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May 11, 2026Production Engineering Archives2 citationsOpen Access

Household Solid Waste Eco-Efficiency in Slovak NUTS-3 Regions: A Two-Stage DEA Analysis

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RLRoman LackoMKMartin KuchtaZHZuzana Hajduová

Key Points

  • The aim is to assess the eco-efficiency of household solid waste management in Slovak regions.
  • Two-stage data envelopment analysis (DEA) was employed to measure eco-efficiency.
  • Efficiency changes and time trends were examined from 2018 to 2023.
  • Environmental variables such as population density and median age were analyzed for their impact.
  • A higher share of economically inactive population negatively impacted efficiency across all waste types.
  • The median age had a positive influence on the efficiency of household solid waste generation.
  • Population density effects showed statistical significance but were less pronounced.

Abstract

Abstract The study assesses the waste-related eco-efficiency across Slovakia’s eight regions at the NUTS-3 level. The study examines time trends and efficiency changes between 2018 and 2023. The Data Envelopment Analysis method is used to measure efficiency, specifically a model assuming variable returns to scale. Three specific models were created for different types of household solid waste. These efficiencies are then bias-corrected using a double-bootstrap approach and subjected to a closer analysis of the effects of selected environmental variables, including population density, median age, and the age dependency index. The results indicated a negative impact of a higher share of the economically inactive population on the efficiency of generating all types of household solid waste. In terms of population density, the effects differed, with statistical significance less pronounced. Median age, i.e., the population’s maturity, positively impacted household solid waste generation efficiency. The paper’s conclusions include recommendations for policies focused on regional disparities, age management, and support for the education of selected population groups. The results of this study can also help identify factors for training AI models for predictive waste management.

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Cite This Study

Lacko et al. (2026) studied this question.

synapsesocial.com/papers/6a01726d3a9f334c282728a2https://doi.org/10.30657/pea.2026.32.23
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