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ORIGINAL RESEARCH
How Does Metal Consumption Decouple?
Evidence from Copper Based upon
Tapio Theory and LMDI Model
1
Faculty of International Trade, Shanxi University of Finance and Economics, Taiyuan 030006, China
2
Institute of Industrial Economics, Chinese Academy of Social Sciences, Beijing 100836, China
3
Faculty of Business and Administration, Shanxi University of Finance and Economics, Taiyuan 030006, China
Submission date: 2024-02-29
Final revision date: 2024-09-12
Acceptance date: 2024-10-13
Online publication date: 2024-12-16
Corresponding author
Shanshan Liang
Faculty of International Trade, Shanxi University of Finance and Economics, Taiyuan 030006, China
Faculty of International Trade, Shanxi University of Finance and Economics, Taiyuan 030006, China
KEYWORDS
TOPICS
ABSTRACT
Copper, the cornerstone metal in sustainable energy initiatives, plays a crucial role in applications
such as electric vehicles, wind turbines, and other green energy projects. This paper aims to study
the evolution and decoupling relationship between copper consumption and economic growth. We
first quantified the trends of the Copper Decoupling Index. Utilizing an expanded Kaya identity
and logarithmic mean Divisia index (LMDI) decomposition method, the analysis explores the key
drivers behind copper consumption. The results are as follows: 1) Developed countries such as the United
Kingdom and the United States have showcased an ideal state of strong decoupling between economic
growth and copper consumption, whereas Germany and Japan have generally shown signs of negative
or weak decoupling. Conversely, in China, the consumption of copper has experienced negative
decoupling growth. 2) During China’s industrialization process, the primary catalyst for changes
in copper consumption was the scale effect, while the structural and efficiency effects exerted negative
regulatory influences. 3) Recent structural adjustments in China have highlighted the inhibitory impact
of structural changes on electricity consumption growth. From 2006–2022, the influence of industrial
structural changes on copper consumption has been predominantly governed by negative regulation,
with its intensity increasing year by year since 2014. These research findings offer valuable insights for
policymakers globally in developing tailored strategies for copper supply and consumption in varying
economic growth scenarios.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (38)
1.
PENG H., GUO L., ZHANG J., ZHONG S., YU H., HAN Y. Research progress and implication of the relationship between regional economic growth and resource-environmental pressure. Resources Science. 42 (4), 593, 2020. https://doi.org/10.18402/resci....
2.
LIANG S., YANG D. Empirical study on typical facts and decoupling of industrialization and mineral resource consumption of great powers from the perspective of evolution. Journal of Central South University (Social Sciences). 29 (1), 66, 2023.
3.
SHEN L., WU N., ZHONG S., SUN Y., KONG H. Study on the supply-side reform strategy of mining industry under the new normal economy in China. China Population, Resources and Environment. 27 (7), 8, 2017.
4.
CHANGJIAN W., FEI W., YUYAO Y. Evolving characteristics and driving mechanism of coal consumption in China based on the perspective of supply and demand. Journal of Natural Resources. 35 (11), 2708, 2020. https://doi.org/10.31497/zrzyx....
5.
MA XJ., CHEN RM., SU H. Driving factors and decoupling analysis on industrial energy consumption in China. Journal of Statistics and Information. 36 (3), 71, 2021.
6.
ZENG XL., LI JH., GENG Y., HAO H., HUANG WR., SUN X. Pathways for medium-and long-term susutainable supply of typical strategic metals in China in context of carbon neutrality. Bulletin of Chinese Academy of Sciences. 38 (8), 1100, 2023.
7.
Duan SF. The status of China's nonferrous metal mineral resources and the changing trends of global mining development pattern. China Nonferrous Metals. (8), 59, 2021.
8.
SUN C., SONG Z., ZHU Y., WU X. Exploitation and utilization status and safe supply strategy of copper, aluminum, lead, and zinc resources in China. Strategic Study of CAE. 21 (1), 133, 2019. https://doi.org/10.15302/J-SSC....
9.
ZHANG R.R., CHEN Q.S., LIU Q.Y., YU W.J., TAN H.C. World demand and analysis of supply and demand of platinum-group metals. Resources Science. 37 (5), 1019, 2015.
10.
YANG X. Double peak and its effects of base metal consumption intensity in China based on IU model: With copper as focus. Scientific and Technological Management of Land and Resources. 32 (5), 55, 2015.
11.
YAN J., ZHAO T., ZHANG X. A sensitivity analysis based on ghosh input-output model: taking high energy-consuming industries in China as an example. Management Review. 27 (3), 39, 2015.
12.
LIU L.-C., CAO D., WEI Y.-M. What drives intersectoral CO2 emissions in China? Journal of Cleaner Production. 133, 1053, 2016. https://doi.org/10.1016/j.jcle....
13.
HUANG J., WANG Z., ZHONG M. The impact of key industries on China's metal consumption: A comprehensive analysis based on a cross industry perspective. Journal of Central South University (Social Sciences). 24 (6), 103, 2018.
14.
WIEDMANN T.O., SCHANDL H., LENZEN M., MORAN D., SUH S., WEST J., KANEMOTO K. The material footprint of nations. Proceedings of the National Academy of Sciences of the United States of America. 112 (20), 6271, 2015. https://doi.org/10.1073/pnas.1... PMid:24003158 PMCid:PMC4443380.
15.
WANG T., YU Y., ZHOU W., LIU B., CHEN D., ZHU B. Dynamics of material productivity and socioeconomic factors based on auto-regressive distributed lag model in China. Journal of Cleaner Production. 137, 752, 2016. https://doi.org/10.1016/j.jcle....
16.
YAO Z., LUO S. Analysis of Energy Consumption Driving Factors in China's Non-ferrous Metal Industry Based on Logarithmic Mean Divisia Index Method. Journal of University of Jinan (Science and Technology). 32 (6), 454, 2018.
17.
CHEN L.F., SUN H.Y., JIAO S.X. Measurement of Interprovincial Implicit Carbon Emissions from Energy Consumption in China and its Drivers. Areal research and development. 42 (4), 120, 2023.
18.
HE Y., XING Y., ZENG X., JI Y., HOU H., ZHANG Y., ZHU Z. Factors influencing carbon emissions from China's electricity industry: Analysis using the combination of LMDI and K-means clustering. Environmental Impact Assessment Review. 93, 106724, 2022. https://doi.org/10.1016/j.eiar....
19.
GUO S., LIU G., LIU S. Driving factors of NOX emission reduction in China's power industry: based on LMDI decomposition model. Environmental Science and Pollution Research. 30 (17), 51042, 2023. https://doi.org/10.1007/s11356... PMid:36807855.
20.
QUAN C., CHENG X., YU S., YE X. Analysis on the influencing factors of carbon emission in China's logistics industry based on LMDI method. Science of the Total Environment. 734 (23), 138473, 2020. https://doi.org/10.1016/j.scit... PMid:32460061.
21.
LIU C., LYU W., ZANG X., ZHENG F., ZHAO W., XU Q., LU J. Exploring the factors effecting on carbon emissions in each province in China: A comprehensive study based on symbolic regression, LMDI and Tapio models. Environmental Science and Pollution Research. 30 (37), 87071, 2023. https://doi.org/10.1007/s11356... PMid:37418189.
22.
DE OLIVEIRA-DE JESUS P.M. Effect of generation capacity factors on carbon emission intensity of electricity of Latin America & the Caribbean, a temporal IDA-LMDI analysis. Renewable and Sustainable Energy Reviews. 101, 516, 2019. https://doi.org/10.1016/j.rser....
23.
ALAJMI R.G. Factors that impact greenhouse gas emissions in Saudi Arabia: Decomposition analysis using LMDI. Energy Policy. 156 (6), 112454, 2021. https://doi.org/10.1016/j.enpo....
24.
ANG B.W., ZHANG F.Q., CHOI K.-H. Factorizing changes in energy and environmental indicators through decomposition. Energy. 23 (6), 489, 1998. https://doi.org/10.1016/S0360-....
25.
ANG B.W. Decomposition analysis for policymaking in energy: which is the preferred method? Energy Policy. 32 (9), 1131, 2004. https://doi.org/10.1016/S0301-....
26.
ANG B.W. The LMDI approach to decomposition analysis: a practical guide. Energy policy. 33 (7), 867, 2005. https://doi.org/10.1016/j.enpo... PMCid:PMC9061863.
27.
FANG D., HAO P., HAO J. Study of the influence mechanism of China's electricity consumption based on multi-period ST-LMDI model. Energy. 170, 730, 2019. https://doi.org/10.1016/j.ener....
28.
JIANG S., ZHU Y., HE G., WANG Q., LU Y. Factors influencing China's non-residential power consumption: Estimation using the Kaya-LMDI methods. Energy. 201 (88), 117719, 2020. https://doi.org/10.1016/j.ener....
29.
MURNI Y., SUDARMAJI E., AMBARWATI S., NASIP I. Technical analysis on household energy consumption: LMDI decomposition index and innovative. IOP Conference Series: Earth and Environmental Science. 1041 (1), 2022. https://doi.org/10.1088/1755-1....
30.
HASAN M.M., LIU K. Decomposition analysis of natural gas consumption in Bangladesh using an LMDI approach. Energy Strategy Reviews. 40, 100724, 2022. https://doi.org/10.1016/j.esr.....
31.
ANG B.W. LMDI decomposition approach: A guide for implementation. Energy Policy. 86, 233, 2015. https://doi.org/10.1016/j.enpo....
32.
ANG B.W., LIU N. Handling zero values in the logarithmic mean Divisia index decomposition approach. Energy policy. 35 (1), 238, 2007. https://doi.org/10.1016/j.enpo....
33.
HUANG C., HAN J., CHEN W.-Q. Changing patterns and determinants of infrastructures' material stocks in Chinese cities. Resources Conservation and Recycling. 123, 47, 2017. https://doi.org/10.1016/j.resc....
34.
LIU M., ZHANG X., ZHANG M., FENG Y., LIU Y., WEN J., LIU L. Influencing factors of carbon emissions in transportation industry based on C-D function and LMDI decomposition model: China as an example. Environmental Impact Assessment Review. 90 (1), 106623, 2021. https://doi.org/10.1016/j.eiar....
35.
TAPIO P. Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transport policy. 12 (2), 137, 2005. https://doi.org/10.1016/j.tran....
36.
WANG J., DONG K., HOCHMAN G., TIMILSINA G.R. Factors driving aggregate service sector energy intensities in Asia and Eastern Europe: A LMDI analysis. Energy Policy. 172 (3), 113315, 2023. https://doi.org/10.1016/j.enpo....
37.
WOOD R., LENZEN M., FORAN B. A material history of Australia: Evolution of material intensity and drivers of change. Journal of Industrial Ecology. 13 (6), 847−862, 2009. https://doi.org/10.1111/j.1530....
38.
YORK R., ROSA E., DIETZ T. STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics. 46, 355, 2003. https://doi.org/10.1016/S0921-....
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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