引用本文:田赢,韩遇春,王炜,胡立聪,赵健,卢闻州.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 Ying, HAN Yu-chun, WANG Wei, HU Li-cong, ZHAO Jian, LU Wen-zhou.‘Bi direction’Analysis of Carbon Emission and Carbon Peak Scenario Analysis and Prediction in Lianyungang[J].Environmental Monitoring and Forewarning,2023,15(1):85-91
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连云港市碳排放“双向”分析及碳达峰情景分析与预测
田赢,韩遇春,王炜,胡立聪,赵健,卢闻州1,2
1.国网江苏省电力有限公司连云港供电分公司,江苏 连云港 222000;2.江南大学物联网工程学院, 江苏 无锡 214122
摘要:
为探究连云港市碳排放特征,实现“碳达峰”“碳中和”的目标,以2011—2020年连云港市的碳排放量数据为核心,采用可拓展的随机性的环境影响评估模型(STIRPAT)以及岭回归方法进行碳排放量数据的“双向”分析。首先,根据连云港市2011—2020年能源消耗数据,“反向”核算出连云港市历年的碳排放量,结果表明,连云港市碳排放量呈现逐年持续上升趋势;其次,选取人口因素、人均地区生产总值、城市化率、能源结构、产业结构为主要因素,构建了STIRPAT模型这一“正向”预测媒介,采用岭回归分析法得到了连云港市的碳排放量拟合模型,结果表明,不同的因素对连云港市碳排放量的影响程度存在差异;最后,通过情景分析法预设6种连云港市未来发展情景模型,对连云港市的碳排放量进行了预测,结果表明,中增长-高减排的发展模式更适合连云港市,将于2030年以4 788.9万t的碳排放量实现达峰。针对这一发展模式要求,提出连云港市应积极调整能源结构比例、加快产业结构优化等相关建议。
关键词:  碳排放  “双向”分析  可拓展的随机性的环境影响评估模型  岭回归  情景分析法  碳达峰预测
DOI:
分类号:X24
基金项目:国家自然科学基金资助项目(62101215);国网江苏省电力有限公司连云港供电分公司业务研究项目(20211260)
‘Bi direction’Analysis of Carbon Emission and Carbon Peak Scenario Analysis and Prediction in Lianyungang
TIAN Ying, HAN Yu-chun, WANG Wei, HU Li-cong, ZHAO Jian, LU Wen-zhou1,2
1. State Grid Jiangsu Electric Power Co. Ltd., Lianyungang Power Supply Company, Lianyungang, Jiangsu 222000, China;2. School of Internet of Things Engineering, Jiangnan University, Wuxi,Jiangsu 214122, China
Abstract:
To analyze Lianyungangs carbon emission characteristics and to achieve ‘Carbon peak’ and ‘Carbon neutrality’ targets, by using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and ridge regression, a bi direction analysis of carbon emissions is done based on Lianyungangs carbon emission data from 2011 to 2020. Firstly, according to Lianyungangs energy 〖JP2〗consumption data from 2011 to 2020, Lianyungangs carbon emissions over the years are ‘backwardly’ calculated, which increase sustainably year by year. Secondly, by selecting population, per capita gross domestic product, urbanization rates, energy structure and industry structure as dominant factors, a STIRPAT model for ‘forward’ analysis is developed. Lianyungangs carbon emission model is established by Ridge regression, which shows different factors have different degrees of impact on carbon emission. Finally, six development models in Lianyungang are set up in advance and carbon emission data is predicted by scenario analysis method, which shows the medium growth high emission reduction model is more suitable for Lianyungang, achieving carbon peak in 2030 with 47.889 million tons. Based on the development model, relevant recommendations are proposed for Lianyungang, such as adjusting the proportion of energy structure actively, accelerating the optimization of industrial structure.
Key words:  Carbon emission  ‘Bi direction’ analysis  STIRPAT model  Ridge regression  Scenario analysis  Carbon peak prediction