Changing patterns and determinants of infrastructures’ material stocks in Chinese cities

Huang, Cheng; Han, Ji; Chen, Weiqiang

doi:10.1016/j.resconrec.2016.06.014

Cited by 83 publications

(78 citation statements)

References 39 publications

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“…For instance, it is 45% higher compared to the center of Wakayama, Japan and 58% higher compared to Manchester, UK (Tanikawa & Hashimoto, ). A large difference (up to 40 times higher) can be seen in comparison to the Chinese cities (Huang et al., ). However, the difference might be actually smaller as indicated by another study, which for Beijing estimated the road stock per capita to 6 t (Guo et al., ).…”

Section: Discussionmentioning

confidence: 96%

“…Similar values (13% lower) were shown for the city of Chiclayo in Peru, which, as of 2016, had residential stock estimated at 55 t per capita (Mesta et al., ). The three largest Chinese cities had two to three times lower residential stock per capita: 16 t for Beijing and 25 t for Tianjin (Huang et al., ). The level of economic development can explain the lower stock in these case studies.…”

Section: Discussionmentioning

confidence: 99%

“…The proportion of the stock among other infrastructure types is not as straightforward. For example, in the case of Japan, the road network makes the second largest contribution to the overall MS (Tanikawa et al., ); for the largest three cities in China, the pipes network is the second highest contributing infrastructure to MS (Huang et al., ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

Gontia

Thuvander

Ebrahimi

et al. 2019

J of Industrial Ecology

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A large share of construction material stock (MS) accumulates in urban built environments. To attain a more sustainable use of resources, knowledge about the spatial distribution of urban MS is needed. In this article, an innovative spatial analysis approach to urban MS is proposed. Within this scope, MS indicators are defined at neighborhood level and clustered with k‐mean algorithms. The MS is estimated bottom‐up with (a) material‐intensity coefficients and (b) spatial data for three built environment components: buildings, road transportation, and pipes, using seven material categories. The city of Gothenburg, Sweden is used as a case study. Moreover, being the first case study in Northern Europe, the results are explored through various aspects (material composition, age distribution, material density), and, finally, contrasted on a per capita basis with other studies worldwide. The stock is estimated at circa 84 million metric tons. Buildings account for 73% of the stock, road transport 26%, and pipes 1%. Mineral‐binding materials take the largest share of the stock, followed by aggregates, brick, asphalt, steel, and wood. Per capita, the MS is estimated at 153 metric tons; 62 metric tons are residential, which, in an international context, is a medium estimate. Denser neighborhoods with a mix of nonresidential and residential buildings have a lower proportion of MS in roads and pipes than low‐density single‐family residential neighborhoods. Furthermore, single‐family residential neighborhoods cluster in mixed‐age classes and show the largest content of wood. Multifamily buildings cluster in three distinct age classes, and each represent a specific material composition of brick, mineral binding, and steel. Future work should focus on megacities and contrasting multiple urban areas and, methodologically, should concentrate on algorithms, MS indicators, and spatial divisions of urban stock.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

Gontia

Thuvander

Ebrahimi

et al. 2019

J of Industrial Ecology

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“…A bottom-up material flow analysis model was employed for material stocks estimation. The details on model description, parameter settings, and data sources can be found in our previous studies [10,25]. Firstly, we adopted the standard Intergovernmental Panel on Climate Change (IPCC) guideline and local carbon emission coefficient to calculate China's provincial CO2 emissions from fossil fuel consumption [19].…”

Section: Materials Stocks Of Infrastructure and Co 2 Emissionsmentioning

confidence: 99%

Section: Materials Stocks Of Infrastructure and Co 2 Emissionsmentioning

confidence: 99%

The Impact of Infrastructure Stock Density on CO2 Emissions: Evidence from China Provinces

et al. 2017

Self Cite

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Abstract:Infrastructure not only plays an import role in socioeconomic development, but also results in a remarkable increase of CO 2 emissions. Based on a panel data analysis of 29 provinces in China from 1995 to 2013, we investigated the relationship between socioeconomic development and CO 2 emissions with special focus on the impact of infrastructure stock density in both the country and regional scales. The results confirmed that a 1% increase of material stocks in infrastructure per built-up area would lead to a 0.11% decrease in CO 2 emissions at the country level. The effect of infrastructure stock density on CO 2 emissions varied across regions, for which elasticity was −0.34, 0.06, and 0.14 for the eastern, central, and western region, respectively. In order to achieve a low-carbon and sustainable development, it is crucial to improve the spatial compactness of infrastructure in the future. Policy implications include upgrading the economic structure to a low-carbon one for the eastern region, accelerating the development of renewable energy infrastructure, and constructing and utilizing infrastructure in an energy-efficient way for the central and western regions.

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Analysis of Material Metabolic Process: Urban Weight

Zhang

2023

Urban Metabolism

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Changing patterns and determinants of infrastructures’ material stocks in Chinese cities

Cited by 83 publications

References 39 publications

Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

The Impact of Infrastructure Stock Density on CO2 Emissions: Evidence from China Provinces

Analysis of Material Metabolic Process: Urban Weight

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