Mapping Sustainable Development Goals (SDGs) Interlinkages For Effective Policy Making

In 2015, the United Nations Member States adopted the 2030 Agenda for Sustainable Development (United Nations, 2015b), with 17 Sustainable Development Goals (SDGs) aimed at both addressing important social issues (e.g., environmental degradation, social inclusion, and climate change) and replacing the impactful Millennium Development Goals (MDGs). The SDGs have adopted a broad scope with the SDGs interacting with each other by design (ICSU, 2017). These interlinkages across SDGs can lead to policy synergies, that can help achieve progress in multiple goals simultaneously, or policy trade-offs, that can impede progress in some goals while achieving progress on others (Pradhan et al., 2017). Due to the broad scope of the SDGs, it has been difficult to map all interlinkages and to fully understand their effects, hampering the ability of governments to make progress on the SDGs. Posed with these challenges, researchers have been mapping the interactions across SDGs and trying to quantify their effects using a diverse set of methods (e.g., qualitative, quantitative, network theory, systems thinking & causal loop diagrams, system dynamics). Among these, System Dynamics (SD) modeling is the most promising since it captures causality transparently, quantifies the functional form of interlinkages, seeks to close all feedback processes, matches historical behavior, and tracks the dynamic impact of policies over time. SD has a long tradition of global modeling and has been applied successfully recently to capture interlinkages across the SDGs (Pedercini et al., 2019). However, there are opportunities to improve upon that work, focusing on (i) a “white box” modeling approach, capturing the high-level interactions across ALL SDGs; (ii) identifying critical feedback processes dominating the overall behavior of the system; and (iii) identifying high leverage policies in the SDG system.Our research aims at contributing to the literature on SDG interlinkages, using a three stage approach. First, we will build a high-level system dynamics model capturing interlinkages across all SDGs. Second, we will develop in-depth understanding of dyadic relationships between two SDG pairs (e.g., No Poverty (SDG1) and No Hunger (SDG2), and No Poverty (SDG1) and Water & Sanitation (SDG6)). Third, we will conduct a series of tests with the different models developed (both independently and in combination). We will test (i) the independent behavior of the high-level SDG model, (ii) the independent behavior of the in-depth SDG pair models, and (iii) the integrated SDG pair models into the high-level one. To develop the high-level model capturing all SDG interlinkages, we would perform step-wise multiple linear regressions on data spanning all SDGs for over 70 developing countries to identify interlinkages in practice. We would cross-check our findings with existing research capturing interlinkages across all SDGs using network and SD approaches (Zhou & Moinuddin 2017 and Pedercini et al., 2019). To develop the models of SDG pairs, we would develop comprehensive Causal Loop Diagram (CLD) capturing the existing linkages, pathways, and feedbacks between a pair of SDGs described in research articles identified in a systematic literature review (SLR). The findings from the literature review will be complemented by expert interviews, country specific data, and sustainable development related reports from governments and UN agencies. The fragmented research findings will then be translated, aligned, and consolidated into an integrated and comprehensive CLD that will serve as the basis for the development of a formal System Dynamics (SD) model. We will then develop a formal SD model, validated through structural and behavioral tests, calibrated to data from different countries, capable of generating a broad range of dynamics, and able to perform policy testing and “what-if” analyses that will allow us to identify policy synergies and trade-offs across the SDG pairs. In addition to structural and behavioral tests to ensure robustness and validity our our research approach, we will conduct several (i) “input-output” tests with the in-depth model for each SDG pair and (ii) integrated model tests, where the in-depth SDG pair model is integrated with the high-level SDG model capturing interlinkages across all SDGs. Third, using the calibrated SD model with data from specific countries, we will (1) test the performance of policies identified in the relevant literature (e.g., SDG interlinkages, sustaniable development, poverty, hunger, etc.), quantifying the short- and long-term impact of such policies within the SDG pair and integrate with all SDG interlinkages (including climate change impacts SDG13), (2) identify high-leverage policies, driven by dominant feedback processes, and test their performance quantifying their short- and long-term impacts, (3) test all our findings in the broader MI’s integrated SDG (iSDG) model, capturing in-depth interlinkages across all SDGs, and (4) identify proxy variables for conceptually vague and data-scarce indicators and elaborate improvement suggestions for indicators within the SDG pairs. Using this approach, we will gain insight and knowledge about specific factors and feedback processes that influence the dynamics of each SDG pair both within the sub-system and across all SDGs, allowing us to not only identify high leverage policies that build on the synergies across those SDGs, but also to inform broader policies across all SDGS. Our feedback rich dynamic models may serve as a framework for future research on other SDG pairs shedding more light on policy synergies and trade-offs.