PREDICTION OF INDUSTRIAL CARBON EMISSIONS IN SHAANXI PROVINCE BASED ON LASSO-GWO-KELM MODEL

ZHANG Xinsheng; WEI Zhizhen; CHEN Zhangzheng; HAN Yiwei

doi:10.13205/j.hjgc.202310018

Volume 41 Issue 10

Oct. 2023

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(10): 141-149

ZHANG Xinsheng, WEI Zhizhen, CHEN Zhangzheng, HAN Yiwei. PREDICTION OF INDUSTRIAL CARBON EMISSIONS IN SHAANXI PROVINCE BASED ON LASSO-GWO-KELM MODEL[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 141-149. doi: 10.13205/j.hjgc.202310018

Citation:

ZHANG Xinsheng, WEI Zhizhen, CHEN Zhangzheng, HAN Yiwei. PREDICTION OF INDUSTRIAL CARBON EMISSIONS IN SHAANXI PROVINCE BASED ON LASSO-GWO-KELM MODEL[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 141-149. doi: 10.13205/j.hjgc.202310018

Citation:

PDF( 1971 KB)

PREDICTION OF INDUSTRIAL CARBON EMISSIONS IN SHAANXI PROVINCE BASED ON LASSO-GWO-KELM MODEL

doi: 10.13205/j.hjgc.202310018

1. School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China;
2. The Research Institute of New Urbanization and Human Settlement in Shaanxi Province, Xi'an 710055, China

Received Date: 2023-07-08
Available Online: 2023-12-26

Abstract

Abstract

A model based on LASSO regression (LASSO), Grey Wolf Optimization algorithm (GWO) and nuclear Extreme Learning Machine (KELM) was established to improve the prediction accuracy of industrial carbon emissions. Firstly, the direct and indirect carbon emissions of the industry during 2000 to 2020 were calculated according to the IPCC formula method and electrothermal allocation method respectively, and the gross domestic product (GDP), energy structure, and fixed asset investment were selected by the STIRPAT model. Then seven significant influencing factors were selected by grey correlation analysis and the LASSO regression model. Secondly, the parameter data of the industrial carbon emission system were preprocessed and input into the KELM model, and the KELM regularization coefficient (C) and kernel function parameter (γ) were optimized using the GWO algorithm. Finally, the forecast results were integrated and summarized, and the forecast results of LASSO-GGO-KELM, LASSO-SSA-KELM, LASSO-SFO-KELM, LASSO-KELM, and LASSO-ELM were compared and analyzed. The results showed that the predicted value of the LASSO-GGO-KELM model fits well with the actual value, and its mean square error, mean absolute error, root mean square error, and mean absolute percentage error was 0.02%, 1.09%, 1.33%, and 1.17% respectively, which was superior to other models, proving that this model can predict industrial carbon emissions more accurately. This study can provide a reference for China to realize the Double Carbon Goal as soon as possible.
- industry,
- LASSO regression,
- kernel extreme learning machine (KELM),
- grey wolf optimization algorithm (GWO),
- carbon emission prediction

FullText(HTML)

References(27)

References

[1]	杨冕, 卢昕, 段宏波.中国高耗能行业碳排放因素分解与达峰路径研究[J].系统工程理论与实践, 2018, 38(10):2501-2511.
[2]	张巍.基于STIRPAT模型的陕西省工业碳排放量预测和情景分析[J].可再生能源, 2017(5):771-777.
[3]	王向前, 夏丹.工业煤炭生产-消费两侧碳排放及影响因素研究:基于STIRPAT-EKC的皖豫两省对比[J].软科学, 2020, 34(8):84-89.
[4]	张国兴, 苏钊贤.黄河流域交通运输碳排放的影响因素分解与情景预测[J].管理评论, 2020, 32(12):283-294.
[5]	刘炳春, 符川川, 李健.基于PCA-SVR模型的中国CO₂排放量预测研究[J].干旱区资源与环境, 2018, 32(4):51-61.
[6]	赵金辉, 李景顺, 王潘乐, 等.基于Lasso-BP神经网络模型的河南省碳达峰路径研究[J].环境工程, 2022, 40(12):151-164.
[7]	徐丽, 曲建升, 李恒吉, 等.西北地区居民生活碳排放现状分析及预测[J].干旱区地理, 2019, 42(5):1166-1175.
[8]	WANG Z X, YE D J.Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models[J].Journal of Cleaner Production, 2017, 142(8):600-612.
[9]	王勇, 毕莹, 王恩东.中国工业碳排放达峰的情景预测与减排潜力评估[J].中国人口·资源与环境, 2017, 27(10):131-140.
[10]	邵帅, 张曦, 赵兴荣.中国制造业碳排放的经验分解与达峰路径:广义迪氏指数分解和动态情景分析[J].中国工业经济, 2017(3):44-63.
[11]	SUN W, LIU M H.Prediction and analysis of the three major industries and residential consumption CO₂ Emissions based on least squares support vector machine in China[J].Journal of Cleaner Production, 2016, 122(2):144-153.
[12]	胡剑波, 罗志鹏, 李峰."碳达峰"目标下中国碳排放强度预测:基于LSTM和ARIMA-BP模型的分析[J].财经科学, 2022(2):89-101.
[13]	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.
[14]	潘思羽, 张美玲.基于BP神经网络的甘肃省二氧化碳排放预测及影响因素研究[J].环境工程, 2023, 41(7):1-12.
[15]	朱盛恺.基于极限学习机模型的空气质量二次预报[J].软件工程, 2022, 25(8):39-42.
[16]	HUANG G B.An insight into extreme learning machines:random neurons, random features and kernels[J].Cognitive Computation, 2014, 6(3):376-390.
[17]	CHENG C, TAY W P, HUANG G B.Extreme learning machines for intrusion detection[C]//Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN).Brisbane, QLD:IEEE, 2012:1-8.
[18]	高金贺, 郑宝珠, 周伟昊, 等.基于GA-SVR的城市交通运输碳排放预测研究[J].东华理工大学学报(自然科学版), 2022, 45(3):269-274.
[19]	王珂珂, 牛东晓, 甄皓, 等.基于WOA-ELM模型的中国碳排放预测研究[J].生态经济, 2020, 36(8):20-27.
[20]	何洋, 李丽敏, 温宗周, 等.基于GWO-ELM算法模型的水体含沙量预测[J].科学技术与工程, 2022, 22(3):910-917.
[21]	PAUSTIAN K, RAVINDRANATH N H, AMSTEL A V.2006 IPCC guidelines for national greenhouse gas inventories[J].International Panel on Climate Change, 2006.DOI: http://dx.doi.org/10.1016/S1462-9011(99)00023-4.
[22]	国家发展和改革委员会应对气候变化司.中国2008年温室气体清单研究[M].北京:中国计划出版社, 2014.
[23]	张新生, 张玥.基于Lasso-PSO-BP神经网络的腐蚀管道失效压力的预测[J].材料保护, 2020, 53(4):46-52.
[24]	SUN W, HUANG C C.Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency[J].Journal of Cleaner Production, 2022, 338:130414.
[25]	汪涛.2035年远景目标和"十四五"规划要点[J].企业观察家, 2021(2):24.
[26]	马高权, 周娜, 谢蒙飞, 等.基于GWO-KELM模型的变压器油纸套管典型绝缘故障辨识方法[J].电网与清洁能源, 2023, 39(5):38-48.
[27]	骆正山, 于瑶如, 骆济豪, 等.基于IAOA-KELM的储气库注采管柱内腐蚀速率预测[J].安全与环境学报, 2023:1-9.doi: 10.13637 /j.issn.1009-6094.2023.0267.

Relative Articles

[1]	LI Denghui, HUANG Bangjie, ZHANG Zongyao, LIU Xiaochen, DU Hongwei, SUN Hongwei, FANG Huaiyang, FANG Xiaohang. A CASE STUDY ON URBAN NON-POINT SOURCE POLLUTION CONTROL: THE HUIZHOU CHATING ECOLOGICAL REGULATION POND IN THE SHAHE RIVER BASIN OF THE DONGJIANG RIVER[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 35-42. doi: 10.13205/j.hjgc.202406005
[2]	CUI Hanwu, DU Xiaoli, ZHAO Min, XU Yao, ZHANG Wenping, LIU Jiaming. IMPACT OF EXTERNAL WATER INFLOW ON FLOODING RISK IN URBAN AREAS AND OPTIMIZATION SCHEMES[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(1): 150-156. doi: 10.13205/j.hjgc.202401020
[3]	GAO Yahong, LIN Bingquan, ZHAO Chen, LIU Yuxuan, AN Xinqi, ZHONG Yin, HU Qian, WANG Zhenbei, QIU Bin, QI Fei, SUN Dezhi. THE CHARACTERISTICS OF INITIAL RAINWATER POLLUTION AND INTERCEPTION AND STORAGE IN HILLY TOWNS IN THE YANGTZE RIVER BASIN[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(9): 191-200. doi: 10.13205/j.hjgc.202409018
[4]	WANG Yihang, FENG Xiaonan, WANG Zongping, YUAN Jianwei, ZHU Zhihuai, LIANG Mu, MA Jie, GUO Gang, WAN Peng, CHEN Zhenbin, ZUO Liang. SCHEDULING OPTIMIZATION OF DOMESTIC WASTE TRANSFER SYSTEMS BASED ON DIGITAL TWINNING[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(5): 199-205. doi: 10.13205/j.hjgc.202405025
[5]	DU Jiamin, WEI Yuanyuan, DING Chao, ZHU Haochuan, LIU Weijing, TANG Baiyang, YANG Shiyao, FENG Qian. RESEARCH ON LAYOUT OF INTERCEPTION COMBINED SEWER OVERFLOW DETENTION TANKS BASED ON THEIR LIFE CYCLE CARBON EMISSIONS[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(11): 50-60. doi: 10.13205/j.hjgc.202411006
[6]	WU Kunlun, GONG Zhiqi, WU Jia. DYNAMIC OPTIMIZATION OF LAYOUT OF CONSTRUCTION WASTE RECYCLING FACILITIES: A CASE STUDY OF XINING, CHINA[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(6): 194-201,258. doi: 10.13205/j.hjgc.202306026
[7]	YU Feng, WANG Kejia, ZHANG Wenlong, LI Yi. PREDICTION OF COAGULANT DOSAGE FOR IN-SITU TURBIDITY CONTROL IN WATER ECOLOGICAL RESTORATION BASED ON BP NEURAL NETWORK OPTIMIZED BY GENETIC ALGORITHM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(4): 154-163. doi: 10.13205/j.hjgc.202304022
[8]	PENG Zhouyang, JIN Xi, SANG Wenjiao. OPTIMIZATION OF DESIGN OF TERMINAL FLOW INTERCEPTION AND STORAGE FACILITIES OF COMBINED DRAINAGE SYSTEM BASED ON NSGA-Ⅲ ALGORITHM[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(8): 143-149. doi: 10.13205/j.hjgc.202208020
[9]	SUN Zheng, WANG Jian-long, ZHANG Zhang-he, WANG Xue-ting, QIU Rong-ting. DISCUSSION ON PATHWAYS FOR CAPACITY UPGRADING OF STORMWATER DRAINAGE AND FLOODING ALLEVIATION IN DEVELOPED URBAN AREAS BASED ON SWMM[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(9): 199-207. doi: 10.13205/j.hjgc.202209027
[10]	ZHENG Qiongqi, LIN Yiyuan, YIN Hailong, XU Zuxin, SU Lei, WU Shanshan. SOURCE TRACKING OF WASTEWATER DISCHARGE INTO RIVERS USING HYDRODYNAMIC DIFFUSION WAVE MODEL AND GENETIC ALGORITHM[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 63-69. doi: 10.13205/j.hjgc.202206008
[11]	WANG Jian-long, QIN Mei-na, HUANG Tao, TU Nan-nan. SEDIMENTATION CHARACTERISTICS OF PARTICULATE MATTERS IN RUNOFF DETENTION TANK VIA CFD METHOD[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(12): 44-50. doi: 10.13205/j.hjgc.202112007
[12]	WANG Shi-jing. EFFECT OF THE WHOLE PROCESS WATERLOGGING CONTROL SYSTEM IN ALLEVIATING URBAN WATERLOGGING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(4): 108-113. doi: 10.13205/j.hjgc.202004019
[13]	XUE Tong-lai, ZHAO Dong-hui, HAN Fei. SVR WATER QUALITY PREDICTION MODEL BASED ON GA OPTIMIZATION[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(3): 123-127. doi: 10.13205/j.hjgc.202003021
[16]	Ren Jinxia Yu Zhiwu You Xin, . MODEL FOR WATER QUALITY EVALUATION BASED ON WAVELET NEURAL NETWORK OF ADAPTIVE GENETIC ALGORITHM[J]. ENVIRONMENTAL ENGINEERING , 2015, 33(5): 144-148. doi: 10.13205/j.hjgc.201505031

Supplements(0)

Cited By

Cited by

Periodical cited type(5)

1.	崔瀚武，杜晓丽，赵敏，徐瑶，张文平，刘家铭. 客水汇入对城区内涝风险的影响及优化方案. 环境工程. 2024(01): 150-156 . 本站查看
2.	吕姚，包学才，彭宇，查小红，黄明坤. 基于改进YOLOX的城市河道智能水位测量算法. 南昌工程学院学报. 2024(03): 13-18 .
3.	武俊槟. 枢纽机场防洪排涝体系的构建与对策研究. 市政技术. 2024(12): 39-46+130 .
4.	杜佳岷，魏源源，丁超，朱浩川，刘伟京，唐柏杨，杨诗瑶，冯骞. 基于全生命周期碳排放的截流式合流制调蓄池布局研究. 环境工程. 2024(11): 50-60 . 本站查看
5.	田甜，胡海英，蒋乐欣，程香菊，章宇达. 雨污水管混接及调蓄池对城市内涝的影响分析——以广州市某高校为例. 给水排水. 2024(S1): 381-388 .