs.
While the SDGs are broadly framed as 17 separate elements, goal areas inherently interlink with one another making up indivisible parts necessary to deliver the holistic sustainability from a systemic perspective. Achieving one goal or target may contribute to achieving other goals or targets. For example, enhanced food security (Goal 2), full and productive employment and decent work (Goal 8) and reduction of inequality (Goal 10) will reinforce poverty eradication (Goal 1). On the other hand, the pursuit of one goal may conflict with the achievement of another. For example, an increase in agricultural production to help end hunger (Goal 2) can result in an increase in water use for irrigation which may compete with water demand for achieving universal access to drinking water (Goal 6).
SDGs and the associated targets form a complicated network of interlinkages. Understanding the structure of the interlinkages helps identify those targets which play special roles in connecting other targets in the network. Addressing these targets as national priorities and analysing the interlinkages between SDG targets can help minimise conflicts, avoid trade-offs, and seek synergies for making achievements inclusively across all 17 SDG areas.
On the one hand, SDG indicators should be limited in number, concise and easily communicable with relevant stakeholders. On the other hand, indicators should be comprehensive enough to cover as much as possible the full spectrum of the 17 SDGs and their respective targets. By combining these two different needs, it is highly desirable to have a core set of SDG indicators, possibly using a limited number of indicators while gaining a relatively wide coverage of the SDGs. This is particularly relevant and useful to developing countries where statistical capacity is often limited and indicator development lags behind. An imminent issue is how to identify such a core set of SDG indicators.
To address these practical issues related to effective implementation and monitoring of SDGs, we applied Social Network Analysis (SNA) techniques to accommodate and visualise the SDG targets in a network of interlinkages. SNA, often used in social and behavioural science, can help understand the structure or pattern of the social relations among actors and use graph theory to distinguish specific actors placed in strategic locations in the network. A network of interlinkages can be visualised in a graph with nodes presenting the actors and edges connecting corresponding actors based on their relations.
The following questions can be addressed through SNA and visualisation:
The analytical framework is shown in Figure 1.
Figure 1 Analytical framework for the analysis and visualisation of the interlinkages between SDG targets
Source: Zhou and Moinuddin (2017).
Interlinkages can refer to those between goals, between a goal and other targets, or between targets, etc. In this project, we focus on the interlinkages between SDG targets. Interlinkages include direct link between two targets and indirect link which connects two targets via a third target or more intermediate ones. Furthermore, interlinkages can be directional links indicating the causality of two targets, or undirectional links. In this project, we study direct and causal/directional links between SDG targets.
Identifying all possible interlinkages between 169 targets is an extremely difficult task. Existing literature on the interlinkages between SDGs is limited due mainly to the relatively short period after the adoption of the SDGs in 2015. Sufficient knowledge on the interlinkages between SDG targets does not exist because SDGs cover 17 broad areas and relate to multiple disciplines. In this project, the interlinkages between SDG targets are identified based on the knowledge obtained from international consultation processes on SDG indicators, such as the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs) and the Indicators and a Monitoring Framework for Sustainable Development Goals initiated by the United Nations Sustainable Development Solutions Network (SDSN) , and through literature review ( see Zhou and Moinuddin, 2017 ). The interlinkages identified in this project can be considered as a pioneer work which can be improved with new knowledge and more scientific studies on the issue. The present work can serve as a sub-set of the full picture. In addition, the identified interlinkages for 169 targets may be biased due to asymmetric knowledge on individual targets and their interlinkages with others.
The identified interlinkages are further quantified to indicate positive or negative relation and how strongly each pair targets link linearly. First, 51 indicators with trackable data mapping with SDG targets ( see the correspondence table ) are identified based on the SDSN’s proposed Global Monitoring Indicators and various corresponding data sources. Among 51 indicators, some are the same as or similar to the SDSN’s indicators, some are indices created based on the SDSN’s indicators and some are proxies for the SDSN’s indicators due to data availability.
Second, time series data (2001 – 2014) for each indicator were collected from publicly-available sources, including the World Bank and various United Nations agencies, for nine selected Asian countries (Bangladesh, Cambodia, China, India, Indonesia, Japan, Korea, the Philippines and Viet Nam). To fill in the data gaps for particular indicators or make the full time series, statistical methods were applied to populate the missing data.
Third, correlation analysis was conducted for each pair of targets based on the indicators for the targets and the corresponding time series data. The correlation coefficients, ranging from [-1, 1], can indicate the linear relationship between each pair targets. Positive coefficients represent positive linear relation and negative ones represent negative linear relation. Coefficients with larger absolute value (e.g. 0.9) indicate stronger linear relationship between the targets and those with smaller absolute value (e.g. - 0.2) indicate weaker linear relationship.
Using Social Network Analysis, SDG targets which place strategic roles in connecting other targets in the network of the interlinkages can be identified based on the statistics of relevant metrics, such as degree centrality, betweenness centrality and closeness centrality, etc. ( Zhou and Moinuddin, 2017 ).
A web tool, SDG Interlinkages and Data Visualisation was developed to enable users to explore and retrieve the data for indicators and visualise the interlinkages between SDG targets.
The development of the SDG indicators at both global and national levels, accumulation of the knowledge on the interlinkages of SDGs, and filling the gaps in trackable data are open-ended processes. With progress made in these areas, the knowledge and results provided by this work can be upgraded.