Methodology


Adopted by the world leaders at the United Nations Sustainable Development Summit in September 2015, the 17 Sustainable Development Goals (SDGs) with 169 targets are heading on a new journey towards achieving the common future we want in 2030.

While the SDGs are broadly framed as 17 separate elements, the goals inherently interlink with each other forming an indivisible framework that aims to achieve holistic sustainability from a systemic perspective. On the one hand, 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 the reduction of inequality (Goal 10) will help achieve poverty eradication (Goal 1). On the other hand, the pursuit of one goal or target may conflict with the achievement of others. For example, an increase in agricultural production to help end hunger (Goal 2) can result in an increase in water use for irrigation that may compete with the water demand for achieving universal access to drinking water (Goal 6).

Goals and their associated targets form a complicated network of interlinkages. Understanding the interlinkages among the goals and between the targets can help identify potential synergies and trade-offs. Removing the trade-offs and maximising the synergies are a key element of SDG integration and policy coherence.

The importance of SDG integration has already been well recognised by many national governments which implementation requires a major shift from the conventional siloed approach to a systems approach. This is where the challenge lies, for there is neither sufficient knowledge on how the SDGs interact with each other, nor any practical tool that can help identify the synergies or trade-offs between the targets. Some experts and institutions have initiated analysing SDG integration, but huge gaps still remain, particularly in terms of the comprehensiveness (covering all the goals and targets), quantification of the SDG interlinkages, and the applications to practical case studies at the national level.

To address the existing knowledge gaps and build the science-to-policy connections, IGES initiated a project on “SDG Interlinkages and Indicators,” under which a methodology with four steps to identify the linkages based on the causalities between relevant targets and then quantify the linkages was developed and applied to 27 selected countries from East Asia (China, Japan, Mongolia, and Republic of Korea), South Asia (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka), and Southeast Asia (Brunei, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Viet Nam), as well as from Africa (Ethiopia, Ghana, Malawi, South Africa, and Tanzania). To enable relevant stakeholders to explore the potential synergies and trade-offs between the targets, IGES developed SDG Interlinkages Analysis & Visualisation Tool, a web-based interface for free access through the internet.

The four steps (see the following Figure) include:

   -   Step I: Identification of the interlinkages between SDG targets based on causalities through literature review;

   -   Step II: Selection of the indicators with trackable data for selected countries based mainly on the Global SDG Indicators;

   -   Step III. Correlation analysis based on the indicator-level time-series data (1990-2019) collected for the selected countries from the UNSD SDG Indicators Global Database as the main source;

   -   Step IV. Quantification of the identified causal relations between relevant SDG targets based on the correlation coefficients.


Figure 1


Methodology for the analysis and visualisation of the SDG interlinkages

Source: Zhou and Moinuddin (forthcoming)

In terms of the scope of SDG interlinkages, interlinkages can refer to those between goals, between a goal and relevant targets, or between targets. Interlinkages include direct relations between two targets or indirect relations that connect two targets via a third target or more intermediate ones. A causal link also has a direction pointing from the cause to the effect. Furthermore, interlinkages can be defined by causalities or by other types of relations. For the SDG Interlinkages Analysis & Visualisation Tool, interlinkages are defined as direct causal relations between the targets.

Identifying the causal relations between relevant SDG targets is a challenging task. Existing knowledge and literature in this area is limited due to the short history of this new research field with most of the existing works started since 2015, before or after the adoption of the SDGs in September, 2015. For the SDG Interlinkages Analysis & Visualisation Tool (V3.0), the interlinkages identified at the target level are based on a comprehensive literature review related to specific goals or targets (see Zhou and Moinuddin, 2017) as well as the knowledge obtained from relevant international consultation processes on SDG indicators, such as the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs).

The identified linkages based on causalities are further quantified to indicate how strong the links are and whether the links are positive or negative. Quantification is based on the correlation analysis of the indicator-level time-series data. For the SDG Interlinkages Analysis & Visualisation Tool (V3.0), a set of indicators with trackable data based on the Global SDG Indicators was used as the main measurement for 113 targets. (Click to see the correspondence table on the SDG goals, targets and indicators.) Time-series data running from 1990 to 2019 for each of the indicators has been collected from publicly available and internationally recognised sources for the selected countries. The UN Statistics Division’s SDG Indicators Global Database has been the primary data source, and some additional data has been collected from the World Bank SDGs database. All the data has been compiled into a database that is publicly available and free of cost from the SDG Interlinkages Analysis & Visualisation Tool.

Correlation analysis was conducted for the identified causal links between relevant targets. The correlation coefficients, ranging from [-1, 1], indicate the linear relationship between relevant targets. Positive coefficients represent positive linear relations and negative ones represent negative linear relations. Coefficients with a larger absolute value (e.g., 0.9) indicate strong linear relationships between targets and those with a smaller absolute value (e.g. - 0.2) indicate weak linear relationships. The SDG Interlinkages Analysis & Visualisation Tool will be updated on a regular basis to reflect the progress made in refining the global indicators, the improvement in data availability and quality, and the advancement in the knowledge on the causalities between relevant SDG targets.