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Using Principal Component Analysis to assess the performance of Romanian wastewater operators

Author

Listed:
  • George BANGHIORE

    (School of Advanced Studies of the Romanian Academy, Doctoral School of Economic Sciences,National Institute of Economic Research "Costin C. Kirițescu";Institute of National Economy,Bucharest,Romania)

Abstract

Objective: In the current global context, characterized by increasing pressures on the environment and the need to optimize resources, the wastewater management sector in Romania faces numerous challenges. These challenges relate in particular to operational efficiency and compliance with environmental standards imposed at national and European level. The present study aims to explore the performance of wastewater operators in Romania, using Principal Component Analysis (PCA) to identify the main operational and financial variables that influence this performance. The main purpose of the research is to evaluate the way different variables contribute to the overall efficiency and the fulfillment of the environmental standards. Method: In order to carry out this study, it was decided to apply the Principal Components Analysis (PCA), a statistical method recognized for its effectiveness in simplifying complex data sets by reducing their dimensionality. For data analysis, the following variables were used: the number of inhabitants connected to sewage services, the volume of wastewater discharged into the outfall without treatment, the volume of wastewater entering the wastewater treatment plants, the total revenues from exploitation, in terms of concerns the wastewater activity, the total investments in the wastewater system and the degree of compliance with the quality of the treated wastewater. Results: The results of the analysis indicate a strong association between the infrastructure and financial capacity of operators and the efficiency of wastewater management. The first two principal components presented distinct dimensions of performance related to operational capacity and compliance with environmental regulations. Originality: The originality of the study consists in using principal component analysis to extract and interpret latent dimensions from a complex data set, providing clear insight into the determinants of performance in the wastewater treatment sector. This method allows efficient and focused analysis, helping to identify key points for strategic interventions and improvements.

Suggested Citation

  • George BANGHIORE, 2024. "Using Principal Component Analysis to assess the performance of Romanian wastewater operators," Romanian Journal of Economics, Institute of National Economy, vol. 59(2(68)), pages 95-110, December.
    • Handle: RePEc:ine:journl:v:59:y:2024:i:68:p:95-110
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    References listed on IDEAS

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    1. Alina (Chiriac) Matei & Kamer-Ainur Aivaz, 2023. "Exploratory Analysis of Assets Nonconformities in Financial Statements," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 978-985, August.
    2. Valentina Vasile & Raluca Mazilescu & Marius-Razvan Surugiu, 2024. "Another Crisis, Different Responses? Determinants of Crisis and Mixed Impact on Economic Development: A Comparative Approach of the EU Countries," Contributions to Economics, in: Valentina Vasile & Elena Bunduchi (ed.), The Economic and Social Impact of the COVID-19 Pandemic, chapter 0, pages 19-48, Springer.
    3. Kinga-Erzsébet Bakó & à rpád-Zoltán Fülöp & Alina STANCIU, 2021. "ECONOMIC AND FINANCIAL STABILITY FOR WATER and WASTEWATER OPERATORS IN ROMANIA," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 30(2), pages 19-25, December.
    4. Eglantina Hysa & Erinda Imeraj & Nerajda Feruni & Mirela Panait & Valentina Vasile, 2022. "COVID-19—A Black Swan for Foreign Direct Investment: Evidence from European Countries," JRFM, MDPI, vol. 15(4), pages 1-21, March.
    5. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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