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

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.

Graphical abstract

Keywords

CO ₂ emissions / demand side regulation / multi-regional input–output model / linear programming model

References

[1] Chang, N ( 2015). Changing industrial structure to reduce carbon dioxide emissions: A Chinese application. Journal of Cleaner Production, 103: 40– 48

[2] Chen, T Y ( 2001). The impact of mitigating CO₂ emissions on Taiwan’s economy. Energy Economics, 23( 2): 141– 151

[3] Cho, C J ( 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

[4] Cortés-Borda, D Ruiz-Hernández, A Guillén-Gosálbez, G Llop, M Guimera, R Sales-Pardo, M ( 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

[5] Fan, Y Zhang, X Zhu, L ( 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

[6] Fe, X Lahr, M Zhang, Y Meng, B ( 2017). Actions on climate change, reducing carbon emissions in China via optimal interregional industry shifts. Energy Policy, 102: 616– 638

[7] Geschke, A Ugon, J Lenzen, M Kanemoto, K Moran, D D ( 2019). Balancing and reconciling large multi-regional input–output databases using parallel optimisation and high-performance computing. Journal of Economic Structures, 8( 1): 2

[8] Green, F Stern, N ( 2017). China’s changing economy: Implications for its carbon dioxide emissions. Climate Policy, 17( 4): 423– 442

[9] Henriques, C O Antunes, C H ( 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

[10] Hristu-Varsakelis, D Karagianni, S Pempetzoglou, M Sfetsos, A ( 2010). Optimizing production with energy and GHG emission constraints in Greece: An input–output analysis. Energy Policy, 38( 3): 1566– 1577

[11] Hsu, G J Chou, F Y ( 2000). Integrated planning for mitigating CO₂ emissions in Taiwan: A multi-objective programming approach. Energy Policy, 28( 8): 519– 523

[12] International Energy Agency (IEA) ( 2021). Greenhouse Gas Emissions from Energy. Technical Report

[13] Kang, J Ng, T S Su, B ( 2020). Optimizing electricity mix for CO₂ emissions reduction: A robust input–output linear programming model. European Journal of Operational Research, 287( 1): 280– 292

[14] Kang, J Ng, T S Su, B Milovanoff, A ( 2021). Electrifying light-duty passenger transport for CO₂ emissions reduction: A stochastic-robust input–output linear programming model. Energy Economics, 104: 105623

[15] Kumanayake, R Luo, H ( 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

[16] Leontief, W W ( 1936). Quantitative input and output relations in the economic systems of the United States. Review of Economics and Statistics, 18( 3): 105– 125

[17] Li, H Zhao, Y Qiao, X Liu, Y Cao, Y Li, Y Wang, S Zhang, Z Zhang, Y Weng, J ( 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

[18] Li, W Xu, D Li, G Su, B ( 2020). Structural path and decomposition analysis of aggregate embodied energy intensities in China, 2012–2017. Journal of Cleaner Production, 276: 124185

[19] Liu, N Ma, Z Kang, J ( 2015). Changes in carbon intensity in China’s industrial sector: Decomposition and attribution analysis. Energy Policy, 87: 28– 38

[20] Medjdoub, B Chalal, M L ( 2017). Impact of household transitions on domestic energy consumption and its applicability to urban energy planning. Frontiers of Engineering Management, 4( 2): 171– 183

[21] Meng, B Xue, J Feng, K Guan, D Fu, X ( 2013). China’s inter-regional spillover of carbon emissions and domestic supply chains. Energy Policy, 61: 1305– 1321

[22] Mi, Z Wei, Y M Wang, B Meng, J Liu, Z Shan, Y Liu, J Guan, D ( 2017). Socioeconomic impact assessment of China’s CO₂ emissions peak prior to 2030. Journal of Cleaner Production, 142: 2227– 2236

[23] Mi, Z F Pan, S Y Yu, H Wei, Y M ( 2015). Potential impacts of industrial structure on energy consumption and CO₂ emission: A case study of Beijing. Journal of Cleaner Production, 103: 455– 462

[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] San, Cristóbal J R ( 2012). A goal programming model for environmental policy analysis: Application to Spain. Energy Policy, 43: 303– 307

[27] Su, B Ang, B W ( 2013). Input–output analysis of CO₂ emissions embodied in trade: Competitive versus non-competitive imports. Energy Policy, 56: 83– 87

[28] Sun, W Li, W Tang, Z Fan, J ( 2016). Industrial structure optimization in central China under the energy constraint. Journal of Geographical Sciences, 26( 9): 1377– 1388

[29] United Nations Framework Convention on Climate Change (UNFCCC)( 2015). Intended Nationally Determined Contributions

[30] Wang, J Wang, K Wei, Y M ( 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

[31] Wang, J Wang, Z Peng, S Li, C Wei, L ( 2020b). Tracing CO₂ emissions of China’s construction sector. Journal of Cleaner Production, 275: 124165

[32] Wang, M Feng, C ( 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

[33] Wang, S H M Williams, P Shi, J Yang, H ( 2015). From green to sustainability trends in the assessment methods of green buildings. Frontiers of Engineering Management, 2( 2): 114– 121

[34] Xu, F Xiang, N Tian, J Chen, L ( 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

[35] Xu, X Zhao, T Liu, N Kang, J ( 2014). Changes of energy-related GHG emissions in China: An empirical analysis from sectoral perspective. Applied Energy, 132: 298– 307

[36] Yu, S Zheng, S Li, X Li, L ( 2018a). China can peak its energy-related carbon emissions before 2025: Evidence from industry restructuring. Energy Economics, 73: 91– 107

[37] Yu, S Zheng, S Zhang, X Gong, C Cheng, J ( 2018b). Realizing China’s goals on energy saving and pollution reduction: Industrial structure multi-objective optimization approach. Energy Policy, 122: 300– 312

[38] Zhang, H Lahr, M L ( 2014). China’s energy consumption change from 1987 to 2007: A multi-regional structural decomposition analysis. Energy Policy, 67: 682– 693

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