Effect of economic growth on CO₂ emission in developing countries: Evidence from a dynamic panel threshold model

Figures & data

Table 1. Summary of the literature review

Authors	Countries	Period	Variables	Methodologies	Main results
Aslanidis and Iranzo (2009)	77 non-OECD countries	1971−1997	Per capita income and carbon dioxide (CO2) emission	Panel smooth transition regressions (PSTR)	No evidence of environmental Kuznets curve (EKC)
Chiu (2012)	52 developing countries	1972−2003	Arable land area, real GDP per capita, rural population density, trade openness, and political freedom	PSTR	EKC relationship exits for deforestation. Strong threshold effect between deforestation and GDP
Duarte et al. (2013)	65 countries	1962−2008	Water use per capita, income per capita, precipitation and political freedom	PSTR	Inverted-U relationship found, with a marked downward limb that dominates the nexus
Chen and Huang (2014)	36 countries	1985−2012	CO2 per capita and GDP per capita, oil consumption, natural gas consumption, and coal consumption	PSTR	Regime switching relationship GDP per capita growth and CO2 emissions. Significant effect of oil consumption, natural gas consumption, and coal consumption
Heidari et al. (2015)	Five ASEAN countries	1980–2008	Economic growth, CO2 emissions and energy consumption	PSTR	Results support the EKC hypothesis
Narayan and Narayan (2010)	43 developing countries	1980–2004	Per capita CO2 emissions and per capita GDP	Pedroni panel cointegration tests and panel VECM	Income elasticity in the long run was smaller than the short run only in two panels thus EKC exists in these two panels
Farhani and Rejeb (2012)	15 MENA countries	1973–2008	Energy consumption, GDP and CO2 emissions	Panel cointegration methods and panel causality test	No causal link between GDP and energy consumption; and between CO2 emissions and energy consumption in the short run. However, in the long run, there is a unidirectional causality running from GDP and CO2 emissions to energy consumption
Saidi and Hammami (2015)	58 countries	1990–2012	Energy consumption per capita, GDP per capita, CO2 emissions per capita, Financial development, Capital stock and Population	Dynamic panel data model with GMM	All variables had positively and mostly significant impact on energy consumption in all four panels.
Kasman and Duman (2015)	15 new EU member	1992–2010	Per capita total primary energy consumption, per capita GDP, per capita CO2 emissions, trade openness, and the share of urban population	Panel cointegration tests by Kao, Pedroni, Westerlund and panel-based error correction model	EKC hypothesis supported. Short-run unidirectional panel causality running from energy consumption, trade openness and urbanization to CO2 emissions. Long-run causal relationship also exists
Al-mulali et al. (2015)	18 Latin America and Caribbean countries	1980−2010	GDP, electricity consumption, financial development and CO2	Kao panel cointegration test, FMOLS, VECM Granger causality test	EKC between GDP and CO2 supported. Financial development has a negative long run effect. Energy consumption had no long-run effect on CO2. Bidirectional causality between CO2 and all variables
Salahuddin et al. (2015)	Six Gulf Cooperation Council (GCC) countries	1980–2012	CO2 emissions, economic growth, electricity consumption and financial development	DOLS, FMOLS, dynamic fixed effect, panel Granger causality test	Electricity consumption and economic growth have a positive long run relationship with CO2 emissions while financial development had a negative effect. Bidirectional causality between economic growth and CO2 emissions. Unidirectional causality from running electricity consumption. No causal link between financial development and CO2
Magazzino (2016)	10 Middle East countries	1971–2006	CO2 emissions, economic growth, and energy use	Panel VAR	For 6 countries, the effect of CO2 emissions on growth is negative. CO2 emissions is driven by energy consumption. CO2 emissions and energy have no impact on growth in the rest 4 four
Ahmed et al. (2017)	106 countries	1971–2011	Energy consumption per capita growth, CO2 emissions per capita growth and real GDP per capita growth	Panel VAR	EKC hypothesis is not supported. Heterogeneous effect of various types of energy consumption. Bidirectional causality between total economic growth and energy consumption
Kais and Ben Mbarek (2017)	Three North African countries	1980–2012	Energy consumption, CO2 emissions and economic growth	Panel co-integration test and panel VECM	Unidirectional relationship from causality running from economic growth to CO2 and also from energy consumption to CO2 emissions
Ahmed et al. (2017)	Five South Asian countries	1971–2013	CO2 emission, energy consumption, income, trade openness and population	Pedroni- Kao- and Johansen-Fisher-panel cointegration tests	In the long run, energy consumption, trade openness and population has negative impact on CO2 and unidirectional causality from them to CO2
Uddin et al. (2017)	27 highest emitting countries	1991−2012	Real income, financial development, trade openness, ecological footprint (EF)	Pedroni co-integration tests, DOLS and FMOLS	Long run relationship found. EF and real income share positive and significant long run relationship. Trade openness has negative effect on EF. Financial development reduces EF
Charfeddine and Mrabet (2017)	15 MENA countries	1975–2007	EF, energy-use, real GDP, life expectancy at birth, fertility rate and political institutional index	Pedroni panel cointegration test and Grange panel VECM	EKC hypothesis is validated in all countries and oil-exporting countries but not in non-oil exporting countries. Long term effect of Urbanization, life expectancy at birth and fertility rate is found. Energy use worsens ecological footprint. Bidirectional causality found between EF, GDP and energy-use variables
Shahbaz et al. (2017)	105	1980–2014	trade openness, CO2 emissions and economic growth	Pedroni and Westerlund panel cointegration tests, panel VECM	The three variables are cointegrated. Trade openness worsens environmental quality. Feedback causality between trade openness and CO2 at the global level and for middle income countries but unidirectional causality from trade openness to CO2 for the high income and low income countries

Table 2. Variable description

Variables	Symbol	Measure	Expected sign	Economic implication
CO2 emission	CO2	CO2 emissions in metric tons per capita	N/A	–
Economic growth	GDP	GDP per capita in constant 2010 US$	±	On one hand, higher level of income CO2 emissions if increases demand for more energy or promotes industries whose activities generate pollution. On the other hand, environmental quality may improve if higher income leads to adoption of environmental friendly production techniques and consumption of clean and green energy
Population	POP	Total population for midyear estimates	+	High population could lead to increased demand for energy and thus fossil fuel emissions. Also it can increase emission through deforestation
Energy consumption	ECONS	Energy use in kg of oil equivalent per capita	±	Electricity consumption will have negative effect on CO2 emissions if the country uses friendly environment energy sources. Otherwise, it will have positive effect on CO2 emissions
Financial development	FD	Domestic credit to private sector (% of GDP)	±	On one hand, financial development may help firms in adopting advanced cleaner and environment friendly technology in the energy sector; it may also attract foreign direct investment, which enhance research and development activities that improve economic activities. On the other hand, financial sector may increase manufacturing activities which in turn leads to an increase in industrial pollution and environmental degradation

Note: N/A implies not applicable since CO₂ is the dependent variable. However, its lag as an explanatory variable can provide indication of its persistence.

Table 3. Descriptive properties of carbon (iv) oxide, GDP, ECONS, POP and FD

	CO2	GDP	ECONS	POP	FD^a
Mean	2,18,149.700	3040.665	760.8745	1.07E + 08	30.242
Maximum	1,02,49,463.000	19,492.680	2979.074	1.36E + 09	166.504
Minimum	260.357	234.922	180.671	6,01,734.000	0.198
Std. Dev.	7,94,536.300	2,934.601	550.389	2.52E + 08	27.356

^a The financial development variable is for only 30 countries as Zimbabwe was dropped due to large missing data points. It is used in the robustness analysis only.

Table 4. Correlation matrix

	CO2	GDP	ECONS	POP
CO2	1.000
GDP	0.418*** (0.000)	1.000
ECONS	0.540*** (0.000)	0.800*** (0.000)	1.000
POP	0.819*** (0.000)	−0.081*** (0.003)	0.086*** (0.002)	1.000

Note: p-values are in parenthesis.

*** Indicates significance at 1%.

Table 5. Unit root tests

	Level				First difference
Test	CO2	GDP	ECONS	POP	CO2	GDP	ECONS	POP
Im, Pesaran and Shin	0.949 (0.829)	4.783 (1.000)	1.569 (0.942)	−16.857*** (0.000)	−21.361*** (0.000)	−14.406*** (0.000)	−18.236*** (0.000)	−10.729*** (0.000)
ADF-Fisher	72.363 (0.173)	31.490 (1.000)	57.531 (0.637)	183.341*** (0.000)	525.637*** (0.000)	330.759*** (0.000)	436.292*** (0.000)	277.269*** (0.000)
PP-Fisher	76.806 (0.098)	34.089 (0.999)	68.272 (0.273)	637.900*** (0.0000	866.732*** (0.000)	550.324*** (0.000)	748.659*** (0.000)	45.265 (0.946)
Levin, Lin & Chu	−0.853 (0.197)	1.388 (0.918)	0.755 (0.775)	−28.732*** (0.001)	−16.303 (0.000)	−10.744*** (0.000)	−14.420*** (0.000)	−18.140*** (0.000)
Breitung	0.545 (0.707)	2.393 (0.992)	1.920 (0.973)	5.637 (1.000)	−10.389*** (0.000)	−11.082*** (0.000)	−9.413*** (0.000)	−4.076*** (0.000)

Note: p-values in parenthesis.

*** Indicates significance at 1%.

Table 6. Economic growth thresholds and CO₂ emission

Threshold estimates:
$\widehat{\mathit{\gamma }}$	0.934%
95% confidence interval	[0.929, 1.073]
Impact of economic growth:
${\widehat{\mathit{\beta }}}_1$	−0.061 (0.512)
${\widehat{\mathit{\beta }}}_2$	0.156 (0.127)
Impact of covariates:
Initial CO2 emission	−0.125 (0.089)
Energy consumption	0.740*** (0.119)
Population	0.503* (0.266)
${\widehat{\mathit{\delta }}}_1$	−0.116 (0.071)
N	31
T	43
Total observation	1,333

* Indicate significance at 10% level, respectively.

*** Indicate significance at 1% level, respectively.

Table 7. Economic growth thresholds and CO₂ emission

	All developing countries	Middle income	Low income
Threshold estimates:
$\widehat{\mathit{\gamma }}$	0.934%	1.053%	0.934%
95% confidence interval	[0.934, 0.937]	[0.978, 1.081]	[0.934, 0.937]
Impact of economic growth:
${\widehat{\mathit{\beta }}}_1$	−0.105 (0.558)	0.328** (0.166)	−0.537 (0.862)
${\widehat{\mathit{\beta }}}_2$	0.149 (0.133)	0.146 (0.147)	−0.104 (0.252)
Impact of covariates:
Initial CO2 emission	−0.127 (0.091)	0.128 (0.092)	−0.121 (0.018)
Energy consumption	0.710*** (0.122)	0.567*** (0.172)	0.862*** (0.167)
Population	1.378* (0.733)	0.899 (0.897)	1.552 (1.133)
Financial development	0.013 (0.028)	−0.004 (0.019)	0.022 (0.043)
${\widehat{\mathit{\delta }}}_1$	−0.123* (0.075)	−0.046 ** (0.019)	−0.192* (0.114)
N	30	12	18
T	41	41
Total observation	1,230	492	738

^* Indicate significance at 10% level, respectively.

^** Indicate significance at 5% level, respectively.

^*** Indicate significance at 1% level, respectively.

Table 8. Dumitrescu and Hurlin (2012) panel causality test in heterogeneous panels

	k = 1			k = 2			k = 3
	W-Stat.	Z bar-Stat.	Prob.	W-Stat.	Z bar-Stat.	Prob.	W-Stat.	Z bar-Stat.	Prob.
GDP does not cause CO2	2.372	4.706	0.000	3.784	4.107	0.000	4.685	2.914	0.004
CO2 does not cause GDP	1.082	0.096	0.924	2.161	0.098	0.922	3.058	−0.279	0.780
ECONS does not cause CO2	2.031	3.489	0.001	2.686	1.395	0.163	3.041	−0.312	0.755
CO2 does not cause ECONS	1.468	1.474	0.141	2.123	0.005	0.996	3.422	0.436	0.663
POP does not cause CO2	1.607	1.971	0.049	1.912	−0.516	0.606	2.967	−0.458	0.647
CO2 does not cause POP	0.741	−1.126	0.260	1.679	−1.093	0.274	3.293	0.182	0.856
FD does not cause CO2	1.574	1.822	0.069	2.413	0.709	0.478	3.317	0.225	0.822
CO2 does not cause FD	1.526	1.656	0.098	3.621	3.645	0.000	4.794	3.078	0.002

Notes: The W bar test statistic corresponds to the cross sectional average of the N standard individual Wald statistics of Granger non causality tests. The Z bar statistic corresponds to the standardized statistic (for fixed T sample). FD results pertain to all countries excluding Zimbabwe. Values in bold indicates rejection of the null hypothesis of homogenous non causality.

Table 9. Dumitrescu and Hurlin (2012) individual wald granger causality test

	GDP does not Granger cause CO2						CO2 does not Granger cause GDP
	k = 1		k = 2		k = 3		k = 1		k = 2		k = 3
	Wi	pi	Wi	pi	Wi	pi	Wi	pi	Wi	pi	Wi	pi
Argentina	0.339	0.560	1.228	0.541	2.125	0.547	0.412	0.521	0.865	0.649	1.041	0.791
Benin	3.816	0.051	8.385	0.015	8.083	0.044	0.246	0.620	4.528	0.104	6.069	0.108
Brazil	0.015	0.904	1.058	0.589	1.236	0.745	0.398	0.528	0.340	0.844	0.332	0.954
China	1.234	0.267	2.470	0.291	2.188	0.534	0.081	0.776	0.318	0.853	1.113	0.774
Côte d’Ivoire	0.605	0.437	1.167	0.558	1.653	0.648	0.499	0.480	0.798	0.671	1.097	0.778
Cameroon	0.980	0.322	1.058	0.589	2.064	0.559	0.220	0.639	1.199	0.549	6.489	0.090
Algeria	5.405	0.020	1.320	0.517	6.687	0.083	0.004	0.952	0.904	0.636	4.423	0.219
Ecuador	2.234	0.135	3.244	0.198	2.051	0.562	0.025	0.875	1.721	0.423	1.336	0.721
Egypt	2.696	0.101	2.749	0.253	2.680	0.444	5.584	0.018	9.316	0.010	8.476	0.037
Gabon	1.974	0.160	3.681	0.159	5.847	0.119	2.763	0.097	3.349	0.187	6.112	0.106
Ghana	2.344	0.126	2.568	0.277	5.236	0.155	0.079	0.779	0.497	0.780	0.765	0.858
Honduras	0.033	0.855	1.033	0.597	2.294	0.514	5.739	0.017	5.183	0.075	5.005	0.171
Indonesia	0.001	0.977	0.428	0.807	1.840	0.606	0.059	0.808	0.178	0.915	1.399	0.706
India	0.037	0.848	1.561	0.458	1.454	0.693	0.173	0.678	0.261	0.878	0.027	0.999
Iran	4.030	0.045	4.979	0.083	4.442	0.218	1.077	0.300	1.550	0.461	3.032	0.387
Kenya	0.740	0.390	2.119	0.347	1.829	0.609	0.003	0.961	0.299	0.861	0.144	0.986
Morocco	0.040	0.842	0.372	0.831	5.762	0.124	0.060	0.806	0.160	0.923	2.102	0.552
Mexico	6.213	0.013	5.216	0.074	9.755	0.021	0.072	0.788	1.821	0.402	4.560	0.207
Nigeria	1.044	0.307	2.911	0.233	4.601	0.204	0.320	0.571	0.375	0.829	1.209	0.751
Pakistan	3.429	0.064	9.747	0.008	10.921	0.012	0.412	0.521	2.067	0.356	4.369	0.224
Peru	5.764	0.016	10.426	0.005	10.366	0.016	0.001	0.982	1.965	0.374	1.795	0.616
Philippines	0.590	0.443	8.480	0.014	9.722	0.021	2.664	0.103	5.548	0.062	4.447	0.217
Sudan	0.067	0.796	0.586	0.746	3.228	0.358	8.359	0.004	14.009	0.001	14.436	0.002
Senegal	3.299	0.069	3.172	0.205	5.267	0.153	0.267	0.606	1.352	0.509	2.012	0.570
Thailand	0.953	0.329	4.433	0.109	2.989	0.393	0.296	0.587	1.698	0.428	0.835	0.841
Tunisia	0.187	0.666	0.272	0.873	0.905	0.824	0.696	0.404	0.910	0.634	4.981	0.173
Turkey	0.023	0.881	0.069	0.966	0.097	0.992	1.508	0.220	1.850	0.396	2.149	0.542
South Africa	2.746	0.098	2.659	0.265	2.727	0.436	0.940	0.332	0.672	0.715	0.613	0.894
Dem. Rep. Congo	12.322	0.000	13.674	0.001	12.652	0.006	0.057	0.811	0.148	0.929	0.234	0.972
Zambia	8.825	0.003	15.127	0.001	13.554	0.004	0.374	0.541	0.349	0.840	0.456	0.928
Zimbabwe	1.545	0.214	1.105	0.576	0.972	0.808	0.164	0.685	2.760	0.252	3.736	0.291

Note: Values in bold indicates rejection of the null hypothesis of homogenous non causality.

Table 10. Panel causality test for homogenous panels

	k = 1		k = 2		k = 3
	F-Stat.	Prob.	F-Stat.	Prob	F-Stat.	Prob.
GDP does not cause CO2	23.624	0.000	18.370	0.000	15.183	0.000
CO2 does not cause GDP	5.511	0.019	4.020	0.018	3.640	0.012
ECONS does not cause CO2	25.147	0.000	17.656	0.000	12.164	0.000
CO2 does not cause ECONS	0.518	0.472	0.566	0.568	0.473	0.701
POP does not cause CO2	0.234	0.629	0.281	0.755	0.322	0.810
CO2 does not cause POP	0.003	0.958	0.043	0.958	0.035	0.991
FD does not cause CO2	3.382	0.066	4.644	0.010	4.208	0.006
CO2 does not cause FD	3.111	0.078	10.665	0.000	7.970	0.000

Note: FD results pertain to all countries excluding Zimbabwe. Values in bold indicates rejection of the null hypothesis of homogenous non causality.

Aslanidis, N., & Iranzo, S. (2009). Environment and development: Is there a Kuznets curve for CO2 emissions? Applied Economics, 41, 803–810.10.1080/00036840601018994

 Web of Science ®Google Scholar

Chiu, Y. B. (2012). Deforestation and the environmental kuznets curve in developing countries: A panel smooth transition regression approach. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 60, 177–194.10.1111/cjag.2012.60.issue-2

 Web of Science ®Google Scholar

Duarte, R., Pinilla, V., & Serrano, A. (2013). Is there an environmental Kuznets curve for water use? A panel smooth transition regression approach. Economic Modelling, 31, 518–527.10.1016/j.econmod.2012.12.010

 Web of Science ®Google Scholar

Chen, J.-H., & Huang, Y.-F. (2014). Nonlinear environment and economic growth nexus: A panel smooth transition regression approach. Journal of International and Global Economic Studies, 7(2), 1–16.

 Google Scholar

Heidari, H., Turan Katircioğlu, S., & Saeidpour, L. (2015). Economic growth, CO2 emissions, and energy consumption in the five ASEAN countries. International Journal of Electrical Power & Energy Systems, 64, 785–791.10.1016/j.ijepes.2014.07.081

 Web of Science ®Google Scholar

Narayan, P. K., & Narayan, S. (2010). Carbon dioxide emissions and economic growth: Panel data evidence from developing countries. Energy Policy, 38, 661–666.10.1016/j.enpol.2009.09.005

 Web of Science ®Google Scholar

Farhani, S., & Rejeb, J. (2012). Link between economic growth and energy consumption in over 90 countries. Interdisciplinary Journal of Contemporary Research in Business, 3, 199–210.

 Google Scholar

Saidi, K., & Hammami, S. (2015). The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Reports, 1, 62–70.10.1016/j.egyr.2015.01.003

 Web of Science ®Google Scholar

Kasman, A., & Duman, Y. S. (2015). CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Economic Modelling, 44, 97–103.10.1016/j.econmod.2014.10.022

 Web of Science ®Google Scholar

Al-mulali, U., Tang, C. F., & Ozturk, I. (2015). Estimating the environment Kuznets Curve hypothesis: Evidence from Latin America and the Caribbean countries. Renewable and Sustainable Energy Reviews, 50, 918–924.10.1016/j.rser.2015.05.017

 Web of Science ®Google Scholar

Salahuddin, M., Gow, J., & Ozturk, I. (2015). Is the long-run relationship between economic growth, electricity consumption, carbon dioxide emissions and financial development in Gulf Cooperation Council Countries robust? Renewable and Sustainable Energy Reviews, 51, 317–326.10.1016/j.rser.2015.06.005

 Web of Science ®Google Scholar

Magazzino, C. (2016). CO2 emissions, economic growth, and energy use in the Middle East countries: A panel VAR approach. Energy Sources, Part B: Economics, Planning, and Policy, 11, 960–968.10.1080/15567249.2014.940092

 Web of Science ®Google Scholar

Ahmed, K., Rehman, M. U., & Ozturk, I. (2017). What drives carbon dioxide emissions in the long-run? Evidence from selected South Asian Countries. Renewable and Sustainable Energy Reviews, 70, 1142–1153.10.1016/j.rser.2016.12.018

 Web of Science ®Google Scholar

Kais, S., & Ben Mbarek, M. (2017). Dynamic relationship between CO2 emissions, energy consumption and economic growth in three North African countries. International Journal of Sustainable Energy, 36, 840–854.10.1080/14786451.2015.1102910

 Web of Science ®Google Scholar

Uddin, G. A., Salahuddin, M., Alam, K., & Gow, J. (2017). Ecological footprint and real income: Panel data evidence from the 27 highest emitting countries. Ecological Indicators, 77, 166–175.10.1016/j.ecolind.2017.01.003

 Web of Science ®Google Scholar

Charfeddine, L., & Mrabet, Z. (2017). The impact of economic development and social-political factors on ecological footprint: A panel data analysis for 15 MENA countries. Renewable and Sustainable Energy Reviews, 76, 138–154.10.1016/j.rser.2017.03.031

 Web of Science ®Google Scholar

Shahbaz, M., Nasreen, S., Ahmed, K., & Hammoudeh, S. (2017). Trade openness–carbon emissions nexus: The importance of turning points of trade openness for country panels. Energy Economics, 61, 221–232.10.1016/j.eneco.2016.11.008

 Web of Science ®Google Scholar

Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29, 1450–1460.10.1016/j.econmod.2012.02.014

 Web of Science ®Google Scholar