Spatial measurement and classification of forest carbon sink demand based on industry emission reduction
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摘要:
目的 对全国28个省级行政区域和深圳市的森林碳汇需求空间进行分类，就提升各类地区未来的森林碳汇需求空间提出针对性的建议，为科学设计碳汇政策以及有针对性地开发森林碳汇需求空间提供客观依据。 方法 以全国28个省级行政区域和深圳市为样本地区，收集整理2008−2017年《统计年鉴》中工业行业的投入产出数据，运用方向性距离函数模型测算各地区工业行业碳边际减排成本，并采用需求空间模型，对全国28个省级行政区域和深圳市10 a的森林碳汇需求空间进行测度，对求得的需求空间数据进行聚类分析和判别分析。 结果 各样本地区的碳边际减排成本和森林碳汇需求空间数据均存在一定的地区性波动。3类地区碳边际减排成本与森林碳汇需求空间的皮尔逊相关系数为0.999，呈显著正相关。聚类结果发现：1、2、3类地区的碳边际减排成本平均值分别为1.59、1.18、0.51万元·t−1；1、2、3类地区森林碳汇需求空间平均值分别为571.91、374.93、174.15万t·a−1。最终判别方程发现：2011、2014年的需求空间数据对地区分类的影响最为显著。 结论 整体来看，分类结果与中国东中西部地区的经济发展水平大致吻合。政策情景模拟显示：差异化开发森林碳汇需求空间要将超排处罚率作为第1、2类地区的重要切入点，同时优化配额发放模式；第3类地区以鼓励引导为主。表6参16 Abstract:Objective This paper classifies the forest carbon sink demand space of 28 provincial administrative regions and Shenzhen City in China, and puts forward some suggestions for improving the future forest carbon sink demand space of various regions, so as to provide an objective basis for scientific design of carbon sink policy and targeted development of forest carbon sink demand space. Method Taking the above 29 areas as sample units, the input and output data of the industrial industries in 2008−2017 statistical yearbook were collected. The directional distance function model was used to calculate the carbon marginal emission reduction cost of the industrial industries in each region, and the demand space model was used to measure the forest carbon sink demand space of 29 areas in the past 10 years. Then cluster analysis and discriminant analysis were carried out on the obtained demand space data. Result There were some regional fluctuations in the marginal carbon emission reduction cost and the spatial data of forest carbon sink demand in each sample area. The Pearson correlation coefficient between marginal carbon emission reduction cost and forest carbon sink demand space was 0.999, showing a significant positive correlation. The clustering results showed that the average marginal carbon emission reduction cost in regions of Category 1, 2 and 3 was 15.9, 11.8 and 5.1 thousand yuan·t−1 respectively. The average spatial value of forest carbon sink demand in Category 1, 2 and 3 was 5 719.1, 3 749.3 and 1 741.5 thousand t·a−1, respectively. Through the final discriminant equation, it was found that the demand spatial data of 2011 and 2014 had the most significant impact on regional classification. Conclusion On the whole, the classification results are roughly consistent with the economic development level of the eastern, central and western regions of China. The policy scenario simulation shows that the penalty rate of over emission should be taken as an important entry point for the regions of Category 1 and 2, and the quota distribution mode should be optimized. The 3rd category should be encouraged and guided.[Ch, 6 tab. 16 ref.] -
表 1 2008−2017年全国29个样本地区工业行业二氧化碳边际减排成本
Table 1. Marginal CO2 emission reduction cost of 29 sample areas in China from 2008 to 2017
年份 边际减排成本/(万元·t−1) 上海市 天津市 北京市 重庆市 深圳市 广东省 湖北省 山西省 海南省 青海省 2008 1.174 3 1.087 1 0.720 3 1.741 4 2.455 1 2.295 2 1.229 8 1.234 8 0.798 8 0.665 5 2009 1.090 2 1.070 4 0.600 3 1.631 8 2.384 0 2.094 5 1.172 9 1.095 0 0.794 6 0.553 4 2010 0.996 8 1.008 9 0.519 1 1.479 0 0.718 9 1.928 5 1.168 1 0.975 3 0.658 9 0.512 6 2011 0.852 6 0.872 0 0.425 1 1.317 0 0.752 8 1.744 5 1.029 2 0.806 1 0.551 1 0.362 3 2012 0.815 7 0.811 6 0.367 9 1.216 9 1.787 6 1.669 0 0.998 7 0.732 9 0.487 2 0.324 5 2013 0.725 6 0.742 1 0.305 8 1.130 2 1.340 7 1.551 1 0.980 6 0.633 1 0.425 7 0.267 1 2014 0.653 2 0.711 7 0.273 4 1.040 4 1.688 2 1.456 4 0.952 8 0.565 9 0.333 7 0.238 1 2015 0.563 4 0.666 2 0.178 0 0.956 1 1.551 5 1.359 0 0.879 5 0.520 8 0.243 2 0.241 8 2016 0.486 2 0.498 6 0.085 8 0.830 0 1.421 7 1.248 2 0.794 8 0.474 0 0.155 2 0.251 7 2017 0.422 9 0.407 0 0.030 3 0.644 0 1.237 7 1.156 1 0.852 9 0.411 3 0.132 9 0.265 8 平均 0.778 1 0.787 6 0.350 6 1.198 7 1.533 8 1.650 3 1.005 9 0.744 9 0.458 1 0.368 3 年份 边际减排成本/(万元·t−1) 山东省 浙江省 江苏省 安徽省 宁夏回族
自治区新疆维吾尔
自治区吉林省 内蒙古
自治区广西壮族
自治区黑龙江省 2008 1.722 1 1.736 1 1.728 3 1.497 8 0.901 9 0.776 1 1.187 6 0.825 3 1.255 0 1.379 3 2009 1.562 2 1.564 4 1.519 6 1.406 3 0.739 3 0.678 4 1.1832 0.759 2 1.353 7 1.303 2 2010 1.400 1 1.475 1 1.425 3 1.280 4 0.629 2 0.548 5 1.053 0 0.689 5 1.369 2 1.050 9 2011 1.187 4 1.146 0 1.212 9 1.118 5 0.502 8 0.420 5 0.913 0 0.533 0 1.198 9 0.862 8 2012 1.114 6 1.131 6 1.154 6 1.071 6 0.420 3 0.361 2 0.854 8 0.453 5 1.148 1 0.829 0 2013 1.026 0 1.058 3 1.099 7 1.005 3 0.356 3 0.243 9 0.767 0 0.370 1 1.061 6 0.765 7 2014 0.904 8 1.015 8 1.078 1 0.955 1 0.252 1 0.215 5 0.717 2 0.284 4 1.005 1 0.688 3 2015 0.825 1 0.961 9 0.964 7 0.855 2 0.169 8 0.188 9 0.662 7 0.205 3 0.976 5 0.630 5 2016 0.755 6 0.909 8 0.886 0 0.801 9 0.082 7 0.145 3 0.609 8 0.159 3 0.912 4 0.555 2 2017 0.683 6 0.857 7 0.809 9 0.722 6 0.030 4 0.112 5 0.511 3 0.082 7 0.839 5 0.621 6 平均 1.118 2 1.185 6 1.187 9 1.071 5 0.408 5 0.369 1 0.846 0 0.436 2 1.112 0 0.868 7 年份 边际减排成本/(万元·t−1) 辽宁省 云南省 甘肃省 湖南省 河北省 河南省 陕西省 四川省 贵州省 2008 1.206 0 0.920 7 1.196 7 1.417 2 1.343 0 1.640 8 1.057 9 1.476 5 1.229 7 2009 1.185 6 0.875 3 1.054 7 1.424 6 1.212 3 1.573 7 0.891 3 1.376 0 1.148 7 2010 1.089 1 0.766 3 0.910 0 1.476 4 1.114 4 1.456 6 0.818 2 1.276 8 1.068 5 2011 0.940 2 0.646 8 0.638 9 1.361 9 1.067 9 1.383 1 0.724 9 1.202 6 1.026 0 2012 0.942 3 0.610 8 0.559 7 1.284 7 0.949 7 1.276 8 0.632 4 1.097 9 0.993 1 2013 0.847 6 0.494 6 0.454 6 1.234 9 0.918 5 1.1624 0.520 6 0.942 1 0.887 7 2014 0.775 7 0.417 6 0.434 5 1.193 0 0.884 3 1.100 6 0.458 3 0.873 5 0.847 4 2015 0.640 2 0.364 6 0.373 7 1.153 9 0.941 0 1.058 0 0.409 9 0.790 2 0.801 3 2016 0.422 3 0.306 5 0.330 4 1.081 6 0.966 6 1.015 6 0.346 1 0.730 1 0.816 2 2017 0.374 1 0.258 5 0.274 5 1.005 6 1.017 9 0.955 0 0.301 9 0.661 8 0.830 9 平均 0.842 3 0.566 2 0.622 8 1.263 4 1.041 6 1.262 3 0.616 1 1.042 8 0.965 0 
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表 2 样本期内全国29个样本地区工业行业的森林碳汇需求空间
Table 2. Forest carbon sink demand space of industrial industries in 29 sample areas in the sample period
年份 森林碳汇需求空间/万t 上海市 天津市 北京市 重庆市 深圳市 广东省 湖北省 山西省 海南省 青海省 2008 425.530 7 395.142 1 261.231 0 645.974 1 880.597 1 832.525 9 457.635 1 463.993 3 295.125 0 250.854 9 2009 395.190 2 389.350 0 217.424 3 604.192 7 853.360 7 759.917 6 435.460 4 412.016 7 294.630 8 208.741 5 2010 360.497 9 366.513 3 187.543 5 540.441 4 257.265 7 697.876 1 431.230 0 365.914 4 243.005 1 192.837 9 2011 307.965 9 316.245 6 153.225 0 479.784 2 268.866 0 630.513 5 377.599 0 301.413 1 203.721 9 136.295 4 2012 294.599 7 294.125 4 132.439 5 442.521 5 637.229 3 603.287 7 365.503 7 274.105 8 179.870 9 122.023 6 2013 262.158 4 268.685 2 109.910 4 409.955 7 477.651 4 559.596 4 358.373 1 237.950 1 157.319 9 100.332 7 2014 235.778 9 257.541 2 97.986 5 376.492 9 601.341 9 524.559 7 347.685 7 212.396 4 123.276 2 89.466 7 2015 203.509 7 240.992 8 63.785 9 345.374 8 552.412 5 489.240 3 320.607 5 195.108 7 90.051 2 90.964 1 2016 175.615 2 180.356 6 30.754 7 299.338 8 506.162 7 448.900 7 289.405 9 177.294 9 57.396 8 94.494 6 2017 152.477 8 147.282 4 10.820 4 233.758 9 441.080 6 415.863 8 311.105 5 153.688 7 49.138 0 99.950 0 平均 281.332 4 285.623 5 126.512 1 437.783 5 547.596 8 596.228 2 369.460 6 279.388 2 169.353 6 138.596 1 年份 森林碳汇需求空间/万t 山东省 浙江省 江苏省 安徽省 宁夏回族
自治区新疆维吾尔
自治区吉林省 内蒙古
自治区广西壮族
自治区黑龙江省 2008 630.253 7 636.862 3 631.536 0 561.117 8 341.099 8 293.080 8 447.657 5 309.865 1 468.059 6 520.871 7 2009 570.442 4 574.530 3 554.929 5 526.258 2 279.604 9 256.392 3 444.933 7 284.253 1 504.069 6 484.984 5 2010 510.358 5 539.758 6 519.268 4 476.582 3 237.946 2 207.244 6 393.885 4 256.297 4 504.212 0 396.492 9 2011 432.256 1 419.122 5 441.849 0 414.075 9 190.128 3 158.851 7 340.667 0 198.503 5 439.809 6 324.935 1 2012 404.694 2 413.084 4 419.890 3 395.802 3 158.935 7 136.430 4 317.902 5 168.827 3 419.973 2 311.821 8 2013 371.902 1 386.086 0 398.849 8 370.895 3 134.723 5 92.130 3 284.560 5 137.679 0 387.779 7 287.227 6 2014 327.233 1 369.979 5 390.672 2 351.813 7 95.322 7 81.418 9 265.078 1 105.345 3 366.252 4 257.924 5 2015 298.444 0 350.371 1 349.241 5 314.428 1 64.194 4 71.117 1 244.464 9 76.513 6 354.816 7 236.649 9 2016 273.220 7 331.187 5 320.749 2 294.116 2 31.289 2 54.715 4 223.876 2 59.277 9 331.148 8 208.483 8 2017 247.480 8 312.980 6 293.207 2 264.811 9 11.487 0 42.332 1 187.970 0 31.045 5 304.477 8 233.843 9 平均 406.628 6 433.396 3 432.019 3 396.990 2 154.473 2 139.371 4 315.099 6 162.760 8 408.059 9 326.323 6 年份 森林碳汇需求空间/万t 辽宁省 云南省 甘肃省 湖南省 河北省 河南省 四川省 陕西省 贵州省 2008 451.759 9 347.007 0 448.955 2 533.319 9 499.975 0 608.348 2 547.278 8 396.237 4 463.326 2 2009 442.426 5 330.176 7 395.524 2 535.025 8 450.870 4 583.354 9 508.845 9 333.362 8 432.676 5 2010 405.310 0 288.590 5 340.103 9 552.182 9 412.466 7 540.003 1 470.701 8 305.461 3 401.732 5 2011 349.301 5 243.184 7 238.099 2 506.962 0 393.870 8 512.764 5 442.425 4 271.917 3 384.434 1 2012 348.947 4 229.295 7 208.365 4 476.968 6 349.810 0 472.053 4 403.810 3 237.160 7 371.192 8 2013 313.450 8 185.657 9 168.930 0 458.259 6 337.873 3 429.481 1 345.111 0 195.399 9 329.724 7 2014 286.771 9 156.370 6 161.298 1 439.817 0 324.878 9 405.864 4 319.321 6 172.046 4 313.636 7 2015 238.740 6 136.265 3 139.103 3 424.683 0 345.130 2 388.950 6 288.475 0 154.146 9 295.465 8 2016 158.807 9 114.412 9 122.971 5 397.644 4 354.087 2 372.605 3 265.916 5 130.088 5 300.254 0 2017 140.602 9 96.409 8 102.433 1 369.643 6 373.191 9 349.506 3 241.167 4 113.420 1 305.912 7 平均 313.612 0 212.737 1 232.578 4 469.450 7 384.215 4 466.293 2 383.305 4 230.924 1 359.835 6
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表 3 样本期内全国29个样本地区工业行业森林碳汇需求空间聚类结果
Table 3. Spatial clustering results of forest carbon sink demand of industrial industry in 29 sample areas in the sample period
样本地区碳汇
需求空间(bi)需求空间
分类(Ck)样本地区碳汇
需求空间(bi)需求空间
分类(Ck)样本地区碳汇
需求空间(bi)需求空间
分类(Ck)样本地区碳汇
需求空间(bi)需求空间
分类(Ck)样本地区碳汇
需求空间(bi)需求空间
分类(Ck)b1 1 b7 2 b13 2 b19 2 b25 3 b2 1 b8 2 b14 2 b20 2 b26 3 b3 2 b9 2 b15 2 b21 3 b27 3 b4 2 b10 2 b16 2 b22 3 b28 3 b5 2 b11 2 b17 2 b23 3 b29 3 b6 2 b12 2 b18 2 b24 3 说明：在聚类分析中，假设全国29个样本地区过去10 a的森林碳汇需求空间分别为 bi, i= 1, 2, ···, 29，即bi=b1, b2, ···, b29 (1~29分别 代表：深圳市、广东省、上海市、天津市、重庆市、湖北省、山东省、浙江省、江苏省、安徽省、吉林省、辽宁省、湖南 省、河北省、河南省、贵州省、四川省、山西省、广西壮族自治区、黑龙江省、北京市、云南省、甘肃省、海南省、青海 省、陕西省、宁夏回族自治区、新疆维吾尔自治区、内蒙古自治区)，需求空间分类为Ck(k为需求空间的分类数)
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表 4 聚类分析的方差分析表
Table 4. ANOVA of cluster analysis
变量 聚类 误差 F值 显著性 均方差 自由度 均方差 自由度 P2008 262 367.420 2 5 624.667 26 46.646 0.000 P2009 259 582.928 2 4 342.022 26 59.784 0.000 P2010 132 329.933 2 7 692.015 26 17.204 0.000 P2011 132 612.742 2 6 034.394 26 21.976 0.000 P2012 212 062.424 2 3 028.819 26 70.015 0.000 P2013 178 662.663 2 3 036.332 26 58.842 0.000 P2014 212 975.584 2 3 037.580 26 70.114 0.000 P2015 200 772.893 2 3 184.382 26 63.049 0.000 P2016 184 581.889 2 3 989.725 26 46.264 0.000 P2017 166 137.990 2 4 388.282 26 37.859 0.000
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表 5 判别分析的 Wilks’ Lambda 检验
Table 5. Wilks’ Lambda test for discriminant analysis
方程检验 Wilks’ Lambda 卡方 自由度 显著性 方程1 0.068 57.923 20 0.000 方程 2 0.068 57.923 20 0.000 方程 3 0.536 13.404 9 0.145
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表 6 逐步回归的Wilks’ Lambda检验
Table 6. Test of Wilks’ Lambda for stepwise regression
Wilks’ Lambda 自由度1 自由度2 自由度3 精确F值 统计 自由度1 自由度2 显著性 0.156 1 2 26 70.114 2 26.000 0.000 0.116 2 2 26 24.221 4 50.000 0.000
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