Authors: Ricardo Alvira
Comments: 658 Pages. Text in Spanish
The present Doctoral Thesis proposes an indicators’ model and a methodology whose objective is enabling the quantitative characterization of different urban situations’ Sustainability Degree and the later use of this parameter in usual urban transformations’ design processes.
In order to do so, we follow a procedure that combines two approaches:
On the one hand, we build on A mathematical theory of Sustainability and Sustainable Development
[Alvira, 2014a], which provides us the following matters:
• General principles for Sustainability logical decomposition / hierarchical structuring of the model.
• Design methodology / mathematical formulation of sustainability indicators.
• Mathematical formulas for indicators aggregation.
• General principles for the design of an operational model.
On the other hand, we review most accepted general recommendations for indicators’ models formulation, including recommendations by more acknowledged organizations / entities.
Although some of these recommendations are actually applied during the drafting process we herein undertake, we do not always explicit it, considering that compliance with main recommendations for the development of indicator models is not a requirement for the formulation of the model but 'guidelines' which provide us a framework for its posterior validation, which we detail in the Conclusions and Annexes.
Combining both approaches we design an operational model, which is complemented by a methodology that allows its use in the usual processes of city conformation.
Later, we validate the proposal in two levels:
• We review its applicability, by using the model to confront the design of a hypothetical transformation of existing urban area: the Palos de Moguer neighborhood in Madrid.
• We contrast obtained results for the E dimension with the economic evolution of the EU 28 countries during the period 2005‐2014, finding a high similarity with reality.
Finally, in the conclusions we detail the main differences / contributions regarding existing models, as well as pending issues that we advance for possible further development.
Category: Social Science