双碳目标背景下宁夏地区碳达峰预测

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Abstract In order to improve the prediction accuracy of peak value and its occurrence time of regional carbon emission, in this study, selecting the Ningxia region as the research target, the IPCC carbon emission factor method was firstly used to calculate historical carbon emission amounts of this region. Secondly, the DTW (Dynamic Time Warping) algorithm was innovatively utilized to identify main influence factors of carbon emission. Then, the PCA (Principal Component Analysis) method and STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model were jointly used for establishiing a regional carbon emission prediction model. Next, the scenario analysis method based on the importance evaluation of influence factors was introduced to design and generate the scenarios reflecting various socio-economic development conditions, technological levels and policy implementation effects by combining the possible future trend of critical factors. Finally, the regional carbon emission amounts under different scenarios were determined, where regional peak carbon value and its occurrence time were estimated accurately, providing the useful reference basis for the formulation and implementation of subsequent regional peak carbon control strategy and countermeasure proposal.

Key words： carbon peak prediction STIRPAT dynamic time warping scenario analysis Ningxia

Received: 06 May 2023

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X21

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	WANG Xue-ting
	ZHENG Fei-fan
	XU Ye
	LI Wei

Cite this article:

WANG Xue-ting,ZHENG Fei-fan,XU Ye等. Carbon peak prediction in Ningxia under the Dual Carbon Background[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(S1): 347-356.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/IS1/347

[1] 项目综合报告编写组.《中国长期低碳发展战略与转型路径研究》综合报告[J].中国人口·资源与环境, 2020,30(11):1-25. Project Comprehensive Report Writing Group. Comprehensive report of "Study on China's Long-term Low-carbon Development Strategy and Transformation Path"[J].China Population Resources and Environment, 2020,30(11):1-25.
[2] 曹炜.“双碳”目标下的流域生态环境保护规划:理念更新与措施调适[J].中国人口·资源与环境, 2022,32(12):31-40. Cao W. Watershed eco-environmental protection planning under the goal of "double carbon": concept renewal and measures adjustment [J].China Population Resources and Environment, 2022,32(12):31-40.
[3] 王晓娟.“双碳”目标与中国钢铁产业优化发展路径研究[D].武汉:中南财经政法大学, 2022. Wang X J. Research on "Double Carbon" goal and development path optimization of Chinese iron and steel industry [D].Wuhan: Zhongnan University of Economics and Law, 2022.
[4] 洪竞科,李沅潮,蔡伟光.多情景视角下的中国碳达峰路径模拟——基于RICE-LEAP模型[J].资源科学, 2021,43(4):639-651. Hong J K, Li Y C, Cai W G, et al. Simulating China’s carbon emission peak path under different scenarios based on RICE-LEAP model [J].Resources Science, 2021,43(4):639-651.
[5] 王倩倩,娄媛媛,张鹏岩,等.能源消费背景下河南省碳排放测算及碳达峰预测[J].河南大学学报(自然科学版), 2023,53(1):47-59. Wang Q Q, Lou Y Y, Zhang P Y, et al. Under the background of energy consumption carbon emission calculation and carbon peak prediction in Henan Province [J].Journal of Henan University (Natural Science Edition), 2023,53(1):47-59.
[6] Ehrlich P R, Holdren J P. Impact of population growth [J].Science, 1971,(171):1212-1217.
[7] Zhao H Q, Hu J, Hao F, et al. Determinants of carbon dioxide emissions and their peaking prospect: evidence from China [J].Frontiers in Environmental Science, 2022,10:913835.
[8] 王琪.基于STIRPAT模型的河北省碳排放峰值预测研究[D].北京:华北电力大学, 2019. Wang Q. Study on carbon emission peak prediction in Hebei Province based on STIRPAT model [D].Beijing: North China Electric Power University, 2019.
[9] 伊德日夫,王路航,解宇龙,等.基于主成分回归的内蒙古自治区碳达峰预测研究[J].内蒙古农业大学学报(自然科学版), 2022,43(6): 102-107. Yi D R F, Wang L H, Xie Y L, et al. Prediction of peaking carbon dioxide emissions in inner mongolia based on principal component regression [J].Journal of Inner Mongolia Agricultural University (Natural science edition), 2022,43(6):102-107.
[10] 孙蒙,李长云,邢振方,等.碳中和目标下中国碳排放关键影响因素分析及情景预测[J].高电压技术, 2023,(6):1-12. Sun M, Li C Y, Xing Z F, et al. Analysis of key influencing factors and scenario prediction of China’s carbon emission under carbon neutrality [J].High voltage technology, 2023,(6):1-12.
[11] 赵守国,徐仪嘉.我国西北地区碳达峰碳中和实现路径研究[J].西北大学学报(哲学社会科学版), 2022,52(4):87-97. Zhao S G, Xu Y J. A research on the pathway of achieving peak carbon neutrality in northwest China [J].Journal of Northwest University (Philosophy and Social Sciences Edition), 2022,52(4):87-97.
[12] 魏泓屹,卓振宇,张宁,等.中国电力系统碳达峰·碳中和转型路径优化与影响因素分析[J].电力系统自动化, 2022,46(19):1-12. Wei H Y, Zhuo Z Y, Zhang N, et al. Transition path optimization and influencing factor analysis of carbon emission peak and carbon neutrality for power system of China [J].Power System Automation, 2022,46(19):1-12.
[13] 刘淳森,曲建升,葛钰洁,等.基于LSTM模型的中国交通运输业碳排放预测[J].中国环境科学, 2023,43(5):2574-2582. Liu C S, Qu J S, Ge Y J, et al. LSTM model-based prediction of carbon emissions from China's transportation sector [J].China Environmental Science, 2023,43(5):2574-2582.
[14] 翁智雄,马忠玉,葛察忠,等.不同经济发展路径下的能源需求与碳排放预测——基于河北省的分析[J].中国环境科学, 2019,39(8): 3508-3517. Weng Z X, Ma Z Y, Ge C Z, et al. Projection on energy demand and carbon emission in various economic developing pathways——A case study in Hebei province [J].China Environmental Science, 2023,43(5): 2574-2582.
[15] 田赢,韩遇春,王炜,等.连云港市碳排放“双向”分析及碳达峰情景分析与预测[J].环境监控与预警, 2023,15(1):85-91. Tian Y, Han Y C, Wang W, et al. ‘Bi-direction’ analysis of carbon emission and carbon peak scenario analysis and prediction in Lianyungang [J].Environmental Monitoring and Early Warning, 2023, 15(1):85-91.
[16] 张丽峰,潘家华.中国区域碳达峰预测与“双碳”目标实现策略研究[J].中国能源, 2021,43(7):54-62. Zhang L F, Pan J H. Study on peak carbon dioxide emissions Forecast and Strategy of Realizing "Double Carbon" in China [J].China Energy, 2021,43(7):54-62.
[17] Kilbourne W E, Thyroff A. STIRPAT for marketing: An introduction, expansion, and suggestions for future use [J].Journal of Business Research, 2020,108:351-361.
[18] Girod S J G, Whittington R. Reconfiguration, restructuring and firm performance: Dynamic capabilities and environmental dynamism [J].Strategic Management Journal, 2017,38(5):1121-1133.
[19] Pan X F, Xu H T, Lu Y D. Long-term forecasting of industrial CO₂ emissions in 31provinces of China [J].Environmental Science and Pollution Research, 2020,27:5168-5191.
[20] Ke H, Xu G Y, Li C T, et al. Optimization of China’s freight transportation structure based on adaptive genetic algorithm under the background of carbon peak [J].Environmental Science and Pollution Research, 2023:1-15.
[21] Qi Y W, Liu H L, Zhao J B, et al. Prediction model and demonstration of regional agricultural carbon emissions based on PCA-GS-KNN: a case study of Zhejiang province, China [J].Environmental Research Communications, 2023,5(5):051001.
[22] Li C, Zhang Z C, Wang L P. Carbon peak forecast and low carbon policy choice of transportation industry in China: scenario prediction based on STIRPAT model [J].Environmental Science and Pollution Research, 2023,30(22):1128-1139.
[23] 宁夏回族自治区统计局.宁夏统计年鉴[M].北京:中国统计出版社, 2022. Statistics Bureau of Ningxia Hui Autonomous Region. Ningxia Statistical Yearbook [M].Beijing: China Statistics Publishing House, 2022.
[24] Gitarskiy M L. The refinement to the 2006 IPCC guidelines for national greenhouse gas inventories [J].Fundamental and Applied Climatology, 2019,2:5-13.
[25] 王波波,朱闯闯,郑小虎,等.基于动态时间规整的纱线毛羽H值周期模式识别方法[J].东华大学学报(自然科学版), 2022,48(6):77-83. Wang B B, Zhu C C, Zheng X H, et al. Identification method of H-value periodic pattern of yarn hairiness based on dynamic time warping [J].Journal of Donghua University (Natural Science Edition), 2022,48(6):77-83.
[26] 胡晓芳.基于主成分分析法对岱海水环境质量的分析研究[J].环境与发展, 2022,34(8):31-37. Hu X F. Analysis of seawater environmental quality in Daihai based on principal component analysis [J].Environment and Development, 2022,34(8):31-37.
[27] Smarnika G, Shaddam M H, Chandra L V, et al. Unveiling the spillover effects of democracy and renewable energy consumption on the environmental quality of BRICS countries: A new insight from different quantile regression approaches [J].Renewable Energy Focus, 2023,46(1):222-235.
[28] 徐鹏,徐千淇,张亚宁,等.情景分析和LCA组合方法在可再生能源政策环评的应用——以山东省为例[J].能源环境保护, 2022, 36(5):27-32. Xu P, Xu Q Q, Zhang Y N, et al. Application of scenario analysis and LCA combination method in environmental assessment of renewable energy policy-taking Shandong Province as an example [J].Energy and Environmental Protection, 2022,36(5):27-32.
[29] 中华人民共和国统计局.中国统计年鉴[M].北京:中国统计出版社, 2022. People's Republic of China Bureau of Statistics. China statistical yearbook [M].Beijing: China Statistical Publishing House, 2022.
[30] 王科,李银坤,郑文刚,等.基于主成分分析的温室内水面蒸发量估算模型构建及验证[J].灌溉排水学报, 2023,42(5):60-66. Wang K, Li Y K, Zheng W G, et al. Construction and verification of estimation model of water surface evaporation in greenhouse based on principal component analysis [J].Journal of Irrigation and Drainage, 2023,42(5):60-66.