The study area
Qingdao with the area of 11293km2, the urban area (Shinan, Shibei, Licang, Laoshan, West Coast New Area, Chengyang and Jimo districts) is 5226km2, and the Jiaozhou, Pingdu and Laixi with the total area of 6067km2. The elevation ranged from 0 to 1090m (Figure 1). From 2000 to 2020, the GDP increased from 118.31 to 1240.06 billion yuan, the proportion of secondary industry decreased from 46.1% to 35.2%, and tertiary industry increased by 19%. The urban built-up area and population increased by 639.1km2 and 2939.2 thousand people, respectively (QingDao Municipal Bureau of Statistics 2021). The carbon emissions per unit of GDP reduced by 45% from 2005 to 2020 due to the low-carbon development policy was implemented from 2006 year. However, the total amount of carbon emissions is increasing and the spatio-temporal heterogeneity is significant with the socio-economic development.
Data source and processing
A time series raster data with resolution of 1km×1km from 2000 to 2020 in Qingdao attained as following: the carbon emissions data of fossil fuels attained from ODIAC fossil fuel emission dataset(https://db.cger.nies.go.jp/dataset/ODIAC/). Annual average temperature data was from National Earth System Science Data Center(http://www.geodata.cn/). Land use and GDP density data were from Resource and Environmental Science and Data Center(https://www.resdc.cn/DOI/doi.aspx?DOIid=33). Population density data was from the World population dataset(https://www.worldpop.org/geodata/listing?id=76). The digital elevation model with resolution of 30m × 30m was from Geospatial data cloud(http://www.gscloud.cn/sources/). Building coverage ratio and floor area ratio were calculated from buildings’ 3D information extracted from high resolution satellite images.
The population density (PD, thousand people/km2), Gross Domestic Product density (GDPD, million yuan/km2), Building coverage ratio (BCR, %), Floor area ratio (FAR), Annual average temperature (T, ℃), Elevation (E, m) and Slope (S, °) are divided into 9 categories by natural breakpoint method, and the land use types (LST) is divided into 8 categories (Table 1). The data of carbon emissions, natural environment and socio-economic factors were extracted by the 10742 grids of 1km×1km, in Qingdao City.
Table 1 Classification of environmental and socioeconomic factors
Types
|
Environmental factors
|
Socioeconomic factors
|
T (℃)
|
S (°)
|
E(m)
|
LST
|
PD (thousand people/km2)
|
GDPD (million yuan/km2)
|
BCR (%)
|
FAR
|
1
|
8.9-10.4
|
0-2.23
|
0-26
|
CL
|
0-0.1
|
1.52-21.03
|
0-2.74
|
0-0.05
|
2
|
10.4-10.9
|
2.23-4.97
|
26-56
|
F
|
0.1-0.3
|
21.03-30.28
|
2.74-8.23
|
0.05-0.16
|
3
|
10.9-11.5
|
4.97-8.20
|
56-94
|
W
|
0.3-0.7
|
30.28-47.10
|
8.23-15.29
|
0.16-0.31
|
4
|
11.5-12.1
|
8.20-11.92
|
94-149
|
RL
|
0.7-1.2
|
47.10-70.29
|
15.29-23.92
|
0.31-0.53
|
5
|
12.1-12.6
|
11.92-16.40
|
149-231
|
UL
|
1.2-1.9
|
70.29-113.77
|
23.92-34.50
|
0.53-0.85
|
6
|
12.6-13.0
|
16.40-21.62
|
231-342
|
GL
|
1.9-3.0
|
113.77-258.17
|
34.50-47.45
|
0.85-1.40
|
7
|
13.0-13.3
|
21.62-27.58
|
342-486
|
UC
|
3.0-4.2
|
258.17-554.58
|
47.45-61.17
|
1.40-2.19
|
8
|
13.3-13.6
|
27.58-35.28
|
486-690
|
R
|
4.2-5.9
|
554.58-1066.55
|
61.17-75.29
|
2.19-3.22
|
9
|
13.6-14.2
|
35.28-63.37
|
690-1088
|
|
5.9-8.3
|
1066.55-9337.56
|
75.29-100
|
3.22-4.82
|
Note: Cultivated land (CL); Forest land (F); Water body (W); Rural construction land (RL); Unused land (UL); Grassland (GL); Urban construction land (UC); Roads (R).
Method
The coefficient of variation, Sen trend analysis and Mann Kendal test based on Python are used to analyze the change stability and variation trend of carbon emissions, in Qingdao City.
The coefficient of variation (Cv) was used for the estimation of the relative variation in carbon emissions for the period 2000–2020, it can be calculated, as shown in Equation (1):
![](data:image/png;base64,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)
where, σ is the standard deviation of carbon emissions, unit (t); μ is the average value of carbon emissions. When Cv≤0.1 means weak variation, Cv≥1 means strong variation, 0.1<Cv<1 means moderate variation.
The Sen’s slope method was used for the estimation of the change in carbon emissions for the period 2000–2020. It is a non-parametric method which has been found highly reliable for estimation of change over time. The Sen’s slope (β) is calculated as the median of all the slopes estimated between all the successive data points of carbon emissions time series as:
![](data:image/png;base64,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)
where, CEi and CEj are the carbon emissions in i and j years, 1<i<j<n.β>0 andβ<0 indicated the carbon emissions are on the rise and on the decline, respectively.
The Mann–Kendall test is the most widely used test, which is recommended by the World Meteorological Organization (WMO) often used as because it does consider the data distribution, and it can cope with the outliers. The significance of the change in carbon emissions was estimated using the MK test. The standardized test static for the Mann–Kendall test (Z) can be calculated, as shown in Equation (3):
![](data:image/png;base64,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)
where, S is the test statistic for carbon emissions can be calculated using Equation in literature (Ali et al. 2019).
The sign of Z indicates the direction of the trend. The negative value of Z indicates a decreasing trend and vice versa. If the absolute value of Z is higher than 1.64 means it passed the significance test with 90% confidence, and it is significant at 10% significance level; If the absolute value of Z is higher than 1.96 means it passed the significance test with 95% confidence, and it is significant at 5% significance level. And when the significance level (P): 0.05<P<0.1 means significant increase or decrease, P<0.05 means extremely significant increase or decrease, others are no significant.
Geodetector is a set of statistical methods to detect spatial differentiation and reveal its driving force (Wang et al. 2017) Factor detector, interaction detector and ecological detector are used to analyze the main factors and interactions of carbon emissions in Qingdao. Factor detector used to explore the influence of different factors on the spatial differentiation of carbon emissions. The factor detector q-statistic can be calculated, as shown in Equation (4):
![](data:image/png;base64,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)
where h is the number of independent variable layers, h∈[1, L]; L is the number of variable or factor layers; N is the number of samples in the study area; is the variance of dependent variable.
The value of q is strictly within [0, 1], q=1 indicates that the spatial distribution of carbon emissions is completely determined by the factor (X); q=0 indicates that there is no relationship between the factors and the change of carbon emissions. The larger value of q is the greater influence or stronger explanatory power on carbon emissions and vice versa.
The interaction detector reveals whether the factors X1 and X2 (and more X) have an interactive influence on a response variable Y. That is, to evaluate whether the interaction of factors will weak or enhance the explanatory power of carbon emissions, or these factors are independent on the carbon emissions. The interaction relationship is defined in a coordinate axis, and it is determined by the q-statistic in the 5 intervals (Table 2).
Table 2 Interaction between explanatory variables (Xs)
![](data:image/png;base64,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)
The ecological detector identifies the difference of the impacts between two explanatory variables X1 and X2 on the spatial distribution of carbon emissions. The ecological detector is measured by the F-statistic, and can be calculated in Equation (5):
![](data:image/png;base64,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)
where NX1 and NX2 are the sample size, SSWX1 and SSWX2 are the sum of variance in the strata, L1 and L2 are the number of strata, of variables X1 and X2, respectively.
At the significance level of 0.05, If Y(X1) was significantly bigger than Y(X2), the associated value is “Y”, means the statistically significant differences between two factors on the distribution of carbon emissions, while “N” expresses the opposite meaning.