Journal of Shanghai Jiaotong University >
Real-Time Calculation of Carbon Emissions in County-Level Administrative Regions Based on ‘Energy Brain’
Received date: 2021-09-18
Online published: 2022-10-09
Existing calculation methods of carbon emission cannot well meet the needs of gradual refinement and real-time of carbon emission regions. In order to ensure the real-time and accuracy of carbon emissions responsibility allocation, a real-time calculation method of carbon emissions in urban regions is proposed. The improved K-means clustering algorithm is used to cluster and combine the operating periods and operating scenarios of the urban area energy load,so as to obtain the typical carbon emission characteristics. The regional unit electricity carbon emission is proposed as a carbon emission indicator, the operating period and scenario are classified, and the unit electricity carbon emission and the total carbon emission of urban regions for each cluster are calculated. The proposed algorithm is verified based on part of the historical data of energy consumption in the energy brain of a certain region in eastern China. The results show that the clustering method and carbon emission indicators can effectively calculate the total carbon emission of urban regions in real-time.
CHEN Yun, SHEN Hao, WANG Jiayu, ZHAO Wenkai, PAN Zhijun, WANG Xiaohui, XIAO Yinjing . Real-Time Calculation of Carbon Emissions in County-Level Administrative Regions Based on ‘Energy Brain’[J]. Journal of Shanghai Jiaotong University, 2022 , 56(9) : 1111 -1117 . DOI: 10.16183/j.cnki.jsjtu.2021.364
| [1] | UNFCCC. Kyoto protocol to the United Nations Framework Convention on Climate Change[EB/OL]. (1998-03-16) [2021-08-24]. http://www.npc.gov.cn/zgrdw/npc/zxft/zxft8/2009-08/24/content_1515037.htm. |
| [2] | 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指南(试行)[EB/OL]. (2012-12-11) [2021-08-24]. https://www.carbonstop.net/static/upload/shanghai_carbonaccounting_guideline.pdf. |
| [2] | Shanghai Municipal Development & Reform Commission. Shanghai greenhouse gas emission accounting and reporting guidelines (trial)[EB/OL]. (2012-12-11) [2021-08-24]. https://www.carbonstop.net/static/upload/shanghai_carbonaccounting_guideline.pdf. |
| [3] | Intergovernmental Panel on Climate Change. 2006 IPCC guidelines for national greenhouse gas inventories-corrected as of July 2020[EB/OL]. (2020-07-22) [2020-07-23] https://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html. |
| [4] | LIU Z, GUAN D B, WEI W, et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China[J]. Nature, 2015, 524(7565): 335-338. |
| [5] | SHAN Y L, LIU J H, LIU Z, et al. New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors[J]. Applied Energy, 2016, 184: 742-750. |
| [6] | CAMBALIZA M O L, SHEPSON P B, CAULTON D R, et al. Assessment of uncertainties of an aircraft-based mass balance approach for quantifying urban greenhouse gas emissions[J]. Atmospheric Chemistry and Physics, 2014, 14(17): 9029-9050. |
| [7] | FIEHN A, KOSTINEK J, ECKL M, et al. Estimating CH4, CO2, and CO emissions from coal mining and industrial activities in the Upper Silesian Coal Basin using an aircraft-based mass balance approach[J]. Atmospheric Chemistry and Physics, 2020, 20(21): 12675-12695. |
| [8] | LAURI M. Analysis: Coronavirus temporarily reduced China’s CO2 emissions by a quarter[EB/OL]. (2020-02-19) [2020-03-30]. https://www.carbonbrief.org/analysis-coronavirus-has-temporarily-reduced-chinas-co2-emissions-by-a-quarter. |
| [9] | 袁文俊. 复杂网络视角下我国省域间贸易隐含碳排放流动研究[D]. 西安: 西安建筑科技大学, 2020. |
| [9] | YUAN Wenjun. Research on the flow of embodied carbon emissions in inter-provincial trade from the perspective of complex network[D]. Xi’an: Xi’an University of Architecture and Technology, 2020. |
| [10] | 邸小龙. 基于复杂网络的中国产业部门间隐含碳排放流动结构演化研究[D]. 西安: 西安建筑科技大学, 2020. |
| [10] | DI Xiaolong. Research on evolution of embodied carbon emissions flow structure among Chinese industrial sectors based on complex network[D]. Xi’an: Xi’an University of Architecture and Technology, 2020. |
| [11] | 李思寰. 跨区域汽车尾气排放减排责任测算与分摊[J]. 统计与决策, 2017(24): 93-96. |
| [11] | LI Sihuan. Cross-regional vehicle exhaust emission responsibility calculation and allocation[J]. Statistics & Decision, 2017(24): 93-96. |
| [12] | CHENG Y H, ZHANG N, WANG Y, et al. Modeling carbon emission flow in multiple energy systems[J]. IEEE Transactions on Smart Grid, 2019, 10(4): 3562-3574. |
| [13] | KANG C Q, ZHOU T R, CHEN Q X, et al. Carbon emission flow from generation to demand: A network-based model[J]. IEEE Transactions on Smart Grid, 2015, 6(5): 2386-2394. |
| [14] | CHEN G, CHEN B, ZHOU H, et al. Life cycle carbon emission flow analysis for electricity supply system: A case study of China[J]. Energy Policy, 2013, 61: 1276-1284. |
| [15] | 李立. 中国国家及区域碳排放分析: 基于LMDI分解和K-均值聚类[D]. 南京: 南京大学, 2017. |
| [15] | LI Li. An analysis on national and regional carbon emissions in China—based on LMDI and K-means[D]. Nanjing: Nanjing University, 2017. |
| [16] | 国家电力投资集团有限公司. 有“能源大脑”,不惧停电[EB/OL]. (2021-02-24) [2021-02-24]. https://power.in-en.com/html/power-2383917.shtml. |
| [16] | State Power Investment Group Co., Ltd.. An ‘energy brain’ make us not afraid of power outages[EB/OL]. (2021-02-24) [2021-02-24]. https://power.in-en.com/html/power-2383917.shtml. |
| [17] | 赵莉, 候兴哲, 胡君, 等. 基于改进k-means算法的海量智能用电数据分析[J]. 电网技术, 2014, 38(10): 2715-2720. |
| [17] | ZHAO Li, HOU Xingzhe, HU Jun, et al. Improved k-means algorithm based analysis on massive data of intelligent power utilization[J]. Power System Technology, 2014, 38(10): 2715-2720. |
| [18] | 张宜浩, 金澎, 孙锐. 基于改进k-means算法的中文词义归纳[J]. 计算机应用, 2012, 32(5): 1332-1334. |
| [18] | ZHANG Yihao, JIN Peng, SUN Rui. Chinese word sense induction based on improved k-means algorithm[J]. Journal of Computer Applications, 2012, 32(5): 1332-1334. |
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