Computational Intelligence for Studying Sustainability Challenges: Tools and Methods for Dealing With Deep Uncertainty and Complexity.

Edmundo Molina-Perez,Hilda Zamora-Maldonado,Oscar A Esquivel-Flores

doi:10.3389/frobt.2020.00111

Edmundo Molina-Perez, Hilda Zamora-Maldonado + Show 1 more

Open Access

https://doi.org/10.3389/frobt.2020.00111

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Abstract

The study of sustainability challenges requires the consideration of multiple coupled systems that are often complex and deeply uncertain. As a result, traditional analytical methods offer limited insights with respect to how to best address such challenges. By analyzing the case of global climate change mitigation, this paper shows that the combination of high-performance computing, mathematical modeling, and computational intelligence tools, such as optimization and clustering algorithms, leads to richer analytical insights. The paper concludes by proposing an analytical hierarchy of computational tools that can be applied to other sustainability challenges.

Highlights

The resolution of contemporary sustainability challenges requires the consideration of coupled systems, long-term time frames, multiple objectives, and deep uncertainty (Liu et al, 2013, 2016; Hull et al, 2015)
The performance of each policy regime is evaluated in terms of its capacity to meet two climate change sustainability objectives: (1) the stabilization of CO2 emissions and (2) the 2◦C temperature rise target
The analysis shows that the structure of optimal environmental regulation changes markedly across the uncertainty space

Summary

Introduction

The resolution of contemporary sustainability challenges requires the consideration of coupled systems, long-term time frames, multiple objectives, and deep uncertainty (Liu et al, 2013, 2016; Hull et al, 2015). Sustainable ecosystem management, water planning, and climate change adaptation and mitigation require the joint consideration of environmental and human systems These spheres (i.e., systems) are inexorably connected as changes in the behavior and constitution of the natural environment often induce changes in human institutions and incentives. The evolution of human preferences, technology, and institutions determines significantly the development trajectories of natural resource systems. Often, if these interactions are not monitored and regulated, one or both systems stop functioning in a sustainable manner (Ostrom, 2009, 2011; Hull et al, 2015). In the context of accelerated global climate change, if anthropogenic emissions continue rising, the growing concentration of greenhouse gases (GHG) in the atmosphere will result in climate imbalances (e.g., changes in precipitation patterns, higher temperatures) that can induce irreversible changes in natural ecosystems (e.g., loss of biodiversity) and in the economy (e.g., higher inequality)

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