Synergies and Trade-offs among Sustainable Development Goals: The Case of Spain

被引:22
|
作者
de Miguel Ramos, Carlos [1 ,2 ]
Laurenti, Rafael [1 ]
机构
[1] Univ Politecn Madrid UPM, ETS Ingenieros Ind, Calle Jose Gutierrez Abascal 2, Madrid 28006, Spain
[2] KTH Royal Inst Technol, Dept Machine Design, Brinellvagen 83, S-11428 Stockholm, Sweden
关键词
sustainable development; Sustainable Development Goals; Agenda; 2030; sustainability; Spain; synergy; trade-off; regression analysis; CORRELATION-COEFFICIENTS; ENERGY;
D O I
10.3390/su122410506
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The United Nations plans have marked global sustainable development for more than two decades. Most of the developed and developing countries have adopted these plans to achieve the Agenda 2030, currently formed by the 17 Sustainable Development Goals (SDGs). The analysis of the interactions between the SDGs is a growing area in research and of interest for governments. However, studies on how positive correlations can improve deteriorated goals are scarce for countries to date. This study aims at filling this gap by finding and quantifying the synergies and trade-offs among the SDGs of Spain. During the years 2000-2019, almost 80% of the SDG targets had significant interactions, either positive (synergy) or negative (trade-off). SDG 4 (quality education), SDG 5 (gender Equality) and SDG 7 (affordable and clean energy) contained the largest number of positive interactions, more than 60% in all of them. SDG 3 (good health and wellbeing) was strongly linked with indicators from SDG 4 (quality education) and also SDG 6 (clean water and sanitation). Furthermore, indicators from SDG 7 (affordable and clean energy) shared a high correlation with the ones from SGD 12 (responsible consumption and production) and SDG 15 (life on land). SDG 1 (no poverty), 2 (zero hunger), 6 (clean water and sanitation), 8 (decent work and economic growth) and 11 (sustainable cities and communities) had the slowest evolution during the years 2000-2019, showing contractions in some instances. We developed a regression model to assess the influence that selected targets have had on a less evolved target (target 8.6-proportion of youth not in education, employment or training). We managed to clarify high influence from target 1.3 (unemployment compensations), target 8.4 (domestic material consumption) and target 10.5 (non-performing loans) on the dependent variable. Identifying numerical dependencies between the SDGs may help nations to develop a roadmap where targets work as cogwheel towards achieving the Agenda 2030.
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页数:14
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