中国石化化工行业二氧化碳排放达峰路径研究

庞凌云; 翁慧; 常靖; 李永亮; 蔡博峰; 雷宇; 严刚; 吕晨; 张立; 戚志强; 孙伟善; 曹丽斌; 张哲; 李淼; 韦志浩

doi:10.13198/j.issn.1001-6929.2021.11.26

中国石化化工行业二氧化碳排放达峰路径研究

doi: 10.13198/j.issn.1001-6929.2021.11.26

庞凌云^1,,
翁慧^2, ,,
常靖²,
李永亮²,
蔡博峰¹,
雷宇^1, ,,
严刚¹,
吕晨¹,
张立¹,
戚志强²,
孙伟善²,
曹丽斌¹,
张哲¹,
李淼²,
韦志浩²

1.
生态环境部环境规划院碳达峰碳中和研究中心, 北京　100012
2.
中国石油和化学工业联合会, 北京　100723

基金项目: 中国工程院战略研究与咨询项目（No.2021-HYZD-14）；国家自然科学基金项目（No.72074154）

详细信息

作者简介:
庞凌云（1980-），女，山东临沂人，博士，助理研究员，主要从事碳达峰、碳中和规划研究，pangly@caep.org.cn

通讯作者:
①翁慧(1988-)，女，内蒙古锡林郭勒盟人，工程师，主要从事石化行业碳减排等领域政策研究及标准制定研究，wenghui_ivy@163.com

②雷宇(1980-)，男，四川攀枝花人，研究员，博士，主要从事大气环境规划管理及污染控制策略研究，leiyu@caep.org.cn

中图分类号: X324
计量
- 文章访问数: 1324
- HTML全文浏览量: 219
- PDF下载量: 695
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-12
- 修回日期: 2021-11-04
- 网络出版日期: 2022-03-01

Pathway of Carbon Emission Peak for China's Petrochemical and Chemical Industries

1.
Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China
2.
China Petroleum and Chemical Industry Federation, Beijing 100723, China

Funds: Strategic Research and Consulting Project of Chinese Academy of Engineering (No.2021-HYZD-14)；National Natural Science Foundation of China (No.72074154)

摘要

摘要: 石化化工行业是高耗能高排放行业之一，约占工业部门碳排放比例的10%，研究石化化工行业碳排放达峰路径不仅能推动工业部门尽早实现达峰，同时也为石化化工行业加快绿色低碳转型指明方向. 基于中国统计年鉴、行业协会、企业碳核查等多来源数据，在分析历史排放趋势的基础上，识别能源集中度高的重点行业和产品，采用情景分析法针对石油和天然气开采业、石油煤炭及其他燃料加工业、化学原料及化学制品制造业三大子行业中的炼油、乙烯、丙烯、对二甲苯和合成氨等重点产品，预测其基准情景和控排情景下的重点产品产量和碳排放强度，以及石化化工行业2021—2035年二氧化碳排放趋势. 石化化工行业在基准情景下排放量无法实现2030年前达峰，控排情景下将于2030年达峰，峰值为17.3×10⁸ t. 通过能源结构调整、节能和低碳技术改造、低碳循环及高效利用等途径可以实现行业减排，与BAU(仅考虑石化产品产量变化，不考虑产品结构、单位产品能耗变化)情景相比，减排贡献最大的路径是化石能源利用清洁化改造，2030年相对BAU减排1.19×10⁸ t，贡献率约44%；其次是加大节能和低碳技术改造力度和资源循环及高效利用，减排量分别为0.8×10⁸和0.6×10⁸ t，减排贡献率分别达到29%和22%.
- 石化化工行业 /
- 碳达峰 /
- 情景预测 /
- 碳排放峰值 /
- 减排路径
Abstract: The petrochemical and chemical industries are one of the high energy-consuming and high-emission industries, accounting for about 10% carbon emissions of the industrial sector. The research on the carbon emission peak pathway in the petrochemical and chemical industries can not only promote the industrial sector to reach the peak as soon as possible, but also point out the direction for accelerating its green and low-carbon transformation. Based on the analysis of historical emission trends with multi-source data from the China Statistical Yearbook, industry associations and enterprise carbon verification, key industries and products with high energy consumption were identified. Focused on the key products of oil refining, ethylene, propylene, p-xylene and synthetic ammonia in the three major sub industries of oil and gas exploitation, petrochemical industry and chemical industry, scenario analysis method is used to predict the output of these key products and carbon emission intensity under the benchmark scenario and emission control scenario. The trend of carbon dioxide emission of petrochemical and chemical industry from 2021 to 2035 is predicted. The emissions of the petrochemical and chemical industries cannot reach the peak before 2030 under the benchmark scenario and will reach the peak in 2030 under the emission control scenario, with a peak value of 1.73 billion tons. The path of emission reduction can be achieved through energy structure adjustment, energy-saving and low-carbon technology transformation, low-carbon cycle and high-efficiency utilization. Compared with business-as-usual (BAU), which only considers the change of petrochemical product output and does not consider the change of product structure and energy consumption per unit product, the path with the largest contribution to emission reduction is clean transformation of fossil energy utilization. In 2030, the emission reduction compared with BAU will be 119 million tons, accounting for about 44% of the emission reduction. Strengthening energy saving and low-carbon technology transformation will reduce emissions by 80 million tons (29% of the total reduction), and resource recycling and efficient utilization will reduce emissions by 60 million tons (22% of the total reduction).
- petrochemical and chemical industries /
- carbon emissions peak /
- scenario prediction /
- carbon dioxide emission peak /
- emission reduction pathway

HTML全文

图 1 石化化工行业达峰预测技术路线

Figure 1. Technical roadmap of peak emission forecasting in petrochemical and chemical industry

下载: 全尺寸图片幻灯片

图 2 2010—2020年石化和化工行业五大子行业能源消费情况

Figure 2. Energy consumption of five major sub industries of petrochemical and chemical industry in 2010-2020

下载: 全尺寸图片幻灯片

图 3 石化行业主要生产过程的碳排放示意

Figure 3. Carbon emission diagram of main production processes in petrochemical and chemical industry

下载: 全尺寸图片幻灯片

图 4 石化化工行业不同情景下碳排放的变化趋势

Figure 4. Carbon emission trends of petrochemical and chemical industry under different scenarios

下载: 全尺寸图片幻灯片

图 5 石化化工行业碳达峰路径和主要减排措施

Figure 5. Carbon emission peak pathway and main emission reduction measures in petrochemical and chemical industry

下载: 全尺寸图片幻灯片

表 1 石化化工行业涉及范围与国民经济行业分类对应

Table 1. Corresponding of petrochemical and chemical industry scope and national economic industry classification

行业名称	国民经济行业分类
油气开采行业	石油和天然气开采业
石化行业	石油煤炭及其他燃料加工业、化学原料及化学制品制造业中的有机化学原料制造、初级形态塑料及合成树脂制造、合成纤维单(聚合)体制造、合成橡胶制造
化工行业	化学原料及化学制品制造业〔扣除有机化学原料制造、初级形态塑料及合成树脂制造、合成纤维单(聚合) 体制造、合成橡胶制造〕

下载: 导出CSV

表 2 基准情景下炼油行业碳排放量预测

Table 2. Carbon emissions prediction of oil refining industry in benchmark scenario

年份	成品油需求量/ (10⁸ t)	出口量/ (10⁴ t)	原油加工量/ (10⁴ t)	成品油收率/%	单位原油加工量碳排放/t	CO₂排放量/(10⁴ t)
2025	37 909	4 500	76 234	55	0.288	21 968
2030	38 234	4 500	85 510	50	0.277	23 668
2035	34 802	4 500	85 510	45	0.268	22 920

下载: 导出CSV

表 3 基准情景乙烯预测参数设置

Table 3. Setting of ethylene prediction parameters in benchmark scenario

参数	消费系数法			类比法
参数	2025年	2030年	2035年	2025年	2030年	2035年
自给率/%	76	85	90	—	—	—
弹性系数	0.9	0.8	0.75	—	—	—
当量消费量/ (10⁴ t)	7 512	9 140	10 673	7 410	8 866	9 581
单位产品碳排放下降率/%	4	3	2.5	4	3	2.5
人均乙烯当量消费量/kg	—	—	—	52	62	67

下载: 导出CSV

表 4 基准情景下乙烯、丙烯碳排放量预测

Table 4. Carbon emission prediction of ethylene and propylene under benchmark scenario

产品	年份	石油基		煤基		碳排放量合计/10⁴ t
产品	年份	产量/ (10⁴ t)	单位产品碳排放强度/t	产量/ (10⁴ t)	单位产品碳排放强度/t	碳排放量合计/10⁴ t
乙烯	2025	4 820	2.533	850	9.626	20 393
	2030	6 642	2.454	1 000	9.326	25 720
	2035	8 054	2.393	1 060	9.093	28 911
丙烯	2025	4 250	0.889	850	9.626	11 960
	2030	5 317	0.863	1 000	9.326	14 008
	2035	6 184	0.843	1 060	9.093	14 850

下载: 导出CSV

参考文献(36)

[1]	生态环境部.关于统筹和加强应对气候变化与生态环境保护相关工作的指导意见[EB/OL].北京:生态环境部,(2021-01-11)[2021-08-01].http://www.mee.gov.cn/xxgk2018/xxgk/xxgk03/202101/t20210113_817221.html.
[2]	国务院办公厅.国务院关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见[EB/OL].北京:中国政府网,(2021-10-24)[2021-11-01].http://www.gov.cn/zhengce/2021-10/24/content_5644613.htm.
[3]	国务院办公厅.国务院关于印发2030年前碳达峰行动方案的通知[EB/OL].北京:中国政府网,(2021-10-24)[2021-11-01].http://www.gov.cn/zhengce/content/2021-10/26/content_5644984.htm.
[4]	HE J K.An analysis of China′s CO₂ emission peaking target and pathways[J].Advances in Climate Change Research,2014,5(4):155-161. doi: 10.1016/j.accre.2015.04.002
[5]	CHAI Q M,XU H Q.Modeling an emissions peak in China around 2030: synergies or trade-offs between economy, energy and climate security[J].Advances in Climate Change Research,2014,5(4):169-180. doi: 10.1016/j.accre.2015.06.001
[6]	YU S W,ZHENG S H,LI X,et al.China can peak its energy-related carbon emissions before 2025: evidence from industry restructuring[J].Energy Economics,2018,73:91-107. doi: 10.1016/j.eneco.2018.05.012
[7]	DING S T,ZHANG M,SONG Y.Exploring China's carbon emissions peak for different carbon tax scenarios[J].Energy Policy,2019,129:1245-1252.
[8]	LI H N,DE QIN Q.Challenges for China's carbon emissions peaking in 2030:a decomposition and decoupling analysis[J].Journal of Cleaner Production,2019,207:857-865.
[9]	赵明轩,吕连宏,王深,等.中国碳达峰路径的meta回归分析[J].环境科学研究,2021,34(9):2056-2064. ZHAO M X,LÜ L H,WANG S,et al.Meta regression analysis of pathway of peak carbon emissions in China[J].Research of Environmental Sciences,2021,34(9):2056-2064.
[10]	马忠,耿文婷.基于假设抽取法的中国区域间碳排放关联分析[J].环境科学研究,2020,33(2):312-323. MA Z,GENG W T.Correlation analysis of regional carbon emission in China based on the hypothetical extraction method[J].Research of Environmental Sciences,2020,33(2):312-323.
[11]	顾阿伦,何崇恺,吕志强.基于LMDI方法分析中国产业结构变动对碳排放的影响[J].资源科学,2016,38(10):1861-1870. GU A,HE C K,LV Z Q.Industrial structure changes impacts on carbon emissions in China based on LMDI method[J].Resources Science,2016,38(10):1861-1870.
[12]	王佳,薛景洁.旅游交通碳排放测算及影响因素分析[J].统计与决策,2016(13):61-64.
[13]	马丁, 陈文颖. 中国2030年碳排放峰值水平及达峰路径研究[J]. 中国人口·资源与环境, 2016, 26(增刊1): 1-4. MA D, CHEN W Y. Analysis of China's 2030 carbon emission peak level and peak path[J]. China Population, Resources and Environment, 2016, 26(Suppl 1): 1-4.
[14]	王勇,王恩东,毕莹.不同情景下碳排放达峰对中国经济的影响: 基于CGE模型的分析[J].资源科学,2017,39(10):1896-1908. WANG Y,WANG E D,BI Y.Impact of a peak in carbon emissions on China′s economy in different situations: analysis based on CGE model[J].Resources Science,2017,39(10):1896-1908.
[15]	段玉婉,杨翠红.中国与日本能源消耗和CO₂排放差异的空间结构分解[J].系统工程理论与实践,2017,37(8):2083-2090. doi: 10.12011/1000-6788(2017)08-2083-08 DUAN Y W,YANG C H.A spatial structural decomposition of Chinese and Japanese energy consumption and CO₂ emission[J].Systems Engineering-Theory & Practice,2017,37(8):2083-2090. doi: 10.12011/1000-6788(2017)08-2083-08
[16]	BUTNAR I,LLOP M.Structural decomposition analysis and input-output subsystems: changes in CO₂ emissions of Spanish service sectors (2000-2005)[J].Ecological Economics,2011,70(11):2012-2019. doi: 10.1016/j.ecolecon.2011.05.017
[17]	CANSINO J M,ROMÁN R,ORDÓÑEZ M.Main drivers of changes in CO₂ emissions in the Spanish economy: a structural decomposition analysis[J].Energy Policy,2016,89:150-159. doi: 10.1016/j.enpol.2015.11.020
[18]	WANG H K,LU X,DENG Y,et al.China′s CO₂ peak before 2030 implied from characteristics and growth of cities[J].Nature Sustainability,2019,2(8):748-754. doi: 10.1038/s41893-019-0339-6
[19]	XU G Y,SCHWARZ P,YANG H L.Adjusting energy consumption structure to achieve China′s CO₂ emissions peak[J].Renewable and Sustainable Energy Reviews,2020,122:109737. doi: 10.1016/j.rser.2020.109737
[20]	QI Y,STERN N,HE J K,et al.The policy-driven peak and reduction of China′s carbon emissions[J].Advances in Climate Change Research,2020,11(2):65-71. doi: 10.1016/j.accre.2020.05.008
[21]	ZHANG X,GENG Y,SHAO S,et al.How to achieve China's CO₂ emission reduction targets by provincial efforts? an analysis based on generalized Divisia index and dynamic scenario simulation[J].Renewable and Sustainable Energy Reviews,2020,127:109892. doi: 10.1016/j.rser.2020.109892
[22]	ZHOU S,WANG Y,YUAN Z Y,et al.Peak energy consumption and CO₂ emissions in China′s industrial sector[J].Energy Strategy Reviews,2018,20:113-123. doi: 10.1016/j.esr.2018.02.001
[23]	SHEINBAUM C,OZAWA L,CASTILLO D.Using logarithmic mean Divisia index to analyze changes in energy use and carbon dioxide emissions in Mexico's iron and steel industry[J].Energy Economics,2010,32(6):1337-1344. doi: 10.1016/j.eneco.2010.02.011
[24]	GIELEN D,MORIGUCHI Y.CO₂ in the iron and steel industry: an analysis of Japanese emission reduction potentials[J].Energy Policy,2002,30(10):849-863. doi: 10.1016/S0301-4215(01)00143-4
[25]	王瑞,许义榕,孟渴欣,等.二氧化碳转化制取燃料及高值化学品研究进展[J].环境工程技术学报,2020,10(4):639-646. doi: 10.12153/j.issn.1674-991X.20190179 WANG R,XU Y R,MENG K X,et al.Development of research on the conversion of carbon dioxide into fuel and high value-added products[J].Journal of Environmental Engineering Technology,2020,10(4):639-646. doi: 10.12153/j.issn.1674-991X.20190179
[26]	赵明轩,吕连宏,张保留,等.中国能源消费、经济增长与碳排放之间的动态关系[J].环境科学研究,2021,34(6):1509-1522. ZHAO M X,LÜ L H,ZHANG B L,et al.Dynamic relationship among energy consumption, economic growth and carbon emissions in China[J].Research of Environmental Sciences,2021,34(6):1509-1522.
[27]	王深,吕连宏,张保留,等.基于多目标模型的中国低成本碳达峰、碳中和路径[J].环境科学研究,2021,34(9):2044-2055. WANG S,LÜ L H,ZHANG B L,et al.Multi objective programming model of low-cost path for China′s peaking carbon dioxide emissions and carbon neutrality[J].Research of Environmental Sciences,2021,34(9):2044-2055.
[28]	李健,李海霞.产业转移视角下京津冀石化产业碳排放因素分解与减排潜力分析[J].环境科学研究,2020,33(2):324-332. LI J,LI H X.Analysis of carbon emission factors decomposition and emission reduction potential of Beijing-Tianjin-Hebei regional petrochemical industry from the perspective of industrial transfer[J].Research of Environmental Sciences,2020,33(2):324-332.
[29]	陈嘉茹,燕菲,陈建荣,等.油气体制改革深入推进 “双碳”目标推动行业低碳发展: 2020年中国油气政策综述[J].国际石油经济,2021,29(2):62-67. doi: 10.3969/j.issn.1004-7298.2021.02.008 CHEN J R,YAN F,CHEN J R,et al.Review of China′s oil and gas policies in 2020[J].International Petroleum Economics,2021,29(2):62-67. doi: 10.3969/j.issn.1004-7298.2021.02.008
[30]	戴彦德,吴凡.基于低碳转型的宏观经济情景模拟与减排策略[J].北京理工大学学报(社会科学版),2017,19(2):1-8. DAI Y D,WU F.Macroeconomic scenario simulation and carbon reduction strategies based on low-carbon transition[J].Journal of Beijing Institute of Technology (Social Sciences Edition),2017,19(2):1-8.
[31]	中国石化经济技术研究院.2021中国能源化工产业发展报告[R].北京: 中国石化出版社,2021.
[32]	中国石油和化学工业联合会,山东隆众信息技术有限公司.中国石化市场产能预警报告(2020)[R].北京: 化学工业出版社,2020.
[33]	中国石油和化学工业联合会.石油和化学工业“十四五”发展指南[R].北京: 中国石油和化学工业联合会,2021.
[34]	严刚,郑逸璇,王雪松,等.基于重点行业/领域的我国碳排放达峰路径研究[J],环境科学研究,2021.doi: 10.13198/j.issn.1001-6929.2021.11.13. YAN G,ZHENG Y X,WANG X S,el al.Pathway to China's carbon emissions peaking based on sectoral analysis[J].Research of Environmental Sciences,2021.doi: 10.13198/j.issn.1001-6929.2021.11.13.
[35]	IPCC.The contribution to the IPCC's fifth assessment report (WGII AR5)[R].Geneva:IPCC,2014.
[36]	中国石油和化学工业联合会.石油和化学工业“十四五”发展指南及二〇三五年远景目标[R].北京: 中国石油和化学工业联合会,2021.