High-resolution mapping and evolution of steel stocks and waste in civil buildings: A case study of Changsha, China

doi:10.21203/rs.3.rs-2497599/v1

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High-resolution mapping and evolution of steel stocks and waste in civil buildings: A case study of Changsha, China

https://doi.org/10.21203/rs.3.rs-2497599/v1

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published 22 Apr, 2023

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Systematic estimation of steel stocks and waste in urban areas and analysis of its historical evolution pattern is crucial for urban buildings steel recycling and environmental sustainability. However, it is a challenging task to collect big data from different sources and estimate accurately with high resolution. In this study, we proposed a novel hybrid approach (GMB model) to estimate building steel stocks and the annual waste rate through combining Geographic Information System, Material Flow Analysis, and Big Data Mining techniques. We estimated the civil-building steel stocks and amount of waste in Changsha urban area from 1985 to 2020 based on the GMB model, and analyzed the historical evolution pattern of steel stocks by using standard deviation ellipse and kernel density. The results showed that the cumulative steel stock in civil buildings grew from 0.66 million tons in 1985 to 8.26 million tons in 2020. The amount of waste increased by 2557 times. The spatiotemporal analysis showed variations in distribution of the steel stocks are mainly concentrated in the central city, indicating a "central-peripheral" distribution, with a southward trend in the standard deviation ellipse and a southeast-northwest direction in the center of gravity of the steel stocks. There is low-high and high-low spatial aggregation patterns. We also compared the experimental results with the observed data to determine the feasibility of the GMB model. Our study can promote the management of steel resources recycling and aid to achieve the green and low-carbon goals in sustainable development policies.

Urban minerals

Civil buildings

Steel stocks and waste

Data Mining

Spatial and temporal analysis

Weber distribution function

The steel industry is one of the basic industries in China’s economy. With rapid economic development, continued industrialization and urbanization, and expanding infrastructure, steel demand is gradually growing (Seok and Hwan 2015). Construction, a major steel-consuming industry, accounts for nearly half of the steel used and recycled (Zhou et al. 2022). According to the usage of buildings, they can be divided into industrial buildings, agricultural buildings, and civil buildings. Civil buildings are places where people live and carry out public activities. Since civil buildings are the major areas for steel consumption in China, as the lifetime of a building is over, it metabolically renewed and begins to enter the recycling period gradually. Assessing the amount of steel scrap in civil buildings is a major initiative to solve the urban resource and environmental problems caused by the massive consumption of steel resources (Bocca et al. 2023; Wang et al. 2015). Therefore, systematic estimation of steel stocks and waste in urban civil buildings provides a better understanding of historical steel evolution trends and has great significance for resource recycling and environmental management.

China’s steel industry has developed rapidly, with steel as the primary consumption area for construction projects. Because of its wide availability, lightweight, and high resistance, it was used in civil construction as early as 1976 and is now one of the preferred materials in the construction industry (Gopinath and Arulvel 2021; Wang et al. 2021; Zhang et al. 2019). According to the latest World Steel Statistics 2021, the global apparent steel consumption reached 1.772 billion tons in 2020, of which China accounts for 56.2%, ranking first in the world. Hunan, a major steel province in China, with an apparent crude steel consumption of 45,933,500 tons in 2020, has become the largest steel consumer in central China. As an important symbol of economic development, the iron and steel industry also puts massive pressure on resources and the environment (Song et al. 2021). Consequently, special attention must be paid to the steel recycling. The steel stocks estimation can provide comprehensive information for the recycling industry and sustainable development. It helps achieving a low carbon path of energy saving and emission reduction.

Studies on metal stock estimation can be traced back to as early as the 1930s (Wang et al. 2007). In recent years, there has been an increasing number of studies on measuring metal stocks, mainly using methods such as material flow analysis (MFA) and light remote sensing to estimate steel stocks. Liu et al. (2019) used a bottom-up material flow approach to estimate iron, copper, and aluminum inventories for 51 products and infrastructure in 10 megacities in China from 1980–2016. Pauliuk et al. (2013) used a top-down material flow approach with per capita stocks as an indicator to estimate the global stocks of steel in use in more than 200 countries. Song et al. (2020) applied dynamic material flow analysis to quantify provincial steel inventories in China from 1978 to 2050. The above studies focused on a large scale, such as the global or national level. They cannot finely estimate the steel stocks of civil buildings at the city level or even at a smaller scale, nor can they reveal their spatial distribution patterns. Zhong et al. (2022) used night-time light remote sensing to quantify the spatial and temporal evolution characteristics of steel stocks in countries along the so-called Belt and Road. They obtained potential hotspot areas for future steel resource flows. Yu et al. (2020) revealed the global accumulation of human resources from 1992 to 2008 at the 1 km resolution level based on nighttime light remote sensing, which effectively supports resource management and environmental protection. However, light remote sensing images have some limitations in capturing information about buildings where there is no light. Therefore, it underestimates the actual steel stock values. Therefore, it is necessary to establish new models and methods to estimate steel stocks and analyze their evolution patterns effectively.

In response to the mentioned gaps, we propose a new method to estimate the steel stock of civil buildings in urban areas of Changsha by using the GMB model. Moreover, we propose a high-resolution map of steel stock for 35 years by utilizing GIS technology and "bottom-up" technique. Using the urban area of Changsha as the study case, the amount of steel waste in civil buildings was estimated by the life cycle function. Spatial autocorrelation, standard deviation ellipse, and kernel density methods are used to explore the evolution pattern of steel stocks from civil buildings in six urban areas of Changsha since 1985. More specifically, we aim to address the following.

(1) The stocks of civil buildings steel in Changsha from 1985–2020.

(2) The waste of steel in Changsha from 1985–2020.

(3) The spatiotemporal distribution and evolution of the civil- building steel stocks in Changsha from 1985–2020.

Combining GIS (Geographic Information System), MFA (Material flow analysis) and BDM (Big data mining), this study proposes a model (GMB) for estimating steel stock in civil buildings. Taking Changsha urban area as the research area, estimate the stock and waste of steel in civil buildings. The specific workflow of this study is shown in Fig. 1. Firstly, we classify buildings based on the point of interest (POI) data, and determine civil buildings to extract buildings attribute. Secondly, geographic data are processed by using vector transformation, spatial connection and overlay analysis. Then the cumulative stocks of civil buildings are estimated by combining the physical properties, and the amount of waste is estimated using the life cycle function. Finally, we analyze the characteristics of the spatial and temporal distribution of steel stocks and the dynamic evolution process of their spatial differences.

2.1. Study Area

The boundary of the GMB model is selected from the urban area of Changsha. Changsha urban (Furong, Tianxin, Wangcheng, Yuelu, Yuhua, and Kaifu) cover an area of 1909.86 km² (Statistics 2012). As an economically important city in China and the capital city of Hunan Province, Changsha has been experiencing rapid urbanization with the urban population growing from 1.12 million to 3.96 million, per capita GDP growing from 900 yuan to 144,649 yuan, the built-up area extending from 57 square kilometers to 409 square kilometers from 1985 to 2020. Given that steel buildings in Changsha mainly were built after 1985, this paper chose to estimate the annual stocks and waste of civil-building steel from 1985 to 2020.

2.2. Data source

The data acquired in this study can be classified into two categories, attribute data and spatial data. Data sources are shown in Table 1. For attribute data, we used Python to crawl the floor height, construction year, area, and neighborhood name of civil buildings on two major real estate agency websites (58. Com Inc, https://cs.58.com; Beke, https://cs.ke.com). For spatial data, geographic data of civil buildings and POI data of buildings were obtained from AutoNavi Company (https://www.amap.com), and AOI (Area of Interest) data were obtained according to the ID of POI. Based on the acquired building POI data, the buildings are classified, and data related to civil buildings are extracted from the classification results. Through this method, we obtained about 96% of the data related to civil buildings in the urban area of Changsha. Since another 4% of the data were missing, we supplemented them by nearest neighbor interpolation. However, using the above data solely is not enough to estimate the steel stocks of civil buildings, and the civil building area and steel intensity per unit area are also necessary. The steel intensity per unit area is obtained through field research and related literature review.

Table 1

List of data source
Date type	Data Name	Data sources	Data Properties	Data Formats	Period
Property Data	Steel intensity per unit area	Literature reading, general standards for architectural design, civil buildings’ design codes		XSLX	1949–2020
	physical properties	https://cs.58.com/ https://cs.ke.com/		XSLX	1985–2020
	Year of construction	https://cs.58.com/ https://cs.ke.com/		XSLX	1985–2020
Spatial Data	Civil buildings’ Geographical data	https://www.amap.com/	Vectors	SHP	1985–2020
	POI Data	https://www.amap.com/	Vectors	SHP	1985–2020
	AOI Data	Combine with the POI's ID to get it in AutoNavi Company	Vectors	SHP	1985–2020

2.3. Model’s framework for estimating steel stocks in civil buildings

2.3.1. Building classification based on POI

As the study area is comprised of various types of buildings, civil and non-civil, we extracted civil buildings through POI data, and applied GIS techniques. In total, there were 23 categories of civil buildings each of them has 267 subcategories. Among them, the first level classification includes automobile service, automobile sales, catering service, government agencies and social organizations; The subcategories make a more detailed classification based on the primary classification. For example, automobile services can be divided into gas stations, other energy stations, gas stations and automobile parts sales. In addition, based on POI dataset, civil buildings are identified by applying vector transformation, spatial connection and overlay analysis.

2.3.2. Deriving Building attribute information based on big data mining

Since the collected geographical data lacks the construction year, we have developed a new tool to collect and mine real estate information, POI/AOI information, extract neighborhood name (property name) and construction year and update them to building geographical data. The specific steps are as follows.

(1) Property data (for sale and sold) were collected from the largest real estate agent websites in China. A total of 101,350 records were collected, and after preprocessing, 92,245 records were suitable for further analysis. The collected information includes title, neighborhood name (property name), area, construction year, house type, address, the total number of floors, unit price, other descriptive information, etc.

(2) Call the AutoNavi Company API interface(URL: https://restapi.amap.com/v3/place/text?parameters) to collect POI based on the keyword of neighborhood name (property name). Parameters include keywords, POI types, city, etc. where keywords were assigned to the neighborhood name (property name), and POI types are assigned to business residential | buildings | commercial and residential buildings | shopping services | malls. The returned data tag information includes ID, name, type, latitude and longitude coordinates, address, administrative district, ParentID, etc.

(3) Extract AOI. Since in most cases, the civil buildings refer to a larger area, such as neighborhoods, shopping malls, etc. the AOI can express the range more precisely. AOI is a faceted entity containing multiple classes of point-of-interest POI(Liu et al. 2021). In this paper, the AOI is obtained by the ID of the POI, and the algorithm “JsonToDataTableForAOI” (string strJson) was developed to extract the shape field value from the returned data in JSON format. If AOI data is not available, POI is used instead. When collecting into the database, to avoid repeated collection and improve the query speed, each neighborhood is hash coded by coordinates. The GeoHash algorithm was used to convert the latitude and longitude data in two-dimensional space into a string, the string is used to indicate the area block where the latitude and longitude are located.

(4) Assigning information such as year of construction to buildings. Spatially connects building vector graphics with property data, and assigns attributes such as year of construction and neighborhood name (property name) to buildings. For those buildings without AOI, we calculated the centroids of the building footprints and matched them with the nearest residential/commercial POI data. Using this method, we obtained nearly 96% of the construction year information, and then the K-nearest neighbor algorithm (KNN) method was used to complete the remaining 4% of information. We used 2-fold cross-validation in the labeled data to calculate an overall accuracy of 92% for the building year information, which is sufficient to meet the minimum standard of 85% proposed by the U.S. Geological Survey(Anderson et al. 1976).

2.3.3. Dataset of steel intensity per unit area of civil buildings

The intensity of steel use per unit area of a civil building is an essential factor in estimating steel stocks. We compiled the civil building steel intensity dataset (CBSIs) by referring to the general standards for architectural design, the design specifications for civil buildings (China 2006), and relevant literature(Izard and Müller 2010; Kozawa and Tsukihashi 2010; Yokoi et al. 2018; Yu et al. 2020; Zhu et al. 2022). Considering the steel intensity in structure, buildings can be classified into two categories. The first class covers the buildings that were built between 1949 to 2005 in which the steel concentration was between 20–40 kg/m² and they were not tall buildings (maximum 6 floors). The second class includes those buildings that were built between 2005–2020 with the various number of floor and they were reinforced to resist against earthquake above 7 in Richter scale. Therefore, the still concentration is higher than those buildings in first class (40–75 kg/m²) (Table 2).

Table 2

Civil building steel intensity in different construction year and period division
Time	Floor	Unit steel strength(Kg/m²)
2006–2020	＜11	40
	＞=11	50
	＞=17	60
	＞=19	70
	＞=28	75
1991–2005		40
1979–1990		35
1958–1978		30
1949–1957		20

2.3.4. Estimation of steel stocks in civil buildings combined GIS and bottom-up material flow analysis

This research adopts the bottom-up material flow analysis method based on the knowledge of GIS techniques. First, load the geographic data of civil buildings in QGIS 3.16, and add attribute fields such as total area and steel stock to each building. Then, select the construction year and the number of floors as the connection fields, and connect the dataset of steel use intensity per unit area of civil buildings with the building geographic data by attributes, so that each building can get the steel use intensity per unit area. Finally, combined with the bottom-up material flow analysis, the steel stock of civil buildings in Changsha urban from 1985 to 2020 is estimated. The calculation formula is shown in Eq. (1).

$$S\left(n\right)={\sum }_{i,j}\left({PS}_{i,j}\left(n\right)\times CBSI\right)$$

Where: S$\left(n\right)$ is the estimated steel stocks in year (n); i is the year of construction; j is the j-th civil building; ${PS}_{i,j}\left(n\right)$ is the steel physical size (unit is m²) of the j-th civil building in year i; CBSI is the civil building steel strength per unit area built.

2.3.5. Steel waste in civil buildings based on weber distribution function

Since the life cycle of civil buildings follows the Weber distribution function(Vu et al. 2022), this study uses the Weber distribution function with different parameters to estimate steel waste in civil buildings. Therefore, the scrap steel in civil buildings should be the total stock of steel in completed civil buildings multiplied by the scrap rate of the year. The probability density distribution function of civil building life is shown in Eq. (2).

$$F\left(t\right)=1-exp\left[-{\left(\frac{t}{\alpha }\right)}^{\beta }\right]$$

Where: $F\left(t\right)$ is the life cycle distribution function (φ > 0) of civil buildings; t stands for year; α is the scale parameter (control the variation of the distribution function in magnitude); β is the shape parameter (control the variation of the shape of the distribution function).

Suppose the scrapping rate of civil buildings in year n is $\phi \left(n\right)$. Then${ \phi }^{{\prime }}\left(n\right)$ is the amount of change in the scrapping rate of civil buildings in year n from previous year $(\phi \left(n\right)$ to $\phi \left(n-1\right))$(Eq. (3)).

$${\phi }^{{\prime }}\left(n\right)=exp\left[-{\left(\frac{n-1}{\beta }\right)}^{\alpha }\right]-exp\left[-{\left(\frac{n}{\beta }\right)}^{\alpha }\right]$$

In summary, the amount of steel waste in civil buildings in year n can be obtained, which is expressed in Eq. (4).

$$C\left(n\right)=\sum _{t=0}^{n-1}{P}_{i}\left(t\right)\times {\phi }^{{\prime }}\left(n-t\right)\times CBSI$$

Where: $C\left(n\right)$ steel waste in civil buildings in the n-th year; $P\left(t\right)$is the civil building area of t in a certain year; $CBSI$is the steel intensity per unit area of civil buildings.

2.4. Analysis of the spatial and temporal distribution of steel stocks in civil buildings

Based on estimating the steel stock of civil buildings, we analyzed the spatial and temporal distribution pattern of the steel stock. Spatial autocorrelation, standard deviation ellipse, and nuclear density analysis were used to explore the aggregation characteristics and the center of gravity of steel stock. In Changsha. Moreover, the evolution trend of the density distribution of civil buildings was investigated.

2.4.1. Spatial autocorrelation analysis

Spatial autocorrelation refers to the degree of similarity between nearby observations (values). Tobler (1970) pointed out the "First Law of Geography: Everything is related to everything else, but things that are near are more related than things that are far away". The current spatial autocorrelation methods are classified into two categories: global spatial autocorrelation and local spatial autocorrelation(Chen et al. 2022). Among them, global spatial autocorrelation can be used to discuss the overall distribution and trend of an attribute. In this study, Moran's I is used to analyze the steel aggregation characteristics of civil buildings in urban areas of Changsha (Eq. (5)).

$$I=\frac{\sum _{i=1}^{n}\sum _{j=1}^{n}{w}_{ij}\left({x}_{i}-x̄\right)\left({x}_{j}-x̄\right)}{{S}^{2}\sum _{i=1}^{n}\sum _{j=1}^{n}{w}_{ij}}$$

Where: -1 ≤ I ≤ 1 is the global Moran index, n is the total number of geographic units in the study area, i and j denote the i-th and j-th geographic units, ${w}_{ij}$is the element value of the spatial weight matrix, ${x}_{i}$and ${x}_{j}$ are the attribute values of units i and j, x̄ is the average value of all unit attribute values. I > 0 is a spatial positive correlation, and it shows clustering of similar values; I < 0 is a spatial negative correlation that indicates dispersion; I = 0 shows a random spatial distribution.

Since global spatial autocorrelation applies to the entire study area, spatial autocorrelation on a local scale cannot be detected. Therefore, we chose local spatial autocorrelation to evaluate the correlation between an attribute of the steel stocks at a local level and a neighboring one. The calculation formula is as follows Eq. (6).

$${I}_{i}=\frac{{x}_{\text{i}}-\text{x}̄}{{\text{S}}^{2}}\sum _{\text{j}=1}^{\text{n}}{\text{w}}_{\text{i}\text{j}}({x}_{j}-\text{x}̄)$$

Where: ${I}_{i}$ is the local Moran index of each study unit, i and j denote the i-th and j-th geographical units, ${w}_{ij}$ is the elementary value of the spatial weight matrix, ${x}_{i}$ and ${x}_{j}$ are the attribute values of units i and j, and x̄ is the average of all unit attribute values, S is the number of regions.

2.4.2. Standard deviation ellipse

Standard Deviational Ellipse (SDE) is an effective analytical method proposed by Lefever in 1926 to measure spatial differences and analyze the spatial distribution characteristics of economic factors(Lefever 1926). The creation of standard deviation ellipses enables the aggregation of distribution characteristics such as central tendency, dispersion, and the directional tendency of geographic elements(Fan et al. 2021). The geographic midpoint or center of gravity is the geographic average position of all points based on each point’s weight (Eq. (7)).

$$M\left(\stackrel{-}{X,}\stackrel{-}{Y}\right)=\left[\begin{array}{cc}\frac{\sum _{i=1}^{n}{w}_{i}{x}_{i}}{\sum _{i=1}^{n}{w}_{i}} ,& \frac{\sum _{i=1}^{n}{w}_{i}{y}_{i}}{\sum _{i=1}^{n}{w}_{i}}\end{array}\right]$$

Where: $M\left(\stackrel{-}{X,}\stackrel{-}{Y}\right)$ denotes the center of gravity of the regions(points); n is the number of regions; ${w}_{i}$ is the weight; (${x}_{i}$,${y}_{i}$)denotes the coordinate of i-th element (i-th building).

The rotation angle θ is the angle formed by the clockwise rotation of the positive north direction to the long axis of the ellipse, which indicates the direction of the main trend of the spatial distribution of the elements (Eq. (8)).

$\text{tan}\theta =\frac{A+B}{C}$

(8)

$A=\sum _{i=1}^{n}\tilde{{x}_{i}^{2}}-\sum _{i=1}^{n}\tilde{{y}_{i}^{2}}$

(9)

$B=\sqrt{{\left(\sum _{i=1}^{n}\tilde{{x}_{i}^{2}}-\sum _{i=1}^{n}\tilde{{y}_{i}^{2}}\right)}^{2}+4{\left(\sum _{i=1}^{n}\tilde{{x}_{i}^{2}}\tilde{{y}_{i}^{2}}\right)}^{2}}$

(10)

$C=2\sum _{i=1}^{n}\tilde{{x}_{i}}\tilde{{y}_{i}}$

(11)

where $\tilde{{x}_{i}}$ and $\tilde{{y}_{i}}$ are the deviations of the i-th subset of regional coordinates from the center of gravity, and A, B, and C denote the long semi-axis, short semi-axis, focal point coordinates of the ellipse when the center of the ellipse is the origin. The long and short semi-axes reflect the dispersion of the elements in the primary and secondary directions, respectively. We use the standard deviation ellipse to analyze the spatial distribution of iron and steel stocks in Changsha and the center of gravity migration trend.

2.4.3. Nuclear density analysis

Kernel density analysis (KDA) is one of the first nonparametric tests to estimate unknown density functions in probability theory(Zhang and Wang 2021). It is an expression of aggregation intensity describing the spatial relationship between points and neighboring points about spatial density. By setting each known data point as the center, the kernel density function can calculate the density contribution value of each known point in each cell within the specified range, which can visually reflect the distribution characteristics of discrete values in the region (Li et al. 2021; Zhang et al. 2022). Calculating each element point in the area and analyzing the superposition of the contribution density at the same location, the final distribution density of the element in the whole area is obtained Eq. (12).

$$fn\left(x\right)=\frac{1}{nh}\sum _{i=1}^{n}k\left(\frac{x-{x}_{i}}{h}\right)$$

Where $fn\left(x\right)$ is the kernel density function; $k\frac{x-{x}_{i}}{\left(h\right)}$ is the kernel function; n is the number of known points; h is the finding radius; $x-{x}_{i}$is the distance from the raster centroid to the known point.

3.1. Historical evolution of steel stocks and waste in civil buildings

With continuous industrialization and urbanization, the stocks and waste of steel in urban areas of Changsha show a rising trend annually. The steel stocks increasing from 0.663 million tons in 1985 to 8.260 million tons in 2020, an increase of 1,246 times. The total stocks of steel, per capita stocks of steel, and amount of waste of civil buildings in urban areas of Changsha increased 1246 times, 359 times, and 2557 times, respectively during 35 years The per capita steel stock increased from 0.0059 tons/person in 1985 to 2.12 tons/person in 2020 (Fig. 2). Since the reform and opening-up, the steel stocks of civil buildings in Changsha have continuously grown. According to the changes in the growth rate of the steel stocks of civil buildings in Changsha, we have divided the steel stocks in urban areas of Changsha into three stages since 1985.

Phase I: Rapid growth period (1985–2001), the steel stocks grew at a rate of 99.8% from 1985 to 2001. The average annual growth rate of steel stocks was 172,700 tons/year, and the amount of waste reached 1,600 tons/year. In 1985, the total steel stocks of civil buildings in urban areas of Changsha were less than 10,000 tons, and the per capita steel stocks were less than 0.01 tons per person. With the reform and opening-up policy and the rapid development of China's economy, the steel stocks of civil buildings in urban areas of Changsha reached 2.91 million tons in 2000, and the per capita steel stocks climbed to 1.6 tons per person.

Phase Ⅱ: stable growth period (2002–2015), the growth rate reached 50.6%. The average annual growth rate of steel stocks was 237,500 tons/year, and the average annual growth rate of waste was 0.5 million tons/year. With the rapid development of urban areas in Changsha during 2002–2015 and the increasing level of urbanization, the steel stocks of civil buildings in urban areas of Changsha grew by 3,087,700 t, and the per capita steel stocks grew to 1.9 t/person. The waste volume increased by 65,900 tons, with a growth rate of 54.8%.

Phase Ⅲ: Slow growth period (2016–2020), with an overall growth rate of 19%. The total steel stock increased by 1,621,500 tons, with an average annual growth rate of 405,400 tons/year, and an average annual growth rate of 4,300 tons of waste. Subject to national policies and the overall impact of the property market economy, the total steel stocks in urban areas of Changsha in 2016 were 6.6390 million tons. The total steel stocks in 2020 will be 8.26 million tons, the growth rate of waste will be 14%, and the per capita steel stocks will increase from 2 tons/person to 2.12 tons/person.

3.2. Historical dynamics of the spatial and temporal distribution of steel stocks in civil buildings

3.2.1. Spatial and temporal changes of steel stocks and waste

To reflect the evolution process of the steel stock of civil buildings more clearly in the urban area of Changsha in the past 35 years, we explored the overall spatial correlation of steel stocks through global spatial autocorrelation analysis. In this paper, we used GeoDa 6.0 software for spatial autocorrelation analysis and visualized the results in QGIS 3.16. The Moran's I changed significantly, from − 0.205 in 1995 to 0.445 in 2005, and then declined, 0.083 and − 0.340 in 2015 and 2020 respectively. According to the change characteristics of civil buildings in Changsha, data sets with different year intervals are selected to present the results. The results show a "central-peripheral" distribution, and the structure is distributed from east to west, and from the center to the south, the central area is dense, and the periphery is sparse. Changsha has distribution characteristics like some other developed cities where buildings with high steel stocks are distributed in the central areas of the city (Guo et al. 2019; Song et al. 2019).

We chose four years, 1995, 2005, 2015, and 2020 (Fig. 3), to reflect the distribution pattern of steel stocks in urban areas of Changsha. The distribution of civil buildings changed significantly from 1995 to 2020, experiencing significant expansion, with the spatial expansion and the amount of steel growth in the central city significantly higher than in the peripheral areas. From Fig. 3 we can see that the civil buildings in Changsha increased dramatically from 1995 to 2005, in which the steel stock also increased significantly. However, the number of buildings and steel stocks increased less from 2015 to 2020, may due to the lack of space. As the core commercial areas of Changsha are in Furong and Yuhua districts, the Furong district is densely populated with civil buildings and high steel stocks. The "central point" (112.97°E, 28.19°N) around the Xiangjiang River drives the development of the surrounding areas in a diffuse manner, creating a strong impetus for sustainable development.

3.2.2. Local spatial autocorrelation of steel stock

The global spatial autocorrelation cannot analyze the characteristics of the spatial distribution of steel stocks in each region. Therefore, local spatial autocorrelation analysis was carried out, and the LISA aggregation diagram was used. The steel stocks in each region were divided into four spatial patterns: high-high, high-low, low-high and low-low. Yuelu and Furong districts showed the spatial pattern of low-high and high-low in 1995, respectively. Their steel stocks were higher than that of other districts where no hotspot areas appeared in the study area (Fig. 4(a)). Diffusion of the low-high region occurred northward in 2005, spreading from Yuelu District to Wangcheng District, and the high-low aggregation region shifted southward, with Tianxin District and Yuhua District expanding into the high-low aggregation pattern (Fig. 4(b)). In 2015, the distribution of low-high aggregation occurred in the civil-building steel stocks shifted again to Yuelu District, and high-low aggregation to Yuhua District (Fig. 4(c)). In 2020, it showed an overall contraction trend, with only Tianxin District showing a low-high aggregation pattern (Fig. 4(d)). In summary, there is no positive aggregation pattern of civil-buildings steel in urban areas of Changsha from 1995 to 2020. Only low-high and high-low aggregation distribution patterns appear.

3.3. Dynamic evolution analysis of spatial difference of steel stocks in civil buildings

3.3.1. Spatial distribution characteristics and evolution process

The standard deviation ellipse of steel stocks in urban areas of Changsha from 1985 to 2020 is centered on the center of gravity, where the center of gravity was mainly distributed at the junction of Kaifu and Yuelu districts in 1995 (Fig. 5). Its main distribution was at the junction of Furong, Tianxin, and Yuhua districts in 2005. The standard deviation ellipse varied considerably during the study period. The overall coverage area was west of Furong District, north of Tianxin and Yuhua Districts, and the junction location of Kaifu and Yuelu Districts, which covered the central area of Changsha. During the period 1995–2020, the overall trend of contraction and then expansion of the steel stock in the urban area of Changsha, the distribution of the center of gravity at each characteristic time point shifted from north to south. The center of gravity of the steel stocks shifted from 1995 to 2005, and the distribution shifted southward, which may be related to the southward shift of Changsha's economic center of gravity. However, the center of gravity from 2005 to 2020 is located at the "center point" around the Xiangjiang River, showing a tendency to spread outward from the center point.

From the rotation angle θ, the spatial pattern of steel stock has a northwest-southeast direction. This is due to the distribution of civil buildings in urban areas of Changsha and the convenience of transportation that bring more opportunities for the development of urban steel buildings. In addition, according to the location of the steel center of gravity, recycling centers can be set up.

3.3.2. Change of distribution density

The cluster analysis of the nuclear density of steel stocks from 1985 to 2020 shows that the steel stocks are mainly centered on the "central point", showing an outward expansion trend (Fig. 6). From 1995–2005, it began to spread outward, with Kaifu District south, Furong District west, and Yuhua District north as the main areas, with uneven overall development and mainly gathered in the central city. From 2005 to 2010, the area was dispersed, involving the central part of Furong District and the northern part of Yuhua District. From 2010 to 2020, it continued to expand and extend, spreading widely and driving the overall development of the southern and eastern parts of Changsha. However, for the whole study area, the coverage is not comprehensive enough. The western and northern parts are not even with other regions, resulting in a low overall living intensity of the city.

4.1. Robustness and limitations of the methods used in this paper

The existing research focuses on the accuracy of estimating steel stocks at the national and even global levels. This study develops a GMB-based model for urban civil building steel stocks estimation, it realizes the analysis of the spatial and temporal evolution of steel stocks from data acquisition to accurate estimation of steel stocks. Estimate the steel stock of civil buildings in six urban areas of Changsha. The results of the study found that the per capita steel stocks of civil buildings in urban areas of Changsha in 2020 are 2.12 tons per person, which is lower than the estimated steel stocks in Xiamen by Song et al. (2019), which is due to the variability of the study population. In addition, comparing the mega-cities Shenyang and Shenzhen(Liu et al., 2019), the per capita stocks in 2015 were 2.2 and 2.1 tons/person. Respectively, which is relatively consistent with our study. In addition, the reliability and accuracy of this study are further supported by the fact that the steel stock data we obtained utilizing questionnaires are less different from our findings.

As with most material flow analyses, our study involves uncertainties and limitations. Although our research covers civil buildings in six urban areas of Changsha, it is incomplete to use data on civil buildings in six urban regions in Changsha as a critique of the entire core area of Changsha. This has led to an underestimation of the total stock of civil buildings in Changsha, which can be improved by updating building data in the future. We selected information from a sample of 72,627 civil buildings newly constructed from 1985 to 2020. Although we obtained as much sample information as possible, the number and representativeness of the sample still lead to uncertainty in the calculation. Our subsequent study should include more help from building constructors and inject new data types to improve the experimental content. The sample size of real estate websites is limited; POI's accuracy affects the year information of buildings.

4.2. Policy implications for resource management and sustainable development

As the largest consumer of steel in China, the useful life of buildings is generally less than 50 years, and the overdevelopment of buildings seriously threatens the sustainable development of cities(Oluleye et al. 2022). According to the estimation of the steel stocks and the measurement of the waste volume in this study, the waste volume has increased as much as a thousand times over the decades. Among them, buildings waste in most cities in China accounts for more than half of the total waste(Guo et al. 2018). Therefore, given the currently identified spatial analysis that shows the inventory and location of urban steel, then decision makers can use our findings for steel recycling management in Changsha.

The estimation of urban civil buildings steel stocks and the calculation of the amount of waste are the drivers of urban resource exploration and its impact on society. The amount of urban civil buildings stocks can alert the steel industry to the problems faced by the industry and enable decision makers to understand how much resources may be available where to replace and recycle, avoid steel surplus, reasonably reduce capacity, eliminate outdated capacity, reasonably develop new capacity, improve industry yield(Uchehara et al. 2022; Xie et al. 2022).. it can also provide a reference for future research on urban-scale steel resources in newly developed cities, especially in China and other countries.

4.3. Opportunities and Challenges under the "Double Carbon" Policy

China has been the world's largest carbon emitter since 2009; China is committed to achieving the peak of its autonomous contribution by 2030. The construction industry, as a major contributor to carbon emissions, has a higher requirement for construction industry with the proposed "double carbon" target (Zhu et al. 2022). Therefore, it is important to estimate the amount of steel stocks and waste for civil buildings. More attention needs to be paid to the existing steel stocks and the future level of steel accumulation. The realization of "de-capacity" is the only way to achieve green and low-carbon development. Especially in the construction industry, there is a lot of waste of resources. After a certain life of housing construction, it is not effectively recycled, or even directly treated as garbage, which hinders the green and low-carbon development of resources such as steel.

Hunan Miluo Circular Economy Industrial Park was awarded as a typical green, low-carbon park with a "low-carbon industry and green infrastructure" in 2021. The processing capacity of recycled copper, aluminum and plastics in the park is 3.5 million tons, among them, the amount of waste steel processing exceeds more than 50% of that of civil buildings in Changsha. It provides necessary support for promoting the recycling of "urban minerals" and building a low-carbon industrial system. Based on the measurement of urban steel inventory, the path of resource recycling development is an important way to rationally optimize and integrate resources, achieve energy conservation and emission reduction, and achieve green low-carbon.

This paper proposes a new approach by establishing a GMB-based steel stocks estimation model framework for urban civil buildings, combining a life-cycle model of civil buildings and Spatio-temporal distribution characteristics analysis. Using urban civil buildings in Changsha as the research object, we assess the historical dynamics of steel stocks and waste volume and explore the pattern of spatial and temporal variation of steel stocks in civil buildings. The method of this study can effectively estimate the building steel stocks and waste, and provide a reference for other regions. The results show that the civil-building steel stocks in urban areas of Changsha grows to 6,639,000 tons in 2020, and steel waste will increase to 152,300 tons. However, the distribution of steel in urban areas of Changsha is not uniform, and the eastern parts showed a higher rate than those regions in the west. In addition, the steel stock in the standard deviation ellipse analysis also showed a spatial distribution in the northwest-southeast direction. Based on the distribution of the center of gravity location in the results, it is recommended to set the recovery center. This study can provide a better reference base for the steel industry and policymakers, showing the sustainable development of the urban steel resource utilization cycle.

To further improve the study, the investigation and analysis of the steel construction industry can be considered in future studies. We can continue to conduct systematic research on the provincial construction steel industry by considering the impact of steel stocks and waste on the circular green economy under different criteria of multiple construction industries.

Author contribution

All authors contributed to the study conception and design. Youliang Chen: Conceptualization, Methodology, Validation, Software. Qu Gong: Investigation, Formal Analysis, Writing-Original Draft Preparation. Yan Shi: Writing-Review & Editing. Min Deng: Supervision, Provision of study materials.

Code availability

The code of the model is available from the authors upon reasonable request.

Data availability

The data will be available upon request.

Funding

This work was sponsored by the Hunan Province High-tech Achievement Transformation and Industrialization Project (No.2020SK2007).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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High-resolution mapping and evolution of steel stocks and waste in civil buildings: A case study of Changsha, China

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Study Area

2.2. Data source

2.3. Model’s framework for estimating steel stocks in civil buildings

2.3.1. Building classification based on POI

2.3.2. Deriving Building attribute information based on big data mining

2.3.3. Dataset of steel intensity per unit area of civil buildings

2.3.4. Estimation of steel stocks in civil buildings combined GIS and bottom-up material flow analysis

2.3.5. Steel waste in civil buildings based on weber distribution function

2.4. Analysis of the spatial and temporal distribution of steel stocks in civil buildings

2.4.1. Spatial autocorrelation analysis

2.4.2. Standard deviation ellipse

2.4.3. Nuclear density analysis

3. Results

3.1. Historical evolution of steel stocks and waste in civil buildings

3.2. Historical dynamics of the spatial and temporal distribution of steel stocks in civil buildings

3.2.1. Spatial and temporal changes of steel stocks and waste

3.2.2. Local spatial autocorrelation of steel stock

3.3. Dynamic evolution analysis of spatial difference of steel stocks in civil buildings

3.3.1. Spatial distribution characteristics and evolution process

3.3.2. Change of distribution density

4. Discussion

4.1. Robustness and limitations of the methods used in this paper

4.2. Policy implications for resource management and sustainable development

4.3. Opportunities and Challenges under the "Double Carbon" Policy

5. Conclusions

Declarations

References

Status:

Journal Publication

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