A Systematic Study of Sustainable Development Goal (SDG) Interactions

Abstract

Sustainable development goals (SDGs) have set the 2030 agenda to transform our world by tackling multiple challenges humankind is facing to ensure well‐being, economic prosperity, and environmental protection. In contrast to conventional development agendas focusing on a restricted set of dimensions, the SDGs provide a holistic and multidimensional view on development. Hence, interactions among the SDGs may cause diverging results. To analyze the SDG interactions we systematize the identification of synergies and trade‐offs using official SDG indicator data for 227 countries. A significant positive correlation between a pair of SDG indicators is classified as a synergy while a significant negative correlation is classified as a trade‐off. We rank synergies and trade‐offs between SDGs pairs on global and country scales in order to identify the most frequent SDG interactions. For a given SDG, positive correlations between indicator pairs were found to outweigh the negative ones in most countries. Among SDGs the positive and negative correlations between indicator pairs allowed for the identification of particular global patterns. SDG 1 (No poverty) has synergetic relationship with most of the other goals, whereas SDG 12 (Responsible consumption and production) is the goal most commonly associated with trade‐offs. The attainment of the SDG agenda will greatly depend on whether the identified synergies among the goals can be leveraged. In addition, the highlighted trade‐offs, which constitute obstacles in achieving the SDGs, need to be negotiated and made structurally nonobstructive by deeper changes in the current strategies.

Participatory, explorative, qualitative modeling: application of the iMODELER software to assess trade-offs among the SDGs

“…Nevertheless, checking against data could help to improve the model by identifying decisive additional factors and including them. Another relevant application of data from correlations (Pradhan et al, 2017) would be to build a model based on the strengths of identified correlations. However, this would not be insightful unless the modeler checks for redundancies from existing indirect connections and addresses other common fallacies relevant to attributing correlation with causation.…”

“…Interestingly, Pradhan et al (2017) in their approach point out correlations between the SDGs' indicators that contradict the ICSU's results. The possibility thus exists that, as they themselves noted, "…correlation does not imply causality…" and "…while the mechanisms generating synergies and trade-offs remain elusive in our analysis, we complement approaches using process-based models by investigating the entire option space in which synergies and trade-offs emerge…".…”

“…A less systemic approach to identify synergies and trade-offs between the goals analyzes data on indicators for each goal using pairwise correlations (Pradhan et al, 2017).…”

Spatial–Temporal Patterns and Driving Forces of Ecological-Living-Production Land in Hubei Province, Central China

“…Based on the results of driving force analysis of ELPL change, the Sustainable Development Goals (e.g., economic growth and urbanization) have synergetic or trade-off relationship with the ELPL change. The trade-offs directly affect the status of regional eco-environment and food production, which constitute obstacles in achieving the SDGs, need to be negotiated and made structurally nonobstructive by deep changes in the current strategies [46].…”

“…These factors were significantly positively correlated with the change of LL and PLL but negatively correlated with the change of EPL and PEL. Referring to the study of Pradhan et al (2017) to analyze the sustainable development goal (SDG) interactions [46,47], the positive and negative correlations between socioeconomic factors and the changes of ELPL can be classified as synergy and trade-off. The economic development and the increase of population, consumption, fiscal revenue, investment and income have a synergetic relationship with the increasing of LL and PLL, whereas trade-offs relationship with EPL and PEL.…”