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Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data

  • Junchang Huang , Shuaijun Yue , Guangxing Ji EMAIL logo , Mingyue Cheng , Hengyun Ma EMAIL logo and Xuanke Hua
Published/Copyright: July 10, 2023
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Open Geosciences
From the journal Open Geosciences Volume 15 Issue 1

Abstract

Iron is one of the most important basic materials in infrastructure development, spatial and temporal variation characteristics analysis of infrastructure iron stocks is conducive to revealing its distribution and change patterns from different scales, which can provide a scientific basis for sustainable urban development and iron resource management in China. In this article, we first calculated provincial infrastructure iron stock data from 2000 to 2020. Then, fitting equations between nighttime lighting data and infrastructure iron stock are constructed to simulate the spatial distribution of China’s infrastructure iron stock at 500 m resolution from 2000 to 2020. Finally, the spatial and temporal dynamics of China’s infrastructure iron stock is analyzed from four scales: national, regional, provincial, and urban agglomeration. The results show as follows: (1) China’s infrastructure iron stock grew at an average annual rate of 26.42% from 2000 to 2020, with China’s infrastructure iron stock increasing 6.28 times over the 21 years. Construction facilities are the most important part of the infrastructure iron stock, and its share is still increasing. (2) On a regional scale, the high-growth type of infrastructure iron stock is mainly distributed in the eastern region, while the no-obvious-growth type is mainly distributed in the western region. The high grade of infrastructure iron stock is mainly distributed in the eastern region, while the low grade is mainly distributed in the western region. (3) On a provincial scale, the highest share of no-obvious-growth type of infrastructure iron stock is in Xinjiang. The highest proportion of infrastructure iron stock of high-growth type is in Jiangsu. The highest proportion of low-grade infrastructure iron stock is in Xinjiang. The highest proportion of infrastructure iron stock of high grade is in Beijing. (4) In terms of urban agglomerations, the high-growth type of infrastructure iron stock is mainly located in Shanghai–Nanjing–Hangzhou, while the no-obvious-growth type is mainly located in the Middle south of Liaoning. The high-grade infrastructure iron stock is mainly distributed in Shanghai–Nanjing–Hangzhou, while the low grade is mainly distributed in Sichuan–Chongqing.

Keywords: night lighting; iron stock; China; infrastructure

1 Introduction

Iron is a fundamental resource for national economic development and is widely used in industrial production and people’s lives [1,2]. To satisfy the needs of a growing population for housing and public facilities, the development of urban infrastructure has become an important part of urban construction and management. Urban expansion consumes a large amount of iron in the form of housing buildings, municipal roads and rail transportation, and other infrastructure. Iron accumulates in this physical form in the socio-economic system and is a potential source of future iron resources [3].

Han et al. measured the iron stock in China’s buildings with dynamic material flow analysis and obtained that the per capita stock has not yet reached the average level of developed countries and future social iron stock will further increase [4,5]. Yang studied the composition of the iron stock of urban buildings and transport infrastructure and found that bridges are the main contributor to the steel stock of roads [6]. Liu et al. used a bottom-up approach to estimate the growth rate of Chongqing’s steel stock from 1985 to 2014, and the results of the study showed that the total steel stock and the per capita stock in the city increased 10-fold and 12-fold, respectively, over the past 30 years, and that economic development and population growth were the main driving factors for the growth of the steel stock in the city [7]. Li et al. used material flow dynamic analysis to account for China’s iron stock since the founding of the country and concluded that among all iron stocks, the iron stock of construction steel was the most dominant and grew at the fastest rate [8]. Wang et al. used dynamic material flow analysis to study the pattern of artificial sedimentation movement of steel at the national level in China during the evolution of industrialization from 1949 to 2012 and concluded that the sedimentation movement of steel was highly correlated with the stage of China’s national industrialization process, with the largest stock of steel accumulated in buildings, followed by mechanical equipment and then transportation equipment [9]. Yue et al. used the average service life method and fixed asset depreciation method to analyze the utilization efficiency of China’s iron stock. The study found that the utilization efficiency of iron showed a slow upward trend during the period 1993–2002, and a continuous downward trend after 2002 [10]. Pauliuk et al. estimated the iron stock of 200 countries and found that in countries with a long industrial history, such as the United States, the United Kingdom, or Germany, the iron stock accumulation is slowing or has stagnated. In countries with a short industrial history, such as South Korea or Portugal, iron stock growth is rapid [11].

Nighttime light remote sensing data can detect light brightness information at the surface and can be used to study population [12], GDP [13,14,15], economy [16,17,18,19,20], city size [21,22,23,24], carbon emissions [25,26,27], anthropogenic heat fluxes [28], energy [29,30,31], pollutants [32,33], etc. In terms of nighttime light modeling of material stocks, Hsu et al. used DMSP-OLS nighttime light remote sensing data to estimate steel stocks for 102 countries or regions in 2006 but did not analyze changes over time series [34]. Hattori et al. optimized the algorithm of Hsu et al. by combining LandScan population data to achieve an assessment of the steel stock for civil engineering and construction works from 2006 to 2010, and the results of the study found that the fusion of multiple sources of data led to better estimation results than the use of a single light data [35]. Yu et al. used DMSP-OLS stable nighttime light data to produce a global 1 km spatially resolved data set of steel, concrete, and aluminum stocks from 1992 to 2008, showing that the combined effects of the natural and social environments are the main drivers of the variation in the distribution of material stocks, with transport networks and urban sprawl being two important drivers, extending the power of material stock estimates and revealing the spatial differentiation of material stocks [36]. However, scholars have less studied the use of nighttime lighting data to analyze the multi-scale spatiotemporal variability characteristics of long-term infrastructure iron stocks.

Therefore, this study establishes a regression model of infrastructure iron stock and nighttime lighting data by accounting for provincial infrastructure iron stock data and conducts the spatial and temporal patterns of iron stock changes in China’s national, regional, provincial, and urban agglomerations. This study analyses the composition and quantity of infrastructure iron stocks in national cities, which can provide a scientific basis for the management of iron resources in cities and is of great significance for the sustainable development of cities.

2 Study areas and data sources

2.1 Study area

Considering the availability and continuity of data, the study period of this article is 2000–2020, and the study area is the regions of China excluding Hong Kong, Macau, and Taiwan, with a total of 31 provinces.

Four regions, namely the Eastern Region, the Central Region, the Western Region, and the Northeast Region, were selected for this study according to their geographical divisions, and eight typical urban agglomerations, namely Beijing–Tianjin–Tangshan, Shanghai–Nanjing–Hangzhou, Pearl River Delta, Middle south of Liaoning, Wuhan Metropolitan Area, Central Plains Urban Agglomeration, Shandong Peninsula, and Sichuan–Chongqing, were selected for this study according to their urban agglomeration scales (Figure 1).

Figure 1 Study area.
Figure 1

Study area.

2.2 Data sources

2.2.1 Infrastructure iron stock

Infrastructure iron stock consists of iron stock in infrastructure such as housing buildings, highway facilities and railway facilities, and underground pipeline networks in municipal support projects. The official China Statistical Yearbook (2000–2020) includes data affecting the size of infrastructure, such as per capita floor area, new residential floor area, year-end total floor area, roads, railways, and length of water pipelines.(https://data.stats.gov.cn/easyquery.htm?cn=C01)

2.2.2 Night light images

Nighttime light remote sensing data have been widely used in research work such as urbanization process monitoring, but the incomparability of the two commonly used nighttime light remote sensing data (DMSP-OLS and NPP-VIIRS) limits the available time series length of nighttime light data. Chen et al. proposed an Auto-encoder (AE)-based correction scheme for cross-sensor (DMSP-OLS and NPP-VIIRS) nighttime lighting data [37]. This new nighttime lighting data set solves the problem that the DMSP-OLS and NPP-VIIRS sets of nighttime lighting data cannot be used simultaneously, extends the length of time that the data are available, and provides a new source of data for related fields such as urban studies. (https://doi.org/10.7910/DVN/YGIVCD).

3 Research methodology

3.1 Calculation of iron stock in infrastructure

The infrastructure in this study typically comprises four main categories: residential, roads, railways, and pipelines. In addition, residential buildings consist of urban and township buildings. Roads are further differentiated into highways and classes 1–2 due to the difference in their service functions and the quality of materials consumed in their construction. Railways are divided into high-speed and ordinary railways. And water pipes contain urban water networks and underground drainage networks, etc.

China’s provincial infrastructure iron stock is estimated using data on floor space, roads, railways, and length of water pipelines included in the statistics.

The iron stock in residential buildings in cities and towns at the end of each year is estimated using equation (1). The iron stock (MS) represents the remaining stock of demolished buildings and the stock of newly built residential buildings in the study area at the end of each year. The equation is as follows:

(1) MS t = Σ n = 1 N [ MS t − n ( 1 − D n ) ] + Δ A t × I t , .

In equation (1), MS represents Material Stock, t represents the year, n represents the number of years since the completion of the residential building, and N represents the maximum service life of the residential building. ΔA represents the new building area, I represents the service strength of the material, and D refers to the building demolition rate, which can be defined by a normal distribution function. This function has been studied by Hu et al. [38]. This state distribution function is given by the following equation:

(2) D = 1 DEV 2 π ∫ − ∞ t exp − ( t − AVG ) 2 2 DE V 2 × ⅆ t .

DEV in equation (2) represents the standard deviation of the statistical analysis data. AVG represents the service life of the building in the statistical analysis.

The iron stock of roads, railways, and underground water pipes is estimated by adding the iron stock of new infrastructure to the old iron stock of the previous year. The calculation equation is shown in (3).

(3) MS t = MS t − 1 + S t × I t .

In equation (3), S represents the scale of the new infrastructure, specifically the road area, the length of the railway, and the length of the underground pipe network, etc. I represents the strength of the material used. For the specific values of I in equations (1) and (3), and the specific values of DEV and AVG in equation (2), the results of Zhang’s study [39] were adopted in this study.

3.2 Spatiotemporal dynamic evaluation of infrastructure iron stock

The average infrastructure iron stock is calculated in China from 2000 to 2020 and the spatial pattern of infrastructure iron stock is analyzed. The equation is described as follows:

(4) P ̅ i = ∑ i = 2000 2020 P i t .

P i is the average iron stock in China’s infrastructure for pixel i from 2000 to 2020, and t is the total number of years, set to 21. Equation (5) is used to describe the time variation of China’s infrastructure iron stock from 2000 to 2020.

(5) P i tem = P i 2020 − P i 2000 ,

P i tem is the time variation of pixel i China’s infrastructure iron stock from 2000 to 2020.

3.3 Linear regression methods

Based on the available research literature on the correlation between material stocks and urban nighttime lighting data, combined with current research results in the field of correction of urban nighttime lighting brightness, the linear regression model between iron stocks in urban infrastructure facilities and urban nighttime lighting initially established in this study is shown in equation (6).

(6) MS = b × VANUI ,

In equation (6), MS represents the stock of iron in the infrastructure within the study area. b is the coefficient in the linear regression model. VANUI is the corresponding night-time light city index for each province.

4 Results and analysis

4.1 Simulation of the spatial distribution of iron stocks

In this study, Shandong Province in the eastern region, Hubei Province in the central region, and Sichuan Province in the western region were selected as the validating provinces for the nighttime light simulation iron stock regression model, and Beijing, Fujian, Guangdong, Hainan, Hebei, Heilongjiang, Jilin, Jiangsu, Liaoning, Shanghai, Tianjin, Zhejiang, Anhui, Henan, Hunan, Jiangxi, Shanxi, Chongqing, Gansu, Guangxi, Guizhou, Inner Mongolia, Ningxia, Qinghai, Shaanxi, Xinjiang, and Yunnan were used as the 27 provincial administrative regions for constructing the regression models for analysis, and the equations of the regression models for each year are shown in Table 1. The simulated and statistical values of iron stocks in Shandong, Hubei, and Sichuan for 2000–2020 were then correlated (Figure 2). The results show that the linear model explains well the relationship between nighttime lighting and iron stocks.

Table 1

Regression model formula

Year Slope R 2
2000 110.17 0.8145
2001 86.821 0.7503
2002 85.602 0.8745
2003 66.657 0.8094
2004 68.158 0.7979
2005 80.776 0.7747
2006 74.824 0.7582
2007 81.227 0.8206
2008 87.7 0.7808
2009 92.583 0.5518
2010 122.59 0.7814
2011 95.138 0.7559
2012 136.42 0.7140
2013 113.43 0.8269
2014 131.17 0.8086
2015 148.93 0.8114
2016 162.14 0.8198
2017 151.56 0.8143
2018 153.59 0.8062
2019 156.77 0.8018
2020 159.12 0.8067
Figure 2 The results were verified in (a) Hubei, (b) Shandong, and (c) Sichuan provinces.
Figure 2

The results were verified in (a) Hubei, (b) Shandong, and (c) Sichuan provinces.

Based on Arcgis 10.8 software, the raster calculator was used to calculate the 500 m resolution raster data for China’s infrastructure iron stock from 2000 to 2020 based on the formula in Table 1 (Figure 3).

Figure 3 Simulation of national iron stock based on night light.
Figure 3

Simulation of national iron stock based on night light.

4.2 Spatial and temporal characteristics of iron stocks

Compared to other statistical data classification methods, the natural breakpoint method has the least variance within categories and the greatest variation between categories. The classification results are not influenced by human factors, and there are significant breaks between categories. Therefore, the natural breakpoint method approach was chosen for this study to examine the statistical variation in infrastructure iron stocks in different regions of China. The natural breakpoint method was used to rank the iron stocks and classify the temporal changes in infrastructure iron stocks from 2000 to 2020 into four categories: no-obvious-growth (<0.02 million tons), low-growth (0.02–0.19 million tons), moderate-growth (0.19–0.50 million tons), and high-growth (>0.50 million tons). The natural breakpoint method was again used to quantify the spatial variation in infrastructure iron stocks from 2000 to 2020. The infrastructure iron stock is divided into five levels: low (<0.56 million tons), relatively low (0.56–24.2 million tons), medium (24.2–52.1 million tons), relatively high (5.21–93.0 million tons), and high (>93.0 million tons). Figure 4a shows the characteristics of the temporal changes in China’s iron stock from 2000 to 2020, while Figure 4b shows the characteristics of the spatial changes in China’s iron stock from 2000 to 2020.

Figure 4 (a) Temporal and (b) spatial changes in China’s infrastructure iron stock from 2000 to 2020. Note: the negative growth is regarded as no significant growth.
Figure 4

(a) Temporal and (b) spatial changes in China’s infrastructure iron stock from 2000 to 2020. Note: the negative growth is regarded as no significant growth.

4.2.1 National scales

Table 2 shows the data on China’s iron stock in infrastructure construction and its composition from 2000 to 2020. China’s infrastructure iron stock grew from 38578.61 × 104 tons in 2000 to 242434.08 × 104 tons in 2020, with an average annual growth rate of 26.42% over the period.

Table 2

China’s iron stock and its composition from 2000 to 2020

Year Iron stock (104 t) China’s infrastructure iron stock composition The proportion of the building (%) The proportion of roads (%) The proportion of pipes (%)
Construction iron stock (104 t) Road iron stock (104 t) Pipe iron stock (104 t)
2000 38578.61 29672.39 1414.97 7491.25 76.91 3.67 19.42
2001 39794.44 30610.03 1694.26 7490.15 76.92 4.26 18.82
2002 41343.91 31953.90 1740.22 7649.79 77.29 4.21 18.50
2003 43380.30 33666.76 1764.64 7948.89 77.61 4.07 18.32
2004 44319.70 35423.23 1803.61 7092.85 79.93 4.07 16.00
2005 47583.07 38252.58 1825.79 7504.70 80.39 3.84 15.77
2006 51899.10 41608.31 1852.23 8438.56 80.17 3.57 16.26
2007 56291.52 45600.74 1875.64 8815.13 81.01 3.33 15.66
2008 61378.91 49979.20 1922.97 9476.74 81.43 3.13 15.44
2009 66907.69 54790.25 2066.53 10050.91 81.89 3.09 15.02
2010 73251.47 60404.11 2197.88 10649.48 82.46 3.00 14.54
2011 86667.27 73056.47 2256.58 11354.22 84.30 2.60 13.10
2012 101536.08 87400.39 2360.94 11774.75 86.08 2.33 11.60
2013 118744.41 103456.29 2489.18 12798.94 87.13 2.10 10.78
2014 136522.84 120384.28 2694.22 13444.34 88.18 1.97 9.85
2015 154272.70 137208.72 2908.90 14155.08 88.94 1.89 9.18
2016 172174.52 154098.25 2991.73 15084.53 89.50 1.74 8.76
2017 189761.84 170854.99 3057.31 15849.53 90.04 1.61 8.35
2018 207810.92 187389.57 3173.40 17247.96 90.17 1.53 8.30
2019 225200.73 203473.28 3371.85 18355.61 90.35 1.50 8.15
2020 242434.08 218857.93 3529.61 20046.54 90.28 1.46 8.27

During the 21-year study period, China’s infrastructure iron stock was the largest in the construction system, the pipeline system iron stock was the second largest in the infrastructure stock, and the road system iron stock was the third largest. The total amount and stock share of iron stock in the building system shows a trend of increasing year by year, with the iron stock in the building system rising from 76.91% in 2000, the starting year of this study, to 90.28% in 2020. The second largest share of iron stocks in the pipeline system and the third largest total of iron stocks in the road transport and rail systems are both gradually decreasing in share, although they are rising. The share of the pipeline system has shrunk most significantly, from 19.42% in 2000 to 8.27% in 2020, and the share of the pipeline and road systems is still on a downward trend. The share of iron stock in the rail system has remained largely stable at below 4%.

The four types and five levels of China’s infrastructure iron stock from 2000 to 2020 are shown in Figure 5. In terms of the pattern of change over time, the high-growth and moderate-growth types account for 0.04 and 0.45% of the total area of the study area, which can be seen in Figure 4 to be concentrated in coastal areas, some inland metropolitan areas and developed cities, including Beijing, Shanghai, Wuhan, Chongqing, Chengdu, Qingdao, and provincial capitals. The no-obvious-growth and low-growth types account for 98.02 and 1.49% of the total area of China, respectively.

Figure 5 (a) Area percentage of each type and (b) area percentage of each grade.
Figure 5

(a) Area percentage of each type and (b) area percentage of each grade.

In addition, Figure 5 shows that the pattern of spatial variation in China’s infrastructure iron stock is similar to the pattern of temporal variation in China’s infrastructure iron stock. Low-grade and relatively low-grade infrastructure iron stocks are mainly distributed in inland areas, accounting for 98.16 and 1.27% of the country’s area, respectively. High grade and relatively high grade are mainly distributed in coastal areas, accounting for 0.03 and 0.15% of China’s total area, respectively.

4.2.2 Regional scale

The spatiotemporal dynamics of the regional scale infrastructure iron stock and the percentage of area in each type and class in the four regions is shown in Figure 6a. In terms of temporal changes, the high-growth type is mainly distributed in the eastern, western, and central regions, accounting for 48.73, 27.1, and 16.74% of the total area of this type, respectively. The no-obvious-growth type is mainly distributed in the western region, accounting for 73.63% of the total area of this type. All four types of infrastructure iron stock in the northeast are low.

Figure 6 (a) Area percentage of each type and (b) area percentage of each grade of the four regions.
Figure 6

(a) Area percentage of each type and (b) area percentage of each grade of the four regions.

In terms of spatial variation (Figure 6b), the high grade infrastructure iron stocks are mainly distributed in the eastern, northeastern, and western regions, accounting for 53.35, 20, and 19.54% of the total area of the grade, respectively. Similarly, relatively high grades are concentrated in the eastern region, accounting for 62.45% of the total area of the grade. The western region, on the other hand, is dominated by low grades, accounting for 72.12% of the low grades area. In addition, the distribution of the five major grades of infrastructure iron stock in the Northeast is significantly less concentrated and relatively uniform. In conclusion, large temporal and spatial variations in infrastructure iron stocks occur mainly in the eastern, western, and central regions.

4.2.3 Provincial scales

By examining the average annual growth rate of infrastructure iron stock in 31 provinces across the country from 2000 to 2020 (Table 3), it can be concluded that Heilongjiang province has the lowest average annual growth rate of 3.83%. There are four provinces with growth rates below 10%, namely Heilongjiang Province, Hainan Province, Qinghai Province, and Shanxi Province. The average annual growth rate of the national infrastructure iron stock was 26.42%, with 20 provinces below the national average, 11 provinces above the national average, and 5 provinces above 40%, namely Chongqing (43.26%), Jiangxi (43.75%), Fujian (45.08%), Jiangsu (60.20%) and Zhejiang (76.14%). Except for Tibet, Zhejiang Province has the highest growth rate, with a growth rate of over 70%. In terms of geographical location, provinces with high average annual growth rates of infrastructure iron stocks are generally in coastal South and East China. Provinces with medium average annual growth rates of infrastructure iron stocks are generally inland in central and northern China. The provinces with low average annual growth rates of infrastructure iron stocks are those in the northeast, southwest, and northwest regions.

Table 3

Average annual growth of iron stocks by province from 2000 to 2020 Rate

Provinces Rate of change (%) Provinces Rate of change (%)
Heilongjiang 3.83 Guizhou 20.15
Hainan 7.38 Shandong 20.60
Qinghai 8.86 Shaanxi 22.72
Shanxi 9.01 Yunnan 26.01
Liaoning 10.25 Henan 28.55
Jilin 10.29 Sichuan 30.15
Tianjin 10.70 Hubei 31.44
Inner Mongolia 10.89 Hunan 34.97
Shanghai 11.57 Anhui 37.91
Guangdong 12.37 Chongqing 43.26
Beijing 14.35 Jiangxi 43.75
Ningxia 15.73 Fujian 45.08
Xinjiang 17.42 Jiangsu 60.20
Hebei 17.68 Zhejiang 76.14
Gansu 17.94 Tibet 139.13
Guangxi 20.00

During the period 2000–2020 (Table 4), provinces with a high share of no-obvious-growth in infrastructure iron stocks include Xinjiang, Inner Mongolia, and Tibet. Provinces with high-growth shares of infrastructure iron stocks are Jiangsu, Zhejiang, and Sichuan. Provinces with a high proportion of low-grade infrastructure iron stocks are Xinjiang, Inner Mongolia, Tibet. Provinces with a high proportion of high-grade infrastructure iron stocks include Beijing, Liaoning, and Shanghai. The spatial and temporal distribution of infrastructure iron stock varies significantly throughout the study and analysis period due to the starting point of economic development, the scale of investment, and the focus of national development planning on different factors.

Table 4

The time and space change of the quantity of infrastructure iron depots in each province

Provinces No-obvious-growth (%) Low growth (%) Moderate growth (%) High growth (%) Low (%) Relatively low (%) Medium (%) Relatively high (%) High (%)
Anhui 1.31 3.92 5.14 4.51 1.33 3.68 4.10 2.19 0.44
Beijing 0.14 2.31 2.39 1.40 0.14 1.85 2.99 6.33 9.67
Fujian 1.09 4.07 3.54 3.26 1.10 3.71 3.30 2.86 2.06
Gansu 4.45 1.21 1.15 1.28 4.44 1.50 1.11 1.25 1.23
Guangdong 1.45 10.63 8.42 4.07 1.47 8.83 12.51 12.78 6.21
Guangxi 2.20 2.14 2.14 0.88 2.21 1.96 1.87 1.26 0.43
Guizhou 1.68 1.96 1.45 1.03 1.69 1.99 1.35 0.52 0.20
Hainan 0.29 0.74 0.57 0.53 0.29 0.69 0.55 0.55 0.32
Hebei 2.01 4.96 2.94 2.36 2.01 5.21 3.64 3.42 2.41
Henan 1.62 5.63 4.67 2.99 1.64 5.34 4.22 2.61 1.72
Heilongjiang 5.70 1.43 1.44 1.75 5.68 2.43 2.37 3.18 7.54
Hubei 1.78 3.07 2.77 2.59 1.80 2.82 2.09 1.47 0.89
Hunan 2.01 2.51 2.33 1.14 2.02 2.36 1.93 0.85 0.16
Jilin 2.22 1.27 1.67 3.83 2.22 1.56 1.59 2.43 6.54
Jiangsu 0.74 9.78 13.66 12.33 0.77 9.76 12.34 11.17 3.46
Jiangxi 1.56 2.14 1.89 0.60 1.57 1.91 1.31 0.34 0.04
Liaoning 1.61 2.84 2.75 3.77 1.59 3.45 3.69 5.11 9.50
Inner Mongolia 13.82 2.10 1.83 3.06 13.79 2.83 2.26 2.87 4.94
Ningxia 0.55 0.81 0.99 1.02 0.55 0.89 0.88 1.11 1.02
Qinghai 7.53 0.45 0.50 1.19 7.52 0.50 0.43 0.44 0.74
Shandong 1.46 8.45 5.62 4.20 1.47 8.23 7.41 5.90 3.54
Shanxi 1.67 2.58 2.35 4.55 1.66 3.44 2.35 2.16 5.14
Shaanxi 2.14 2.54 3.07 5.62 2.14 3.18 2.64 2.87 3.71
Shanghai 0.02 1.67 3.89 3.47 0.02 1.43 3.72 7.60 7.87
Sichuan 4.82 3.87 5.03 6.75 4.83 3.87 3.52 2.48 1.42
Tianjin 0.08 1.60 2.33 2.36 0.08 1.62 2.20 3.42 7.36
Tibet 12.03 0.25 0.25 0.96 12.01 0.22 0.17 0.24 0.36
Xinjiang 18.73 3.02 3.12 5.05 18.71 3.33 3.02 3.40 5.59
Yunnan 3.64 2.00 2.27 3.40 3.63 2.18 2.07 2.58 3.25
Zhejiang 0.83 8.44 7.83 8.51 0.86 7.67 6.80 5.87 2.10
Chongqing 0.79 1.62 2.01 1.51 0.80 1.58 1.56 0.75 0.15

4.3 Urban agglomeration scale

The eight urban agglomerations account for 10.99% of China’s total area. While the stock of iron infrastructure in these eight urban agglomerations is growing continuously between 2000 and 2020, the growth rates and increases vary widely. In terms of area share, 90.03% of the Middle south of Liaoning and 89.42% of Sichuan–Chongqing urban agglomerations are no-obvious-growth types (Figure 7); 3.2 and 12.73% of the area of the Shanghai–Nanjing–Hangzhou urban agglomeration is high-growth and moderate-growth; 1.08 and 9.63% of the area of the Pearl River Delta region is high growth and moderate growth. In summary, the spatial pattern of infrastructure iron stock in the Shanghai–Nanjing–Hangzhou urban agglomeration and the Pearl River Delta has changed significantly over the past 21 years, while the areas with insignificant changes in infrastructure iron stock are mainly concentrated in the Middle south of Liaoning and Sichuan–Chongqing regions.

Figure 7 (a) Area proportion of each type and (b) area proportion of each grade of eight urban agglomerations.
Figure 7

(a) Area proportion of each type and (b) area proportion of each grade of eight urban agglomerations.

The percentage of areas with low-grade infrastructure iron stock in Sichuan–Chongqing is 96.37%. The percentage of area with low-grade infrastructure iron stock in the Middle south of the Liaoning region is 95.23%. The percentage of the area with a high grade of infrastructure iron stock in the Shanghai–Nanjing–Hangzhou region is 0.38%. The percentage of area with a high grade of infrastructure iron stock in the Pearl River Delta region is 0.34%. On the whole, the high-grade infrastructure iron stock is mainly concentrated in Shanghai, Nanjing, and Hangzhou, and the Pearl River Delta, while the low-grade infrastructure iron stock is mainly concentrated in the Middle south of Liaoning and Sichuan–Chongqing regions.

5 Conclusion

Based on the calculated data of infrastructure iron stock in various provinces across China from 2000 to 2020, this article constructs a fitting equation between nighttime lighting data and infrastructure iron stock and simulates the spatial distribution of China’s infrastructure iron stock at a resolution of 500 m from 2000 to 2020. The spatiotemporal dynamic characteristics of China’s infrastructure iron stock is analyzed at four scales: national, regional, provincial, and urban agglomeration. The focus of China’s urbanization construction is mainly in the field of housing construction in cities, with the national focus on solving the housing problem of residents. The construction of urban infrastructure also remains in the stage of meeting people’s basic housing needs. Among the entire iron stock of infrastructure, the contribution of the residential system is the largest, with the second largest contribution being the pipeline system closely related to residential construction. The contribution of the iron stock of the highway and railway systems in infrastructure construction ranks third. This result also indirectly indicates that in the study of steel material stock in different scale research areas in China, the selection of steel materials from residential, highway, railway, and water distribution networks for infrastructure research can reflect the basic laws of national infrastructure investment and construction. Since the year 2000, the rapid development of China’s economy has led to a continuous and rapid increase in the iron stock in infrastructure in various provinces and cities. Overall, the distribution of iron and other substances in infrastructure in various provinces and cities across the country is very uneven. The overall performance is as follows: the eastern and coastal regions have the most, while the central and western regions are in the middle and western provinces have the least. Although the national development strategy continues to tilt toward the central and western regions, due to the different urbanization rates and economic levels of each province in the country, the gap in iron stock in infrastructure between inland and coastal areas cannot be narrowed in a short period of time. Especially as the national economic development strategy gradually enters the regional development strategy, the gap between coastal and central and western development continues to widen, what it shows is that the gap in infrastructure construction scale across the country is constantly strengthening, and correspondingly, the gap in the stock of basic materials such as steel in infrastructure is also continuously widening. Due to their developed economic level, the eight major urban agglomerations will also have the more complete urban infrastructure, resulting in a relatively concentrated inventory of infrastructure iron in these areas. For some provinces and regions, the level of infrastructure iron stock is constantly increasing, which is closely related to the population base of the province or region, as well as factors such as population mobility caused by economic development. The increase in population has a huge promoting effect on the residential area in various regions. As a huge reservoir for the use and stock of steel and other materials in infrastructure construction, residential buildings have caused a huge increase in the iron stock in infrastructure. China has a large population and a rapid urbanization process. Faced with environmental pollution and overcapacity issues, this study can provide a scientific basis for the management and secondary utilization of urban iron resources, which is of great significance for the sustainable development of cities.

  1. Funding information: Henan Soft Science Research Project (232400410328) and Research Project of Henan Federation of Social Sciences (2023-ZZJH-189).

  2. Conflict of interest: Authors state no conflict of interest.

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Received: 2023-04-04
Revised: 2023-06-18
Accepted: 2023-06-19
Published Online: 2023-07-10

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2022-0510
Keywords for this article
night lighting; iron stock; China; infrastructure
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Diagenesis and evolution of deep tight reservoirs: A case study of the fourth member of Shahejie Formation (cg: 50.4-42 Ma) in Bozhong Sag
  3. Petrography and mineralogy of the Oligocene flysch in Ionian Zone, Albania: Implications for the evolution of sediment provenance and paleoenvironment
  4. Biostratigraphy of the Late Campanian–Maastrichtian of the Duwi Basin, Red Sea, Egypt
  5. Structural deformation and its implication for hydrocarbon accumulation in the Wuxia fault belt, northwestern Junggar basin, China
  6. Carbonate texture identification using multi-layer perceptron neural network
  7. Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
  8. Assessments of recent Global Geopotential Models based on GPS/levelling and gravity data along coastal zones of Egypt
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  10. Uncertainty assessment of 3D geological models based on spatial diffusion and merging model
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  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
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  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
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  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
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  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
  51. Lignin analysis of sediments from around 2,000 to 1,000 years ago (Jiulong River estuary, southeast China)
  52. Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi
  53. Transforming text into knowledge graph: Extracting and structuring information from spatial development plans
  54. A symmetrical exponential model of soil temperature in temperate steppe regions of China
  55. A landslide susceptibility assessment method based on auto-encoder improved deep belief network
  56. Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm
  57. Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt
  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
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