Environmental degradation, energy use, and globalization in Ghana: New empirical evidence from regime switching and neural network autoregression models

Figures & data

Figure 1. Globalization and CO₂ emissions in Ghana, 1971–2016. Source: Authors’ calculations based on CO₂ discharge and globalization index from the World Bank’s World Development Indicators and KOF Swiss Economic Institute.

Figure 2. Multilayer-feed-forward system of 4 inputs. Source: Adopted from As’ad, Sujito, and Setyowibowo (2020).

Table 1. Summary statistics.

	lnCO2	lnY	lnEU	lnGI	lnPGI	lnSGI	lnEGI
Mean	8.546	6.196	5.823	3.795	4.226	3.070	3.635
Median	8.477	5.933	5.840	3.784	4.251	2.970	3.653
Maximum	9.721	7.760	6.012	4.094	4.397	3.763	3.941
Minimum	7.739	5.433	5.584	3.401	3.722	2.804	3.329
Std. Dev.	0.601	0.652	0.111	0.195	0.156	0.284	0.192
Skewness	0.448	1.175	−0.617	−0.046	−1.394	1.380	−0.008
Kurtosis	2.024	2.942	2.493	1.813	5.319	3.593	1.593
Jarque-Bera	3.365	10.600	3.436	2.718	25.213	15.279	3.797
Probability	0.186	0.225	0.179	0.257	0.061	0.120	0.150
Sum	393.125	284.998	267.880	174.565	194.415	141.211	167.195
Sum Sq. Dev.	16.239	19.146	0.556	1.716	1.095	3.636	1.651
Observations	46	46	46	46	46	46	46

Table 2. ADF and P − P stationarity test results.

ADF test					PP test
Variable	Constant	Order of Int.	Constant and trend	Order of Int.	Constant	Order of Int.	Constant and trend	Order of Int.
Level
lnCO2	2.01		−3.01		1.34		−2.78
lnY	0.26		−1.15		0.26		−1.24
lnEU	−1.66		−2.06		−1.85		−2.28
lnGI	−1.37		−2.27		−1.36		−2.49
lnPGI	−4.04***	I (0)	−4.32***	I (0)	−4.18***	I (0)	−4.48***	I (0)
lnSGI	−1.24		−1.03		1.11		−0.97
lnEGI	−1.65		−3.12		−1.52		−2.51
First-difference
lnCO2	−5.99***	I (1)	−6.81***	I (1)	−12.28***	I (1)	−22.95***	I (1)
lnY	−5.94***	I (1)	−5.97***	I (1)	−5.94***	I (1)	−5.98***	I (1)
lnEU	−6.18***	I (1)	−6.10***	I (1)	−6.17***	I (1)	−6.10***	I (1)
lnGI	−6.55***	I (1)	−6.57***	I (1)	−6.59***	I (1)	−6.62***	I (1)
lnPGI	−5.51***	I (1)	−6.16***	I (1)	−5.59***	I (1)	−6.24***	I (1)
lnSGI	−5.31***	I (1)	−3.77**	I (1)	−5.33***	I (1)	−6.19***	I (1)
lnEGI	−7.31***	I (1)	7.22***	I (1)	−7.48***	I (1)	−7.38***	I (1)

Note. *** and ** represent 1%, and 5% level of importance respectively. The appropriate lag (s) is automatically chosen by the AIC for the ADF test and Newey-West Bandwidth accordingly. The optimal lag for the ADF and P − P tests are 1 and 3, respectively.

Table 3. Markov-switching models results.

	MS1 model		MS2 model		MS3 model
Variable	Regime 1	Regime 2	Regime 1	Regime 2	Regime 1	Regime 2
Constant	17.504***	7.549**	−1.111	18.297***	−15.894***	−9.802***
lnY	−3.033***	−0.580	0.471	−3.833***	−0.539	−0.913***
lnY2	0.263***	0.113	−0.059	0.340***	0.144	0.147***
lnGI			2.212***	0.899**
lnEU			0.335**	−0.478	0.561***	−0.412***
lnPGI					5.325***	−0.806***
lnSGI					0.588**	−0.938***
lnEGI					−0.452	2.019***

Note. *** and ** denote the significance levels at 1% and 5% respectively; MS1 model considers the traditional EKC theory without any control variable; MS2 model investigates the EKC theory, while controlling for an overall globalization and energy use; MS3 model focuses on the EKC theory as well as the effect of the three disaggregated globalization variables—political, social, and economic.

Figure 3. Smoothed probablity for MS1 Model.

Figure 4. Smoothed probability for MS2 Model.

Table 4. Transition probabilities and model specification test results.

Transition probability	MS1 model	MS2 model	MS3 model
$P_{11}$	0.949	0.942	0.843
$P_{12}$	0.018	0.058	0.290
$P_{22}$	0.998	0.942	0.710
$P_{21}$	0.051	0.079	0.157
Diagnostic test
Model specification test [p-values]	0.507	0.974	0.642

Note. $P_{11}$ and $P_{22}$ represent the likelihood that the model stays in regime 1 and regime 2 respectively. $P_{12}$ and $P_{21}$ denote the likelihood that the model transitions from regime 1 to regime 2 and from regime 2 to regime 1 respectively.

Figure 5. Smoothed probability for MS3 Model.

Figure 6. Q–Q, ACF, and PACF plots from MS1 Model.

Figure 7. Q–Q, ACF, and PACF plots from MS2 Model.

Figure 8. Q–Q, ACF, and PACF plots from MS3 Model.

Figure 9. Series plot, ACF, and PACF of ${\mathit{CO}}_2$ emisions in Ghana.

Table 5. Measures of accuracy of the NNAR(p, k) trainning models.

Model	Weight	RMSE	MAPE
NNAR(11, 6)	79	0.00131	0.00980
NNAR(12, 6)	85	0.00164	0.00855
NNAR(13, 7)	106	0.00033	0.00283
NNAR(14, 8)	129	0.00020	0.00175
NNAR(15, 8)	137	0.00041	0.00320

Figure 10. NNAR(14, 8) prediction (Annual ${CO}_2$ growth in Ghana, 2011–2030).

Table 6. Predicted values of the NNAR(14, 8) model.

Year	NNAR(14, 8) forecast	Actual	Difference
2011	9.3033	9.3523	0.0490
2012	9.2596	9.6066	0.3470
2013	9.0913	9.5944	0.5032
2014	9.2011	9.6334	0.4323
2015	9.3166	9.7143	0.3977
2016	9.2814	9.7214	0.4396
2017	9.2318	9.6894	0.4576
2018	9.2174	9.6949	0.4776
2019	9.3011	9.7313	0.4302
2020	9.2814
2021	9.2654
2022	9.2504
2023	9.2830
2024	9.2824
2025	9.2715
2026	9.2659
2027	9.2740
2028	9.2780
2029	9.2731
2030	9.2724

Figure 11. Predicted NNAR(14, 8) values.

Figure A1. Time-series plots of the variables used in the study (level and first difference).

Table A1. Zivot-Andrews (ZA) stationarity test results.

Variable	Constant	Order of It.	Constant and trend	Order of Int.	Break points
Level
lnCO2	−4.57		−5.37		1983
lnY	−4.76		−4.04		2005
lnEU	−4.72		−4.65		1999
lnGI	−3.25		−4.12		1992
lnPGI	−9.33***	I (0)	−9.33***	I (0)	1972
lnSGI	−3.08		−3.23		2009
lnEGI	−4.78		−5.08		1991
First difference
lnCO2	−9.46***	I (1)	−9.68***	I (1)	2008
lnY	−7.32***	I (1)	−7.46***	I (1)	2000
lnEU	−6.72***	I (1)	−7.63***	I (1)	2007
lnGI	−8.14***	I (1)	−8.74***	I (1)	1975, 1987
lnPGI	−13.98***	I (1)	−13.98***	I (1)	1973
lnSGI	−6.87***	I (1)	−10.08***	I (1)	2008, 2011
lnEGI	−7.98***	I (1)	−7.99***	I (1)	1990

Note: *** denotes the significance levels at 1%.

As’ad, M., S. Sujito, and S. Setyowibowo. 2020. “Prediction of Daily Gold Prices Using an Autoregressive Neural Network.” Journal Inform 5 (2): 69–73. doi:10.25139/inform.v0i1.2715.

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