The Implementation of the Sustainable Development Goals at the Local Level. The Case of the Districts of Kuyavian-Pomeranian Province

Abstract

The aim of the paper is to provide the objective assessment of progress in the implementation of the goals of the local sustainable development strategy by the districts of Kuyavian-Pomeranian Province. In order to achieve our aim, we have devised, using methods of taxonomic analysis, the synthetic indicator (taking a lot of different measures of sustainable development into account), which shows the degree of the accomplishment of goals adopted in the sustainable development goals. In the collective of units under study, there is a significant differentiation of the level of indicators analyzed within the framework of the sustainable development strategy. It must be emphasized that there are as many as 12 districts in the lower quartile of the distribution of the synthetic measure, i.e., its level was below 0.25. 11 self-government units were in the second quartile of the distribution, i.e., in range between 0.25 and 0.5 of the synthetic indicator. Relatively big taxonomic distances between synthetic measures for individual districts, with quite a large set of features, show significant differentiation among these parts of Kuyavian-Pomeranian Province in terms of local sustainable development. Despite the fact that the examined objects belong to the same administrative unit, which would appear to be relatively coherent, they do not develop at the same pace. Moreover, given a broad set of indicators, they differ significantly when it comes to the rate of their sustainable development. The findings of this paper highlight the use of multidimensional taxonomic analysis for measuring the progress of local units in the degree of the accomplishment of the sustainable development goals. First of all, it helps to show differences in the implementation of the development strategies across districts. What is more, it shows these differences on the basis of the complete set of diverse features rather than single parameters (e.g., pace of growth). Thus, the multidimensional approach is a lot more precise when it comes to showing differences among the examined objects.

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