IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i14p2745-d249305.html
   My bibliography  Save this article

Economic-Engineering Modelling of the Buildings Sector to Study the Transition towards Deep Decarbonisation in the EU

Author

Listed:
  • Theofano Fotiou

    (Researcher in E3MLab, National Technical University of Athens, 15773 Athens, Greece)

  • Alessia de Vita

    (Researcher in E3MLab, National Technical University of Athens, 15773 Athens, Greece)

  • Pantelis Capros

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

Abstract

The paper presents a newly developed economic-engineering model of the buildings sector and its implementation for all the European Union (EU) Member States (MS), designed to study in detail ambitious energy efficiency strategies and policies, in the context of deep decarbonisation in the long term. The model has been used to support the impact assessment study that accompanied the European Commission’s communication “A Clear Planet for All”, in November 2018. The model covers all EU countries with a fine resolution of building types, and represents agent decision-making in a complex and dynamic economic-engineering mathematical framework. Emphasis is given to behaviours driving the energy renovation of buildings and the ensuing choice of equipment for heating and cooling. The model represents several market and non-market policies that can influence energy decisions in buildings and promote deep energy renovation. Moreover, the paper presents key applications for supporting policies targeting ambitious reduction of energy consumption and carbon emissions in buildings across Europe. The results illustrate that the achievement of ambitious energy-efficiency targets in the long-term heavily depends on pursuing a fast and extensive renovation of existing buildings, at annual rates between 1.21% and 1.77% for the residential sector and between 0.92% to 1.35% for the services sector. In both cases, the renovation rates are far higher past trends. Strong policies aimed at removing non-market barriers are deemed necessary. Electrification constitutes a reasonable choice for deeply renovated buildings and, as a result, almost 50% of households chooses electric heating over gas heating in the long term. However, heat pumps need to exploit further their learning potential to be economical and implementable for the various climatic conditions in Europe. The results also show that the cost impacts are modest even if renovation and decarbonisation in buildings develop ambitiously in the EU. The reduced energy bills due to energy savings can almost offset the increasing capital expenditures. Fundraising difficulties and the cost of capital are, however, of concern.

Suggested Citation

  • Theofano Fotiou & Alessia de Vita & Pantelis Capros, 2019. "Economic-Engineering Modelling of the Buildings Sector to Study the Transition towards Deep Decarbonisation in the EU," Energies, MDPI, vol. 12(14), pages 1-28, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2745-:d:249305
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/14/2745/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/14/2745/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brown, Marilyn A., 2001. "Market failures and barriers as a basis for clean energy policies," Energy Policy, Elsevier, vol. 29(14), pages 1197-1207, November.
    2. Weiss, Martin & Patel, Martin K. & Junginger, Martin & Blok, Kornelis, 2010. "Analyzing price and efficiency dynamics of large appliances with the experience curve approach," Energy Policy, Elsevier, vol. 38(2), pages 770-783, February.
    3. Small, Kenneth A & Rosen, Harvey S, 1981. "Applied Welfare Economics with Discrete Choice Models," Econometrica, Econometric Society, vol. 49(1), pages 105-130, January.
    4. Andreas Uihlein & Peter Eder, 2009. "Towards Additional Policies to Improve the Environmental Performance of Buildings. Part II: Quantitative Assessment," JRC Research Reports JRC53640, Joint Research Centre.
    5. Miranowski, John & Dinan, T., 1989. "Estimating the Implicit Price of Energy Efficiency Improvement in the Residential Housing Market: A Hedonic Approach," Staff General Research Papers Archive 10698, Iowa State University, Department of Economics.
    6. Friege, Jonas & Chappin, Emile, 2014. "Modelling decisions on energy-efficient renovations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 196-208.
    7. Tanatvanit, Somporn & Limmeechokchai, Bundit & Chungpaibulpatana, Supachart, 2003. "Sustainable energy development strategies: implications of energy demand management and renewable energy in Thailand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 7(5), pages 367-395, October.
    8. Dorothée Charlier & Alejandro Mosino & Aude Pommeret, 2011. "Energy-saving Technology Adoption under Uncertainty in the Residential Sector," Annals of Economics and Statistics, GENES, issue 103-104, pages 43-70.
    9. Sergey Paltsev & Pantelis Capros, 2013. "Cost Concepts For Climate Change Mitigation," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 4(supp0), pages 1-26.
    10. Kannan, Ramachandran & Strachan, Neil, 2009. "Modelling the UK residential energy sector under long-term decarbonisation scenarios: Comparison between energy systems and sectoral modelling approaches," Applied Energy, Elsevier, vol. 86(4), pages 416-428, April.
    11. Christine H Pout, 2000. "N-DEEM: The National Nondomestic Buildings Energy and Emissions Model," Environment and Planning B, , vol. 27(5), pages 721-732, October.
    12. Martin Jakob, 2007. "The drivers of and barriers to energy efficiency in renovation decisions of single-family home-owners," CEPE Working paper series 07-56, CEPE Center for Energy Policy and Economics, ETH Zurich.
    13. Dinan, Terry M. & Miranowski, John A., 1989. "Estimating the implicit price of energy efficiency improvements in the residential housing market: A hedonic approach," Journal of Urban Economics, Elsevier, vol. 25(1), pages 52-67, January.
    14. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, September.
    15. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762, September.
    16. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    17. Jerry A. Hausman, 1979. "Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables," Bell Journal of Economics, The RAND Corporation, vol. 10(1), pages 33-54, Spring.
    18. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
    19. repec:adr:anecst:y:2011:i:103-104:p:04 is not listed on IDEAS
    20. Natarajan, Sukumar & Levermore, Geoffrey J., 2007. "Predicting future UK housing stock and carbon emissions," Energy Policy, Elsevier, vol. 35(11), pages 5719-5727, November.
    21. Andreas Uihlein & Peter Eder, 2009. "Towards Additional Policies to Improve the Environmental Performance of Buildings," JRC Research Reports JRC50149, Joint Research Centre.
    22. Shorrock, LD & Dunster, JE, 1997. "The physically-based model BREHOMES and its use in deriving scenarios for the energy use and carbon dioxide emissions of the UK housing stock," Energy Policy, Elsevier, vol. 25(12), pages 1027-1037, October.
    23. Chateau, B. & Lapillonne, B., 1978. "Long-term energy demand forecasting A new approach," Energy Policy, Elsevier, vol. 6(2), pages 140-157, June.
    24. John S. Chipman, 2006. "Aggregation and Estimation in the Theory of Demand," History of Political Economy, Duke University Press, vol. 38(5), pages 106-129, Supplemen.
    25. Bhattacharyya, Subhes C. & Timilsina, Govinda R., 2009. "Energy demand models for policy formulation : a comparative study of energy demand models," Policy Research Working Paper Series 4866, The World Bank.
    26. Clinch, J. Peter & Healy, John D., 2003. "Valuing improvements in comfort from domestic energy-efficiency retrofits using a trade-off simulation model," Energy Economics, Elsevier, vol. 25(5), pages 565-583, September.
    27. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    28. Richard B. Howarth & Alan H. Sanstad, 1995. "Discount Rates And Energy Efficiency," Contemporary Economic Policy, Western Economic Association International, vol. 13(3), pages 101-109, July.
    29. Daioglou, Vassilis & van Ruijven, Bas J. & van Vuuren, Detlef P., 2012. "Model projections for household energy use in developing countries," Energy, Elsevier, vol. 37(1), pages 601-615.
    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. Zhu, Tong & Curtis, John & Clancy, Matthew, 2023. "Modelling barriers to low-carbon technologies in energy system analysis: The example of renewable heat in Ireland," Applied Energy, Elsevier, vol. 330(PA).
    2. Catherine Willan & Kathryn B. Janda & David Kenington, 2021. "Seeking the Pressure Points: Catalysing Low Carbon Changes from the Middle-Out in Offices and Schools," Energies, MDPI, vol. 14(23), pages 1-21, December.
    3. Theofano Fotiou & Pantelis Capros & Panagiotis Fragkos, 2022. "Policy Modelling for Ambitious Energy Efficiency Investment in the EU Residential Buildings," Energies, MDPI, vol. 15(6), pages 1-29, March.
    4. Barbara Kryk & Małgorzata Klaudia Guzowska, 2021. "Implementation of Climate/Energy Targets of the Europe 2020 Strategy by the EU Member States," Energies, MDPI, vol. 14(9), pages 1-18, May.
    5. Peep Pihelo & Kalle Kuusk & Targo Kalamees, 2020. "Development and Performance Assessment of Prefabricated Insulation Elements for Deep Energy Renovation of Apartment Buildings," Energies, MDPI, vol. 13(7), pages 1-20, April.
    6. Rita Remeikienė & Ligita Gasparėnienė & Aleksandra Fedajev & Marek Szarucki & Marija Đekić & Jolita Razumienė, 2021. "Evaluation of Sustainable Energy Development Progress in EU Member States in the Context of Building Renovation," Energies, MDPI, vol. 14(14), pages 1-22, July.
    7. Malgorzata Klaudia Guzowska & Barbara Kryk, 2021. "Efficiency of Implementing Climate/Energy Targets of the Europe 2020 Strategy and the Structural Diversity between Old and New Member States," Energies, MDPI, vol. 14(24), pages 1-18, December.
    8. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    9. Borja Izaola & Ortzi Akizu-Gardoki & Xabat Oregi, 2022. "Life Cycle Analysis Challenges through Building Rating Schemes within the European Framework," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    10. Andreas Andreou & Panagiotis Fragkos & Theofano Fotiou & Faidra Filippidou, 2022. "Assessing Lifestyle Transformations and Their Systemic Effects in Energy-System and Integrated Assessment Models: A Review of Current Methods and Data," Energies, MDPI, vol. 15(14), pages 1-24, July.

    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. Theofano Fotiou & Pantelis Capros & Panagiotis Fragkos, 2022. "Policy Modelling for Ambitious Energy Efficiency Investment in the EU Residential Buildings," Energies, MDPI, vol. 15(6), pages 1-29, March.
    2. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    3. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    4. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    5. Lianne Foti & Avis Devine, 2019. "High Involvement and Ethical Consumption: A Study of the Environmentally Certified Home Purchase Decision," Sustainability, MDPI, vol. 11(19), pages 1-11, September.
    6. Scott Kelly & Michael Pollitt & Doug Crawford-Brown, 2011. "Building performance evaluation and certification in the UK: a critical review of SAP?," Working Papers EPRG 1219, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    7. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    8. Ward, David O. & Clark, Christopher D. & Jensen, Kimberly L. & Yen, Steven T. & Russell, Clifford S., 2011. "Factors influencing willingness-to-pay for the ENERGY STAR® label," Energy Policy, Elsevier, vol. 39(3), pages 1450-1458, March.
    9. Andreas Mense, 2018. "The Value of Energy Efficiency and the Role of Expected Heating Costs," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(3), pages 671-701, November.
    10. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    11. Blasch, Julia & Filippini, Massimo & Kumar, Nilkanth, 2019. "Boundedly rational consumers, energy and investment literacy, and the display of information on household appliances," Resource and Energy Economics, Elsevier, vol. 56(C), pages 39-58.
    12. Pukšec, Tomislav & Vad Mathiesen, Brian & Duić, Neven, 2013. "Potentials for energy savings and long term energy demand of Croatian households sector," Applied Energy, Elsevier, vol. 101(C), pages 15-25.
    13. Hassett, Kevin A. & Metcalf, Gilbert E., 1995. "Energy tax credits and residential conservation investment: Evidence from panel data," Journal of Public Economics, Elsevier, vol. 57(2), pages 201-217, June.
    14. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    15. Caroline Löffler & Harald Hecking, 2017. "Greenhouse Gas Abatement Cost Curves of the Residential Heating Market: A Microeconomic Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 68(4), pages 915-947, December.
    16. Lee, Timothy & Yao, Runming, 2013. "Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis," Energy Policy, Elsevier, vol. 52(C), pages 363-372.
    17. Li, Jia & Just, Richard E., 2018. "Modeling household energy consumption and adoption of energy efficient technology," Energy Economics, Elsevier, vol. 72(C), pages 404-415.
    18. Min, Jihoon & Azevedo, Inês L. & Michalek, Jeremy & de Bruin, Wändi Bruine, 2014. "Labeling energy cost on light bulbs lowers implicit discount rates," Ecological Economics, Elsevier, vol. 97(C), pages 42-50.
    19. Marcin Zygmunt & Dariusz Gawin, 2021. "Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock," Energies, MDPI, vol. 14(24), pages 1-15, December.
    20. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.

    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:12:y:2019:i:14:p:2745-:d:249305. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.