1
|
Liu X, Wei Y, Jin X, Luo X, Zhou Y. County-level carbon compensation zoning based on China's major function-oriented zones. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 367:121988. [PMID: 39067344 DOI: 10.1016/j.jenvman.2024.121988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2024] [Accepted: 07/20/2024] [Indexed: 07/30/2024]
Abstract
Large but overlooked carbon inequalities among counties in China matter for the design of mitigation strategies. Here, we investigated the spatial heterogeneity of carbon inequality across 2236 county-level units nationwide from 2000 to 2020, refining carbon compensation zone types based on land functional zoning and estimating their carbon compensation values using a modified compensation model. Our results showed that China's carbon inequality consistently exceeded the cautionary threshold of 0.4 on the Gini coefficient. Significant spatial variations in carbon intensity were observed, notably concentrated in the North China Plain and Yangtze River Delta, indicating a pronounced core-periphery structure. The nonlinear relationships among carbon emission pressure (CEP), land use intensity (LUI), economy contributive coefficient (ECC), and ecological support coefficient (ESC) were identified. CEP and ECC posed initial increases followed by decreases with LUI, while ESC decreased with increasing LUI. The inverted U-curve between ECC and CEP suggested that most county-level cities have yet to reach the decoupling tipping point. Based on spatial comparative advantage, we identified 625 payment zones, 666 equilibrium zones, and 945 recipient zones, culminating in nine types of carbon compensation zones aligned with land functional objectives. Our study provides a new county-level carbon compensation zoning approach that can achieve carbon equity.
Collapse
Affiliation(s)
- Xiaojie Liu
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing, 210023, China; Key Laboratory of Carbon Neutrality and Territory Optimization, Ministry of Natural Resources, Nanjing, 210023, China
| | - Yongping Wei
- School of Earth and Environmental Sciences, The University of Queensland, Brisbane, 4072, Australia
| | - Xiaobin Jin
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing, 210023, China; Key Laboratory of Carbon Neutrality and Territory Optimization, Ministry of Natural Resources, Nanjing, 210023, China.
| | - Xiuli Luo
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing, 210023, China; Key Laboratory of Carbon Neutrality and Territory Optimization, Ministry of Natural Resources, Nanjing, 210023, China
| | - Yinkang Zhou
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources, Nanjing, 210023, China; Key Laboratory of Carbon Neutrality and Territory Optimization, Ministry of Natural Resources, Nanjing, 210023, China
| |
Collapse
|
2
|
Tao J, Hu Y, Jiang J, Yang W, Zhao T, Su S. Prediction of Potential Suitable Distribution Areas for an Endangered Salamander in China. Animals (Basel) 2024; 14:1390. [PMID: 38731395 PMCID: PMC11083405 DOI: 10.3390/ani14091390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/20/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024] Open
Abstract
Climate change has been considered to pose critical threats for wildlife. During the past decade, species distribution models were widely used to assess the effects of climate change on the distribution of species' suitable habitats. Among all the vertebrates, amphibians are most vulnerable to climate change. This is especially true for salamanders, which possess some specific traits such as cutaneous respiration and low vagility. The Wushan salamander (Liua shihi) is a threatened and protected salamander in China, with its wild population decreasing continuously. The main objective of this study was to predict the distribution of suitable habitat for L. shihi using the ENMeval parameter-optimized MaxEnt model under current and future climate conditions. Our results showed that precipitation, cloud density, vegetation type, and ultraviolet radiation were the main environmental factors affecting the distribution of L. shihi. Currently, the suitable habitats for L. shihi are mainly concentrated in the Daba Mountains, including northeastern Chongqing and western Hubei Provinces. Under the future climate conditions, the area of suitable habitats increased, which mainly occurred in central Guizhou Province. This study provided important information for the conservation of L. shihi. Future studies can incorporate more species distribution models to better understand the effects of climate change on the distribution of L. shihi.
Collapse
Affiliation(s)
- Jiacheng Tao
- College of Fisheries, Southwest University, Chongqing 400715, China; (J.T.); (Y.H.)
| | - Yifeng Hu
- College of Fisheries, Southwest University, Chongqing 400715, China; (J.T.); (Y.H.)
| | - Jianping Jiang
- CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China;
| | - Wanji Yang
- Shengnongjia National Park Administration, Huibei Provincial Key Laboratory on Conservation Biology of the Shennongjia Golden Snub-Nosed Monkey, Shennongjia 442421, China;
| | - Tian Zhao
- College of Fisheries, Southwest University, Chongqing 400715, China; (J.T.); (Y.H.)
| | - Shengqi Su
- College of Fisheries, Southwest University, Chongqing 400715, China; (J.T.); (Y.H.)
| |
Collapse
|
3
|
Fang M, Lyu L, Wang N, Zhou X, Hu Y. Application of unsupervised clustering model based on graph embedding in water environment. Sci Rep 2023; 13:22774. [PMID: 38123700 PMCID: PMC10733311 DOI: 10.1038/s41598-023-50301-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 12/18/2023] [Indexed: 12/23/2023] Open
Abstract
Surface water monitoring data has spatiotemporal characteristics, and water quality will change with time and space in different seasons and climates. Data of this nature brings challenges to clustering, especially in terms of obtaining the temporal and spatial characteristics of the data. Therefore, this paper proposes an improved TADW algorithm and names it RTADW to obtain the spatiotemporal characteristics of surface water monitoring points. We improve the feature matrix in TADW and input the original time series data and spatial information into the improved model to obtain the spatiotemporal feature vector. When the improved TADW model captures watershed information for clustering, it can simultaneously extract the temporal and spatial characteristics of surface water compared with other clustering algorithms such as the DTW algorithm. We applied the proposed method to multiple different monitoring sites in the Liaohe River Basin, analyzed the spatiotemporal regional distribution of surface water monitoring points. The results show that the improved feature extraction method can better capture the spatiotemporal feature information between surface water monitoring points. Therefore, this method can provide more potential information for cluster analysis of water environment monitoring, thereby providing a scientific basis for watershed zoning management.
Collapse
Affiliation(s)
- Meng Fang
- University of Chinese Academy of Sciences, Beijing, China.
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, China.
| | - Li Lyu
- University of Chinese Academy of Sciences, Beijing, China
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, China
| | - Ning Wang
- University of Chinese Academy of Sciences, Beijing, China.
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, China.
| | - Xiaolei Zhou
- University of Chinese Academy of Sciences, Beijing, China
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, China
| | - Yankun Hu
- University of Chinese Academy of Sciences, Beijing, China
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, China
| |
Collapse
|