Analysis and prediction of industrial carbon emission of Linyi City based on STIRPAT model

doi:10.3969/j.issn.1674-1951.2021.06.006

Abstract

Abstract:

To stimulate the accomplishment of provincial and municipal carbon emission targets, a Stochastic Impacts by Regression on Population, Affluence and Technology （STIRPAT） model is established based on the industrial carbon emission data of Linyi City from 2009 to 2019. The model quantitatively analyzes the relationship between industrial carbon emission and enterprises' fixed assets, per capita industrial production added value, energy intensity and energy structure in Linyi City. The collinearity between the variables is eliminated by ridge regression. For every 1% variation in enterprises' fixed assets, per capita industrial production added value, energy intensity, the proportion of raw coal in energy consumption and net purchased electricity will lead to 0.069 37%,0.016 30%,0.214 60%,0.550 00% and 0.214 60% fluctuation in the industrial carbon emission, respectively. The per capita industrial production added value is predicted by a Grey Model, GM（1,1）. The industrial carbon emissions in Linyi City from 2020 to 2030 under six different prediction scenarios are analyzed. The comparison results conclude that maintaining a moderate growth of industrial product, increasingly optimizing energy structure and controlling energy intensity are the effective ways to reduce the industrial carbon emission of Linyi City.

Key words: industrial carbon emission, carbon neutrality, carbon peaking, STIRPAT model, ridge regression, scenario prediction, energy intensity, energy structure, low-carbon economy

CLC Number:

X511：F206

WU Tong, ZHANG Xingyu, CHENG Xingxing, SUN Rongfeng, WANG Zhiqiang, GENG Wenguang, WANG Luyuan, FENG Tai. Analysis and prediction of industrial carbon emission of Linyi City based on STIRPAT model[J]. Huadian Technology, 2021, 43(6): 47-54.

Figures/Tables 14

Tab.1

Tab.2

Tab.3

Tab.4

Model coefficients

项目	非标准化系数		标准系数	T检验统计值	显著性系数	共线性统计量
项目	回归系数	标准误差	标准系数	T检验统计值	显著性系数	容差	方差膨胀系数
常数项	-0.522	2.095		-0.249	0.041
$ln Q$	0.033	0.065	0.103	0.517	0.062	0.065	15.381
$ln U$	0.113	0.039	0.297	2.864	0.035	0.238	4.207
$ln O$	0.657	0.135	0.702	4.881	0.005	0.123	8.106
$ln M$	0.006	0.204	0.004	0.028	0.067	0.149	6.708
$ln N$	0.548	0.116	0.966	4.744	0.005	0.062	16.230

Tab.4

Fig.1

Fig.2

Tab.5

Tab.6

Coefficients of the ridge regression model

项目	回归系数	标准误差	标准化系数	T检验统计值	显著性系数
$ln Q$	0.069 37	0.024	0.210	2.838	0.036
$ln U$	0.016 30	0.035	0.043	0.460	0.046
$ln O$	0.214 60	0.076	0.229	2.820	0.037
$ln M$	-0.550 00	0.131	-0.353	-4.168	0.009
$ln N$	0.214 60	0.040	0.377	5.276	0.003
常数项	6.839 00	0.913	0.000	7.487	0.000

Tab.6

Tab.7

Tab.8

Fig.3

Tab.9

Tab.10

Tab.11

References 17

[1]	姜克隽, 胡秀莲, 庄幸, 等. 中国2050年低碳情景和低碳发展之路[J]. 中外能源, 2009,6(6):21-26.
	JIANG Kejun, HU Xiulian, ZHUANG Xing, et al. China's low-carbon scenarios and roadmap for 2050[J]. Sino-Global Energy, 2009,6(6):21-26.
[2]	佟昕, 陈凯, 李刚. 中国碳排放影响因素分析和趋势预测:基于STIRPAT和GM(1,1)模型的实证研究[J]. 东北大学学报(自然科学版), 2015,36(2):297-300.
	TONG Xin, CHEN Kai, LI Gang. Influencing factors analysis and trend forecasting of China's carbon emissions:Empirical study based on STIRPAT and GM(1,1) models[J]. Journal of Northeastern University (Natural Science), 2015,36(2):297-300.
[3]	渠慎宁, 郭朝先. 基于STIRPAT模型的中国碳排放峰值预测研究[J]. 中国人口·资源与环境, 2010,20(12):10-15.
	QU Shenning, GUO Chaoxian. Forecast of China's carbon emissions based on STIRPAT model[J]. China Population, Resources and Environment, 2010,20(12):10-15.
[4]	许华, 王莹. EKC视角下陕西经济增长与碳排放量实证研究[J]. 调研世界, 2021(1):54-59.
	XU Hua, WANG Ying. An empirical research on economic growth and carbon emissions of Shaanxi Province from the perspective of EKC[J]. The World of Survey and Research, 2021(1):54-59.
[5]	颜伟, 黄亚茹, 张晓莹, 等. 基于STIRPAT模型的山东半岛蓝色经济区碳排放预测[J]. 济南大学学报(自然科学版), 2021,35(2):125-131.
	YAN Wei, HUANG Yaru, ZHANG Xiaoying, et al. Prediction of carbon emission in Shandong Peninsula Blue Economic Zone based on STIRPAT model[J]. Journal of University of Jinan(Science and Technology), 2021,35(2):125-131
[6]	韩媛媛, 廖剑宇, 张旺, 等. 长沙市规模以上工业能耗碳排放测算与预测研究[J]. 国土与自然资源研究, 2017(5):46-50.
	HAN Yuanyuan, LIAO Jianyu, ZHANG Wang, et al. Calculation and prediction of carbon emission from industrial scale energy consumption in Changsha[J]. Territory & Natural Resources Study, 2017(5):46-50.
[7]	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, 2017,35(5):771-777.
	ZHANG Wei. Prediction and scenario analysis of industrial carbon emissions in Shaanxi Province based on STIRPAT model[J]. Renewable Energy Resources, 2017,35(5):771-777.
[8]	解品磊. 基于LEAP模型的吉林省工业碳排放模拟研究及碳减排路径选择[D]. 长春:吉林大学, 2018.
[9]	郭晓芳. 遵义市规模以上工业能源消费碳排放测度及影响因素[J]. 贵州科学, 2020,38(6):57-63.
	GUO Xiaofang. Carbon emission from energy consumption of industries above designated size in Zunyi: Measurement and influencing factors[J]. Guizhou Science, 2020,38(6):57-63.
[10]	王海静, 王红蕾. 基于STIRPAT模型的贵州省电力行业碳峰值预测[J]. 生产力研究, 2021(3):108-113.
	WANG Haijing, WANG Honglei. The electricity industry carbon peak forecast based on the STIRPAT model[J]. Productivity Research, 2021(3):108-113.
[11]	王莉叶, 陈兴鹏, 庞家幸, 等. 基于LMDI的能源消费碳排放因素分解及情景分析——以兰州市为例[J]. 生态经济, 2019,35(9):38-44.
	WANG Liye, CHEN Xingpeng, PANG Jiaxing, et al. Decomposition and scenario analysis of carbon emission from energy consumption based on LMDI method: Taking Lanzhou as an example[J]. Ecological Economy, 2019,35(9):38-44.
[12]	刘晓燕. 基于STIRPAT模型的工业能源消费碳排放影响因素分析[J]. 生态经济, 2019,35(3):27-31.
	LIU Xiaoyan. Study on influencing factors of carbon emissions from industrial energy consumption based on STIRPAT model[J]. Ecological Economy, 2019,35(3):27-31.
[13]	田泽, 张怀婧, 黄萌萌, 等. 扬子江城市群工业能源碳排放与经济增长脱钩水平及驱动因素[J]. 资源与产业, 2020,22(6):29-36.
	TIAN Ze, ZHANG Huaijing, HUANG Mengmeng, et al. Decoupling level and driving factors of industrial energy carbon emission and economic growth in Yangzi River urban agglomeration[J]. Resources & Industries, 2020,22(6):29-36.
[14]	马艳梅, 吴玉鸣. 山东省工业碳减排政策研究——基于空间效应视角[J]. 生态经济, 2019,35(7):39-43.
	MA Yanmei, WU Yuming. Policies for industrial carbon emission reduction in Shandong Province from the perspective of spatial effects[J]. Ecological Economy, 2019,35(7):39-43.
[15]	邓光耀, 马蓉, 张忠杰. 中国各行业能源消费碳排放效应的分解研究[J]. 统计与决策, 2018,34(15):124-127.
	DENG Guangyao, MA Rong, ZHANG Zhongjie. Decomposition of carbon emission effect of energy consumption in China's various industries[J]. Statistics & Decision, 2018,34(15):124-127.
[16]	EHRLICH P R, HOLDREN J P. Impact of population growth[J]. Science, 1971,171(3977):1212-1217. doi: 10.1126/science.171.3977.1212
[17]	杨楠, 李艳霞, 吕晨, 等. 唐山市钢铁行业碳排放核算及达峰预测[J]. 环境工程, 2020,38(11):44-52.
	YANG Nan, LI Yanxia, LV Chen, et al. Carbon emission accounting and peak forecasting of iron & steel industry in Tangshan[J]. Environmental Engineering, 2020,38(11):44-52.

变量	定义	符号	单位
工业环境压力	工业生产产生的碳排放量	C	万t
工业规模	工业固有资产	Q	亿元
人均工业产值	人均工业产值	U	万元/人
能源强度	单位工业产值的综合能耗	O	t/万元^①
能源结构	原煤占总燃料的消耗比	M	%
能源结构	净购入电力	N	MW·h

项目	平方和	自由度	均方	F检验统计值	显著性系数
回归	0.140	5	0.028	77.324	0.000
残差	0.002	5	0.000
总计	0.142	10

年份	碳排放量实际值/万t	碳排放量预测值/万t	误差/%
2009	3 666.19	3 826.29	4.30
2010	3 682.33	3 755.35	2.00
2011	3 899.33	3 967.43	1.70
2012	4 398.08	4 161.67	-5.40
2013	4 883.81	4 799.55	-1.70
2014	5 325.22	5 032.89	-5.50
2015	4 242.59	4 305.67	1.50
2016	4 822.40	4 797.21	-0.50
2017	4 579.60	4 691.59	2.40
2018	4 627.89	4 671.95	0.90
2019	4 235.07	4 238.17	0.07

年份	年度生产总值/ 亿元		工业占GDP 比重/%		工业生产增加值/ 亿元
年份	实际值	预测值	实际值	预测值	实际值	预测值
2009	1 977.10	1 977.10	42.60	42.60	842.24	842.24
2010	2 360.60	2 502.05	42.10	42.52	993.81	1 063.87
2011	2 642.90	2 682.08	41.40	41.31	1 094.16	1 107.96
2012	2 870.90	2 875.06	39.90	40.14	1 145.48	1 154.04
2013	3 175.60	3 081.93	38.80	39.01	1 232.13	1 202.26
2014	3 388.10	3 303.68	38.50	37.90	1 304.41	1 252.09
2015	3 599.40	3 541.38	37.30	36.83	1 342.57	1 304.29
2016	3 810.50	3 796.19	35.70	35.79	1 360.34	1 358.65
2017	4 062.00	4 069.34	35.40	34.77	1 437.98	1 414.90
2018	4 367.80	4 362.13	34.80	33.79	1 519.99	1 473.96
2019	4 600.30	4 676.00	31.00	32.83	1 426.09	1 535.13

年份	工业生产增加值预测值/亿元	工业平均用工人数/人	人均工业生产增加值预测值/（万元·人^-1）
2020	1 599.17	393 000	40.69
2021	1 665.71	393 000	42.38
2022	1 735.02	393 000	44.14
2023	1 807.21	391 086	46.21
2024	1 882.41	364 737	51.61
2025	1 960.73	340 109	57.65
2026	2 042.31	317 227	64.38
2027	2 127.29	295 826	71.91
2028	2 215.81	275 907	80.31
2029	2 308.00	257 302	89.70
2030	2 404.03	239 971	100.18