Carbon capture and storage (CCS) could be an effective measurement for carbon emission reduction in China. This paper summarizes the development of power sector in 2020, 2030, and 2050, and it classifies 18 scenarios including with and without CCS, respectively, in G1:low, G2:middle, and G3:high in 2020, 2030, and 2050. It adopts China’s input-output table (IO table) and analyzes the different mitigation strategies for power sector. In particular, this paper builds a new China’s input-output table based on aggregating the sectors in IO table and disaggregating the power sector into 11 different technologies which are coal-fire power, coal-fire power with CCS, natural gas power, natural gas power with CCS, hydropower, nuclear power, wind power, solar power, biomass power, geothermal power, and ocean power. Through input-output model, this paper estimates gross value added (GVA) and employment effects of different scenarios of different technologies in power sector in China. It finds that the differences of GVA and employment effects among different scenarios are large. In CCS scenarios, the coal-fire power with CCS contribute 1.48–1.63 × 1010 RMB in 2020, 1.09–1.55 × 1010 RMB in 2030, and 0.85–1.20 × 1010 RMB in 2050 for gross value added. Meanwhile, the employments of coal-fire power with CCS can add the jobs of 11,966–17,159 in 2020; 10,419–16,228 in 2030; and 8977–12,571 in 2050. CCS sector contributes the higher employment than in the renewable power sectors. Meanwhile, coal mining industry, equipment manufacturing industry, and metallic industry take main contribution to the employment of CCS sector.
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Ashworth P, Wade S, Reiner D, Liang X (2015) Developments in public communications on CCS. IntJ Greenh Gas Con (40):449–458
Azzolina NA, Nakles DV, Gorecki CD, Peck WD, Ayash SC, Melzer LS, Chatterjee S (2015) CO2 storage associated with CO2 enhanced oil recovery: a statistical analysis of historical operations. Int J Greenh Gas Con 37(37):384–397
Bon R (1986) Comparative stability analysis of demand-side and supply-side input-output models. Int J Forecast 2(2):231–235
Chen ZA, Li Q, Liu LC, Zhang X, Kuang LP, Jia L, Liu GZ (2015) A large national survey of public perceptions of CCS technology in China. Appl Energy (158):366–377
CMLTSEDSG (2011) Studies on China’s middle- and long-term strategic energy development (2030 and 2050): renewable energy. In: China’s Middle- and long-term Strategic Energy Development Study Group. Science Press, Beijing
Cooney G, Littlefield J, Marriott J, Skone TJ (2015) Evaluating the climate benefits of CO2-enhanced oil recovery using life cycle analysis. Environ Sci Technol 49(12):7491–7500
Davis H (1990) Regional economic impact analysis and project evaluation. University of British Columbia Press, Vancouver, BC
Evans EJ, Li H, Yu WY, Mullen GM, Henkelman G, Mullins CB (2017) Mechanistic insights on ethanol dehydrogenation on Pd-Au model catalysts: a combined experimental and DFT study. Phys Chem Chem Phys 19(45):30578–30589
Ferioli F, Schoots K, Zwaan BVD (2009) Use and limitations of learning curves for energy technology policy: a component-learning hypothesis. Energy Policy 37(37):2525–2535
GCCSI, Global CCS Institute (2018a) The global status of CCS: 2018. https://www.globalccsinstitute.com/. Accessed 25 Mar 2019
GCCSI, Global CCS Institute (2018b) Large-scale CCS facilities. https://www.globalccsinstitute.com/projects/large-scale-ccs-projects. Accessed 25 Mar 2019
Gerlagh R, van der Zwaan BCC (2004) A sensitivity analysis of timing and costs of greenhouse gas emission reductions. Clim. Change 65:39–71
Gough C, Cunningham R, Mander S (2018) Understanding key elements in establishing a social license for CCS: an empirical approach. Int J Greenh Gas Con (68):16–25
Gowdy JM (1992) Labour productivity and energy intensity in Australia 1974-87: an input-output analysis. Energy Econ 14(1):43–48
Harkin T, Hoadley A, Hooper B (2012) Optimisation of power stations with carbon capture plants the trade-off between costs and net power. J. Clean. Prod. 34:98–109
Hussain D, Dzombak DA, Jaramillo P, Lowry GV (2013) Comparative lifecycle inventory (LCI) of greenhouse gas (GHG) emissions of enhanced oil recovery (EOR) methods using different CO2 sources. Int J Greenh Gas Con 16:129–144
IPCC, Intergovernmental Panel on Climate Change (2005) Special report on carbon dioxide capture and storage. Cambridge University Press, Cambridge
IPCC, Intergovernmental Panel On Climate Change (2018) Special report on global warming of 1.5 °C (SR15). http://www.ipcc.ch/report/sr15/. Accessed 25 Mar 2019
Jaramillo P, Michaelgriffin W, Mccoy ST (2009) Life cycle inventory of CO2 in an enhanced oil recovery system. Environ Sci Technol (43):8027–8032. Jiang Y, Lei YL, Li L, Ge JP (2016) Mechanism of fiscal and taxation policies in the geothermal industry in China. Energies 9(9)
Jiang Y, Lei YL, Yang YZ, Wang F (2018a) Life cycle CO2 emission estimation of CCS-EOR system using different CO2 sources. Pol J Environ Stud 27(6):1–11
Jiang Y, Lei YL, Yang YZ (2018b) Wang F. Factors affecting pilot trading market of carbon emission in China. Pet Sci (2):1–9
Jung JY, Huh C, Kang SG, Seo Y, Chang D (2013) CO2 transport strategy and its cost estimation for the offshore CCS in Korea. Appl Energy 111:1054–1060
Kerschner C, Hubacek K (2009) Assessing the suitability of input-output analysis for enhancing our understanding of potential economic effects of peak oil. Energy 34:284–290
Koelbl BS, Wood R, van den Broek MA, Sanders MWJL, Faaij APC, van Vuuren DP (2015) Socio-economic impacts of future electricity generation scenarios in Europe: potential costs and benefits of using CO2 Capture and Storage (CCS). Int J Greenh Gas Con 42(42):471–484
Laude A, Jonen C (2013) Biomass and CCS: the influence of technical change. Energy Policy 60:916–924
Leontief W W (1936) Quantitative input-output relations in the economic system of the U.S. Rev Econ Stat 18(3):105–125.
Li H, Henkelman G (2017) Dehydrogenation selectivity of ethanol on close-packed transition metal surfaces: a computational study of monometallic, Pd/Au, and Rh/Au catalysts. J Phys Chem C 121(49):27504–27510
Li H, Zhang Z (2018) Mining the intrinsic trends of CO2 solubility in blended solutions. J CO2 Util (26):496–502.
Li KW, Bian HY, Liu CW, Zhang DF, Yang YN (2015) Comparison of geothermal with solar and wind power generation systems. Renew Sust Energ Rev 42:1464–1474
Li H, Zhang Z, Liu Z (2017) Application of artificial neural networks for catalysis: a review. Catalysts 7(10):306
Li H, Evans EJ, Mullins CB, Henkelman G (2018) Ethanol decomposition on Pd-Au alloy catalysts. J Phys Chem C 122(38):22024–22032
Lindner S, Julien Legault J, Guan DB (2012) Disaggregating input-output models with incomplete information. Econ Syst Res 24(4):329–347
Lindner S, Julien Legault J, Guan DB (2013) Disaggregating the electricity sector of China’s input–output table for improved environmental life-cycle assessment. Econ Syst Res 25(3):300–320
Llop M (2008) Economic impact of alternative water policy scenarios in the Spanish production system: an input-output analysis. Ecol Econ 68(1):288–294
Mabon L, Kita J, Xue ZQ (2017) Challenges for social impact assessment in coastal regions: a case study of the Tomakomai CCS demonstration project. Mar Policy 83:243–251
McKinsey (2008) Carbon capture & storage: assessing the economics, from: McKinsey & Company, Inc, New York http://www.mckinsey.com/clientservice/ccsi/pdf/ CCS_Assessing_the_Economics.pdf. Accessed 25 Mar 2019
MIT (2007) The future of coal, Massachusetts Institute of Technology, Cambridge, from: http://web.mit.edu/coal/The_Future_of_Coal.pdf. Accessed 25 Mar 2019
Norhasyima RS, Mahlia TMI (2018) Advances in CO2 utilization technology: a patent landscape review. J CO2 Util 26(26):323–335
Odenberger M, Kjärstad J, Johnsson F (2008) Ramp-up of CO2 capture and storage within Europe. Int J Greenh Gas Con 2(4):417–438
Renner M (2014) Carbon prices and CCS investment: a comparative study between the European Union and China. Energy Policy 75(2014):327–340
Seigo SL, Dohle S, Siegrist M (2014) Public perception of carbon capture and storage (CCS): a review. Renew Sust Energ Rev 38(38):848–863
Wise M, Dooley J (2009) The value of post-combustion carbon dioxide capture and storage technologies in a world with uncertain greenhouse gas emissions constraints. Int J Greenh Gas Con 3(1):39–48
Wu RH, Chen CY (1990) On the application of input-output analysis to energy issues. Energy Econ 12(1):71–76
Yan D, Lei YL, Li L (2017) Driving factor analysis of carbon emissions in China’s power sector for low-carbon economy. Math Probl Eng (11):1–10
Zhang XH, Qi TY, Zhang XL (2015) The impact of climate policy on carbon capture and storage deployment in China. https://dspace.mit.edu/handle/1721.1/100541. Accessed 25 Mar 2019
Zhong ZQ, He LY, Wang Z (2017) Geographic sources and the structural decomposition of emissions embodied in trade by Chinese megacities: the case of Beijing, Tianjin, Shanghai, and Chongqing. J Clean Prod 158:59–72
Zhu L, Fan Y (2011) A real options–based CCS investment evaluation model: case study of China’s power generation sector. Appl Energy 88:4320–4333
This study was financially supported by the National Science and Technology Major Project under Grant No. 2016ZX05016005-003 and Beijing Propaganda Culture High-level Talent Training Subsidy Program under Grant No. 2017XCB031.
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Jiang, Y., Lei, Y., Yan, X. et al. Employment impact assessment of carbon capture and storage (CCS) in China’s power sector based on input-output model. Environ Sci Pollut Res 26, 15665–15676 (2019). https://doi.org/10.1007/s11356-019-04928-2
- Employment impact
- China’s power sector
- Input-output model