Prediction of carbon emissions in Shanxi Province based on PSO-BP neural network
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摘要:
山西作为能源使用和碳排放大省，推动“双碳”战略对全国具有重要示范意义。基于IPCC（政府间气候变化专门委员会）排放系数法测算山西省2000—2020年的碳排放量，运用Tapio脱钩模型分析碳排放与经济发展之间的脱钩关系，利用LMDI法对影响碳排放变化的因素进行分解，采用PSO-BP神经网络模型对山西省的碳排放量进行模拟和预测。结果表明：2000—2020年山西省碳排放量呈增长趋势，碳排放强度呈下降趋势，脱钩系数为0.585，整体处于弱脱钩状态。经济增长是碳排放量增长的决定因素，而产业结构与能源强度的优化调整是抑制碳排放的主导因素。引入PSO（粒子群优化算法）有效提高了BP神经网络的预测精度。预测结果显示，在基准情景、低碳情景和强化低碳情景下，山西省碳排放分别于2032年、2029年和2027年达峰。针对预测结果，提出了相关政策建议。
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关键词:
- BP神经网络 /
- 粒子群优化算法(PSO) /
- 碳排放 /
- 预测 /
- 山西省
Abstract:Shanxi, as a major province of energy use and carbon emission, has an important demonstration significance for the whole country to promote the "dual carbon" strategy. The carbon emissions of Shanxi Province from 2000 to 2020 were calculated based on IPCC emission coefficient method. Tapio decoupling model was used to analyze the decoupling relationship between carbon emissions and economic development, LMDI method was used to decompose the factors affecting carbon emission changes, and PSO-BP neural network model was used to simulate and forecast the carbon emissions of Shanxi Province. The results showed that the carbon emission in Shanxi Province increased during 2000-2020, while the carbon emission intensity decreased, and the decoupling coefficient was 0.585, indicating a weak decoupling state as a whole. Economic growth was the determining factor of carbon emission growth, and the optimization and adjustment of industrial structure and energy intensity was the leading factor to restrain carbon emission. The introduction of particle swarm optimization (PSO) improved the prediction accuracy of BP neural network effectively. The predicted results showed that carbon emissions in Shanxi Province would peak in 2032, 2029 and 2027 under three scenarios: baseline scenario, low carbon scenario and intensive low carbon scenario, respectively. In view of the forecast results, relevant policy suggestions were put forward.
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Key words:
- BP neural network /
- particle swarm optimization (PSO) /
- carbon emissions /
- prediction /
- Shanxi Province
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图 2 2000—2020年山西省碳排放情况
Figure 2. Carbon emissions in Shanxi Province from 2000 to 2020
图 3 山西省碳排放量模拟值与真实值
Figure 3. Comparison of simulated and real carbon emissions in Shanxi Province
表 1 脱钩状态分类
Table 1. Decoupling status classification
类型 状态 $\Delta C$ $\Delta G$ UGC 含义 脱钩 强脱钩 − + （−∞，0） 经济增长，碳排放量减少 弱脱钩 + + [0，0.8） 碳排放量增速低于经济增速 衰退脱钩 − − （1.2，+∞） 碳排放量降低速度高于
经济衰退速度负脱钩 强负脱钩 + − （−∞，0） 经济衰退，碳排放量增加 弱负脱钩 − − [0，0.8） 碳排放量降低速度低于
经济衰退速度增长负脱钩 + + （1.2，+∞） 碳排放量增速快于经济增速 连接 增长连接 + + [0.8，1.2] 经济增长，碳排放量增加 衰退连接 − − [0.8，1.2] 经济衰退，碳排放量减少 
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表 2 不同能源的碳排放系数
Table 2. Carbon emission coefficient of different energy sources
类型 平均低位
发热值1)单位热值含碳
量/
(t/TJ)碳氧化
率/%碳排放系数/
（t/t）原煤 20 908 26.4 94 1.900 3 洗精煤 26 340 25.4 98 2.405 0 其他洗煤 8 360 25.4 90 0.701 0 型煤 15 440 33.6 90 1.712 0 焦炭 28 440 29.5 93 2.860 9 焦炉煤气 16 746 13.6 99 0.825 2 其他煤气 9 796 12.2 99 0.433 6 其他焦化产品 33 450 29.5 93 3.364 9 汽油 43 070 18.9 98 2.925 1 煤油 43 070 19.6 98 3.033 4 柴油 42 650 20.2 98 3.095 8 燃料油 41 820 21.1 98 3.170 8 液化石油气 50 180 17.2 98 3.101 4 其他石油制品 41 820 20.0 98 3.005 5 天然气 38 460 15.3 99 2.136 0 液化天然气 49 030 15.3 99 2.723 1 1)固体、液体单位为kJ/kg，气体单位为kJ/m3。
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表 3 山西省经济增长与碳排放脱钩状态
Table 3. Status of decoupling between economic growth and carbon emissions in Shanxi Province
年份 $ \Delta C $ $ \Delta G $ UGC $ \mathrm{脱}\mathrm{钩}\mathrm{状}\mathrm{态} $ 2001 0.314 0.101 3.113 扩张负脱钩 2002 0.099 0.129 0.771 弱脱钩 2003 0.118 0.119 0.990 增长连接 2004 0.069 0.122 0.566 弱脱钩 2005 0.070 0.105 0.666 弱脱钩 2006 0.104 0.099 1.046 增长连接 2007 0.062 0.132 0.472 弱脱钩 2008 0.086 0.083 1.041 增长连接 2009 0.015 0.055 0.282 弱脱钩 2010 0.066 0.108 0.613 弱脱钩 2011 0.088 0.100 0.885 增长连接 2012 0.056 0.092 0.606 弱脱钩 2013 0.017 0.090 0.192 弱脱钩 2014 −0.022 0.049 −0.455 强脱钩 2015 −0.057 0.030 −1.913 强脱钩 2016 0.012 0.041 0.295 弱脱钩 2017 0.063 0.068 0.924 增长连接 2018 0.022 0.066 0.329 弱脱钩 2019 0.041 0.061 0.668 弱脱钩 2020 0.022 0.036 0.611 弱脱钩 均值 0.062 0.084 0.585 弱脱钩
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表 4 LMDI碳排放因素分解结果
Table 4. Decomposition results of LMDI carbon emission factors
万t 年份 人口效应
$(\Delta p)$经济增长效应
$(\Delta g) $产业结构效应
$(\Delta v) $能耗强度效应
$(\Delta e) $能源结构效应
（$ \Delta s $）综合效应
$(\Delta C) $2001 137.38 1 670.81 148.46 2 766.86 431.27 5 154.78 2002 276.06 4 002.48 657.27 1 667.27 695.15 7 298.23 2003 422.41 6 455.76 1 985.77 649.49 574.49 10 087.92 2004 571.56 9 022.75 2 660.15 −955.58 617.88 11 916.76 2005 721.40 11 361.13 3 760.77 −3 229.73 1 333.27 13 946.84 2006 896.05 14 068.00 4 234.19 −3 000.66 834.51 17 032.09 2007 1 050.07 17 328.72 4 886.46 −5 084.95 936.48 19 116.78 2008 1 224.60 19 878.65 4 907.45 −5 246.53 1 419.84 22 184.00 2009 1 360.21 21 303.15 3 852.04 −5 266.43 1 532.43 22 781.40 2010 2 503.27 23 621.86 4 958.97 −6 374.13 664.17 25 374.14 2011 2 526.67 27 385.98 5 792.55 −7 490.51 853.90 29 068.59 2012 2 496.53 30 873.70 4 914.36 −7 638.88 958.75 31 604.46 2013 2 397.21 33 496.29 3 973.19 −8 293.88 861.60 32 434.40 2014 2 307.26 34 336.78 2 754.11 −9 095.35 1 042.10 31 344.89 2015 2 144.90 33 903.57 −1 090.63 −7 721.92 1 369.01 28 604.94 2016 2 146.28 35 556.41 −1 783.74 −7 399.25 629.95 29 149.65 2017 2 192.75 38 748.05 −313.96 −8 928.03 311.70 32 010.51 2018 2 144.78 40 924.88 −1 108.29 −9 332.00 431.82 33 061.19 2019 2 134.40 43 363.64 −1 316.55 −9 822.89 667.72 35 026.31 2020 2 107.94 44 999.20 −1 665.93 −9 936.84 703.39 36 207.76 累计贡献率/% 5.82 124.28 −4.60 −27.44 1.94 100
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表 5 相关性分析结果
Table 5. Correlation analysis results
驱动因素 含义 |r| 人口 人口数量 0.863** 经济增长 人均GDP 0.962** 产业结构 第二产业增加值/GDP 0.878** 能耗强度 能源消耗量/GDP 0.924** 能源结构 煤炭消费量/能源消耗量 0.955** 注：**表示在 0.01 水平相关性显著。
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表 6 2种模型预测效果对比
Table 6. Comparison of prediction effects of two models
预测模型 BP PSO-BP R2 0.953 4 0.990 6 MAE/亿t 0.392 8 0.080 0 RMSE/亿t 0.228 7 0.102 9 MAPE/% 9.59 2.15
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表 7 山西省各驱动因素变化率
Table 7. Change rate of various driving factors in Shanxi Province
情景 变化速率 p g v e s 基准情景 高 高 低 低 低 低碳情景 中 中 中 中 中 强化低碳情景 低 中 高 高 高
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