H_E	Hellwig’s method with equal weights and Euclidean distance
H_EE	Hellwig’s method with entropy-based weights and Euclidean distance
H_M	Hellwig’s method with equal weights and Mahalanobis distance
H_MM	Hellwig’s method with entropy-based weights and Mahalanobis distance
DM	Decision maker
TODIM	An acronym in Portuguese for Interactive and Multi-criteria Decision Making
MCDM	Multi-criteria decision-making
SDG	Sustainable Development Goal
TOPSIS	Technique for Ordering Preferences by Similarity to Ideal Solution
Notation
The most important notations used in this manuscript:
$A_1,A_2,\dots ,A_m$	Alternatives
$C_1,C_2,\dots ,C_n$	Decision criteria
$D=\left[x_{ij}\right]$	Decision matrix
$W=\left[w_1,\dots ,w_n\right]$	Vector of weights
$\mathrm{I}=\left[x_1^{+},\dots ,x_n^{+}\right]$	Ideal (pattern of development)
$\overline{D}=\left[{\overline{x}}_{ij}\right]$	Normalized matrix
$\tilde{D}=\left[{\tilde{x}}_{ij}\right]$	Weighted normalized matrix
$d_{i0}(A_i,I)$	The distances of $i$-th alternative $A_i$ from the ideal $I$
$H_i$	Hellwig’s measure for the $i$-th alternative
$E_j$	Extended and normalized entropy
$M(x,y)$	Weighted Mahalanobis distance between points $x$ and $y$