We Put the “UN” in FUN: The Mathematical Guide to Saving the World
DOI:
https://doi.org/10.33011/cuhj20242839Keywords:
SustainabilityAbstract
Imagine a world without hunger, without poverty, with equality and education. Could this ever truly be a worldwide possibility? While some of these goals may appear distant, the United Nations (UN), with their Sustainable Development Goals (SDGs), aims to create change across seventeen different categories by 2030. These goals focus on various contemporary challenges that can improve the quality of life for people across the globe. However, with such a broad range of topics and the short time frame, it becomes necessary to prioritize some goals over others in order to have the greatest chance of success and impact.
In searching for a method to prioritize a subsection of the seventeen different goals, our team used a weighted graph model with nodes and edges as a method to represent some set of elements and relationships between them. For our model, we constructed a network of connections with each node representing one of the SDGs. The edges between the nodes are weighted, representing the positive or negative impact correlation between two goals. Each node is connected to every other node in the graph. To determine the proper weighting between each node, we analyzed data from a 2017 study which utilized Spearman’s correlation ranking to determine interactions between different goals [Pradhan et al., 2017]. Another popular metric for measuring correlation between SDGs is the 7-point scale, where correlations are ranked from -3 to +3, where -3 represents the most negative correlation and +3 represents the most positive (Pradhan et al., 2017). However, there are no current global values measured with this scale, so we combined both approaches to scale the Spearman's correlation rankings and the 7-point scale to create our scale, which ranges from -1 to +1.
As a metric to model the synergy between the SDGs, we used an achievement score, a value between 0 and 1, where 1 indicates complete achievement and 0 indicates no progress. The achievement scores can be propagated through the network as a function of the weights and distance from the parent vertex. Using our model, we experimented with various connection weights and initial achievement values to determine the most interconnected and most influential goals. From this analysis, we determined that SDG 1: No Poverty holds the highest positive priority, while SDG 12: Responsible Consumption and Production holds the greatest negative priority. This indicates that SDG 1 holds the most positive correlations with the other goals and SDG 12 holds the most negative correlations. This model was also used as a foundation for what we might expect to be accomplished in 10 years if actions based on our priorities were enacted. We believe that while focusing on SDG 1 would allow the UN to better meet the holistic needs of people worldwide, the initial value for SDG 1 is not high enough to expect completion by 2030.
We also included constant multipliers within each node to represent the probable impact that certain worldwide events could have on the achievement level of each goal. Constant multipliers represent a percentage change in the achievement levels of each of the SDGs and are calculated for each potential event separately. The COVID-19 pandemic, which erased four years’ worth of progress towards ending poverty, is one example (United Nations, 2015). Statistics like this impacted the relative values of our multipliers for war, technological advancements, pandemics, climate change, and refugee movements. From implementing the multipliers, we determined that SDG 1: No Poverty, SDG 2: Zero Hunger, SDG 4: Quality Education, SDG 10: Reduced Inequalities, and SDG 16: Peace, Justice, and Strong Infrastructure would be most impacted by a variety of possible future events.
Downloads
Published
How to Cite
Issue
Section
License
Authors whose work is accepted and who publish with The University of Colorado Honors Journal agree to the following terms:
1. The Author retains copyright in the Work, where the term “Work” shall include all objects that may result in subsequent electronic or print publication or distribution.
2. Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
3. The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a non-exclusive license, which grants the Journal the right to copy, distribute, and transmit the Work under the following conditions: a. Right of publication in the print format of the journal; b. Right of publication in the online and/or digital format of the journal; c. Right to use in promotional or other journal-related activities, as defined by the journal.
4. Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
5. The Author represents and warrants that: a. the Work is the Author’s original work; b. the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party; c. the Work is not pending review or under consideration by another publisher; d. the Work has not previously been published; e. the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and f. the Work contains no libel, invasion of privacy, or other unlawful matter.
6. The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.
11-August-2014