Changes in land-use-related carbon emissions in Xiangxi and their prediction

doi:10.6046/zrzyyg.2024185

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Abstract

Investigating land-use-related carbon emissions (LCE) plays a vital role in achieving goals of peak carbon dioxide emissions and carbon neutrality (also referred to as the “dual carbon” goals). Research on the changes and prediction of LCE in Xiangxi Tujia and Miao Autonomous Prefecture (also referred to as the Xiangxi Prefecture) can provide a theoretical reference for the region to develop policies on the achievement of the “dual carbon” goals and for local balanced development and protection. Based on five sets of land use data from 2000 to 2020, this study analyzed the land use conditions and the spatiotemporal evolution of historical carbon emissions in Xiangxi Prefecture. The factors influencing LCE were determined using a decoupling model and a logarithmic mean Divisia index (LMDI) model. Furthermore, three land use scenarios were established: natural development, priority of cultivated land protection, and ecological protection priority. Using these scenarios, this study predicted the land use and carbon emissions in Xiangxi Prefecture in 2030. The results indicate that forest land represents the dominant land use type in Xiangxi Prefecture. Regarding land use transition, the period from 2000 to 2020 witnessed a significant increase in construction land, which encroached into substantial areas of forest land and cultivated land. Concurrently, water bodies and grassland decreased in area, being converted into forest land and cultivated land. From the perspective of carbon emissions, land use in the region exhibited a transformation from carbon sinks to carbon sources in general. During the 20-year span, the total LCE continued to increase. Construction land was identified as the primary land type as a carbon source, while forest land was the main land type as a carbon sink. Within the 20 years, carbon emissions decreased only in Huayuan County but increased in all other counties and cities. After 2010, the original regions with elevated carbon emissions showed a decrease in carbon emissions, while other regions witnessed growing carbon emissions to varying degrees. These regional changes in carbon emissions were largely attributed to the increased carbon emissions from construction land. Xiangxi Prefecture maintained a weak decoupling effect generally, with counties and cities fluctuating between weak decoupling and strong decoupling states. The economic output effect and energy efficiency effect served as the primary factors influencing carbon emissions. The overall land pattern remained relatively stable across the three scenarios. The carbon emissions of the three scenarios increased in the order of ecological protection priority, natural development, and priority of cultivated land protection. In the future, construction land will still represent the dominant factor causing overall changes in carbon emissions, while forest land will remain as the primary carbon sink.

Keywords land use carbon emission logarithmic mean Divisia index (LMDI) CA-Markov scenario forecasting Xiangxi Tujia and Miao Autonomous Prefecture

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Issue Date: 03 September 2025

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	Siying XIA
	Jingzhi LI
	Yujia ZHENG

Cite this article:

Siying XIA,Jingzhi LI,Yujia ZHENG. Changes in land-use-related carbon emissions in Xiangxi and their prediction[J]. Remote Sensing for Natural Resources, 2025, 37(4): 220-231.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024185 OR https://www.gtzyyg.com/EN/Y2025/V37/I4/220

Fig.1 Schematic diagram of the location and scope of the study area

Tab.1 Carbon emission coefficient of land use

Tab.2 Carbon emission coefficient of energy

脱钩状态	脱钩类型	$M C$	$M G$	$T$
脱钩	强脱钩	<0	>0	<0
	弱脱钩	>0	>0	[0,0.8)
	衰退脱钩	<0	<0	>1.2
负脱钩	强负脱钩	>0	<0	T<0
	弱负脱钩	<0	<0	[0,0.8)
	扩张负脱钩	>0	>0	>1.2
连接	扩张连接	>0	>0	[0.8,1.2)
连接	衰退连接	<0	<0	[0.8,1.2)

Tab.3 Classification of decoupling types

Fig.2 Land use map of Xiangxi Prefecture from 2000 to 2020

Tab.4 Land use dynamics from 2000 to 2020

Fig.3 Land use type transfer

Tab.5 Carbon emissions of various land use types from 2000 to 2020(万t)

Tab.6 Carbon emissions of eight counties and cities

Fig.4 Spatiotemporal distribution of carbon emissions

影响因素	2000— 2005年	2005— 2010年	2010— 2015年	2015— 2020年
$M C$	0.033	0.102	0.053	0.014
$M G$	0.829	1.470	0.647	0.401
$T$	0.040	0.069	0.082	0.036
脱钩类型	弱脱钩	弱脱钩	弱脱钩	弱脱钩

Tab.7 Decoupling coefficient of Xiangxi Prefecture

地区	2000—2005年		2005—2010年		2010—2015年		2010—2015年
地区	$T$	状态	$T$	状态	$T$	状态	$T$	状态
吉首市	-0.071	强脱钩	0.341	弱脱钩	-0.037	强脱钩	-0.021	强脱钩
泸溪县	-0.116	强脱钩	0.168	弱脱钩	-0.314	强脱钩	-0.469	强脱钩
凤凰县	-0.052	强脱钩	-0.104	强脱钩	0.269	弱脱钩	0.262	弱脱钩
花垣县	-0.007	强脱钩	0.297	弱脱钩	-1.591	强脱钩	0.598	弱脱钩
保靖县	0.100	弱脱钩	-0.047	强脱钩	0.082	弱脱钩	0.113	弱脱钩
古丈县	-0.191	强脱钩	-0.096	强脱钩	3.044	扩张负脱钩	-0.265	强脱钩
永顺县	0.217	弱脱钩	-0.158	强脱钩	0.412	弱脱钩	-0.039	强脱钩
龙山县	0.230	弱脱钩	-0.111	强脱钩	0.565	弱脱钩	0.139	弱脱钩

Tab.8 Decoupling index of land use carbon emissions in counties and cities

Tab.9 Decomposition of LMDI factors of land use carbon emissions(万t)

Tab.10 Scenario types and transition principles

Fig.5 Projected land use proportions under different scenarios in 2030

Tab.11 Land use projections under different scenario in 2030(km²)

Tab.12 Energy consumption forecast inspection

Tab.13 Projected carbon emissions from land use under different scenario in 2030(万t)

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