Socio-Environmental Development Index

for the State of Bahía

for the State of Bahía

Building the Environmental Conditions Index

for the Municipalities of Bahía

for the Municipalities of Bahía

The Environmental Conditions Index (eci ) is used to measure the proportion of environmental quality in an area of a specified municipality. It was constructed in two stages. In the first, the Partial Index of Environmental Conditions (piec) was developed by means of a Multivariate Factor Analysis. In the second, based on the ipca, the weighting attributed to each of the variables that made up eci ’s composition was estimated, using regression analysis, applying the restricted least square method (rls). The factors estimated by the technique should explain a significant part of the variation of the group of original variables, taking into account that the first factor has the largest explanation percentage of the total variation, the second factor has the second largest percentage, and so forth successively (Corrar et al., 2007).

The orthoganality property of the estimated factors point was used to construct the partial index. Meanwhile, it should be noted that the orthoganality associated with the matrix of factors, does not necessarily imply orthoganality of the factor point, the factor points being evaluated to establish if they are orthogonal by means of the variation and covariance matrix between these points. (Corrar *et al*., 2007)

The piec may be estimated through the following equation:

(1) |

in which piec_{i} is the Partial Index of Environmental Conditions associated with the *i ^{ th}* Bahía municipality, and F

The Partial Index only reflects a ranking of municipalities in terms of environmental conditions. It cannot therefore be used to estimate environmental quality as a percentage for each of the municipalities. For this the eci is used. This index was constructed by taking into account the weighting of each of the variables used in the previously mentioned Partial Index (Equation 1). These weightings were obtained through regression analysis using the restricted least square method (rls) in which the piec is the dependent variable and the indicators (X1), (X2), (X3), (X4), (X5) and (X8) are the explanatory variables. Equations (2) and (3) show how the eci is made up.

(2) |

(3) |

in which P_{j} are the parameters estimated for Equation (3) which together are equal to 1; and X_{i} are the variables used to construct the piec.