Evaluation of provincial carbon emission efficiency based on partial order set
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摘要:
为解决评价碳排放效率时面板数据的时间权重难以表示的问题，应用偏序集评价模型开展我国碳排放效率评价。该模型无需指标的具体权重值，只需知道权重顺序即可。通过收集我国30个省（区、市）2000—2017年的面板数据，求出历年碳排放效率，以时间逆序为权重顺序，进行碳排放效率评价。结果表明：我国东部碳排放效率较高，中部次之，西北部碳排放效率最差，影响各地区碳排放效率的因素不同，因此制定碳排放政策时要因地制宜；不同地区的碳排放效率呈空间聚集的趋势，一个地区的碳排放政策不仅会影响本地区的碳排放效果，还会影响相近地区的碳排放效果，因此要加强各地区间的碳减排合作。
Abstract:To address the problematic time weights of panel data in the evaluation of carbon emission efficiency, a partial-order set evaluation model was used to evaluate the carbon emission efficiency of China. In this model, it was not necessary to know the specific weight values of the indicators, but only the weight order was needed. By collecting the panel data of 30 Chinese provinces (autonomous regions and municipalities) from 2000 to 2017, calculating each year′s carbon emission efficiency, and sequencing the weight values in time reverse order, the carbon emission efficiency was evaluated. The results showed that the carbon emission efficiency was the highest in East China, followed by the Central China, and the worst in Northwest. The factors affecting the carbon emission efficiency in each region were different, which required that specific carbon emission policies should be developed according to local conditions. The carbon emission efficiency of each region tended to be aggregated, and the local carbon emission policy would not only affect the carbon emission effect of the region, but also affect the carbon emission of the adjacent areas, so, it was necessary to strengthen the cooperation of carbon emission reduction among all the regions.
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图 1 2000—2011年碳排放效率Hasse图
Figure 1. Hasse chart of carbon emission efficiency from 2000 to 2011
图 2 2000—2017年碳排放效率Hasse图
Figure 2. Hasse chart of carbon emission efficiency from 2000 to 2017
表 1 碳排放效率评价指标
Table 1. Evaluation indexes of carbon emission efficiency
一级指标 二级指标 三级指标 碳排放效率 产出指标 碳排放量 GDP 投入指标 劳动力 资本存量 能源消费 
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表 2 部分地区2000—2017年碳排放效率
Table 2. Carbon emission efficiency from 2000 to 2017 in some areas
决策单元 2017年 2016年 2015年 2014年 2013年 2012年 2011年 2010年 2009年 北京 1 739 1 637 1 528 1 402 1 412 1 304 1 618 1 412 1 239 天津 931 889 905 954 961 854 906 986 1 058 河北 1 000 923 943 974 1 000 952 768 755 752 上海 1 250 1 259 1 398 1 390 1 380 1 248 1 412 1 399 1 411 陕西 771 764 807 856 882 873 728 718 706 甘肃 799 753 782 854 903 885 599 599 569 青海 781 779 787 812 821 727 583 571 528 宁夏 590 534 587 507 533 588 512 504 499 新疆 855 884 925 1 000 1 000 1 000 679 679 622 决策单元 2008年 2007年 2006年 2005年 2004年 2003年 2002年 2001年 2000年 北京 1 151 1 139 1 133 1 031 1 028 1 018 1 016 1 012 1 010 天津 1 015 1 012 978 964 957 951 945 933 913 河北 772 805 810 829 843 828 825 1 177 868 上海 1 391 1 377 1 364 1 232 1 363 1 420 1 418 1 378 1 385 陕西 697 689 694 677 621 623 571 545 546 甘肃 590 615 617 599 563 565 530 513 543 青海 516 496 482 489 496 589 450 434 455 宁夏 485 856 472 477 429 449 427 419 426 新疆 664 644 655 653 633 647 552 554 575 注：为了计算方便，将计算数据扩大了1000倍，因为只比较不同地区碳排放效率的相对大小，所以不影响最后结果。
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表 3 部分地区2000—2017年Hasse矩阵
Table 3. Hasse matrix from 2000 to 2017 in some areas
决策单元 北京 天津 河北 上海 陕西 甘肃 青海 宁夏 新疆 北京 0 0 0 0 0 0 0 0 0 天津 0 0 0 0 0 0 0 0 0 河北 0 0 0 0 0 0 0 0 1 上海 0 0 0 0 0 0 0 0 0 云南 0 0 0 0 0 0 0 0 0 陕西 0 0 0 0 0 0 0 0 0 甘肃 0 0 0 0 0 0 0 1 0 青海 0 0 0 0 0 0 0 1 0 宁夏 0 0 0 0 0 0 0 0 0 新疆 0 0 0 0 1 1 1 0 0
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表 4 部分地区2000—2017年秩均值
Table 4. Rank mean values from 2000 to 2017 in some areas
决策单元 秩均值 决策单元 秩均值 上海 0.97 湖北 0.41 北京 0.97 四川 0.40 广东 0.90 重庆 0.37 江苏 0.88 江西 0.35 海南 0.88 陕西 0.29 天津 0.86 甘肃 0.27 山东 0.86 河南 0.25 福建 0.84 云南 0.22 河北 0.76 黑龙江 0.21 安徽 0.70 辽宁 0.20 广西 0.65 吉林 0.14 浙江 0.64 贵州 0.11 新疆 0.55 青海 0.08 湖南 0.54 内蒙古 0.03 山西 0.43 宁夏 0.03
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