IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i9p2302-d167100.html
   My bibliography  Save this article

Social and Infrastructural Conditioning of Lowering Energy Costs and Improving the Energy Efficiency of Buildings in the Context of the Local Energy Policy

Author

Listed:
  • Maria Mrówczyńska

    () (Architecture and Environmental Engineering, Faculty of Civil Engineering, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland)

  • Marta Skiba

    () (Architecture and Environmental Engineering, Faculty of Civil Engineering, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland)

  • Anna Bazan-Krzywoszańska

    () (Architecture and Environmental Engineering, Faculty of Civil Engineering, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland)

  • Dorota Bazuń

    () (Psychology and Sociology, Faculty of Education, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland)

  • Mariusz Kwiatkowski

    () (Psychology and Sociology, Faculty of Education, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland)

Abstract

The main problem in creating successful efficiency improvement policies is adjusting objectives to local development programs, dependent on public awareness. This article attempts to find a framework for the costs of changing energy policies using neural networks to identify the social-infrastructure conditions. An analysis model is presented of social-infrastructure conditions of energy costs reduction and buildings’ efficiency improvement. Data were obtained from standardized interviews with Zielona Góra, Poland inhabitants and the Town Energy Audit documentation. The data were analyzed using an artificial neural network. This allowed the creation of a model to estimate the cost inhabitants will incur if the energy is sourced from RES (Renewable Energy Systems). The city social-infrastructural correlation model enabled diagnosing its fragments that can support decision-making. The paper contributes to the current knowledge demonstrating the possibilities of hierarchical investments, different for various buildings and neighborhoods, that allow for rational public funding. Knowledge of the correlation conditions matters when implementing effective local policy. This work is based on pilot studies not financed by the parties concerned. Multiple themes were intentionally investigated: emission control, reducing energy consumption, renovating buildings, supplying with RES, and energy poverty, to show methods to match the goal (hard) to social conditions (soft), rarely presented in studies.

Suggested Citation

  • Maria Mrówczyńska & Marta Skiba & Anna Bazan-Krzywoszańska & Dorota Bazuń & Mariusz Kwiatkowski, 2018. "Social and Infrastructural Conditioning of Lowering Energy Costs and Improving the Energy Efficiency of Buildings in the Context of the Local Energy Policy," Energies, MDPI, Open Access Journal, vol. 11(9), pages 1-16, September.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2302-:d:167100
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/9/2302/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/9/2302/
    Download Restriction: no

    References listed on IDEAS

    as
    1. Jan K. Kazak, 2018. "The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland)," Sustainability, MDPI, Open Access Journal, vol. 10(4), pages 1-15, April.
    2. Skiba, Marta & Mrówczyńska, Maria & Bazan-Krzywoszańska, Anna, 2017. "Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: The town of Zielona Góra," Applied Energy, Elsevier, vol. 188(C), pages 356-366.
    3. Fabbri, Kristian, 2015. "Building and fuel poverty, an index to measure fuel poverty: An Italian case study," Energy, Elsevier, vol. 89(C), pages 244-258.
    4. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    5. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    6. Aleksander Szpor & Maciej Lis, 2016. "Ograniczenie ubostwa energetycznego w Polsce - od teorii do praktyki," IBS Policy Papers 06/2016, Instytut Badan Strukturalnych.
    7. Michał Juszczyk & Agnieszka Leśniak & Krzysztof Zima, 2018. "ANN Based Approach for Estimation of Construction Costs of Sports Fields," Complexity, Hindawi, vol. 2018, pages 1-11, March.
    8. Gorbacheva, Natalya V. & Sovacool, Benjamin K., 2015. "Pain without gain? Reviewing the risks and rewards of investing in Russian coal-fired electricity," Applied Energy, Elsevier, vol. 154(C), pages 970-986.
    9. Sovacool, Benjamin K., 2015. "Fuel poverty, affordability, and energy justice in England: Policy insights from the Warm Front Program," Energy, Elsevier, vol. 93(P1), pages 361-371.
    10. Staszczuk, Anna & Wojciech, Magdalena & Kuczyński, Tadeusz, 2017. "The effect of floor insulation on indoor air temperature and energy consumption of residential buildings in moderate climates," Energy, Elsevier, vol. 138(C), pages 139-146.
    11. Luciana Maria Miu & Natalia Wisniewska & Christoph Mazur & Jeffrey Hardy & Adam Hawkes, 2018. "A Simple Assessment of Housing Retrofit Policies for the UK: What Should Succeed the Energy Company Obligation?," Energies, MDPI, Open Access Journal, vol. 11(8), pages 1-22, August.
    12. Teller-Elsberg, Jonathan & Sovacool, Benjamin & Smith, Taylor & Laine, Emily, 2016. "Fuel poverty, excess winter deaths, and energy costs in Vermont: Burdensome for whom?," Energy Policy, Elsevier, vol. 90(C), pages 81-91.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arkadiusz Dobrzycki & Dariusz Kurz & Stanisław Mikulski & Grzegorz Wodnicki, 2020. "Analysis of the Impact of Building Integrated Photovoltaics (BIPV) on Reducing the Demand for Electricity and Heat in Buildings Located in Poland," Energies, MDPI, Open Access Journal, vol. 13(10), pages 1-19, May.
    2. Agnieszka Leśniak & Filip Janowiec, 2019. "Risk Assessment of Additional Works in Railway Construction Investments Using the Bayes Network," Sustainability, MDPI, Open Access Journal, vol. 11(19), pages 1-15, September.
    3. Mrówczyńska, Maria & Skiba, Marta & Bazan-Krzywoszańska, Anna & Sztubecka, Małgorzata, 2020. "Household standards and socio-economic aspects as a factor determining energy consumption in the city," Applied Energy, Elsevier, vol. 264(C).
    4. Sergio Gómez Melgar & Miguel Ángel Martínez Bohórquez & José Manuel Andújar Márquez, 2020. "uhuMEBr: Energy Refurbishment of Existing Buildings in Subtropical Climates to Become Minimum Energy Buildings," Energies, MDPI, Open Access Journal, vol. 13(5), pages 1-35, March.
    5. Tsagarakis, Konstantinos P., 2020. "Shallow geothermal energy under the microscope: Social, economic, and institutional aspects," Renewable Energy, Elsevier, vol. 147(P2), pages 2801-2808.

    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. Skiba, Marta & Mrówczyńska, Maria & Bazan-Krzywoszańska, Anna, 2017. "Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: The town of Zielona Góra," Applied Energy, Elsevier, vol. 188(C), pages 356-366.
    2. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    3. Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A. & Javier Guevara-García, Fco., 2018. "Fuel Poverty Potential Risk Index in the context of climate change in Chile," Energy Policy, Elsevier, vol. 113(C), pages 157-170.
    4. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    5. Soutullo, S. & Giancola, E. & Heras, M.R., 2018. "Dynamic energy assessment to analyze different refurbishment strategies of existing dwellings placed in Madrid," Energy, Elsevier, vol. 152(C), pages 1011-1023.
    6. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Seyedzadeh, Saleh & Pour Rahimian, Farzad & Oliver, Stephen & Rodriguez, Sergio & Glesk, Ivan, 2020. "Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making," Applied Energy, Elsevier, vol. 279(C).
    8. Qi Dong & Kai Xing & Hongrui Zhang, 2017. "Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions," Sustainability, MDPI, Open Access Journal, vol. 10(1), pages 1-15, December.
    9. Sandrine Meyer & Laurence Holzemer & Thiago Nyssens Moraes Da Silva & Kevin Maréchal, 2016. "Things are not always what it is measured: On the importance of adequately assessing energy poverty," Working Papers CEB 16-025, ULB -- Universite Libre de Bruxelles.
    10. Belaïd, Fateh, 2018. "Exposure and risk to fuel poverty in France: Examining the extent of the fuel precariousness and its salient determinants," Energy Policy, Elsevier, vol. 114(C), pages 189-200.
    11. Okushima, Shinichiro, 2017. "Gauging energy poverty: A multidimensional approach," Energy, Elsevier, vol. 137(C), pages 1159-1166.
    12. Jan K. Kazak & Małgorzata Świąder, 2018. "SOLIS—A Novel Decision Support Tool for the Assessment of Solar Radiation in ArcGIS," Energies, MDPI, Open Access Journal, vol. 11(8), pages 1-12, August.
    13. Zhengxun Jin & Jonghyeob Kim & Chang-taek Hyun & Sangwon Han, 2019. "Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction," Sustainability, MDPI, Open Access Journal, vol. 11(14), pages 1-18, July.
    14. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.
    15. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    16. Dorothee Charlier and Sondes Kahouli, 2019. "From Residential Energy Demand to Fuel Poverty: Income-induced Non-linearities in the Reactions of Households to Energy Price Fluctuations," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    17. Sovacool, Benjamin K. & Lipson, Matthew M. & Chard, Rose, 2019. "Temporality, vulnerability, and energy justice in household low carbon innovations," Energy Policy, Elsevier, vol. 128(C), pages 495-504.
    18. Maria Mrówczyńska & Małgorzata Sztubecka & Marta Skiba & Anna Bazan-Krzywoszańska & Przemysław Bejga, 2019. "The Use of Artificial Intelligence as a Tool Supporting Sustainable Development Local Policy," Sustainability, MDPI, Open Access Journal, vol. 11(15), pages 1-17, August.
    19. Edyta Plebankiewicz & Damian Wieczorek, 2020. "Prediction of Cost Overrun Risk in Construction Projects," Sustainability, MDPI, Open Access Journal, vol. 12(22), pages 1-1, November.
    20. Shan Zhou & Douglas S. Noonan, 2019. "Justice Implications of Clean Energy Policies and Programs in the United States: A Theoretical and Empirical Exploration," Sustainability, MDPI, Open Access Journal, vol. 11(3), pages 1-20, February.

    More about this item

    Keywords

    local energy policy; energy efficiency of buildings; neural network; social-infrastructural correlation;
    All these keywords.

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - 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:gam:jeners:v:11:y:2018:i:9:p:2302-:d:167100. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team). General contact details of provider: https://www.mdpi.com/ .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.