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An Entangled Model for Sustainability Indicators
Pável Vázquez1, Jesús A Del Río2, Karla G Cedano3
1Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México.
Plos One
|August 22, 2015
Summary
Selecting sustainability indicators is crucial for monitoring development pathways. This study introduces a model using evolutionary dynamics to identify essential indicators, ensuring resource efficiency and resilience for various scales, from cities to the planet.
Area of Science:
- Complex Systems Science
- Environmental Science
- Urban Planning
Background:
- Sustainable development requires effective monitoring via sustainability indicators.
- Existing indicators vary in importance based on local context and time.
- Resource scarcity necessitates strategic selection of indicators, especially for smaller regions.
Purpose of the Study:
- To propose a model for identifying and selecting a representative set of sustainability indicators for human systems.
- To provide decision-makers with a tool for monitoring sustainability strategies effectively.
- To adapt indicator selection for diverse geographical scales.
Main Methods:
- Application of evolutionary dynamics within a framework of interconnected sustainability indicators.
- Utilizing a fixed interaction matrix simulating a specific regional context (Cuernavaca, Mexico).
- Employing the Pareto principle for efficient indicator set identification.
Main Results:
- Identification of a core set of sixteen sustainability indicators capturing over 80% of system strength.
- Demonstration of the selected indicator set's resilience to perturbations.
- Adaptation of the Tangled Nature framework for scalable indicator selection across different geographical contexts.
Conclusions:
- The proposed model offers a valuable tool for selecting context-specific sustainability indicators.
- The coevolutionary framework supports indicator selection for diverse scales, from local to global.
- Ensuring social legitimacy through community-prioritized indicator selection is highlighted.
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