需求侧视角下中国隐含能源消费量及强度的影响因素

doi:10.18402/resci.2021.09.02

摘要/Abstract

摘要：

当前中国对能耗总量和强度实行了“双控”,在需求侧改革背景下,分析中国能源消费总量与能源强度变动的影响因素是制定有效能源政策的基础。基于最终需求视角测算了2012、2015和2017年中国5类最终需求的隐含能源消费及其强度,使用结构分解分析（SDA）和双层归因分析探究影响中国能源消费总量和能源强度变动的因素,并针对求解过程中可能存在的零值与负值问题给出了两种处理方法。结果表明：①中国近一半的能源消耗是由于满足资本形成需求而产生的,资本形成和出口的隐含能源强度是全国能源强度的1.2倍以上,而消费的隐含能源强度低于全国能源强度;②能源效率效应和生产结构效应分别是导致2012—2015年和2015—2017年全国能源消费总量及强度下降的主要因素;③第一层归因分析发现各影响因素主要通过影响资本形成需求进而影响全国能源消费总量及强度;④第二层归因分析发现重制造业和建筑业是影响资本形成需求变动的主要行业。因此未来从需求侧控制能源消耗具有更大潜力,经济增长模式需要进一步从投资和出口驱动向消费驱动转变,加快促进国内大循环。同时要注重优化居民消费结构,推动其向绿色化、低碳化和节能化转变。

关键词: 隐含能源消费, 隐含能源强度, 结构分解分析, 双层归因分析, 需求侧改革

Abstract:

At present, China has implemented the “double control” on total energy consumption and energy intensity. Under the background of demand-side reform, analyzing the influencing factors of total energy consumption and energy intensity is the basis for formulating effective energy policy in China. Based on the perspective of final demand, this study measured the embodied energy consumption and embodied energy intensity of five types of final demand in China in 2012, 2015, and 2017. Structural decomposition analysis (SDA) and two-level attribution analysis were used to explore the factors that affect China’s total energy consumption and energy intensity. Two methods were proposed to deal with the problem of zero value and negative value. The results show that: (1) Nearly half of China’s energy consumption is due to meeting the demand for capital formation. The embodied energy intensity of capital formation and export is more than 1.2 times that of the country average, while the embodied energy intensity of consumption is lower than that of the average. (2) The energy efficiency effect and the production structure effect are the main factors leading to the decline of the total energy consumption and its intensity in 2012-2015 and 2015-2017 respectively. (3) The first level of attribution analysis found that the influencing factors mainly affect the capital formation demand, and then affect the total energy consumption and its intensity. (4) The second level of attribution analysis found that heavy manufacturing and construction industries are the main industries that affect the change of capital formation demand. Therefore, it has greater potential to control energy consumption from the demand side in the future, the economic growth model needs to be further changed from investment and export-driven to consumption-driven, and to speed up the promotion of domestic circulation. At the same time, we should pay attention to optimizing the consumption structure of residents, and promote its transformation to green, low-carbon, and energy-saving.

Key words: embodied energy consumption, embodied energy intensity, structural decomposition analysis, two-level attribution analysis, demand-side reform

李虹, 王帅. 需求侧视角下中国隐含能源消费量及强度的影响因素[J]. 资源科学, 2021, 43(9): 1728-1742.

LI Hong, WANG Shuai. Research on influencing factors of China’s energy consumption and intensity:Based on the demand-side perspective[J]. Resources Science, 2021, 43(9): 1728-1742.

图/表 9

表1

所有可能的 μ fi变动情形及其处理方法

情形	处理方法
1. $μ fi$ 变动为 $+ ↔ +$ 或 $- ↔ -$	无需进行额外处理,直接使用两级分解法求解
2. $μ fi$ 变动为 $0 ↔ 0$	$μ fi$ 和 $AEE I fi$ 的变动均不影响全国能源强度的变动。在实际操作中,可根据解析极限策略直接得到最终结果,也可以使用小值策略得到十分接近的结果
3. $μ fi$ 变动为 $0 ↔ +$ 、 $0 ↔ -$ 或 $- ↔ +$	方法1： $μ fi$ 的变动贡献了 $μ fi 1 AEE I fi 1 - μ fi 0 AEE I fi 0$ 的全部变化,而 $AEE I fi$ 的变动对全国能源强度变动无影响。在实际操作中,可根据解析极限策略直接得到最终结果,也可以使用小值策略得到十分接近的结果方法2： $μ fi$ 的变动贡献了 $Δ μ fi AEE I fi 1 + AEE I fi 0 2$ , $AEE I fi$ 的变动贡献了 $Δ AEE I fi μ fi 1 + μ fi 0 2$ 。在实际操作中,可据此得到最终结果

表1

表2

表3

2017年中国各类最终需求各行业的隐含能源消费、增加值与强度及其比例

行业	隐含能源消费/万t标准煤					隐含增加值/亿元					隐含能源强度/（t标准煤/万元)
行业	农村居民消费	城市居民消费	政府消费	资本形成	出口	农村居民消费	城市居民消费	政府消费	资本形成	出口	隐含能源强度/（t标准煤/万元)
农业	1895.57	3833.26	270.26	524.14	274.97	7315.38	14793.27	1042.97	2022.77	1061.16	0.26
采掘业	23.04	15.43	0.00	98.07	250.97	59.13	39.60	0.00	253.29	550.40	0.43
轻制造业	5609.78	18831.25	0.00	1554.50	15430.01	16366.45	51262.24	0.00	3665.54	27649.99	0.42
能源工业	608.09	4961.76	0.00	326.60	1410.68	868.53	6548.23	0.00	376.62	1713.05	0.77
重制造业	2772.36	13902.22	0.00	37412.92	56685.57	3574.04	19032.29	0.00	57983.33	74083.31	0.72
建筑业	0.00	0.00	0.00	122924.86	466.43	0.00	0.00	0.00	174280.20	661.30	0.71
服务业	5125.66	25089.76	23240.51	9849.63	4407.68	19710.97	96573.58	85571.18	37423.44	18336.60	0.26
交通运输业	1842.94	7126.46	1797.59	2279.21	5060.96	1650.92	6383.93	1610.29	2041.73	4533.64	1.12
总计	17877.45	73760.14	25308.36	174969.94	83987.26	49545.42	194633.14	88224.45	278046.92	128589.44	0.51
	隐含能源消费比例/%					隐含增加值比例/%					隐含能源强度比值
行业	农村居民消费	城市居民消费	政府消费	资本形成	出口	农村居民消费	城市居民消费	政府消费	资本形成	出口	隐含能源强度比值
农业	10.60	5.20	1.07	0.30	0.33	14.77	7.60	1.18	0.73	0.83	0.51
采掘业	0.13	0.02	0.00	0.06	0.30	0.12	0.02	0.00	0.09	0.43	0.84
轻制造业	31.38	25.53	0.00	0.89	18.37	33.03	26.34	0.00	1.32	21.50	0.82
能源工业	3.40	6.73	0.00	0.19	1.68	1.75	3.36	0.00	0.14	1.33	1.51
重制造业	15.51	18.85	0.00	21.38	67.49	7.21	9.78	0.00	20.85	57.61	1.41
建筑业	0.00	0.00	0.00	70.25	0.56	0.00	0.00	0.00	62.68	0.51	1.39
服务业	28.67	34.02	91.83	5.63	5.25	39.78	49.62	96.99	13.46	14.26	0.52
交通运输业	10.31	9.66	7.10	1.30	6.03	3.33	3.28	1.83	0.73	3.53	2.19
总计	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	1.00

表3

图1

图2

图3

表4

2012—2015年全国能源消费总量的加法SDA双层归因分析结果（单位：万t标准煤）

行业	农村居民消费	城市居民消费	政府消费	资本形成	出口	总计
能源效率效应
农业	-12.06	-23.76	-2.50	-5.94	-2.05	-46.32
采掘业	-21.94	-12.68	0.00	-55.51	-49.14	-139.27
轻制造业	-184.16	-634.82	0.00	-129.67	-1104.27	-2052.92
能源工业	152.92	1142.70	0.00	32.93	-100.05	1228.50
重制造业	-233.93	-973.12	0.00	-2503.76	-3828.05	-7538.85
建筑业	0.00	0.00	0.00	-3160.59	-18.48	-3179.07
服务业	64.06	272.56	206.29	92.01	104.39	739.30
交通运输业	160.54	639.51	219.32	232.92	682.23	1934.52
总计	-74.57	410.38	423.11	-5497.60	-4315.43	-9054.10
增加值率效应
农业	-85.72	-142.59	-9.30	-64.10	-10.26	-311.96
采掘业	-1.00	-0.61	0.00	3.60	-23.25	-21.25
轻制造业	-227.78	-683.30	0.00	-137.12	-579.21	-1627.41
能源工业	-365.20	-2089.12	0.00	-46.85	-40.46	-2541.63
重制造业	-144.08	-535.99	0.00	-1920.87	-2182.45	-4783.39
建筑业	0.00	0.00	0.00	-10284.85	-60.67	-10345.52
服务业	-394.86	-1646.90	-1619.70	-534.77	-395.97	-4592.20
交通运输业	-198.56	-787.99	-268.03	-283.18	-835.06	-2372.82
总计	-1417.20	-5886.50	-1897.02	-13268.13	-4127.33	-26596.18
生产结构效应
农业	165.13	280.10	19.66	118.95	20.65	604.49
采掘业	0.53	0.30	0.00	-5.29	16.49	12.03
轻制造业	119.47	373.45	0.00	137.22	487.44	1117.58
能源工业	211.22	818.15	0.00	10.13	72.46	1111.95
重制造业	196.84	748.75	0.00	2973.94	3549.14	7468.68
建筑业	0.00	0.00	0.00	12071.82	71.06	12142.88
服务业	691.10	2860.06	3069.67	922.49	547.76	8091.08
交通运输业	87.25	342.35	113.51	117.94	355.38	1016.43
总计	1471.54	5423.17	3202.84	16347.19	5120.37	31565.11
最终需求效应
农业	-464.47	-293.07	104.22	-747.03	23.33	-1377.02
采掘业	63.74	32.46	0.00	-117.89	-6.67	-28.36
轻制造业	1553.89	4404.91	0.00	1050.30	396.10	7405.20
能源工业	937.69	1514.57	0.00	-509.31	-51.04	1891.91
重制造业	1998.31	6966.79	0.00	-2624.76	9280.81	15621.15
建筑业	0.00	0.00	0.00	35339.16	188.09	35527.25
服务业	1028.99	4536.44	3258.86	1205.53	910.02	10939.84
交通运输业	195.43	256.23	-303.52	-584.53	-714.56	-1150.95
总计	5313.57	17418.33	3059.56	33011.47	10026.08	68829.01

表4

表5

2015—2017年全国能源消费总量的加法SDA双层归因分析结果（万t标准煤）

行业	农村居民消费	城市居民消费	政府消费	资本形成	出口	总计
能源效率效应
农业	-42.69	-85.67	-6.20	-13.05	-6.22	-153.84
采掘业	-4.14	-2.49	0.00	-7.26	-30.00	-43.89
轻制造业	-64.21	-7.06	0.00	10.81	942.75	882.29
能源工业	-211.81	-1084.29	0.00	-29.30	45.18	-1280.21
重制造业	-93.56	-337.07	0.00	540.90	185.25	295.52
建筑业	0.00	0.00	0.00	-3460.71	-11.97	-3472.67
服务业	-24.72	-178.53	-104.09	-94.42	-45.72	-447.49
交通运输业	397.57	1508.36	410.45	447.69	1245.01	4009.07
总计	-43.55	-186.76	300.15	-2605.35	2324.28	-211.23
增加值率效应
农业	173.62	340.16	26.64	68.55	25.62	634.58
采掘业	8.28	4.88	0.00	13.32	47.95	74.44
轻制造业	487.44	1512.47	0.00	207.09	1094.37	3301.37
能源工业	352.35	1762.89	0.00	36.64	108.93	2260.81
重制造业	569.21	2446.35	0.00	5580.98	8754.76	17351.29
建筑业	0.00	0.00	0.00	19426.12	90.46	19516.58
服务业	379.29	1749.71	1715.71	646.60	309.21	4800.52
交通运输业	-198.92	-752.54	-207.02	-220.75	-634.18	-2013.41
总计	1771.28	7063.91	1535.33	25758.55	9797.11	45926.18
生产结构效应
农业	-250.91	-488.76	-38.99	-104.37	-37.13	-920.15
采掘业	-14.16	-8.25	0.00	-22.13	-63.15	-107.69
轻制造业	-543.65	-1518.80	0.00	-307.81	-956.27	-3326.53
能源工业	-301.96	-1289.93	0.00	-16.15	-218.14	-1826.18
重制造业	-757.70	-3490.12	0.00	-11926.11	-12941.12	-29115.05
建筑业	0.00	0.00	0.00	-32294.24	-155.79	-32450.03
服务业	-869.46	-3890.41	-4042.18	-1394.58	-618.29	-10814.92
交通运输业	-117.44	-446.51	-120.50	-133.68	-362.76	-1180.90
总计	-2855.28	-11132.77	-4201.67	-46199.07	-15352.66	-79741.46
最终需求效应
农业	526.12	1301.06	34.09	-299.87	66.96	1628.36
采掘业	-49.51	-24.71	0.00	52.35	71.66	49.79
轻制造业	127.01	2361.76	0.00	-1184.87	1520.92	2824.83
能源工业	-779.31	162.06	0.00	506.18	671.83	560.76
重制造业	-1189.67	-1446.48	0.00	4453.65	2351.23	4168.73
建筑业	0.00	0.00	0.00	16809.94	-167.71	16642.24
服务业	63.34	4009.87	3332.97	3255.60	-732.01	9929.76
交通运输业	520.20	2190.92	367.36	915.33	481.49	4475.31
总计	-781.82	8554.49	3734.42	24508.30	4264.38	40279.77

表5

图4

参考文献 35

[1]	王锋, 高长海. 中国产业部门隐含能源的测度、分解与跨境转移: 基于CRIO模型的研究[J]. 经济问题探索, 2020, (9):1-11.
	[ Wang F, Gao C H. Measurement, decomposition, and cross-border transfer of embodied energy ofChina’s industrial sectors: Based on the CRIO model[J]. Inquiry into Economic Issues, 2020, (9):1-11.]
[2]	韩梦瑶, 熊焦, 刘卫东. 中国跨境能源贸易及隐含能源流动对比: 以“一带一路”能源合作为例[J]. 自然资源学报, 2020, 35(11):2674-2686.
	[ Han M Y, Xiong J, Liu W D. China’s cross-border energy relations between direct trade and embodied transfers: Based on “the Belt and Road” energy cooperation[J]. Journal of Natural Resources, 2020, 35(11):2674-2686.] doi: 10.31497/zrzyxb.20201109
[3]	Chen C C, Liu G Y, Meng F X, et al. Energy consumption and carbon footprint accounting of urban and rural residents in Beijing through consumer lifestyle approach[J]. Ecological Indicators, 2019, 98:575-586. doi: 10.1016/j.ecolind.2018.11.049
[4]	Fu F, Liu H T, Polenske K R, et al. Measuring the energy consumption of China’s domestic investment from 1992 to 2007[J]. Applied Energy, 2013, 102:1267-1274. doi: 10.1016/j.apenergy.2012.06.062
[5]	Fu F, Ma L W, Li Z, et al. The implications of China’s investment-driven economy on its energy consumption and carbon emissions[J]. Energy Conversion and Management, 2014, 85:573-580. doi: 10.1016/j.enconman.2014.05.046
[6]	Wiedmann T, Lenzen M. Environmental and social footprints of international trade[J]. Nature Geoscience, 2018, 11(5):314-321. doi: 10.1038/s41561-018-0113-9
[7]	Song Z Y, Zhu Q L, Han M Y. Tele-connection of global crude oil network: Comparisons between direct trade and embodied flows[J]. Energy, 2021, DOI: 10.1016/j.energy.2020.119359. doi: 10.1016/j.energy.2020.119359
[8]	郭朝先, 胡雨朦. 全球生产分工体系下隐含能源跨境转移研究[J]. 中国人口·资源与环境, 2019, 29(12):26-35.
	[ Guo C X, Hu Y M. Research on the cross-border transfer of embodied energy under global production division system[J]. China Population, Resources and Environment, 2019, 29(12):26-35.]
[9]	汪艺晗, 杨谨, 刘其芸, 等. “一带一路”国家粮食贸易下虚拟水和隐含能源流动[J]. 资源科学, 2021, 43(5):974-986. doi: 10.18402/resci.2021.05.11
	[ Wang Y H, Yang J, Liu Q Y, et al. Virtual water and embodied energy transfer in grain trade across the countries along the “Belt and Road”[J]. Resources Science, 2021, 43(5):974-986.]
[10]	郭珊, 韩梦瑶, 杨玉浦. 中国省际隐含能源流动及能效冗余解析[J]. 资源科学, 2021, 43(4):733-744. doi: 10.18402/resci.2021.04.08
	[ Guo S, Han M Y, Yang Y P. Multiregional embodied energy flows and energy efficiency redundancy in China[J]. Resources Science, 2021, 43(4):733-744.]
[11]	Su B, Ang B W. Multiplicative structural decomposition analysis of aggregate embodied energy and emission intensities[J]. Energy Economics, 2017, 65:137-147. doi: 10.1016/j.eneco.2017.05.002
[12]	Su B, Ang B W, Li Y Z. Structural path and decomposition analysis of aggregate embodied energy and emission intensities[J]. Energy Economics, 2019, 83:345-360. doi: 10.1016/j.eneco.2019.07.020
[13]	Yan J N, Su B. What drive the changes in China’s energy consumption and intensity during 12th Five-Year Plan period?[J]. Energy Policy, 2020, DOI: 10.1016/j.enpol.2020.111383. doi: 10.1016/j.enpol.2020.111383
[14]	高鹏, 岳书敬. 中国产业部门全要素隐含能源效率的测度研究[J]. 数量经济技术经济研究, 2020, 37(11):61-80.
	[ Gao P, Yue S J. Measurement of total-factor embodied energy efficiency of China’s industry sectors[J]. The Journal of Quantitative & Technical Economics, 2020, 37(11):61-80.]
[15]	Shepard J U, Pratson L F. Hybrid input-output analysis of embodied energy security[J]. Applied Energy, 2020, DOI: 10.1016/j.apenergy.2020.115806. doi: 10.1016/j.apenergy.2020.115806
[16]	Su B, Ang B W. Attribution of changes in the generalized Fisher index with application to embodied emission studies[J]. Energy, 2014, 69:778-786. doi: 10.1016/j.energy.2014.03.074
[17]	Su B, Ang B W. Demand contributors and driving factors of Singapore’s aggregate carbon intensities[J]. Energy Policy, 2020, DOI: 10.1016/j.enpol.2020.111817. doi: 10.1016/j.enpol.2020.111817
[18]	李虹, 王帅. 中国行业隐含能源消费及其强度的变动与影响因素[J]. 中国人口·资源与环境, 2021, 31(5):47-57.
	[ Li H, Wang S. Changes and influencing factors of embodied energy consumption and intensity in China’s industries[J]. China Population, Resources and Environment, 2021, 31(5):47-57.]
[19]	Li Y L, Chen B, Chen G Q, et al. An embodied energy perspective of urban economy: a three-scale analysis for Beijing 2002-2012 with headquarter effect[J]. Science of The Total Environment, 2020,DOI: 10.1016/j.scitotenv.2020.139097. doi: 10.1016/j.scitotenv.2020.139097
[20]	Zheng H M, Li A M, Meng F X, et al. Energy flows embodied in China’s interregional trade: Case study of Hebei Province[J]. Ecological Modelling, 2020,DOI: 10.1016/j.ecolmodel.2020.109061. doi: 10.1016/j.ecolmodel.2020.109061
[21]	Guo S, Han M Y, Yang Y P, et al. Embodied energy flows in China’s economic zones: Jing-Jin-Ji, Yangtze-River-Delta and Pearl-River-Delta[J]. Journal of Cleaner Production, 2020, DOI: 10.1016/j.jclepro.2020.121710. doi: 10.1016/j.jclepro.2020.121710
[22]	Ang B W, Mu A R, Zhou P. Accounting frameworks for tracking energy efficiency trends[J]. Energy Economics, 2010, 32(5):1209-1219. doi: 10.1016/j.eneco.2010.03.011
[23]	张中华, 赵玉焕, Su Bin, 等. 能源需求与碳排放驱动因素分解模型发展评述[J]. 生态经济, 2019, 35(4):13-19
	[ Zhang Z H, Zhao Y H, Su B, et al. Review on the decomposition analysis of driving factors applied to energy demand and carbon emission[J]. Ecological Economy, 2019, 35(4):13-19.]
[24]	Choi K H, Ang B W. Decomposition of aggregate energy intensity changes in two measures: Ratio and difference[J]. Energy Economics, 2003, 25(6):615-624. doi: 10.1016/S0140-9883(03)00038-0
[25]	Dietzenbacher E, Los B. Structural decomposition techniques: Sense and sensitivity[J]. Economic Systems Research, 1998, 10(4):307-324. doi: 10.1080/09535319800000023
[26]	Ang B W, Liu N. Handling zero values in the logarithmic mean Divisia index decomposition approach[J]. Energy Policy, 2007, 35(1):238-246. doi: 10.1016/j.enpol.2005.11.001
[27]	Ang B W, Liu N. Negative-value problems of the logarithmic mean Divisia index decomposition approach[J]. Energy Policy, 2007, 35(1):739-742. doi: 10.1016/j.enpol.2005.12.004
[28]	Wang Q, Song X X, Liu Y. China’s coal consumption in a globalizing world: Insights from multi-regional input-output and structural decomposition analysis[J]. Science of the Total Environment, 2020, DOI: 10.1016/j.scitotenv.2019.134790 doi: 10.1016/j.scitotenv.2019.134790
[29]	Su B, Huang H C, Ang B W, et al. Input-output analysis of CO₂ emissions embodied in trade: The effects of sector aggregation[J]. Energy Economics, 2010, 32(1):166-175. doi: 10.1016/j.eneco.2009.07.010
[30]	彭水军, 刘安平. 中国对外贸易的环境影响效应: 基于环境投入-产出模型的经验研究[J]. 世界经济, 2010, 33(5):140-160.
	[ Peng S J, Liu A P. Environmental impacts of foreign trade in China: An input-output analysis[J]. The Journal of World Economy, 2010, 33(5):140-160.]
[31]	Shi X H, Chu J H, Zhao C Y. Exploring the spatiotemporal evolution of energy intensity in China by visual technology of the GIS[J]. Energy, 2021, DOI: 10.1016/j.energy.2021.120650. doi: 10.1016/j.energy.2021.120650
[32]	Zhu B Z, Su B, Li Y Z, et al. Embodied energy and intensity in China’s (normal and processing) exports and their driving forces, 2005-2015[J]. Energy Economics, 2020,DOI: 10.1016/j.eneco.2020.104911. doi: 10.1016/j.eneco.2020.104911
[33]	Guo S, Zheng S P, Hu Y H, et al. Embodied energy use in the global construction industry[J]. Applied Energy, 2019, DOI: 10.1016/j.apenergy.2019.113838. doi: 10.1016/j.apenergy.2019.113838
[34]	林伯强. 能源革命促进中国清洁低碳发展的“攻关期”和“窗口期”[J]. 中国工业经济, 2018, (6):15-23.
	[ Lin B Q. The period of carrying out energy revolution to promote low carbon clean development in China[J]. China Industrial Economics, 2018, (6):15-23.]
[35]	Guo S, Li Y, Hu Y, et al. Embodied energy in service industry in global cities: A study of six Asian cities[J]. Land Use Policy, 2020, 91:104264. doi: 10.1016/j.landusepol.2019.104264

行业大类	行业名称
农业	S1农林牧渔业
采掘业	S2煤炭、石油和天然气开采业;S3金属矿采选业;S4非金属矿和其他矿采选业
轻制造业	S5食品加工及烟草制造业;S6纺织业;S7纺织服装皮革制造业;S8木材加工与家具制造业;S9造纸印刷和文教体育制造业
能源工业	S10石油、炼焦产品和核燃料加工业;S22电力、热力生产和供应业;S23燃气和水的生产供应业
重制造业	S11化学工业;S12非金属矿物制品业;S13金属冶炼和压延加工业;S14金属制品业;S15通用设备制造业;S16专用设备制造业;S17交通运输设备制造业;S18电气机械和器材制造业;S19计算机、通信和其他电子设备制造业;S20仪器仪表制造业;S21废品废料、设备修理与其他制造业
建筑业	S24建筑业
服务业	S25批发、零售、住宿和餐饮业;S27其他服务业
交通运输业	S26交通运输、仓储和邮政业