Development of a Sustainability Index at the Local Level: methodological proposal and its application in the case study of Rio de Janeiro, Brazil

Silva, Ana Luiza Mendes; Barros, Matheus Ferreira de; Santos, Luan

doi:10.1590/0103-6513.20210034

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Thematic Section - Future of Energy-efficient Operations and Production Systems • Prod. 31 • 2021 • https://doi.org/10.1590/0103-6513.20210034 copy

Development of a Sustainability Index at the Local Level: methodological proposal and its application in the case study of Rio de Janeiro, Brazil

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Paper aims

This paper presents the methodological development of a sustainability indicator focused on the local level, based on the Municipal Human Development Index (MHDI).

Originality

The proposed indicator (MHDI+S) was built on the use of an indicator with simple methodology based on information provided by reliable Brazilian research institutes, making it easier to be updated and reproduced in other regions and states.

Research method

By adding the environmental factor to this well-known index, the proposed indicator (MHDI+S) is then applied to the state of Rio de Janeiro, Brazil, so that the municipalities have been categorized into different clusters.

Main findings

The results of this categorization were validated by statistical assessment (cluster analysis) showing that the groups have internal homogeneity and external heterogeneity.

Implications for theory and practice

The proposed indicator (MHDI+S) can assist public management at the local level, decentralizing the well-established federal perspective,

Keywords
Indicator; Sustainability; Municipal management; Cluster analysis

	Step 1	Step 2	Step 3	Step 4
	Problem detection and contextualization	Survey of the theoretical framework and definition of the indicator	Application of the indicator in the case study of the state of Rio de Janeiro	Cluster analysis and study validation and conclusions
Description	The theme is presented and delimited in order to contextualize the problem, justifying its applicability and defining the main objectives.	The most pertinent aspects of the context of the sustainability agenda are discussed and the main concepts about cluster analysis are discussed through a bibliographic study.	The developed indicator is applied to the database of municipalities in the state of Rio de Janeiro and the observations are grouped into subgroups.	The groups formed are analyzed, observing their main characteristics. In addition, a statistical analysis of the cluster is performed to confirm its validity.
Obtained Results	- Introduction of the theme	- Approach to sustainable development and its concepts related to the study	- Preliminary database analysis	- Cluster analysis
	- Justification and motivation	- Definition of the methodological process	- Application of the indicator to municipalities	- Validation of results
	- General and specifics objectives

	Terms of search	Scopus	Web of Science
Method [A]	[multivariate analysis OR multivariate data OR cluster analysis OR hierarchical cluster analysis OR non-hierarchical cluster analysis]	1.847	2.934
Method [A]	[A] AND [sustainability OR environment OR sustainable development OR sustainable environment OR triple bottom line]	9	191
+
Context [B]
Method [A]	[A] AND [B] AND [indicator OR index OR public management OR public policy OR development index OR sustainability index]	8	37
+
Context [B]
+
Measure [C]

Connection Method	Distance Measure	Number of Clusters	Similarity
Mean	Euclidian	4	73.748
Mean	Euclidian²	4	92.373
Mean	Manhatan	4	70.870
Centroid	Euclidian	3	83.204
Centroid	Euclidian²	5	95.489
Centroid	Manhatan	3	81.400
Complete	Euclidian	3	47.630
Complete	Euclidian²	3	72.574
Complete	Manhatan	3	33.714
Ward	Euclidian	5	21.177
Ward	Euclidian²	4	64.835
Ward	Manhatan	6	21.091

Cluster	N	Population	Income	Longevity	Education	EI	MHDI+S	MHDI
1	31	79,207.16	0.709	0.817	0.634	0.753	0.725	0.716
2	26	207,776.31	0.706	0.818	0.648	0.868	0.755	0.721
3	4	32,468.50	0.692	0.813	0.595	0.509	0.641	0.694
4	19	20,783.68	0.674	0.806	0.558	0.676	0.672	0.671
5	6	83,970.33	0.739	0.833	0.659	0.595	0.700	0.740
State	86	103,327.81	0.702	0.816	0.621	0.748	0.717	0.708
Range		Classification
< 0,550		Very Low
0,550-0,699		Medium
0,700-0,799		High
> 0,800		Very High

	Normality Test (Kolmogorov-Smirnov)	Equality of Variance Test (Bartlett/Levene)	Equality of Means Test (ANOVA/Kruskal-Wallis)
INCOME	P-value = 0.150 → Normal Distribution	Bartlett: P-value = 0.338 → Equal Variances	ANOVA: P-value = 0.000 → At least 1 pair of different means
LONGEVITY	P-value = 0.010 → Non-normal Distribution	Levene: P-value = 0.757 → Equal Variances	Kruskal-Wallis: P-value = 0.017 → At least 1 pair of different medians
EDUCATION	P-value = 0.099 → Normal Distribution	Bartlett: P-value = 0.219 → Equal Variances	ANOVA: P-value = 0.000 → At least 1 pair of different means
EI	P-value = 0.150 → Normal Distribution	Bartlett: P-value = 0.977 → Equal Variances	ANOVA: P-value = 0.000 → At least 1 pair of different means
IDHM+S	P-value = 0.150 → Normal Distribution	Bartlett: P-value = 0.245 → Equal Variances	ANOVA: P-value = 0.000 → At least 1 pair of different means

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[1] * luan.santos@pep.ufrj.br