气候变化领域集成服务门户(CCPortal): Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

CCPortal

DOI	10.3390/rs16101678
	Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment
	Chen, Lijie; Wang, Zhe; Ma, Xiaogang; Zhao, Jingwen; Que, Xiang; Liu, Jinfu; Chen, Ruohai; Li, Yimin
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	10
卷号	16 期号:10
英文摘要	With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use-natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a weakly effective status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the slack variables yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the 3060 dual-carbon target.
英文关键词	safety assessment; carbon stocks; Super-SBM; machine learning; remote sensing inversion
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001233289500001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302229
作者单位	Fujian Agriculture & Forestry University; Idaho; University of Idaho; Fujian Agriculture & Forestry University
推荐引用方式 GB/T 7714	Chen, Lijie,Wang, Zhe,Ma, Xiaogang,et al. Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment[J],2024,16(10).
APA	Chen, Lijie.,Wang, Zhe.,Ma, Xiaogang.,Zhao, Jingwen.,Que, Xiang.,...&Li, Yimin.(2024).Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment.REMOTE SENSING,16(10).
MLA	Chen, Lijie,et al."Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment".REMOTE SENSING 16.10(2024).