Advertisement

Environmental Science and Pollution Research

, Volume 25, Issue 31, pp 31630–31655 | Cite as

Markov-switching vector autoregressive neural networks and sensitivity analysis of environment, economic growth and petrol prices

  • Melike Bildirici
  • Özgür Ersin
Research Article
  • 44 Downloads

Abstract

The paper aims at evaluating the nonlinear and complex relations between CO2 emissions, economic development, and petrol prices to obtain new insights regarding the shape of the environmental Kuznets curve (EKC) in the USA and in the UK in addition to introducing a newly proposed nonlinear approach. Within this respect, the paper has three purposes: the first one is to combine the multilayer perceptron neural networks (MLP) with Markov-switching vector autoregressive (MS-VAR) type nonlinear models to obtain the MS-VAR-MLP model. The second is to utilize one of the largest datasets in the literature covering the 1871–2016 period, a long span of data starting from the late eighteenth century. Since the emission, economic development, and petrol price relation is subject to nonlinearity and trajectory changes due to many historical events, the development of the MS-VAR-MLP model is a necessity to contribute to the ongoing debate regarding the shape of the EKC curve and the stability of the relation. The third purpose is to develop the MS-VAR-MLP-based regime-dependent sensitivity analysis, which eases the visual interpretation of the nonlinear causal relationships, which are allowed to have asymmetric interactions in different phases of the expansionary and recessionary periods of the business cycles. Our results provide clear deviations from the findings in the literature: (i) the shape of the EKC curve cannot be assumed to be stable and is subject to regime dependency, nonlinearity, and magnitude dependency; (ii) the forecast results suggest that incorporation of regime switching and neural networks provide significant improvement over the MS-VAR counterpart; and (iii) for both USA and UK and for the 1871–2016 period, the positive impacts of economic growth on emissions cannot be rejected for the majority of the phases of the business cycles; however, the magnitude of this effect is at various degrees. In addition, the incorporation of petrol price provides significant findings considering its effects on emission and economic growth rates. The analysis suggest clear deviations from the expected shape of the EKC curve and puts forth the necessity to utilize more complex empirical methodologies to evaluate the EKC since the emissions-economic development relation is more complex than it was assumed. Following these findings, several policy recommendations are provided. Lastly, the proposed MS-VAR-MLP methodology is compared with the MS-VAR model and various advantages and disadvantages are enumerated.

Keywords

Neural networks Regime switching Nonlinear econometrics Granger causality Environmental economics 

References

  1. Alam, Begum IA, Buysse J, Huylenbroeck GV (2012) Energy consumption, carbon emissions and economic growth nexus in Bangladesh: cointegration and dynamic causality analysis. Energy Policy 45:217–225CrossRefGoogle Scholar
  2. Al-Mulali U, Solarin SA, Sheau-Ting L, Ozturk I (2016) Does moving towards renewable energy cause water and land inefficiency? An empirical investigation. Energy Policy 93:303–314CrossRefGoogle Scholar
  3. Ang JB (2007) CO2, emission, energy consumption and output in France. Energy Policy 35:4772–4778CrossRefGoogle Scholar
  4. Apergis N (2016) Environmental Kuznets curves: new evidence on both panel and country-level CO2 emissions. Energy Econ 54:263–271CrossRefGoogle Scholar
  5. Arouri MEH, Jawadi F, Nguyen DK (2012a) Nonlinearities in carbon spot-futures price relationships during Phase II of the EU ETS. Econ Model 29(3):884–892CrossRefGoogle Scholar
  6. Arouri ME, Youssef A, M’henni H, Rault C (2012b) Energy consumption, economic growth and CO2 emissions in Middle East and North African countries. Energy Policy 45:342–349CrossRefGoogle Scholar
  7. Aslanidis N, Iranzo S (2009) Environment and development: is there a Kuznets curve for CO2 emissions? Appl Econ 41(6):803–810CrossRefGoogle Scholar
  8. Atasoy BS (2017) Testing the environmental Kuznets curve hypothesis across the U.S.: evidence from panel mean group estimators. Renew Sus. Energ Rev 77:731–747CrossRefGoogle Scholar
  9. Bello MO, Solarin SA, Yen YY (2018) The impact of electricity consumption on CO2 emission, carbon footprint, water footprint and ecological footprint: the role of hydropower in an emerging economy. J Environ Manag 219:218–230CrossRefGoogle Scholar
  10. Bergin T (2008) Oil majors’ output growth hinges on strategy shift. Reuters. https://www.reuters.com/article/us-oilmajors-production/oil-majors-output-growth-hinges-on-strategy-shift-idUSL169721220080801. Accessed 19.01.2018
  11. Bildirici M (2013) Economic growth and electricity consumption: MS-VAR and MS-Granger causality analysis. OPEC Ener Rev 37(4):447–476CrossRefGoogle Scholar
  12. Bildirici M, Ersin Ö (2009) Improving forecasts of GARCH family models with the artificial neural networks: an application to the daily returns in Istanbul stock exchange. Exp Sys with App 36:7355–7362CrossRefGoogle Scholar
  13. Bildirici M, Ersin Ö (2013) Forecasting oil prices: smooth transition and neural network augmented GARCH family models. J Pet Sci Eng 109:230–240CrossRefGoogle Scholar
  14. Bildirici M, Ersin Ö (2014) Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns. Sci World J 2014:1–22Google Scholar
  15. Bildirici M, Ersin Ö (2018) Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800–2014 period in the USA. Environ Sci Pollut Res 25(1):200–219CrossRefGoogle Scholar
  16. Bildirici M, Gökmenoğlu S (2017) Environmental pollution, hydropower energy consumption and economic growth: evidence from G7 countries. Renew Sust Energ Rev 75:68–85CrossRefGoogle Scholar
  17. Bishop C (1995) Neural networks for pattern recognition, 1st edn. Oxford, New YorkGoogle Scholar
  18. Bloomberg Businessweek (2018) Company Overview of BP Exploration & Production Inc. https://www.bloomberg.com/research/stocks/private/snapshot.asp?privcapId=2414356. Accessed 19 Jan 2018
  19. BP (2018) History of BP. https://www.bp.com/en/global/corporate/who-we-are/our-history.html. Accessed 18 Jan 2018
  20. CDIAC, 2016. Carbon dioxide information analysis center database. http://cdiac.ornl.gov. Accessed 11 Nov 2016
  21. Charfeddine L (2017) The impact of energy consumption and economic development on ecological footprint and CO2 emissions: evidence from a Markov switching equilibrium correction model. Energy Econ 65:355–374CrossRefGoogle Scholar
  22. Cheng B, Titterington DM (1994) Neural networks: a review from statistical perspective. Stat Sci 9(1):49–54CrossRefGoogle Scholar
  23. Chevallier J (2011a) Macroeconomics, finance, commodities: interactions with carbon markets in a data-rich model. Econ Model 28(1–2):557–567CrossRefGoogle Scholar
  24. Chevallier J (2011b) A model of carbon price interactions with macroeconomic and energy dynamics. Energy Econ 33:1295–1312CrossRefGoogle Scholar
  25. Cole MA, Rayner AJ, Bates JM (1997) The environmental Kuznets curve: an empirical analysis. Environ Dev Econ 2:401–416CrossRefGoogle Scholar
  26. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2:303–314CrossRefGoogle Scholar
  27. Dimopoulos Y, Bourret P, Lek S (1995) Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Process Lett 2(6):1–4CrossRefGoogle Scholar
  28. Dimopoulos I, Chronopoulos J, Chronopoulou-Sereli A, Lek S (1999) Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecol Mod 120(2–3):157–165CrossRefGoogle Scholar
  29. Engelbrecht AP, Cloete I, Zurada JM (1995) Determining the significance of input parameters using sensitivity analysis. In: Mira J, Sandoval F (eds) From natural to artificial neural computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, HeidelbergGoogle Scholar
  30. Engelbrecht AP, Flectcher L, Cloete I (1999) Variance analysis of sensitivity information for pruning neural networks. In: IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).  https://doi.org/10.1109/IJCNN.1999.832657
  31. Ersin Ö (2009) Türkiye’de Fiyatlar Genel Düzeyine İlişkin Maliye Teorisinin Doğrusal Olmayan Zaman Serisi Modelleri Bakımından İncelenmesi. PhD. Thesis, Yıldız Tech. Uni., Inst. of Soc. Sci., Dept. of Econ., IstanbulGoogle Scholar
  32. Ersin Ö (2016) The nonlinear relationship of environmental degradation and income for the 1870–2011 period in selected developed countries: the dynamic panel-STAR approach. Procedia Econ Fin 38:318–339CrossRefGoogle Scholar
  33. Esteve V, Tamarit C (2012) Threshold cointegration and nonlinear adjustment between CO 2 and income: the environmental Kuznets curve in Spain, 1857–2007. Energy Econ 34(6):2148–2156CrossRefGoogle Scholar
  34. Fezzi C, Bunn DW (2009) Structural interactions of European carbon trading and energy prices. The Journal of Energy Markets 2(4):53CrossRefGoogle Scholar
  35. Fodha M, Zaghdoud O (2010) Economic growth and pollutant emissions in Tunisia: an empirical analysis of the environmental Kuznets curve. Energy Policy 38(2):1150–1156CrossRefGoogle Scholar
  36. Fosten J, Morley B, Taylor T (2012) Dynamic misspecification in the environmental Kuznets curve: evidence from CO2 and SO2 emissions in the United Kingdom. Ecol Econ 76:25–33CrossRefGoogle Scholar
  37. Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160:249–264CrossRefGoogle Scholar
  38. Ghosh S (2010) Examining carbon emissions economic growth nexus for India: a multivariate cointegration approach. Energy Policy 38:3008–3014CrossRefGoogle Scholar
  39. Gil-Alana LA, Solarin SA (2018) Have U.S. environmental policies been effective in the reduction of U.S. emissions? A new approach using fractional integration. Atmos Pollut Res 9:53–60CrossRefGoogle Scholar
  40. Granger CWJ, Terasvirta T (1993) Modelling dynamic nonlinear economic relationships, first edn. Oxford Uni. Press, OxfordGoogle Scholar
  41. Grossman G, Krueger A (1991) Environmental impacts of a North American free trade agreement. NBER Working Papers 3914, pp 1–57. http://www.nber.org/papers/w3914. Accessed 7 Sept 2018 
  42. Guo Z, Ward M, Rundensteiner E, Ruiz C (2011) Pointwise local pattern exploration for sensitivity analysis. In: 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp 131–140.  https://doi.org/10.1109/VAST.2011.6102450
  43. Hadzima-Nyarko M, Nyarko E, Moric D (2011) A neural network based modelling and sensitivity analysis of damage ratio coefficient. Expert Syst Appl 38:13405–13413CrossRefGoogle Scholar
  44. Halkos GE, Tsionas EG (2001) Environmental Kuznets curves: Bayesian evidence from switching regime models. Energy Econ 23:191–210CrossRefGoogle Scholar
  45. Hamilton JD (1990) Analysis of time series subject to regime changes. J Econ 45:39–70CrossRefGoogle Scholar
  46. Hamilton JD (2011) Historical oil shocks. NBER Working Paper 16790, pp 1–52. http://www.nber.org/papers/w16790.pdf. Accessed 9 Sept 2018
  47. Jalil A, Mahmud SF (2009) Environment Kuznets curve for CO2 emissions: a cointegration analysis for China. Energy Policy 37(12):5167–5172CrossRefGoogle Scholar
  48. Kim S, Lee K, Nam K (2010) The relationship between CO2 emissions and economic growth: the case of Korea with nonlinear evidence. Energy Policy 38(10):5938–5946CrossRefGoogle Scholar
  49. Krolzig HM (1998) Econometric modelling of Markov-switching vector autoregressions using MSVAR for Ox. http://fmwww.bc.edu/ec-p/software/ox/Msvardoc.pdf. Accessed 21 Jan. 2018
  50. Krolzig HM (2000) Predicting Markov-switching vector autoregressive processes. Department of Economics and Nuffield College, Oxford. https://pdfs.semanticscholar.org/2b99/ebe2736ea800384370db30325ec27f6d5347.pdf. Accessed 7 Sept 2018Google Scholar
  51. Krolzig HM, Clements MP (2002) Can oil shocks explain asymmetries in the US business Cycle? Empir Econ 27(2):185–204CrossRefGoogle Scholar
  52. Krolzig HM, Toro J (2005) Classical and modern business cycle measurement: the European case. Span Econ Rev 7(1):1–21CrossRefGoogle Scholar
  53. Kuan C-M, White H (1994) Artificial neural networks: an econometric perspective (with discussions). Econometric Rev 13:1–91 139–143CrossRefGoogle Scholar
  54. Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecol Model 90(1):39–52CrossRefGoogle Scholar
  55. Lindmark M (2002) An EKC-pattern in historical perspective: carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870–1997. Ecol Econ 42(1–2):333–347CrossRefGoogle Scholar
  56. Liu J, Zhang X, Song X (2018) Regional carbon emission evolution mechanism and its prediction approach driven by carbon trading-a case study of Beijing. J Clean Prod 172:2793–2810CrossRefGoogle Scholar
  57. Martinez-Zarzoso I, Maruotti A (2013) The environmental Kuznets curve: functional form, time-varying heterogeneity and outliers in a panel setting. Environmetrics 24:461–475CrossRefGoogle Scholar
  58. Mensah JT (2014) Carbon emissions, energy consumption and output: a threshold analysis on the causal dynamics in emerging African economies. Energy Policy 70:172–182CrossRefGoogle Scholar
  59. Menyah K, Wolde-Rufael Y (2010) Energy consumption. pollutant emissions and economic growth in South Africa. Energy Econ 32(6):1374–1382CrossRefGoogle Scholar
  60. Molas G, Yamazaki F (1995) Neural networks for quick earthquake damage estimation. Earthq Eng Struct Dyn 24:505–516CrossRefGoogle Scholar
  61. Olivier J, Janssens-Maenhout G, Muntean M, Peters J (2015) Trends in the global CO2 emissions: 2015 report. PBL Netherlands Environmental Assessment Agency Publication 1803, pp 1–98. http://www.pbl.nl/en/publications/trends-in-global-co2-emissions-2015-report. Accessed 18 Sept 2016
  62. Olteanu M, Rynkiewicz J, Maillet B (2004) Nonlinear analysis of shocks when financial markets are subject to changes in regime. ESANN 2004 proceedings, pp 87–92Google Scholar
  63. Özokçu S, Özdemir Ö (2017) Economic growth, energy, and environmental Kuznets curve. Renew Sustain Energy Rev 72:639–647CrossRefGoogle Scholar
  64. Pan Z, Wang Y, Wu C, Yin L (2017) Oil price volatility and macroeconomic fundamentals: a regime switching GARCH-MIDAS model. J Empir Financ 43:130–142CrossRefGoogle Scholar
  65. Pao HT, Tsai CM (2010) CO2 emissions, energy consumption and economic growth in BRIC countries. Energy Policy 38(12):7850–7860CrossRefGoogle Scholar
  66. Park J, Hong T (2013) Analysis of South Korea’s economic growth, carbon dioxide emission, and energy consumption using the Markov switching model. Renew Sustain Energy Rev 18:543–551CrossRefGoogle Scholar
  67. Plassmann F, Khanna N (2006) Household Income and Pollution: Implications for the Debate About the Environmental Kuznets Curve Hypothesis. J Environ Dev 15(1):22–41CrossRefGoogle Scholar
  68. Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econ 16(3):289–326CrossRefGoogle Scholar
  69. Rasli A, Qureshi M, Isah-Chikaji A, Zaman K, Ahmad M (2018) New toxics, race to the bottom and revised environmental Kuznets curve: the case of local and global pollutants. Renew Sustain Energy Rev 81(2):3120–3130CrossRefGoogle Scholar
  70. Rehman MU (2018) Do oil shocks predict economic policy uncertainty?. Physica A Stat Mech Appl 498:123–136CrossRefGoogle Scholar
  71. Richmond AK, Kaufmann RK (2006) Is there a turning point in the relationship between income and energy use and/or carbon emissions? Ecol Econ 56:176–189CrossRefGoogle Scholar
  72. Roach T (2015) Hidden regimes and the demand for carbon dioxide from motor-gasoline. Energy Econ 52:306–315CrossRefGoogle Scholar
  73. Rostow WW (1960) The stages of economic growth: a non-communist manifesto, third edn. Cambridge University Press, CambridgeGoogle Scholar
  74. Sanjari F, Delangizan S (2010) Carbon emissions and economic growth: the Iranian experience. SSRN, 1–7. http://ssrn.com/abstract=1635233. Accessed 21 Jan 2018
  75. Selden TM, Song D (1994) Environmental quality and development: is there a Kuznets curve for air pollution? J Environ Econ Environ Mgmt 27:147–162CrossRefGoogle Scholar
  76. Shafik N, Bandyopadhyay S (1992) Economic growth and environmental quality: time series and cross-country evidence. World Bank Working Paper Series 904, pp 1–55Google Scholar
  77. Shen J, Wei YD, Yang Z (2017) The impact of environmental regulations on the location of pollution-intensive industries in China. J Clean Prod 148:785–794CrossRefGoogle Scholar
  78. Sims CA (1980) Macroeconomics and reality. Econometrica 48(1):1–48CrossRefGoogle Scholar
  79. Sinha A, Shahbaz M (2018) Estimation of environmental Kuznets curve for CO2 emission: role of renewable energy generation in India. Renew Energy 119:703–711CrossRefGoogle Scholar
  80. Solarin SA, Al-Mulali U (2018) Influence of foreign direct investment on indicators of environmental degredation. Environ Sci Pollut Res.  https://doi.org/10.1007/s11356-018-2562-5 CrossRefGoogle Scholar
  81. Stern DI, Enflo K (2013) Causality between energy and output in the long-run. Energy Econ 39:135–146CrossRefGoogle Scholar
  82. Stern DI, Common MS, Barbier EB (1996) Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development. World Dev 24(7):1151–1160CrossRefGoogle Scholar
  83. Sun W, Liu M (2016) Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China. J Clean Prod 112:144–153CrossRefGoogle Scholar
  84. Swanson N, White H (1997) A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks. Rev Econ Stat 79:540–550CrossRefGoogle Scholar
  85. Terasvirta T (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. J Am Stat Assoc 89:208–218Google Scholar
  86. Tong H (1990) Non-linear time series: a dynamical system approach, first edn. Oxford University Press, OxfordGoogle Scholar
  87. Tucker M (1995) Carbon dioxide emissions and global GDP. Ecol Econ 15(3):215–223CrossRefGoogle Scholar
  88. U.S. Energy Information Administration EIA (2018) https://www.eia.gov/todayinenergy/detail.php?id=16971. Accessed 19 Jan 2018
  89. Unruh GC, Moomaw WR (1998) An alternative analysis of apparent EKC-type transitions. Ecol Econ 25:221–229CrossRefGoogle Scholar
  90. Wang SS, Zhou DQ, Zhou P, Wang QW (2011) CO2 emissions, energy consumption and economic growth in China: a panel data analysis. Energy Policy 39:4870–4875CrossRefGoogle Scholar
  91. White H (1992) Artificial neural networks: approximation and learning theory, first edn. Blackwell, OxfordGoogle Scholar
  92. Winchester N, Ledvina K (2017) The impact of oil prices on bioenergy, emissions and land use. Energy Econ 65:219–227CrossRefGoogle Scholar
  93. Wutsqa DU, Guritno S, Guritno Z (2006) Forecasting performance of VAR-NN and VARMA models. In: Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications Universiti Sains Malaysia. http://staff.uny.ac.id/sites/default/files/132048772/penang%20paperbaru.pdf. Accessed 7 Sept 2018 
  94. Xu T (2018) Investigating environmental Kuznets curve in China–aggregation bias and policy implications. Energy Policy 114:315–322CrossRefGoogle Scholar
  95. Yavuz NC, Yilanci V (2013) Convergence in per capita carbon dioxide emissions among G7 countries: a TAR panel unit root approach. Environ Resour Econ 54(2):283–291CrossRefGoogle Scholar
  96. Zambrano-Monserrate M, Silva-Zambrano C, Davalos-Penafiel J, Zambrano-Monserrate A, Ruano M (2018) Testing environmental Kuznets curve hypothesis in Peru: the role of renewable electricity, petroleum and dry natural gas. Renew Sustain Energy Rev 82:4170–4178CrossRefGoogle Scholar
  97. Zhang XP, Cheng XM (2009) Energy consumption, carbon emissions, and economic growth in China. Ecol Econ 68:2706–2712CrossRefGoogle Scholar
  98. Zi C, Jie W, Hong-Bo C (2016) CO2 emissions and urbanization correlation in China based on threshold analysis. Ecol Indic 61:193–201CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EconomicsYildiz Technical UniversityIstanbulTurkey
  2. 2.Department of EconomicsBeykent UniversityIstanbulTurkey

Personalised recommendations