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Ming L, Wang Y, Chen X, Meng L. Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties. Heliyon 2024; 10:e29647. [PMID: 38655335 PMCID: PMC11036051 DOI: 10.1016/j.heliyon.2024.e29647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/22/2024] [Accepted: 04/11/2024] [Indexed: 04/26/2024] Open
Abstract
Cities are the main carriers of social and economic development, and they are also important sources of carbon emissions. Therefore, it is essential to explore the impact of urban expansion and form changes on carbon emissions. Here, we attempted to analyzes the relationship between urban expansion and carbon emissions at the county level in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1997 to 2017. It further decomposes the driving effects of carbon emissions from multiple factors, and considers the spatial heterogeneity between different urban form changes and driving effects. The results show that: The relationship between urban expansion and carbon emissions in the GBA has gone through three stages from 1997 to 2017, with 2012 as a turning point. Optimization of economic development models and strict protection of the ecological environment can effectively control carbon emissions. After 2012, the economic development effect (GE) and population scale effect (PE) are the driving factors of carbon emissions, while the carbon emission intensity effect (CE) and urban land intensity effect (UE) are the inhibitory factors of carbon emissions. The contribution rate of UE to carbon emission reduction can reach 86 %. The impact of urban form changes on carbon emissions has spatial heterogeneity. The changes in urban form have a significant impact on the carbon emissions of counties in Dongguan and Shenzhen. The increase in fragmentation indirectly promotes carbon emissions. In 2007-2012, the increase in centrality significantly weakened the economic development effect, which is conducive to emission reduction. After 2007, the increase in compactness in counties in the eastern part of the GBA, including Zhongshan and Zhuhai, is not conducive to emission reduction.
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Affiliation(s)
- Lei Ming
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, 341000, China
- Jiangxi Provincial Key Laboratory of Urban Solid Waste Low Carbon Circulation Technology, Ganzhou, 341000, China
- Institute of National Land Space Planning, Gannan Normal University, China
| | - Yuandong Wang
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, 341000, China
- Jiangxi Provincial Key Laboratory of Urban Solid Waste Low Carbon Circulation Technology, Ganzhou, 341000, China
- Institute of National Land Space Planning, Gannan Normal University, China
| | - Xiaojie Chen
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, 341000, China
- Jiangxi Provincial Key Laboratory of Urban Solid Waste Low Carbon Circulation Technology, Ganzhou, 341000, China
| | - Lihong Meng
- School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, 341000, China
- Jiangxi Provincial Key Laboratory of Urban Solid Waste Low Carbon Circulation Technology, Ganzhou, 341000, China
- Basic Geography Experimental Center, Gannan Normal University, China
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Guo B, Xie T, Zhang W, Wu H, Zhang D, Zhu X, Ma X, Wu M, Luo P. Rasterizing CO 2 emissions and characterizing their trends via an enhanced population-light index at multiple scales in China during 2013-2019. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:167309. [PMID: 37742983 DOI: 10.1016/j.scitotenv.2023.167309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/31/2023] [Accepted: 09/21/2023] [Indexed: 09/26/2023]
Abstract
Climate change caused by CO2 emissions (CE) has received widespread global concerns. Obtaining precision CE data is necessary for achieving carbon peak and carbon neutrality. Significant deficiencies of existing CE datasets such as coarse spatial resolution and low precision can hardly meet the actual requirements. An enhanced population-light index (RPNTL) was developed in this study, which integrates the Nighttime Light Digital Number (DN) Value from the National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and population density to improve CE estimation accuracy. The CE from the Carbon Emission Accounts & Datasets (CEADS) was divided into three sectors, namely urban, industrial, and rural, to differentiate the heterogeneity of CE in each sector. The ordinary least square (OLS), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models were employed to establish the quantitative relationship between RPNTL and CE for each sector. The optimal model was determined through model comparison and precision evaluation and was utilized to rasterize CE for urban, industrial, and rural areas. Additionally, hot spot analysis, trend analysis, and standard deviation ellipses were introduced to demonstrate the spatiotemporal dynamic characteristics of CE at multiple scales. The performance of the GTWR outperformed other methods in estimating CE. The enhanced RPNTL demonstrated a higher coefficient of determination (R2 = 0.95) than the NTL (R2 = 0.92) in predicting CE, particularly in rural regions where the R2 value increased from 0.76 to 0.81. From 2013 to 2019, high CE was observed in eastern and northern China, while a decreasing trend was detected in northeastern China and Chengdu-Chongqing. Conversely, the Yangtze River Delta, Pearl River Delta, Fenwei Plain, and Henan Province showed an increasing trend. The center of gravity for industrial and rural CE is shifting towards western regions, whereas that for urban CE is moving northward. This study provides valuable insights for decision-making on CE control.
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Affiliation(s)
- Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
| | - Tingting Xie
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Wencai Zhang
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Dingming Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Xiaowei Zhu
- Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, USA
| | - Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Min Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Pingping Luo
- School of Water and Environment, Chang'an University, Xi'an, Shaanxi 710054, China.
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Li L, Li Y, Mei Z. A Low Degree of Physical Exercise Adherence in College Students: Analyzing the Impact of Interpersonal Skills on Exercise Adherence in College Students. J Racial Ethn Health Disparities 2023:10.1007/s40615-023-01747-7. [PMID: 37682424 DOI: 10.1007/s40615-023-01747-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/25/2023] [Accepted: 08/02/2023] [Indexed: 09/09/2023]
Abstract
Physical exercise adherence (PEA) is multifaceted and significantly influenced by elements such as physical prowess, personality traits, psychosocial traits, and demographics. At present, there are problems such as the low degree of PEA in college students. Studies have shown that exercise adherence (EA) can be improved by adjusting psychological factors. Social abilities are one of the important manifestations of mental health, so this study aims to explore the intrinsic influence mechanism of social abilities on college students' PEA. Shanghai Sports University consistently ranks first among Chinese institutions that specialize in sports in the list of the best Chinese institutions. Therefore, this study decided to survey Shanghai University students. Valid data were collected from 1278 students from 6 universities in Shanghai using a questionnaire survey method. The ordinary least square (OLS) regression analysis technique was utilized in the study. The study has shown that (1) boys have stronger social abilities than girls; (2) the exercise attitude and exercise persistence of junior students are better than those of freshmen and sophomores; (3) social abilities, emotion regulation strategies, exercise needs satisfaction, exercise attitude, and EA were significantly positively associated with each other. Emotion regulation strategies and exercise attitude had a negative predictive effect on PEA, and exercise needs satisfaction and social abilities had a significant predictive effect on exercise adherence. (4) Exercise needs satisfaction and exercise attitude were used as mediating variables to regulate the influence of college students' social abilities on EA.
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Affiliation(s)
- Lingshu Li
- Department of Physical Education, Shanghai International Studies University, Shanghai, China.
| | - Yan Li
- Huzhou No. 5 Middle School Education Group, Huzhou, China
| | - Zi Mei
- School of Education, Shanghai International Studies University, Shanghai, China
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Zhao C, Liu Y, Yan Z. Effects of land-use change on carbon emission and its driving factors in Shaanxi Province from 2000 to 2020. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:68313-68326. [PMID: 37119487 DOI: 10.1007/s11356-023-27110-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 04/15/2023] [Indexed: 05/27/2023]
Abstract
Exploring the process of carbon emissions under the "carbon peaking and carbon neutrality goals" can contribute to sustainable economic development. This research takes Shaanxi Province as an example. We elaborated on the spatial and temporal characteristics of land-use change from 2000 to 2020 and adopted the carbon emission model method to calculate land-use carbon emissions, also used urban morphological indicators to reveal the main factors of carbon emission changes. The results show that from 2000 to 2020, the land-use change in Shaanxi Province is mainly reflected in the increase in construction land area and the decrease in agricultural land area. Among them, the construction land area increased by 2192 km2, and the agricultural land area decreased by 5006 km2. Land-use carbon emissions increased by 1.28 × 1011 kg during this period. Construction land is a major contributor to carbon emissions. The forestland is the main carbon sink. Carbon emissions showed a spatial pattern of "high in the north, low in the south, and concentrated in the middle." Urban form change is the driving factor affecting land-use carbon emissions in Shaanxi Province. The results of the research contribute to the understanding of regional carbon emission mechanisms and provide a scientific basis for reducing carbon emissions.
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Affiliation(s)
- Chenxu Zhao
- State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi'an University of Technology, No. 5, South Jinhua Road, Xi'an, 710048, Shaanxi, China
| | - Yuling Liu
- State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi'an University of Technology, No. 5, South Jinhua Road, Xi'an, 710048, Shaanxi, China.
| | - Zixuan Yan
- State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi'an University of Technology, No. 5, South Jinhua Road, Xi'an, 710048, Shaanxi, China
- State Key Laboratory of National Forestry Administration On Ecological Hydrology and Disaster Prevention in Arid Regions, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China
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Zhang P, Miao L, Wang F, Li X. Discovering Geographical Flock Patterns of CO 2 Emissions in China Using Trajectory Mining Techniques. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4265. [PMID: 36901276 PMCID: PMC10002456 DOI: 10.3390/ijerph20054265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/24/2023] [Accepted: 02/26/2023] [Indexed: 06/18/2023]
Abstract
Carbon dioxide (CO2) emissions are considered a significant factor that results in climate change. To better support the formulation of effective policies to reduce CO2 emissions, specific types of important emission patterns need to be considered. Motivated by the flock pattern that exists in the domain of moving object trajectories, this paper extends this concept to a geographical flock pattern and aims to discover such patterns that might exist in CO2 emission data. To achieve this, a spatiotemporal graph (STG)-based approach is proposed. Three main parts are involved in the proposed approach: generating attribute trajectories from CO2 emission data, generating STGs from attribute trajectories, and discovering specific types of geographical flock patterns. Generally, eight different types of geographical flock patterns are derived based on two criteria, i.e., the high-low attribute values criterion and the extreme number-duration values criterion. A case study is conducted based on the CO2 emission data in China on two levels: the province level and the geographical region level. The results demonstrate the effectiveness of the proposed approach in discovering geographical flock patterns of CO2 emissions and provide potential suggestions and insights to assist policy making and the coordinated control of carbon emissions.
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Affiliation(s)
- Pengdong Zhang
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
| | - Lizhi Miao
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
| | - Fei Wang
- East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
| | - Xinting Li
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
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