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Research Article

An LSTM-STRIPAT model analysis of China’s 2030 CO2 emissions peak

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References

  • Dietz T, Rosa EA. Effects of population and affluence on CO2 emissions. Proc Natl Acad Sci USA. 1997;94(1):175–179. doi:10.1073/pnas.94.1.175.
  • Bong CPC, Lim LY, Ho WS, et al. A review on the global warming potential of cleaner composting and mitigation strategies. J Cleaner Prod. 2017;146:149–157.
  • Liu Z. China's carbon emissions report 2016. Cambridge (MA): Harvard Belfer Center for Science and International Affairs; 2016.
  • Zhang M, Mu H, Ning Y, et al. Decomposition of energy-related CO2 emission over 1991–2006 in China. Ecol Econ. 2009;68(7):2122–2128.
  • Chen CC, Liu CL, Wang H, et al. Examining the impact factors of energy consumption related carbon footprints using the STIRPAT model and PLS model in Beijing. China Environ Sci. 2014; 34(6):1622–1632.
  • Fang D, Zhang X, Yu Q, et al. A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression. J Cleaner Prod. 2018; 173:143–150.
  • Pao HT, Fu HC, Tseng CL. Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model. Energy. 2012; 40(1):400–409.
  • Niu D, Wang K, Wu J, et al. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network. J Cleaner Prod. 2020; 243:118558.
  • Dai S, Niu D, Han Y. Forecasting of energy-related CO2 emissions in China based on GM (1, 1) and least squares support vector machine optimized by modified shuffled frog leaping algorithm for sustainability. Sustainability. 2018; 10(4):958.
  • Ding S, Dang YG, Li XM, et al. Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model. J Cleaner Prod. 2017; 162:1527–1538.
  • Yuan J, Xu Y, Hu Z, et al. Peak energy consumption and CO2 emissions in China. Energy Policy. 2014; 68:508–523.
  • Li F, Xu Z, Ma H. Can China achieve its CO2 emissions peak by 2030? Ecol Indic. 2018;84:337–344.
  • Sun W, Liu M. Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China. J Cleaner Prod. 2016; 122:144–153.
  • Reijnders L, Huijbregts MAJ. Palm oil and the emission of carbon-based greenhouse gases. J Cleaner Prod. 2008; 16(4):477–482.
  • Say NP, Yücel M. Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth. Energy Policy. 2006; 34(18):3870–3876.
  • Auffhammer M, Carson RT. Forecasting the path of China's CO2 emissions using province-level information. J Environ Econ Manage. 2008; 55(3):229–247.
  • Wu L, Liu S, Liu D, et al. Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model. Energy. 2015;79:489–495.
  • Pao HT, Tsai CM. Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy. 2011; 36(5):2450–2458.
  • Lin CS, Liou FM, Huang CP. Grey forecasting model for CO2 emissions: A Taiwan study. Appl Energy. 2011; 88(11):3816–3820.
  • Wang X, Wei Y, Shao Q. Decomposing the decoupling of CO2 emissions and economic growth in China’s iron and steel industry. Resour Conserv Recycl. 2020; 152:104509.
  • Sun W, Wang C, Zhang C. Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization. J Cleaner Prod. 2017; 162:1095–1101.
  • Kavoosi H, Saidi MH, Kavoosi M, et al. Forecast global carbon dioxide emission by use of genetic algorithm (GA). Int J Computer Sci Issues (IJCSI). 2012; 9(5):418.
  • Chang H, Sun W, Gu X. Forecasting energy CO2 emissions using a quantum harmony search algorithm-based DMSFE combination model. Energies. 2013; 6(3):1456–1477.
  • Zhao J, Deng F, Cai Y, et al. Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. Chemosphere. 2019; 220:486–492. doi:10.1016/j.chemosphere.2018.12.128.
  • Yang B, Sun S, Li J, et al. Traffic flow prediction using LSTM with feature enhancement. Neurocomputing. 2019; 332:320–327.
  • Nair R, Jhingan S, Jain D. Testing the consistency between cosmological measurements of distance and age. Phys Lett B. 2015; 745:64–68.
  • Baraldi P, Mangili F, Zio E. A prognostics approach to nuclear component degradation modeling based on Gaussian process regression. Prog Nucl Energy. 2015; 78:141–154.
  • Haseeb M, Hassan S, Azam M. Rural–urban transformation, energy consumption, economic growth, and CO2 emissions using STRIPAT model for BRICS countries. Environ Prog Sustainable Energy. 2017; 36(2):523–531.
  • Fan Y, Liu LC, Wu G, et al. Analyzing impact factors of CO2 emissions using the STIRPAT model. Environ Impact Assess Rev. 2006; 26(4):377–395.
  • Shahbaz M, Chaudhary AR, Ozturk I. Does urbanization cause increasing energy demand in Pakistan? Empirical evidence from STIRPAT model. Energy. 2017; 122:83–93.
  • Shahbaz M, Loganathan N, Muzaffar AT, et al. How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model. Renewable Sustainable Energy Rev. 2016; 57:83–93.
  • Mi Z, Wei YM, Wang B, et al. Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030. J Cleaner Prod. 2017; 142:2227–2236.
  • Wang ZX, Ye DJ. Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models. J Cleaner Prod. 2017; 142:600–612.
  • Su B, Ang BW, Li Y. Input-output and structural decomposition analysis of Singapore's carbon emissions. Energy Policy. 2017; 105:484–492.
  • Cansino JM, Román R, Ordóñez M. Main drivers of changes in CO2 emissions in the Spanish economy: A structural decomposition analysis. Energy Policy. 2016; 89:150–159.
  • Yang L, Yang Y, Zhang X, et al. Whether China's industrial sectors make efforts to reduce CO2 emissions from production?-A decomposed decoupling analysis. Energy. 2018; 160:796–809.
  • Ito K. CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries. Int Econ. 2017; 151:1–6.
  • Shen L, Wu Y, Lou Y, et al. What drives the carbon emission in the Chinese cities?—A case of pilot low carbon city of Beijing. J Cleaner Prod. 2018;174:343–354.
  • Shuai C, Chen X, Shen L, et al. The turning points of carbon Kuznets curve: evidences from panel and time-series data of 164 countries. J Cleaner Prod. 2017; 162:1031–1047.
  • Behera SR, Dash DP. The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region. Renewable Sustainable Energy Rev. 2017; 70:96–106.
  • Li B, Liu X, Li Z. Using the STIRPAT model to explore the factors driving regional CO 2 emissions: a case of Tianjin, China. Nat Hazards. 2015; 76(3):1667–1685.
  • Yang T, Pan Y, Yang Y, et al. CO2 emissions in China's building sector through 2050: a scenario analysis based on a bottom-up model. Energy. 2017;128:208–223.
  • Zhang Q, Yang J, Sun Z, et al. Analyzing the impact factors of energy-related CO2 emissions in China: what can spatial panel regressions tell us? J Cleaner Prod. 2017; 161:1085–1093.
  • Wang Z, Yin F, Zhang Y, et al. An empirical research on the influencing factors of regional CO2 emissions: evidence from Beijing city, China. Appl Energy. 2012; 100:277–284.
  • Li H, Mu H, Zhang M, et al. Analysis on influence factors of China's CO2 emissions based on Path–STIRPAT model. Energy Policy. 2011;39(11):6906–6911.
  • Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997; 9(8):1735–1780. doi:10.1162/neco.1997.9.8.1735.
  • Graves A, Mohamed A, Hinton G. Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing; IEEE; 2013. p. 6645–6649.
  • Ehrlich PR, Holdren JP. Impact of population growth. Science. 1971; 171(3977):1212–1217. doi:10.1126/science.171.3977.1212.
  • York R, Rosa EA, Dietz T. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ. 2003;46(3):351–365.
  • Huang Y, Shen L, Liu H. Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China. J Cleaner Prod. 2019; 209:415–423.
  • Liu Z, Guan D, Crawford-Brown D, et al. Energy policy: A low-carbon road map for China. Nature. 2013; 500(7461):143–145. doi:10.1038/500143a.
  • Maddala GS, Wu S. A comparative study of unit root tests with panel data and a new simple test. Oxford Bull Econ Stat. 1999; 61(S1):631–652.
  • Choi I. Unit root tests for panel data. J Int Money Finance. 2001; 20(2):249–272.
  • Shen H, Tao S, Chen Y, et al. Urbanization-induced population migration has reduced ambient PM2.5 concentrations in China. Sci Adv. 2017; 3(7):e1700300. doi:10.1126/sciadv.1700300.

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