PREDICTION OF CARBON DIOXIDE EMISSION IN GANSU PROVINCE BASED ON BP NEURAL NETWORK AND ITS INFLUENCING FACTORS

PAN Siyu; ZHANG Meiling

doi:10.13205/j.hjgc.202307009

Volume 41 Issue 7

Jul. 2023

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PAN Siyu, ZHANG Meiling. PREDICTION OF CARBON DIOXIDE EMISSION IN GANSU PROVINCE BASED ON BP NEURAL NETWORK AND ITS INFLUENCING FACTORS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 61-68,85. doi: 10.13205/j.hjgc.202307009

Citation:

PAN Siyu, ZHANG Meiling. PREDICTION OF CARBON DIOXIDE EMISSION IN GANSU PROVINCE BASED ON BP NEURAL NETWORK AND ITS INFLUENCING FACTORS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 61-68,85. doi: 10.13205/j.hjgc.202307009

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PREDICTION OF CARBON DIOXIDE EMISSION IN GANSU PROVINCE BASED ON BP NEURAL NETWORK AND ITS INFLUENCING FACTORS

doi: 10.13205/j.hjgc.202307009

PAN Siyu,
ZHANG Meiling^,

School of Science, Gansu Agricultural University, Lanzhou 730070, China

Received Date: 2022-11-04

Abstract

Abstract

The estimation of direct CO₂ emissions from three major industries and domestic energy consumption in Gansu province from 2000 to 2020 by emission factor method was carried out, to describe and analyze its evolution characteristics. Then we established a BP neural network model and forecasted the CO₂ emissions of Gansu province from 2021 to 2030. Finally, a STIRPAT expansion model of influencing factors of CO₂ emission in Gansu province was constructed, the influence degree and internal mechanism of each factor on CO₂ emissions were quantitatively explored by using multiple regression analysis, and the important influencing factors were further identified by combining random forests. The results showed that:the direct CO₂ emission from industrial and domestic energy consumption in Gansu province were generally fluctuating, and the secondary industry accounted for more than 70% of the total CO₂ emissions, which was the main source of carbon dioxide emissions. The prediction error of the BP neural network model was 2×10^-4, and the correlation coefficient was greater than 0.99, which had high accuracy for predicting CO₂ emissions in Gansu province, also it was concluded that the direct CO₂ emissions of energy consumption in Gansu province would reach the maximum value in 2026. The driving factors of CO₂ emissions in Gansu province were significantly different, the intensity of CO₂ emissions, economic development and urban and rural consumption had a greater positive effect on CO₂ emissions, and per capita consumption expenditure of urban residents was the main influencing factor. The growth of forest coverage inhibited CO₂ emissions, and the reduction of energy intensity contributed to the reduction of CO₂ emissions. The adjustment and transformation of the three major industrial structures could lead to a decline in CO₂ emissions. The research provided a theoretical basis and scientific basis for Gansu province to promote low-carbon emission reduction.
- CO₂ emissions,
- BP neural network,
- STIRPAT model,
- multiple regression analysis,
- random forest

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References(33)

References

[1]	方恺,李帅,叶瑞克,等.全球气候治理新进展:区域碳排放权分配研究综述[J].生态学报,2020,40(1):10-23.
[2]	ZHANG L, LEI J, ZHOU X, et al.Changes in carbon dioxide emissions and LMDI-based impact factor decomposition:the Xinjiang Uygur autonomous region as a case[J].Journal of Arid Land,2014,6(2):145-155.
[3]	李欢,杨珊,陈建宏,等.湖南省能源消费碳排放驱动因素及趋势预测实证分析[J].环境工程,2018,36(2):152-157.
[4]	PATA U K.Renewable and non-renewable energy consumption, economic complexity, CO₂ emissions, and ecological footprint in the USA:testing the EKC hypothesis with a structural break[J].Environmental Science and Pollution Research,2021,28(1):846-861.
[5]	顾张锋,徐丽华,马淇蔚,等.浙江省都市区碳排放时空演变及其影响因素[J].自然资源学报,2022,37(6):1524-1539.
[6]	张君宇,宋猛,刘伯恩.中国二氧化碳排放现状与减排建议[J].中国国土资源经济,2022,35(4):38-44.
[7]	MENG B, WANG J G, ANDREW R, et al.Spatial spillover effects in determining China's regional CO₂ emissions growth:2007-2010[J].Energy Economics,2017,63(3):161-173.
[8]	田成诗,陈雨.中国省际农业碳排放测算及低碳化水平评价:基于衍生指标与TOPSIS法的运用[J].自然资源学报,2021,36(2):395-410.
[9]	燕振刚,李薇,YAN T H,等.BP神经网络算法在河西绿洲玉米生产碳排放评估中的应用及算法有效性研究[J].中国生态农业学报,2018,26(8):1100-1106.
[10]	彭猛,吴剑,陈柳芮,等.基于生产函数理论的重庆市碳排放预测[J].江苏大学学报(自然科学版),2021,42(4):451-457.
[11]	张帆,徐宁,吴锋.共享社会经济路径下中国2020-2100年碳排放预测研究[J].生态学报,2021,41(24):9691-9704.
[12]	潘栋,李楠,李锋,等.基于能源碳排放预测的中国东部地区达峰策略制定[J].环境科学学报,2021,41(3):1142-1152.
[13]	张哲,任怡萌,董会娟.城市碳排放达峰和低碳发展研究:以上海市为例[J].环境工程,2020,38(11):12-18.
[14]	李艳红.山东省碳减排系统仿真及政策优化研究[J].环境工程技术学报,2020,10(1):150-159.
[15]	LEI W, ZHANG J, SONG Q Q.A scenario analysis of Chinese carbon neutral based on STIRPAT and system dynamics model[J].Environmental Science and Pollution Research International,2022,29(36):55105-55130.
[16]	胡振,龚薛,刘华.基于BP模型的西部城市家庭消费碳排放预测研究:以西安市为例[J].干旱区资源与环境,2020,34(7):82-89.
[17]	LEI W, LU B, ZHANG E N.System dynamic modeling and scenario simulation on Beijing industrial carbon emissions[J].Environmental Engineering Research,2016,21(4):355-364.
[18]	刘丽娜,王春妤,袁子薇,等.区域农业碳排放LMDI分解和脱钩效应分析[J].统计与决策,2019,35(23):95-99.
[19]	王雅楠,谢艳琦,谢丽琴,等.基于LMDI模型和Q型聚类的中国城镇生活碳排放因素分解分析[J].环境科学研究,2019,32(4):539-546.
[20]	LIU Y X, JIANG Y J, LIU H, et al.Driving factors of carbon emissions in China's municipalities:a LMDI approach[J].Environmental Science and Pollution Research International,2021,29(15).21789-21802.
[21]	戴小文,何艳秋,钟秋波.中国农业能源消耗碳排放变化驱动因素及其贡献研究:基于Kaya恒等扩展与LMDI指数分解方法[J].中国生态农业学报,2015,23(11):1445-1454.
[22]	陈占明,吴施美,马文博,等.中国地级以上城市二氧化碳排放的影响因素分析:基于扩展的STIRPAT模型[J].中国人口·资源与环境,2018,28(10):45-54.
[23]	卢娜,曲福田,冯淑怡,等.基于STIRPAT模型的能源消费碳足迹变化及影响因素:以江苏省苏锡常地区为例[J].自然资源学报,2011,26(5):814-824.
[24]	XIONG C H, CHEN S, XU L T.Driving factors analysis of agricultural carbon emissions based on extended STIRPAT model of Jiangsu Province, China[J].Growth and Change,2020,51(3):1401-1416.
[25]	曾晓莹,邱荣祖,林丹婷,等.中国交通碳排放及影响因素时空异质性[J].中国环境科学,2020,40(10):4304-4313.
[26]	董莹,许宝荣,华中,等.基于LMDI的甘肃省碳排放影响因素分解研究[J].兰州大学学报(自然科学版),2020,56(5):606-614.
[27]	李小军,朱青祥,漆志强,等.基于STIRPAT模型的碳排放峰值预测研究:以甘肃省为例[J].环保科技,2022,28(5):38-44.
[28]	国家统计局能源统计司,国家能源局综合司.中国能源统计年鉴2008[M].北京:中国统计出版社,2008.
[29]	HOERL A E, KENNARD R W.Ridge regression:biased estimation for nonorthogonal problems[J].Technometrics, 1970, 12(1):55-67.
[30]	WANG S H,YU W Y.Sensitivity analysis of primary energy consumption structural change and carbon intensity[J].Resources Science,2013,35(7):1438-1446.
[31]	BREIMAN L.Random forests[J].Machine Learning, 2001, 45(1):5-32.
[32]	刘卫东,唐志鹏,夏炎,等.中国碳强度关键影响因子的机器学习识别及其演进[J].地理学报,2019,74(12):2592-2603.
[33]	周四军,江秋池.基于动态SDM的中国区域碳排放强度空间效应研究[J].湖南大学学报(社会科学版),2020,34(1):40-48.