基于夜间遥感和POI的荆门市能耗空间定量化分析

doi:10.12082/dqxxkx.2021.200375

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (5): 891-902.doi: 10.12082/dqxxkx.2021.200375

• 地理空间分析综合应用 • 上一篇下一篇

基于夜间遥感和POI的荆门市能耗空间定量化分析

高楠楠¹^,²(), 曾辉¹, 李芬²^,³^,^*()

1.北京大学深圳研究生院,深圳 518055
2.深圳市建筑科学研究院股份有限公司,深圳 518031
3.中国城市科学研究会,北京 100835

收稿日期:2020-07-16 修回日期:2020-08-19 出版日期:2021-05-25 发布日期:2021-07-25
通讯作者: 李芬
作者简介:高楠楠（1985— ）,女,河南周口人,博士,助理研究员,主要从事可持续发展城市、低碳管理政策研究。E-mail:gaonannan@pku.edu.cn
基金资助:
深圳协同创新科技计划-国际科技合作项目(GJHZ20190822173805220)

Spatial Quantitative Analysis of Urban Energy Consumption based on Night-Time Remote Sensing Data and POI

GAO Nannan¹^,²(), ZENG Hui¹, LI Fen²^,³^,^*()

1. Shenzhen Graduate School, Peking University, Shenzhen 518055, China
2. Shenzhen Institute of Building Research Company Limited, Shenzhen 518031, China
3. Chinese Society for Urban Studies, Beijing 100835, China

Received:2020-07-16 Revised:2020-08-19 Online:2021-05-25 Published:2021-07-25
Contact: LI Fen
Supported by:
The 2020 International Cooperation Projects of Shenzhen Science and Technology Innovation Committee, China(GJHZ20190822173805220)

摘要/Abstract

摘要：

以全球变暖和极端气候为主要特征的气候变化已成为世界各国普遍关注的重大环境问题,全球性的碳排放问题亟待解决已是非常明确的科学共识。然而城市能源消耗尤其是在街道街区尺度能源消耗空间定量化研究目前较少,不利于城市采取精准控制、优化能源结构和减少碳排放措施。本文以资源型城市荆门作为案例城市,以夜间遥感数据、POI等空间数据为基础,定量化分析影响交通、产业和建筑部门碳排放的关键因素车流量、建筑面积和主要用能企业的空间分布数据,实现城市能源消费街道尺度空间可视化,并探讨城镇化和工业化对街道尺度城市能源消费的影响。结果发现工业部门能源消费的持续增长是该市能源消费总量增长的主要驱动因子,72个乡镇（街道）中,以产业能耗为主的10个乡镇（街道）占荆门市能源消费总量达68%。荆门市总用能量在2005—2015年增长82.82万 tce,然而同时用能量高于10 000 tce的乡镇减少了4个,说明荆门市能源消耗提高并呈现集中化趋势。研究结论能够填补以城市或城区为最小单元统计城市能源消费情况所不能发现问题,提出了更加精准的降低荆门市能耗的途径,以期为同类中小资源型城市转型实现绿色发展提供借鉴。

关键词: 气候变化, 能源消费, 夜间遥感, 城镇化, 空间数据, POI, 街道尺度, 能源消费部门

Abstract:

Climate change has become a major global environmental issue that is widely concerned by countries around the world. It has been a very clear scientific consensus that the global carbon emission has to be cut urgently under the context of the global warming and extreme climate. Currently, few studies on the urban energy consumption have been performed, especially the quantitative research on the scale of urban blocks, which is actually required by cities in order to adopt precise control, optimize energy structure, and reduce carbon emissions. This paper took Jingmen, a resource-based city, as a case city, and applied night-time remote sensing data, POI, and other big data. Quantitative analysis of the spatial data on key factors affecting carbon emissions in transportation, industry, and construction sectors, respectively, was applied to realize block-scale spatial visualization of urban energy consumption, and furthermore, to discuss the impact of urbanization and industrialization on urban energy consumption. It is found that the continuous growth of energy consumption in the industrial sector was the main driving factor of the city's total energy consumption growth. Among the 72 towns (blocks), 10 towns (blocks) were dominated by industrial energy consumption which accounted for up to 68% the energy consumption of Jingmen. From 2005 to 2015, the total energy consumption of Jingmen City increased by 828,200 tons of standard coal equivalent(tce), while the number of towns (blocks) with more than 10,000 tons of standard coal equivalent(tce) decreased by 4. Therefore, the energy consumption of Jingmen City showed a trend of increase and concentration. The conclusions of this study can fill up the problems that cannot be found in the energy consumption statistics of cities, and propose a more accurate way to reduce energy consumption in Jingmen City, which provide a reference for the green transformation of similar small and medium-sized resource-based cities.

Key words: climate change, energy consumption, night-time remote sensing, urbanization, spatial data, POI, block scale, energy consumption sector

高楠楠, 曾辉, 李芬. 基于夜间遥感和POI的荆门市能耗空间定量化分析[J]. 地球信息科学学报, 2021, 23(5): 891-902.DOI:10.12082/dqxxkx.2021.200375

GAO Nannan, ZENG Hui, LI Fen. Spatial Quantitative Analysis of Urban Energy Consumption based on Night-Time Remote Sensing Data and POI[J]. Journal of Geo-information Science, 2021, 23(5): 891-902.DOI:10.12082/dqxxkx.2021.200375

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参考文献 37

[1]	本刊编辑部. 联合国气候变化大会通过《巴黎协议》[J]. 中国能源, 2015,37(12):1.
	[ The Editorial Department of this Journal. The United Nations Climate Change Conference adopted the "Paris Agreement"[J]. Energy of China, 2015,37(12):1. ]
[2]	United Nations. Sustainable development Goal 11-Sustainable cities: Why they matter[R]. 2015. https://www.un.org/sustainabledevelopment.
[3]	B P. Statistical Review of World Energy 2019: An unsustainable road to development[R]. www.bp.com/statisticalreview, 2019.
[4]	Dhakal S. Urban energy use and carbon emissions from cities in China and policy implications[J]. Energy Policy, 2009,37:4208-4219. doi: 10.1016/j.enpol.2009.05.020
[5]	郎一环, 李红强. 构建城市低碳能源体系的国际经验与中国行动[J]. 中国能源, 2010,32(7):11-16.
	[ Lang Y H, Li H Q. International experiences for constructing city's low carbon energy system and China's action[J]. Energy of China, 2010,32(7):11-16. ]
[6]	王蕾, 魏后凯. 中国城镇化对能源消费影响的实证研究[J]. 资源科学, 2014,36(6):1235-1243.
	[ Wang L, Wei H K. The impacts of Chinese urbanization on energy consumption[J]. Resources Science, 2014,36(6):1235-1243. ]
[7]	Oda T, Maksyutov S. A very high-resolution (1 km×1 km) global fossil fuel CO₂ emission inventory derived using a point source database and satellite observations of nighttime lights[J]. Atmospheric Chemistry and Physics, 2011,11(2):543-556. doi: 10.5194/acp-11-543-2011
[8]	Meng L, Graus W, Worrell E, Huang B. Estimating CO₂ (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a case study for China[J]. Energy, 2014,71:468-478. doi: 10.1016/j.energy.2014.04.103
[9]	Ghosh T, Elvidge C D, Sutton P C, et al. Creating a global grid of distributed fossil fuel CO₂ emissions from nighttime satellite imagery[J]. Energies, 2010,3:1895-1913. doi: 10.3390/en3121895
[10]	苏泳娴. 基于DMSP/OLS夜间灯光数据的中国能源消费碳排放研究[D]. 广州:中国科学院研究生院, 2015.
	[ Su Y X. Study on the carbon emissions from energy consumption in China using DMSP/OLS night light imageries[D]. Guangzhou: Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, 2015. ]
[11]	梁竞, 张力小. 中国省会城市能源消费的空间分布特征分析[J]. 资源科学, 2009,31(12):2086-2092.
	[ Liang J, Zhang L X. Analysis on spatial distribution characteristics of urban energy consumption among capital cities in China[J]. Resources Science, 2009,31(12):2086-2092. ]
[12]	刘竹, 耿涌, 薛冰, 等. 城市能源消费碳排放核算方法[J]. 资源科学, 2011,33(7):1325-1330.
	[ Liu Z, Geng Y, Xue B, et al. A Calculation method of CO₂ emission from urban energy consumption[J]. Resources Science, 2011,33(7):1325-1330. ]
[13]	吴笛, 毛建素. 中国重点城市产业与能源消费基本特征研究[J]. 环境科学与技术, 2010,33(2):184-191.
	[ Wu D, Mao J S. Comparative study of energy consumption and industrial structure in China's five major cities[J]. Environmental Science and Technology, 2010,33(2):184-191. ]
[14]	石玉淳. 基于LEAP模型的大连市工业能源消费分析研究[D]. 大连:大连海事大学, 2014.
	[ Shi Y C. Analysis of industrial energy consumption in Dalian City based on LEAP model[D]. Dalian: Dalian Maritime University, 2014. ]
[15]	Li G, Kou C, Wang H. Estimating city-level energy consumption of residential buildings: A life-cycle dynamic simulation model[J]. Journal of Environmental Management. 2019,240:451-462. doi: 10.1016/j.jenvman.2019.03.130
[16]	黄莹, 郭洪旭, 廖翠萍, 等. 基于LEAP模型的城市交通低碳发展路径研究——以广州市为例[J]. 气候变化研究进展, 2019,15(6):670-683.
	[ Huang Y, Guo H X, Liao C P, et al. Study on low-carbon development path of urban transportationsector based on LEAP model: Take Guangzhou as an example[J]. Climate Change Research, 2019,15(6):670-683. ]
[17]	贾涛, 杨仕浩, 李欣, 等. 武汉居民建筑物碳排放反演计算和时空分析[J]. 地球信息科学学报, 2020,22(5):1063-1072. doi: 10.12082/dqxxkx.2020.190727
	[ Jia T, Yang S H, Li X, et al. Computation of carbon emissions of residential buildings in Wuhan and its spatiotemporal analysis[J]. Journal of Geo-information Science, 2020,22(5):1063-1072. ]
[18]	荆门市政府办公室. “十三五”能源发展规划[R]. 湖北省荆门市, 2016.
	[ Municipal Government Office of Jingmen. The 13^th Five-Year Plan for Energy development[R]. Hubei, Jingmen, 2016. ]
[19]	荆门市政府办公室. 荆门市新型城镇化规划(2016—2020年)[R]. 湖北省荆门市, 2016.
	[ Municipal Government Office of Jingmen. Jingmen new-type urbanization plan (2016-2020)[R]. Hubei Jingmen, 2016. ]
[20]	江亿. 我国建筑耗能状况及有效的节能途径[J]. 暖通空调, 2005,35(5):30-40.
	[ Jiang Y. Current building energy consumption in China and effective energy efficiency measures[J]. Journal Heating Ventilating and Airconditioning, 2005,35(5):30-40. ]
[21]	严晗, 吴烨, 张少君, 等. 北京典型道路交通环境机动车黑碳排放与浓度特征研究[J]. 环境科学学报, 2014,34(8):1891-1899.
	[ Yan H, Wu Y, Zhang S J, et al. Emission characteristics and concentrations of vehicular black carbon in a typical freeway traffic environment of Beijing[J]. Acta Scientiae Circumstantiae, 2014,34(8):1891-1899. ]
[22]	许盛. 南京市温室气体排放清单及其空间分布研究[D]. 南京:南京大学, 2011.
	[ Xu S. Research on greenhouse gas emission inventory and its spatial distribution in Nanjing[D]. Nanjing: Nanjing University, 2011. ]
[23]	Environmental Protection . Development of methodology for estimating VMT weighting by facility type[R]. 1999, Report EPA420-R-01-009, M6. SPD.003
[24]	张玉军. 贵阳市道路网交叉口流量反推研究[D]. 大连:大连海事大学, 2010.
	[ Zhang Y J. Research on reverse deduction of traffic flow at road network intersections in Guiyang city[D]. Dalian: Dalian Maritime University, 2010. ]
[25]	李立源, 曹大铸. 道路交通流量优预测与交叉口量优控制[J]. 控制理论与应用, 1993,10(1):67-72.
	[ Li L Y, Cao D Z. The optimal prediction of road traffic flow and optimal control of intersection signal[J]. Control Theory & Applications, 1993,10(1):67-72. ]
[26]	Raupach M, Rayner P, Paget M. Regional variations in spatial structure of night lights, population density and fossil fuel CO₂ emissions[J]. Energy Policy, 2009,2:61-65.
[27]	苏泳娴, 陈修治, 叶玉瑶, 等. 基于夜间灯光数据的中国能源消费碳排放特征及机理[J]. 地理学报, 2013,68(11):1513-1526.
	[ Su Y X, Chen X Z, Ye Y Y, et al. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries[J]. Acta Geographica Sinica, 2013,68(11):1513-1526. ]
[28]	荆门市住建局. 荆门市中心城区民用集中供热管理办法(草案)[R]. 湖北省荆门市, 2020.
	Housing and Urban-rural Construction Bureau of Jingmen. Administrative Measures for Civil Central Heating in Jingmen City Center (Draft)[R]. Hubei, Jingmen, 2021.
[29]	赵彦婷. 基于多源数据的城市路网交通能耗和排放模型与算法[D]. 北京:北京交通大学, 2012.
	[ Zhao Y T. Model and algorithms for estimating fuel consumption and emissions on urban road network based on multi-source data[D]. Beijing: Beijing Jiaotong University, 2012. ]
[30]	Gao N N, Li F, Zeng H, et al. Can more accurate night-time remote sensing data simulate a more detailed population distribution?[J]. Sustainability, 2019,11:4488. doi: 10.3390/su11164488
[31]	舒松, 余柏蒗, 吴健平, 等. 基于夜间灯光数据的城市建成区提取方法评价与应用[J]. 遥感技术与应用, 2011,26(2):169-176.
	[ Shu S, Yu B L, Wu J P, et al. Methods for deriving urban built-up area using night-light data: Assessment and application[J]. Remote Sensing Technology and Application, 2011,26(2):169-176. ]
[32]	荆州市统计局. 荆门市2015年国民经济和社会发展统计公报[R]. 湖北荆门, 2015
	[ Jingmen Bureau of Statistics. 2015 Jingzhou National Economic and Social Development Statistical Bulletin[R]. Hubei, Jingmen, 2015. ]
[33]	谢菲菲. 城市交通碳排放量影响因素与低碳交通发展研究[D]. 北京:北京交通大学, 2013.
	[ Xie F F. Research on the Influencing factors of carbon emissions from urban transport and the development of low carbon transport[D]. Beijing: Beijing Jiaotong University, 2013. ]
[34]	李新佳. 欧洲智能交通建设情况及启发[J]. 城市交通, 2004,2(2):58-62.
	[ Li X J. The development of ITS in Europe and its suggestions for China[J]. Urban Transport of China, 2004,2(2):58-62. ]
[35]	宋慧峰. 产业空间分布对碳排放影响的实证研究[D]. 广州:暨南大学, 2017.
	[ Song H F. Empirical research of the effects between industry spatial distribution and CO₂[D]. Guangzhou: Jinan University, 2017. ]
[36]	马海良, 王若梅, 丁元卿, 等. 城镇化对工业能源消费的门槛效应研究——以长江经济带省份为例[J]. 中国人口·资源与环境, 2017,27(3):56-62.
	[ Ma H L, Wang R M, Ding Y Q, et al. Threshold effect between the urbanization and industrial energy consumption of provinces in the Yangtze River Economic Belt[J]. China Population Resources and Environment, 2017,27(3):56-62. ]
[37]	傅立新, 郝吉明, 何东全, 等. 城市街区空气污染扩散模拟研究[C]// 全国大气环境学术会议, 1998.
	[ Fu L X, Hao J M, He D Q, et al. Modeling Air Pollution from traffic in street canyon[C]// the National Conference on Atmospheric Environment of China, 1998. ]

能量来源	煤			焦炭	其他煤制品	天然气	液化天然气	原油	石油制品	电力	热力	其他能源	总和
能量来源		原煤	精洗煤	焦炭	其他煤制品	天然气	液化天然气	原油	石油制品	电力	热力	其他能源	总和
Ⅰ供应	480.89	478.81	2.08	1.51	0.69	8.25		685.36	-444.38	17.97	-3.47	26.03	722.85
1.年初库存（+）	53.59	53.59						4.16	1.17				58.95
2.年末库存（–）	-39.28	-39.28							-1.32				-40.60
3.一次能源生产	98.14	98.14								6.34			104.48
-水能										0.20			0.20
-风能										0.97			0.97
-太阳能										0.01			0.01
-生物质										1.89			1.89
-余热										3.27			3.27
4.调入（+）/调出（-）	368.44	366.36	2.08	1.51	0.66	8.25		681.20	-444.23	11.63	-3.47	26.03	650.02
Ⅱ转换投入（-）产出（+）	-216.80	-216.80	0	0	0	0	0	-685.36	689.72	90.05	5.98	-19.75	-136.16
1.发电	-209.33	-209.33							-1.29	90.05		-12.88	-133.45
-生物质												-4.70
-余热												-8.18
2.供热	-7.47	-7.47							-1.22		5.98		-2.71
3.炼油及煤制品								-685.36				-6.87	-692.23
4.加工转换产出（+）									692.23				692.23
5.回收利用（+）													0
Ⅲ终端消费量	252.66	250.58	2.08	1.51	0.69	8.25	0	0	245.35	103.32	2.46	6.28	620.52
1.农林牧渔	3.51	3.51							22.44	2.13		0.20	28.28
2.工业	221.03	218.95	2.08	1.51	0.69	3.19			73.55	78.84	2.41	5.91	387.13
3.建筑业
4.交通运输						1.20			72.13	1.56			74.89
5.商业和公共	20.50	20.50				0.80			38.38	6.80			66.48
6.居民生活	7.62	7.62				3.06			38.85	13.99	0.05	0.17	63.74
Ⅳ损失	11.43	11.43								4.71	0.04		16.18
Ⅴ统计平衡差额	0	0	0	0	0	0	0	0	-0.01	-0.01	0.01	0	-0.01
Ⅵ可用最终消费量合计	252.66	250.58	2.08	1.51	0.69	8.25			245.34	103.31	2.47	6.28	620.51

化工企业	概况	详细信息
污染化工企业	排放废水企业6家、排放废气11家、污水处理厂5家、排放重金属企业15家、排放危险废物企业7家	根据2015年第二季度空气环境报告
排放废气企业	排放废气国控源企业上报数据8家。达标率87.5%	湖北天宇玻璃制品有限公司的二氧化硫、颗粒物、氮氧化物等项目超标
能源利用项目及企业	① 煤制氢项目	2015年4月,荆门化工循环产业园内,盈德气体集团投资建设煤制氢项目
能源利用项目及企业	② 2万吨/年废硫酸再生装置建设项目	2014年12月,荆门市渝楚化工有限公司2万吨/年废硫酸再生装置建设项目属于污染严重项目。采用高温裂解工艺生产98%的工业硫酸,回用到厂内异辛烷装置的生产中

基于夜间遥感和POI的荆门市能耗空间定量化分析

Spatial Quantitative Analysis of Urban Energy Consumption based on Night-Time Remote Sensing Data and POI

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