IDEAS home Printed from https://ideas.repec.org/a/url/izvest/v19y2018i4p94-106.html
   My bibliography  Save this article

Environmental Economic Evaluation of Resource Saving at Small Energy Suppliers in a Region

Author

Listed:
  • Valery P. Anufriev

    (Ural Federal University named after the first President of Russia Boris N. Yeltsin)

  • Aleksandr G. Mokronosov

    (Ural State University of Economics President of Russia Boris N. Yeltsin)

  • Nikolay G. Mikhailov

    (Russian State Vocational Pedagogical University)

Abstract

The article aims to develop theoretical propositions and methodological tools of environmental economic evaluation of resource saving at small energy suppliers in a region. To reveal the synergistic effect from the influence of environmental economic factors in the process of resource saving in house? hold heating the authors suggest a method of hierarchical clustering of territorial heating systems and a method of principal components to form the most informative model of them. The researchers use the described toolkit to hold a multidimensional analysis and multifactorial evaluation of resource saving (economic, social, ecological) of small energy suppliers in a region, and identify its new emergent prop? erties and strategic directions of development as an element of the territorial socioeconomic system. The paper presents the results of estimating the volume of polluting substances emitted by low power boiler plants in Sverdlovsk oblast and calculates the index of environmental economic potential of resource saving in small energy industry in municipalities of the region. The analysis indicates an uneven spatial distribution of boiler plants and identifies the clusters (groups), which are the most promising for im? plementing energy-efficient technologies and improving ecological situation in the region. The results of environmental and economic assessment can be useful when developing the regional fuel and energy balance as well as substantiating strategic investment-innovative development directions of municipali? ties’ energy supply.

Suggested Citation

  • Valery P. Anufriev & Aleksandr G. Mokronosov & Nikolay G. Mikhailov, 2018. "Environmental Economic Evaluation of Resource Saving at Small Energy Suppliers in a Region," Journal of New Economy, Ural State University of Economics, vol. 19(4), pages 94-106, August.
    • Handle: RePEc:url:izvest:v:19:y:2018:i:4:p:94-106
    DOI: 10.29141/2073-1019-2018-19-4-7
    as

    Download full text from publisher

    File URL: http://izvestia.usue.ru/images/download/78/7.pdf
    Download Restriction: no
    File URL: http://izvestia.usue.ru/en/-2018/751
    Download Restriction: no
    File URL: https://libkey.io/10.29141/2073-1019-2018-19-4-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bien, Jacob & Tibshirani, Robert, 2011. "Hierarchical Clustering With Prototypes via Minimax Linkage," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1075-1084.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kenjiro Yagi & Ramteen Sioshansi, 2023. "Simplifying capacity planning for electricity systems with hydroelectric and renewable generation," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.
    2. Elvira Pelle & Roberta Pappadà, 2021. "A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1507-1533, December.
    3. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    4. Liao Zhu & Ningning Sun & Martin T. Wells, 2022. "Clustering Structure of Microstructure Measures," Applied Economics and Finance, Redfame publishing, vol. 9(1), pages 85-95, December.
    5. Cristiano Varin & Manuela Cattelan & David Firth, 2016. "Statistical modelling of citation exchange between statistics journals," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 1-63, January.
    6. Sheng Liu & Long He & Zuo-Jun Max Shen, 2021. "On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors," Management Science, INFORMS, vol. 67(7), pages 4095-4119, July.
    7. Gautier Marti & Sébastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Post-Print hal-01400395, HAL.
    8. Liao Zhu & Robert A. Jarrow & Martin T. Wells, 2021. "Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-30, December.
    9. Barrera-Santana, J. & Sioshansi, Ramteen, 2023. "An optimization framework for capacity planning of island electricity systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    10. Liao Zhu & Ningning Sun & Martin T. Wells, 2021. "Clustering Structure of Microstructure Measures," Papers 2107.02283, arXiv.org, revised Dec 2021.
    11. Robert A. Jarrow & Rinald Murataj & Martin T. Wells & Liao Zhu, 2023. "The Low-Volatility Anomaly And The Adaptive Multi-Factor Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 26(04n05), pages 1-33, August.
    12. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    13. Gautier Marti & S'ebastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Papers 1603.04017, arXiv.org, revised Apr 2016.
    14. Liao Zhu & Haoxuan Wu & Martin T. Wells, 2021. "A News-based Machine Learning Model for Adaptive Asset Pricing," Papers 2106.07103, arXiv.org.
    15. Liao Zhu & Sumanta Basu & Robert A. Jarrow & Martin T. Wells, 2020. "High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-52, December.

    More about this item

    Keywords

    ecological economic evaluation; resource saving; greenhouse gases; small energy suppli? ers; region; emergence; system approach; municipality; hierarchical clustering;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • P49 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:url:izvest:v:19:y:2018:i:4:p:94-106. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Victor Blaginin (email available below). General contact details of provider: https://edirc.repec.org/data/usueeru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.