Built Environment Factors and Residential Land Carbon Emissions: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
xiaoping zhang
,
,
Xiaoping Zhang
,
Hu Zhao

: Evaluating the effects of built environment factors (BEF) on residential land carbon emissions (RLCE) is an effective way to reduce RLCE and promote low-carbon development from the perspective of urban planning. In this study, the Grey correlation analysis method and Universal global optimization method were proposed to explore the effects of BEF on RLCE using advanced metering infrastructure (AMI) data in Zibo, a representative resource-based city in China. The results indicated that RLCE can be significantly affected by BEF such as intensity, density, morphology, and land. The morphology is the most critical BEF in reducing RLCE. Among them, the building height (BH) and building shape coefficient (BSC) had positive effects on RLCE, while the high-rise buildings ratio (HRBR) and RLCE decreased first and then increased. The R2 of BH, BSC, and HRBR are 0.684, 0.754, and 0.699. The land had limited effects in reducing RLCE, and the R2 of the land construction time (LCT) is only 0.075, which has the least effect on RLCE. The results suggest that urban design based on BEF optimization would be effective in reducing the RLCE.

  • residential land carbon emissions
  • built environment factors
  • advanced metering infrastructure (AMI)
  • grey relation analysis
  • Universal global optimization
  • Zibo

1. Introduction

Urban construction land (UCL) is not only the main spatial carrier of human living, entertainment, and industrial production, but also the main carrier of carbon emissions. Although UCL accounts for only 2.4% of the global area, it carries about 80% of the carbon emissions [1][2], while in China UCL is responsible for about 73% of the country’s total carbon emissions—and it is still rising [3]. If nothing is done, climate change will hit the threshold of 1.5 °C in the coming decades, with more serious consequences for the environment, economy, and society to follow [4][5]. Therefore, reducing UCL carbon emissions is of great significance to deal with global warming and achieve low-carbon development [6][7]. Buildings, industry, and transportation are the three main sources of UCL carbon emissions [8][9]. Among them, building energy consumption accounts for about 40% of carbon emissions, while in developed countries such as the United States, building energy consumption has exceeded 60% [10]. According to the data from the China Energy Statistics Yearbook, the total energy consumption of residential buildings has increased year by year, with an average annual growth rate of 10.12% from 2010 to 2019. Residential land is the most basic unit of residential building energy consumption, and its energy consumption and carbon emissions are closely related to built environment factors (BEF) [11]. Many studies have illustrated that residential land carbon emissions (RLCE) can be reduced by using clean energy, adjusting the proportion of green buildings, and promoting new energy-saving technologies, but these cannot completely solve carbon emissions caused by BEF such as intensity, density, morphology, and land [12][13]. By defining the relationship between BEF and RLCE and carrying out low-carbon optimization, urban planning can achieve the lock-in effect of RLCE. The optimization of BEF can reduce RLCE by 18–24%, and the overall carbon reduction potential can reach 78% [14]. Therefore, optimization of BEF is an important means to reduce RLCE from the perspective of urban planning.

2. Built Environment Factors and Residential Land Carbon Emissions

During recent decades, some scholars have made preliminary explorations on BEF that affect RLCE, among which the BEF of building scale and block scale have been widely discussed [15]. For BEF of building scale, the studies were mainly based on the established statistical database of building energy consumption, combined with the energy consumption simulation method and the analyzed effects of BEF of building scale on RLCE [15][16]. For example, Kragh et al. extracted the building area and building age data of 1.60 × 106 residential buildings from the Danish National Research Database, then classified the building types and simulated the classified typical building energy consumption. The results show that the error between simulated data and official statistical data was less than 4% [17]. Streltsov et al. studied the RLCE in Gainesville, Florida and San Diego, California based on the data of utility companies and high-altitude images, and considered that the building shape coefficient and building aspect ratio have more important effects [18]. Li et al. divided residential building types by building height, building aspect ratio, and building compactness ratio, and simulated the energy consumption of typical buildings. The results show that the error between simulated and true was within 3% [19]. Luana et al. selected the building construction time, building type, and building scale as the important BEF affecting building energy consumption in Sicily. The energy consumption data for 12 different building types were obtained through energy consumption simulations and compared with the measured data, with an average error of about 7.7% [20]. Ifigeneia et al. classified the stock of residential buildings in Greece by construction time, building type, building height, and other factors based on data from the Greek Bureau of Statistics and simulated the energy consumption of typical buildings of different types through Energyplus. By comparing with the measured values, the error was between 15 and 18% [21].
In contrast to the effects of BEF of building scale on RLCE, especially when buildings are arranged in groups, the RLCE is not the sum of single residential buildings’ carbon emissions, and the effects of BEF of block scale should be considered.
For BEF of block scale, the common methods include statistical methods, simulation methods, and comprehensive methods combining statistics and simulation [22][23]. For example, Wilson et al. found that BEF such as building density and land area have important and long-term effects on RLCE [24]. Garbasevschi et al. found significant differences in RLCE by comparing eight residential lands of the same type but with different construction times in Germany [25]. Wang et al. conducted a correlation analysis between carbon emissions and landscape characteristics of 6754 districts in Eindhoven based on the cluster analysis method and random forest method and found that the function, density, and building floors of different districts have a significant impact on carbon emissions [26]. Sundus used energy simulation software to analyze the effects of the ratio of high-rise buildings on RLCE and found that when floor area ratio was certain, the ratio of high-rise buildings has a significant effect on RLCE and can reduce carbon emissions by 4.6% [13]. Kamal et al. evaluated the impacts of the greening rate on urban microclimate and building energy loads by using the open weather data of the Marina district in the city of Lusail and found that the energy consumption of 250 kW h could be reduced with an increase in greening rate [27]. Leng et al. established the relationship between seven BEF and building energy consumption by using correlation analysis and multiple linear regression analysis. The results indicated that the greater building site cover, floor area ratio, building height, road height–width ratio, total wall surface area, and lower green space ratio were beneficial to the reduction of building energy consumption [12].
As shown above, the effects of BEF on RLCE have been widely explored. These studies also demonstrate that, from the perspective of urban planning, carbon emissions can be effectively reduced by regulating the range of the BEF. However, due to the different research scales, objects, and problems selected by different studies, there are still some disputes about the effects of some BEF, such as building area, building density, and land area on RLCE, and the impact mechanism is complex. Therefore, the significant correlation and influence relationship between the BEF and RLCE have not yet formed a unified conclusion, and more empirical research needs to be further carried out. Moreover, the relevant studies are mainly based on the questionnaire data or software simulation data, resulting in a certain deviation between the conclusions and the true values.
Fortunately, with the extensive installation and use of the national smart grid and smart sensing equipment, especially the popularization of advanced measurement infrastructure (AMI), power supply companies have obtained a large amount of data with geographical indications and time information. These data record the space-time information of power users in detail, including name, ownership, location, active electric energy, voltage, current, power factor, forward and reverse power, and other power grid status information. Compared with the data of questionnaires and model simulations, which are limited by manual survey costs, survey scale, sample size, and computer simulation performance, AMI data has the characteristics of large amount of data, good compatibility, and high accuracy [28][29]. Therefore, studies based on AMI data are gradually increasing, such as user behavior analysis and classification, load prediction, power network planning, distribution network operation status evaluation and early warning, etc. [30][31]. However, few studies have used AMI data to investigate the effects of BEF on RLCE.
On the one hand, the empirical study on the effects of BEF on RLCE can be enriched, thus filling in the gaps of existing studies to some extent. On the other hand, it provides recommendations to carry out low-carbon-oriented urban planning in the future.
In the representative resource-based city of Zibo, China, 45 residential lands are selected as research objects. The Grey correlation analysis method and Universal global optimization method are proposed to explore the effects of BEF on RLCE using advanced metering infrastructure (AMI) data provided by the Zibo power supply company. The study starts by emphasizing the significance of the effects of BEF on RLCE. The Section 2 elaborates on prior research and the knowledge gaps in the literature. The Section 3 explains the study area, research data, and methods. In the Section 4, the study presents the results of this study in three subsections: RLCE and BEF, Correlations between BEF and RLCE, and Regression of BEF and RLCE. The Section 5 further discusses the effects of intensity, density, morphology, and land on RLCE, as well as the perspective of urban planning strategies to reduce RLCE and the detailed applications of the results. The Section 6 presents the conclusions.

This entry is adapted from the peer-reviewed paper 10.3390/buildings12050508

References

  1. Yang, J.; Zhan, Y.; Xiao, X.; Xia, J.C.; Sun, W.; Li, X. Investigating the diversity of land surface temperature characteristics in different scale cities based on local climate zones. Urban Clim. 2020, 34, 100700.
  2. Zhang, Y.; Liu, Y.; Wang, Y.; Liu, D.; Xia, C.; Wang, Z.; Wang, H.; Liu, Y. Urban expansion simulation towards low-carbon development: A case study of Wuhan, China. Sustain. Cities Soc. 2020, 63, 102455.
  3. Liu, L.C.; Wu, G.; Wang, J.N.; Wei, Y.M. China’s carbon emissions from urban and rural households during 1992–2007. J. Clean. Prod. 2011, 19, 1754–1762.
  4. He, B.J.; Zhao, D.; Dong, X.; Xiong, K.; Feng, C.; Qi, Q.; Darko, A.; Sharifi, A.; Pathak, M. Perception, physiological and psychological impacts, adaptive awareness and knowledge, and climate justice under urban heat: A study in extremely hot-humid Chongqing, China. Sustain. Cities Soc. 2022, 79, 103685.
  5. Yang, J.; Wang, Y.; Xue, B.; Li, Y.; Xiao, X.; Xia, J.; He, B. Contribution of urban ventilation to the thermal environment and urban energy demand: Different climate background perspectives. Sci. Total Environ. 2021, 795, 148791.
  6. Yang, J.; Jin, S.; Xiao, X.; Jin, C.; Xia, J.; Li, X.; Wang, S. Local climate zone ventilation and urban land surface temperatures: Towards a performance-based and wind-sensitive planning proposal in megacities. Sustain. Cities Soc. 2019, 47, 101487.
  7. He, B.J. Potentials of meteorological characteristics and synoptic conditions to mitigate urban heat island effects. Urban Clim. 2018, 24, 26–33.
  8. Olu-Ajayi, R.; Alaka, H.; Sulaimon, I.; Sunmola, F.; Ajayi, S. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J. Build. Eng. 2022, 45, 103406.
  9. He, B.J. Towards the next generation of green building for urban heat island mitigation: Zero UHI impact building. Sustain. Cities Soc. 2019, 50, 101647.
  10. Duan, H.; Chen, S.; Song, J. Characterizing regional building energy consumption under joint climatic and socioeconomic impacts. Energy 2022, 12, 3290.
  11. Ren, J.; Yang, J.; Zhang, Y.; Xiao, X.; Xia, J.C.; Li, X.; Wang, S. Exploring thermal comfort of urban buildings based on local climate zones. J. Clean. Prod. 2022, 340, 130744.
  12. Leng, H.; Chen, X.; Ma, Y.; Wong, N.H.; Ming, T. Urban morphology and building heating energy consumption: Evidence from Harbin, a severe cold region city. Energy Build. 2020, 224, 110143.
  13. Shareef, S. The impact of urban morphology and building’s height diversity on energy consumption at urban scale. The case study of Dubai. Build. Environ. 2021, 194, 107675.
  14. Hukkalainen, M.; Virtanen, M.; Paiho, S.; Airaksinen, M. Energy planning of low carbon urban areas-Examples from Finland. Sustain. Cities Soc. 2017, 35, 715–728.
  15. Wong, C.H.H.; Cai, M.; Ren, C.; Huang, Y.; Liao, C.; Yin, S. Modelling building energy use at urban scale: A review on their account for the urban environment. Build. Environ. 2021, 205, 108235.
  16. Torabi Moghadam, S.; Toniolo, J.; Mutani, G.; Lombardi, P. A GIS-statistical approach for assessing built environment energy use at urban scale. Sustain. Cities Soc. 2018, 37, 70–84.
  17. Kragh, J.; Wittchen, K.B. Development of two Danish building typologies for residential buildings. Energy Build. 2014, 68, 79–86.
  18. Streltsov, A.; Malof, J.M.; Huang, B.; Bradbury, K. Estimating residential building energy consumption using overhead imagery. Appl. Energy 2020, 280, 116018.
  19. Li, X.; Yao, R.; Liu, M.; Costanzo, V.; Yu, W.; Wang, W.; Short, A.; Li, B. Developing urban residential reference buildings using clustering analysis of satellite images. Energy Build. 2018, 169, 417–429.
  20. Filogamo, L.; Peri, G.; Rizzo, G.; Giaccone, A. On the classification of large residential buildings stocks by sample typologies for energy planning purposes. Appl. Energy 2014, 135, 825–835.
  21. Theodoridou, I.; Papadopoulos, A.M.; Hegger, M. A typological classification of the Greek residential building stock. Energy Build. 2011, 43, 2779–2787.
  22. Loeffler, R.; Österreicher, D.; Stoeglehner, G. The energy implications of urban morphology from an urban planning perspective—A case study for a new urban development area in the city of Vienna. Energy Build. 2021, 252, 111453.
  23. Ji, Q.; Li, C.; Makvandi, M.; Zhou, X. Impacts of urban form on integrated energy demands of buildings and transport at the community level: A comparison and analysis from an empirical study. Sustain. Cities Soc. 2022, 79, 103680.
  24. Wilson, B. Urban form and residential electricity consumption: Evidence from Illinois, USA. Landsc. Urban Plan. 2013, 115, 62–71.
  25. Garbasevschi, O.M.; Schmiedt, E.J.; Verma, T.; Lefter, I.; Korthals Altes, W.K.; Droin, A.; Schiricke, B.; Wurm, M. Spatial factors influencing building age prediction and implications for urban residential energy modelling. Comput. Environ. Urban Syst. 2021, 88, 101637.
  26. Wang, G.; Han, Q.; de Vries, B. Assessment of the relation between land use and carbon emission in Eindhoven, The Netherlands. J. Environ. Manag. 2019, 247, 413–424.
  27. Kamal, A.; Abidi, S.M.H.; Mahfouz, A.; Kadam, S.; Rahman, A.; Hassan, I.G.; Wang, L.L. Impact of urban morphology on urban microclimate and building energy loads. Energy Build. 2021, 253, 111499.
  28. Wang, B.; Rong, J.; Zhang, S.; Liu, L. Research on data security of multicast transmission based on certificateless multi-recipient signcryption in AMI. Int. J. Electr. Power Energy Syst. 2020, 121, 106123.
  29. Ribeiro, I.C.G.; Albuquerque, C.; Rocha, A.A.D.A.; Passos, D. THOR: A framework to build an advanced metering infrastructure resilient to DAP failures in smart grids. Future Gener. Comput. Syst. 2019, 99, 11–26.
  30. Kreuwel, F.P.M.; Mol, W.B.; Vilà-Guerau de Arellano, J.; van Heerwaarden, C.C. Characterizing solar PV grid overvoltages by data blending advanced metering infrastructure with meteorology. Sol. Energy 2021, 227, 312–320.
  31. Liang, D.; Zeng, L.; Chiang, H.D.; Wang, S. Power flow matching-based topology identification of medium-voltage distribution networks via AMI measurements. Int. J. Electr. Power Energy Syst. 2021, 130, 106938.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback