Evaluating and simulating the impacts of land use patterns on carbon emissions in coal resource-based regions: A case study of shanxi province, China

被引:5
作者
Wang, Kunpeng [1 ]
Li, Zhe [1 ]
Xu, Zhanjun [1 ]
Wang, Jiakang [1 ]
Jia, Mingxuan [1 ]
Wang, Lu [1 ]
Yue, Xin [1 ]
Duo, Xin [1 ]
机构
[1] Shanxi Agr Univ, Coll Resources & Environm, Jinzhong 030801, Shanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Trends in carbon emissions; Land use mix degree; Multi-scenario simulation; Decoupling analysis; Coal resources; DRIVING FACTORS; ECONOMIC-GROWTH; DECOUPLING ANALYSIS; MULTIPLE SCENARIOS; ECOSYSTEM SERVICES; SPATIAL SCALE; CO2; EMISSIONS; MORANS I; MODEL; CITY;
D O I
10.1016/j.jclepro.2024.142494
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
It is essential for sustainable development worldwide to reduce carbon emissions (CE), however, how land use patterns in coal resource-based regions (CRBRs) affect CE remains less explored. Based on socioeconomic data and GlobeLand30, this study evaluated and predicted the influence of land use patterns on CE in Shanxi Province from 2000 to 2030. The key findings are as follows: (1) From 2000 to 2020, the area of cropland in the study area decreased by 4.85 x 10 5 ha and the area of construction land increased by 4.38 x 10 5 ha, with the increase in construction land coming mainly from cropland. (2) There was an increasing trend in the overall net CE, from 3.17 x 10 7 t in 2000 to 3.89 x 10 7 t in 2010 and 6.38 x 10 7 t in 2020. The land use CE had a spatial distribution characteristic of being high in the west and south and low in the east and north. The total CE was the largest in 2030 in business-as-usual scenario (BAUs), and the CE class of the cities in the study area was mainly high CE, while the ecological and economic balance scenario (EEBs) was higher CE. In 2030, the BAUs will have the largest total CE, and the CE levels of the cities will mainly high CE. (3) The decoupling state between the land use mix degree (LUM) and CE in the study area was mainly an expansionary negative decoupling over the past 20 years and changed from long negative decoupling to weak decoupling in the economic development priority scenario (EDPs) and strong decoupling in EEBs, which might be better suited to the future development of Shanxi Province. This study makes it easier to comprehend how different land use patterns affect the number of CE in CRBRs. Additionally, it provides other CRBRs with a theoretical framework and instances needed to research CE.
引用
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页数:19
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共 87 条
[21]   The oceanic sink for anthropogenic CO2 from 1994 to 2007 [J].
Gruber, Nicolas ;
Clement, Dominic ;
Carter, Brendan R. ;
Feely, Richard A. ;
van Heuven, Steven ;
Hoppema, Mario ;
Ishii, Masao ;
Key, Robert M. ;
Kozyr, Alex ;
Lauvset, Siv K. ;
Lo Monaco, Claire ;
Mathis, Jeremy T. ;
Murata, Akihiko ;
Olsen, Are ;
Perez, Fiz F. ;
Sabine, Christopher L. ;
Tanhua, Toste ;
Wanninkhof, Rik .
SCIENCE, 2019, 363 (6432) :1193-+
[22]   An integrated modeling approach for ecological risks assessment under multiple scenarios in Guangzhou, China [J].
Guo, Hongjiang ;
Cai, Yanpeng ;
Li, Bowen ;
Tang, Yijia ;
Qi, Zixuan ;
Huang, Yaping ;
Yang, Zhifeng .
ECOLOGICAL INDICATORS, 2022, 142
[23]   Coupled MOP and PLUS-SA Model Research on Land Use Scenario Simulations in Zhengzhou Metropolitan Area, Central China [J].
Guo, Pengfei ;
Wang, Haiying ;
Qin, Fen ;
Miao, Changhong ;
Zhang, Fangfang .
REMOTE SENSING, 2023, 15 (15)
[24]   Spatio-temporal evolution and optimization analysis of ecosystem service value-A case study of coal resource-based city group in Shandong, China [J].
Han, Jiazheng ;
Hu, Zhenqi ;
Wang, Peijun ;
Yan, Zhigang ;
Li, Gensheng ;
Zhang, Yuhang ;
Zhou, Tao .
JOURNAL OF CLEANER PRODUCTION, 2022, 363
[25]   How does international technology spillover affect China's carbon emissions? A new perspective through intellectual property protection [J].
Hao, Yu ;
Ba, Ning ;
Ren, Siyu ;
Wu, Haitao .
SUSTAINABLE PRODUCTION AND CONSUMPTION, 2021, 25 :577-590
[26]   Distribution of iodine concentration in drinking water in China mainland and influence factors of its variation [J].
Hou, Xin ;
Zhao, Meng ;
Li, Jia ;
Du, Yang ;
Li, Ming ;
Liu, Lixiang ;
Liu, Peng ;
Meng, Fangang ;
Fan, Lijun ;
Shen, Hongmei ;
Sun, Dianjun .
SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 892
[27]   Evolution of spatial network structure for land-use carbon emissions and carbon balance zoning in Jiangxi Province: A social network analysis perspective [J].
Huang, Hanzhi ;
Jia, Junsong ;
Chen, Dilan ;
Liu, Shuting .
ECOLOGICAL INDICATORS, 2024, 158
[28]   China's CO2 emissions: A systematical decomposition concurrently from multi-sectors and multi-stages since 1980 by an extended logarithmic mean divisia index [J].
Jia, Junsong ;
Xin, Lele ;
Lu, Chengfang ;
Wu, Bo ;
Zhong, Yexi .
ENERGY STRATEGY REVIEWS, 2023, 49
[29]   A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level [J].
Jiang, Hao ;
Hu, Hao ;
Zhong, Renhai ;
Xu, Jinfan ;
Xu, Jialu ;
Huang, Jingfeng ;
Wang, Shaowen ;
Ying, Yibin ;
Lin, Tao .
GLOBAL CHANGE BIOLOGY, 2020, 26 (03) :1754-1766
[30]   Decomposition Analysis in Electricity Sector Output from Carbon Emissions in China [J].
Jiang, Xue-Ting ;
Su, Min ;
Li, Rongrong .
SUSTAINABILITY, 2018, 10 (09)