Environmental Kuznets Curve for Carbon Dioxide Emissions and Economic Growth in Algeria

Abstract

This article examines how economic growth affects the environment through the lens of carbon dioxide (CO₂) emissions, by testing the validity of the environmental Kuznets curve (EKC) for the case of Algeria during the period 1973–2016. For this, the auto regressive distributed lag (ARDL) method is used. The results of the econometric analysis confirm the existence of a positive long-term relationship between CO₂ emissions and real GDP and show that the direction of this relationship goes from economic growth to CO₂ emissions according to Granger causality tests. Specifically, for a developing country like Algeria, economic growth determines the level of emissions. This implies that an energy policy in favour of the environment can be put in place without risking negative repercussions on economic growth. In addition, the results of the ARDL regression validate EKC’s hypothesis: in the first phase, economic growth leads to a higher level of CO₂ emissions, however, when a given threshold (inflection point) is reached, these emissions decrease.

JEL Codes: O10, O47, Q50, Q53

Keywords

Environmental Kuznets curve (EKC)Carbon Dioxide emission Economic growth ARDL model Algeria

1. Introduction

Nowadays, rapid environmental degradation and modern infrastructure development are causing critical challenges to human life (Aguila, 2020). Huge economic growth is, in one way or another, related to fossil fuel consumption, which contributes toward warming the atmosphere by producing massive greenhouse gases (GHGs) in the environment (Hanaki & Portugal-Pereira, 2018; Toma et al., 2020). GHG production is considered the main factor that influences carbon dioxide (CO₂) emissions (Abeydeera et al., 2019).

The relationship between economic activity and environmental degradation has been widely studied within the framework of environmental economics, becoming one of the topics most addressed by this discipline in the past 20 years. Building on the work of Grossman and Krueger (1991), who found evidence for the existence of an inverted U-shaped relationship between per capita income and certain specific pollutants, the literature has extensively delved into the theoretical and empirical analysis of this relationship, known as the environmental Kuznets curve (EKC). The EKC hypothesis proposes that environmental deterioration is a growing function of economic activity up to a certain critical level of income (turning point), from which higher incomes are progressively associated with less environmental degradation. Since the hypothesis can be analysed for any factor that negatively affects environmental conditions, studies to verify its validity have been extended to too many areas of environmental economics.

According to the theory of the EKC hypothesis, the relationship between these two variables is presented in an inverted U-shape, indicating that when countries increase their levels of development, pollution levels increase in a decreasing manner until they reach a certain level after which they gradually begin to decrease. However, verification of this hypothesis has yielded inconclusive results (Karsch, 2019). In fact, there are studies showing no evidence of the effect of GDP on CO₂ emissions (Salahuddin & Khan, 2013) and where economic growth does not imply less long-term environmental degradation (Roca & Padilla, 2003).

Algeria has experienced significant economic and demographic development in recent years. Although it is a low-greenhouse-gas emitting country, it remains vulnerable to the adverse effects of climate disruption, and is experiencing increasing pressure on its natural resources. Thus, aware of this danger, it is strongly committed to the fight against climate change. Algeria has thus adopted various sectorial strategies, which integrate the environmental dimension in various key areas of the economy such as energy, transport, agriculture, tourism, and so on. In addition, it has aligned itself with measures adopted at the global level, in particular within the framework of the United Nations Framework Convention on Climate Change (UNFCCC). It aims to move towards a new climate policy in accordance with its socio-economic development; however, international rankings indicate that greater efforts remain to be made. While the environmental performance index places Algeria in 83rd place in 2016 (out of a total of 179 countries), the natural resources governance index (RGI, 2017) places Algeria 73rd (total of 86 countries), and the climate change performance index (CCPI, 2015) at 39th (out of 61 countries), clearly behind other Arabian countries such as Tunisia, Morocco and Egypt.

In this study, we aim to analyse the EKC hypothesis and the relationships between CO₂, GDP, and the square of the GDP and ENE by the ARDL model. According to the EKC hypothesis, CO₂ levels initially increase as the country’s GDP grows. After a certain level of GDP is achieved, CO₂ levels start to decline. This study is important since there is a scarcity of single-country studies in the literature on the EKC for Algeria, and also because the country is heavily dependent on fossil fuels for its energy needs. The investigation of the relationship between economic growth and emissions is also important given the topicality of climate change in the last decade.

After this introduction, the second section discusses the most important studies in this field, both globally and based on a specific country. In the third section, the environmental and economic variables selected for the econometric models needed to find the relationship between economic growth and environment are defined. The last two sections discuss the results and the main conclusions.

2. Literature Review

There are several empirical works that can be considered as the foundation of EKC, among which are Grossman and Krueger (1993, 1995), Shafik and Bandyopadhyay (1992), Cropper and Griffiths (1994), Selden and Song (1995), Antle and Heidebrink (1995), Holtz-Eakin and Selden (1995) and Tucker (1995). The common denominator for these works is that they used a polynomial regression model (in level or in logarithm) to study the existing relationship between certain indicators of environmental degradation and income per capita.

For the most up-to-date research studies for the ARDL-model-investigated relationships between economic growth and environmental degradation, the relevant literature is given in Table 1.

Table 1.
Summary of Selected Relevant Studies

Author(s) Country/Countries Period Methodology Results

Lau et al. (2014) Malaysia 1970–2008 ARDL bound tests, VECM EKC valid in short and long run

Shahbaz et al. (2014) Tunisia 1971–2010 ARDL bound tests, VECM EKC valid in short and long run

Yavuz (2014) Turkey 1960–2007 Johansen co-integration ARDL EKC is valid in long run

Al-Mulali et al. (2015) Vietnam 1981–2011 ARDL EKC is invalid in short and long run

Lacheheb et al. (2015) Algeria 1971–2009 ARDL Confirms EKC is invalid

Allali et al. (2015) Algeria 1971–2010 ARDL Confirms EKC is invalid

Mohammed and Pablo-Romero (2016) Algeria 1970–2010 ARDL Confirms EKC is invalid

Li et al. (2016) China 1996–2012 ARDL and GMM Inverted U-shape relationship between CO₂ and GDP

Rafindadi (2016) Japan 1971–2012 ARDL Inverted U-shape association between CO₂ and GDP

Pal and Mitra (2017) China and India 1971–2012 ARDL N-shape association between CO₂ and GDP

Shahbaz et al. (2017) China 1970–2012 ARDL Inverted U-shape relationship between CO₂ and GDP

Shahbaz et al. (2018) France 1955–2016 ARDL Inverted U-shape association between CO₂ and GDP

Alam and Adil (2019) India 1971–2016 ARDL Insignificant relationship between CO₂ and GDP

Cosmas et al. (2019) Nigeria 1981–2016 ARDL & NARDL N-shaped relationship between emissions and GDP

Gessesse and He (2020) China 1971–2015 ARDL and ECM Validates the existence of the EKC

Zhang (2021) China 1971–2014 ARDL N-shaped relationship between CO₂ and GDP in the long run

Özden and Beşe (2021) Australia 1994–2014 ARDL and NARDL EKC hypothesis is not confirmed

Author(s)	Country/Countries	Period	Methodology	Results
Lau et al. (2014)	Malaysia	1970–2008	ARDL bound tests, VECM	EKC valid in short and long run
Shahbaz et al. (2014)	Tunisia	1971–2010	ARDL bound tests, VECM	EKC valid in short and long run
Yavuz (2014)	Turkey	1960–2007	Johansen co-integration ARDL	EKC is valid in long run
Al-Mulali et al. (2015)	Vietnam	1981–2011	ARDL	EKC is invalid in short and long run
Lacheheb et al. (2015)	Algeria	1971–2009	ARDL	Confirms EKC is invalid
Allali et al. (2015)	Algeria	1971–2010	ARDL	Confirms EKC is invalid
Mohammed and Pablo-Romero (2016)	Algeria	1970–2010	ARDL	Confirms EKC is invalid
Li et al. (2016)	China	1996–2012	ARDL and GMM	Inverted U-shape relationship between CO₂ and GDP
Rafindadi (2016)	Japan	1971–2012	ARDL	Inverted U-shape association between CO₂ and GDP
Pal and Mitra (2017)	China and India	1971–2012	ARDL	N-shape association between CO₂ and GDP
Shahbaz et al. (2017)	China	1970–2012	ARDL	Inverted U-shape relationship between CO₂ and GDP
Shahbaz et al. (2018)	France	1955–2016	ARDL	Inverted U-shape association between CO₂ and GDP
Alam and Adil (2019)	India	1971–2016	ARDL	Insignificant relationship between CO₂ and GDP
Cosmas et al. (2019)	Nigeria	1981–2016	ARDL & NARDL	N-shaped relationship between emissions and GDP
Gessesse and He (2020)	China	1971–2015	ARDL and ECM	Validates the existence of the EKC
Zhang (2021)	China	1971–2014	ARDL	N-shaped relationship between CO₂ and GDP in the long run
Özden and Beşe (2021)	Australia	1994–2014	ARDL and NARDL	EKC hypothesis is not confirmed

Source: The authors.

3. Data and Methodology

3.1 Data

Testing the EKC hypothesis for Algeria is carried out in this study through the annual time series dataset from 1973 to 2016. The data used is constructed from two sources: the World Development Indicators (WDI) and the International Energy Agency (IEA). To measure environmental pollution, yearly CO₂ per capita in metric tons is extracted from the WDI, which is calculated by the Carbon Dioxide Information Analysis Center (the CO₂ series used in this study only includes emissions from the burning of fossil fuels and the manufacture of cement.). Annual real GDP per capita (at constant 2010 US$) from the WDI is used for the estimation as a proxy for income. The annual energy consumption per capita in kilograms of oil equivalent is available up to 2016 from the WDI and IEA. Logarithms were applied to all variables to ameliorate heteroscedasticity issues.

3.2 Methodology

This article follows the methodology of recent studies carried out on the ‘economic growth–environmental pollution’ nexus (Ang, 2008; Soytas et al., 2007) integrating energy consumption and foreign trade as explanatory variables. In order to test the long-term relationship between CO₂ emissions, economic growth, energy consumption and foreign trade, and to assess the validity of the EKC hypothesis, the linear logarithmic form is proposed:

L C O 2_{t} = β_{0} + β_{1} L E C_{t} + β_{2} L G D P P_{t} + β_{3} L G D P P_{​}^{2} + β_{4} L C O_{t} + ε_{t}

(1)

Where¹

LCO2_t: CO₂ emissions per capita

LEC_t: Energy consumption per capita

LGDPP_t: Real GDP per capita

$L G D P P_{t}^{2}$ : Real GDP per capita squared

LCO_t: Commercial openness ratio that is used as a trade proxy

ε_t: Error term.

We have opted for auto regressive distributed lag (ARDL), introduced by Pesaran and Shin (1999), because this method is suitable for small sample sizes, it can be applied to non-stationary time series without the constraint of the same order of integration, and endogeneity does not represent a problem (Harris & Sollis, 2003).

The ARDL approach goes through several stages. Equation (1) is transformed as follows:

\begin{matrix} △ L C O 2_{t} = β_{0} + \sum_{i = 1}^{p} δ_{i} △ L C O 2_{t - 1} + \sum_{i = 1}^{p} α_{i} △ L E C_{t - 1} + \\ \sum_{i = 1}^{p} ω_{i} △ L G D P P_{t - 1} + \sum_{i = 1}^{p} γ_{i} △ L G D P P_{t - 1}^{2} + \\ \sum_{i = 1}^{p} θ_{i} △ L C O_{t - 1} + ρ_{1} L C O 2_{t - 1} + ρ_{2} L E C_{t - 1} + ρ_{3} L G D P P_{t - 1} + \\ ρ_{4} L G D P P_{​}^{2} + ρ_{5} L C O_{t - 1} + U_{t} \end{matrix}

(2)

Where, β₀: β₀ Is the drift component, and U_t: White noise. The terms with summation signs represent the error correction model, while those with the coefficient ρ_t represent the long-term relationship.

Verification of the cointegration relation is carried out by the Bounds Test, which carries out an F-test on the hypothesis ρ₁ = ρ₂ = ρ₃ = ρ₄ = ρ₅ = 0 against the alternative hypothesis ρ₁ ≠ ρ₂ ≠ ρ₃ ≠ ρ₄ ≠ ρ₅ ≠ 0.

In order to choose an optimal lag for each variable, the ARDL method estimates the (p + 1)^k regressions, where p is the maximum number of lags and k is the number of variables in the equation. The model is chosen based on the Schawrtz–Bayesian criteria (SBC) and the Akaike information criterion (AIC). The long-term relationships are estimated, after which the error correction model is estimated:

\begin{matrix} Δ L C O 2_{t} = β_{0} + \sum_{i = 1}^{p} δ_{i} Δ L C O 2_{t - 1} + \\ \sum_{i = 1}^{p} α_{i} Δ L E C_{t - 1} + \sum_{i = 1}^{p} ω_{i} Δ L G D P P_{t - 1} + \\ \sum_{i = 1}^{p} γ_{i} Δ L G D P P_{t - 1}^{2} + \sum_{i = 1}^{p} θ_{i} Δ L C O_{i - 1} + μ E C M_{t - 1} + U_{t} \end{matrix}

(3)

4. Results and Analysis

To determine the order of integration of the time series, this study uses the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) stationary tests. In fact, in order to use the Bound Test, it is necessary to ensure beforehand that no series is integrated of order 2. The results of these tests are reported in Table A1. They indicate that all the series are not stationary in level but stationary in the 1st difference. They are therefore integrated of order 1. The criteria of AIC, SC, LR and HQ are then used to select the optimal delay number of the vector autoregressive (VAR) (Table A2). Four VAR models (p = 0, 1, 2, 3) are estimated for the period 1973–2016. The AIC criterion implies a delay of 3, while the LR, SC and HQ criteria imply a delay of 1. It is this last criterion, which is retained for this study.

Table 2.
Result of the ARDL Bound Test

F-statistics 6.4723

Critical Values^a

Threshold of Signification (%) I (0) Bound I (1) Bound

5 2.65 3.85

1 3.37 4.91

F-statistics	6.4723
	Critical Values^a
Threshold of Signification (%)	I (0) Bound	I (1) Bound
5	2.65	3.85
1	3.37	4.91

Source: The authors.

Note: ^aSignificance level 5%.

Then, we use the ARDL approach for cointegration to determine the long-term relationship between the variables. The Bound Test is used for this, which calculates an statistic. From the results in Table 2 we reject the null hypothesis of no cointegration, and conclude that there is a long-term relationship between the model variables.

The results of the Granger causality test (Table 3) show that there is a causal relationship between the two main variables of interest and that this relationship is in the direction of economic growth towards CO₂ emissions. This implies that an energy policy in favour of the environment can be implemented without affecting economic growth.

Table 3.

Granger Causality Test^a

Null Hypothesis	F-statistics	Prob.
LCO2_t does not cause LGDPP_t in the sense of Granger	2.8198	0.1009
LGDPPt_t does not cause LCO2 in the sense of Granger	8.5309	0.0057*
LCO2_t does not cause LGDPP_t2 in the sense of Granger	2.4851	0.1232
LGDPP_t2 does not cause LCO2 in the sense of Granger	8.0467	0.0071*

Source: The authors.

Notes: * indicates a 5% significance level.

^aIf p-value> 0.05 we accept the null hypothesis. If p-value <0.05 we reject the null hypothesis.

Equation (2) is used to estimate the long and short-term coefficients of the ARDL model by considering CO₂ emissions per capita (LCO2_t) as a dependent variable. The estimated long-run coefficients, which also represent the long-run elasticities, are displayed in Table 4. The coefficient of the LCE2 variable is equal to 1.1369 and is statistically significant, implying that an increase of 1 per cent of per capita energy consumption would lead to a 1.14 per cent increase in per capita CO₂ emissions. The positive sign of this coefficient is consistent with the work of Liu (2005) and Ang (2008, 2009). Similarly, the long-run elasticity of CO₂ emissions per capita to GDP per capita (LGDPP_t) is equal to 4.7461 and is statistically significant, implying that a 1 per cent increase in real GDP per capita inhabitant would imply a 4.75 per cent increase in per capita CO₂ emissions. The negative sign of the coefficient of the variable LPIBP2—which is also statistically significant—confirms the hypothesis of the decline in CO₂ emissions, when the country (Algeria) reaches high-income levels. This result supports the EKC’s hypothesis that the level of CO₂ emissions first increases with income, then stabilises before declining.

Table 4.

ARDL Model and Estimated Coefficients of the Variables (Long Term)

Dependent Variable: LCO2_t
Explanatory	Coefficients	Standard Deviation	t-statistics	Prob.
Variables
LECt	1.1369	0.1874	6.0668	0.0000
LGDPP_t	4.7461	1.4273	3.3253	0.0017
LGDPP_t2	–0.2429	0.0726	–3.3458	0.0019
LCO_t	–0.0092	0.0654	–0.1407	0.0793
C	–28.2746	6.2184	–4.5469	0.001
R²	0.9929	R²-adjusted	0.9917
F-statistics	853.1362	Durbin–Watson statistics	1.6798

Source: The authors.

Note: ARDL (1,0,0,0,1) selected on the basis of the SC criterion.

The sign of the coefficient of the variable LOC_t is negative but it is not significant. This coefficient is equal to –0.0092, which suggests that the contribution of foreign trade to CO₂ emissions is minimal. On the other hand, the R² and adjusted R² parameters are 0.9931 and 0.9919, respectively, showing that the model is well-adjusted.²

The error correction mechanism (ECM) is used to test the short-term relationship between the variables (Table A3). The results show that the coefficient of the error-corrected term (–1) is significant, implying that the speed of short-term adjustment to reach equilibrium is significant. On the other hand, this term equals approximately –0.8627, suggesting that when per capita CO₂ emissions are above or below their equilibrium value, they would adjust by 86 per cent per year. The coefficients of the lagged variables represent the short-run elasticities. The latter are significant with the expected signs for all variables, except LOC. For example, a 1 per cent increase in per capita energy consumption would imply a 0.98 per cent increase in per capita CO₂ emissions in the short term.

Diagnostic tests on the residuals of the ARDL regression were also carried out in order to validate the model (Table A4). The LM auto-correlation test as well as the correlogram of the regression residuals confirm the absence of auto-correlation. The White test confirms the absence of heteroskedasticity of the residuals, while the Jarque–Bera test shows that they follow a normal distribution. Ramsey’s test, on the other hand, shows that there are no missing variables or functional form issues in the model.

The last step in the ARDL estimation is to check the stability of the long and short-term parameters of equation (2). The techniques of CUSUM based on the cumulative sum of recursive residuals and CUSUMQ based on the cumulative sum of the square of recursive residuals are applied (Figure A.1). The results show that the graph of the CUSUM and CUSUMQ statistics remain within the critical value interval at the 5 per cent threshold, which implies that the model’s coefficients are stable.

In addition, a dynamic deterministic simulation was carried out on the long-term equation obtained following the econometric analysis in order to estimate the CO₂ emissions per capita for the period 1973–2016 in equation (4) and compare them with the historical values.

\begin{matrix} L C O 2_{t} = 1, 6301 * L E C_{t} + 5, 1411 * L G D P P_{t} - \\ 0, 2421 * L G D P P_{t}^{2} - 0, 0091 L C O_{t} - 28, 9786 \end{matrix}

(4)

Figure 1 shows that the model works quite well. Indeed, the historical values (in blue) and the estimated values (in green) of CO₂ emissions per capita follow a similar trend. Indeed, the simulation gives a value of 3.91 tCO2 per capita in 2016 against 3.85 tCO2 per capita according to historical values.

5. Conclusions

The main objective of this study is to examine how economic growth affects the environment in Algeria through an analysis of the validity of the EKC for the period 1973–2016. The EKC hypothesis was investigated using the ARDL methodology and considering per capita CO₂ emissions as an indicator of environmental conditions.

Empirical results confirm the presence of a robust long-term relationship in Algeria between per capita CO₂ emissions and per capita income. In addition, the positive sign of the coefficient of the latter and the negative sign of the coefficient of the quadratic term of per capita income seem to validate the existence of the EKC for the case of Algeria. However, only actual observations of future values of CO₂ emissions could categorically confirm this, given that Algeria is still a developing country, suggesting that it has not yet reached its turning point.

Figure 1.

Source: The authors.

The estimate of the long-term relationship shows that a 1 per cent increase in per capita energy consumption would lead to a 1.63 per cent increase in CO₂ emissions per capita and that a 1 per cent increase of real GDP per capita would imply a 5.14 per cent increase in CO₂ emissions per capita. The Granger causality test indicates the existence of a one-way causality in the direction of per capita income towards CO₂ emissions.

This means that to implement a policy in favour of the environment and without risking negative repercussions on economic growth, we should target energy consumption or trade; a change in the efficiency of home appliances is needed, along with a change in the electricity mix with an increase in the share of renewables. If we change our consumption pattern, both domestic and exports, we could reduce CO₂ emissions.

Our results are in line with the strategies to reduce greenhouse gases (GHGs) including carbon dioxide CO₂ by 2030. That being said, one of the sectors where emissions is greatest is electricity from traditional sources. As a future line of research, we could analyse the Algerian economy from a sectorial perspective, considering the different electricity subsectors and distinguishing among different electricity sources.

In summary, this article contributes to the literature offering an estimation of the relationship between economic growth and CO₂ emissions in Algeria, and finding that an EKC exists there over the period 1973–2016. Future research directions may use nonlinear cointegration models for further studies on the Algerian economy, since the majority of studies have used symmetric cointegration models. The EKC relationship in Algeria may be analysed only between CO₂ and GDP by taking ENE out of the equation, such as has been done by Jaforullah and King (2017) for Denmark, Iceland, Canada, Finland, Norway, the US and Sweden.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix A

Notes

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Environmental Kuznets Curve for Carbon Dioxide Emissions and Economic Growth in Algeria

Abstract

Keywords

1. Introduction

2. Literature Review

3.1 Data

3.2 Methodology

Table 2. Result of the ARDL Bound Test F-statistics 6.4723 Critical Valuesa Threshold of Signification (%) I (0) Bound I (1) Bound 5 2.65 3.85 1 3.37 4.91

Footnotes

Declaration of Conflicting Interests

Funding

Appendix A

Notes

References

Table 2.
Result of the ARDL Bound Test

F-statistics 6.4723

Critical Values^a

Threshold of Signification (%) I (0) Bound I (1) Bound

5 2.65 3.85

1 3.37 4.91