Remote sensing-based monitoring and analysis of residential carbon emissions

doi:10.6046/zrzyyg.2022310

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Abstract

In recent years, the research on residents’ carbon emissions has mostly focused on the economic level and direct energy consumption, and less involved in the area of residential areas, and most of the research has relied on traditional surface measured data. In order to improve data accuracy and make more targeted policies, this paper selected China as the research object by taking advantage of the features of strong timeliness, wide coverage and small constraints of remote sensing images, and analyzed the correlation between residential area and residential carbon emissions in China in 2019. After determining the significance of the two, combined with the influencing factor of GDP, a multiple linear regression model was established between residents’ carbon emissions and residential area and GDP. The results show that there is a linear correlation between residents’ carbon emissions and the area of residential areas and GDP. With the development of economic level, the expansion of residential area is the main driving force for the increase of residential carbon emissions, and the driving effect of GDP on the increase of residential carbon emissions has decreased. Therefore, it is necessary to reasonably control the expansion of residential areas while considering economic development, so as to make more refined emission reduction policies and achieve the country's future green and low-carbon goals.

Keywords residential carbon emissions residential area remote sensing technology linear regression GDP

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TP79

Issue Date: 21 December 2023

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	Zhao TIAN
	Ailin LIANG

Cite this article:

Zhao TIAN,Ailin LIANG. Remote sensing-based monitoring and analysis of residential carbon emissions[J]. Remote Sensing for Natural Resources, 2023, 35(4): 43-52.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022310 OR https://www.gtzyyg.com/EN/Y2023/V35/I4/43

Fig.1 Carbon emission distribution chart of Chinese residents

Fig.2 Distribution chart of residential area in China

Tab.1 Statistical table of residents’ carbon emissions and residential area in various provinces in China

Tab.2 OLS regression analysis results

Fig.3 Trend chart of residential area and residents’ carbon emissions in various provinces in China

Fig.4 Trend chart of residential area and carbon emission in some provinces and cities

拟合模型	公式	可拟合度R²
线性函数	$G D P =$ 0.051TNL+343.091	0.797
二次多项式函数	$G D P =$ 1.293 $×$ 10^-8 $T N L 2$ +0.028TNL+6 710.071	0.816
指数函数	$G D P =$ 7 646.177 $e 1.701 × 10 - 6 T N L$	0.587

Tab.3 Comparison of different models in different provinces in China

Tab.4 Fitting results of a quadratic polynomial model

Fig.5 GDP and TNL trend chart of Chinese provinces

Tab.5 Fitting results of index models for cities in Guangdong Province

Fig.6 GDP and TNL trend chart of cities in Guangdong Province

Fig.7 Comparison chart of Corrected GDP and real GDP

自变量	回归系数	t	p	R²	F	德宾-沃森
常数	5 439 354.454	2.009	0.043^*	0.703	F(2,31)=36.752, F=0.000	2.032
$x 1$ (居民区面积)	2 255.327	5.710	0.000^**
$x 2$ (GDP)	184.146	3.063	0.005^**

Tab.6 OLS regression results of the whole model

尺度	模型	R²
中国省(自治区、直辖市)级	$Y$ =2 255.327 $x 1$ +184.146 $x 2$ +5 439 354.454	0.703
广东省市级	$Y$ =5 640.426 $x 1$ +215.392 $x 2$ +104 706.689 1	0.961
黑龙江省市级	$Y$ =1 027.658 $x 1$ +831.069 $x 2$ -82 248.171	0.932
陕西省市级	$Y$ =2 607.585 $x 1$ +338.204 $x 2$ +223 457.023	0.954
山西省市级	$Y$ =2 234.288 $x 1$ +673.122 $x 2$ -47 130.692	0.964

Tab.7 Model results at different scales

Fig.8 Comparison chart between the real value and the fitted value of residential carbon emission in provinces

Fig.9 Comparison chart between the real value and the fitted value of residential carbon emission in cities

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