上海交通大学学报

上海交通大学学报 >

2022 , Vol. 56 >Issue 3: 293 - 302

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.436

新型电力系统与综合能源

粤港澳大湾区电力系统低碳转型

展开
  • 1.山东大学 威海蓝绿发展研究院,山东 威海 2642209
    2.上海理工大学 管理学院,上海 200093
    3.中国城市和小城镇改革发展中心,北京 100045
    4.上海市气候中心, 上海 200030
    5.上海交通大学 中英国际低碳学院,上海 201306
    6.上海交通大学 国际与公共事务学院,上海 200030
张鹏飞(1992-),男,河南省焦作市人,博士生,主要研究方向为能源环境经济学、资源环境管理.

收稿日期: 2021-11-01

  网络出版日期: 2022-04-01

基金资助

国家重点研发计划(2019YFC1908501);国家自然科学基金(72088101);国家自然科学基金(71904125);国家自然科学基金(71690241);国家自然科学基金(71810107001)

收起

Low-Carbon Transformation of the Power System in the Guangdong-Hong Kong-Macao Greater Bay Area

Expand
  • 1. Institute of Blue and Green Development, Shandong University, Weihai 2642209, Shandong, China
    2. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
    3. China Center for Urban Development, Beijing 100045, China
    4. Shanghai Climate Center, Shanghai 200030, China
    5. China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 201306, China
    6. School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China

Received date: 2021-11-01

  Online published: 2022-04-01

Fold

摘要

我国“碳达峰、碳中和”目标的实现在很大程度上依赖于电力系统的低碳转型,而现有研究很少从区域尺度研究电力系统低碳转型.综合使用政府间气候变化专门委员会温室气体清单编制方法、网络模型分析法量化了粤港澳大湾区电力生产和消费引致的碳排放,并使用对数平均迪氏指数分析法分析了社会经济因素对大湾区电力碳排放的影响.结果表明:香港、澳门电力低碳转型进展缓慢,广东省低碳电力份额持续提高;快速扩大的经济规模和电力需求是大湾区排放增长的最主要驱动因素;外部低碳电力的输入以及部门用电效率的提升抵消了部分排放增长.

关键词： 粤港澳大湾区; 电力传输; 碳排放; 碳中和

本文引用格式

张鹏飞, 徐静怡, 郭巍, 吴蔚, 钟晨, 魏文栋 . 粤港澳大湾区电力系统低碳转型[J]. 上海交通大学学报, 2022 , 56(3) : 293 -302 . DOI: 10.16183/j.cnki.jsjtu.2021.436

Abstract

China’s “carbon peaking and carbon neutrality” goal relies greatly on the low-carbon transition of the power system, but the existing research rarely explores the low-carbon transition of the regional power system. By using the intergovernmental panel on climate change (IPCC) greenhouse gas inventory compilation method and the network model analysis, the carbon emissions caused by the power generation and the power consumption in Guangdong-Hong Kong-Macao Greater Bay Area (the Greater Bay Area) was quantified. The logarithmic mean Divisia index (LMDI) method was used to quantify the influence of socio-economic factors on the electricity-related carbon emissions in the Greater Bay Area. The results show that Hong Kong and Macao have made slow progress in the low-carbon transition of the power system, and Guangdong’s share of the low-carbon power continues to increase. The rapidly expanding economic scale and the power demand were the most important drivers of the emissions growth in the Greater Bay Area. The low-carbon electricity imported from outside regions and the improved efficiency in the sectoral electricity consumption offset part of the emission growth.

Key words： Guangdong-Hong Kong-Macao Greater Bay Area (Greater Bay Area); power transmission; carbon emissions; carbon neutrality

参考文献

[1] United Nations. United nations digital library [DB/OL]. (2020-09-22)[2021-07-28]. https://digitallibrary.un.org/record/3893029?ln=en
[2] CHEN H, KANG J N, LIAO H, et al. Costs and potentials of energy conservation in China’s coal-fired power industry: A bottom-up approach considering price uncertainties[J]. Energy Policy, 2017, 104:23-32.
[3] ADEDOYIN F F, GUMEDE M I, BEKUN F V, et al. Modelling coal rent, economic growth and CO2 emissions: Does regulatory quality matter in BRICS economies?[J]. Science of the Total Environment, 2020, 710:136284.
[4] ZHANG P F, CAI W Q, YAO M T, et al. Urban carbon emissions associated with electricity consumption in Beijing and the driving factors[J]. Applied Energy, 2020, 275:115425.
[5] TONG D, ZHANG Q, DAVIS S J, et al. Targeted emission reductions from global super-polluting power plant units[J]. Nature Sustainability, 2018, 1(1):59-68.
[6] GAVRILOVA O, JONAS M, ERB K, et al. International trade and Austria’s livestock system: Direct and hidden carbon emission flows associated with production and consumption of products[J]. Ecological Economics, 2010, 69(4):920-929.
[7] QU S, WANG H X, LIANG S, et al. A Quasi-Input-Output model to improve the estimation of emission factors for purchased electricity from inter-connected grids[J]. Applied Energy, 2017, 200:249-259.
[8] 魏文栋, 张鹏飞, 李佳硕. 区域电力相关碳排放核算框架的构建和应用[J]. 中国人口·资源与环境, 2020, 30:38-46.
[8] WEI Wendong, ZHANG Pengfei, LI Jiashuo, et al. Construction and application of regional power related to carbon emission accounting framework[J]. Chinese Journal of Population, Resources and Environment, 2020, 30:38-46.
[9] TONG D, ZHANG Q, ZHENG Y X, et al. Committed emissions from existing energy infrastructure jeopardize 1.5 ℃ climate target[J]. Nature, 2019, 572(7769):373-377.
[10] CUI R Y, HULTMAN N, EDWARDS M R, et al. Quantifying operational lifetimes for coal power plants under the Paris goals[J]. Nature Communications, 2019, 10:4759.
[11] ZAPPA W, JUNGINGER M, VAN DEN BROEK M. Is a 100% renewable European power system feasible by 2050?[J]. Applied Energy, 2019, 233/234:1027-1050.
[12] ZHONG J, BOLLEN M, RÖNNBERG S. Towards a 100% renewable energy electricity generation system in Sweden[J]. Renewable Energy, 2021, 171:812-824.
[13] COLE W J, GREER D, DENHOLM P, et al. Quantifying the challenge of reaching a 100% renewable energy power system for the United States[J]. Joule, 2021, 5(7):1732-1748.
[14] GULAGI A, ALCANZARE M, BOGDANOV D, et al. Transition pathway towards 100% renewable energy across the sectors of power, heat, transport, and desalination for the Philippines[J]. Renewable and Sustainable Energy Reviews, 2021, 144:110934.
[15] SCHMIDT T S, STEFFEN B, EGLI F, et al. Adverse effects of rising interest rates on sustainable energy transitions[J]. Nature Sustainability, 2019, 2(9):879-885.
[16] EGLI F, STEFFEN B, SCHMIDT T S. A dynamic analysis of financing conditions for renewable energy technologies[J]. Nature Energy, 2018, 3(12):1084-1092.
[17] PENG X, TAO X, FENG K, et al. Drivers toward a low-carbon electricity system in China’s provinces[J]. Environmental Science & Technology, 2020, 54(9):5774-5782.
[18] KANG J D, NG T S, SU B, et al. Optimizing the Chinese electricity mix for CO2 emission reduction: An input-output linear programming model with endogenous capital[J]. Environmental Science & Technology, 2020, 54(2):697-706.
[19] TONG D, ZHANG Q, LIU F, et al. Current emi-ssions and future mitigation pathways of coal-fired power plants in China from 2010 to 2030[J]. Environmental Science & Technology, 2018, 52(21):12905-12914.
[20] CUI Y R, HULTMAN N, EDWARDS M, et al. A high ambition coal phaseout in China: Feasible strategies through a comprehensive plant-by-plant assessment[R]. University of Maryland Center for Global Sustainability: The Energy Research Institute of the National Development and Reform Commission of China, 2020.
[21] WANG M, YAO M T, WANG S S, et al. Study of the emissions and spatial distributions of various power-generation technologies in China[J]. Journal of Environmental Management, 2021, 278:111401.
[22] WEI W D, WANG X B, ZHU H, et al. Carbon emissions of urban power grid in Jing-Jin-Ji region: Characteristics and influential factors[J]. Journal of Cleaner Production, 2017, 168:428-440.
[23] WANG J X, ZHONG H W, YANG Z F, et al. Exploring the trade-offs between electric heating policy and carbon mitigation in China[J]. Nature Communications, 2020, 11:6054.
[24] WEI W D, HAO S J, YAO M T, et al. Unbalanced economic benefits and the electricity-related carbon emissions embodied in China’s interprovincial trade[J]. Journal of Environmental Management, 2020, 263:110390.
[25] WEI W D, ZHANG P F, YAO M T, et al. Multi-scope electricity-related carbon emissions accounting: A case study of Shanghai[J]. Journal of Cleaner Production, 2020, 252:119789.
[26] LI W B, LONG R Y, ZHANG L L, et al. Greenhouse gas emission transfer of inter-provincial electricity trade in China[J]. International Journal of Environmental Research and Public Health, 2020, 17(22):8375.
[27] 中国共产党中央委员会. 中共中央关于制定国民经济和社会发展第十三个五年计划的建议[M]. 北京: 中央文献出版社, 2016.
[27] Central Committee of the Communist Party of China. Recommendations of the central committee of the communist party of china on the formulation of the 13th five-year plan for national economic and social development[M]. Beijing: Central Literature Press, 2016.
[28] QU S, LIANG S, XU M. CO2 emissions embodied in interprovincial electricity transmissions in China[J]. Environmental Science & Technology, 2017, 51(18):10893-10902.
[29] QU S, LI Y, LIANG S, et al. Virtual CO2 emission flows in the global electricity trade network[J]. Environmental Science & Technology, 2018, 52(11):6666-6675.
[30] DE CHALENDAR J A, TAGGART J, BENSON S M. Tracking emissions in the US electricity system[J]. PNAS, 2019, 116(51):25497-25502.
[31] CHINI C M, DJEHDIAN L A, LUBEGA W N, et al. Virtual water transfers of the US electric grid[J]. Nature Energy, 2018, 3(12):1115-1123.
[32] ANG B W. The LMDI approach to decomposition analysis: A practical guide[J]. Energy Policy, 2005, 33(7):867-871.
[33] ANG B W. LMDI decomposition approach: A guide for implementation[J]. Energy Policy, 2015, 86:233-238.
[34] 国家统计局. 国家数据[DB/OL].(2021-09-20)[2021-10-20]. https://data.stats.gov.cn/
[34] National Bureau of Statistics of China. National data[DB/OL].(2021-09-20)[2021-10-20]. https://data.stats.gov.cn/
[35] 国家统计局. 中国能源统计年鉴2018[M]. 北京: 中国统计出版社, 2019.
[35] National Bureau of Statistics. China energy statistical yearbook 2018[M]. Beijing: China Statistics Press, 2018.
[36] 《中国电力年鉴》编辑委员会. 2019中国电力年鉴[M]. 北京: 中国电力出版社, 2019.
[36] Editorial Committee of China Electricity Yearbook. 2019 China electricity yearbook[M]. Beijing: China Electric Power Press, 2019.
[37] 澳门特别行政区政府统计暨普查局. 能源统计[DB/OL]. (2021-04-25)[2021-07-23]. https://www.dsec.gov.mo/en-US/
[37] Government of Macao Special Administrative Region Statistics and Census Service. Energy statistics[DB/OL]. (2021-04-25)[2021-07-23]. https://www.dsec.gov.mo/en-US/
[38] 澳门环境保护局. 澳门环境状况报告2018[M]. 澳门: 环境保护局, 2018.
[38] Environmental Protection Bureau in Macao Special Administrative Region. Macao environmental status report 2018[M]. Macao: Environmental Protection Bureau, 2018.
[39] 香港特别行政区政府统计处. 香港能源统计[M]. 香港: 政府统计处刊物出版组, 2019.
[39] Census and Statistics Department in Hong Kong Special Administrative Region. Hong Kong energy statistics[M]. Hong Kong: Publications Unit of C&SD, 2019.
[40] 中国电力企业联合会. 电力工业统计资料汇编2017[M]. 北京: 中国电力企业联合会, 2018.
[40] China Electricity Council. Electricity industry statistics compilation 2017[M]. Beijing: China Electricity Council, 2018.
[41] 广东省人民政府. 广东省国民经济和社会发展第十四个五年规划和2035年远景目标纲要[R/OL]. (2021-04-25) [2021-07-30]. http://www.gd.gov.cn/zwgk/wjk/qbwj/yf/content/post_3268751.html
[41] The People’s Government of Guangdong Province. The fourteenth five-year plan for national economic and social development of Guangdong province and the outline of long-term goals for 2035[R/OL]. (2021-04-25) [2021-07-30]. http://www.gd.gov.cn/zwgk/wjk/qbwj/yf/content/post_3268751.html
[42] 香港特别行政区政府. 香港气候行动蓝图2030[R/OL]. (2017-01-20)[2021-07-30]. https://sc.isd.gov.hk/TuniS/www.info.gov.hk/gia/general/201701/20/P2017012000724.htm
[42] Hong Kong Special Administrative Region Government. Hong Kong's climate action plan 2030+[R/OL]. (2017-01-20)[2021-07-30]. https://sc.isd.gov.hk/TuniS/www.info.gov.hk/gia/general/201701/20/P2017012000724.htm
[43] 香港特别行政区政府. 粤港澳大湾区发展规划纲要[R/OL]. (2019-02-20)[2021-07-30]. http://www.shunde.gov.cn/data/2019/05/23/1558575813.pdf
[43] State Council of the People’s Republic of China. Outline of development plan for Guangdong-Hong Kong-Macao Greater Bay Area[R/OL]. (2019-02-20)[2021-07-30]. http://www.shunde.gov.cn/data/2019/05/23/1558575813.pdf
Options
文章导航

/

〈 〉