Cutting CO2 emissions through demand side regulation: Implications from multi-regional input–output linear programming model

doi:10.1007/s42524-022-0209-1

Frontiers of Engineering Management

2022, Vol. 9

Issue (3): 452-461 https://doi.org/10.1007/s42524-022-0209-1

本期目录

Cutting CO₂ emissions through demand side regulation: Implications from multi-regional input–output linear programming model

Nan LIU¹, Jidong KANG²(

), Tsan Sheng NG³, Bin SU³

¹. School of Management, Tianjin Normal University, Tianjin 300387, China; Energy Studies Institute, National University of Singapore, Singapore 119620, Singapore
². Energy Studies Institute, National University of Singapore, Singapore 119620, Singapore
³. Energy Studies Institute, National University of Singapore, Singapore 119620, Singapore; Department of Industrial & Systems Engineering and Management, National University of Singapore, Singapore 117576, Singapore

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Abstract：

This study combines multi-regional input–output (MRIO) model with linear programming (LP) model to explore economic structure adjustment strategies for the reduction of carbon dioxide (CO₂) emissions. A particular feature of this study is the identification of the optimal regulation sequence of final products in various regions to reduce CO₂ emissions with the minimum loss in gross domestic product (GDP). By using China’s MRIO tables 2017 with 28 regions and 42 economic sectors, results show that reduction in final demand leads to simultaneous reductions in GDP and CO₂ emissions. Nevertheless, certain demand side regulation strategy can be adopted to lower CO₂ emissions at the smallest loss of economic growth. Several key final products, such as metallurgy, nonmetal, metal, and chemical products, should first be regulated to reduce CO₂ emissions at the minimum loss in GDP. Most of these key products concentrate in the coastal developed regions in China. The proposed MRIO–LP model considers the inter-relationship among various sectors and regions, and can aid policy makers in designing effective policy for industrial structure adjustment at the regional level to achieve the national environmental and economic targets.

Key words： CO₂ emissions demand side regulation multi-regional input–output model linear programming model

收稿日期: 2022-01-31 出版日期: 2022-09-05

Corresponding Author(s): Jidong KANG

引用本文:

. [J]. Frontiers of Engineering Management, 2022, 9(3): 452-461.
Nan LIU, Jidong KANG, Tsan Sheng NG, Bin SU. Cutting CO₂ emissions through demand side regulation: Implications from multi-regional input–output linear programming model. Front. Eng, 2022, 9(3): 452-461.

链接本文:

https://academic.hep.com.cn/fem/CN/10.1007/s42524-022-0209-1
https://academic.hep.com.cn/fem/CN/Y2022/V9/I3/452

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1	N Chang, ( 2015). Changing industrial structure to reduce carbon dioxide emissions: A Chinese application. Journal of Cleaner Production, 103: 40– 48 https://doi.org/10.1016/j.jclepro.2014.03.003
2	T Y Chen, ( 2001). The impact of mitigating CO2 emissions on Taiwan’s economy. Energy Economics, 23( 2): 141– 151 https://doi.org/10.1016/S0140-9883(00)00060-8
3	C J Cho, ( 1999). The economic-energy-environmental policy problem: An application of the interactive multiobjective decision method for Chungbuk province. Journal of Environmental Management, 56( 2): 119– 131 https://doi.org/10.1006/jema.1999.0264
4	D Cortés-Borda, A Ruiz-Hernández, G Guillén-Gosálbez, M Llop, R Guimera, M Sales-Pardo, ( 2015). Identifying strategies for mitigating the global warming impact of the EU-25 economy using a multi-objective input–output approach. Energy Policy, 77: 21– 30 https://doi.org/10.1016/j.enpol.2014.11.020
5	Y Fan, X Zhang, L Zhu, ( 2010). Estimating the macroeconomic costs of CO2 emission reduction in China based on multi-objective programming. Advances in Climate Change Research, 1( 1): 27– 33 https://doi.org/10.3724/SP.J.1248.2010.00027
6	X Fe, M Lahr, Y Zhang, B Meng, ( 2017). Actions on climate change, reducing carbon emissions in China via optimal interregional industry shifts. Energy Policy, 102: 616– 638 https://doi.org/10.1016/j.enpol.2016.10.038
7	A Geschke, J Ugon, M Lenzen, K Kanemoto, D D Moran, ( 2019). Balancing and reconciling large multi-regional input–output databases using parallel optimisation and high-performance computing. Journal of Economic Structures, 8( 1): 2 https://doi.org/10.1186/s40008-019-0133-7
8	F Green, N Stern, ( 2017). China’s changing economy: Implications for its carbon dioxide emissions. Climate Policy, 17( 4): 423– 442 https://doi.org/10.1080/14693062.2016.1156515
9	C O Henriques, C H Antunes, ( 2012). Interactions of economic growth, energy consumption and the environment in the context of the crisis: A study with uncertain data. Energy, 48( 1): 415– 422 https://doi.org/10.1016/j.energy.2012.04.009
10	D Hristu-Varsakelis, S Karagianni, M Pempetzoglou, A Sfetsos, ( 2010). Optimizing production with energy and GHG emission constraints in Greece: An input–output analysis. Energy Policy, 38( 3): 1566– 1577 https://doi.org/10.1016/j.enpol.2009.11.040
11	G J Hsu, F Y Chou, ( 2000). Integrated planning for mitigating CO2 emissions in Taiwan: A multi-objective programming approach. Energy Policy, 28( 8): 519– 523 https://doi.org/10.1016/S0301-4215(00)00006-9
12	International Energy Agency (IEA) ( 2021). Greenhouse Gas Emissions from Energy. Technical Report
13	J Kang, T S Ng, B Su, ( 2020). Optimizing electricity mix for CO2 emissions reduction: A robust input–output linear programming model. European Journal of Operational Research, 287( 1): 280– 292 https://doi.org/10.1016/j.ejor.2020.04.026
14	J Kang, T S Ng, B Su, A Milovanoff, ( 2021). Electrifying light-duty passenger transport for CO2 emissions reduction: A stochastic-robust input–output linear programming model. Energy Economics, 104: 105623 https://doi.org/10.1016/j.eneco.2021.105623
15	R Kumanayake, H Luo, ( 2018). Life cycle carbon emission assessment of a multi-purpose university building: A case study of Sri Lanka. Frontiers of Engineering Management, 5( 3): 381– 393 https://doi.org/10.15302/J-FEM-2018055
16	W W Leontief, ( 1936). Quantitative input and output relations in the economic systems of the United States. Review of Economics and Statistics, 18( 3): 105– 125 https://doi.org/10.2307/1927837
17	H Li, Y Zhao, X Qiao, Y Liu, Y Cao, Y Li, S Wang, Z Zhang, Y Zhang, J Weng, ( 2017). Identifying the driving forces of national and regional CO2 emissions in China: Based on temporal and spatial decomposition analysis models. Energy Economics, 68: 522– 538 https://doi.org/10.1016/j.eneco.2017.10.024
18	W Li, D Xu, G Li, B Su, ( 2020). Structural path and decomposition analysis of aggregate embodied energy intensities in China, 2012–2017. Journal of Cleaner Production, 276: 124185 https://doi.org/10.1016/j.jclepro.2020.124185
19	N Liu, Z Ma, J Kang, ( 2015). Changes in carbon intensity in China’s industrial sector: Decomposition and attribution analysis. Energy Policy, 87: 28– 38 https://doi.org/10.1016/j.enpol.2015.08.035
20	B Medjdoub, M L Chalal, ( 2017). Impact of household transitions on domestic energy consumption and its applicability to urban energy planning. Frontiers of Engineering Management, 4( 2): 171– 183 https://doi.org/10.15302/J-FEM-2017029
21	B Meng, J Xue, K Feng, D Guan, X Fu, ( 2013). China’s inter-regional spillover of carbon emissions and domestic supply chains. Energy Policy, 61: 1305– 1321 https://doi.org/10.1016/j.enpol.2013.05.108
22	Z Mi, Y M Wei, B Wang, J Meng, Z Liu, Y Shan, J Liu, D Guan, ( 2017). Socioeconomic impact assessment of China’s CO2 emissions peak prior to 2030. Journal of Cleaner Production, 142: 2227– 2236 https://doi.org/10.1016/j.jclepro.2016.11.055
23	Z F Mi, S Y Pan, H Yu, Y M Wei, ( 2015). Potential impacts of industrial structure on energy consumption and CO2 emission: A case study of Beijing. Journal of Cleaner Production, 103: 455– 462 https://doi.org/10.1016/j.jclepro.2014.06.011
24	National Bureau of Statistics of China (NBSC) ( 2018). Chinese Provincial Statistics Yearbook for various provinces (in Chinese)
25	NBSC ( 2021). Chinese Statistics Yearbook (in Chinese)
26	Cristóbal J R San, ( 2012). A goal programming model for environmental policy analysis: Application to Spain. Energy Policy, 43: 303– 307 https://doi.org/10.1016/j.enpol.2012.01.007
27	B Su, B W Ang, ( 2013). Input–output analysis of CO2 emissions embodied in trade: Competitive versus non-competitive imports. Energy Policy, 56: 83– 87 https://doi.org/10.1016/j.enpol.2013.01.041
28	W Sun, W Li, Z Tang, J Fan, ( 2016). Industrial structure optimization in central China under the energy constraint. Journal of Geographical Sciences, 26( 9): 1377– 1388 https://doi.org/10.1007/s11442-016-1333-9
29	United Nations Framework Convention on Climate Change (UNFCCC)( 2015). Intended Nationally Determined Contributions
30	J Wang, K Wang, Y M Wei, ( 2020a). How to balance China’s sustainable development goals through industrial restructuring: A multi-regional input–output optimization of the employment–energy–water–emissions nexus. Environmental Research Letters, 15( 3): 034018 https://doi.org/10.1088/1748-9326/ab666a
31	J Wang, Z Wang, S Peng, C Li, L Wei, ( 2020b). Tracing CO2 emissions of China’s construction sector. Journal of Cleaner Production, 275: 124165 https://doi.org/10.1016/j.jclepro.2020.124165
32	M Wang, C Feng, ( 2017). Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors. Applied Energy, 190: 772– 787 https://doi.org/10.1016/j.apenergy.2017.01.007
33	S H M Wang, P Williams, J Shi, H Yang, ( 2015). From green to sustainability trends in the assessment methods of green buildings. Frontiers of Engineering Management, 2( 2): 114– 121 https://doi.org/10.15302/J-FEM-2015018
34	F Xu, N Xiang, J Tian, L Chen, ( 2017). 3ES-based optimization simulation approach to support the development of an eco-industrial park with planning towards sustainability: A case study in Wuhu, China. Journal of Cleaner Production, 164: 476– 484 https://doi.org/10.1016/j.jclepro.2017.06.192
35	X Xu, T Zhao, N Liu, J Kang, ( 2014). Changes of energy-related GHG emissions in China: An empirical analysis from sectoral perspective. Applied Energy, 132: 298– 307 https://doi.org/10.1016/j.apenergy.2014.07.025
36	S Yu, S Zheng, X Li, L Li, ( 2018a). China can peak its energy-related carbon emissions before 2025: Evidence from industry restructuring. Energy Economics, 73: 91– 107 https://doi.org/10.1016/j.eneco.2018.05.012
37	S Yu, S Zheng, X Zhang, C Gong, J Cheng, ( 2018b). Realizing China’s goals on energy saving and pollution reduction: Industrial structure multi-objective optimization approach. Energy Policy, 122: 300– 312 https://doi.org/10.1016/j.enpol.2018.07.034
38	H Zhang, M L Lahr, ( 2014). China’s energy consumption change from 1987 to 2007: A multi-regional structural decomposition analysis. Energy Policy, 67: 682– 693 https://doi.org/10.1016/j.enpol.2013.11.069

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