A process-level hierarchical structural decomposition analysis (SDA) of energy consumption in an integrated steel plant

Abstract

A hierarchical structural decomposition analysis (SDA) model has been developed based on process-level input-output (I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2011-2013. By combining the principle of hierarchical decomposition into D&L method, a hierarchical decomposition model for multilevel SDA is obtained. The developed hierarchical IO-SDA model would provide consistent results and need less computation effort compared with the traditional SDA model. The decomposition results of the steel plant suggest that the technology improvement and reduced steel final demand are two major reasons for declined total energy consumption. The technical improvements of blast furnaces, basic oxygen furnaces, the power plant and the by-products utilization level have contributed mostly in reducing energy consumption. A major retrofit of ancillary process units and solving fuel substitution problem in the sinter plant and blast furnace are important for further energy saving. Besides the empirical results, this work also discussed that why and how hierarchical SDA can be applied in a process-level decomposition analysis of aggregated indicators.

Keywords

structural decomposition analysis / input-output table / energy consumption / embodied energy / integrated steel plant

References

[1] SuB, AngB W. Structural decomposition analysis applied to energy and emissions: Some methodological developments [J]. Energy Economics, 2012, 34(1): 177-188

[2] ShahiduzzamanM, LaytonA. Changes in CO₂ emissions over business cycle recessions and expansions in the United States: A decomposition analysis [J]. Applied Energy, 2015, 150: 25-35

[3] SumabatA K, LopezN S, YuK D, HaoH, LiR, GengY, ChiuA S. Decomposition analysis of Philippine CO₂ emissions from fuel combustion and electricity generation [J]. Applied Energy, 2016, 164: 795-804

[4] CansinoJ M, RománR, OrdóñezM. Main drivers of changes in CO₂ emissions in the Spanish economy: A structural decomposition analysis [J]. Energy Policy, 2016, 89: 150-159

[5] AndreoniV, GalmariniS. Drivers in CO₂ emissions variation: A decomposition analysis for 33 world countries [J]. Energy, 2016, 103: 27-37

[6] TimmaL, ZossT, BlumbergaD. Life after the financial crisis. Energy intensity and energy use decomposition on sectorial level in Latvia [J]. Applied Energy, 2016, 162: 1586-1592

[7] Fernández GonzálezP. Exploring energy efficiency in several European countries. An attribution analysis of the Divisia structural change index [J]. Applied Energy, 2015, 137: 364-374

[8] YuanB, RenS, ChenX. The effects of urbanization, consumption ratio and consumption structure on residential indirect CO₂ emissions in China: A regional comparative analysis [J]. Applied Energy, 2015, 140: 94-106

[9] LanJ, MalikA, LenzenM, McbainD, KanemotoK. A structural decomposition analysis of global energy footprints [J]. Applied Energy, 2016, 163: 436-451

[10] ShaoS, LiuJ, GengY, MiaoZ, YangY. Uncovering driving factors of carbon emissions from China’s mining sector [J]. Applied Energy, 2016, 166: 220-238

[11] GambhirA, TseL K C, TongD, Martinez-BotasR. Reducing China’s road transport sector CO₂ emissions to 2050: Technologies, costs and decomposition analysis [J]. Applied Energy, 2015, 157: 905-917

[12] LiuZ, GengY, AdamsM, DongL, SunL, ZhaoJ, DongH, WuJ, TianX. Uncovering driving forces on greenhouse gas emissions in China’ aluminum industry from the perspective of life cycle analysis [J]. Applied Energy, 2016, 166: 253-263

[13] KarmellosM, KopidouD, DiakoulakiD. A decomposition analysis of the driving factors of CO₂ (Carbon dioxide) emissions from the power sector in the European Union countries [J]. Energy, 2016, 94: 680-692

[14] AlisesA, VassalloJ M, AymerichM. Comparison of road freight transport trends in Europe: Results of an input-output structural decomposition analysis [C]//. Transportation Research Board 94th Annual Meeting, 2015Washington, D.C.TRB1-15

[15] TanX, DongL, ChenD, GuB, ZengY. China’s regional CO₂ emissions reduction potential: A study of Chongqing city [J]. Applied Energy, 2016, 162: 1345-1354

[16] LuQ, YangH, HuangX, ChuaiX, WuC. Multi-sectoral decomposition in decoupling industrial growth from carbon emissions in the developed Jiangsu Province, China [J]. Energy, 2015, 82: 414-425

[17] WangZ, LiuW. Determinants of CO₂ emissions from household daily travel in Beijing, China: Individual travel characteristic perspectives [J]. Applied Energy, 2015, 158: 292-299

[18] XiaX H, HuY, AlsaediA, HayatT, WuX D, ChenG Q. Structure decomposition analysis for energy-related GHG emission in Beijing: Urban metabolism and hierarchical structure [J]. Ecological Informatics, 2015, 26(1): 60-69

[19] ZhiY, YangZ, YinX A, HamiltonP B, ZhangL. Evaluating and forecasting the drivers of water use in a city: Model development and a case from Beijing [J]. Journal of Water Resources Planning and Management, 2015, 142(1): 04015042

[20] LinX, PolenskeK R. Input-output modeling of production processes for business management [J]. Structural Change and Economic Dynamics, 1998, 9(2): 205-226

[21] AlbinoV, DietzenbacherE, KühtzS. Analysing materials and energy flows in an industrial district using an enterprise input–output model [J]. Economic Systems Research, 2003, 15(4): 457-480

[22] JungJ v d, AssenN, BardowA. Comparative LCA of multi-product processes with non-common products: A systematic approach applied to chlorine electrolysis technologies [J]. The International Journal of Life Cycle Assessment, 2013, 18(4): 828-839

[23] MillerR E, BlairP DInput-output analysis: Foundations and extensions [M], 2009

[24] DietzenbacherE, LosB. Structural decomposition techniques: sense and sensitivity [J]. Economic Systems Research, 1998, 10(4): 307-324

[25] SeibelSDecomposition analysis of carbon dioxide-emission changes in Germany-Conceptual framework and empirical results [J], 2003

[26] SonisM, HewingsG J, GazelR. The structure of multi-regional trade flows: Hierarchy, feedbacks and spatial linkages [J]. The Annals of Regional Science, 1995, 29(4): 409-430

[27] DietzenbacherE, LosB. Structural decomposition analyses with dependent determinants [J]. Economic Systems Research, 2000, 12(4): 497-514

[28] KagawaS, InamuraH. A structural decomposition of energy consumption based on a hybrid rectangular input-output framework: Japan’s case [J]. Economic Systems Research, 2001, 13(4): 339-363

[29] WuJ H, ChenY Y, HuangY H. Trade pattern change impact on industrial CO₂ emissions in Taiwan [J]. Energy Policy, 2007, 35(11): 5436-5446

[30] LiuX, WangH-t, ChenJ, HeQ, ZhangH, JiangR, ChenX. Method and basic model for development of Chinese reference life cycle database of fundamental industries [J]. Acta Scientiae Circumstantiae, 2010, 30(10): 2136-2144

[31] WORLDSTEEL.CO₂ emissions data collection (User Guide, version 6) [R], 2012

[32] NorgateT, HaqueN. Energy and greenhouse gas impacts of mining and mineral processing operations [J]. Journal of Cleaner Production, 2010, 18(3): 266-274

[33] MarinkovicS, RadonjaninV, MaleševM, IgnjatovicI. Comparative environmental assessment of natural and recycled aggregate concrete [J]. Waste management, 2010, 30(11): 2255-2264

[34] JiangR, WangH, ZhangH, ChenX. Life cycle assessment of cement technologies in China and recommendations [J]. Acta Scientiae Circumstantiae, 2010, 30(11): 2361-2368

[35] ChenC, HabertG, BouzidiY, JullienA, VenturaA. LCA allocation procedure used as an incitative method for waste recycling: An application to mineral additions in concrete [J]. Resources, Conservation and Recycling, 2010, 54(12): 1231-1240