Driving factors of CO2 emissions in South American countries: An application of Seemingly Unrelated Regression model

doi:10.1016/j.regsus.2024.100182

Abstract

Abstract:

Carbon emissions have become a critical concern in the global effort to combat climate change, with each country or region contributing differently based on its economic structures, energy sources, and industrial activities. The factors influencing carbon emissions vary across countries and sectors. This study examined the factors influencing CO₂ emissions in the 7 South American countries including Argentina, Brazil, Chile, Colombia, Ecuador, Peru, and Venezuela. We used the Seemingly Unrelated Regression (SUR) model to analyse the relationship of CO₂ emissions with gross domestic product (GDP), renewable energy use, urbanization, industrialization, international tourism, agricultural productivity, and forest area based on data from 2000 to 2022. According to the SUR model, we found that GDP and industrialization had a moderate positive effect on CO₂ emissions, whereas renewable energy use had a moderate negative effect on CO₂ emissions. International tourism generally had a positive impact on CO₂ emissions, while forest area tended to decrease CO₂ emissions. Different variables had different effects on CO₂ emissions in the 7 South American countries. In Argentina and Venezuela, GDP, international tourism, and agricultural productivity significantly affected CO₂ emissions. In Colombia, GDP and international tourism had a negative impact on CO₂ emissions. In Brazil, CO₂ emissions were primarily driven by GDP, while in Chile, Ecuador, and Peru, international tourism had a negative effect on CO₂ emissions. Overall, this study highlights the importance of country-specific strategies for reducing CO₂ emissions and emphasizes the varying roles of these driving factors in shaping environmental quality in the 7 South American countries.

Key words: CO₂ emissions, Urbanization, Industrialization, International tourism, Agricultural productivity, Seemingly Unrelated Regression (SUR) model, South American countries

Gadir BAYRAMLI, Turan KARIMLI. Driving factors of CO₂ emissions in South American countries: An application of Seemingly Unrelated Regression model[J]. Regional Sustainability, 2024, 5(4): 100182.

Figures/Tables 9

Table 1

Fig. 1.

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Estimation result of the Seemingly Unrelated Regression (SUR) model for the 7 South American countries."

Country	Dependent variable	Independent variable	Coefficient	Z-value	P-value
Argentina	CO₂ emissions	GDP	0.253	2.426	0.016
		Renewable energy use	-0.014	-2.045	0.042
		Urbanization	0.009	1.741	0.089
		Industrialization	0.024	3.868	0.000
		International tourism	4.102	6.992	0.000
		Agricultural productivity	0.054	0.723	0.473
		Forest area	-7.747	-6.476	0.000
		Constant	10.505	7.026	0.000
Brazil	CO₂ emissions	GDP	0.002	2.354	0.018
		Renewable energy use	-0.033	-18.612	0.000
		Urbanization	0.009	2.139	0.033
		Industrialization	0.007	1.151	0.252
		International tourism	-0.480	-2.850	0.039
		Agricultural productivity	-0.082	-1.762	0.078
		Forest area	-6.413	-5.474	0.000
		Constant	11.115	4.288	0.000
Chile	CO₂ emissions	GDP	-0.003	-0.832	0.404
		Renewable energy use	-0.017	-2.039	0.042
		Urbanization	0.002	2.292	0.047
		Industrialization	0.099	2.320	0.020
		International tourism	1.643	2.911	0.037
		Agricultural productivity	-0.117	-2.961	0.034
		Forest area	-6.312	-1.893	0.059
		Constant	-34.991	-2.887	0.004
Colombia	CO₂ emissions	GDP	0.003	2.753	0.045
		Renewable energy use	-0.002	-4.180	0.024
		Urbanization	0.025	3.533	0.000
		Industrialization	0.107	4.074	0.000
		International tourism	0.723	4.446	0.000
		Agricultural productivity	-0.051	-1.558	0.120
		Forest area	-1.035	-4.673	0.004
		Constant	0.816	0.281	0.779
Ecuador	CO₂ emissions	GDP	-0.002	-3.193	0.014
		Renewable energy use	-0.039	-16.021	0.000
		Urbanization	0.003	1.669	0.097
		Industrialization	0.007	2.465	0.044
		International tourism	0.543	3.574	0.069
		Agricultural productivity	-0.158	-3.435	0.001
		Forest area	-1.417	-4.385	0.017
		Constant	16.593	0.289	0.778
Peru	CO₂ emissions	GDP	-0.001	-2.852	0.040
		Renewable energy use	-0.052	-16.241	0.000
		Urbanization	0.004	2.267	0.021
		Industrialization	0.007	0.423	0.676
		International tourism	0.036	3.040	0.024
		Agricultural productivity	-0.003	-2.145	0.041
		Forest area	-1.604	-3.223	0.026
		Constant	34.584	0.386	0.765
Venezuela	CO₂ emissions	GDP	0.001	3.393	0.069
		Renewable energy use	-0.045	-9.274	0.000
		Urbanization	0.002	3.557	0.058
		Industrialization	0.012	4.479	0.035
		International tourism	3.781	10.273	0.000
		Agricultural productivity	0.078	2.072	0.038
		Forest area	-12.409	-8.488	0.000
		Constant	-24.935	-8.834	0.000

Table 7

Table 8

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Variable	Unit	Description	Data sources
CO₂ emissions	10³ t	The total amount of CO₂ emissions	World Bank (2023)
Gross domestic product (GDP)	USD	The total value of all final goods and services produced by a country or region in a given period of time
Renewable energy use	%	Percentage of the total final energy use
Urbanization	persons	Number of urban population
Industrialization	%	Industrial value added as a percentage of GDP
International tourism	persons	Number of tourist arrivals
Agricultural productivity	%	Agricultural value added as a percentage of GDP
Forest area	km²	-

Variable	Mean	Minimum	Maximum	Standard Deviation
CO₂ emissions (×10⁸ t)	1.306	0.221	5.116	1.208
GDP (×10¹¹ USD)	5.130	0.663	36.300	7.720
Renewable energy use (%)	25.475	7.650	50.050	11.982
Urbanization (×10⁷ persons)	4.470	0.761	18.900	5.130
Industrialization (%)	31.244	18.189	53.089	7.759
International tourism (×10⁷ persons)	0.331	0.043	0.762	0.218
Agricultural productivity (%)	6.283	3.275	15.405	2.126
Forest area (km²)	1,083,250	124,335	5,510,886	1,683,224

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7
Model 1	1.000
Model 2	-0.043	1.000
Model 3	-0.244	0.482	1.000
Model 4	-0.275	0.107	-0.223	1.000
Model 5	-0.159	-0.481	-0.237	0.554	1.000
Model 6	-0.135	0.229	0.732	-0.089	0.219	1.000
Model 7	0.305	-0.406	0.095	-0.396	0.330	0.431	1.000
Breusch-Pagan Lagrange Multiplier test: Chi²(45)=340.781; P-value=0.000

Independent variable	VIF	1/VIF	Mean VIF
GDP	4.566	0.219	2.534
Renewable energy use	3.215	0.311
Urbanization	2.841	0.352
Industrialization	2.212	0.452
International tourism	2.203	0.454
Agricultural productivity	1.531	0.653
Forest area	1.200	0.833

Model	RMSE	R²	Chi²	Model	RMSE	R²	Chi²	P-value
Model 1	0.030	0.945	444.482	Model 5	0.023	0.987	1847.552	0.000
Model 2	0.016	0.989	2180.511	Model 6	0.025	0.991	3178.612	0.000
Model 3	0.056	0.933	337.414	Model 7	0.054	0.949	524.887	0.000
Model 4	0.046	0.898	205.637

Driving factors of CO₂ emissions in South American countries: An application of Seemingly Unrelated Regression model

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Variable	Cross-sectional dependence test	Variable	Cross-sectional dependence test
CO₂ emissions	13.944	Industrialization	8.893
GDP	11.891	International tourism	11.664
Renewable energy use	11.092	Agricultural productivity	3.825
Urbanization	20.613	Forest area	9.316

Independent variable	Coefficient	Standard error	t-statistic	Independent variable	Coefficient	Standard error	t-statistic
Forest area	-5.277	0.568	-9.284^***	Renewable energy use	-0.029	0.010	-2.843^***
International tourism	1.401	0.151	9.309^***	Urbanization	0.008	0.002	5.193^***
Industrialization	0.038	0.015	2.577^***	Agricultural productivity	-0.040	0.075	-0.532
GDP	0.036	0.015	2.385^***	Constant	1.955	18.612	0.105

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