참고문헌
- Jiang X, Zhu K, Wang S. The potential for reducing China's carbon dioxide emissions: Role of foreign-invested enterprises. Global Environ. Chang. 2015;35:22-30. https://doi.org/10.1016/j.gloenvcha.2015.07.010
- Gurney KR. Global change: China at the carbon crossroads. Nature 2009;458:977-979. https://doi.org/10.1038/458977a
- Holditch SA, Chianelli RR. Factors that will influence oil and gas supply and demand in the 21st century. MRS Bull. 2008;33:317-323. https://doi.org/10.1557/mrs2008.65
- Longwell HJ. The future of the oil and gas industry: Past approaches, new challenges. World Energ. 1998;5:100-104.
-
Say NP, Yucel M. Energy consumption and
$CO_2$ emissions in Turkey: Empirical analysis and future projection based on an economic growth. Energ. Policy 2006;34:3870-3876. https://doi.org/10.1016/j.enpol.2005.08.024 - Aydin G, Jang H, Topal E. Energy consumption modeling using artificial neural networks: The case of the world's highest consumers. Energ. Source. Part B. 2016;11:212-219. https://doi.org/10.1080/15567249.2015.1075086
- Azadeh A, Tarverdian S. Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energ. Policy 2007;35:5229-5241. https://doi.org/10.1016/j.enpol.2007.04.020
- Utgikar PV, Scott PJ. Energy forecasting: Predictions, reality and analysis of cause of error. Energ. Policy 2006;34:3087-3092. https://doi.org/10.1016/j.enpol.2005.06.006
-
Aydin G. The development and validation of regression models to predict energy-related
$CO_2$ emissions in Turkey. Energ. Source. Part B. 2016;10:176-182. -
Aydin G. The modeling of coal-related
$CO_2$ emissions and projections into future planning. Energ. Source. Part A. 2014;36: 191-201. https://doi.org/10.1080/15567036.2012.760018 - Feng YY, Zhang LX. Scenario analysis of urban energy saving and carbon abatement policies: A case study of Beijing city, China. Procedia Environ. Sci. 2012;13:632-644. https://doi.org/10.1016/j.proenv.2012.01.055
- Lin BQ, Moubarak M. Decomposition analysis: Change of carbon dioxide emissions in the Chinese textile industry. Renew. Sust. Energ. Rev. 2013;26:389-396. https://doi.org/10.1016/j.rser.2013.05.054
-
Gonzalez PF, Landajo M, Presno MJ. The driving forces behind changes in
$CO_2$ emission levels in EU-27. Differences between member states. Environ. Sci. Policy 2013;38:11-16. - Zhang Y, Wang HK, Liang S, et al. Temporal and spatial variations in consumption-based carbon dioxide emissions in China. Renew. Sust. Energ. Rev. 2014;40:60-68. https://doi.org/10.1016/j.rser.2014.07.178
- Wang YF, Zhao HY, Li LY, et al. Carbon dioxide emission drivers for a typical metropolis using input-output structural decomposition analysis. Energ. Policy 2013;58:312-318. https://doi.org/10.1016/j.enpol.2013.03.022
-
Andres AR, Rustemoglu H. Determinants of
$CO_2$ emissions in Brazil and Russia between 1992 and 2011: A decomposition analysis. Environ. Sci. Policy 2016;58:95-106. https://doi.org/10.1016/j.envsci.2016.01.012 - Li Q, Wei YN, Dong YF. Coupling analysis of China's urbanization and carbon emissions: example from Hubei Province. Nat. Hazards 2016;81:1333-1348. https://doi.org/10.1007/s11069-015-2135-6
-
Li W, Ou Q, Chen Y. Decomposition of China's
$CO_2$ emissions from agriculture utilizing an improved Kaya identity. Environ. Sci. Pollut. Res. 2014;21:13000-13006. https://doi.org/10.1007/s11356-014-3250-8 - Wang QW, Chiu YH, Chiu CR. Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis. Energ. Econ. 2015;51:252-260. https://doi.org/10.1016/j.eneco.2015.07.009
-
Ahmed K. The sheer scale of China's urban renewal and
$CO_2$ emissions: multiple structural breaks, long-run relationship, and short-run dynamics. Environ. Sci. Pollut. Res. 2016;23: 16115-16126. https://doi.org/10.1007/s11356-016-6765-3 -
Ali HS, Law SH, Zannah TI. Dynamic impact of urbanization, economic growth, energy consumption, and trade openness on
$CO_2$ emissions in Nigeria. Environ. Sci. Pollut. Res. 2016;23:12435-12443. https://doi.org/10.1007/s11356-016-6437-3 -
Deng MX, Li W, Hu Y. Decomposing industrial energy-related
$CO_2$ emissions in Yunnan Province, China: Switching to low-carbon economic growth. Energies 2016;9:23. https://doi.org/10.3390/en9010023 - Tang CF, Tan BW. The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam. Energy 2015;79:447-454. https://doi.org/10.1016/j.energy.2014.11.033
- Lin BQ, Moubarak M, Ouyang XL. Carbon dioxide emissions and growth of the manufacturing sector: Evidence for China. Energy 2014;76:830-837. https://doi.org/10.1016/j.energy.2014.08.082
- Wang CJ, Wang F, Zhang HG, et al. Carbon emissions decomposition and environmental mitigation policy recommendations for sustainable development in Shandong Province. Sustainability 2014;6:8164-8179. https://doi.org/10.3390/su6118164
- Bian YW, He P, Xu H. Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach. Energ. Policy 2013;63:962-971. https://doi.org/10.1016/j.enpol.2013.08.051
-
Kang YQ, Zhao T, Wu P. Impacts of energy-related
$CO_2$ emissions in China: A spatial panel data technique. Nat. Hazards 2016;81:405-421. https://doi.org/10.1007/s11069-015-2087-x - Sheng PF, Guo XH. The long-run and short-run impacts of urbanization on carbon dioxide emissions. Econ. Model. 2016;53:208-215. https://doi.org/10.1016/j.econmod.2015.12.006
-
Wu LF, Liu SF, Liu DL et al. Modelling and forecasting
$CO_2$ emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model. Energy 2015;79:489-495. https://doi.org/10.1016/j.energy.2014.11.052 -
Perez-Suarez R, Lopez-Menendez AJ. Growing green? Forecasting
$CO_2$ emissions with environmental Kuznets curves and logistic growth models. Environ. Sci. Policy 2015;54: 428-437. https://doi.org/10.1016/j.envsci.2015.07.015 -
Xu B, Lin BQ. Assessing
$CO_2$ emissions in China's iron and steel industry: A dynamic vector autoregression model. Appl. Energ. 2016;161:375-386. https://doi.org/10.1016/j.apenergy.2015.10.039 - Baareh AK. Solving the carbon dioxide emission estimation problem: An artificial neural network model. J. Softw. Eng. Appl. 2013;6:338-342. https://doi.org/10.4236/jsea.2013.67042
- Behrang MA, Assareh E, Assari MR et al. Using bees algorithm and artificial neural network to forecast world carbon dioxide emission. Energ. Source 2011;33:1747-1759. https://doi.org/10.1080/15567036.2010.493920
- Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing 2006;70:489-501. https://doi.org/10.1016/j.neucom.2005.12.126
- Liu H, Tian HQ, Li YF. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms. Energ. Convers. Manage. 2015;100:16-22. https://doi.org/10.1016/j.enconman.2015.04.057
- Omer FE. Forecasting electricity load by a novel recurrent extreme learning machines approach. Int. J. Elec. Power. 2016;78:429-435. https://doi.org/10.1016/j.ijepes.2015.12.006
- Yu L, Dai W, Tang L. A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Eng. Appl. Artif. Intel. 2015;47:110-121.
- Pearson K. On lines and planes of closest fit to systems of points in space. Philos. Mag. 1901;2:559-572. https://doi.org/10.1080/14786440109462720
- Hotelling H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 1933;24:417-441. https://doi.org/10.1037/h0071325
- Li S, Goel L, Wang P. An ensemble approach for short-term load forecasting by extreme learning machine. Appl. Energ. 2016;170:22-29. https://doi.org/10.1016/j.apenergy.2016.02.114
- Deo RC, Sahin M. Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmos. Res. 2015;153:512-525. https://doi.org/10.1016/j.atmosres.2014.10.016
- Lombardi AM. Some reasoning on the RELM-CSEP likelihood- based tests. Earth Planets Space 2014;66:286-301.
- Zhang K, Luo MX. Outlier-robust extreme learning machine for regression problems. Neurocomputing 2015;151:1519-1527. https://doi.org/10.1016/j.neucom.2014.09.022
- Energy Research Institute National Development And Reform Commission. The comprehensive report of China sustainable development of energy and carbon emission scenarios analysis [cited May, 2003]. Available from: http://www.docin.com/p-419481261.html.
-
Asongu S, Montasser GE, Toumi H. Testing the relationships between energy consumption,
$CO_2$ emissions, and economic growth in 24 African countries: A panel ARDL approach. Environ. Sci. Pollut. Res. 2015;23:6563-6573. -
Auffhammer M, Carson RT. Forecasting the path of China's
$CO_2$ emissions using province-level information. J. Environ. Econ. Manag. 2008;55:229-247. https://doi.org/10.1016/j.jeem.2007.10.002 -
Farhani S, Ozturk I. Causal relationship between
$CO_2$ emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environ. Sci. Pollut. Res. 2015;22:15663-15676. https://doi.org/10.1007/s11356-015-4767-1 - Song JK. China's carbon emissions prediction model based on support vector regression. J. China Univ. Pet. 2012;36: 182-187.
피인용 문헌
- Forecasting CO2 emissions in Hebei, China, through moth-flame optimization based on the random forest and extreme learning machine vol.25, pp.29, 2018, https://doi.org/10.1007/s11356-018-2738-z
- Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm vol.11, pp.9, 2018, https://doi.org/10.3390/en11092475
- Measuring and Fitting the Relationship between Socioeconomic Development and Environmental Pollution: A Case of Beijing-Tianjin-Hebei Region, China vol.2019, pp.None, 2017, https://doi.org/10.1155/2019/1570364
- Research on Green Management Effect Evaluation of Power Generation Enterprises in China Based on Dynamic Hesitation and Improved Extreme Learning Machine vol.7, pp.7, 2017, https://doi.org/10.3390/pr7070474
- Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency vol.11, pp.2, 2021, https://doi.org/10.3390/app11020763
- Forecast of China’s Carbon Emissions Based on ARIMA Method vol.2021, pp.None, 2017, https://doi.org/10.1155/2021/1441942
- Modelling approach for carbon emissions, energy consumption and economic growth: A systematic review vol.37, pp.None, 2021, https://doi.org/10.1016/j.uclim.2021.100849
- Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia vol.11, pp.11, 2017, https://doi.org/10.3390/agronomy11112344