基于Mann-Kendall趋势检验的城市能源碳达峰评估方法

doi:10.16183/j.cnki.jsjtu.2021.524

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (7): 928-938.doi: 10.16183/j.cnki.jsjtu.2021.524

所属专题：《上海交通大学学报》2023年“新型电力系统与综合能源”专题

• 新型电力系统与综合能源 • 上一篇

基于Mann-Kendall趋势检验的城市能源碳达峰评估方法

陈赟¹, 沈浩¹, 王晓慧¹, 赵文恺¹, 潘智俊¹, 王佳裕¹, 李思源², 韩冬²()

1.国网上海浦东供电公司总师室(互联网办公室),上海 200122
2.上海理工大学电气工程系,上海 200093

收稿日期:2021-12-21 修回日期:2022-02-21 接受日期:2022-04-11 出版日期:2023-07-28 发布日期:2023-07-28
通讯作者: 韩冬 E-mail:han_dong@usst.edu.cn
作者简介:陈赟(1982-),高级工程师,主要从事分布式能源、电网数字化转型及双碳技术研究等.
基金资助:
国家电网有限公司科技项目(5209212100AC)

Assessment Method for Urban Energy Carbon Emission Peak Based on Mann-Kendall Trend Test

CHEN Yun¹, SHEN Hao¹, WANG Xiaohui¹, ZHAO Wenkai¹, PAN Zhijun¹, WANG Jiayu¹, LI Siyuan², HAN Dong²()

1. Director of the Head Office (Internet Office), State Grid Shanghai Pudong Electric Power Supply Company, Shanghai 200122, China
2. Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2021-12-21 Revised:2022-02-21 Accepted:2022-04-11 Online:2023-07-28 Published:2023-07-28
Contact: HAN Dong E-mail:han_dong@usst.edu.cn

摘要/Abstract

摘要：

能源是城市碳排放的重要组成,评估城市能源碳达峰是践行国家“双碳”战略的必要手段.为此,针对城市能源的碳排放水平,提出了一种基于Mann-Kendall趋势检验的能源碳达峰评估方法.通过构建涵盖能源碳排放量、清洁能源发电量、交通电能替代量等要素的碳监测体系,结合历史数据计算城市的能源碳排放总量.鉴于能源碳排放具有季节性和随机性,采用Mann-Kendall趋势检验法,建立城市能源碳达峰判断模型,衡量不同时期区域碳排放水平.以上海某行政区为例,从年度、季度视角,判断该区域的能源碳达峰状态.计算结果表明,基于年度数据,该地区在2020年已实现能源碳达峰;基于季度数据,夏季与秋季已实现能源碳达峰,春季和冬季仍处于平台期.该方法可应用于评估城市级的碳达峰状态,为检验各省市的碳达峰进程提供参考.

关键词: 能源碳监测体系, Mann-Kendall趋势检验, 城市能源, 碳达峰

Abstract:

Energy is an important component of urban carbon emissions. Assessing the peak of urban energy carbon is a necessary means to implement the national “double carbon” strategy. For this reason, this paper proposes an energy carbon peaking assessment method based on Mann-Kendall trend test for carbon emission of urban energy. By constructing a carbon monitoring system covering elements such as energy carbon emissions, clean energy generation, and transportation electric energy substitution, the total energy carbon emissions of the city are calculated by combining historical data. In view of the seasonality and randomness of energy carbon emissions, the Mann-Kendall trend test was used to establish a model for determining urban energy carbon peaking and to measure regional carbon emissions in different periods. Taking an administrative region in Shanghai as an example, the peak status of energy carbon in this region is judged from the perspective of year and quarter. The results show that based on the annual data, the region has reached its peak energy carbon in 2020. Based on quarterly data, peak energy carbon has been achieved in summer and autumn, while spring and winter are still in plateau. The methods proposed in this paper can be used to assess the carbon peak status in the city, and provide a reference for examining the carbon peak process in other provinces and cities.

Key words: energy carbon monitoring system, Mann-Kendall trend test, urban energy, carbon peaking

中图分类号:

TK01+2

陈赟, 沈浩, 王晓慧, 赵文恺, 潘智俊, 王佳裕, 李思源, 韩冬. 基于Mann-Kendall趋势检验的城市能源碳达峰评估方法[J]. 上海交通大学学报, 2023, 57(7): 928-938.

CHEN Yun, SHEN Hao, WANG Xiaohui, ZHAO Wenkai, PAN Zhijun, WANG Jiayu, LI Siyuan, HAN Dong. Assessment Method for Urban Energy Carbon Emission Peak Based on Mann-Kendall Trend Test[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 928-938.

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参考文献 31

[1]	黄超明, 刘海龙. 气候变暖如何影响天气变化[J]. 上海交通大学学报, 2021, 55(Sup.1): 72-73.
	HUANG Chaoming, LIU Hailong. How will the weather change along with global warming?[J]. Journal of Shanghai Jiao Tong University, 2021, 55(Sup.1): 72-73.
[2]	黄以天. 国际碳交易机制的演进与前景[J]. 上海交通大学学报(哲学社会科学版), 2016, 24(1): 28-37.
	HUANG Yitian. Evolution and prospect of International carbon trading mechanisms[J]. Journal of Shanghai Jiao Tong University (Philosophy and Social Sciences Edition), 2016, 24(1): 28-37.
[3]	张华. 低碳城市试点政策能够降低碳排放吗?--来自准自然实验的证据[J]. 经济管理, 2020, 42(6): 25-41.
	ZHANG Hua. Can low carbon city construction reduce carbon emissions? Evidence from a quasi-natural experiment[J]. Business Management Journal, 2020, 42(6): 25-41.
[4]	张森林. 基于“双碳”目标的电力市场与碳市场协同发展研究[J]. 中国电力企业管理, 2021(10): 50-54.
	ZHANG Senlin. Research on coordinated development of power market and carbon market based on “double carbon” goal[J]. China Power Enterprise Management, 2021 (10): 50-54.
[5]	KUZNETS S. Economic growth and income inequality[J]. American Economic Review, 1955, 45(1): 1-28.
[6]	郭志玲. 甘肃省碳排放峰值预测与应对策略研究[D]. 重庆: 重庆大学, 2015.
	GUO Zhiling. Study on peak prediction of carbon emission and control strategies in Gansu Province[D]. Chongqing: Chongqing University, 2015.
[7]	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. doi: 10.18402/resci.2021.04.01
	HONG Jingke, LI Yuanchao, CAI Weiguang. Simulating China’s carbon emission peak path under different scenarios based on RICE-LEAP model[J]. Resources Science, 2021, 43(4): 639-651. doi: 10.18402/resci.2021.04.01
[8]	李庚欣, 胡纯, 梅运军, 等. 基于Kaya模型的湖北省农业碳排放时空特征及影响因素研究[J]. 绿色科技, 2020(4): 217-220.
	LI Gengxin, HU Chun, MEI Yunjun, et al. Study on the spatiotemporal characteristics and impact factors of agricultural carbon emissions in Hubei based on Kaya Model[J]. Journal of Green Science and Technology, 2020(4): 217-220.
[9]	王利兵, 张赟. 中国能源碳排放因素分解与情景预测[J]. 电力建设, 2021, 42(9): 1-9. doi: 10.12204/j.issn.1000-7229.2021.09.001
	WANG Libing, ZHANG Yun. Factors decomposition and scenario prediction of energy-related CO₂ emissions in China[J]. Electric Power Construction, 2021, 42(9): 1-9. doi: 10.12204/j.issn.1000-7229.2021.09.001
[10]	王杰. 中国高耗能行业碳排放的脱钩和影响因子研究[D]. 天津: 天津大学, 2018.
	WANG Jie. Study on the decoupling relationship and influencing factors of carbon emissions in China’s energy-intensive industry[D]. Tianjin: Tianjin University, 2018.
[11]	杜强, 陈乔, 杨锐. 基于Logistic模型的中国各省碳排放预测[J]. 长江流域资源与环境, 2013, 22(2): 143-151.
	DU Qiang, CHEN Qiao, YANG Rui. Forecast carbon emission of provinces in China based on logistic model[J]. Resources and Environment in the Yangtze Basin, 2013, 22(2): 143-151.
[12]	SU Y, LIU X, JI J, et al. Role of economic structural change in the peaking of China’s CO₂ emissions: An input-output optimization model[J]. Science of the Total Environment, 2021, 761: 143306. doi: 10.1016/j.scitotenv.2020.143306 URL
[13]	上海市发展和改革委员会. 上海市温室气体排放核算与报告技术文件: SH/MRV-002-2012[S]. 上海:上海发改委,
	Shanghai Municipal Development and Reform Commission. Shanghai greenhouse gas emission accounting and reporting technical document: SH/MRV-002-2012[S]. Shanghai: Shanghai Municipal Development and Reform Commission, 2012.
[14]	陈玉珊, 秦琳琳, 吴刚, 等. 基于渐消记忆递推最小二乘法的电动汽车电池荷电状态在线估计[J]. 上海交通大学学报, 2020, 54(12): 1340-1346.
	CHEN Yushan, QIN Linlin, WU Gang, et al. Online state of charge estimation for battery in electric vehicles based on forgetting factor recursive least squares[J]. Journal of Shanghai Jiao Tong University, 2020, 54(12): 1340-1346.
[15]	中华人民共和国国家发展和改革委员会. 国家发展改革委办公厅关于印发首批10个行业企业温室气体排放核算方法与报告指南(试行)的通知[EB/OL]. (2013-10-15)[2021-12-21]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/201311/t20131101_963960_ext.html.
	National Development and Reform Commission. Notice of the general office of the national development and reform commission on printing and distributing the accounting methods and reporting guidelines for greenhouse gas emissions of the first 10 industrial enterprises (for trial implementation)[EB/OL]. (2013-10-15)[2021-12-21]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/201311/t20131101_963960_ext.html.
[16]	刘永奇, 陈龙翔, 韩小琪. 能源转型下我国新能源替代的关键问题分析[J]. 中国电机工程学报, 2022, 42(2): 515-524.
	LIU Yongqi, CHEN Longxiang, HAN Xiaoqi. The key problem analysis on the alternative new energy under the energy transition[J]. Proceedings of the CSEE, 2022, 42(2): 515-524.
[17]	李玲芳, 陈占鹏, 胡炎, 等. 基于灵活性和经济性的可再生能源电力系统扩展规划[J]. 上海交通大学学报, 2021, 55(7): 791-801.
	LI Lingfang, CHEN Zhanpeng, HU Yan, et al. Expansion planning of renewable energy power system considering flexibility and economy[J]. Journal of Shanghai Jiao Tong University, 2021, 55(7): 791-801.
[18]	游广增, 汤翔鹰, 胡炎, 等. 基于典型运行场景聚类的电力系统灵活性评估方法[J]. 上海交通大学学报, 2021, 55(7): 802-813.
	YOU Guangzeng, TANG Xiangying, HU Yan, et al. Flexibility evaluation method for power system based on clustering of typical operating scenarios[J]. Journal of Shanghai Jiao Tong University, 2021, 55(7): 802-813.
[19]	阿迪拉·阿力木江, 蒋平, 董虹佳, 等. 推广新能源汽车碳减排和大气污染控制的协同效益研究--以上海市为例[J]. 环境科学学报, 2020, 40(5): 1873-1883.
	ADILA Alimujiang, JIANG Ping, DONG Hongjia, et al. Synergy and co-benefits of reducing CO₂ and air pollutant emissions by promoting new energy vehicles: A case of Shanghai[J]. Acta Scientiae Circumstantiae, 2020, 40(5): 1873-1883.
[20]	王从飞, 曹锋, 李明佳, 等. 碳中和背景下新能源汽车热管理系统研究现状及发展趋势[J]. 科学通报, 2021, 66(32): 4112-4128.
	WANG Congfei, CAO Feng, LI Mingjia, et al. Research status and future development of thermal management system for new energy vehicles under the background of carbon neutrality[J]. Chinese Science Bulletin, 2021, 66(32): 4112-4128.
[21]	龚小祥, 常思勤, 蒋李晨, 等. 电动汽车新型线控制动单元及其控制系统[J]. 上海交通大学学报, 2016, 50(3): 395-400.
	GONG Xiaoxiang, CHANG Siqin, JIANG Lichen, et al. A novel brake-by wire unit and control system for electric vehicle[J]. Journal of Shanghai Jiao Tong University. 2016, 50(3): 395-400.
[22]	广东省发展和改革委员会.广东省自行车骑行碳普惠方法学: 2019001-V01[S]. 广东: 广东省发改委, 2019.
	Guangdong Provincial Development and Reform Commission. Guangdong province cycling carbon inclusion methodology: 2019001-V01[S]. Guangdong: Guangdong Provincial Development and Reform Commission, 2019.
[23]	MALLICK J, TALUKDAR S, ALSUBIH M. et al. Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis[J]. Theoretical and Applied Climatology, 2021, 143, 823-841. doi: 10.1007/s00704-020-03448-1
[24]	符艳红, 谢世友, 高洁. 基于Mann-Kendall法的嘉陵江流域降水量时空分布规律[J]. 西南大学学报(自然科学版), 2018, 40(6): 132-139.
	FU Yanhong, XIE Shiyou, GAO Jie. Temporal and spatial distribution of precipitation in Jialing River basin based on Mann-Kendall method[J]. Journal of Southwest University (Natural Science), 2018, 40(6): 132-139.
[25]	白瑞泉, 周子江, 吴昊, 等. 基于Mann-Kendall法的大运河枫桥站水文变化分析[J]. 长江技术经济, 2021, 5(Sup.1): 103-105.
	BAI Ruiquan, ZHOU Zijiang, WU Hao, et al. Hydrological variation analysis of Fengqiao station of grand canal based on Mann-Kendall method[J]. Technology and Economy of Changjiang, 2021, 5(Sup.1): 103-105.
[26]	王佳, 王博. 基于改进蚁群算法和Mann-Kendall法的涡桨发动机性能预测[J]. 航空动力学报, 2022, 37(6): 1306-1313.
	WANG Jia, WANG Bo. Performance prediction of turboprop engine based on improved ant colony algorithm and Mann-Kendall method[J]. Journal of Aerospace Power, 2022, 37(6): 1306-1313.
[27]	NYIKADZINO B, CHITAKIRA M, MUCHURU S. Rainfall and runoff trend analysis in the Limpopo river basin using the Mann Kendall statistic[J]. Physics and Chemistry of the Earth, Parts A/B/C, 2020, 117: 102870. doi: 10.1016/j.pce.2020.102870 URL
[28]	张立, 谢紫璇, 曹丽斌, 等. 中国城市碳达峰评估方法初探[J]. 环境工程, 2020, 38(11): 1-5.
	ZHANG Li, XIE Zixuan, CAO Libin, et al. Discussion on evaluation method on carbon dioxide emissions peaking for Chinese cities[J]. Environmental Engineering, 2020, 38(11): 1-5.
[29]	C40. 27 cities have reached peak greenhouse gas emissions whilst populations increase and economies grow[R/OL].(2018-09-13)[2021-12-21]. https://www.c40.org/press_releases/27-cities-have-reached-peak-greenhouse-gas-emissions-whilst-populations-increase-and-economies-grow.
[30]	GILBERT R O. Statistical methods for environmental pollution monitoring[M]. New York: John Wiley and Sons, 1987: 209.
[31]	杨涛, 方莉, 王海林. 北京市某区碳排放总量核算及达峰判定[J]. 节能与环保, 2021(8): 37-39.
	YANG Tao, FANG Li, WANG Hailin. Total carbon emission accounting and the determination of carbon peak at the district and county scale[J]. Energy Conservation & Environmental Protection, 2021(8): 37-39.

年份	S	V(S)	Z_mk	P	发展趋势
2012	-1	1.00	0.00	1.00	无趋势
2013	-3	3.65	-1.03	0.31	无趋势
2014	-6	8.66	-1.71	0.09	无趋势
2015	-8	16.66	-1.72	0.09	无趋势
2016	-9	28.34	-1.51	0.12	无趋势
2017	-9	44.32	-1.19	0.22	无趋势
2018	-12	65.33	-1.36	0.17	无趋势
2019	-18	92.00	-1.75	0.09	无趋势
2020	-27	125.00	-2.33	0.02	下降趋势

季节	S	V(S)	Z_mk	P	发展趋势
春	-7	125.00	-0.54	0.590	无趋势
夏	-29	125.00	-2.50	0.010	下降趋势
秋	-35	125.00	-3.04	0.002	下降趋势
冬	-10	65.33	-1.11	0.270	无趋势

年份	S	V(S)	Z_mk	P	发展趋势
2016	-1	1.00	0.00	1.00	无趋势
2017	-3	3.67	-1.04	0.30	无趋势
2018	-6	0.12	-1.70	0.09	无趋势
2019	-8	16.67	-1.71	0.09	无趋势
2020	-9	28.33	-1.50	0.13	无趋势

基于Mann-Kendall趋势检验的城市能源碳达峰评估方法

Assessment Method for Urban Energy Carbon Emission Peak Based on Mann-Kendall Trend Test

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