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遥感技术与应用  2022, Vol. 37 Issue (2): 319-332    DOI: 10.11873/j.issn.1004-0323.2022.2.0319
面向双碳的观测与模拟专栏     
夜间灯光数据估算中国省域碳排放与国际碳数据库分配的碳排放比较
刘贤赵(),杨旭
湖南科技大学 地球科学与空间信息工程学院,湖南 湘潭 411201
The Accuracy of Nighttime Light Data to Estimate China's Provincial Carbon Emissions: A Comparison with Carbon Emissions Allocated by International Carbon Database
Xianzhao Liu(),Xu Yang
School of Earth Science and Space Information Engineering,Hunan University of Science and Technology,Xiangtan 411201,China
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摘要:

快速准确获取省域碳排放数据是实时制定差异化碳减排政策的前提。基于DMSP/OLS和NPP-VIIRS夜间灯光数据,采用统计数据比较法提取1997~2017年中国大陆各省域(不包括西藏)建成区的夜间灯光总值(用TDN表示),并利用1997~2014年各省域的TDN值与同期核算的碳排放量建立各省域碳排放预测模型。然后,以2015~2017年的TDN值为自变量估算中国各省域的碳排放量;同时,利用熵值法和碳排放分配模型将四大国际权威数据库(IEA、EIA、EDGAR和CEADs)发布的中国碳排放量分配至各省;最后,将估算结果与四大典型碳数据库分配的省域碳排放值进行比较。研究表明:估算的省域碳排放量与分配的省域碳排放量大体一致,平均绝对百分比误差(MAPE)为6.45%~9.12%,并且基于夜间灯光数据估算的省域碳排放量与IEA和EIA数据库分配的碳排放量更为接近;各省域估算的碳排放量与分配的碳排放量均落在1∶1线附近;单个省域的MAPE值变化在0.68%~14.85%,且多数省域的MAPE值均在10.0%以内。上述结果证明,基于夜间灯光数据通过提取TDN值估算省域碳排放量具有可行性和准确性

关键词: 夜间灯光数据碳排放估算国际碳排放数据库    
Abstract:

Fast and accurate access to provincial carbon emission data is the premise of real-time development of differentiated carbon emission reduction policies. Based on the DMSP/OLS and NPP-VIIRS night lighting data, the statistical data comparison method was used to extract the total nighttime light value (Expressed by TDN) of provincial built-up area in China's mainland (excluding Tibet) from 1997 to 2017, and the carbon emission prediction models of provinces were established by using the TDN values of 1997 to 2014 and the carbon emissions in the same period. Then, the TDN value from 2015 to 2017 is used as the independent variable to estimate the carbon emissions of China's provinces; at the same time, the total carbon emissions of China published by four international authoritative databases (IEA, EIA, EDGAR and CEADs) are allocated to each province by using entropy method and carbon emission allocation model. Finally, the estimated results are compared with the provincial carbon emission values assigned by four typical carbon databases. The results show that the estimated provincial carbon emissions are generally consistent with the allocated provincial carbon emissions, and the Mean Absolute Percentage Error (MAPE) is only 6.45%~9.12%. Meanwhile, the provincial carbon emissions estimated based on night light data are closer to the carbon emission values assigned by IEA and EIA databases. The estimated and allocated carbon emissions of each province fall near the 1∶1 line; the MAPE value of a single province varies from 0.68% to 14.85%, and the MAPE values of most provinces are within 10.0%. The above results prove the feasibility and accuracy of estimating provincial carbon emissions by extracting TDN values based on night light data.

Key words: Night lighting data    Carbon emission estimation    International carbon emission database
收稿日期: 2020-12-25 出版日期: 2022-06-17
ZTFLH:  X24  
基金资助: 国家社科基金项目(17BGL138);湖南省社科基金项目(18YBA151)
作者简介: 刘贤赵(1970-),男,湖南隆回人,教授,主要从事空间数据处理及环境管理研究。E?mail:xianzhaoliu@sina.com
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引用本文:

刘贤赵,杨旭. 夜间灯光数据估算中国省域碳排放与国际碳数据库分配的碳排放比较[J]. 遥感技术与应用, 2022, 37(2): 319-332.

Xianzhao Liu,Xu Yang. The Accuracy of Nighttime Light Data to Estimate China's Provincial Carbon Emissions: A Comparison with Carbon Emissions Allocated by International Carbon Database. Remote Sensing Technology and Application, 2022, 37(2): 319-332.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.2.0319        http://www.rsta.ac.cn/CN/Y2022/V37/I2/319

数据库名称起止时间碳排放空间边界碳排放核算边界数据库网址
IEA1960~2017年中国大陆、香港化石燃料https:∥webstore.iea.org
EDGAR1970~2018年中国大陆、港澳台化石燃料和工业过程https:∥data.jrc.ec.europa.eu/collection/EDGAR
EIA1979~2017年中国大陆化石燃料https:∥eia.gov/international/data/world
CEADs1997~2017年中国大陆及各省区化石燃料和水泥生产http:∥www.ceads.net
表1  国际权威碳排放数据库基本信息
图1  中国夜间灯光影像空间分布 审图号:GS(2020)4619
图2  研究框架
选取原则指标指标度量影响方向数据来源
公平性原则人口规模人口数量正向中国统计年鉴
经济增长GDP数值正向中国统计年鉴
历史碳排放量化石能源消费产生的碳排放正向中国能源统计年鉴
效率性原则碳排放强度碳排放量/GDP正向中国能源统计年鉴/中国统计年鉴
可行性原则产业结构第三产业比重负向中国统计年鉴
表2  国家层面碳排放量省域分配的指标体系
省份回归结果省份回归结果
回归方程R2FP回归方程R2FP
北京y=0.0003x+36.670.85587.030.000河南y=0.0048x-275.570.894134.360.000
天津y=0.0022x-55.200.887125.570.000湖北y=0.0037x-135.600.925196.840.000
河北y=0.0066x-312.230.954321.230.000湖南y=0.0041x-115.360.918178.430.000
山西y=0.0217x-927.170.874111.410.000广东y=0.0011x-65.860.916173.850.000
内蒙古y=0.0134x-525.890.903136.720.000广西y=0.0033x-117.720.884100.080.000
辽宁y=0.0043x-367.740.964423.640.000海南y=0.0053x-40.700.83279.100.000
吉林y=0.0038x-107.770.84785.380.000重庆y=0.0019x+10.570.865103.000.000
黑龙江y=0.0052x-423.720.869105.950.000四川y=0.0027x-41.930.84787.940.000
上海y=0.0012x+11.380.82778.180.000贵州y=0.0131x-118.830.86494.490.000
江苏y=0.0024x-56.560.960371.730.000云南y=0.0038x-28.450.933221.260.000
浙江y=0.0030x-76.640.943266.600.000陕西y=0.0083x-327.950.929210.540.000
安徽y=0.0035x-128.890.882120.060.000甘肃y=0.0046x-86.370.895136.980.000
福建y=0.0028x-57.680.9861140.460.000青海y=0.0150x-75.010.931216.080.000
江西y=0.0024x-7.810.952307.820.000宁夏y=0.0084x-86.280.898141.050.000
山东y=0.0040x-206.050.936233.760.000新疆y=0.0086x-124.030.927193.880.000
表3  基于1997~2014年中国省域建成区提取的TDN值与历史碳排放量建立的碳排放预测模型
省份IEAEDGAREIACEADs省份IEAEDGAREIACEADs
北京5.173.745.147.15河南5.7711.209.984.78
天津14.857.957.1714.19湖北4.877.8110.343.39
河北13.2213.3212.126.82湖南7.137.597.889.04
山西3.713.613.299.74广东11.412.390.693.74
内蒙古5.699.7011.296.38广西3.6512.458.9411.73
辽宁6.059.795.707.63海南2.479.906.325.99
吉林5.165.875.844.45重庆6.1612.5711.3613.70
黑龙江5.795.274.396.82四川5.025.247.295.71
上海12.588.7011.813.61贵州5.379.6612.435.89
江苏11.6010.1812.9411.67云南2.8514.558.5814.29
浙江2.414.777.396.55陕西10.352.582.379.44
安徽0.684.136.746.44甘肃6.368.706.029.04
福建4.5911.925.4010.19青海8.9614.1310.852.85
江西9.556.773.664.40宁夏5.6711.859.655.50
山东14.6511.887.169.30新疆5.6010.986.0710.11
表4  2015~2017年主要省域估算的碳排放量与国际数据库分配的碳排放量的平均绝对百分比误差(MAPE, %)
图3  2015~2017年国际数据库分配至省的碳排放与估算的省域碳排放比较
省份1997年1998年1999年2000年2001年2002年2003年2004年2005年2006年2007年2008年2009年2010年2011年2012年2013年2014年
北京72 74872 75072 76372 858109 060135 214153 397153 690154 897158 207164 833165 123168 991163 397168 207169 843170 833172 931
天津47 89449 22150 68151 76156 13058 55662 75865 09867 94469 34872 74580 30380 35485 70485 90386 22589 86389 983
河北79 33281 54883 43885 77087 66094 249104 046106 638111 893117 606121 333124 973127 673130 165135 684198 647139 957147 247
山西43 93346 44648 39957 44060 20562 41765 28066 63868 65469 33672 92574 87878 81482 30587 70191 08792 08694 398
内蒙古39 44842 06442 64443 03651 30751 66755 30158 97763 76867 49174 77577 16779 98879 98881 96388 61896 04897 821
辽宁140 263144 545147 679148 755152 907160 283161 662165 844168 506172 324178 674183 946186 666201 998206 673209 561209 871212 737
吉林54 03154 30852 68454 51857 12258 75361 65363 56967 20880 23682 61688 99580 37683 52785 71587 08088 66897 537
黑龙江108 519109 865107 806116 262116 456116 536117 191126 745129 139131 434132 534134 969137 687141 730146 815149 185151 681152 218
上海58 14373 83976 07576 08376 09576 17576 275103 315106 474114 246117 121117 129117 2211 174 246117 521117 531120 769123 835
江苏93 88696 862102 699105 861122 340151 009158 977166 997178 722188 515199 275213 502223 542244 214253 479276 107286 767297 166
浙江52 47959 94865 78272 15982 42786 64596 977101 602112 703120 185122 580133 715133 715144 719144 719153 600160 816169 537
安徽52 45058 68766 24175 60876 18180 35985 42591 89799 857103 275105 073111 047116 864121 986129 266129 266133 896137 161
福建31 19433 74035 83338 36041 07143 44053 03755 21057 89662 03367 18671 49374 56484 93092 16394 16395 51399 519
江西18 23119 35420 80026 96428 53231 49337 48041 67442 61945 75547 28749 23854 45158 22060 43962 14666 93369 473
山东109 654114 468122 926129 673144 029156 950176 031186 841200 688218 280223 811244 417253 996263 472273 482298 132321 071334 859
河南73 87478 60481 73991 11797 284105 353111 150115 754128 476136 117142 841148 633151 593157 473166 056174 882178 148180 318
湖北59 69960 40665 88770 29672 24477 01481 07882 03983 19397 267101 826109 932103 800109 932115 860117 568124 101124 638
湖南41 08443 06348 44250 04353 41755 84462 18362 68364 18766 42971 40777 72880 71682 17489 06590 69792 15494 546
广东151 524164 755177 684196 348218 782258 966319 463334 299343 321353 215373 721398 141407 293416 151431 491454 112468 180576 298
广西42 63043 84045 36745 54049 52156 53059 59460 18564 36663 34670 32471 24773 81477 286186 87284 36288 22890 697
海南7 6677 6708 2348 2458 7349 25811 59510 27612 53514 33916 07316 45313 67413 92114 40317 42017 53619 335
重庆17 30021 70522 42223 62824 07534 17135 14734 79739 27147 71351 40652 04952 57261 30567 38170 04372 01775 965
四川42 20147 32952 83659 50564 52971 83682 87484 51284 90387 07191 95592 20697 341103 887108 920115 928121 281130 180
贵州13 95114 51114 32115 29116 00217 43518 90620 83021 27821 27824 13324 42825 86822 55827 35327 40832 24336 789
云南18 28522 30622 36924 48025 57026 42835 11835 87844 50241 16543 95846 01049 35350 24452 62754 56461 83262 613
陕西41 66541 84746 78249 17551 55452 60156 17558 14859 85768 94266 31972 85875 04581 36581 50586 48892 58093 297
甘肃31 44431 89831 95032 11531 82339 29040 52041 27543 50046 76347 55349 33048 71449 12450 73751 94154 45556 091
青海5 7065 7736 1226 2786 4446 3196 4116 5656 6436 7696 7116 7416 7927 1167 6688 6589 44510 853
宁夏9 13110 68110 72111 05811 67314 50217 31019 55221 07122 73625 00926 52626 36627 52128 36630 13232 18732 371
新疆19 23125 13725 61925 63125 85127 05027 25428 02228 43230 60531 10140 99444 65945 67148 74348 74349 98952 922
  
省份201520162017
PCEIEAEDGAREIACEADsPCEIEAEDGAREIACEADsPCEIEAEDGAREIACEADs
平均MAPE ―7.428.207.288.386.459.057.397.647.589.128.078.26
北京89.4193.6389.6583.8483.3989.8492.6493.5683.5585.9490.0397.8296.7891.1688.06
天津182.30169.67201.73197.68161.24195.31170.99204.45197.48170.35214.32172.59206.69202.78203.35
河北633.86598.05592.14580.26689.37650.15596.03600.13579.67664.57755.75606.62606.71595.23841.90
山西1258.181312.901255.231246.101404.501335.781381.341361.371345.641433.111338.471389.481466.431457.611521.41
内蒙古791.45761.22729.46720.84753.80787.50759.75735.25720.41754.62831.33767.43740.02731.70765.07
辽宁549.16562.78531.31522.66502.37581.09561.31537.13522.23508.94638.47569.02541.935633.56615.65
吉林270.46244.57290.77285.94268.85254.36243.57264.69274.64274.18249.94248.77267.92262.29264.52
黑龙江377.41358.38387.19381.03347.80379.95357.33361.33360.72365.37383.55362.82354.75358.79355.33
上海163.69205.28193.51187.42161.65164.63184.25177.60187.12158.23165.22178.67170.98185.09156.58
江苏698.44642.14644.56631.63634.16720.12639.94653.26630.98653.12739.37651.47660.42647.92645.05
浙江455.72476.79447.97438.98481.50474.48475.26454.01438.53509.18496.00483.27458.99450.31461.44
安徽364.24362.81360.01352.79392.85387.32381.58364.87352.43361.18387.51388.00368.87361.89397.02
福建233.83258.05176.80260.64264.47247.83257.00280.93261.33267.15260.31262.48284.34258.40232.92
江西168.76203.941822.47237.61171.41173.00203.12185.74237.36176.67177.25207.45188.44243.74179.01
山东1191.501029.581048.521133.501052.181164.121027.021058.611132.751096.701195.461040.411066.931052.421101.80
河南604.86551.53536.83586.05537.07627.20549.69544.07525.52536.84686.78559.29650.03639.63557.62
湖北339.83334.47397.66389.68352.88340.65333.11363.02389.28353.98376.84340.23387.44399.73366.93
湖南281.54308.34306.59309.24250.52286.60307.09311.54308.87265.51294.71313.64315.61318.50275.97
广东690.03585.78696.45682.47697.95679.29583.40705.84681.77706.86695.89695.86713.58700.08733.20
广西191.42205.85244.74209.83173.23207.93205.02248.04219.58182.48214.66209.39250.76246.02193.85
海南60.9061.9468.1365.9665.3557.1760.5769.5855.8562.2662.5762.5161.7968.6967.75
重庆164.06169.77189.40184.39139.19164.88188.92202.76174.14142.18169.23203.38205.53205.70157.77
四川320.88347.95332.58321.90353.58363.19346.14339.76321.36350.14363.99355.66345.68335.36379.76
贵州352.32362.86312.52306.25327.57365.61361.80316.74305.94348.36322.78367.39320.21314.15340.34
云南218.31219.65261.15255.91178.65225.94218.76264.67225.65254.54233.82223.43267.58262.51198.22
陕西481.34439.57484.83479.12429.36504.49438.60488.67478.83570.05578.98543.69591.84586.32607.83
甘肃177.28192.50188.88184.28206.58183.50191.73201.96194.05169.72182.36195.82204.51200.07173.99
青海90.67106.59104.29100.7984.05103.61106.00116.64100.6293.15108.85109.12108.58105.2088.68
宁夏196.86188.50224.12219.62193.38199.16187.74227.14219.39189.7204.21191.75229.63225.28226.24
新疆347.62362.33311.89305.64379.09358.28361.27316.10345.32410.85410.52366.85319.57413.52452.26
  
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