重庆市钢铁存量估算及驱动力分析

doi:10.18402/resci.2018.12.02

资源科学 ›› 2018, Vol. 40 ›› Issue (12): 2341-2350.doi: 10.18402/resci.2018.12.02

• 专栏:中国钢铁物质流研究 • 上一篇下一篇

重庆市钢铁存量估算及驱动力分析

刘仟策^1,^2,^3,⁴(), 刘立涛³, 刘剑⁵, 李胜功^3,⁶(), 白晧⁷, 刘刚²

1. 中国科学院大学中丹学院,北京 100049
2. 南丹麦大学生命周期工程研究中心,丹麦欧登塞 5230
3. 中国科学院地理科学与资源研究所,北京 100101
4. 中国-丹麦科研教育中心,北京 100049
5. 中国科学院办公厅,北京 100864
6. 中国科学院大学资源与环境学院,北京 100049
7. 北京科技大学冶金与生态工程学院,北京 100083

收稿日期:2018-09-21 修回日期:2018-10-30 出版日期:2018-12-20 发布日期:2018-12-10
作者简介:
作者简介：刘仟策,男,吉林长春人,硕士生,研究方向为物质流分析。E-mail: lqc_ustbeco@163.com
基金资助:
国家自然科学基金项目（41728002）;中国地质调查局项目（121201103000150015）

In-use iron and steel stock estimation and driving force analysis in Chongqing

Qiance LIU^1,^2,^3,⁴(), Litao LIU³, Jian LIU⁵, Shenggong LI^3,⁶(), Hao BAI⁷, Gang LIU²

1. Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
2. SDU Life Cycle Engineering, University of Southern Denmark, Odense 5230, Denmark
3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4. Sino-Danish Centre for Education and Research, Beijing 100049, China
5. Department of General Administration of Chinese Academy of Sciences, Beijing 100864, China
6. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
7. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China

Received:2018-09-21 Revised:2018-10-30 Online:2018-12-20 Published:2018-12-10

摘要/Abstract

摘要：

钢铁存量的准确估算可为预测钢铁需求量、理论报废量、制定资源环境管理政策提供科学依据。本文以重庆市全行政区面积为研究边界,采用自下而上的方法估算了1985—2014年重庆市钢铁存量及其行业分布。研究结果表明,重庆市的钢铁总存量及人均存量在过去30年间分别增长了10倍和12倍,于2014年分别达到0.59亿t和1748kg/人;在钢铁存量的行业分布结构上,建筑钢铁存量的历年占比均达到50%以上,该结果与邯郸市、美国纽黑文市的钢铁存量行业分布模式相似。基于钢铁存量的估算结果,本文利用IPAT模型对重庆市钢铁存量的变化进行了驱动力分析,研究发现,经济发展与人口增长是重庆市钢铁存量增长的主要驱动因素,其中经济发展始终是最强劲的驱动力,而技术的负向作用表明,重庆市未来对钢铁的需求将降低。

关键词: 物质流分析, 钢铁存量, 自下而上方法, IPAT方程, 驱动力分析, 重庆市

Abstract:

A precise accounting of urban iron and steel stocks could provide scientific basis for predicting future iron (including steel) demand, forecasting scrap steel generation, and informing relevant resource and environmental management strategies and policies. Studies on in-use stocks at city level, especially in China, have far less been paid attention to by scientific community comparing to them at country level, although it is still crucial to solve urban waste management and resource recycling problem. Chongqing, one of the four municipalities in China, is a representative city in West China with the largest population between all Chinese cities. Hence, in this study, we selected Chongqing to estimate its in-use iron stock levels and its sector distribution from 1985 to 2014 by applying a statistics-based bottom-up approach within its whole administrative boundary. A huge effort was made to establish a more elaborate iron and steel containing product inventory and collect data of product amount and iron intensities of each product. Social-economic drivers of stocks growth were further analyzed by IPAT equation that could easily quantify the impact for each factor. Our main findings include: (1) Both total and per capita iron stocks have increased by more than 10 times in the last 30 years and reached 59 million tons and 1.748t/cap in 2014, respectively. (2) Chongqing shows the lowest iron and steel stock among it and other two cities reported in the literature (Handan, China and New Haven, USA). However, it has similar distribution structure among different sectors (e.g., with the largest share in the building sector). (3) Economic growth and population positively contribute to stock growth. Alternatively, the technology shows a negative effect, leading to a decreasing trend for future iron demand in Chongqing.

Key words: material flow analysis, iron and steel stocks, bottom-up method, IPAT equation, driving force analysis, Chongqing

刘仟策, 刘立涛, 刘剑, 李胜功, 白晧, 刘刚. 重庆市钢铁存量估算及驱动力分析[J]. 资源科学, 2018, 40(12): 2341-2350.

Qiance LIU, Litao LIU, Jian LIU, Shenggong LI, Hao BAI, Gang LIU. In-use iron and steel stock estimation and driving force analysis in Chongqing[J]. Resources Science, 2018, 40(12): 2341-2350.

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类别	子类	单位含铁量	参考文献序号	类别	子类	单位含铁量	参考文献序号
建筑	城镇住宅	40 kg/m²	[29]	家用电器	洗衣机	18 kg/台	[31,34]
	农村住宅	6 kg/m²	[29]		电冰箱	31 kg/台	[31,34]
	非住宅	31 kg/m²	[4]		微波炉	8 kg/台	[31,34]
基础设施	普通公路	1.5 t/km	[30]		空调	26 kg/台	[31,34]
	高速公路	190 t/km	[30]		淋浴热水器		12 kg/台	[31,34]
	城市道路	1.5 t/km	[30]	移动电话		0.05 kg/部	[31,34]
	桥梁	3.6 t/km	[4]	黑白电视机		0.8 kg/台	[31,34]
	铁路	68 t/km	[31]	彩色电视机		0.8 kg/台	[31,34]
	供水管道	90 t/km	[32]	计算机		1.5 kg/台	[31,34]
	排水管道	8 t/km	[32]	摄像机		0.1 kg/部	[31,34]
	火电	18 t/MW	[4]	照相机		0.1 kg/部	[31,34]
	水电	35 t/MW	[4]	健身器材		10 kg/套	[31,34]
	电力输送	25 t/km	[4]	自行车		8 kg/辆	[31,34]
	路灯	0.2 kg/盏	[4]	抽油烟机		20 kg/台	[31,34]
	码头岸线	0.03 t/m	[4]	钢琴		40 kg/架	[31,34]
	泊位数	240 t/个	[4]	中高档乐器		4 kg/件	[31,34]
交通工具	载货汽车	3.7 t/辆	[33]	普通电话	0.08 kg/部	[31,34]
	载客汽车	0.94 t/辆	[33]	影碟机		3 kg/台	[31,34]
	其他汽车	2.5 t/辆	[33]	电风扇		0.5 kg/台	[31,34]
	摩托车	0.085 t/辆	[33]	缝纫机		10 kg/台	[31,34]
	其他机动车	2.5 t/辆	[33]	农业机械	大中型拖拉机	3.5 t/台	[4]
	机动船	157 t/艘	[4]		小型拖拉机	1.2 t/台	[4]
	驳船	62 t/艘	[4]		机动脱粒机	1.2 t/台	[4]
	铁路机车	19 t/艘	[4]		载重汽车	2 t/台	[4]
	铁路客车	26 t/艘	[4]		农用排灌动力机械	0.4 t/台	[4]
					农用水泵	0.05 t/台	[4]

		人均存量 /kg	建筑 /%	基础设施 /%	机械 /%	家用电器 /%	交通工具 /%	人均GDP /USD	人口 /万	城市化率 /%
邯郸	2005年	1 333	66.5	19.7	3.5	2.3	7.9	1 656^[37]	863.0^[37]	21^[37]
纽黑文	2004年	8 750	42.5	14.4	17.3*		25.8	49 682^[35]	12.4^[35]	85^[36]
重庆	2005年	761	67.5	13.0	10.6	1.7	7.2	1 350^[28]	3 169.0^[28]	56^[28]

重庆市钢铁存量估算及驱动力分析

In-use iron and steel stock estimation and driving force analysis in Chongqing

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	1985—1995年	1995—2005年	2005—2014年
∆I	3	16	35
∆P	1	1	2
∆A	11	16	53
∆T	-8	0	-20