Input, stocks and output flows of urban residential building system in Beijing city, China from 1949 to 2008

Abstract

a b s t r a c tUrban residential buildings are formed, maintained and reformed by different external material and energy flows, and their behaviors of input, accumulation and output are characterized by their architectural factors and modes of use that usually determine the consumption of material and energy of a building at its overall life cycle.In this research, we took Beijing city, a rapid developing city as a case study, and examined material flows of urban residential building system based on a survey of typical residential buildings in the urban areas of Beijing city. The quantitative analysis were made to describe the input, transformation/accumulation, and output of building materials from the year 1949 to 2008, and a comparative analysis was done to identify the differences of material uses among the buildings with different architectural structures as masonry-concrete, and steel-concrete.During the period from 1949 to 2008, there were six main materials of cement, sand, gravel, steel, bricks and timber used in urban residential building system in Beijing. The total amount of material imported into the system was 5.1 × 10 8 t, among which the accumulated amount was 4.7 × 10 8 t, an accumulation rate of 92.5%, and the total of building wastes reached 3.9 × 10 7 t. Among the buildings with two architectural structures, the total amount of material use for buildings with steel-concrete structure was larger than masonry-concrete. It was found that the buildings with steel-concrete structure experienced a rapid increase since the year 1979 in Beijing. As a result of rapid urban development, the large-scale reformation and demolishment of urban old buildings also led to a rapid growth of the amount of building wastes. And the building wastes generated in the process of reformation and demolition began to exceed that produced in the process of new buildings construction. The amount of building wastes generated from 2004 to 2008 accounted for 52.2% of the total that generated from 1949 to 2008.From this research, the rapid development of Beijing's residential building system in the past 60 years became a big ecological pressure for urban sustainable building development. It is important to change the traditional model of urban construction, and develop some sustainable or ecologically friendly construction technologies to enhance the capacity of recycling and reuse of residential building wastes for realizing a sustainable urban building construction and management in Beijing.

Taking Stock of Built Environment Stock Studies: Progress and Prospects

“…Wakayama (City Center). Urban studies: Oslo (NO) 166 , Salford Quays Manchester (GB) , Vienna (AT) 84,85,167 , Frankfurt (DE) 3 , Esch-sur-Alzette (LU) 40 , Geneva (CH) 168 , Bruxelles (BE) 169 , Orléans (FR) 170 , Paris (FR) 171 , Kitakyushu (JP) 172 , Wakayama (JP) 48 , Taipei City (TW) 17 , Melbourne (AU) 39 , Jakarta (ID) 173 , Bandung (ID) 173 , Beijing (CN) [80][81][82] , Tianjing (CN) 80 , Shanghai (CN) 80 , Ezhou City (CN) 127 , Philadelphia (US) 174 , Rio de Janeiro (BR) 163 , and Chiclayo (PE) 15 . National studies: Austria 175 , Germany 5,77,114 , Switzerland 162,176 , Japan 28,72,78,94,125,153 , China 75,105,[177][178][179] , and USA 28,142 .…”

“…*2.6 km 2 in Philadelphia (University City); 11 km 2 in Manchester (Salford); 8 km 2 in Wakayama (City Center). Urban studies: Oslo (NO), Salford Quays Manchester (GB), Vienna (AT), ,, Frankfurt (DE), Esch-sur-Alzette (LU), Geneva (CH), Bruxelles (BE), Orléans (FR), Paris (FR), Kitakyushu (JP), Wakayama (JP), Taipei City (TW), Melbourne (AU), Jakarta (ID), Bandung (ID), Beijing (CN), − Tianjing (CN), Shanghai (CN), Ezhou City (CN), Philadelphia (US), Rio de Janeiro (BR), and Chiclayo (PE) . National studies: Austria, Germany, ,, Switzerland, , Japan, ,,,,, China, ,,− and United States. , …”

Taking Stock of Built Environment Stock Studies: Progress and Prospects

“…Wakayama (City Center). Urban studies: Oslo (NO) 166 , Salford Quays Manchester (GB) , Vienna (AT) 84,85,167 , Frankfurt (DE) 3 , Esch-sur-Alzette (LU) 40 , Geneva (CH) 168 , Bruxelles (BE) 169 , Orléans (FR) 170 , Paris (FR) 171 , Kitakyushu (JP) 172 , Wakayama (JP) 48 , Taipei City (TW) 17 , Melbourne (AU) 39 , Jakarta (ID) 173 , Bandung (ID) 173 , Beijing (CN) [80][81][82] , Tianjing (CN) 80 , Shanghai (CN) 80 , Ezhou City (CN) 127 , Philadelphia (US) 174 , Rio de Janeiro (BR) 163 , and Chiclayo (PE) 15 . National studies: Austria 175 , Germany 5,77,114 , Switzerland 162,176 , Japan 28,72,78,94,125,153 , China 75,105,[177][178][179] , and USA 28,142 .…”

“…*2.6 km 2 in Philadelphia (University City); 11 km 2 in Manchester (Salford); 8 km 2 in Wakayama (City Center). Urban studies: Oslo (NO), Salford Quays Manchester (GB), Vienna (AT), ,, Frankfurt (DE), Esch-sur-Alzette (LU), Geneva (CH), Bruxelles (BE), Orléans (FR), Paris (FR), Kitakyushu (JP), Wakayama (JP), Taipei City (TW), Melbourne (AU), Jakarta (ID), Bandung (ID), Beijing (CN), − Tianjing (CN), Shanghai (CN), Ezhou City (CN), Philadelphia (US), Rio de Janeiro (BR), and Chiclayo (PE) . National studies: Austria, Germany, ,, Switzerland, , Japan, ,,,,, China, ,,− and United States. , …”

What matters most to the material intensity coefficient of buildings? Random forest‐based evidence from China

“…In many previous studies related to quantifying MS of Chinese buildings, MICs of archetype buildings are determined by the mean values (M. Hu, Pauliuk, et al., 2010; Huang et al., 2013; Shi et al., 2012), usually categorized based on structure (Guo et al., 2019; D. Hu, You, et al., 2010; Wang et al., 2015) and use type (Huang et al., 2013; Shi et al., 2012), with a few studies also taking the construction year into consideration (Han & Xiang, 2013; Huang et al., 2013; Shi et al., 2012). This method is executed by assigning equal weight to each feature.…”

“…However, it is often difficult to grasp a definite and comprehensive set of building information. So far, various sources such as GIS‐based data (Heeren et al., 2013; Kleemann et al., 2017; Miatto et al., 2019), remote sensing image (Haberl et al., 2021; Liang et al., 2017; Schandl et al., 2020), and conventional statistical data (Han & Xiang, 2013; D. Hu, You, et al., 2010; M. Hu, Pauliuk, et al., 2010) have been used as common ways to access physical inventory of buildings, but the ability of different tools to obtain the building typology information is varied and limited. For example, the GIS tool has been widely used due to its spatially explicit characteristics and it has a relatively greater potential to obtain different properties (Heeren et al., 2013; Kleemann et al., 2017; Tanikawa & Hashimoto, 2009).…”

Dynamic Assessment of Construction Materials in Urban Building Stocks: A Critical Review

“…Demand-driven models, used in six articles, ,,,,, seems especially well suited to capture temporal dynamics. Nevertheless, three ,, of these studies also include spatial dynamic input parameters, either for population or affluence .…”

Taking Stock of Built Environment Stock Studies: Progress and Prospects

“…Wakayama (City Center). Urban studies: Oslo (NO) 166 , Salford Quays Manchester (GB) , Vienna (AT) 84,85,167 , Frankfurt (DE) 3 , Esch-sur-Alzette (LU) 40 , Geneva (CH) 168 , Bruxelles (BE) 169 , Orléans (FR) 170 , Paris (FR) 171 , Kitakyushu (JP) 172 , Wakayama (JP) 48 , Taipei City (TW) 17 , Melbourne (AU) 39 , Jakarta (ID) 173 , Bandung (ID) 173 , Beijing (CN) [80][81][82] , Tianjing (CN) 80 , Shanghai (CN) 80 , Ezhou City (CN) 127 , Philadelphia (US) 174 , Rio de Janeiro (BR) 163 , and Chiclayo (PE) 15 . National studies: Austria 175 , Germany 5,77,114 , Switzerland 162,176 , Japan 28,72,78,94,125,153 , China 75,105,[177][178][179] , and USA 28,142 .…”

“…*2.6 km 2 in Philadelphia (University City); 11 km 2 in Manchester (Salford); 8 km 2 in Wakayama (City Center). Urban studies: Oslo (NO), Salford Quays Manchester (GB), Vienna (AT), ,, Frankfurt (DE), Esch-sur-Alzette (LU), Geneva (CH), Bruxelles (BE), Orléans (FR), Paris (FR), Kitakyushu (JP), Wakayama (JP), Taipei City (TW), Melbourne (AU), Jakarta (ID), Bandung (ID), Beijing (CN), − Tianjing (CN), Shanghai (CN), Ezhou City (CN), Philadelphia (US), Rio de Janeiro (BR), and Chiclayo (PE) . National studies: Austria, Germany, ,, Switzerland, , Japan, ,,,,, China, ,,− and United States. , …”

What matters most to the material intensity coefficient of buildings? Random forest‐based evidence from China

“…In many previous studies related to quantifying MS of Chinese buildings, MICs of archetype buildings are determined by the mean values (M. Hu, Pauliuk, et al., 2010; Huang et al., 2013; Shi et al., 2012), usually categorized based on structure (Guo et al., 2019; D. Hu, You, et al., 2010; Wang et al., 2015) and use type (Huang et al., 2013; Shi et al., 2012), with a few studies also taking the construction year into consideration (Han & Xiang, 2013; Huang et al., 2013; Shi et al., 2012). This method is executed by assigning equal weight to each feature.…”

“…However, it is often difficult to grasp a definite and comprehensive set of building information. So far, various sources such as GIS‐based data (Heeren et al., 2013; Kleemann et al., 2017; Miatto et al., 2019), remote sensing image (Haberl et al., 2021; Liang et al., 2017; Schandl et al., 2020), and conventional statistical data (Han & Xiang, 2013; D. Hu, You, et al., 2010; M. Hu, Pauliuk, et al., 2010) have been used as common ways to access physical inventory of buildings, but the ability of different tools to obtain the building typology information is varied and limited. For example, the GIS tool has been widely used due to its spatially explicit characteristics and it has a relatively greater potential to obtain different properties (Heeren et al., 2013; Kleemann et al., 2017; Tanikawa & Hashimoto, 2009).…”

Dynamic Assessment of Construction Materials in Urban Building Stocks: A Critical Review

“…Demand-driven models, used in six articles, ,,,,, seems especially well suited to capture temporal dynamics. Nevertheless, three ,, of these studies also include spatial dynamic input parameters, either for population or affluence .…”

Taking Stock of Built Environment Stock Studies: Progress and Prospects

“…Wakayama (City Center). Urban studies: Oslo (NO) 166 , Salford Quays Manchester (GB) , Vienna (AT) 84,85,167 , Frankfurt (DE) 3 , Esch-sur-Alzette (LU) 40 , Geneva (CH) 168 , Bruxelles (BE) 169 , Orléans (FR) 170 , Paris (FR) 171 , Kitakyushu (JP) 172 , Wakayama (JP) 48 , Taipei City (TW) 17 , Melbourne (AU) 39 , Jakarta (ID) 173 , Bandung (ID) 173 , Beijing (CN) [80][81][82] , Tianjing (CN) 80 , Shanghai (CN) 80 , Ezhou City (CN) 127 , Philadelphia (US) 174 , Rio de Janeiro (BR) 163 , and Chiclayo (PE) 15 . National studies: Austria 175 , Germany 5,77,114 , Switzerland 162,176 , Japan 28,72,78,94,125,153 , China 75,105,[177][178][179] , and USA 28,142 .…”

“…*2.6 km 2 in Philadelphia (University City); 11 km 2 in Manchester (Salford); 8 km 2 in Wakayama (City Center). Urban studies: Oslo (NO), Salford Quays Manchester (GB), Vienna (AT), ,, Frankfurt (DE), Esch-sur-Alzette (LU), Geneva (CH), Bruxelles (BE), Orléans (FR), Paris (FR), Kitakyushu (JP), Wakayama (JP), Taipei City (TW), Melbourne (AU), Jakarta (ID), Bandung (ID), Beijing (CN), − Tianjing (CN), Shanghai (CN), Ezhou City (CN), Philadelphia (US), Rio de Janeiro (BR), and Chiclayo (PE) . National studies: Austria, Germany, ,, Switzerland, , Japan, ,,,,, China, ,,− and United States. , …”

What matters most to the material intensity coefficient of buildings? Random forest‐based evidence from China

“…In many previous studies related to quantifying MS of Chinese buildings, MICs of archetype buildings are determined by the mean values (M. Hu, Pauliuk, et al., 2010; Huang et al., 2013; Shi et al., 2012), usually categorized based on structure (Guo et al., 2019; D. Hu, You, et al., 2010; Wang et al., 2015) and use type (Huang et al., 2013; Shi et al., 2012), with a few studies also taking the construction year into consideration (Han & Xiang, 2013; Huang et al., 2013; Shi et al., 2012). This method is executed by assigning equal weight to each feature.…”

“…However, it is often difficult to grasp a definite and comprehensive set of building information. So far, various sources such as GIS‐based data (Heeren et al., 2013; Kleemann et al., 2017; Miatto et al., 2019), remote sensing image (Haberl et al., 2021; Liang et al., 2017; Schandl et al., 2020), and conventional statistical data (Han & Xiang, 2013; D. Hu, You, et al., 2010; M. Hu, Pauliuk, et al., 2010) have been used as common ways to access physical inventory of buildings, but the ability of different tools to obtain the building typology information is varied and limited. For example, the GIS tool has been widely used due to its spatially explicit characteristics and it has a relatively greater potential to obtain different properties (Heeren et al., 2013; Kleemann et al., 2017; Tanikawa & Hashimoto, 2009).…”

Dynamic Assessment of Construction Materials in Urban Building Stocks: A Critical Review

“…Demand-driven models, used in six articles, ,,,,, seems especially well suited to capture temporal dynamics. Nevertheless, three ,, of these studies also include spatial dynamic input parameters, either for population or affluence .…”