IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i7p2982-d342995.html
   My bibliography  Save this article

Energy Efficiency and GHG Emissions Mapping of Buildings for Decision-Making Processes against Climate Change at the Local Level

Author

Listed:
  • Edgar Lorenzo-Sáez

    (ICT against Climate Change Research Group, ITACA—Information and Telecommunication Research Institute, Universitat Politècnica de València, Camí de Vera, s/n, 46022 València, Spain)

  • José-Vicente Oliver-Villanueva

    (ICT against Climate Change Research Group, ITACA—Information and Telecommunication Research Institute, Universitat Politècnica de València, Camí de Vera, s/n, 46022 València, Spain)

  • Eloina Coll-Aliaga

    (ICT against Climate Change Research Group, ITACA—Information and Telecommunication Research Institute, Universitat Politècnica de València, Camí de Vera, s/n, 46022 València, Spain)

  • Lenin-Guillermo Lemus-Zúñiga

    (ICT against Climate Change Research Group, ITACA—Information and Telecommunication Research Institute, Universitat Politècnica de València, Camí de Vera, s/n, 46022 València, Spain)

  • Victoria Lerma-Arce

    (ICT against Climate Change Research Group, ITACA—Information and Telecommunication Research Institute, Universitat Politècnica de València, Camí de Vera, s/n, 46022 València, Spain)

  • Antonio Reig-Fabado

    (Department of Applied Physics, Universitat Politècnica de València, Camí de Vera, s/n, 46022 València, Spain)

Abstract

Buildings have become a key source of greenhouse gas (GHG) emissions due to the consumption of primary energy, especially when used to achieve thermal comfort conditions. In addition, buildings play a key role for adapting societies to climate change by achieving more energy efficiency. Therefore, buildings have become a key sector to tackle climate change at the local level. However, public decision-makers do not have tools with enough spatial resolution to prioritise and focus the available resources and efforts in an efficient manner. The objective of the research is to develop an innovative methodology based on a geographic information system (GIS) for mapping primary energy consumption and GHG emissions in buildings in cities according to energy efficiency certificates. The developed methodology has been tested in a representative medium-sized city in Spain, obtaining an accurate analysis that shows 32,000 t of CO 2 emissions due to primary energy consumption of 140 GWh in residential buildings with high spatial resolution at single building level. The obtained results demonstrate that the majority of residential buildings have low levels of energy efficiency and emit an average of 45 kg CO 2 /m 2 . Compared to the national average in Spain, this obtained value is on the average, while it is slightly better at the regional level. Furthermore, the results obtained demonstrate that the developed methodology is able to directly identify city districts with highest potential for improving energy efficiency and reducing GHG emissions. Additionally, a data model adapted to the INSPIRE regulation has been developed in order to ensure interoperability and European-wide application. All these results have allowed the local authorities to better define local strategies towards a low-carbon economy and energy transition. In conclusion, public decision-makers will be supported with an innovative and user-friendly GIS-based methodology to better define local strategies towards a low-carbon economy and energy transition in a more efficient and transparent way based on metrics of high spatial resolution and accuracy.

Suggested Citation

  • Edgar Lorenzo-Sáez & José-Vicente Oliver-Villanueva & Eloina Coll-Aliaga & Lenin-Guillermo Lemus-Zúñiga & Victoria Lerma-Arce & Antonio Reig-Fabado, 2020. "Energy Efficiency and GHG Emissions Mapping of Buildings for Decision-Making Processes against Climate Change at the Local Level," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2982-:d:342995
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/7/2982/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/7/2982/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Theodoridou, Ifigeneia & Karteris, Marinos & Mallinis, Georgios & Papadopoulos, Agis M. & Hegger, Manfred, 2012. "Assessment of retrofitting measures and solar systems' potential in urban areas using Geographical Information Systems: Application to a Mediterranean city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 6239-6261.
    2. Bentzen, Jan & Engsted, Tom, 2001. "A revival of the autoregressive distributed lag model in estimating energy demand relationships," Energy, Elsevier, vol. 26(1), pages 45-55.
    3. Diakaki, Christina & Grigoroudis, Evangelos & Kabelis, Nikos & Kolokotsa, Dionyssia & Kalaitzakis, Kostas & Stavrakakis, George, 2010. "A multi-objective decision model for the improvement of energy efficiency in buildings," Energy, Elsevier, vol. 35(12), pages 5483-5496.
    4. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    5. Caputo, Paola & Costa, Gaia & Ferrari, Simone, 2013. "A supporting method for defining energy strategies in the building sector at urban scale," Energy Policy, Elsevier, vol. 55(C), pages 261-270.
    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. Shaban R. S. Aldhshan & Khairul Nizam Abdul Maulud & Wan Shafrina Wan Mohd Jaafar & Othman A. Karim & Biswajeet Pradhan, 2021. "Energy Consumption and Spatial Assessment of Renewable Energy Penetration and Building Energy Efficiency in Malaysia: A Review," Sustainability, MDPI, vol. 13(16), pages 1-26, August.
    2. Ionica Oncioiu & Ioana Duca & Mirela Anca Postole & Georgiana Camelia Georgescu (Crețan) & Rodica Gherghina & Robert-Adrian Grecu, 2021. "Transforming the COVID-19 Threat into an Opportunity: The Pandemic as a Stage to the Sustainable Economy," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    3. Hongjiang Liu & Fengying Yan & Hua Tian, 2020. "A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method," Sustainability, MDPI, vol. 12(23), pages 1-21, December.
    4. Tahir, Muhammad Faizan & Haoyong, Chen & Guangze, Han, 2022. "Evaluating individual heating alternatives in integrated energy system by employing energy and exergy analysis," Energy, Elsevier, vol. 249(C).
    5. Marta Monzón-Chavarrías & Silvia Guillén-Lambea & Sergio García-Pérez & Antonio Luis Montealegre-Gracia & Jorge Sierra-Pérez, 2021. "Heating Energy Consumption and Environmental Implications Due to the Change in Daily Habits in Residential Buildings Derived from COVID-19 Crisis: The Case of Barcelona, Spain," Sustainability, MDPI, vol. 13(2), pages 1-19, January.

    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. Yanxia Li & Chao Wang & Sijie Zhu & Junyan Yang & Shen Wei & Xinkai Zhang & Xing Shi, 2020. "A Comparison of Various Bottom-Up Urban Energy Simulation Methods Using a Case Study in Hangzhou, China," Energies, MDPI, vol. 13(18), pages 1-23, September.
    2. Talebi, Behrang & Haghighat, Fariborz & Tuohy, Paul & Mirzaei, Parham A., 2018. "Validation of a community district energy system model using field measured data," Energy, Elsevier, vol. 144(C), pages 694-706.
    3. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    4. Soares, N. & Bastos, J. & Pereira, L. Dias & Soares, A. & Amaral, A.R. & Asadi, E. & Rodrigues, E. & Lamas, F.B. & Monteiro, H. & Lopes, M.A.R. & Gaspar, A.R., 2017. "A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 845-860.
    5. Jacopo Gaspari & Michaela De Giglio & Ernesto Antonini & Vincenzo Vodola, 2020. "A GIS-Based Methodology for Speedy Energy Efficiency Mapping: A Case Study in Bologna," Energies, MDPI, vol. 13(9), pages 1-19, May.
    6. 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.
    7. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    8. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    9. Schlör, Holger & Venghaus, Sandra & Hake, Jürgen-Friedrich, 2018. "The FEW-Nexus city index – Measuring urban resilience," Applied Energy, Elsevier, vol. 210(C), pages 382-392.
    10. Jinhui Ma & Haijing Huang & Mingxi Peng & Yihuan Zhou, 2024. "Investigating the Heterogeneity Effects of Urban Morphology on Building Energy Consumption from a Spatio-Temporal Perspective Using Old Residential Buildings on a University Campus," Land, MDPI, vol. 13(10), pages 1-24, October.
    11. Zhang, Yi & Ji, Qiang & Fan, Ying, 2018. "The price and income elasticity of China's natural gas demand: A multi-sectoral perspective," Energy Policy, Elsevier, vol. 113(C), pages 332-341.
    12. Wei Yu & Baizhan Li & Yarong Lei & Meng Liu, 2011. "Analysis of a Residential Building Energy Consumption Demand Model," Energies, MDPI, vol. 4(3), pages 1-13, March.
    13. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    14. Dergiades, Theologos & Tsoulfidis, Lefteris, 2008. "Estimating residential demand for electricity in the United States, 1965-2006," Energy Economics, Elsevier, vol. 30(5), pages 2722-2730, September.
    15. Liang Chen & Yuanfan Zheng & Jia Yu & Yuanhang Peng & Ruipeng Li & Shilingyun Han, 2024. "A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution," Energies, MDPI, vol. 17(17), pages 1-24, August.
    16. Yumin Li & Yan Jiang & Shiyuan Li, 2022. "Price and income elasticities of electricity in China: Estimation and policy implications," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S2), pages 76-90, November.
    17. Hanan S.S. Ibrahim & Ahmed Z. Khan & Shady Attia & Yehya Serag, 2021. "Classification of Heritage Residential Building Stock and Defining Sustainable Retrofitting Scenarios in Khedivial Cairo," Sustainability, MDPI, vol. 13(2), pages 1-26, January.
    18. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    19. Henze, Gregor P. & Pavlak, Gregory S. & Florita, Anthony R. & Dodier, Robert H. & Hirsch, Adam I., 2015. "An energy signal tool for decision support in building energy systems," Applied Energy, Elsevier, vol. 138(C), pages 51-70.
    20. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).

    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:jsusta:v:12:y:2020:i:7:p:2982-:d:342995. 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.