Construction and demolition wastes in Beijing: Where they come from and where they go?

Chen, Chong; Liu, Yupeng

doi:10.1177/0734242x20980819

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…Building information modelling, for example, can provide information about a building from the design to the demolition stage, contributing to the prediction of project-level CDW (Won and Cheng, 2017). In addition, geographic information systems (GISs) are often adopted to predict regional CDW quantity according to building stock, which is considered to be innovative when estimating the CDW output (Chen and Liu, 2021; Rašković et al, 2020; Wu et al, 2016a), and conduct real-time monitoring of the demolition activities on a spatial and temporal basis (Kleemann et al, 2017). Moreover, CDW online prediction tools and simulation tools have also been developed to help predict CDW generation (Gómez Soberón et al, 2016; WRAP, 2009).…”

Section: Context Of the Researchmentioning

confidence: 99%

Efficient supervision strategy for illegal dumping of construction and demolition waste: A networked game theory decision-making model

2021

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While the construction industry has brought substantial economic benefits to society, it has also generated substantial construction and demolition waste (CDW). Illegal dumping, which refers to dumping CDW in an unauthorized non-filling location, has become widespread in many countries and regions. Illegally dumping CDW destroys the environment, causing groundwater pollution and forest fires and causing significant economic impacts. However, there is a lack of research on the decision-making behaviours and logical rules of the main participants, construction contractors and the government in the illegal CDW dumping process. This paper constructs an evolutionary game model on a small-world network considering government supervision to portray the decision-making behaviours of illegal dumping participants and conducts a numerical simulation based on empirical equations to propose an effective supervision strategy for the government to manage illegal CDW dumping efficiently. It is found that the illegal dumping behaviours of contractors are mainly affected by the intensity of government supervision, the cost of fines and the income of illegal dumping; while for government, a supervision strategy is found to be necessary, and a supervision intensity of approximately 0.7 is the optimal supervision probability given supervision efficiency. Notably, under a low-level supervision probability, increasing the penalty alone does not curb illegal dumping, and a certain degree of supervision must be maintained. The results show that in addition to setting fines for illegal dumping, the government must enforce a certain level of supervision and purify the market environment to steadily reduce illegal dumping.

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Section: Context Of the Researchmentioning

confidence: 99%

Efficient supervision strategy for illegal dumping of construction and demolition waste: A networked game theory decision-making model

2021

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“…At the end of service lives, spatiotemporal maps of material outputs provide hotspots of CDW generation and can thus help to design the effective planning of reuse, recycle, and landfill facilities. It can also help to optimize transportation systems according to the trade‐off between transportation cost and treatment subsidy and this optimization could change the current territory‐based and self‐sufficient scheme (PGBM, 2020) to a cross‐regional one (Chen & Liu, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…We also collected total amount of CDW generation during 2012-2018 in Xiamen from XCB. We excluded the proportion of excavated soil generated from building construction (Chen & Liu, 2021) and on the mainland part of Xiamen (because we only selected the Xiamen Island, the downtown area of Xiamen, as the study area) from the statistic CDW data and finally used the rest of the values to compare with the output flow simulated by our model.…”

Section: Study Area and Data Compilationmentioning

confidence: 99%

Quantifying urban mass gain and loss by a GIS‐based material stocks and flows analysis

Liu

Chen

et al. 2022

J of Industrial Ecology

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Rapid urbanization generates substantial demand, use, and demolition waste of construction materials. However, the existing top-down or bottom-up frameworks combining material flow analysis (MFA) and geographic information system (GIS) tend to underestimate both input and output of construction material flows due to insufficient descriptions of key processes in building construction and demolition. To address this limitation, this study identifies four important and complementary processesconstruction, demolition, replacement, and maintenance, and integrates them into an improved framework to capture all material flows. We take Xiamen, a rapidly urbanizing city, as a case study to verify this framework. The results show that ∼40% of material inputs and ∼65% of outputs are underestimated by previous frameworks because they fail to capture material inputs in building maintenance and outputs in construction. These findings indicate a better estimation of such key flows in the modeling framework helps to accurately characterize building material metabolism. Based on systematic counting of material stocks and flows, the improved framework can help design effective policies for urban resource management by explicitly recognizing the spatiotemporal patterns and processes of material metabolism. K E Y W O R D Sgeographic information systems (GIS), high-resolution urban grids (HUGs), industrial ecology, material flow analysis, urbanization INTRODUCTIONCities are continually resulting in the exchange of materials, water, and energy between natural and artificial environments (Chini & Stillwell, 2019;Haberl et al., 2019). When the material inputs of a city exceed its outputs, an imbalance occurs and contributes to the city's mass gain and vice versa (Tanikawa & Hashimoto, 2009;Tanikawa et al., 2015). Many studies have been conducted to quantify urban material inputs and outputs dynamically to understand how a city's mass has changed over time (

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“…The high-resolution maps of in-use material stocks could provide a benchmark of potential amounts of reusing or recycling materials. Meanwhile, the spatial hotspots of CDW generation indicate key areas for planning local recycling facilities or logistic centers according to the trade-off between the cost of transportation and subsidy of treatment (Tong et al, 2018;Chen and Liu, 2021;Liu et al, 2022b). Therefore, both the spatial features of material stock and potential CDW generation could further help to design regional transportation and treatment facilities and optimize the waste management system for Shenzhen.…”

Section: Implications Of Spatio-temporal Dynamics Of Materials Metabo...mentioning

confidence: 99%

“…Previous studies have illustrated the relationship between the living space per capita and personal income (or GDP per capita) (Olaya et al, 2017), which would be of great help to forecast building volume in the future. Combining with the scenarios of land use/cover changes, especially for the built-up area, above predictions of building volume could be allocated in space and used to predict the birth and decay of buildings in a city (Chen and Liu, 2021). Characterization of the these dynamics of buildings and materials can provide insightful information that may better support and design effective policies for CDW management.…”

Section: Uncertainties and Outlookmentioning

confidence: 99%

Quantifying spatiotemporal dynamics of urban building and material metabolism by combining a random forest model and GIS-based material flow analysis

et al. 2022

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Understanding building metabolism is critical for guiding urban resource management and addressing challenges in urban sustainable development. Key attributes of buildings, including geolocation, footprint, height, and vintage, are crucial to characterizing spatiotemporal patterns of building metabolism. However, these attributes are usually challenging to obtain broadly and automatically, which obscures a comprehensive understanding and accurate assessment of urban metabolism. Moreover, the lack of a finer spatial pattern of these attributes shadows a spatially explicit characterization of material stock and flow in cities. In this study, we took Shenzhen—whose urbanization over the past three decades has been unprecedented in China and even around the world— has been taken as an example to develop a city-level building dataset based on a random-forest model and quantify the spatiotemporal patterns of material metabolism at relatively high spatial resolution (in 500 m × 500 m grids) by combing material flow analysis (MFA) with geographic information system (GIS). The results show that Shenzhen grew from a small town with 281.02 × 106 m3 of buildings in the 1990s to a mega-city with 3585.5 × 106 m3 of buildings in 2018 and expanded both outward and upward from downtown to suburban areas. The urban “weight” (material stock) increased from 92.69 Mt in the 1990s to 1667.8 Mt in 2018 and tended to be saturated, with an average growth rate of 9.5% per year. Spatially, the south-central areas were the largest container of material stocks and generated the most demolition waste. The spatially explicit maps of building three-dimensional (3-D) form and vintage provide detailed information for architectural conservation and could support the decision-making for urban renewal planning. The spatiotemporal patterns of in-use material stocks and potential generation of construction and demolition waste (CDW) provide a benchmark of environmental risk assessment and potential secondary resources to reduce “original” material consumption, which could help alter urban renewal to an environmental-friendly and sustainable trajectory.

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Construction and demolition wastes in Beijing: Where they come from and where they go?

Cited by 14 publications

References 29 publications

Efficient supervision strategy for illegal dumping of construction and demolition waste: A networked game theory decision-making model

Efficient supervision strategy for illegal dumping of construction and demolition waste: A networked game theory decision-making model

Quantifying urban mass gain and loss by a GIS‐based material stocks and flows analysis

Quantifying spatiotemporal dynamics of urban building and material metabolism by combining a random forest model and GIS-based material flow analysis

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