Trend analysis of variations in carbon stock using stock big data

被引:0
|
作者
Yanbin Wu
Yiqiang Guo
Lin Liu
Ni Huang
Li Wang
机构
[1] Hebei University of Economics and Business,College of Management Science and Engineering
[2] Ministry of Land and Resources,Land Consolidation and Rehabilitation Center
[3] Ministry of Land and Resources,Key Laboratory of Land Consolidation and Rehabilitation
[4] Shijiazhuang Engineering and Technology School,The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth
[5] Chinese Academy of Sciences,undefined
来源
Cluster Computing | 2017年 / 20卷
关键词
Land use; Carbon stock; Trend analysis; Big data;
D O I
暂无
中图分类号
学科分类号
摘要
Changes in land use affect the terrestrial carbon stock through changes in the land cover. Research on land use and analysis of variations in carbon stock have practical applications in the optimization of land use and the mitigation of climate change effects. This study was conducted in Baixiang and Julu counties in the Taihang Piedmont by employing the trend analysis method to characterize the variation in county land use and carbon stock. The findings show that in both counties, agricultural and unused land areas decreased while built-up land area increased, and the reduction in cropland was the main reason behind the agricultural land reduction. An inflection point appeared on the cropland curves of Julu, because the cropland area decreased by 1576.97 hm2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{2}$$\end{document} from 2004 to 2006. Cropland area in Baixiang decreased from 1996 to 1998 by a total of 129.89 hm2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{2}$$\end{document} and then remained relatively stable after 1998. The total carbon storage and variation in land use in the two counties displayed similar trends. Total carbon reserves in Julu increased by 2.76 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 104\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{4}$$\end{document} tC (carbon equivalent), while those in Baixiang decreased by 0.63 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 104\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{4}$$\end{document} tC. Carbon stock of built-up land in Julu and Baixiang increased by 2.44 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 104\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{4}$$\end{document} and 1.22 ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 104\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{4}$$\end{document} tC, respectively.
引用
收藏
页码:989 / 1005
页数:16
相关论文
共 50 条
  • [1] Trend analysis of variations in carbon stock using stock big data
    Wu, Yanbin
    Guo, Yiqiang
    Liu, Lin
    Huang, Ni
    Wang, Li
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2017, 20 (02): : 989 - 1005
  • [2] Analysis of Investor Sentiment and Stock Market Volatility Trend Based on Big Data Strategy
    Peng, Du
    2019 INTERNATIONAL CONFERENCE ON ROBOTS & INTELLIGENT SYSTEM (ICRIS 2019), 2019, : 269 - 272
  • [3] Stock trend prediction using sentiment analysis
    Xiao, Qianyi
    Ihnaini, Baha
    PEERJ COMPUTER SCIENCE, 2023, 9
  • [4] Stock Price Prediction based on Stock Big Data and Pattern Graph Analysis
    Jeon, Seungwoo
    Hong, Bonghee
    Kim, Juhyeong
    Lee, Hyun-jik
    IOTBD: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND BIG DATA, 2016, : 223 - 231
  • [5] A Fused Intelligent Computing Approach Using Stock Big Data for Near Future Trend Prediction
    Yan, Tao
    Han, Chongzhao
    Jia, Yong
    BDCAT'19: PROCEEDINGS OF THE 6TH IEEE/ACM INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING, APPLICATIONS AND TECHNOLOGIES, 2019, : 113 - 116
  • [6] Big data based stock trend prediction using deep CNN with reinforcement-LSTM model
    Ishwarappa
    Anuradha, J.
    INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2021,
  • [7] Survey on Sentiment Analysis based Stock Prediction using Big data Analytics
    Balaji, S. Naveen
    Paul, P. Victer
    Saravanan, R.
    2017 INNOVATIONS IN POWER AND ADVANCED COMPUTING TECHNOLOGIES (I-PACT), 2017,
  • [8] Development of stock correlation network models using maximum likelihood method and stock big data
    Guo, Xue
    Zhang, Hu
    Jiang, Feng
    Tian, Tianhai
    2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP), 2018, : 455 - 461
  • [9] Predicting the Stock Market Trend: An Ensemble Approach Using Impactful Exploratory Data Analysis
    Rouf, Nusrat
    Malik, Majid Bashir
    Arif, Tasleem
    INFORMATION, COMMUNICATION AND COMPUTING TECHNOLOGY (ICICCT 2021), 2021, 1417 : 223 - 234
  • [10] Material intensity database for the Dutch building stock: Towards Big Data in material stock analysis
    Sprecher, Benjamin
    Verhagen, Teun Johannes
    Sauer, Marijn Louise
    Baars, Michel
    Heintz, John
    Fishman, Tomer
    JOURNAL OF INDUSTRIAL ECOLOGY, 2022, 26 (01) : 272 - 280