TY - JOUR
T1 - Variations in sustainable development goal interactions
T2 - Population, regional, and income disaggregation
AU - Warchold, Anne
AU - Pradhan, Prajal
AU - Kropp, Jürgen P.
N1 - Funding Information:
A. Warchold and P. Pradhan acknowledges funding from the German Federal Ministry of Education and Research (BMBF) for the SUSFOOD project (grant agreement No. 01DP17035) and the German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety for the Sustainable Amazonian Landscapes project (42206‐6157). The funders had no role in the design, data collection and analysis, decision to publish, or preparation of the study. Open access funding enabled and organized by Projekt DEAL.
Publisher Copyright:
© 2020 The Authors. Sustainable Development published by ERP Environment and John Wiley & Sons Ltd.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - To fulfill the 2030 Agenda, the complexity of sustainable development goal (SDG) interactions needs to be disentangled. However, this understanding is currently limited. We conduct a cross-sectional correlational analysis for 2016 to understand SDG interactions under the entire development spectrum. We apply several correlation methods to classify the interaction as synergy or trade-off and characterize them according to their monotony and linearity. Simultaneously, we analyze SDG interactions considering population, location, income, and regional groups. Our findings highlight that synergies always outweigh trade-offs and linear outweigh non-linear interactions. SDG 1, 5, and 6 are associated with linear synergies, SDG 3, and 7 with non-linear synergies. SDG interactions vary according to a country's income and region along with the gender, age, and location of its population. In summary, to achieve the 2030 Agenda the detected interactions and inequalities across countries need be tracked and leveraged to “leave no one behind.”.
AB - To fulfill the 2030 Agenda, the complexity of sustainable development goal (SDG) interactions needs to be disentangled. However, this understanding is currently limited. We conduct a cross-sectional correlational analysis for 2016 to understand SDG interactions under the entire development spectrum. We apply several correlation methods to classify the interaction as synergy or trade-off and characterize them according to their monotony and linearity. Simultaneously, we analyze SDG interactions considering population, location, income, and regional groups. Our findings highlight that synergies always outweigh trade-offs and linear outweigh non-linear interactions. SDG 1, 5, and 6 are associated with linear synergies, SDG 3, and 7 with non-linear synergies. SDG interactions vary according to a country's income and region along with the gender, age, and location of its population. In summary, to achieve the 2030 Agenda the detected interactions and inequalities across countries need be tracked and leveraged to “leave no one behind.”.
KW - development pathways
KW - disaggregation
KW - inequalities
KW - non-linearity
KW - SDG interactions
KW - SDGs
KW - synergies and trade-offs
U2 - 10.1002/sd.2145
DO - 10.1002/sd.2145
M3 - Article
SN - 0968-0802
VL - 29
SP - 285
EP - 299
JO - Sustainable Development
JF - Sustainable Development
IS - 2
ER -