# The Impact of Renewable Energy and Economic Complexity on Carbon Emissions in BRICS Countries under the EKC Scheme

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

_{2}emissions. Moreover, economic complexity and renewable energy aim to improve environmental damage and climate change.

## 1. Introduction

_{2}emissions as applied to the BRICS group of countries.

## 2. Literature Review and Empirical Studies

_{2}emissions. The arguments of environmental Kuznets are also considered.

_{2}emissions to 28 countries of OECD for the period 1990–2014 was investigated by Dogan et al. [6]. The authors used as explanatory variables economic complexity, renewable energy, population, and income per capita. Considering panel data FMOLS–Fully Modified Least Squares, DOLS–Dynamic Ordinary Least Squares, and ARDL–Autoregressive Lag Distribution, the econometric results reveal that population and income per capita are positively correlated with carbon dioxide emissions. Moreover, renewable energy and economic complexity variables negatively affect CO

_{2}emissions, showing that renewable energy and economic complexity encourage environmental improvements.

_{2}emissions. In this context, the experience of BRICS was considered by Khattak et al. [3]. The authors considered panel data for the period 1980–2016. As explanatory variables, they selected the EKC hypothesis, i.e., U-shaped curve (income per capita and squared income per capita), innovation measured by patents, and renewable energy. Considering the CCEMG estimator, we observe that BRICS countries validate the EKC assumptions. The innovation variable presents a positive effect on CO

_{2}emissions and a negative impact of renewable energy on CO

_{2}emissions. The same tendency is valid for Brazil, China, and Russia; nonetheless, for India and South Africa, the hypotheses of ECK are not valid. In this line, Azevedo et al. [2] considered two groups of countries. The first group is composed of Brazil and Russia. The authors evaluated the impact of the lagged variable of carbon dioxide emissions and income per capita on CO

_{2}emissions. The results showed that in the long run (lagged variable of carbon dioxide emissions) presents a positive effect, indicating that climate change increases and economic growth are positively correlated with carbon dioxide emissions. In the second group, Azevedo et al. [2] showed that lagged variable of carbon dioxide emissions increased, reflecting the environmental damage.

_{2}emissions proving an environmental improvement. Finally, natural resources and globalization are positively associated with carbon dioxide emissions. In this line, Elshimy and El-Aasar [25] tested the Arabic experience for the period 1980–2014. The authors used the econometric strategy panel cointegration FMOLS–Fully Modified Least Squares, DOLS–Dynamic Ordinary Least Squares, VECM–vector error correction model, Granger causality, and the results demonstrated that income per capita and squared income per capita are according to EKC hypotheses. The variables of bovine meat and non-renewable electricity present a positive effect on carbon footprint. Besides, renewable electricity aims to decrease climate change, i.e., they found a negative relationship between renewable energy and carbon footprint.

_{2}emissions. The author applied panel data for Portugal, Spain, Italy, Ireland, and Greece for 1995–2015. The econometric results demonstrated that economic growth and corruption accelerate carbon dioxide emissions. Nevertheless, international trade and renewable energy presented a negative linkage with CO

_{2}emissions, indicating an improvement in the environment.

## 3. Materials and Methods

_{2}emissions are used in this article. The approach considered in this research is panel data for the period 1990–2015, and the sample covers BRICS countries (Brazil, Russia, India, China, and South Africa). The selected period took into account establishing a balanced data panel. Furthermore, the series for the variable of renewable energies is accessible up to 2015.

**Hypothesis**

**1**

**(H1).**

^{2}< 0).

^{2}) on carbon dioxide emissions for the experience of BRICS countries.

**Hypothesis**

**2**

**(H2).**

_{ij}> 1 means that the country has a comparative advantage (higher economic complexity) and when RCA

_{ij}< 1 (lower economic complexity). According to the literature (e.g., Romero and Gramkow [8]; Chu [7] Can and Gozgor [5], Dogan et al. [6]), economic complexity aims to improve the environment (ECI < 0).

**Hypothesis**

**3**

**(H3).**

^{2}, ECI, REW)

_{it}= β

_{0}+ β

_{1}LogGDP

_{it}+ β

_{2}LogGDP

^{2}

_{it}+ β

_{3}ECI

_{it}+ β

_{4}LogREW

_{it}+ δ

_{t}+ η

_{i}+ ε

_{it}

^{2}—symbolizes the logarithm of squared income per capita expressed in US dollars.

_{y}(τ|CE

_{it}) = (α

_{i}+ δ

_{i}q(τ)) + X

_{it}′β + Z

_{it}′γ q(τ)

_{y}(τ|CE

_{it}) indicates the quantile distribution of carbon dioxide emissions (CE), and X

_{it}′ denotes the independent variables of income per capita (LogGDP), squared income per capita (LogGDP

^{2}), economic complexity index (ECI), and renewable energy (LogREW). The function (α

_{i}+ δ

_{i}q(τ)) reveals the scalar effect (the quantile fixed by the individual in the analysis). Furthermore, the vector of components X is designated by Z

_{t.}

## 4. Empirical Results and Discussion

_{2}emissions is also considered.

^{2}) and carbon dioxide emissions (LogCE) represent the higher value of Maximum. Moreover, all variables demonstrate a negative skew. The variable of renewable energy (LogREW) indicates the low Kurtosis value, and carbon dioxide emissions (LogCO

_{2}) present the low Kurtosis value.

_{2}), income per capita (LogGDP), economic complexity (ECI), and renewable energy (LogREW) are integrated at first difference.

^{2}), economic complexity (ECI), renewable energy (LogREW), and CO

_{2}emissions (LogCE).

^{2}), and renewable energy (LogREW) are statistically significant at 1% levels. Nevertheless, the economic complexity index (ECI) is statistically significant at a 5% level with FMOLS–panel fully modified least squares, FE–fixed effects, and 1% level with DOLS panel Dynamic Least Squared.

^{2}) confirm a positive and negative effect on carbon dioxide emissions, and these are statistically significant at a 1% level (Figure 5). The previous studies of Khattak et al. [3], Aziz et al. [24], Elshimy and El-Aasar [25] give support to our result. Further, the economic complexity (ECI) and renewable energy (LogREW) variables are negatively associated with carbon dioxide emissions. These results are according to Can and Gozgor [5], Dogan et al. [6], Chu [7], Romero and Gramkow [8]), i.e., these studies also found a negative correlation between economic complexity and pollution emissions. Furthermore, Balsalobre-Lorente et al. [13], Koc and Bulus [28], and Leitão [29] also demonstrated a negative correlation between renewable energy and CO

_{2}emissions.

## 5. Conclusions

^{2}), renewable energy (REW), and economic complexity index (ECI) are cointegrated in the long run.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Panel data distribution of carbon dioxide emissions. Source: Authors’ elaboration based on World Development Indicators (2021).

**Figure 2.**Panel data distribution of income per capita. Source: Authors’ elaboration based on World Development Indicators (2021).

**Figure 3.**Panel data distribution of economic complexity index. Source: Authors’ elaboration based in Atlas Database (2021).

**Figure 4.**Panel data distribution of renewable energy. Source: Authors’ elaboration based on World Development Indicators (2021).

Dependent Variable | Source | |
---|---|---|

LogCE—Logarithm of carbon dioxide emissions in kilotons | World Development Indicators (2021) from world bank institution and Carbon Dioxide Information Analysis Center. | |

Explanatory Variables | Expected Signs | Source |

LogGDP—Logarithm of income per capita established on purchasing power parity (PPP) | Positive impact on CE | World Development Indicators (2021) from world bank institution and OECD National Account. |

LogGDP^{2}—Logarithm of squared income per capita established on purchasing power parity (PPP) | Negative impact on CE | World Development Indicators (2021) from world bank institution and OECD National Account. |

ECI—Economic complex | Negative impact on CE | Atlas Database. |

LogREW—Logarithm of a percentage of renewable energy use. | Negative impact on CE | World Development Indicators (2021) from world bank institution and OECD National Account. |

Descriptive Statistics | LogCE | LogGDP | LogGDP^{2} | ECI | LogREW |
---|---|---|---|---|---|

Mean | 5.711 | 3.797 | 14.284 | 0.088 | 1.299 |

Median | 6.016 | 3.859 | 14.512 | 0.234 | 1.406 |

Maximum | 7.013 | 4.416 | 21.350 | 1.164 | 1.768 |

Minimum | 3.760 | 2.992 | 7.9057 | −1.442 | 0.508 |

Std. Dev. | 0.928 | 0.345 | 3.214 | 0.581 | 0.413 |

Skewness | −0.878 | −0.541 | −0.086 | −0.948 | −0.813 |

Kurtosis | 2.576 | 2.501 | 2.421 | 3.031 | 2.354 |

Probability | 0.000 | 0.0 24 | 0.383 | 0.000 | 0.000 |

Observations | 126 | 126 | 126 | 126 | 126 |

Variables | Level | First Difference | ||
---|---|---|---|---|

Carbon Dioxide Emissions | LogCE | DLogCE | ||

Method | Statistic | p-value | Statistic | p-value |

Levin, Lin & Chu t | 3.935 | (1.000) | −7.856 *** | (0.000) |

ADF—Fisher Chi-square | 2.987 | (0.987) | 63.135 *** | (0.000) |

PP—Fisher Chi-square | 3.296 | (0.974) | 69.846 *** | (0.000) |

Income per capita | LogGDP | DLogGDP | ||

Method | Statistic | p-value | Statistic | p-value |

Levin, Lin & Chu t | 3.489 | (0.992) | −2.244 ** | (0.012) |

ADF—Fisher Chi-square | 1.397 | (0.999) | 17.215 * | (0.069) |

PP—Fisher Chi-square | 0.077 | (1.000) | 20.376 ** | (0.025) |

Level | First difference | |||

Economic Complexity | ECI | DECI | ||

Method | Statistic | p-value | Statistic | p-value |

Levin, Lin & Chu t | −1.050 | (0.147) | −5.6043 *** | (0.000) |

ADF—Fisher Chi-square | 11.169 | (0.344) | 43.912 *** | (0.000) |

PP—Fisher Chi-square | 11.682 | (0.307) | 54.697 *** | (0.000) |

Level | First difference | |||

Renewable Energy | LogREW | DLogREW | ||

Method | Statistic | p-value | Statistic | p-value |

Levin, Lin & Chu t | −2.275 ** | (0.011) | −4.074 *** | (0.000) |

ADF—Fisher Chi-square | 15.725 | (0.101) | 47.462 *** | (0.000) |

PP—Fisher Chi-square | 32.948 *** | (0.000) | 75.539 *** | (0.000) |

Within-Dimension | |||||
---|---|---|---|---|---|

Weighted | |||||

Statistic | Prob. | Statistic | Prob. | ||

Panel v-Statistic | 0.257 | (0.080) | −1.128 | (0.870) | |

Panel rho-Statistic | −0.207 | (0.482) | −0.005 | (0.482) | |

Panel PP-Statistic | −2.442 *** | (0.007) | −3.702 *** | (0.000) | |

Panel ADF-Statistic | −1.951 *** | (0.000) | −3886 *** | (0.000) | |

Between-Dimension | |||||

Statistic | Prob. | ||||

Group rho-Statistic | 0.830 | (0.875) | |||

Group PP-Statistic | −3.517 *** | (0.000) | |||

Group ADF-Statistic | −3.200 *** | (0.000) | |||

t-Statistic | Prob. | ||||

ADF | −2.206 ** | (0.014) | |||

Residual variance | 0.000710 | ||||

Heteroskedasticity– and autocorrelation—consistent (HAC) variance | 0.000745 |

Variables | Variance Inflation Factor (VIF) | 1/VIF |
---|---|---|

LogGDP | 1.23 | 0.812 |

ECI | 1.34 | 0.749 |

LogREW | 1.33 | 0.755 |

Mean VIF | 1.30 |

**Table 6.**Panel Fully Modified Least Squares (FMOLS), Panel Dynamic Least Squared (DOLS), and Fixed Effects (FE).

Variables | FMOLS | DOLS | Fixed Effects |
---|---|---|---|

LogGDP | Coef. | Coef. | Coef. |

1.080 *** | 1.471 *** | 2.214 *** | |

(0.000) | (0.007) | (0.000) | |

LogGDP^{2} | −0.076 *** | −0.130 *** | −0.204 *** |

(0.000) | (0.006) | (0.000) | |

ECI | −0.119 ** | −0.227 *** | −0.216 ** |

(0.018) | (0.000) | (0.016) | |

LogREW | −0.708 *** (0.002) | −1.681 *** (0.000) | −0.642 *** (0.004) |

Constant | 1.077 *** (0.000) | ||

Observations | 121 | 111 | 126 |

Adj R^{2} | 0.389 | ||

Hausman Test: Chi-Sq. Statistics (4) = | 214.275 *** |

Variables | Quantile 25th | Quantile 50th | Quantile 75th |
---|---|---|---|

LogGDP | Coef. | Coef. | Coef. |

3.607 *** | 4.387 *** | 5.594 *** | |

(0.000) | (0.000) | (0.000) | |

LogGDP^{2} | −0.415 *** | −0. 579 *** | −0.820 *** |

(0.000) | (0.000) | (0.000) | |

ECI | −1.234 *** | −0.849 *** | −0.069 ** |

(0.000) | (0.006) | (0.577) | |

LogREW | −1.880 *** (0.000) | −2.088 *** (0.000) | −2.561 *** (0.000) |

Observations | 126 | 126 | 126 |

Wald test | 0.230 (0.994) | 0.230 (0.994) | 0.230 (0.994) |

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**MDPI and ACS Style**

Leitão, N.C.; Balsalobre-Lorente, D.; Cantos-Cantos, J.M. The Impact of Renewable Energy and Economic Complexity on Carbon Emissions in BRICS Countries under the EKC Scheme. *Energies* **2021**, *14*, 4908.
https://doi.org/10.3390/en14164908

**AMA Style**

Leitão NC, Balsalobre-Lorente D, Cantos-Cantos JM. The Impact of Renewable Energy and Economic Complexity on Carbon Emissions in BRICS Countries under the EKC Scheme. *Energies*. 2021; 14(16):4908.
https://doi.org/10.3390/en14164908

**Chicago/Turabian Style**

Leitão, Nuno Carlos, Daniel Balsalobre-Lorente, and José María Cantos-Cantos. 2021. "The Impact of Renewable Energy and Economic Complexity on Carbon Emissions in BRICS Countries under the EKC Scheme" *Energies* 14, no. 16: 4908.
https://doi.org/10.3390/en14164908