• Overview of Chinese core journals
  • Chinese Science Citation Database(CSCD)
  • Chinese Scientific and Technological Paper and Citation Database (CSTPCD)
  • China National Knowledge Infrastructure(CNKI)
  • Chinese Science Abstracts Database(CSAD)
  • JST China
  • SCOPUS
HE Miao, ZHANG Yang, LUAN Shaohan, GAO Hui, ZHANG Wenxia, HUANG Junli. Analysis of China's Urban and Rural Consumption Carbon Emissions Based on LMDI-Tapio-GA-BP[J]. Journal of South China Normal University (Natural Science Edition), 2024, 56(6): 1-16. DOI: 10.6054/j.jscnun.2024071
Citation: HE Miao, ZHANG Yang, LUAN Shaohan, GAO Hui, ZHANG Wenxia, HUANG Junli. Analysis of China's Urban and Rural Consumption Carbon Emissions Based on LMDI-Tapio-GA-BP[J]. Journal of South China Normal University (Natural Science Edition), 2024, 56(6): 1-16. DOI: 10.6054/j.jscnun.2024071

Analysis of China's Urban and Rural Consumption Carbon Emissions Based on LMDI-Tapio-GA-BP

More Information
  • Received Date: May 29, 2024
  • Based on IPCC-CLA method, the carbon emissions from household consumption in China were traced. And the driving factors were decomposed and decoupled by the LMDI-Tapio method. Then the total carbon emissions and driving effects trends in 2030 were predicted by the ARMI-GA-BP method. The results show that the carbon emissions from living consumption of urban and rural residents in China were rising from 2003 to 2019, and in general, the carbon emissions from living consumption of urban residents are relatively higher, and the growth of indirect carbon emissions from living consumption of urban residents is the main source of the widening gap between urban and rural carbon emissions. The decoupling of main driving factors indicates the negative decoupling between urban and rural energy utilization intensity, energy utilization structure, population and household consumption carbon emissions. Urban per capita income level and household consumption carbon emissions show a phased transition between expansionary negative connection and expansionary connection, and the decoupling state of rural per capita income level shows a good transition to weak decoupling. In the influence trend of driving factors, the driving role of urban population will further rise, the driving role of rural population will further decline, and the driving role of energy intensity will further decline. The prediction of urban direct carbon emissions and rural direct carbon emissions from 2020 to 2030 will always be on the rise, but the growth rate of carbon emissions will peak in 2021 and then show a downward trend, with a large decline in urban direct carbon emissions and a relatively slow decline in rural direct carbon emissions. The impacts of changes in income and urban population are obvious. Taking into account the overall development of urban and rural areas, and paying attention to the energy utilization in urban and rural areas and the innovation of consumption patterns in rural areas are effective ways to promote residents consumption carbon emission reduction.

  • [1]
    中华人民共和国国务院. 2030年前碳达峰行动方案[EB/OL]. (2021-10-26)[2024-05-30]. https://www.gov.cn/zhengce/content/2021-10/26/content_5644984.htm.
    [2]
    LIU Z, DENG Z, DAVIS S J, et al. Global carbon emissions in 2023[J]. Nature Reviews Earth & Environment, 2024, 5(4): 253-254.
    [3]
    联合国环境规划署. 2023年排放差距报告[R]. (2023-11-20)[2024-05-30]. https://www.unep.org/zh-hans/resources/2023nianpaifangchajubaogao.
    [4]
    张志强. 中国居民生活碳排放评估报告[M]. 北京: 科学出版社, 2019.
    [5]
    HERENDEEN R, TANAKA J. Energy cost of living[J]. Energy, 1976, 1(2): 165-178. doi: 10.1016/0360-5442(76)90015-3
    [6]
    耿涌, 董会娟, 郗凤明, 等. 应对气候变化的碳足迹研究综述[J]. 中国人口·资源与环境, 2010, 20(10): 6-12.

    GENG Y, DONG H J, XI F M, et al. Review of carbon footprint studies in response to climate change[J]. China Population, Resources and Environment, 2010(10): 6-12.
    [7]
    GUO D, CHEN H, LONG R, et al. An integrated measurement of household carbon emissions from a trading-oriented perspective: a case study of urban families in Xuzhou, China[J]. Journal of Cleaner Production, 2018, 188: 613-624. doi: 10.1016/j.jclepro.2018.04.025
    [8]
    计志英, 赖小锋, 贾利军. 家庭部门生活能源消费碳排放: 测度与驱动因素研究[J]. 中国人口·资源与环境, 2016, 26(5): 64-72. doi: 10.3969/j.issn.1002-2104.2016.05.008

    JI Z Y, LAI X F, JIA L J. Carbon emissions from household energy consumption in the household sector: measurement and driver factors[J]. China Population, Resources and Environment, 2016, 26(5): 64-72. doi: 10.3969/j.issn.1002-2104.2016.05.008
    [9]
    华怡婷, 石宝峰. 互联网使用与家庭间接碳排放: 测度及影响因素分析[J]. 重庆大学学报(社会科学版), 2023, 29(1): 117-134.

    HUA Y T, SHI B F. Internet use and household indirect carbon emissions: an analysis of measures and influencing factors[J]. Journal of Chongqing University(Social Science Edition), 2023, 29(1): 117-134.
    [10]
    贾晋, 温虎, 张齐圣. 正规信贷对农村家庭消费隐含碳排放的影响效应分析——来自中国农村家庭追踪调查数据的证据[J]. 农村经济, 2022(9): 98-106.

    JIA J, WEN H, ZHANG Q S. Analysis of the effect of formal credit on the consumption of rural households: Evidence from the tracking survey data of rural households in China[J]. Rural Economy, 2022(9): 98-106.
    [11]
    孙悦. 居民消费碳排放及其影响因素研究——基于家庭生命周期视角的实证分析[J]. 人口学刊, 2022, 44(5): 86-98.

    SUN Y. Research on household consumption carbon emission and its influencing factors: empirical analysis based on family life cycle perspective[J]. Journal of Demographic Studies, 2022, 44(5): 86-98.
    [12]
    尚梅, 徐紫瑞, 闫晓霞, 等. 中国家庭居民消费碳排放动态演进及驱动因素研究[J]. 生态经济, 2023, 39(3): 23-30.

    SHANG M, XU Z R, YAN X X, et al. Research on the dynamic evolution and driving factors of carbon emission of Chinese residents[J]. Ecological Economy, 2023, 39(3): 23-30.
    [13]
    陈英姿, 胡亚琪. 人口老龄化对居民消费碳排放的影响路径研究[J]. 人口学刊, 2022, 44(5): 99-112.

    CHEN Y Z, HU Y Q. Research on the influence path of population aging on the carbon emission of household consumption[J]. Demographic Journal, 2022, 44(5): 99-112.
    [14]
    尚梅, 张风斌, 胡振. 家庭异质性视角下城乡居民家庭碳排放研究——以陕西为例[J]. 生态经济, 2021, 37(2): 13-21;34.

    SHANG M, ZHANG W B, HU Z. Research on carbon emission by consumption of urban and rural residents from the perspective of family heterogeneity: take Shaanxi Province as an example[J]. Ecological Economy, 2021, 37(2): 13-21;34.
    [15]
    王亚红, 蔡亚平. 农户贫困脆弱性、收入水平和消费升级对居民消费碳排放的影响研究[J]. 中国物价, 2023(2): 55-58.

    WANG Y H, CAI Y P. Research on the impact of peasant household poverty vulnerability, income level and consumption upgrading on carbon emissions of household consumption[J]. China Prices, 2023(2): 55-58.
    [16]
    姜璐, 丁博文鹏, 周学伟, 等. 青海高原西宁城镇社区家庭能耗直接碳排放研究[J]. 地理科学, 2023, 43(1): 119-129.

    JIANG L, DING B W P, ZHOU X W, et al. Research on direct carbon emission of household energy consumption in Urban Community in Xining, Qinghai Plateau[J]. Geographic Sciences, 2023, 43(1): 119-129.
    [17]
    CHEN Y, JIANG L. Influencing factors of direct carbon emissions of households in urban villages in Guangzhou, China[J]. International Journal of Environmental Research and Public Health, 2022, 19(24): 17054/1-12. doi: 10.3390/ijerph192417054
    [18]
    李治国, 王杰. 中国城乡居民消费碳排放核算及驱动因素分析[J]. 统计与决策, 2021, 37(20): 48-52.

    LI Z G, WANG J. Accounting of carbon emissions and driving factors of urban and rural residents in China[J]. Statistics and Decision-making, 2021, 37(20): 48-52.
    [19]
    ZHANG S, SHI B, JI H. How to decouple income growth from household carbon emissions: a perspective based on urban-rural differences in China[J]. Energy Economics, 2023, 125: 106816/1-14. doi: 10.1016/j.eneco.2023.106816
    [20]
    李娜娜, 赵月, 王军锋. 中国城市居民收入和储蓄增长对家庭能耗碳排放的区域异质性及政策应对[J]. 生态经济, 2022, 38(1): 30-35.

    LI N N, ZHAO Y, WANG J F. Regional heterogeneity and policy response to household energy consumption and carbon emissions from the growth of income and savings of urban residents in China[J]. Ecological Economy, 2022, 38(1): 30-35.
    [21]
    HUANG S, YANG L, YANG C, et al. Obscuring effect of income inequality and moderating role of financial literacy in the relationship between digital finance and China's household carbon emissions[J]. Journal of Environmental Management, 2024, 351: 119927/1-13. doi: 10.1016/j.jenvman.2023.119927
    [22]
    CHENG S, WANG K, MENG F, et al. The unanticipated role of fiscal environmental expenditure in accelerating household carbon emissions: evidence from China[J]. Energy Policy, 2024, 185: 113962/1-15. doi: 10.1016/j.enpol.2023.113962
    [23]
    CHEN X P, WANG G K, GUO X J, et al. An analysis based on SD model for energy-related CO2 mitigation in the Chinese household sector[J]. Energies, 2016, 9(12): 1062/1-5. doi: 10.3390/en9121062
    [24]
    AN K X, ZHANG S H, HUANG H, et al. Socioeconomic impacts of household participation in emission trading scheme: a computable general equilibrium-based case study[J]. Applied Energy, 2021, 288: 116647/1-12. doi: 10.1016/j.apenergy.2021.116647
    [25]
    赵润菲. 收入来源、消费结构与家庭隐含碳排放——基于CHFS数据的实证分析[J]. 时代经贸, 2023, 20(9): 25-31.

    ZHAO R F. Income sources, consumption structure and household implied carbon emissions: empirical analysis based on CHFS data[J]. Times economy and Trade, 2023, 20(9): 25-31.
    [26]
    MENG W L, YUAN G C, SUN Y P. Expansion of social networks and household carbon emissions: evidence from household survey in China[J]. Energy Policy, 2023, 174: 113460/1-13. doi: 10.1016/j.enpol.2023.113460
    [27]
    冯建平. 电子商务、支付便捷性对家庭消费升级的影响效应研究[J]. 商业经济研究, 2024(8): 61-64.

    FENG J P. Study on the effect of e-commerce and payment convenience on family consumption upgrading[J]. Business Economics Research, 2024(8): 61-64.
    [28]
    彭湣皓. 跨境电商发展对我国居民家庭消费的溢出效应——基于2017—2021年家庭追踪调查数据的实证[J]. 商业经济研究, 2024(2): 71-74.

    PENG M H. The spillover effect of cross-border e-commerce development on Chinese household consumption: based on the empirical evidence of 2017—2021 family tracking survey data[J]. Business Economics Research, 2024(2): 71-74.
    [29]
    赵方方. 智慧城市建设对老年家庭消费的影响研究——基于智慧城市试点的准自然实验[J]. 商业经济研究, 2024(8): 69-72.

    ZHAO F F. Research on the influence of smart city construction on the consumption of elderly families: quasi-natural experiment based on the smart city pilot[J]. Business Economics Research, 2024(8): 69-72.
    [30]
    谢强, 唐珏, 吕思诺, 等. 失业保险、流动性约束及家庭消费[J]. 经济学, 2024, 24(2): 481-498.

    XIE Q, TANG J, LU S N, et al. Unemployment insurance, liquidity constraints, and household consumption[J]. Economics, 2024, 24(2): 481-498.
    [31]
    包文, 赵春明. 就业质量促进家庭消费升级的理论与实证分析[J]. 财经理论与实践, 2024, 45(2): 121-127.

    BAO W, ZHAO C M. Theory and empirical analysis of employment quality to promote household consumption upgrading[J]. Financial Theory and Practice, 2024, 45(2): 121-127.
    [32]
    朱健, 李伟, 李子芳. 长期照护保险对农村家庭消费水平的影响——基于CHARLS数据的实证分析[J]. 财经理论与实践, 2024, 45(2): 104-111.

    ZHU J, LI W, LI Z F. The effect of long-term care insurance on the consumption level of rural households: an empirical analysis based on CHARLS data[J]. Financial Theory and Practice, 2024, 45(2): 104-111.
    [33]
    杨碧云, 梁子昊, 易行健, 等. 机会不平等影响居民消费的机制与效应——基于CFPS数据的经验研究[J]. 南开经济研究, 2024(3): 20-40.

    YANG B Y, LIANG Z H, YI Y J, et al. Mechanism and effect of unequal opportunity affecting household consumption: empirical study based on CFPS data[J]. Nankai Economic Research, 2024(3): 20-40.
    [34]
    解垩, 高梦桃. 公共转移支付对家庭消费相对剥夺的影响[J]. 中南财经政法大学学报, 2024(2): 40-51.

    JIE E, GAO M T. The effect of public transfer payments on the relative deprivation of household consumption[J]. Journal of Zhongnan University of Economics and Law, 2024(2): 40-51.
    [35]
    班梓瑜, 任羽卓. 住房财富对城镇居民消费的影响——基于CHFS数据分析[J]. 商业经济研究, 2024(5): 51-54.

    BAN Z Y, REN Y Z. The impact of housing wealth on urban resident consumption: is based on the CHFS data analysis[J]. Business Economics Research, 2024(5): 51-54.
    [36]
    王平, 刘淼淼, 陈建东, 等. 2019年个人所得税改革对家庭消费升级的影响研析[J]. 税务研究, 2024(3): 93-101.

    WANG P, LIU M M, CHEN J D, et al. Analysis of the impact of personal income tax reform on family consumption upgrading in 2019[J]. Tax Research, 2024(3): 93-101.
    [37]
    刘娜, 陈安平. 创新能促进居民消费吗?[J]. 消费经济, 2023, 39(2): 57-69.

    LIU N, CHEN A P. Can innovation boost household consumption?[J]. Consumer Economy, 2023, 39(2): 57-69.
    [38]
    代洪娜, 曾煜磊, 施庆利, 等. 碳达峰与碳中和背景下省域高速公路网碳排放精细化测算方法[J]. 华南师范大学学报(自然科学版), 2023, 55(4): 1-13. doi: 10.6054/j.jscnun.2023044

    DAI H N, ZENG Y L, SHI Q L, et al. Fine calculation method of carbon emission of provincial expressway network under the background of carbon peak and carbon neutrality[J]. Journal of South China Normal University(Natural Science Edition), 2023, 55(4): 1-13. doi: 10.6054/j.jscnun.2023044
    [39]
    付云鹏, 马树才, 宋宝燕. 中国城乡居民消费碳排放差异及影响因素——基于面板数据的实证分析[J]. 经济问题探索, 2016(10): 43-50.

    FU Y P, MA S C, SONG B Y. Differences in carbon emission consumption between urban and rural residents and influencing factors: empirical analysis based on panel data[J]. Exploration of Economic Issues, 2016(10): 43-50.
    [40]
    TAPIO P. Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in finland between 1970 and 2001[J]. Transport Policy, 2005, 12(2): 137-151.
    [41]
    文扬, 马中, 吴语晗, 等. 京津冀及周边地区工业大气污染排放因素分解——基于LMDI模型分析[J]. 中国环境科学, 2018, 38(12): 4730-4736.

    WEN Y, MA Z, WU Y H, et al. Decomposition of industrial air pollution emission factors in the Beijing-Tianjin-Hebei region and its surrounding areas: based on LMDI model analysis[J]. Environmental Science in China, 2018, 38(12): 4730-4736.
    [42]
    WANG Q, JIANG R, ZHAN L. Is decoupling economic growth from fuel consumption possible in develop countries?-A comparison of China and India[J]. Journal of Cleaner Production, 2019, 229: 806-817.

Catalog

    Article views (84) PDF downloads (32) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return