Introducing the SDG policy integration tool
The proposed tool uses data from the 2020 UN The Gambia Common Country Analysis (CCA) to assist policy makers to identify the multidimensional risks threatening SDGs and also understand the root causes driving or being driven by these risks.
The integrated and indivisible nature of SDGs means that that there are synergies as well as trade-offs in the implementation of SDGs with the achievement of one target impacting on the achievement of others. By extension; the successful implementation of any one SDG is confronted by several risks at the same time. In turn, any risk to the successful implementation of any one SDG affects several SDGs at the same time
Successful implementation of the 2030 Agenda therefore creates a policy challenge as it depends, to a large extent, on the identification of the multidimensional risks threatening the success of SDGs as well as the root causes that drive them (or vice versa). The UN recommends identifying the risks and categorizing them according to the 12 dimensions of risks as illustrated in Table 1 below.
The 2020 UN The Gambia CCA — an independent UN system impartial impartial assessment and analysis of a country’s situation — identified 117 multidimensional risks to SDGs as illustrated in Table 2 below. The relationship of each of these risks (listed in the column titled ‘Risk scope’) with SDGs was then assessed.
The tool uses three dashboards to illustrate the results of the assessment. The first dashboard maps the 117 multidimensional risks and the SDGs they threaten, the second dashboard maps 89 multidimensional risks and how they are related with 42 root causes of development problems that were identified in the CCA and the final dashboard ranks the risks across the 12 UN SDG risk-based dimensions. The next sections aim to show the reader how they can use the tool
Figure 1 below illustrates the tool’s landing dashboard. It has an SDG Logo on top; a sankey chart showing the 117 risks (represented by the lines) and the SDGs they threaten (illustrated by the bar on the left) and the 12 dimensions of the SDG-based risk framework established by the UN that they are associated with (illustrated by the bar on the right).
The thickness of the line illustrates the relative likelihood of the risk materializing. At the landing, it is not easy to distinguish the lines. This is not a problem as the tool is interactive allowing policy makers to discover the multidimensional risks to SDGs by filtering the chart. For example, suppose a user was interested in looking at the development risks threatening to hinder the attainment of SDG 11 — sustainable cities and communities.
This is done by clicking on the SDG 11 logo. The resultant sankey chart is illustrated in Figure 2 below. The greyed out lines each represent a development risk associated with SDG 11 (These are also listed under the “short description of risks” section below the chart ). The 3 bold lines represent the proposed 3 top risks to SDG11 in the country that are relatively most likely to materialize.
From Figure 2 below, there are 49 risks associated with SDG 11. These risks cover all the 12 UN SDG risk-based framework dimensions. Among these 49, the fast pace of urbanization, marginalization of the disabled and inadequate disability inclusive infrastructure are the most likely risks to happen if nothing is done.
Let us suppose further that after identifying urbanization as one of the major risks to SDG 11, a policy maker is interested in understanding the root causes associated with unsustainable urbanization.
This is done by clicking on the root causes dashboard which takes the user to a sankey chart that illustrates the root causes. Unlike the multidimensional risks sankey chart which has two nodes (SDG and Risk Area), the root cause sankey has three nodes (Broad Risk Area, SDG and Broad Policy Problem). The chart shows the associated root causes of the risks to achieving the 2030 Agenda in the country.
Extending the example of a policy maker interested in understanding the root causes of unsustainable urbanization; in the root causes dashboard, the user would select ‘urbanization’ from the risks listed below the chart resulting in the sankey chart shown in Figure 3 below.
To summarize the proposition of Figure 3 (below): unsustainable urbanization in The Gambia poses the greatest risk to SDG1 (no poverty). It also threatens SDG 8 (Decent work and economic growth), SDG 10 (reduced inequalities), SDG 11 (Sustainable cities and communities and SDG 17 (Partnerships for the goals). Overall it poses the greatest risk to economic stability through facilitating weak governance and poor economic performance. There are 14 root causes proposed as exacerbating the problem with the biggest being corruption - others include low women participation rates, concentration of the electricity grid to urban areas, limited local government capacity and inadequate investments in public infrastructure.
Besides using the SDGs to discover associated risks, the tool also allows a user to discover the SDGs a risk (or group of risks) threaten. Let’s assume a user was interested in the SDGs threatened by insufficient production and distribution of electricity. This is done by selecting the risk (in the multidimensional risk dashboard) which would result in the sankey chart illustrated by Figure 4 below.
Figure 4 below proposes that insufficient production and distribution of electricity in The Gambia directly affects at least 13 SDGs and threatens economic stability while exacerbating risks due to limited access to physical infrastructure or basic services.
To explore the root causes, a user would select the relevant risk in the root cause dashboard resulting in the sankey chart illustrated by Figure 5 below with the following proposition: Of the 13 SDGs insufficient production and generation of electricity threatens, 7(SDGs 4, 5, 6, 8, 9, 10 and 17) can be said to be affected with greater intensity and SDG 7 is the most affected. Fourteen root causes and 2 intermediate causes are proposed as driving the problem and exacerbating weak governance and poor economic performance which threatens the country’s economic stability. It is worth observing that some of the root causes are similar to the ones identified in the urbanization example above emphasizing the multidimensional nature of the risks to the 2030 Agenda.
Overall, the tool is a prototype as we continue to explore tools to assist SDG integration in policy planning and implementation. We encourage you to try the tool and give us any suggestions you may have on how to improve it. The tool can be found at this link: https://public.tableau.com/profile/george.lwanda#!/vizhome/SDGPolicyIntegrationTool/SDG-Policyinterlinkages?publish=yes
