1 |
Faria AP, Fernandes GW, Franca MGC. Predicting the impact of increasing carbon dioxide concentration and temperature on seed germination and seedling establishment of African grasses in Brazilian Cerrado. Austral Ecol. 2015;40:962-973.
DOI
|
2 |
Auffhammer M, Carson RT. Forecasting the path of China's emissions using province-level information. J. Environ. Econ. Manag. 2008;55:229-247.
DOI
|
3 |
Sheta AF, Ghatasheh N, Faris H. Forecasting global carbon dioxide emission using auto-regressive with exogenous input and evolutionary product unit neural network models. Information and Communication Systems (ICICS), 2015 6th International Conference on. IEEE; 2015. p. 182-187.
|
4 |
Asumadu-Sarkodie S, Owusu PA. Energy use, carbon dioxide emissions, GDP, industrialization, financial development, and population, a causal nexus in Sri Lanka: With a subsequent prediction of energy use using neural network. Energ. Source. Part B. 2016;11:889-899.
DOI
|
5 |
Mohiuddin O, Asumadu-Sarkodie S, Obaidullah M. The relationship between carbon dioxide emissions, energy consumption, and GDP: A recent evidence from Pakistan. Cogent Eng. 2016;3:1210491.
|
6 |
Sun W, Liu M. Prediction and analysis of the three major industries and residential consumption emissions based on least squares support vector machine in China. J. Clean. Prod. 2016;122:144-153.
DOI
|
7 |
Asumadu-Sarkodie S, Owusu PA. Carbon dioxide emissions, GDP, energy use, and population growth: A multivariate and causality analysis for Ghana, 1971-2013. Environ. Sci. Pollut. Res. 2016;23:13508-13520.
DOI
|
8 |
Suykens JAK, Vandewalle J. Least squares support vector machine classifiers. Neural Process. Lett. 1999;9:293-300.
DOI
|
9 |
Ramanathan R. A multi-factor efficiency perspective to the relationships among world GDP, energy consumption and carbon dioxide emissions. Technol. Forecast. Soc. 2006;73:483-494.
DOI
|
10 |
Lin SJ, Lu IJ, Lewis C. Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan. Energ. Policy 2007;35:1948-1955.
DOI
|
11 |
Ismail S, Shabri A, Samsudin R. A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting. Expert Syst. Appl. 2011;38:10574-10578.
DOI
|
12 |
Li HZ, Guo S, Li CJ, Sun JQ. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl-Based Syst. 2013;37:378-387.
DOI
|
13 |
Chang YS, Jeon S. Using the experience curve model to project carbon dioxide emissions through 2040. Carbon Manag. 2015;6:51-62.
DOI
|
14 |
Salahuddin M, Gow J, Ozturk I. Is the long-run relationship between economic growth, electricity consumption, carbon dioxide emissions and financial development in Gulf Cooperation Council Countries robust? Renew. Sust. Energy Rev. 2015;51:317-326.
DOI
|
15 |
Asumadu-Sarkodie S, Owusu PA. Multivariate co-integration analysis of the Kaya factors in Ghana. Environ. Sci. Pollut. Res. 2016;23:9934-9943.
DOI
|
16 |
Pauzi HM, Abdullah L. Neural network training algorithm for carbon dioxide emissions forecast: A performance comparison. Advanced Computer and Communication Engineering Technology. Springer International Publishing; 2015. p. 717-726.
|
17 |
Chang H, Sun W, Gu X. Forecasting energy emissions using a quantum harmony search algorithm-based DMSFE combination model. Energies 2013;6:1456-1477.
DOI
|
18 |
Cheng J, Qian JS, Guo YN. Least squares support vector machines for gas concentration forecasting in coal mine. Int. J. Comput. Sci. Network Secur. 2006;6:125-129.
|
19 |
Pan WT. A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst. 2012;26:69-74.
DOI
|
20 |
Wang XI, Wang MW. Short-term wind speed forecasting based on wavelet decomposition and least square support vector machine. Power Syst. Technol. 2010;34:179-184.
|
21 |
Harman HH. Modern factor analysis. Oxford: Univ. of Chicago Press; 1960. p. 10-23.
|
22 |
Levesque R. SPSS programming and data management: A guide for SPSS and SAS users. Chicago: Spss Inc.; 2005.
|
23 |
Green SB, Salkind NJ. Using SPSS for windows and macintosh: Analyzing and understanding data. 6th ed. New Jersey: Prentice Hall Press; 2010.
|
24 |
Solomon S, Plattner GK, Knutti R, Friedlingstein P. Irreversible climate change due to carbon dioxide emissions. Proc. Natl. Acad. Sci. USA. 2009:106:1704-1709.
DOI
|
25 |
O'connor BP. SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test. Behav. Res. Meth. Instrum. Comput. 2000;32:396-402.
DOI
|
26 |
Worrell E, Price L, Martin N, Hendriks C, Meida LO. Carbon dioxide emissions from the global cement industry. Annu. Rev. Energ. Environ. 2001;26:303-329.
DOI
|
27 |
Marland G, Andres RJ, Boden TA. Global, regional, and national emissions. Trends 93: A Compendium of Data on Global Change; 1994. p. 505-584.
|
28 |
Du L, Wei C, Cai S. Economic development and carbon dioxide emissions in China: Provincial panel data analysis. China Econ. Rev. 2012;23:371-384.
DOI
|
29 |
Suganthi L, Samuel AA. Energy models for demand forecasting-A review. Renew. Sust. Energy. Rev. 2012;16:1223-1240.
DOI
|
30 |
Rout UK, VoB A, Singh A, Fahl U, Blesl M, Gallachoir BPO. Energy and emissions forecast of China over a long-time horizon. Energy 2011;36:1-11.
DOI
|