Source Jouranl of CSCD
Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Environmental Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
Volume 41 Issue 10
Oct.  2023
Turn off MathJax
Article Contents
REN Hongyang, DU Ruolan, XIE Guilin, JIN Wenhui, LI Xi, DENG Yuanpeng, MA Wei, WANG Bing. RESEARCH STATUS OF INFLUENCING FACTORS AND IDENTIFICATION METHODS OF CARBON EMISSIONS IN CHINA[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 195-203,244. doi: 10.13205/j.hjgc.202310023
Citation: REN Hongyang, DU Ruolan, XIE Guilin, JIN Wenhui, LI Xi, DENG Yuanpeng, MA Wei, WANG Bing. RESEARCH STATUS OF INFLUENCING FACTORS AND IDENTIFICATION METHODS OF CARBON EMISSIONS IN CHINA[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 195-203,244. doi: 10.13205/j.hjgc.202310023

RESEARCH STATUS OF INFLUENCING FACTORS AND IDENTIFICATION METHODS OF CARBON EMISSIONS IN CHINA

doi: 10.13205/j.hjgc.202310023
  • Received Date: 2023-06-27
    Available Online: 2023-12-26
  • The change of carbon emissions affects the realization of China's carbon peaking and neutrality goals. Study on the influencing factors of carbon emissions is an important part. Currently, scholars at home and abroad have conducted a lot of research on the influencing factors of carbon emissions, involving national, regional, and provincial levels, and industry levels, but those factors have the complexity of spatial and temporal dimensions. The importance of influencing factors of carbon emissions changes dynamically with space and time, and the identification methods for the factors have different applicability, and the changing influencing factors of carbon emissions put forward higher requirements for the identification methods. These characteristics lead to the large and complex research results of existing carbon emissions research and lack of systematic combing. Therefore, from the perspective of identification content and theory, this paper comprehensively analyzes the influencing factors of carbon emissions and the change process of the identification methods, determines the main influencing factors of China's carbon emissions, and puts forward trend and directions for the further research.
  • loading
  • [1]
    FU R, JIN G, CHEN J, et al.The effects of poverty alleviation investment on carbon emissions in China based on the multiregional input-output model[J].Technological Forecasting and Social Change, 2021, 162:120344.
    [2]
    王正, 樊杰.能源消费碳排放的影响因素特征及研究展望[J].地理研究, 2022(10):2587-2599.
    [3]
    LIU L C, FAN Y, WU G, et al.Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use:an empirical analysis[J].Energy Policy, 2007, 35(11):5892-5900.
    [4]
    YANG J, CAI W, MA M D, et al.Driving forces of China's CO2 emissions from energy consumption based on Kaya-LMDI methods[J].Science of the Total Environment, 2020, 711:134569.
    [5]
    ZHOU Y, LIU Y S.Does population have a larger impact on carbon dioxide emissions than income?Evidence from a cross-regional panel analysis in China[J].Applied Energy, 2016, 180:800-809.
    [6]
    王利兵, 张赟.中国能源碳排放因素分解与情景预测[J].电力建设, 2021, 42(9):1-9.
    [7]
    SHUAI C Y, CHEN X, WU Y, et al.Identifying the key impact factors of carbon emission in China:results from a largely expanded pool of potential impact factors[J].Journal of Cleaner Production, 2018, 175:612-623.
    [8]
    杜俊慧, 张克勇, 张雪姣.山西省碳排放影响因素分解及峰值预测[J].中北大学学报(自然科学版), 2018, 39(3):334-343.
    [9]
    王长建, 汪菲, 张虹鸥.新疆能源消费碳排放过程及其影响因素-基于扩展的Kaya恒等式[J].生态学报, 2016, 36(8):2151-2163.
    [10]
    陈军华, 李乔楚.成渝双城经济圈建设背景下四川省能源消费碳排放影响因素研究:基于LMDI模型视角[J].生态经济, 2021, 37(12):30-36.
    [11]
    DONG J, LI C B, WANG Q Q.Decomposition of carbon emission and its decoupling analysis and prediction with economic development:a case study of industrial sectors in Henan Province[J].Journal of Cleaner Production, 2021, 321:129019.
    [12]
    张金良, 贾凡.中国火电行业多模型碳达峰情景预测[J].电力建设, 2022, 43(5):18-28.
    [13]
    YU A, LIN X R, ZHANG Y T, et al.Analysis of driving factors and allocation of carbon emission allowance in China[J].Science of the Total Environment, 2019, 673:74-82.
    [14]
    杨宇, 于宏源, 鲁刚, 等.世界能源百年变局与国家能源安全[J].自然资源学报, 2020, 35(11):2803-2820.
    [15]
    WANG Q, SU M.Drivers of decoupling economic growth from carbon emission-an empirical analysis of 192 countries using decoupling model and decomposition method[J].Environmental Impact Assessment Review, 2020, 81:106356.
    [16]
    LIDDLE B, LUNG S.Age-structure, urbanization, and climate change in developed countries:revisiting STIRPAT for disaggregated population and consumption-related environmental impacts[J].Population and Environment, 2010, 31:317-343.
    [17]
    OKON E O.Population structure and environmental degradation:Implication for EKC hypothesis[J].Bussecon Review of Social Sciences, 2019, 1(2):18-27.
    [18]
    KIM J, LIM H, JO H H.Do aging and low fertility reduce carbon emissions in Korea?Evidence from IPAT augmented EKC analysis[J].International journal of environmental research and public health, 2020, 17(8):2972.
    [19]
    张伟, 朱启贵, 李汉文.能源使用、碳排放与我国全要素碳减排效率[J].经济研究, 2013, 48(10):138-150.
    [20]
    龚利, 屠红洲, 龚存.基于STIRPAT模型的能源消费碳排放的影响因素研究:以长三角地区为例[J].工业技术经济, 2018, 37(8):95-102.
    [21]
    张京玉.影响中国能源消耗碳排放因素分析:基于LMDI分解模型[J].煤炭经济研究, 2019, 39(11):23-28.
    [22]
    邵帅, 张可, 豆建民.经济集聚的节能减排效应:理论与中国经验[J].管理世界, 2019(1):36-60.
    [23]
    王锋, 林翔燕, 刘娟, 等.城镇化对区域碳排放效应的研究综述[J].生态环境学报, 2018, 27(8):1576-1584.
    [24]
    胡雷.我国城镇化对二氧化碳排放的影响机理研究[J].气候变化研究进展, 2016, 12(4):341-347.
    [25]
    LI R R, WANG Q, LIU Y, et al.Per-capita carbon emissions in 147 countries:the effect of economic, energy, social, and trade structural changes[J].Sustainable Production and Consumption, 2021, 27:1149-1164.
    [26]
    李小冬, 朱辰.我国建筑碳排放核算及影响因素研究综述[J].安全与环境学报, 2020, 20(1):317-327.
    [27]
    ZHOU P, ANG B W.Decomposition of aggregate CO2 emissions:a production-theoretical approach[J].Energy Economics, 2008, 30(3):1054-1067.
    [28]
    王永琴, 周叶, 张荣.碳排放影响因子与碳足迹文献综述:基于研究方法视角[J].环境工程, 2017, 35(1):155-159.
    [29]
    ANG B W, LIU N.Handling zero values in the logarithmic mean Divisia index decomposition approach[J].Energy Policy, 2007, 35(1):238-246.
    [30]
    安庆贤, 邹雨晴, 熊贝贝.基于PDA-IDA分解法的碳强度影响因素研究[J].运筹与管理, 2023, 32(4):140-146.
    [31]
    郑蕊, 刁书琪.基于LMDI-PDA-MMI分解法的我国产业体系碳排放驱动因素研究[J].生态经济, 2022, 38(5):33-39.
    [32]
    WANG S J, SHI C Y, FANG C L, et al.Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using geographically weighted regression model[J].Applied Energy, 2019, 235:95-105.
    [33]
    YANG X H, JIA Z, YANG Z M, et al.The effects of technological factors on carbon emissions from various sectors in China:a spatial perspective[J].Journal of Cleaner Production, 2021, 301:126949.
    [34]
    刘卫东, 唐志鹏, 夏炎, 等.中国碳强度关键影响因子的机器学习识别及其演进[J].地理学报, 2019, 74(12):2592-2603.
    [35]
    SU B, ANG B W.Structural decomposition analysis applied to energy and emissions:some methodological developments[J].Energy Economics, 2012, 34(1):177-188.
    [36]
    WANG M, FENG C.Decomposing the change in energy consumption in China's nonferrous metal industry:an empirical analysis based on the LMDI method[J].Renewable & Sustainable Energy Reviews, 2018, 82(3):2652-2663.
    [37]
    DU K R, LIN B Q.Understanding the rapid growth of China's energy consumption:a comprehensive decomposition framework[J].Energy, 2015, 90(1):570-577.
    [38]
    钟兴菊, 龙少波.环境影响的IPAT模型再认识[J].中国人口·资源与环境, 2016, 26(3):61-68.
    [39]
    ZUO Z L, GUO H X, CHENG J H.An LSTM-STRIPAT model analysis of China's 2030 CO2 emissions peak[J].Carbon Management, 2020, 11(6):577-592.
    [40]
    ANG B W.The LMDI approach to decomposition analysis:a practical guide[J].Energy Policy, 2005, 33(7):867-871.
    [41]
    ANG B W.Decomposition analysis for policymaking in energy:which is the preferred method?[J].Energy Policy, 2004, 32(9):1131-1139.
    [42]
    董莹, 许宝荣, 华中, 等.基于LMDI的甘肃省碳排放影响因素分解研究[J].兰州大学学报(自然科学版), 2020, 56(5):606-614.
    [43]
    SMITH S J, van AARDENNE J, KLIMONT Z, et al.Anthropogenic sulfur dioxide emissions:1850-2005[J].Atmospheric Chemistry and Physics, 2011, 11(3):1101-1116.
    [44]
    WANG C J, ZHANG X L, WANG F, et al.Decomposition of energy-related carbon emissions in Xinjiang and relative mitigation policy recommendations[J].Frontiers of Earth Science, 2015, 9:65-76.
    [45]
    何永贵, 于江浩.基于STIRPAT模型的我国碳排放和产业结构优化研究[J].环境工程, 2018, 36(7):174-178

    , 84.
    [46]
    田涛源.基于机器学习的长三角碳排放估算与趋势变化研究[D].上海:东华大学, 2022.
    [47]
    张连发.基于流数据的地理加权回归建模方法的研究[D].武汉:武汉大学, 2019.
    [48]
    WEN L, YUAN X Y.Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO[J].Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China, 2020, 718:137194.
    [49]
    余文梦, 张婷婷, 沈大军.基于随机森林模型的我国县域碳排放强度格局与影响因素演进分析[J].中国环境科学, 42(6):2788-2798.
    [50]
    武炜杰.随机森林算法的应用与优化方法研究[D].无锡:江南大学, 2021.
    [51]
    WEN L, LI Z K.Provincial-level industrial CO2 emission drivers and emission reduction strategies in China:combining two-layer LMDI method with spectral clustering[J].Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China, 2020, 700:134374.
    [52]
    王雅楠, 谢艳琦, 谢丽琴, 等.基于LMDI模型和Q型聚类的中国城镇生活碳排放因素分解分析[J].环境科学研究, 2019, 32(4):539-546.
    [53]
    HE Y, XING Y, ZENG X, et al.Factors influencing carbon emissions from China's electricity industry:analysis using the combination of LMDI and K-means clustering[J].Environmental Impact Assessment Review, 2022, 93:106724.
    [54]
    JIANG J J, YE B, XIE D J, et al.Provincial-level carbon emission drivers and emission reduction strategies in China:combining multi-layer LMDI decomposition with hierarchical clustering[J].Journal of Cleaner Production, 2017, 169:178-190.
    [55]
    陈腾飞.中国碳排放的智能预测及减碳对策研究[D].郑州:华北水利水电大学, 2016.
    [56]
    HUANG Y S, SHEN L, LIU H.Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China[J].Journal of Cleaner Production, 2019, 209:415-423.
    [57]
    SINGPAI B, WU D D.An integrative approach for evaluating the environmental economic efficiency[J].Energy, 2021, 215:118940.
    [58]
    WANG Q W, WANG Y Z, HANG Y, et al.An improved production-theoretical approach to decomposing carbon dioxide emissions[J].Journal of Environmental Management, 2019, 252:109577.
    [59]
    范丹.中国能源消费碳排放变化的驱动因素研究:基于LMDI-PDA分解法[J].中国环境科学, 2013, 33(9):1705-1713.
    [60]
    DU K R, XIE C P, OUYANG X L.A comparison of carbon dioxide (CO2) emission trends among provinces in China[J].Renewable and Sustainable Energy Reviews, 2017, 73:19-25.
    [61]
    李珊珊, 罗良文."十二五"时期中国碳生产率的因素分解与增长动力:基于LMDI-PDA分解法[J].技术经济, 2018, 37(8):77-86.
    [62]
    WANG Q W, HANG Y, SU B, et al.Contributions to sector-level carbon intensity change:an integrated decomposition analysis[J].Department of Industrial and Systems Engineering, National University of Singapore, Singapore;Energy Studies Institute, National University of Singapore, Singapore, 2018, 70:12-25.
    [63]
    ZHA D L, YANG G L, WANG Q W.Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method[J].Energy Economics, 2019, 84:104521.
    [64]
    张洋.基于IPSO-LSTM模型的中国能源消费碳排放预测研究[D].北京:华北电力大学, 2021.
    [65]
    余瑶.基于PSO-RF-BP的中国碳排放预测模型研究[D].太原:山西大学, 2022.
    [66]
    李蓉蓉.长江经济带产业结构、技术创新对碳排放强度的影响研究[D].镇江:江苏大学, 2022.
    [67]
    李欢, 杨珊, 陈建宏, 等.湖南省能源消费碳排放驱动因素及趋势预测实证分析[J].环境工程, 2018, 36(2):152-157.
    [68]
    李健, 马晓芳, 苑清敏.区域碳排放效率评价及影响因素分析[J].环境科学学报, 2019, 39(12):4293-4300.
    [69]
    A K D, B L H, C K Y.Sources of the potential CO2 emission reduction in China:a nonparametric metafrontier approach[J].Applied Energy, 2014, 115(4):491-501.
    [70]
    YU J, ZHOU K, YANG S.Regional heterogeneity of China's energy efficiency in ‘new normal’:a meta-frontier Super-SBM analysis[J].Energy Policy, 2019, 134:110941.
    [71]
    成鑫.技术进步对中国区域碳排放的影响研究[D].镇江:江苏大学.
    [72]
    王锋, 林翔燕, 刘娟, 等.城镇化对区域碳排放效应的研究综述[J].生态环境学报, 2018, 27(8):1576-1584.
    [73]
    WANG Q, LIN J, ZHOU K, et al.Does urbanization lead to less residential energy consumption?a comparative study of 136 countries[J].Energy, 2020, 202:117765.
    [74]
    黄蕊, 王铮, 丁冠群, 等.基于STIRPAT模型的江苏省能源消费碳排放影响因素分析及趋势预测[J].地理研究, 2016, 35(4):781-789.
    [75]
    吴昊, 车国庆.中国人口年龄结构如何影响了地区碳排放?基于动态空间STIRPAT模型的分析[J].吉林大学社会科学学报, 2018, 58(3):67-77.
    [76]
    杨涵墨.中国人口老龄化新趋势及老年人口新特征[J].人口研究, 2022, 46(5):104-116.
    [77]
    江文渊, 曾珍香, 张征云, 等.城市水土资源利用碳排放系统动力学仿真研究:以天津市为例[J].安全与环境学报, 2021, 21(2):882-892.
    [78]
    孙艺璇, 程钰, 张含朔.城市工业土地集约利用对碳排放效率的影响研究:以中国15个副省级城市为例[J].长江流域资源与环境, 2020, 29(8):1703-1712.
    [79]
    张杰, 陈海, 刘迪, 等.基于县域尺度土地利用碳排放的时空分异及影响因素研究[J].西北大学学报(自然科学版), 2022, 52(1):21-31.
    [80]
    周新.基于结构方程模型的中国碳排放影响因素分析[D].北京:北京交通大学, 2019.
    [81]
    CHENG S L, CHEN Y T, MENG F X, et al.Impacts of local public expenditure on CO2 emissions in Chinese cities:a spatial cluster decomposition analysis[J].Resources, Conservation and Recycling, 2021, 164:105217.
    [82]
    周迪, 罗东权.绿色税收视角下产业结构变迁对中国碳排放的影响[J].资源科学, 2021, 43(4):693-709.
    [83]
    YU S W, WEI Y M, GUO H X, et al.Carbon emission coefficient measurement of the coal-to-power energy chain in China[J].Applied Energy, 2014, 114:290-300.
    [84]
    董聪, 董秀成, 蒋庆哲, 等.《巴黎协定》背景下中国碳排放情景预测:基于BP神经网络模型[J].生态经济, 2018, 34(2):18-23.
    [85]
    彭旭.中国能源结构与碳排放强度的关系研究[D].北京:华北电力大学, 2016.
    [86]
    侯亚荣.能源结构、碳排放与经济高质量发展的动态关系研究:基于真实发展指标估算[D].大连:东北财经大学, 2022.
    [87]
    郭正权, 张兴平, 郑宇花.能源价格波动对能源-环境-经济系统的影响研究[J].中国管理科学, 2018, 26(11):22-30.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (804) PDF downloads(23) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return