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Mapping the evolution of building material stocks in three eastern coastal urban agglomerations of China

https://doi.org/10.1016/j.resconrec.2022.106651Get rights and content

Highlights

  • Applied nighttime lights to estimate material stock (MS) at urban micro-cell scale.

  • MS in China's three major urban agglomerations was 64,402 Tg in 2020.

  • MS increased significantly in each urban agglomeration with specific patterns.

  • Urban agglomeration exhibited three integration trends: global, partial and primary.

Abstract

The building material stock (MS) plays a vital role in managing and optimizing the urban built environment. This study proposed a novel approach for mapping the evolution of building MS in China's three eastern coastal urban agglomerations (Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Greater Bay Area (GBA)). We divided urban built-up areas into micro-cell units according to the spatial distribution characteristics of nighttime lights (NTL), to estimate the MS, and examine the evolutionary patterns of MS from 2000 to 2020, to reflect the internal urban agglomeration. The results highlighted the total MS in the three urban agglomerations increased from 9.3 billion to 64.4 billion tons, and the YRD had the largest MS scale and the highest growth rate. The spatial-temporal evolution of MS reflected the various development mode of each urban agglomeration. Finally, we provide the prospects in urban renewal and sustainable resource management using our present MS findings.

Introduction

Over the past two decades, China has experienced rapid and large-scale urbanization, resulting in surging flows in and from the urban system. China accounts for approximately 50% of the global annual growth of built-up areas, which contributes to an annual net increase of 1.5-2 billion m2, creating a vast stock of materials and significant life cycle environmental impacts behind the materials flows (Fernández, 2007). Material stocks (MS) play a pivotal role in transforming resource flow into services (Weisz et al., 2015). As such, MS have increasingly received attention in recent years with the collective push toward sustainability (Haberl et al., 2021). This is in part because of their environmental impacts, such as land demand (Bren d'Amour et al., 2017; Liu et al., 2020), construction and demolition waste generation (Mao et al., 2020), and greenhouse gas (GHG) emissions (Krausmann et al., 2020). Building materials, particularly steel, concrete, and brick, constitute a significant portion (about 95%) of building materials(Chen et al., 2016). Greenhouse gas emissions accompany the entire life cycle of these materials, from production to removal. In 2018, the annual embodied carbon of building materials in China was 1,757 Tg. Cement, steel, brick, lime, and linoleum accounted for 34.0%, 20.2%, 24.7%,13.4%, and 3.4% of the total embodied carbon, respectively (Chen et al., 2022).

In other words, MS plays a critical role in managing and optimizing the urban system more sustainably, which is critical to achieving urban sustainability. Notably, from the perspective of urban metabolism, understanding the spatial and temporal features of MS in cities could support better planning and management of urban agglomeration. Investigating the magnitude and trend of MS in urban agglomerations, and elaborating its features and relationship to urban patterns, is key to address sustainable resource management and forward the circular economy. China, which, with fast urbanization, thus very relevant to study in terms of MS. Though some existing studies have been performed in the Chinese context, the diversity in methodology, scope of the study, and data sources make the consolidation of this knowledge base difficult. More case studies are needed to draw strong conclusions.

Various methods have been developed to determine the quantity and scale of MS. Traditionally, building MS have been calculated from statistical data mainly using either top-down or bottom-up methods (B. Müller, 2006; Tanikawa et al., 2015). The top-down approach quantifies MS by calculating inflows and subtracting outflows from stocks over time (Wiedenhofer et al., 2019). This method relies on highly aggregated statistical data and is suitable for large-scale accounting over extended periods of time. However, the top-down method cannot reflect the spatial distribution of MS, and thus renders it unsuitable for high-resolution mapping. The spatial distribution characteristics of MS are important for formulating construction waste recycling paths and resource utilization strategies. Methods to map the spatial characteristics of MS generally employ a stock-driven bottom-up approach. This relies on a detailed inventory of items that include physical dimensions, such as the area and length of buildings and infrastructure. Subsequently, the mass of the material inventory may be calculated in combination with material intensity (Gontia et al., 2019; Lanau et al., 2019; Schandl et al., 2020). Four-dimensional geographic Information System (4D-GIS) data is a kind of data that can reflect the change in geographic information over time. It is increasingly used in MS research to clarify the spatio-temporal change of material accumulation (Tanikawa and Hashimoto, 2010). However, the applicability of these traditional methods is limited. MS calculation using conventional methods is often hindered by the lack of statistical data (Mao et al., 2020). Due to data availability and the complexity of data processing, MS analysis based on the 4D-GIS database is often limited to single cities within a specified period of time (Gontia et al., 2020).

Remotely sensed satellite data can compensate for such limitations by offering a broad spatial scope and relatively high (The commonly used NTL data has a spatial resolution of 500 × 500m) spatial-temporal resolutions. Nighttime light (NTL) remote sensing data has gradually been applied to the study of urban and socio-economic development because of its ability to observe spatial and temporal distribution. Researchers found that NTL often correlates well with several indicators of socioeconomic activity. These relationships provide new technologies for the spatial visualization of the social economy and have been successfully used by researchers in many urban social aspects, such as urban expansion (Shi et al., 2014; Yu et al., 2018), gross domestic product (Liu et al., 2021), poverty (Wang et al., 2012), population density (Sutton et al., 2010), and energy and carbon emissions (Ghosh et al., 2010; Min and Gaba, 2014). Many attempts have been made to estimate the amount of MS using NTL. Some researchers quantified the relationship between specific in-used MS (e.g., steel, copper) and NTL, and used this to estimate MS at the national, sub-national or city level (Hattori et al., 2014; Hsu et al., 2011; Hsu et al., 2012; Liang et al., 2017; Takahashi et al., 2010). However, the estimation models used in these studies were only applicable at larger scales than the city; as such, they cannot be used to characterize the structure and distribution of MS within cities. Understanding the distribution and development characteristics of intra-urban MS in eastern coastal urban agglomerations of China is important to formulate a reasonable urban agglomeration development strategy. This means the need to develop an effective estimation model for MS at the suburban scale based, for which remote sensing technology seems to provide a good data source.

To fill the above research gaps, this study proposes a method to divide built-up areas within cities into several micro-cells (also referred to as built-up area cells; BAC) based on the spatial distribution characteristics of NTL (Fig. 3). The minimum area of BAC divided by this method can reach 0.15km2, and detailed area figures of BAC are given in Table S1. These urban micro-cells were used to establish an MS estimation model at the urban micro-cell scale to reflect the internal urban MS. The model was subsequently applied to long-term NTL data to reveal the spatial characteristics of urban MS in eastern coastal urban agglomerations of China and explore the development of MS in different urban agglomerations. The proposed approach and analytical results are expected to provide guidance to improve and optimize urban resource management policies and offer critical and enlightening insights into the development of urban agglomerations and sustainable urban renewal in the rapid urbanizing economy.

The outline of this study is as follows: Section 2 describes the study area, data, and methods used in this study. Section 3 presents the performance of the MS estimation model, estimation results, and spatial-temporal variation of the MS. Section 4 discusses the application of this study and its differences with other related studies, while section 5 finishes with a series of summaries.

Section snippets

Study area

The study area included the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), Yangtze River Delta (YRD), and Beijing-Tianjin-Hebei (BTH) urban agglomerations; these are the most modern and economically dynamic regions experiencing the highest levels of urbanization in China (Fig. 1). Table 1 details the economic and urbanization indicators for these urban agglomerations; these indicators were obtained from the China Statistical Yearbook (NSBC, 2020). Each urban agglomeration has its unique

Building volume estimation model performance

The building volume estimation models for the five types of BACs are shown in Fig. 5; overall, these five models showed good performance, with R2 values >0.65. Fig. 6 presents the validation results for the models. The building volume data for Shenzhen, Suzhou, and Tianjin were used as validation data. The observed building volumes in each BAC of these three cities were compared with the value calculated using the model established in the Building Volume Estimation section to verify the

Comparison with former study

The findings from this study were compared with the spatially explicit MS analysis of buildings in metropolises of eastern China published by Guo et al. (2019). Guo et al. (2019) used the latest GIS dataset of buildings in 14 representative eastern Chinese metropolises to quantify the current status of MS by employing the bottom-up method. Notably, this study covered a broader area of research compared to Guo et al. (2019). The bottom-up MS estimation method based on building base data adopted

Conclusions

In the context of urban sustainability and global climate change, there is an increasing need for spatially explicit characterization of existing building material stocks to analyze the prospective dynamics of stocks and flows and implement a circular economy. This study utilized the MS accounting method in which building volume was introduced as an intermediary between NTL and MS. The model described the relationship between the NTL (representing the intensity of human activity) and building

Funding

The authors acknowledge the support of the National Natural Science Foundation of China [grant number 42001240], the National Key Research and Development Program of China [grant number 2019YFC1510203], Funds for International Cooperation and Exchange of the National Natural Science Foundation of China [grant number 42161144003], and College Students Practice and Innovation Training Program in Jiangsu province, China [grant number 202210300080Y]. The corresponding author (L.D.) also thanks the

Ethics approval

This paper is not submitted to more than one journal for simultaneous consideration.

This paper is original and not have been published elsewhere in any form or language. Re-use of material (one figure) is clearly marked with reference.

This paper doesn't involve the single study split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time.

This paper doesn't involve the concurrent or secondary publication.

The results of this

Availability of data and material

Not applicable.

Code availability

Not applicable.

Consent to participate

Not applicable.

CRediT authorship contribution statement

Hanwei Liang: Conceptualization, Supervision, Writing – original draft. Xin Bian: Methodology, Data curation, Visualization, Writing – original draft. Liang Dong: Resources, Writing – review & editing. Wenrui Shen: Software, Validation. Sophia Shuang Chen: Funding acquisition. Qian Wang: Project administration.

Declaration of Competing Interest

The authors declare no conflict of interest.

Acknowledgments

We are grateful for the support of Zuoqi Chen, Bailang Yu, and colleagues at Fuzhou University and East China Normal University for providing the extended time series (2000–2020) nighttime light data, and Jing Guo at Nagoya University for providing the comparative data. We also thank Renzhe Pan in Chang Wang School of Honors (NUIST) for his contribution to data collection and processing.

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