中国煤炭铁路运输生命周期温室气体排放研究

doi:10.18402/resci.2021.03.16

摘要/Abstract

摘要：

中国煤炭储量丰富、消耗量大且其资源禀赋与经济发展水平呈逆向分布,大宗煤炭铁路运输所导致的温室效应不容忽视。由于铁路机车的技术应用存在时空异质性,因而有必要从生命周期视角解析煤炭铁路运输温室气体排放的时空变异特征,探索减排潜力。本文在构建煤炭铁路运输生命周期温室气体排放（Life Cycle Greenhouse Gas Emissions, LC-GHGEs）核算模型的基础上,量化1999—2017年3类机车的省级LC-GHGEs因子,刻画LC-GHGEs的总体变化趋势与时空分异特征。结果显示：①随着西北电力机车工作量占比大幅提升与西南水电的大力发展,西北、西南铁路运输的LC-GHGEs因子呈显著下降,而华北、华东呈略微上升,省际差异逐步缩小。②1999—2017年,煤炭铁路运输LC-GHGEs总量首先呈现相对稳定的增长趋势,经历2002—2011年的煤炭“黄金十年”后开始迅速下降,然后在2017年再次上升。自2002年起,电力机车替代内燃机车成为温室气体排放最大的贡献者。③内蒙古替代山西成为LC-GHGEs贡献最高的煤炭输出省份,重点输出省份的西向转移加剧了煤炭铁路运输的温室效应。煤炭铁路运输LC-GHGEs在输入省份的分布较为分散,钢铁生产大省的贡献较为突出。最后,基于上述研究结果,从机车技术升级、能源结构调整、调运系统优化等方面提出了如何有效降低煤炭铁路运输温室气体排放的对策建议。

关键词: 煤炭流动, 碳排放, 生命周期, 气候变化, 煤炭贸易

Abstract:

The greenhouse gas emissions from China’s coal railway transport should not be ignored considering the rich coal reserves and large consumption, as well as the spatial difference between the distribution of coal resources and economic activities. Due to the significant spatial and temporal heterogeneity of locomotive technology application, it is necessary to analyze the spatial and temporal variations of life cycle greenhouse gas emissions (LC-GHGEs) from coal railway transport and explore its reduction potential. Based on the calculation model of LC-GHGEs from coal railway transport, the provincial LC-GHGEs factors of the three types of locomotives from 1999 to 2017 were calculated. Then the overall change trend and spatial and temporal variations of LC-GHGEs from coal railway transport were characterized quantitatively. The results show that: (1) With the rising proportion of electric locomotive in the northwest and the development of hydropower in the southwest, their LC-GHGEs factors had a significant downward trend in Northwest and Southwest China, while increased slightly in North China and East China. Therefore, the inter-provincial differences have gradually narrowed. (2) From 1999 to 2017, the LC-GHGEs of coal railway transportation showed a relatively stable growth trend first. After the coal “golden decade” from 2002 to 2011, it began to decline rapidly, and rose again in 2017. Electric locomotive has become the largest contributor to greenhouse gas emissions by replacing diesel locomotive since 2002. (3) Inner Mongolia has replaced Shanxi as the coal exporting province with the highest contribution to LC-GHGEs. And the westward shift of key coal exporting provinces intensified the greenhouse gas emissions. Compared with export, the contribution of importing provinces was more scattered, and the main contributors are large iron and steel production province. Finally, suggestions are proposed from locomotive technology upgrade, energy structure adjustment, and transportation system optimization to effectively reduce greenhouse gas emissions from coal railway transport.

Key words: coal flow, carbon emissions, life cycle, climate change, coal trade

张优, 程明今, 刘雪薇. 中国煤炭铁路运输生命周期温室气体排放研究[J]. 资源科学, 2021, 43(3): 601-611.

ZHANG You, CHENG Mingjin, LIU Xuewei. Life cycle greenhouse gas emissions from China’s coal railway transport[J]. Resources Science, 2021, 43(3): 601-611.

图/表 7

图1

图2

表1

LC-GHGEs核算模型重要参数"

参数缩写	参数含义
$GH G y - sum$	$y$ 年份煤炭铁路运输LC-GHGEs总量/t
$CT G yi - x$	$y$ 年 $i$ 省第 $x$ 类机车的LC-GHGEs因子/(t/t·km)
$CT G yi - ele$	$y$ 年 $i$ 省电力机车的LC-GHGEs因子/(t/t·km)
$CT G yi - k$	$y$ 年 $i$ 省内燃或蒸汽机车的LC-GHGEs因子/(t/t·km)
$CT G k$	柴油或煤炭上游生产的LC-GHGEs因子/(t/t)
$A yi - ele$	$y$ 年 $i$ 省电力机车的电力消耗强度/(kW·h/t·km)
$A yi - d / c$	$y$ 年 $i$ 省内燃或蒸汽机车的柴油消耗强度/(t/t·km)
$M yij$	$y$ 年从 $i$ 省通过铁路运输至 $j$ 省的煤炭量/t
$q yi - x$	$y$ 年 $i$ 省第 $x$ 类机车的工作量占比
$c yi$	$y$ 年 $i$ 省燃煤发电过程的煤炭消耗强度/(t/kW·h)
$E yi - ele$	$y$ 年 $i$ 省电力生产过程的GHG排放因子/(t/kW·h)
$Q yi - m$	$y$ 年 $i$ 省第 $m$ 种火力发电能源的消耗量/t
$T yi - n$	$y$ 年 $i$ 省第 $n$ 种发电方式的发电总量/(kW·h)
$r yi - n$	$y$ 年 $i$ 省第 $n$ 种发电方式的自用电率
$LH V m$	第 $m$ 种能源的平均低位发热量/(MJ/t)
$C m$	第 $m$ 种能源的单位低位发热量的含碳量/(t/MJ)
$O R m$	第 $m$ 种能源的碳氧化率
$F m - C H 4$	第 $m$ 种能源的单位低位发热量所对应的CH₄的排放因子/(t/MJ)
$F m - N 2 O$	第 $m$ 种能源的单位低位发热量所对应的N₂O的排放因子/(t/MJ)
$E k$	柴油或煤炭燃烧过程的GHG排放因子/(t/t)
$D ij$	$i$ 省的近似半径/km

表1

表2

1999—2017年铁路运输LC-GHGEs因子的时空分异"

地区	1999年	地区	2005年	地区	2010年	地区	2017年	地区	变化量
西藏	251.5	陕西	162.8	陕西	150.0	陕西	149.4	青海	-116.2
青海	251.5	西藏	158.1	西藏	128.7	北京	138.1	西藏	-116.2
重庆	153.3	青海	158.1	青海	128.7	天津	138.1	重庆	-72.8
四川	153.3	云南	153.6	云南	127.7	河北	138.1	四川	-72.8
贵州	143.1	山西	148.0	北京	119.3	西藏	135.4	贵州	-64.6
云南	137.5	甘肃	142.0	天津	119.3	青海	135.4	云南	-43.5
甘肃	129.5	宁夏	142.0	河北	119.3	山东	134.5	甘肃	-41.5
宁夏	129.5	重庆	133.5	甘肃	118.8	辽宁	127.6	宁夏	-41.5
黑龙江	118.4	四川	133.5	宁夏	118.8	吉林	127.6	湖北	-40.5
北京	115.4	贵州	126.1	山西	111.8	福建	111.9	河南	-39.2
天津	115.4	北京	111.2	山东	111.6	黑龙江	103.1	湖南	-24.1
河北	115.4	天津	111.2	内蒙古	108.3	内蒙古	102.5	广西	-23.7
山西	115.4	河北	111.2	辽宁	107.8	上海	102.5	海南	-23.5
辽宁	111.7	湖南	110.2	吉林	107.8	江苏	102.5	广东	-23.0
吉林	111.7	辽宁	107.0	重庆	107.2	浙江	102.5	山西	-18.1
新疆	109.8	吉林	107.0	四川	107.2	安徽	102.5	黑龙江	-15.4
陕西	108.1	广东	104.8	新疆	105.5	新疆	100.3	江西	-11.0
河南	105.3	河南	104.6	贵州	105.0	山西	97.4	新疆	-9.5
湖北	105.3	新疆	102.5	福建	97.4	云南	93.9	内蒙古	3.9
内蒙古	98.6	海南	101.2	江西	91.4	甘肃	88.1	上海	6.1
湖南	97.9	黑龙江	96.8	黑龙江	91.4	宁夏	88.1	江苏	6.1
上海	96.4	湖北	95.7	河南	88.6	重庆	80.5	浙江	6.1
江苏	96.4	福建	93.5	湖南	88.3	四川	80.5	安徽	6.1
浙江	96.4	江西	90.9	广东	86.7	贵州	78.5	辽宁	16.0
安徽	96.4	广西	88.9	海南	86.7	江西	77.8	吉林	16.0
海南	95.7	内蒙古	81.4	湖北	82.4	湖南	73.7	北京	22.7
广东	95.0	上海	77.4	上海	80.4	海南	72.2	天津	22.7
广西	93.5	江苏	77.4	江苏	80.4	广东	72.0	河北	22.7
福建	88.8	浙江	77.4	浙江	80.4	广西	69.8	福建	23.1
江西	88.8	安徽	77.4	安徽	80.4	河南	66.0	陕西	41.3
山东	79.2	山东	74.9	广西	80.2	湖北	64.8	山东	55.3
CV/%	32.7		23.9		17.2		24.8

表2

图3

图4

图5

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