Spatial analysis of the pattern of energy consumption in the domestic sector in the neighborhoods of Birjand city

Document Type : Research Paper


1 Assistant professor, Department of Geography at Ferdowsi University of Mashhad, Iran

2 Ma student of geography and urban planning at Ferdowsi University of Mashhad, Iran.


Energy and its consumption pattern are among the basic issues related to sustainable development in urban areas.Today, an important part of research in the field of urban studies focuses on this issue. Meanwhile, in countries such as Iran, due to the cheapness of energy consumption, this issue has received less attention from planners. In a city, an important part of the difference in the energy consumption pattern is explained by the city form and its spatial characteristics. Therefore, paying attention to spatial analysis models and methods can help to understand this issue. Spatial analysis models help the role of spatial differences in energy consumption by using tools and software related to spatial database.
Therefore, the present study aims to measure the pattern of gas consumption in the domestic part of Birjand city by relying on spatial analysis methods. The study is descriptive-analytical and research variables include 1) energy consumption, 2) population, 3) building quality, 4) building age, 5) parts size, 6) building facade and 7) urban form. The studied area also includes 5353 residential plots in 5 neighborhoods of Birjand city with different physical, socio-economic characteristics. These neighborhoods include "Zafar", "Bazar", "Sarab", "Edari" and "Resalat". which are located in the north, west, south and east of Birjand city. In the first step, after collecting data related to gas consumption by residential units in each of the studied localities, a spatial database was created in ArcGis.
Also, the physical characteristics of each piece were collected in this database.
The research variables include gas consumption by 5353 residential plots in the target areas on one hand and physical variables related to the characteristics of each plot including size, building quality, building age, building facade and also the population living in each residential unit on the other hand. In addition to this, the compression index was also considered as an index of texture form, as another variable related to the subject.
Further, by using classical statistical methods (correlation coefficient, analysis of variance and Shannon's entropy) and spatial statistical methods (such as density models, two-way Moran and spatial autocorrelation), the findings were analyzed.
The results obtained at two macro levels (at the level of all neighborhoods) and local (at the level of residential plots in each neighborhood) are prepared in the form of maps, charts and tables, and finally, by putting the prepared layers together, the data is combined. with each other and the final conclusion was reached

The results showed that energy consumption has an inverse relationship with the variables of building age, population and building quality, and a direct relationship with the entropy coefficient. This means that new tissues, in addition to their short lifespan and lower population density, have not been able to be effective in reducing energy consumption. On the other hand, despite the high population, organic tissues show a lower amount of energy consumption. This issue shows the necessity of paying attention to tissue renewal processes and its effect on energy consumption compared to older tissues. On the other hand, it shows that paying attention to the spatial patterns of consumption will be one of the key points in improving the energy consumption pattern.
It is not possible to optimize energy consumption and modify the consumption pattern, regardless of the characteristics and spatial differences (even in urban scales). This issue is especially visible in big cities that show a high variety of living patterns and lifestyles. Therefore, setting a single pattern for them is not a good job. As in this study, spatial analysis accurately showed the spatial differences in energy consumption and the different status of physical indicators in each of the target neighborhoods in Birjand city. The general result of this study shows that an important part of the differences in the consumption pattern is explained by the spatial differences. Therefore, the modification of the consumption pattern is an issue that is achieved under the synergy of various specialties. Part of which is related to the understanding of spatial patterns. In this regard, setting up and completing the location database of subscribers and identifying consumption patterns is perhaps one of the most important basic measures, in order to move in the direction of the optimal consumption pattern. In such a way that during this preparation, an accurate picture of energy consumption in different sectors and its relationship with other issues will be clarified. In order to obtain a detailed analysis of consumption and its pattern in its shadow, and adjust appropriate frameworks for each area in line with optimal energy consumption.


Aksoezen, M., Daniel, M., Hassler, U., & Kohler, N. (2015). Building age as an indicator for energy consumption. Energy and Buildings, 87, 74-86. [In Persian]
Amod Consulting Engineers (2013). studies of master revision plan of Birjand city. [In Persian]
Amoruso, G., Donevska, N., & Skomedal, G. (2018). German and Norwegian policy approach to residential buildings’ energy efficiency—A comparative assessment. Energy Efficiency, 11(6), 1375-1395
Anderson, J. E., Wulfhorst, G., & Lang, W. (2015). Energy analysis of the built environment—A review and outlook. Renewable and Sustainable Energy Reviews, 44, 149-158.
Barakpur, N., & Mosannenzadeh, F. (2012). Comparative Study on Energy Efficiency Policies in the Area of Land Use Planning in Iran and England. Motaleate Shahri, 1(1), 41-60. [In Persian] 
Beykaei, S. A., & Miller, E. (2017). Testing uncertainty in ILUTE—an integrated land use-transportation micro-simulation model of demographic updating. J Civil Environ Eng, 7(1), 1-9.
Bhatia, S. (2014). Energy resources and their utilisation. In Advanced Renewable Energy Systems,(Part 1 and 2) (pp. 18-48). WPI Publishing.
Commission, E., & Energy, D.-G. f. (2019). Comprehensive study of building energy renovation activities and the uptake of nearly zero-energy buildings in the EU : final report. Publications Office.
De Pascali, P., & Bagaini, A. (2019). Energy Transition and Urban Planning for Local Development. A Critical Review of the Evolution of Integrated Spatial and Energy Planning. Energies, 12(1), 35.
Dong, F., Li, Y., Li, K., Zhu, J., & Zheng, L. (2022). Can smart city construction improve urban ecological total factor energy efficiency in China? Fresh evidence from generalized synthetic control method. Energy, 241.
Eskandari Sani, M., Moradi, M., & Gadei Moghadam, P. (2019). Assessing the Potentials of Smart Cities with an Emphasis on Urban Transportion: A Case Study of the City of Birjand. The Journal of Geographical Research on Desert Areas, 6(2), 159-185. [In Persian] 
Farrokhi, M., Izadi, M. S., & Karimi Moshaver, M. (2018). Analysis of Energy Efficiency of Urban Fabrics in the Hot and Dry Climates, Case Study: Isfahan. Journal of Iranian Architecture Studies, 7(13), 127-147.[In Persian]
Fazeli, A., & Heydarim Sh. (2013). Energy efficiency in residential areas of Tehran using Rotterdam Energy Approach Planning (REAP). Quarterly Journal of Energy Policy and Planning Research, 1(3), 83-96. [In Persian]
Fernandez, N. P. (2008). The Influence of Construction Materials on Life-cycle Energy Use and Carbon Dioxide Emissions of Medium Size Commercial Buildings: A Thesis Submitted in Fulfilment of the Requirements for the Degree of Master of Building Science Victoria University of Wellington.
Gegraphic information system of Birjand city (1395). [In Persian]
Hajipour, K., & Foroozan, N. (2015). Study of the Urban Form Effect on Operational Energy Consumption; the Case of Shiraz. Honar-Ha-Ye-Ziba: Memary Va Shahrsazi, 19(4), 17-26. [In Persian] DOI:10.22059/JFAUP.2015.55692
Jiang, H., Yao, R., Han, S., Du, C., Yu, W., Chen, S., Li, B., Yu, H., Li, N., & Peng, J. (2020). How do urban residents use energy for winter heating at home? A large-scale survey in the hot summer and cold winter climate zone in the Yangtze River region. Energy and Buildings, 223, 110131.
Ji, Q., Li, C., Makvandi, M., & Zhou, X. (2022). Impacts of urban form on integrated energy demands of buildings and transport at the community level: A comparison and analysis from an empirical study. Sustainable Cities and Society, 79, 103680.
Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., Stevanovic, Z., & Djurovic-Petrovic, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and environment, 45(7), 1683-1697.
Kofoworola, O. F., & Gheewala, S. H. (2009). Life cycle energy assessment of a typical office building in Thailand. Energy and Buildings, 41(10), 1076-1083.
Leng, H., Chen, X., Ma, Y., Wong, N. H., & Ming, T. (2020). Urban morphology and building heating energy consumption: Evidence from Harbin, a severe cold region city. Energy and Buildings, 224, 110143.
Li, C., Song, Y., & Kaza, N. (2018). Urban form and household electricity consumption: A multilevel study. Energy and Buildings, 158, 181-193.
Li, Z., & Shi, J. (2015). Comparative analysis of residential building 75% energy efficiency design standards of Shandong Province and Germany building energy efficiency standards. 2015 3rd International Conference on Education, Management, Arts, Economics and Social Science.
Liu, Y., Dong, F.(2022). What are the roles of consumers, automobile production enterprises, and the government in the process of banning gasoline vehicles? Evidence from a tripartite evolutionary game model. Energy, 238, 12200. DOI: 10.1016/
Malhotra, A., Bischof, J., Nichersu, A., Häfele, K.-H., Exenberger, J., Sood, D., Allan, J., Frisch, J., van Treeck, C., O’Donnell, J., & Schweiger, G. (2022). Information modelling for urban building energy simulation—A taxonomic review. Building and environment, 208, 108552.
Marique, A.-F., & Reiter, S. (2012). A method to evaluate the energy consumption of suburban neighborhoods. HVAC&R Research, 18(1-2), 88-99.
Muñiz, I., & Rojas, C. (2019). Urban form and spatial structure as determinants of per capita greenhouse gas emissions considering possible endogeneity and compensation behaviors. Environmental Impact Assessment Review, 76, 79-87.
Mutani, G., & Todeschi, V. (2021). GIS-based urban energy modelling and energy efficiency scenarios using the energy performance certificate database. Energy Efficiency, 14(5).
Nichols, B. G., & Kockelman, K. M. (2015). Urban form and life-cycle energy consumption: Case studies at the city scale. Journal of Transport and Land Use, 8(3), 115-128.
Nikpour, A., Lotfi, S., Reza Zadeh, M., & Allahgholitabar Nesheli, F. (2018). An Analysis of the Relationship between Urban Form and Energy Consumption in the Housing Sector (Case Study: Babolsar). Geography and Urban Space Development, 5(1), 71-92. [In Persian]
Norman, J., MacLean, H. L., & Kennedy, C. A. (2006). Comparing high and low residential density: life-cycle analysis of energy use and greenhouse gas emissions. Journal of urban planning and development, 132(1), 10-21.
Quan, S. J., & Li, C. (2021). Urban form and building energy use: A systematic review of measures, mechanisms, and methodologies. Renewable and Sustainable Energy Reviews, 139, 110662.
Rafiyan, M., Fath Jalali, A., & Dadashpoor, H. (2011). Evaluating the Effect of Building Form and Density on Urban Energy Consumption (Case Study: Hashtgerd New Town). Armanshahr Architecture & Urban Development, 4(6), 107-116. [In Persian]
Sahraei Nejad, N. (2021). The Experience of Creating Intelligent, Green and Garden City of Putrajaya, Malaysia. Human & Environment, 19(3), 97-113. [In Persian]
 Sanaieian, H., Tenpierik, M., Van Den Linden, K., Seraj, F. M., & Shemrani, S. M. M. (2014). Review of the impact of urban block form on thermal performance, solar access and ventilation. Renewable and Sustainable Energy Reviews, 38, 551-560.
Shoja, S., Pourjafar, M., & Tabibian, M. (2019). Meta-Analysis of the Relationship between Urban Form and Energy: A Review of Approaches, Methods, Scales and Variables. Urban Planning Knowledge, 3(1), 85-107. [In Persian]
Silva, M. C., Horta, I. M., Leal, V., & Oliveira, V. (2017). A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand. Applied Energy, 202, 386-398.
South Korea's Smart Cities (2016). South Korea Ministry of Land, Infrastructure and Transport. Translated by Ketayun Moshoudi. Tehran: specialized mother company for new cities construction. [In Persian]
Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 13(8), 1819-1835.
Van der Hoeven, M. (2013). World energy outlook 2012. International Energy Agency: Tokyo, Japan.
Yang, Z., Roth, J., & Jain, R. K. (2018). DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis. Energy and Buildings, 163, 58-69.
Yin, Y., Mizokami, S., & Aikawa, K. (2015). Compact development and energy consumption: Scenario analysis of urban structures based on behavior simulation. Applied Energy, 159, 449-457.
Yu, H., Selvakkumaran, S., & Ahlgren, E. O. (2021). Integrating the urban planning process into energy systems models for future urban heating system planning: A participatory approach. Energy Reports, 7, 158-166.
Yu, H., Wang, M., Lin, X., Guo, H., Liu, H., Zhao, Y., Wang, H., Li, C., & Jing, R. (2021). Prioritizing urban planning factors on community energy performance based on GIS-informed building energy modeling. Energy and Buildings, 249, 111191.
Zhuang, Z., Chen, J., & Luo, X. (2019). Parallel computational building-chain model for rapid urban-scale energy simulation. Energy and Buildings, 201.