留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions

Ruiling HAN Lingling LI Xiaoyan ZHANG Zi LU Shaohua ZHU

HAN Ruiling, LI Lingling, ZHANG Xiaoyan, LU Zi, ZHU Shaohua, 2022. Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions. Chinese Geographical Science, 32(2): 218−236 doi:  10.1007/s11769-021-1247-z
Citation: HAN Ruiling, LI Lingling, ZHANG Xiaoyan, LU Zi, ZHU Shaohua, 2022. Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions. Chinese Geographical Science, 32(2): 218−236 doi:  10.1007/s11769-021-1247-z

doi: 10.1007/s11769-021-1247-z

Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions

Funds: Under the auspices of the National Natural Science Foundation of China (No. 42071266), the Third Batch of Hebei Youth Top Talent Project, Natural Science Foundation of Hebei Province (No. D2021205003)
More Information
    Corresponding author: LU Zi. E-mail: luzi1960@126.com
  • The eastern region includes Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Hebei, Guangdong, and Hainan. The western region includes Inner Mongolia, Tibet Autonomous Region, Xinjiang Uygur Autonomous Region, and Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai and Ningxia. The central region includes Anhui, Jiangxi, Henan, Hubei, Hunan and Shanxi provinces. The northeastern region includes Heilongjiang, Jilin and Liaoning provinces.
    • 关键词:
    •  / 
    •  / 
    •  / 
    •  / 
    •  
    The eastern region includes Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Hebei, Guangdong, and Hainan. The western region includes Inner Mongolia, Tibet Autonomous Region, Xinjiang Uygur Autonomous Region, and Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai and Ningxia. The central region includes Anhui, Jiangxi, Henan, Hubei, Hunan and Shanxi provinces. The northeastern region includes Heilongjiang, Jilin and Liaoning provinces.
    注释:
  • Figure  1.  China’s aviation carbon emissions and growth rate from 2000 to 2019

    Figure  2.  Cumulative contribution rate of various influencing factors on aviation carbon emissions

    Figure  3.  Annual contribution rate of various influencing factors on aviation carbon emissions

    Figure  4.  Trends of changes of the four influencing factors on China’s aviation carbon emissions

    Figure  5.  The difference evolution (trends) of the effect contribution degree of air transportation revenue to the aviation carbon emission of various provinces in China. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    Figure  6.  The difference evolution (trends) of the effect contribution degree of aviation route structure to the aviation carbon emission of various provinces in China. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    Figure  7.  The difference evolution(trends) of the effect contribution degree of air transportation intensity to the aviation carbon emission of various provinces in China. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    Figure  8.  The difference evolution (trends) of the effect contribution degree of aviation energy intensity to the aviation carbon emission of various provinces in China. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    Figure  9.  Decoupling efforts of various provinces’ route structure effects, air transportation intensity effects, and aviation energy intensity effects. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    Table  1.   The decoupling state of China’s air transportation revenue and aviation carbon emissions

    Decoupling typeAir carbon emissions
    variation (∆C)
    Air transportation revenue
    variation (∆Q)
    Decoupling elasticity index
    $ {{\varepsilon }} $ range
    Decoupling discriminationQuadrant distribution
    decoupling < 0 > 0 ≤ 0 Strong decoupling
    > 0 > 0 (0, 0.8) Weak decoupling
    < 0 < 0 ≥ 1.2 Decreasing decoupling
    Negative decoupling > 0 < 0 ≤ 0 Strong negative decoupling
    < 0 < 0 (0, 0.8) Weak negative decoupling
    > 0 > 0 ≥ 1.2 Expansion negative decoupling
    Connection > 0 > 0 [0.8,1.2) Increase connection
    < 0 < 0 [0.8,1.2) Decay connection
    Notes: source, Lai et al., 2020; Zhang et al., 2019
    下载: 导出CSV

    Table  2.   Logarithmic Mean Divisia Index (LDMI) analysis and calculation results

    TimeOverall annual effectAir transportation revenueAviation route structureAir transportation intensityAviation energy intensity
    Annual effectCumulative effectAnnual effectCumulative effectAnnual effectCumulative effectAnnual effectCumulative effectAnnual effectCumulative effect
    2000−20011.091.091.081.080.990.991.101.100.900.90
    2001−20021.112.201.502.580.931.930.661.750.891.79
    2002−20031.013.210.973.550.952.881.162.920.942.74
    2003−20041.314.521.455.001.043.920.873.790.943.68
    2004−20051.125.641.156.151.004.920.974.750.984.65
    2005−20061.126.761.237.380.995.900.935.680.925.57
    2006−20071.147.911.178.560.996.891.066.740.926.49
    2007−20081.038.941.059.601.007.890.917.661.007.48
    2008−20091.1310.071.0210.620.868.751.419.071.008.48
    2009−20101.1611.231.4212.041.089.830.799.860.859.33
    2010−20111.0812.311.1913.230.9410.770.8510.711.0110.34
    2011−20121.0913.391.0714.300.9911.760.9611.671.0611.39
    2012−20131.1214.511.0115.311.0212.781.1612.831.0312.42
    2013−20141.1115.621.0816.391.0213.801.0513.890.9913.42
    2014−20151.1316.751.0417.441.0614.861.1615.050.9914.40
    2015−20161.1317.881.0718.500.9915.851.1416.190.9915.39
    2016−20171.1319.001.1219.631.0016.851.0117.191.0016.39
    2017−20181.0920.091.1520.781.0217.870.9218.120.9617.35
    2018−20191.0621.161.1421.921.0418.900.8518.960.9918.34
    Total21.16212.2821.91224.0618.91187.3418.96187.9318.36180.55
    下载: 导出CSV

    Table  3.   China aviation carbon emissions and air transportation revenue decoupling relationship distribution table in 2000–2019

    Region and
    ratio
    Total number of samplesExpansion negative decouplingWeak decouplingGrowth connectionStrong negative decouplingStrong decouplingWeak negative decouplingRecession
    decoupling
    Nation 620 281 146 113 50 26 2 2
    Ratio / % 100 45.32 23.55 18.23 8.06 4.19 0.32 0.32
    Eastern region 200 78 59 33 17 11 17 0
    Ratio / % 100.00 390.. 29.50 16.50 8.50 5.50 8.50 0
    Western region 240 106 10 47 21 10 0 2
    Ratio / % 100.00 44.17 4.17 19.58 8.75 4.17 0 0.83
    Central region 120 66 21 23 6 4 0 0
    Ratio / % 100.00 55.00 17.50 19.17 5.00 3.33 0 0
    Northeast region 60 31 12 10 6 1 0 0
    Ratio / % 100.00 51.67 20.00 16.67 10.00 1.67 0 0
    下载: 导出CSV
  • [1] Chen Qiting, Lu Chenting, Zhou Dequn, 2014. Index decomposition of the factors influence China civil aviation markets’ carbon emissions based on LMDI method. Journal of Tianjin University (Social Sciences), 16(5): 397–403. (in Chinese)
    [2] Chinese Department of Planning and Development of Civil Aviation Administration, 2000−2019. Look at civil aviation from statistics. Beijing: China Civil Aviation Press.
    [3] Civil Aviation Administration of China, 2019. Statistical Bulletin on development of Civil Aviation Industry. Beijing: China Civil Aviation Press.
    [4] Dai Rufeng, Chou Jieming, Dong Wenjie et al., 2017. Historical characteristics and future trend of carbon emission in China. Journal of Beijing Normal University (Natural Science), 53(1): 80–86. (in Chinese)
    [5] Dietz T, Rosa E A, 1994. Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review, 1(2): 277–300.
    [6] Dong Jiankang, Zong Miao, Chen Jingjie, 2014. A method to predict the carbon emissions of civil aviation based on stirpat model. Environmental Engineering, 32(7): 165–169. (in Chinese)
    [7] Fan W Y, Sun Y F, Zhu T L et al., 2012. Emissions of HC, CO, NOx, CO2, and SO2 from civil aviation in China in 2010. Atmospheric Environment, 56: 52–57. doi:  10.1016/j.atmosenv.2012.03.052
    [8] Fan Shengyue, Liu Wenwen, Zhou Ning, 2019. Environmental pressure analysis of desertification in inner mongolia based on STIRPAT model. Journal of Desert Research, 39(3): 117–125. (in Chinese)
    [9] Guan D B, Hubacek K, Weber C L et al., 2008. The drivers of Chinese CO2 emissions from 1980 to 2030. Global Environmental Change, 18(4): 626–634. doi:  10.1016/j.gloenvcha.2008.08.001
    [10] Han Ruiling, Lu Zi, Yao Haifang, 2019. Study on dynamic transformation, application and comparison of aviation carbon emission environmental damage assessment methods. Advances in Earth Science, 34(7): 688–696. (in Chinese)
    [11] He J C, Xu Y Q, 2012. Estimation of the aircraft CO2 emissions of China’s civil aviation during 1960–2009. Advances in Climate Change Research, 3(2): 99–105. doi:  10.3724/SP.J.1248.2012.00099
    [12] Huang F, Zhou D Q, Hu J L et al., 2020. Integrated airline productivity performance evaluation with CO2 emissions and flight delays. Journal of Air Transport Management, 84: 101770. doi:  10.1016/j.jairtraman.2020.101770
    [13] Jiang Wanbei, Han Mengyao, Tang Zhipeng et al., 2019. Carbon emission effect of location vicissitude of international manufacturing industry. Scientia Geographica Sinica, 39(10): 1553–1560. (in Chinese)
    [14] Khoo H L, Teoh L E, 2014. A bi-objective dynamic programming approach for airline green fleet planning. Transportation Research Part D:Transport and Environment, 33: 166–185. doi:  10.1016/j.trd.2014.06.003
    [15] Lai Wenting, Wang Yuan, Huang Linlin et al., 2020. Decomposition of driving factors of industry-related CO2 emissions and its decoupling with economic growth in Fujian Province, China. Chinese Journal of Applied Ecology, 31(10): 3529–3538. (in Chinese)
    [16] Lee K C, Tsai W H, Yang C H et al., 2018. An MCDM approach for selecting green aviation fleet program management strategies under multi-resource limitations. Journal of Air Transport Management, 68: 76–85. doi:  10.1016/j.jairtraman.2017.06.011
    [17] Li Jian, Ma Xiaofang, Yuan Qingmin, 2019. Evaluation and influencing factors’ analysis of regional carbon emission efficiency. Acta Scientiae Circumstantiae, 39(12): 4293–4300. (in Chinese)
    [18] Lin B Q, Ouyang X L, 2014. Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry. Energy, 68: 688–697. doi:  10.1016/j.energy.2014.01.069
    [19] Lin B Q, Zhu J P, 2019. The role of renewable energy technological innovation on climate change: empirical evidence from China. Science of the Total Environment, 659: 1505–1512. doi:  10.1016/j.scitotenv.2018.12.449
    [20] Liu X, Hang Y, Wang Q W et al., 2020. Flying into the future: a scenario-based analysis of carbon emissions from China’s civil aviation. Journal of Air Transport Management, 85: 101793. doi:  10.1016/j.jairtraman.2020.101793
    [21] Liu Hongming, 2019. Thinking about carbon emission reduction path of international aviation industry under target of zero growth of carbon emission in 2020. World Environment, (1): 33–35. (in Chinese)
    [22] Loo B P Y, Li L N, 2012. Carbon dioxide emissions from passenger transport in China since 1949: implications for developing sustainable transport. Energy Policy, 50: 464–476. doi:  10.1016/j.enpol.2012.07.044
    [23] Niewiadomski P, 2017. Global production networks in the passenger aviation industry. Geoforum, 87: 1–14. doi:  10.1016/j.geoforum.2017.09.013
    [24] Picard P M, Tampieri A, Wan X, 2019. Airport capacity and inefficiency in slot allocation. International Journal of Industrial Organization, 62: 330–357. doi:  10.1016/j.ijindorg.2017.10.003
    [25] Qiu Fangdao, Yuan He, Bai Liangyu et al., 2017. Spatial-temporal heterogeneity of industrial structure transformation and carbon emission effect in Xuzhou Metropolitan Area. Chinese Geographical Science, 27(6): 904–917. doi:  10.1007/s11769-017-0920-8
    [26] Shi Yuting, Wu Weiwei, Li Xiaoxia, 2019. Study on development and influencing factors of China’s aviation carbon emission. Journal of East China Jiaotong University, 36(6): 32–38. (in Chinese)
    [27] Tapio P, 2005. Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transport Policy, 12(2): 137–151. doi:  10.1016/j.tranpol.2005.01.001
    [28] Wang Guoxia, Shi Xiaowei, Cui Haiyan et al., 2020a. Impacts of migration on urban environmental pollutant emissions in China: a comparative perspective. Chinese Geographical Science, 30(1): 45–58. doi:  10.1007/s11769-020-1096-1
    [29] Wang Z L, Xu X D, Zhu Y F et al., 2020b. Evaluation of carbon emission efficiency in China’s airlines. Journal of Cleaner Production, 243: 118500. doi:  10.1016/j.jclepro.2019.118500
    [30] Wang Zhongfengyan, Tian Yong, Wan Lili et al., 2017. Progress in the study of environmental impacts of high altitude flight. Environmental Protection Science, 53(3): 100–105. (in Chinese)
    [31] Xu Chen, Zhu Jialin, Hu Rong, 2018. Index decomposition of airport carbon emission factors in Jiangsu Province based on log mean divisia index. Aeronautical Computing Technique, 48(3): 86–90. (in Chinese)
    [32] Xu Xiaohu, Liao Xiawei, Ma Xiaoming, 2013. Research on the impacts of european union aviation carbon trading on China’s aviation passenger transport. Acta Scientiarum Naturalium Universitatis Pekinensis, 49(4): 699–706. (in Chinese)
    [33] Yu J L, Shao C F, Xue C Y et al., 2020. China’s aircraft-related CO2 emissions: decomposition analysis, decoupling status, and future trends. Energy Policy, 138: 111215. doi:  10.1016/j.enpol.2019.111215
    [34] Zhang Hengquan, Chen Zhuoran, Zhang Chenjun, 2019. Decoupling efforts of environmental pressure of industrial wastewater and economic growth in Yangtze River economic belt. Areal Research and Development, 38(2): 13–18, 30. (in Chinese)
    [35] Zhao Yanhui, Lu Zi, 2018. Airline network carbon emission model and the analysis of external factors. Advances in Earth Science, 33(1): 103–111. (in Chinese)
    [36] Zhou N, Price L, Dai Y D et al., 2019. A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030. Applied Energy, 239: 793–819. doi:  10.1016/j.apenergy.2019.01.154
    [37] Zhou W J, Wang T, Yu Y D et al., 2016. Scenario analysis of CO2 emissions from China’s civil aviation industry through 2030. Applied Energy, 175: 100–108. doi:  10.1016/j.apenergy.2016.05.004
    [38] Zhu Jialin, Hu Rong, Zhang Junfeng et al., 2020. Research on the measurement and evolution characteristics of aircraft carbon emissions in China. Journal of Wuhan University of Technology (Transportation Science & Engineering), 44(3): 558–563. (in Chinese)
  • [1] Haipeng ZHANG, Hanchu LIU, Yong SUN, Renwei HE.  Spatial Differentiation Characteristics of Human Settlements and Their Responses to Natural and Socioeconomic Conditions in the Marginal Zone of an Uninhabited Area, Changtang Plateau, China . Chinese Geographical Science, 2022, 32(3): 506-520. doi: 10.1007/s11769-022-1280-6
    [2] Xiaohong CHEN, Mingxuan ZHANG, Ying WANG, Xiaoqing XU, Shuang LIU, Lingyu MA.  Coupling and Coordination Characteristics and Influencing Factors of the Livable Environment System for the Elderly in China . Chinese Geographical Science, 2022, (): 1-17. doi: 10.1007/s11769-022-1283-3
    [3] Dalai MA, Fengtai ZHANG, Lei GAO, Guangming YANG, Qing YANG, Youzhi AN.  Spatiotemporal Dynamics of Green Total-factor Water-use Efficiency and Its Influencing Factors in China . Chinese Geographical Science, 2021, 31(5): 795-814. doi: 10.1007/s11769-021-1227-3
    [4] Jiawei WANG, Shilin YE, Xinhua QI.  Regional Equity and Influencing Factor of Social Assistance in China . Chinese Geographical Science, 2021, 31(4): 611-628. doi: 10.1007/s11769-021-1195-7
    [5] Xingchuan GAO, Tao LI, Dongqi SUN.  Regional Differentiation Regularity and Influencing Factors of Population Change in the Qinghai-Tibet Plateau, China . Chinese Geographical Science, 2021, 31(5): 888-899. doi: 10.1007/s11769-021-1223-7
    [6] Xuelan TAN, Hangling YU, Yue AN, Zhenkai WANG, Lingxiao JIANG, Hui REN.  Spatial Differentiation and Influencing Factors of Poverty Alleviation Performance Under the Background of Sustainable Development: A Case Study of Contiguous Destitute Areas in Hunan Province, China . Chinese Geographical Science, 2021, 31(6): 1029-1044. doi: 10.1007/s11769-021-1242-4
    [7] DU Yan, QIN Weishan, SUN Jianfeng, WANG Xiaohui, GU Haoxin.  Spatial Pattern and Influencing Factors of Regional Ecological Civilisa-tion Construction in China . Chinese Geographical Science, 2020, 30(5): 776-790. doi: 10.1007/s11769-020-1145-9
    [8] LI Jinfeng, XU Haicheng, LIU Wanwan, WANG Dongfang, ZHOU Shuang.  Spatial Pattern Evolution and Influencing Factors of Cold Storage in China . Chinese Geographical Science, 2020, 30(3): 505-515. doi: 10.1007/s11769-020-1124-1
    [9] DIAO Shuo, YUAN Jiadong, WU Yanyan.  Performance Evaluation of Urban Comprehensive Carrying Capacity of Harbin, Heilongjiang Province in China . Chinese Geographical Science, 2019, 20(4): 579-590. doi: 10.1007/s11769-019-1056-9
    [10] XUE Shuyan, LI Gang, YANG Lan, LIU Ling, NIE Qifan, Muhammad Sajid MEHMOOD.  Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China . Chinese Geographical Science, 2019, 29(6): 1078-1094. doi: 10.1007/s11769-019-1086-3
    [11] SUN Zhe, ZHAN Dongsheng, JIN Fengjun.  Spatio-temporal Characteristics and Geographical Determinants of Air Quality in Cities at the Prefecture Level and Above in China . Chinese Geographical Science, 2019, 20(2): 316-324. doi: 10.1007/s11769-019-1031-5
    [12] MA Zhenbang, CHEN Xingpeng, CHEN Huan.  Multi-scale Spatial Patterns and Influencing Factors of Rural Poverty: A Case Study in the Liupan Mountain Region, Gansu Province, China . Chinese Geographical Science, 2018, 28(2): 296-312. doi: 10.1007/s11769-018-0943-9
    [13] LI Bo, SHI Zhaoyuan, TIAN Chuang.  Spatio-temporal Difference and Influencing Factors of Environmental Adaptability Measurement of Human-sea Economic System in Liaoning Coastal Area . Chinese Geographical Science, 2018, 28(2): 313-324. doi: 10.1007/s11769-018-0948-4
    [14] GONG Li, LIU Guohua, WANG Meng, YE Xin, WANG Hao, LI Zongshan.  Effects of Vegetation Restoration on Soil Organic Carbon in China: A Meta-analysis . Chinese Geographical Science, 2017, 27(2): 188-200. doi: 10.1007/s11769-017-0858-x
    [15] LI Jing, LO Kevin, ZHANG Pingyu, GUO Meng.  Relationship Between Built Environment, Socio-economic Factors and Carbon Emissions from Shopping Trip in Shenyang City, China . Chinese Geographical Science, 2017, 27(5): 722-734. doi: 10.1007/s11769-017-0904-8
    [16] DAI Dandan, ZHOU Chunshan, YE Changdong.  Spatial-temporal Characteristics and Factors Influencing Commuting Activities of Middle-class Residents in Guangzhou City, China . Chinese Geographical Science, 2016, 26(3): 410-428. doi: 10.1007/s11769-016-0806-1
    [17] YU Chao, MA Yanji.  Carbon Emission Trends of Manufacturing and Influencing Factors in Jilin Province, China . Chinese Geographical Science, 2016, 26(5): 656-669. doi: 10.1007/s11769-016-0823-0
    [18] LI Linshan, MA Yanji.  Spatial-temporal Pattern Evolution of Manufacturing Geographical Agglomeration and Influencing Factors of Old Industrial Base: A Case of Jilin Province, China . Chinese Geographical Science, 2015, 25(4): 486-497. doi: 10.1007/s11769-014-0730-1
    [19] SHEN Yuming, QIU Ling, REN Wangbing, CAO Yi, HU Dan, SONG Yujing.  Basic Characteristics, Spatial Disparity and Its Major Influencing Factors of Service Industry in China . Chinese Geographical Science, 2009, 19(4): 314-324. doi: 10.1007/s11769-009-0314-7
    [20] WANG Kaiyong, GAO Xiaolu, CHEN Tian.  Influencing Factors for Formation of Urban and Rural Spatial Structure in Metropolis Fringe Area——Taking Shuangliu County of Chengdu in China as a Case . Chinese Geographical Science, 2008, 18(3): 224-234. doi: 10.1007/s11769-008-0224-0
  • 加载中
图(9) / 表ll (3)
计量
  • 文章访问数:  210
  • HTML全文浏览量:  82
  • PDF下载量:  45
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-04-12
  • 录用日期:  2021-08-10
  • 刊出日期:  2022-04-01

Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions

doi: 10.1007/s11769-021-1247-z
    基金项目:  Under the auspices of the National Natural Science Foundation of China (No. 42071266), the Third Batch of Hebei Youth Top Talent Project, Natural Science Foundation of Hebei Province (No. D2021205003)
    通讯作者: LU Zi. E-mail: luzi1960@126.com

English Abstract

HAN Ruiling, LI Lingling, ZHANG Xiaoyan, LU Zi, ZHU Shaohua, 2022. Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions. Chinese Geographical Science, 32(2): 218−236 doi:  10.1007/s11769-021-1247-z
Citation: HAN Ruiling, LI Lingling, ZHANG Xiaoyan, LU Zi, ZHU Shaohua, 2022. Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions. Chinese Geographical Science, 32(2): 218−236 doi:  10.1007/s11769-021-1247-z
    • China’s aviation carbon emissions continue to increase, and with the expansion of air traffic demand and the increase in the number of civil aircraft, aviation carbon emissions will continue to grow (Wang et al., 2020b). As an important engine to the growth of global aviation industry, by the end of 2019, China’s civil aviation transportation scale has ranked second in the world for 15 successive years, and it is expected to surpass the United States in 2035 to become the world’s largest air transportation market. In 2019, China’s total air transport turnover volume was 12.93 × 1011 t/km, and total passenger transport volume was 65.99 × 107, an increase of 394.99% and 377.28% respectively over 2005 (Zhu et al., 2020); China’s aviation fuel consumption in 2019 was 3.68 × 107 t, an increase of 319.10% compared to 2005. Aviation carbon emissions are mainly emitted from the nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), unburned hydrocarbons (UHC), carbon dioxide (CO2), soot particles (Soot), and pollutants such as Atmospheric Particulate Matters (PM) (Han et al., 2019) when aviation fuel mixed with air during combustion, which not only affect air quality but also cause the greenhouse effect. During the flight of a civil aviation aircraft, every 1 kg of aircraft fuel can produce 3.16 kg of CO2, 0.011 kg of NOx and 1.25 kg of water vapor (Khoo and Teoh, 2014). At present, the aviation industry has become one of the top ten greenhouse gas emission industries in the world (Liu, 2019). The CO2 emitted by air transportation accounts for 2.0%–2.5% of the total global anthropogenic CO2 emissions (Wang et al., 2017), and China’s aviation carbon emissions account for 0.22% of the global anthropogenic carbon emissions (Niewiadomski, 2017), and the issue of aviation carbon emissions has been a major concern (Zhou et al., 2016).

      In 2007, the 36th Congress of the International Civil Aviation Organization (ICAO) decided to give priority to international aviation emission reduction work in the future; in 2008, the European Union brought the aviation industry into the European Emissions Trading System (EU-ETS) (Chen et al., 2014), limit the carbon emissions of civil aircraft of all airlines flying through the European Union, 33 Chinese airlines are included in the system (Xu et al., 2013); in 2016, the 38th ICAO Congress adopted the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA), requiring all major airlines compulsorily report the emissions of international flights from January 2019 (Lee et al., 2018) and undertake carbon offset and emission reduction task. In 2017, the Civil Aviation Administration of China issued the 13th Five-Year Plan for China’s Civil Aviation Development, which proposed that by 2020 China plans to reduce carbon intensity by 40%–50% and establish a green civil aviation standard system; in 2018, the Civil Aviation Administration of China (CAAC) identified 39 key regional airports including Beijing Capital and Beijing Daxing, as well as 31 other regional airports with an annual passenger throughput of more than 5 million passengers, as the main positions for civil aviation to win the battle against the blue sky. CAAC put forward specific requirements to increase the electrification rate of airports and reduce the airport area air pollution emissions. In September 2020, president Xi Jinping stated at the United Nations General Assembly that China’s carbon emissions will strive to reach its peak by 2030 (Zhou et al., 2019), and achieve carbon neutrality by 2060, provide guidance for civil aviation emissions reduction.

      The current research on aviation carbon emissions mainly focuses on three aspects: 1) Total aviation carbon emissions estimation. Loo and Li (2012) calculated aviation carbon emissions from 1949 to 2009 from the perspective of air passenger transportation, found that aviation carbon emissions have become the second largest contributor to China’s transportation carbon emissions since 1998. He and Xu (2012) found that from 1960 to 2009, China’s aviation carbon emissions increased from 120 × 103 t to 41.44 × 106 t, while CO2 emission intensity showed a downward trend, with an average annual decrease of 0.04 kg/km; from 1980 to 2005, aviation CO2 emissions accounted for an average share of 6.6% of total CO2 emissions from the transportation, storage, and postal sectors. 2) The issue of airline carbon emission efficiency. Wang et al. (2020b) studied the carbon emission efficiency of 13 Chinese airlines from 2009 to 2013. Among them, the static efficiency showed an inverted ‘U’ curve, and the dynamic efficiency of each airline was different, mainly affected by technical efficiency and technical progress. Fan et al. (2012) found that China Southern Airlines’ fuel consumption and carbon emissions accounted for the largest proportion in China in 2010, with 27% and 25%–28% respectively. Huang et al. (2020) found that the carbon emission advantages of European and American airlines from 2011 to 2017 benefited more from advanced technology, while most airlines in Asia and Oceania still benefited from operational efficiency advantages. 3) Analysis and prediction of aviation carbon emissions influencing factors. Zhou et al. (2016), Guan et al. (2008) and Lin and Ouyang (2014) analyzed factors affecting carbon emission changes such as low-carbon fuel, technical progress, and traffic demand, by setting different aviation carbon emission scenarios, and found that traffic demand has the most significant impact on aviation carbon emissions, technical progress is most likely to promote carbon emission reduction. In-depth analysis of carbon emissions influencing factors has become an important cornerstone and research direction for exploring the road to aviation’s low-carbon sustainable development (Xu et al., 2018). Effectively identifying the driving factors of aviation carbon emissions and analyzing the impact of each factor is the key to reduce emissions (Shi et al., 2019).

      Factor decomposition research has been a research hotspot in the field of international carbon emissions since 1980s (Jiang et al., 2019). In order to actively explore the influencing factors of aviation carbon emissions, domestic and foreign scholars have established a variety of research models and methods, common ones include factor decomposition methods (Li et al., 2019), a production-theoretical decomposition analysis (PDA) (Lin and Zhu, 2019), the Environmental Pressure Equation (IPAT) (Qiu et al., 2017) and the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) (Wang et al., 2020a). Among them, the Logarithmic Mean Divisia Index (LMDI) (Dai et al., 2017) is an important branch of factor decomposition methods, which corrects the problem of large residuals, allowing no residual value and data containing 0, which is an international important method to deal with energy and environmental issues, and has been increasingly used in empirical research on aviation carbon emissions in recent years; the Kaya identity that assists in the deformation of the main driving factors and contribution rate of aviation carbon emissions can also be used (Dai et al., 2017; Xu et al., 2018). Most studies have found that transportation volume growth (Yu et al., 2020), transportation scale effect (Zhou et al., 2019), economic development scale, industrial scale, and population scale (Shi et al., 2019) play a positive leading driving role in carbon emissions. Airport transportation revenue (Liu et al., 2020), unit income, changes in passenger throughput also lead to an increase in carbon emissions. While changes in energy intensity (Dai et al., 2017), transportation intensity, airport scale (Xu et al., 2018), and unit transportation revenue turnover (Chen et al., 2014) reduce carbon emissions.

      Faced with the huge challenges brought by the development of low-carbon aviation to the domestic and foreign aviation industry and the transformation of emission reduction mechanism (Zhao and Lu, 2018), it is necessary to fully explore the influencing factors of aviation carbon emissions and find suitable and feasible carbon emission reduction strategies. Existing research on the influencing factors of aviation carbon emissions mainly focus on the national scale, pay less attention to the provincial scale. Therefore, based on the calculation of China’s aviation carbon emissions from 2000 to 2019, this paper conducts a comprehensive systemic analysis of the influencing factors of aviation carbon emissions at national and provincial level, based on the LDMI model, and determines the main influencing factors’ characteristic, influencing direction and degree on aviation carbon emissions in different levels and regions of China, as well as analyzes the decoupling influence and decoupling effort degree between the relationship of air carbon emissions and air transport-ationrevenue of each influencing factor. We are expected to provide theoretical reference for Chinese airlines to reduce carbon emissions and comprehensively respond to the new carbon emission reduction rules.

    • The STIRPAT model is a more recognized and common method in the study of carbon emissions in the transportation industry (Dong et al., 2014). It is an extension of the environmental pressure equation (I = PAT), where I, P, A, and T represent environmental pressure, population, wealth and technology respectively. Dietz and Rosa (1994) transformed the IPAT model into a stochastic form, the STIRPAT model: I=a Pb Ac Td e. Where a is a constant term, b, c and d are used as exponential terms of P, A, and T respectively. The STIRPAT model can analyze the degree of influence of various humanistic driving factors by decomposing technical indicators of environmental pressure (Fan et al., 2019), it has an important value to effectively determine the drive mechanism of China’s aviaion carbon emissions. The calculation model of China’s aviation carbon emissions constructed in this paper is as follows:

      $$ {I=3.252 \times }{{10}}^{{-4}} \times {{P}}^{{}{6.610\;95}} \times {\left({TG}\right)}^{{}{0.929\;35}} \times {\left({ET}\right)}^{{}{0.645\;61}} $$ (1)

      where I represents CO2 emissions (t); P represents population (109 person), use permanent population at the year-end to represent the population size of current development model, reflecting the support ability of passenger source of regional aviation market; TG represents total air transport turnover per unit of GDP (t·km/104 yuan (RMB)), representing air transport capacity of various provinces; ET represents energy consumption per unit of total air transport turnover (kg/(t·km)) (Dong et al., 2014), representing aviation carbon emissions consumption efficiency. The specific index of P, TG, and ET are referenced to Dong’s researches (2014).

    • Drawing on existing research (Xu et al., 2018), according to the three main influencing factors as air transportation revenue, total air transport turnover and aviation fuel consumption (Chen et al., 2014), the LMDI decomposition model of aviation carbon emission factors is established based on the characteristics of China’s civil aviation transportation. The specific formula is as follows:

      $$ {C}=\varSigma _{{i}}{{C}}_{{i}}=\varSigma _{{i}}{Q} \times \frac{{{Q}}_{{i}}}{{Q}} \times \frac{{{T}}_{{i}}}{{{Q}}_{{i}}} \times \frac{{{E}}_{{i}}}{{{T}}_{{i}}} \times \frac{{{C}}_{{i}}}{{{E}}_{{i}}} $$ (2)

      setting $ {{S}}_{{i}}{=}\dfrac{{{Q}}_{{i}}}{{Q}} $, $ {{P}}_{{i}}{=}\dfrac{{{T}}_{{i}}}{{{Q}}_{{i}}} $, $ {{R}}_{{i}}{=}\dfrac{{{E}}_{{i}}}{{{T}}_{{i}}} $, $ {{U}}_{{i}}{=}\dfrac{{{C}}_{{i}}}{{{E}}_{{i}}} $, then:

      $$ {C=}\varSigma _{i}Q \times {{S}}_{{i}}{{ \times P}}_{{i}}{{ \times R}}_{{i}}{{ \times U}}_{{i}} $$ (3)

      In the formula: C is the total aviation carbon emissions (t); i is the route classification. When i = 1, it is the domestic route, when i = 2, it is the international and regional routes; Ci, Qi, Ti, Ei represent the airline carbon emissions (t) of class i, total air transportation revenue (104 yuan), total air transportation turnover (t/km), and energy consumption (t), respectively. Si is the air route structure, which means that the air transportation business is divided into two parts: domestic routes and international and regional routes; Pi is air transportation intensity (t·km/104 yuan); Ri is aviation energy intensity (t/t); Ui is the CO2 emission coefficient.

      Use the LMDI additive decomposition formula to define Ct +1 and Ct as the decomposition formula of aviation carbon emission change for the t + 1 period and base period t respectively:

      $$ \Delta {C}={{C}}^{{t+1}}-{{C}}^{{t}}{=\Delta }{{C}}_{{Q}}+\Delta {{C}}_{{S}}+\Delta {{C}}_{{P}}+\Delta {{C}}_{{R}}+\Delta {{C}}_{{U}} $$ (4)

      In the formula, ΔC represents the total amount of changes in aviation carbon emissions (t), ΔCQ, ΔCS, ΔCP, ΔCR, and ΔCU represent the contribution value of air transportation revenue, air route structure, air transportation intensity, aviation energy intensity and the carbon emission coefficient to the total aviation carbon emissions respectively. Since the carbon emission coefficient remains unchanged, the effect of carbon emission coefficient is not included in the analysis framework.

      Use the LMDI multiplicative decomposition formula to define the decomposition formula of the annual contribution rate of carbon emissions:

      $$ {D}=\frac{{{C}}^{{t+1}}}{{{C}}^{{t}}}={{D}}_{{Q}}\times {{D}}_{{S}}\times {{D}}_{{P}}\times {{D}}_{{R}}{{ \times D}}_{{U}} $$ (5)

      D represents the overall annual effect, which means the influence degree of five influencing factors on the total aviation carbon emissions of that year. The annual effect mainly presents two driving modes: positive (greater than 1) and negative (less than 1). The greater the positive driving force, the more obvious the effect of this influencing factor on the incremental contribution in aviation carbon emissions, and vice versa. t reprent the base period. DQ, DS, DP, DR, and DU respectively represent the annual effects of total air transportation revenue, aviation route structure, air transportation intensity, aviation energy intensity, and carbon emission coefficient on total aviation carbon emissions, which represent the influence degree of each influencing factor on total aviation carbon emission. The specific formula is:

      $$ \begin{split} &{{D}}_{{Q}}={{\rm{exp}}}\left[\displaystyle\sum\limits_{{i}}{{w}}_{{i}}{{\rm{In}}}\left(\frac{{{Q}}^{{t+1}}}{{Q}}\right)\right],{{D}}_{{S}}={{\rm{exp}}}\left[\displaystyle\sum\limits_{{i}}{{w}}_{{i}}{{\rm{{\rm{In}}}}}\left(\frac{{{S}}_{{i}}^{{t+1}}}{{{S}}_{{i}}^{{t}}}\right)\right],\\ &{{D}}_{{P}}={{\rm{exp}}}\left[\displaystyle\sum\limits_{{i}}{{w}}_{{i}}{{\rm{In}}}\left(\frac{{{p}}_{{i}}^{{t+1}}}{{{p}}_{{i}}^{{t}}}\right)\right],{{D}}_{{R}}={{\rm{exp}}}\left[\displaystyle\sum\limits_{{i}}{{w}}_{{i}}{{\rm{In}}}\left(\frac{{{R}}_{{i}}^{{t+1}}}{{{R}}_{{i}}^{{t}}}\right)\right], \\ &{{D}}_{{U}}{{\rm{exp}}}\left[\displaystyle\sum\nolimits_{{i}}{{w}}_{{i}}{{\rm{In}}}\left(\frac{{{U}}_{{i}}^{{t+1}}}{{{U}}_{{i}}^{{t}}}\right)\right] \end{split} $$ (6)

      In the formula, Q, Si, Pi, Ri, Ui index is the same as the above; wi is the index weight, and the specific formula is:

      $$ {{w}}_{{i}}=\frac{{{C}}_{{i}}^{{t+1}}-{{C}}_{{i}}^{{t}}}{{{\rm{ln}}}{{C}}_{{i}}^{{t+1}}{-{\rm{ln}}}{{C}}_{{i}}^{{t}}}/\frac{{{C}}^{{t+1}}-{{C}}^{{t}}}{{{\rm{ln}}}{{C}}^{{t+1}}{-{\rm{ln}}}{{C}}^{{t}}} $$ (7)

      In addition, three indexes are also required in the decomposition of air carbon emissions: 1) The annual contribution rate is the ratio of the respective annual effects of the four influencing factors to the overall annual effect (D). The larger the annual contribution rate is, indicates that it is the main contributor to the growth of total aviation carbon emissions, and contrariwise is the main restraining factor. 2) Cumulative effect represents the sum of annual effect of influencing factors on total aviation carbon emissions, and is also divided into positive and negative driving modes. 3) The cumulative contribution rate is the ratio of respective cumulative effect of four influencing factors to total cumulative effect (D), expressed as the cumulative contribution degree of four influencing factors to total aviation carbon emissions.

    • Based on the Tapio decoupling model, a decoupling index formula between China’s aviation carbon emissions and total air transportation revenue is constructed (Lai et al., 2020):

      $$ {\rm{\varepsilon }}=\frac{{\Delta }{C}/{{C}}_{{t}}}{{\Delta }{Q}/{{Q}}_{{t}}} $$ (8)

      $ \varepsilon $ is decoupling index; ΔC is the change in aviation carbon emissions, ΔQ is the change in total air transportation revenue, Ct is the base period aviation carbon emissions, and Qt is the total air transportation revenue of base period. The decoupling elasticity index mainly refers to the ratio of the growth rate of the pressure state variable to the growth rate of the economic driving force variable, which aims to distinguish the decoupling tense of economic development and aviation carbon emissions. The quadrant distribution of each decoupling state is referenced to Lai’s researches (2020). Among them, the second and fourth quadrants represent the worst and the best decoupling results respectively, the first and third quadrants represent the excessive state. When the decoupling result is in the first quadrant, the smaller ‘ε’ means the higher the degree of decoupling, and the opposite is true in the third quadrant. Tapio divides the decoupling model state into eight types (Table 1) (Tapio, 2005; Zhang et al., 2019).

      Table 1.  The decoupling state of China’s air transportation revenue and aviation carbon emissions

      Decoupling typeAir carbon emissions
      variation (∆C)
      Air transportation revenue
      variation (∆Q)
      Decoupling elasticity index
      $ {{\varepsilon }} $ range
      Decoupling discriminationQuadrant distribution
      decoupling < 0 > 0 ≤ 0 Strong decoupling
      > 0 > 0 (0, 0.8) Weak decoupling
      < 0 < 0 ≥ 1.2 Decreasing decoupling
      Negative decoupling > 0 < 0 ≤ 0 Strong negative decoupling
      < 0 < 0 (0, 0.8) Weak negative decoupling
      > 0 > 0 ≥ 1.2 Expansion negative decoupling
      Connection > 0 > 0 [0.8,1.2) Increase connection
      < 0 < 0 [0.8,1.2) Decay connection
      Notes: source, Lai et al., 2020; Zhang et al., 2019
    • Decoupling efforts refer to all measures that directly or indirectly contribute to the reduction of aviation carbon emissions without harming air transportation revenue. In the study of aviation carbon emissions, the effectiveness of the decoupling efforts of all factors can be further evaluated when removing the carbon emissions caused by air transportation revenue (Lai et al., 2020). Based on the Tapio decoupling index model and the LMDI index additive decomposition model (Formula (4)), this study derives the decoupling effort model, which decomposes the change in aviation carbon emissions into air route structure effect, air transportation intensity effect, and aviation energy intensity effect, and at the same time, air transportation revenue effect is eliminated, and the sum of each effect is obtained:

      $$ \Delta {E=}\Delta {C}-{\Delta {C}}_{{Q}}=\Delta {{C}}_{{S}}+\Delta {{C}}_{{P}}+\Delta {{C}}_{{R}} $$ (9)
      $$ {B}=-{\Delta }{E}{/}{\Delta }{C}{Q} $$ (10)

      ΔE is the decoupling effort, ΔC, ΔCQ, ΔCs, ΔCP, ΔCR index explanation are the same as above, B is the decoupling effort index after eliminate the air transportation revenue effect. B ≤ 0, no decoupling effort, indicating that the driving factors will not promote the decoupling between air transportation revenue and aviation carbon emissions, and in turn promote the growth of carbon emissions; 0 < B < 1, it is a weak decoupling effort, indicating the driving force to promote the decoupling between air transportation revenue and aviation carbon emissions are weaker than the promotion effect of air transportation revenue; B ≥ 1, which is a strong decoupling effort, indicating that the driving factors have a promotion effect in the decoupling between air transportation revenue and aviation carbon emissions.

      Combined with the LMDI calculation method, the decoupling effort degree of various driving factors can be investigated. The formula is as follows:

      $$ {B=}-\frac{\Delta {{C}}_{{S}}}{{{\Delta C}}_{{Q}}}-\frac{\Delta {{C}}_{{P}}}{{{\Delta C}}_{{Q}}}-\frac{\Delta {{C}}_{{R}}}{{{\Delta C}}_{{Q}}}={{B}}_{{CS}}+{{B}}_{{CP}}+{{B}}_{{CR}} $$ (11)

      In the formula: BCS, BCP, and BCR respectively refer to the decoupling effort degree of airline structure effect, air transportation intensity effect, and aviation energy intensity effect between aviation carbon emissions and aviation transportation revenue.

    • In order to fully show the temporal and spatial evolution characteristics of the influencing factors on aviation carbon emissions, this paper selects 31 provincial administrative units across China (not including Hong Kong, Macao, and Taiwan of China due to incomplete data) as the basic research area. The indexes for aviation carbon emission research are air transportation revenue (the sum of air passenger and cargo revenue, excluding revenue from airport services), total air transportation turnover (reflecting transportation volume and transportation distance, it is the comprehensive production index for passengers, goods and mails to achieve displacement in the air) and the amount of aviation fuel (the consumption of aviation kerosene, the main energy of civil aviation aircraft), with all of the data from ‘Statistical Data On Civil Aviation of China’ (Chinese Department of Planning and Development of Civil Aviation Administration, 2000−2019).The research index of aviation carbon emission factor decomposition, the total aviation carbon emissions and the emissions of each airline are calculated by Formula (1); the aviation routes are divided into domestic routes and international and regional routes (international routes and China Hong Kong and Macao) according to the ‘Statistic Bulletin of Civil Aviation Industry Development’ (Civil Aviation Administration of China, 2019); the energy (aviation kerosene) consumption index for each route is calculated based on the specific proportions of the total number of air passengers and cargo and mail turnover in each year in China’s domestic routes and international and regional routes. The aviation kerosene carbon emission coefficient refers to the ‘Supplementary Data Sheet for the Greenhouse Gas Emission Report of Civil Aviation Companies (Airlines)’ issued by the CAAC, and the default value is 3.15 t. All data are converted to constant prices in 2000.

    • From 2000 to 2019, China’s total aviation carbon emissions continued to grow, while the growth rate of aviation carbon emissions showed a fluctuating downward trend (Fig. 1). In the past 20 yr, China’s total aviation carbon emissions increased from 1.56 × 107 t to 11.60 × 107 t, an increase of 6.44 times, with an average annual growth rate of 14.34%; China’s total air transportation increased from 0.69 × 107 t to 5.79 × 107 t,an increase of 7.38 times, with an average annual growth rate of 11.99%; China’s air transportation revenue increased from 52.81 × 109 yuan to 648.72 × 109 yuan, an increase of 11.28 times, with an average annual growth rate of 14.66%. Since 2000, it has been affected by favorable factors such as the rapid economic growth for many years and the increasingly frequent foreign trade exchanges, coupled with the gradual popularization of air transportation. Indexes such as air transportation turnover and passenger transportation volume have been significantly improved and steadily developed, which have driven the growth of air transportation revenue and the annual growth of aviation carbon emissions. In the past 20 yr, the growth rate ofChina’s aviation carbon emissions dropped from 71.95% to 6.38%. Among them, the growth rate of aviation carbon emissions fluctuated greatly from 2000 to 2010, with an average annual growth rate of 17.65%. The highest growth rate in 2000 was mainly due to the substantial increase in cargo and mail transportation in that year. The growth rate of aviation carbon emissions in 2003 was the lowest (0.8%), which was mainly affected by SARS. The growth rate of total amount of air transportation in 2003 was only 4% higher than that in 2002, which affected the sudden increase of aviation carbon emissions to 30.4% in 2004. In 2008, affected by the global financial crisis and rising costs after fuel tax adjustments, aviation capacity was weakened and carbon emissions growth was reduced. From 2011 to 2016, the growth rate of total aviation carbon emissions increased slowly, from 7.50% to 12.38%, with an average annual growth rate of 10.70%. At that time China was implementing The 12th Five-Year Plan, and put forward quantitative requirements for aviation carbon emissions for the first time, and the whole industry actively responded to optimize the aviation energy-saving operating environment, improve aviation transportation efficiency, and implement green airport construction, etc. Fuel consumption per ton-kilometer and aviation carbon emissions declined by an average of 4.20%, making aviation carbon emissions reduction results gradually appeared during this period. From 2017 to 2019, the growth rate of aviation carbon emissions fluctuated greatly, and its average annual growth rate dropped to 9.46%, mainly due to the fact that the growth rate of aviation carbon emissions in 2018 was higher than the growth rate of air transportation turnover, and fuel consumption per ton-kilometer increased slightly, but compared with the United States and the European Union, China’s aviation fuel efficiency is still at a higher level. Therefore, according to the current emission level, it will affect the emission reduction potential of China’s aviation industry to a certain extent. In general, except in 2000, the average annual growth rate of total aviation carbon emissions was lower than that of the total volume of air transportation and air transportation revenue, indicating that China has achieved results in aviation energy conservation and emission reduction.

      Figure 1.  China’s aviation carbon emissions and growth rate from 2000 to 2019

    • Using the LMDI multiplication decomposition model to analyze the influencing factors of China’s aviation carbon emissions from 2000 to 2019, the calculation results can be obtained in Table 2.

      Table 2.  Logarithmic Mean Divisia Index (LDMI) analysis and calculation results

      TimeOverall annual effectAir transportation revenueAviation route structureAir transportation intensityAviation energy intensity
      Annual effectCumulative effectAnnual effectCumulative effectAnnual effectCumulative effectAnnual effectCumulative effectAnnual effectCumulative effect
      2000−20011.091.091.081.080.990.991.101.100.900.90
      2001−20021.112.201.502.580.931.930.661.750.891.79
      2002−20031.013.210.973.550.952.881.162.920.942.74
      2003−20041.314.521.455.001.043.920.873.790.943.68
      2004−20051.125.641.156.151.004.920.974.750.984.65
      2005−20061.126.761.237.380.995.900.935.680.925.57
      2006−20071.147.911.178.560.996.891.066.740.926.49
      2007−20081.038.941.059.601.007.890.917.661.007.48
      2008−20091.1310.071.0210.620.868.751.419.071.008.48
      2009−20101.1611.231.4212.041.089.830.799.860.859.33
      2010−20111.0812.311.1913.230.9410.770.8510.711.0110.34
      2011−20121.0913.391.0714.300.9911.760.9611.671.0611.39
      2012−20131.1214.511.0115.311.0212.781.1612.831.0312.42
      2013−20141.1115.621.0816.391.0213.801.0513.890.9913.42
      2014−20151.1316.751.0417.441.0614.861.1615.050.9914.40
      2015−20161.1317.881.0718.500.9915.851.1416.190.9915.39
      2016−20171.1319.001.1219.631.0016.851.0117.191.0016.39
      2017−20181.0920.091.1520.781.0217.870.9218.120.9617.35
      2018−20191.0621.161.1421.921.0418.900.8518.960.9918.34
      Total21.16212.2821.91224.0618.91187.3418.96187.9318.36180.55

      From 2000 to 2019, the cumulative contribution effect of each influencing factor on aviation carbon emissions was 21.16, that is, the overall cumulative contribution of each influencing factor to the total cumulative effect of China’s aviation carbon emissions was positively driven, which also indicates that China’s aviation carbon emissions have generally remained at growth trend. In the past twenty years, cumulative contribution rate of each influencing factor in descending order were air transportation revenue (103.6%), aviation route structure (89.8%), air transportation intensity (89.6%), and aviation energy intensity (86.7%) (Fig. 2). The increase of total aviation carbon emissions caused by changes in the cumulative contribution rate of the four influencing factors in the past twenty years were 1.15 × 108 t, 2.58 × 105 t, –5.77 × 106 t, and –1.05 × 107 t, respectively. It is clear that air transportation revenue and the structure of aviation route have a positive effect on the growth of total aviation carbon emissions, and air transportation intensity and aviation energy intensity have a restraining effect on the growth of total aviation carbon emissions. Among them, aviation energy intensity is the most prominent on emission reduction effect of aviation carbon emissions during the study period. From 2000 to 2019, the annual average contribution rate of various factors affecting aviation carbon emissions in descending order are air transportation revenue (103.5%), aviation route structure (90.0%), air transportation intensity (89.9%), and aviation energy intensity (87.0%) (Fig. 3).

      Figure 2.  Cumulative contribution rate of various influencing factors on aviation carbon emissions

      Figure 3.  Annual contribution rate of various influencing factors on aviation carbon emissions

    • Air transportation revenue (DQ) has the highest annual contribution rate to total aviation carbon emissions, and its growth has played a decisive role in positive driving the increase of China’s aviation carbon emissions (Fig. 4). The overall DQ curve shows a fluctuating downtrend. Among them, the air transportation revenue of 2001–2002 and 2009–2010 showed a high level of operation, with annual contribution rates of 135.6% and 122.0% respectively, while the air transportation revenue of other years hovered between 90.0% and 110.0%. It shows that air transportation revenue has maintained the largest incremental impact on the growth of total aviation carbon emissions, but the influence degree has decreased year by year. The main reason is that the demand of air transportation has increased with the economic development, leading to the increase of air transportation revenue, which will continue to promote the growth of China’s aviation carbon emissions. However, as the growth rate of air transportation scale slowed down, the growth rate of air transportation revenue also dropped, so its impact on aviation carbon emissions showed a slow downward trend, too. The growth of air transportation revenue has played a driving role in boosting China’s aviation carbon emissions. Among them, from 2009 to 2010, the impact of air transportation revenue on aviation carbon emissions increased significantly, which made the total amount of aviation carbon emissions increase to 4.92 × 107 t. From 2013 to 2019, the impact of air transportation revenue on aviation carbon emissions showed a rising W trend, which eventually increased the total aviation carbon emissions to 1.17 × 108 t.

      Figure 4.  Trends of changes of the four influencing factors on China’s aviation carbon emissions

    • The annual contribution rate of the aviation route structure (DS) to the total aviation carbon emissions has shown a slow upward trend, which is an insignificant factor affecting the total aviation carbon emissions. From 2000 to 2019, the annual contribution rate of aviation route structure to total aviation carbon emissions increased from 90.6% to 97.3%. Among them, it showed a significant downward trend in 2003–2004 and 2008–2009 since the structure of China’s aviation route has undergone major changes, and international and domestic routes have decreased to varying degrees mainly under the influence of SARS, natural disasters, and the fluctuations in the international financial order. From 2009 to 2019, with the recovery and development of the aviation industry, the proportion of international and regional routes has increased, causing the slow rise of the impact of route structure factors on aviation carbon emissions. The main reason is that international and regional routes generate more aviation carbon emissions than domestic short-haul routes because they cover larger distance and take longer time. If the operating efficiency can be further improved, the goal of aviation carbon emission reduction can be achieved. The impact of aviation route structure on China’s aviation carbon emissions is unstable, with both positive and negative driving effects. From 2000 to 2019, the overall growth of aviation carbon emissions under the influence of aviation route structure showed a fluctuating growth trend. 2003–2004 and 2014–2019 were positive drives, and 2000–2003, 2004–2007, 2007–2014 obviously showed negative drives. The main reason is that the increase of domestic routes in the proportion of aviation route structure led to the decrease of total amount of aviation carbon emissions.

    • The annual contribution rate of air transportation intensity (DP) to total aviation carbon emissions also shows a fluctuating downward trend, which is especially obvious in the later stage of the study; air transportation intensity has a negative drive effect on the growth of total aviation carbon emissions. From 2000 to 2019, the annual contribution rate of the aviation route structure to the total aviation carbon emissions decreased from 100.2% to 79.5%. Among them, the annual contribution rate of 2001–2002, 2003–2004, and 2009–2010 has decreased significantly, falling to 59.4%, 66.3%, and 68.3%, respectively. From 2011 to 2019, with the continuous increase of unit transportation revenue turnover, the aviation carbon emissions continued to decrease, which made China’s air transportation intensity gradually increase and its impact on aviation carbon emissions showed a downtrend. The increase in the intensity of air transportation has led to a decline in total aviation carbon emissions, which is the main factor to inhibit the increase in total aviation carbon emissions. The overall negative driving trend from 2001 to 2008 reduced the total amount of aviation carbon emissions by 6.08 × 106 t. From 2008 to 2009, the impact of aviation transportation intensity on aviation carbon emissions increased significantly. The total aviation carbon emissions caused by the aviation transportation intensity increased by 5.9 × 106 t, and the total aviation carbon emissions decreased by 1.8 × 105 t from 2000 to 2009. From 2009 to 2018, with 2014 as the dividing line, the impact of air transportation intensity on aviation carbon emissions was first negatively suppressed and then positively driven. Therefore, increasing the air transportation intensity can significantly reduce the total amount of aviation carbon emissions, that is, increasing the per unit transportation revenue turnover is one of the effective means to restrain the growth of aviation carbon emissions.

    • The annual contribution rate of aviation energy intensity (DR) to total aviation carbon emissions rises in fluctuation from 82.1% to 92.6%; aviation energy intensity presents a negative driving effect to total aviation carbon emissions growth. Among them, the annual contribution rate of aviation energy intensity to total aviation carbon emissions from 2003 to 2004, and from 2007 to 2010 has shown a significant downward trend, from 93.7% to 71.7%, and from 96.8% to 72.9%, respectively; but starting from 2014, the contribution rate of aviation energy intensity has shown a slow upward trend year by year. It shows that a series of emission reduction measures adopted by the aviation industry have achieved remarkable effects, but due to the law of increasing marginal cost and diminishing marginal utility, energy efficiency will have little improvement space in the future, which makes a negative contribution capacity of aviation energy intensity on aviation carbon emissions begin to decline, and the emission reduction potential of aviation energy intensity factors will face severe challenges in the future. Aviation energy intensity is another important factor in restraining the increase of total aviation carbon emissions, and its restraining effect is obvious and stable. From 2000 to 2010, there was a negative driving trend, which resulted in a total decrease of aviation carbon emissions by 9.91 × 106 t, with an obvious restraint effect. From 2010 to 2013, the overall impact of aviation energy intensity on aviation carbon emissions increased by 8.25 × 105 t with the decline of aviation carbon emissions by 7.38 × 106 t in total. From 2013 to 2019, the impact of aviation energy intensity on aviation carbon emissions decreased overall, with a cumulative reduction of 1.14 × 107 t. The increase in aviation energy intensity is mainly due to the renewal of many airlines’ transport fleets (replacement of old aircraft types by new types of energy saving and emission reduction) and the use of large aircraft for transportation, which greatly improves aviation energy intensity and air transportation efficiency.

    • From 2000 to 2019, the scope of positive influence of air transportation revenue on aviation carbon emissions of various provinces tends to decrease, while the contribution degree of negative influence gradually increases. The scope of change is mainly in North China and Northeast China (Fig. 5). Among them, from 2000 to 2019, the number of provinces with low positive impact decreased from 22 to 17; the number of provinces with high positive impact decreased from 8 to 7, mainly concentrated in the southwest and southeast regions of China. From 2000 to 2019, the number of provinces where air transportation revenue factors negatively affected aviation carbon emissions increased from 1 (Xinjiang) to 7. The air transportation revenue of each province decreased slightly from 2004 to 2005, resulting in the reduction of the aviation carbon emissions in 18 provinces. However, with the recovery and development of the aviation industry in each province, by 2013, the air transportation revenue factor of each province has had a positive effect on aviation carbon emissions. Since 2014, China’s National Development and Reform Commission has issued the ‘China’s Policies and Actions for Addressing Climate Change (2014–2020) stating that China will be committed to the low-carbon development of the aviation industry, and all provinces have responded actively to reduce aviation carbon emissions, thus increasing the scope of air transportation revenue factors’ negative impact on the total aviation carbon emissions, with its impact scattered in western and northeastern China. In general, the impact of air transportation revenue on China’s northwestern regions (Xinjiang, Gansu) and southeastern regions (Zhejiang, Fujian) was on an increasing trend, changing from negative drive to positive drive; while the southwest regions (Yunnan, Sichuan) and Beijing, Shanghai, and Guangdong were relatively stable and high positive influence areas; Northeast China (Heilongjiang, Jilin, Liaoning, Inner Mongolia) and some northern provinces (Hebei, Tianjin, Shandong) have a downward trend in the degree of influence, changing from positive to negative influence zone. On the one hand, due to the slowdown in regional economic growth in recent years, the vitality needs to be improved, on the other hand, because of the relative conservative transportation status of these provincial air markets, the air transportation volume has not increased much, so the impact of aviation transport revenue on air carbon emissions has weakened.

      Figure 5.  The difference evolution (trends) of the effect contribution degree of air transportation revenue to the aviation carbon emission of various provinces in China. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    • From 2000 to 2019, the scope of positive drive influence of aviation route structure on total aviation carbon emissions in various provinces increased, gradually shifting from south to north and expanding; the scope of negative drive influence effect gradually shrunk, mainly concentrated in the central and northwest regions of China (Fig. 6). From 2000 to 2019, the number of provinces with positive impacts on aviation carbon emissions increased from 16 to 18. Among them, the number of provinces with high positive impacts increased from 1 to 3. Due to the rise of the proportion of international and regional air routes in the aviation route structure, Beijing, Shanghai have changed from positive low impact area to high impact area. The provinces with positive low impact have increased from 14 to 15, obviously distributed in the northern region of Huaihe River and Qinling Mountains, which shows that the pressure of emission reduction in the northern region of China is more severe, and to some extent reflects the faster development of the civil aviation industry in the southern provinces. From 2000 to 2019, the number of provinces with negative impacts on aviation carbon emissions from the aviation route structure factors decreased from 16 to 13, mainly in the central and southern regions of China. It is notable that, from 2012 to 2013, the number has increased to 18. For example, Beijing, Jiangsu and other places have decreased from a positive influence to a negative influence because the proportion of domestic routes has increased more than that of international and regional routes. In general, some areas in the central and eastern parts of China (Anhui, Gansu, Hainan, Henan, Hubei, Hunan, Jiangxi, Xinjiang, Chongqing, Beijing, Yunnan, Shanghai) have experienced rapid development and increased proportion in international air routes in recent years. Therefore, the degree of influence on aviation route structure has gradually increased, which has enhanced the influence degree on the total increase of aviation carbon emissions. The influence degree on aviation route structure factors in the northern and northwestern regions of China (Guangdong, Guangxi, Hebei, Heilongjiang, Ningxia, Qinghai, Shandong, Shaanxi) has decreased. For example, Guangdong and Shandong have raised the proportion of domestic routes, whose transportation distance and flight time, compared with international routes, are shorter, which led to a gradually decrease in the influence of aviation route structure on the increase of total aviation carbon emissions.

      Figure 6.  The difference evolution (trends) of the effect contribution degree of aviation route structure to the aviation carbon emission of various provinces in China. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    • From 2000 to 2019, the scope of positive driving influence of air transportation intensity on total aviation carbon emissions of various provinces gradually decreased, while the degree and scope of negative driving influence gradually increased (Fig. 7). Among them, the number of provinces with a positive impact on aviation carbon emissions has declined from 25 to 15, and the number of provinces with a positive low impact has fallen from 20 to 12; the number of provinces with high impact has dropped from 5 to 3, namely Beijing, Shanghai, and Yunnan. However, the number of provinces where air transportation intensity factors had a negative driving impact on total aviation carbon emissions has increased from 6 to 16. It is notable that during 2004–2005, each province’s unit transportation revenue turnover increased significantly, resulting in a negative impact of air transportation intensity factors on aviation carbon emissions in 23 provinces. The degree and scope of air transportation intensity factors have declined year by year after 2005. In general, the impact of air transportation intensity on the four provinces (Hainan, Hubei, Shanghai, Yunnan) was on the rise, and these provinces became the dominant carbon emission areas of air transportation intensity, mainly because the unit transportation revenue turnover was relatively small, and the growth rate of their aviation transport total turnover was less than that of air transportation revenue. The influence degree of air transportation intensity, by contrast, has shown a downward trend in the northern regions of China’s north of the Qinling Mountains and Huaihe River and Guangdong, Sichuan, Zhejiang and other places, mainly because these areas have made synchronous progress in air transportation revenue and volume, as well as in the quantity and quality. The air transportation intensity of these areas also has gradually increased, which not only improved the economic efficiency of air transport, but also reduced the influence degree of aviation carbon emissions by air transportation intensity factor, thus achieving environmental benefits.

      Figure 7.  The difference evolution(trends) of the effect contribution degree of air transportation intensity to the aviation carbon emission of various provinces in China. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    • From 2000 to 2019, aviation energy intensity factors mainly had a negative driving impact on the total aviation carbon emissions of various provinces (Fig. 8), but in 2000–2001 and 2012–2013 they exerted a low positive driving influence, which shows that aviation energy intensity factors play a considerable role in promoting aviation carbon emission reduction, but their negative driving impact on the total aviation carbon emissions is decreasing year by year. Among them, from 2000 to 2001, aviation energy intensity factors had a low positive impact on aviation carbon emissions in 29 provinces except Hebei and Shanxi. From 2012 to 2013, they had a low positive impact on the total aviation carbon emissions of all 31 provinces. Other years in the study period, aviation energy intensity factors had a negative driving impact on the total aviation carbon emissions of 31 provinces. In general, many airports have supported the ‘Civil Aviation Bureau to win the Blue Sky Defense War’, and improved energy efficiency from multi-angles and multi-levels. Meanwhile, with the updated airplane models, improved energy structure and quality, and the application of sustainable aviation fuel, ‘the regular carbon neutralization routes’ have come into being, which gradually increases the aviation energy intensity but reduces the aviation carbon emissions.

      Figure 8.  The difference evolution (trends) of the effect contribution degree of aviation energy intensity to the aviation carbon emission of various provinces in China. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

    • Using the Tapio decoupling model, the decoupling relationship between China’s aviation carbon emissions and air transportation revenue from 2000 to 2019 was calculated (Table 3). From the overall scope of 31 provinces, the three decoupling results of expansion negative decoupling, weak decoupling, and growth connection take up the highest proportions, which means the increase of air transportation revenue brings the varying degrees of growth in aviation carbon emissions. Among them, the decoupling states of quadrants I, II, III, and IV accounted for 87.10%, 8.06%, 0.64% and 4.19%, respectively. It can be said that the decoupling relationship between aviation carbon emissions and air transportation revenue is basically in the transitional state between quadrants I and III. Meanwhile, the proportion of expansion negative decoupling is the highest, the proportion of weak decoupling and growth connection decrease in turn, the proportion of weak negative decoupling and recession decoupling are the smallest, and the proportion of the strong negative decoupling state in the second quadrant is not high, which is in line with expectations. However, the proportion of strong decoupling is not high, indicating that great efforts still need to be made to reduce aviation carbon emissions. From a regional perspective, the highest proportion of decoupling in each region is expansion negative decoupling, that is, both aviation carbon emissions and air transportation revenue have increased to varying degrees, and the decoupling index is more than 1.2. Among them, the expansion negative decoupling in the central and northeastern regions accounted for more than 50.00%, and the expansion negative decoupling in the eastern and western regions accounted for 39.00% and 44.17%, respectively. In terms of weak decoupling, the four major regions are east > northeast > central > west. In terms of growth connection, the western and central regions both accounted for more than 19%, and the northeastern and western regions both accounted for more than 16%. It can be said that the eastern region is better than other regions in terms of the state of decoupling degree, because of its highest ratio in the state of strong decoupling and weak decoupling, which also shows that the eastern region has a prominent advantage in aviation carbon emissions and has made greater efforts in this respect. Moreover, the central and northeastern regions have done a good job in expansion negative decoupling state, but further control on the increase of the total amount of aviation carbon emissions is still needed. As for the western region, more attention should be paid to the double pressure of economic benefits and environmental benefits, in order to further avoid recession decoupling and strong negative decoupling.

      Table 3.  China aviation carbon emissions and air transportation revenue decoupling relationship distribution table in 2000–2019

      Region and
      ratio
      Total number of samplesExpansion negative decouplingWeak decouplingGrowth connectionStrong negative decouplingStrong decouplingWeak negative decouplingRecession
      decoupling
      Nation 620 281 146 113 50 26 2 2
      Ratio / % 100 45.32 23.55 18.23 8.06 4.19 0.32 0.32
      Eastern region 200 78 59 33 17 11 17 0
      Ratio / % 100.00 390.. 29.50 16.50 8.50 5.50 8.50 0
      Western region 240 106 10 47 21 10 0 2
      Ratio / % 100.00 44.17 4.17 19.58 8.75 4.17 0 0.83
      Central region 120 66 21 23 6 4 0 0
      Ratio / % 100.00 55.00 17.50 19.17 5.00 3.33 0 0
      Northeast region 60 31 12 10 6 1 0 0
      Ratio / % 100.00 51.67 20.00 16.67 10.00 1.67 0 0
    • From the perspective of the decoupling effort driving factors, from 2000 to 2019, the air transportation effect has made a strong decoupling effort, the aviation energy intensity effect has made a weak decoupling effort, and the route structure effect has not made a decoupling effort (Fig. 9). It shows that the increase in the scale of air transportation has played a leading role in reducing aviation carbon emissions and increasing air transportation revenue; aviation energy intensity is helpful to reduce aviation carbon emissions, but it does not function well in the growth of air transportation revenue; the air route structure still needs great adjustment.

      Figure 9.  Decoupling efforts of various provinces’ route structure effects, air transportation intensity effects, and aviation energy intensity effects. Data don not include Hong Kong, Macao, Taiwan of China due to incomplete data

      From the provincial distribution of decoupling efforts, 31 provinces did not make strong decoupling efforts in the route structure effect, and weak decoupling efforts were made only in Fujian, Gansu, Inner Mongolia, Qinghai, Shandong, and Tibetan Autonomous Region. Except those provinces in the western region, other provinces did not make decoupling efforts at all, which shows that the route structure of most provinces in China still needs to be further optimized. At present, the routes of most domestic airlines are mostly short, medium haul routes and transit routes, which is not conducive to the decoupling between aviation carbon emissions and air transportation revenue. The air transportation intensity effect has made weak decoupling efforts in nine provinces including Guangdong, Guangxi, and Henan, strong decoupling efforts in other 20 provinces, and non-decoupling efforts in Shandong and Tianjin. It can be said that the decoupling effort of air transportation intensity effect is remarkable, and strong decoupling efforts have been made in most of the study areas, indicating that the transportation scale of each province has increased to varying degrees during the study period. This not only has contributed to the growth of air transportation revenue considerably, but also reduced aviation carbon emissions under the influence of scale. Aviation energy intensity effects have made strong decoupling efforts only in Shandong, weak decoupling efforts in 16 places including Anhui and Beijing, and non-decoupling efforts in 15 places including Fujian and Gansu. Improving energy efficiency is an important means to promote aviation carbon emission reduction. However, the extremely high technical performance of new aircraft dramatically reduces the possibility of human intervention, which makes it difficult to achieve technology-driven aviation carbon emission reduction in the short term, and limits the contribution rate of technological innovation to the reduction of global aviation carbon emissions per year per kilometer to 1.0%–1.5% (Picard et al., 2019). Therefore, the decoupling efforts of aviation energy intensity effects in many provinces of China are not great, which is also a problem that the civil aviation industry needs to overcome in the future.

    • In order to achieve the ‘3060’ goal, the national carbon emission rights trade market has been officially opened. As one of the eight key emissions industries in China’s carbon market, air transportation carbon emission reduction is under great pressure and extremely difficult due to its high-altitude and mobility emissions. The civil aviation industry is characterized by high cost and low profitability, carbon emissions growth will greatly increase the operating pressure of civil aviation industry. China is in the crucial period moving from the civil aviation big power to the civil aviation super power, so it is imperative to push forward the low-carbon development of civil aviation industry. Promoting aviation carbon emissions reduction is conducive to accelerating civil aviation industry to adapt to global emission reduction trends, which is of great significance for building China’s high-quality modernized economic system and improving international influence of China’s civil aviation industry.

      This paper studies the aviation carbon emissions reduction issues in 31 provinces in China in 2000–2019, chooses four major influencing factors on China’s aviation carbon emissions: air transportation revenue, aviation route structure, air transportation intensity, and aviation energy intensity, and through undertaking a meticulous analysis of temporal and spatial evolution of influencing degree, influencing direction and evolution trend judges the decoupling relationship and decoupling effort degree between aviation carbon emissions and air transportation revenue in various provinces. This study clearly shows the basic trend of China’s aviation carbon emissions, explains the results of the four main influencing factors, and dissects the difference trend of aviation carbon emissions under the development of regional aviation markets, that is, the aviation industries in the southern China and the main aviation hub provinces develop faster, and the aviation carbon emissions are also more prominent, while the air carbon emission reduction is satisfactory. Based on the research results, the driving element, emission mechanism and reduction formula of air carbon emissions in China’s provincial level can be deeply explored. All provinces, in order to respond to aviation carbon emissions, by reference to the current development strategy of China’s civil aviation industry, should encourage and support the development and application of new energy technology, and make a list of civil aviation carbon emissions; they should make the best use of the influence of traffic structure to promote structural optimization in route planning, design and management, etc., which will benefit carbon emission reduction a lot; they should replace the old airplane model, improve aviation energy utilization efficiency, and actively promote the decoupling between aviation carbon emissions and air transportation revenue while promoting air carbon emission reduction.

      However, due to the complexity of the issue itself, this paper mainly uses macroscopic factors when selecting the influencing factors of aviation carbon emission. In the future, apart from those macroscopic factors, we should also take the impact of various microscopic factors such as air routes, airspace, aircraft types, and aircraft flight stages into consideration to make the research results more refined.

    • First, from 2000 to 2019, China’s total aviation carbon emissions continued to grow, while the growth rate of aviation carbon emissions showed a fluctuating downward trend, indicating that China has achieved remarkable results in aviation energy conservation and emission reduction. The emission reduction in the northern China is more prominent. On the one hand, it shows that economic growth is slow down and the growth rate of aviation scale is not rapid enough; on the other hand, it shows that the development of civil aviation industry in the southern China is faster, and with advantages in route structure and aviation energy intensity, the civil aviation industry in the southern China has greater potentiality in air carbon emission reduction. The decoupling relationship between aviation carbon emissions and air transportation revenue nationwide is mostly in a transitional state of decoupling, with expansion negative decoupling accounting for the highest proportion, weak decoupling and growth connection decreasing in turn, and weak negative decoupling and recession decoupling accounting for the smallest proportion. Among the four major regions, the eastern region has the best decoupling results.

      Second, the effects and scope of the four influencing factors on carbon emission reduction are different. 1) The growth of air transportation revenue has played a decisive positive driving role in the growth of China’s aviation carbon emissions, but its contribution rate has been declining year by year; its impact on the total aviation carbon emissions of various provinces is mainly focused in North China and Northeast China. 2) Aviation energy intensity has the most prominent reduction effect on total aviation carbon emissions, and the contribution rate is increasing year by year; the scope of positive driving influence on total aviation carbon emissions in various provinces tends to decrease, and the contribution of negative driving influence effects gradually expands to the whole of China. However, in terms of decoupling efforts, it is necessary to further coordinate the relationship between aviation carbon emissions and air transportation revenue. 3) The aviation route structure promotes the growth of total aviation carbon emissions; the scope of positive driving influence on total aviation carbon emissions in various provinces tends to decrease while the contribution of negative driving influence effects gradually increases; its impact on total emissions in various provinces is mainly focused in the central and northwestern regions; greater efforts need to be made in aviation carbon emission reduction and decoupling efforts. 4) The aviation transportation intensity has a restraining effect on the growth of total aviation carbon emissions; the scope of influence on the total aviation carbon emissions of various provinces is mainly in the areas to the west and south of the Qinling Mountains and Huaihe River; the aviation carbon emission reduction and decoupling efforts are both outstanding.

参考文献 (38)

目录

    /

    返回文章
    返回