IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p7627-d845209.html
   My bibliography  Save this article

Study on the Spatial and Temporal Differentiation Pattern of Carbon Emission and Carbon Compensation in China’s Provincial Areas

Author

Listed:
  • Hequ Huang

    (College of Geographical Science, Harbin Normal University, Harbin 150025, China
    Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China)

  • Jia Zhou

    (College of Geographical Science, Harbin Normal University, Harbin 150025, China
    Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China)

Abstract

Excessive carbon emissions lead to global warming, which has attracted widespread attention in the global society. Carbon emissions and land use are closely related. An analysis of land use carbon emissions and carbon fairness can provide guidance for the formulation of energy conservation and emission reduction policies. This study uses data on agricultural production activities, land use and energy consumption and uses the carbon emission coefficient method to calculate carbon emissions and carbon absorption. The tendency value is used to analyze trends in land use carbon emissions and carbon absorption. The Gini coefficient, ecological support coefficient and economic contributive coefficient are used to analyze the fairness and difference of carbon emissions. The results showed that: (1) During the study period, there were fewer provinces with rapid growth in carbon emissions and carbon absorption and more provinces with slow growth. (2) Cultivated land and woodland are the main carriers of land use carbon absorption, and most provinces steadily maintain the type of carbon absorption to which they belong. (3) Carbon emissions from construction land are the main source of total carbon emissions, and the high concentration areas of carbon emissions are mainly located in the more economically developed areas. (4) There are obvious regional differences in the net carbon emissions. By 2015, Shanxi–Shandong High–High agglomeration areas and Yunnan–Guangxi Low–Low agglomeration areas were finally formed. (5) The distribution of carbon emissions in different provinces is not fair, and the spatial distribution is obviously different. Based on the analysis results, relevant suggestions are made from the perspectives of carbon emission reduction and carbon sink enhancement.

Suggested Citation

  • Hequ Huang & Jia Zhou, 2022. "Study on the Spatial and Temporal Differentiation Pattern of Carbon Emission and Carbon Compensation in China’s Provincial Areas," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7627-:d:845209
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/7627/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/13/7627/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Laamrani & Paul R. Voroney & Adam W. Gillespie & Abdelghani Chehbouni, 2021. "Development of a Land Use Carbon Inventory for Agricultural Soils in the Canadian Province of Ontario," Land, MDPI, vol. 10(7), pages 1-20, July.
    2. Jinjie Zhao & Lei Kou & Haitao Wang & Xiaoyu He & Zhihui Xiong & Chaoqiang Liu & Hao Cui, 2022. "Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    3. Fan, Jianshuang & Zhou, Lin & Zhang, Yan & Shao, Shuai & Ma, Miao, 2021. "How does population aging affect household carbon emissions? Evidence from Chinese urban and rural areas," Energy Economics, Elsevier, vol. 100(C).
    4. Zhao, Jun & Shahbaz, Muhammad & Dong, Xiucheng & Dong, Kangyin, 2021. "How does financial risk affect global CO2 emissions? The role of technological innovation," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hui Wen & Yi Li & Zirong Li & Xiaoxue Cai & Fengxia Wang, 2022. "Spatial Differentiation of Carbon Budgets and Carbon Balance Zoning in China Based on the Land Use Perspective," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    2. Jia Peng & Xianli Hu & Xinyue Fan & Kai Wang & Hao Gong, 2023. "The Impact of the Green Economy on Carbon Emission Intensity: Comparisons, Challenges, and Mitigating Strategies," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    3. Zhenhua Wu & Linghui Zhou & Yabei Wang, 2022. "Prediction of the Spatial Pattern of Carbon Emissions Based on Simulation of Land Use Change under Different Scenarios," Land, MDPI, vol. 11(10), pages 1-19, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmad, Munir & Wu, Yiyun, 2022. "Natural resources, technological progress, and ecological efficiency: Does financial deepening matter for G-20 economies?," Resources Policy, Elsevier, vol. 77(C).
    2. Ahmad, Munir & Zhu, Xiwei & Wu, Yiyun, 2022. "The criticality of international tourism and technological innovation for carbon neutrality across regional development levels," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    3. Zhao, Jun & Shahbaz, Muhammad & Dong, Kangyin, 2022. "How does energy poverty eradication promote green growth in China? The role of technological innovation," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Dong, Kangyin & Dong, Xiucheng & Jiang, Qingzhe & Zhao, Jun, 2021. "Assessing energy resilience and its greenhouse effect: A global perspective," Energy Economics, Elsevier, vol. 104(C).
    5. Lei, Mingyu & Cai, Wenjia & Liu, Wenling & Wang, Can, 2022. "The heterogeneity in energy consumption patterns and home appliance purchasing preferences across urban households in China," Energy, Elsevier, vol. 253(C).
    6. Usman, Ahmed & Ozturk, Ilhan & Ullah, Sana & Hassan, Ali, 2021. "Does ICT have symmetric or asymmetric effects on CO2 emissions? Evidence from selected Asian economies," Technology in Society, Elsevier, vol. 67(C).
    7. Wang, Jianda & Dong, Kangyin & Sha, Yezhou & Yan, Cheng, 2022. "Envisaging the carbon emissions efficiency of digitalization: The case of the internet economy for China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    8. Mu Lin & Jingxin Gao & Yongjie Du & Pengyu Ren, 2023. "Mismatch in Urban Construction Land Use and Economic Growth: Empirical Evidence from China," Land, MDPI, vol. 12(2), pages 1-21, February.
    9. Shahzad, Umer & Ferraz, Diogo & Nguyen, Huu-Huan & Cui, Lianbiao, 2022. "Investigating the spill overs and connectedness between financial globalization, high-tech industries and environmental footprints: Fresh evidence in context of China," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    10. Lu, Zhou & Mahalik, Mantu Kumar & Mahalik, Hrushikesh & Zhao, Rui, 2022. "The moderating effects of democracy and technology adoption on the relationship between trade liberalisation and carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    11. Zhang, Shaobin & Shi, Baofeng & Ji, Hao, 2023. "How to decouple income growth from household carbon emissions: A perspective based on urban-rural differences in China," Energy Economics, Elsevier, vol. 125(C).
    12. Shengqiang Yang & Donglin Li & Heping Liao & Lin Zhu & Miaomiao Zhou & Zhicong Cai, 2023. "Analysis of the Balance between Supply and Demand of Arable Land in China Based on Food Security," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
    13. Liu, Qiang & Zhao, Zhongwei & Liu, Yiran & He, Yao, 2022. "Natural resources commodity prices volatility, economic performance and environment: Evaluating the role of oil rents," Resources Policy, Elsevier, vol. 76(C).
    14. Zhu, Penghu & Lin, Boqiang, 2022. "Do the elderly consume more energy? Evidence from the retirement policy in urban China," Energy Policy, Elsevier, vol. 165(C).
    15. Lin, Boqiang & Ma, Ruiyang, 2022. "Green technology innovations, urban innovation environment and CO2 emission reduction in China: Fresh evidence from a partially linear functional-coefficient panel model," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    16. Yuhong Zhao & Ruirui Liu & Zhansheng Liu & Liang Liu & Jingjing Wang & Wenxiang Liu, 2023. "A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    17. Ahmad, Mahmood & Ahmed, Zahoor & Khan, Sana Akbar & Alvarado, Rafael, 2023. "Towards environmental sustainability in E−7 countries: Assessing the roles of natural resources, economic growth, country risk, and energy transition," Resources Policy, Elsevier, vol. 82(C).
    18. Yiqun Wu & Yuan Sun & Congyue Zhou & Yonghua Li & Xuanli Wang & Huifang Yu, 2023. "Spatial–Temporal Characteristics of Carbon Emissions in Mixed-Use Villages: A Sustainable Development Study of the Yangtze River Delta, China," Sustainability, MDPI, vol. 15(20), pages 1-21, October.
    19. Liu, Yang & Dong, Kangyin & Jiang, Qingzhe, 2023. "Assessing energy vulnerability and its impact on carbon emissions: A global case," Energy Economics, Elsevier, vol. 119(C).
    20. Badeeb, Ramez Abubakr & Wang, Bo & Zhao, Jun & Khan, Zeeshan & Uktamov, Khusniddin Fakhriddinovich & Zhang, Changyong, 2023. "Natural resources extraction and financial inclusion: Linear and non-linear effect of natural resources on financial sector," Resources Policy, Elsevier, vol. 85(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7627-:d:845209. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.