Economics Working Papers 2026, 10(4):1-76 | DOI: 10.32725/ewp.2026.00292

Measuring regional sustainability in the EU: Developing and applying adjusted HDI variants with functional data analysis

Antonín Hořčica
Faculty of Economics, University of South Bohemia in České Budějovice

Addressing the gap in regional sustainability monitoring in the EU, this study assesses the applicability of composite sustainability indicator families at the NUTS 2 level and de-velops a methodology for regional variants of the Human Development Index (HDI) family. Nine indicator families were systematically assessed against four applicability criteria: communicability, relationship to GDP, sustainability dimension coverage, and territorial applicability. The HDI family emerged as the only methodologically sound basis for the NUTS 2 sustainability assessments. Four regional indices (R-HDI, R-IHDI, R-GDI, and the planetary pressures-adjusted R-PHDI) were derived from Eurostat and EDGAR data and piloted on 84 NUTS 2 regions across six Central European countries (V4, Germany, Austria) for 2000–2024. Functional data analysis with global envelope tests confirmed that conclu-sions about convergence depend on indicator choice: statistically significant V4 vs DE+AT differences under R-HDI (p = 0,001) disappeared following environmental adjustment via R-PHDI (p = 0,487), with emission-intensive regions substantially penalised. The R-IHDI and R-GDI required methodological compromises owing to the absence of microdata at the NUTS 2 level. This study presents the first systematic derivation and empirical application of the full HDI family at EU NUTS 2 level.

Keywords: sustainability indicators; NUTS regions; Human Development Index; functional data analy-sis; European Union; regional development
JEL classification: C14, Q01, R11, R58

Received: May 12, 2026; Revised: May 13, 2026; Accepted: July 2, 2026; Published: July 2, 2026  Show citation

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Hořčica, A. (2026). Measuring regional sustainability in the EU: Developing and applying adjusted HDI variants with functional data analysis. Economics Working Papers, 10(4), 1-76. doi: 10.32725/ewp.2026.002
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