# Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Theoretical Background

_{2}) consumption), together with a high interest in studying its impact on economic activity [12,13,14].

_{2}compared to other fossil fuels) and, in this sense, the study proposes a mathematical model (i.e., a Grey Model with an error latent information function) in order to forecast the annual production of natural gas consumption for the period 2016–2018 in the US and China. The novelty of this study is the extension of the classical Grey Model—GM (1,1)—via the inclusion of corrected outliers—GMCO (1,1)—such that the accuracy of the model improves remarkably. The evidence shows that this mathematical model was able to identify the series of variations and changes in natural gas consumption at the level of the two states mentioned. Specifically, in the short term, China will face a significant increase in its dependence on natural gas imports, with multiple implications for future global price trends. In the case of the United States, the increasing capacity for future natural gas exports to European and Asian countries has been demonstrated and confirmed by the current situation (e.g., the US exports an average of 5.6 billion cubic feet of natural gas per day).

_{2}emissions, energy intensity of GDP, share of natural gas consumption in the energy mix, HDD index, and annual natural gas consumption), it was found that the model leads to better results, in terms of increased performance and level of accuracy, in the analysis of forecasting annual natural gas consumption and production per capita. This proposed mathematical model can also be a valuable tool for forecasting natural gas consumption at the territorial level; the authors believe that its main advantage is the possibility of considering the cognitive uncertainty related to forecasts of socio-economic development, which are input variables to the model for forecasting demand for natural gas or other energy sources.

_{2}emissions, and energy use). Specifically, for Armenia, a unidirectional Granger relationship was found between energy use, CO

_{2}emissions, and GDP, confirming the economic conservation hypothesis; for Georgia and Azerbaijan, a bidirectional relationship was found between energy use and GDP, confirming the feedback and growth hypotheses. In the case of Turkey, the validation of the neutrality hypothesis indicates that GDP and energy use are not cointegrated, implying that energy conservation policies would have no impact on real GDP. Furthermore, the policy implications of the study are aimed at maintaining and consolidating cooperative relations between Georgia, Armenia, Azerbaijan, and Turkey, which illustrates that energy has become an important precondition for the South Caucasus states to reduce poverty and promote regional economic growth and prosperity. Consequently, more and more countries are focusing on building a sustainable environment, and reducing CO

_{2}emissions is one of their priorities. For these reasons, natural gas is becoming the best option for implementing a clean and low carbon energy system compared to traditional energy sources (especially fossil fuels). However, these priorities can only be met if government authorities establish a well-developed strategic energy plan, the core of which is a sound assessment and forecast of natural gas consumption, production, and price issues.

_{2}-emission-free energy system in the Caspian region [29,30].

_{2}emissions and GDP, while renewable energy resources have a positive impact on the level of economic growth in both the short and long term, indicating significant changes in the diversification of energy sources and the reduction of fossil fuel dependency for Romania in the immediate future.

## 3. Research Methodology

^{2}) has been considered in predictive modelling as one of the most widely used and reliable statistical tools to test the goodness of fit of a model or to compare the performance of different models. In fact, the adjusted form of R-squared (adj. R

^{2}) is adj. (adjusted) R

^{2}is considered a basic and essential tool for making a final decision about the relationship between the dependent variable and a set of explanatory variables.

## 4. Results

#### 4.1. Results after the Application of the Mathematical Model at the Individual Level (for Each Caspian Country)

#### 4.2. Results of the Mathematical Model Applied to the Caspian Sea Region (the Panel Mathematical Approach)

_{0}, there is no correlation between the estimators, and for H

_{1}, there are random effects.

## 5. Discussion and Implications

_{2}emissions in the 25th–50th quantile provinces due to the difference in fixed asset investment and heavy industry output. The impact of urbanisation on CO

_{2}emissions is lower in the 10th–25th quantile provinces than in other quantile provinces because these provinces have the lowest number of university graduates. Energy efficiency has a smaller impact on CO

_{2}emissions in the upper 90th quantile province due to differences in R&D personnel investment and the number of patents granted.

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ADF | Augmented Dickey–Fuller |

CO_{2} | Carbon Dioxide |

EU | European Union |

GDP | Gross Domestic Product |

FE | Fixed Effects |

FMOLS | Fully Modified Ordinary Least Squares |

IRF | Impulse Response Function |

PP | Phillips–Perron |

OLS | Ordinary Least Squares |

RE | Random Effects |

VAR | Vector Autoregression |

VEC | Vector Error Correction |

MENA | Middle East and Northen Africa countries |

BRICS | Brazil, Russia, India, China and South Africa countries |

R&D | Research and Development |

## Appendix A

GDP_CAP | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | |
---|---|---|---|---|---|---|

Mean | 2108.05 | 56.89 | 14,305.82 | 37,889.32 | 85.74 | 3.75 |

Median | 2754.71 | 54.35 | 17,274.47 | 41,295.13 | 81.46 | 3.44 |

Maximum | 3159.09 | 111.63 | 32,956.67 | 648,868.70 | 121.50 | 7.41 |

Minimum | 625.10 | 12.76 | 5318.37 | 13,122.89 | 69.48 | 1.91 |

Standard deviation | 1028.32 | 31.90 | 7979.88 | 16,944.71 | 13.49 | 1.23 |

Skewness | −0.39 | 0.31 | 0.57 | −0.08 | 1.11 | 0.86 |

Kurtosis | 1.33 | 1.88 | 2.51 | 1.63 | 3.60 | 3.82 |

Jarque–Bera | 3.96 | 1.93 | 1.81 | 2.21 | 6.25 | 4.25 |

Probability | 0.13 | 0.37 | 0.40 | 0.32 | 0.04 | 0.11 |

Observations | 28 | 28 | 28 | 28 | 28 | 28 |

GDP_CAP | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | |
---|---|---|---|---|---|---|

Mean | 154,000,000.00 | 56.89 | 17,130.21 | 30,587.34 | 43.91 | 1.54 |

Median | 163,000,000.00 | 54.35 | 17,558.27 | 32,400.97 | 43.94 | 1.45 |

Maximum | 177,000,000.00 | 111.63 | 29,293.84 | 35,477.69 | 58.38 | 2.84 |

Minimum | 116,000,000.00 | 12.76 | 5241.89 | 19,234.66 | 29.22 | 0.73 |

Standard deviation | 20,750,843.00 | 31.90 | 7688.58 | 4873.03 | 6.97 | 0.56 |

Skewness | −0.70 | 0.31 | 0.02 | −0.93 | −0.07 | 0.49 |

Kurtosis | 1.91 | 1.88 | 1.77 | 2.55 | 2.53 | 2.45 |

Jarque–Bera | 3.67 | 1.93 | 1.74 | 4.32 | 0.27 | 1.50 |

Probability | 0.15 | 0.37 | 0.41 | 0.11 | 0.86 | 0.47 |

Observations | 28 | 28 | 28 | 28 | 28 | 28 |

GDP_CAP | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | |
---|---|---|---|---|---|---|

Mean | 561,136.00 | 56.89 | 12,287.00 | 43,360.60 | 75.98 | 4.11 |

Median | 596,784.80 | 54.35 | 13,984.81 | 50,321.65 | 73.42 | 4.04 |

Maximum | 798,597.20 | 111.63 | 21,133.98 | 56,687.63 | 105.69 | 6.12 |

Minimum | 259,138.90 | 12.76 | 2551.87 | 14,601.26 | 53.04 | 2.85 |

Standard deviation | 195,282.30 | 31.90 | 5829.95 | 14,012.88 | 15.41 | 0.86 |

Skewness | −0.35 | 0.31 | −0.41 | −0.94 | 0.24 | 0.76 |

Kurtosis | 1.60 | 1.88 | 1.75 | 2.34 | 1.75 | 3.24 |

Jarque–Bera | 2.84 | 1.93 | 2.62 | 4.66 | 2.08 | 2.83 |

Probability | 0.24 | 0.37 | 0.26 | 0.09 | 0.35 | 0.24 |

Observations | 28 | 28 | 28 | 28 | 28 | 28 |

GDP_CAP | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | |
---|---|---|---|---|---|---|

Mean | 491,306.30 | 56.89 | 40,754.32 | 37,198.91 | 52.44 | 6.33 |

Median | 545,096.30 | 54.35 | 40,981.98 | 40,571.29 | 50.45 | 6.34 |

Maximum | 645,614.10 | 111.63 | 48,387.56 | 45,755.51 | 69.39 | 7.05 |

Minimum | 282,990.80 | 12.76 | 35,388.52 | 23,762.61 | 43.77 | 5.53 |

Standard deviation | 126,625.40 | 31.90 | 3384.61 | 7965.10 | 6.51 | 0.41 |

Skewness | −0.49 | 0.31 | 0.25 | −0.75 | 1.08 | −0.20 |

Kurtosis | 1.64 | 1.88 | 2.39 | 1.92 | 3.57 | 2.07 |

Jarque–Bera | 3.31 | 1.93 | 0.73 | 3.97 | 5.90 | 1.18 |

Probability | 0.19 | 0.37 | 0.69 | 0.13 | 0.052 | 0.55 |

Observations | 28 | 28 | 28 | 28 | 28 | 28 |

GDP_CAP | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | |
---|---|---|---|---|---|---|

Mean | 5735.09 | 56.89 | 96,106.03 | 21,695.38 | 80.73 | 0.02 |

Median | 5060.68 | 54.35 | 105,329.40 | 23,112.48 | 81.64 | 0.02 |

Maximum | 10,005.24 | 111.63 | 125,016.50 | 26,682.88 | 168.17 | 0.03 |

Minimum | 2425.83 | 12.76 | 27,105.81 | 11,199.03 | 36.10 | 0.01 |

Standard deviation | 2795.98 | 31.90 | 26,405.32 | 4248.12 | 32.87 | 0.008 |

Skewness | 0.33 | 0.31 | −1.30 | −1.04 | 0.78 | −0.06 |

Kurtosis | 1.51 | 1.88 | 3.70 | 3.34 | 3.36 | 1.47 |

Jarque–Bera | 3.10 | 1.93 | 8.46 | 5,23 | 3.05 | 2.71 |

Probability | 0.21 | 0.37 | 0.01 | 0.07 | 0.21 | 0.25 |

Observations | 28 | 28 | 28 | 28 | 28 | 28 |

GDP_CAP | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | |
---|---|---|---|---|---|---|

Mean | 270,132.00 | 56.89 | 18,966.08 | 2181.20 | 56.50 | 3.16 |

Median | 2,564,433.00 | 54.35 | 19,326.71 | 1942.59 | 56.98 | 3.17 |

Maximum | 4,503,783.00 | 111.63 | 22,008.47 | 3920.01 | 79.74 | 4.28 |

Minimum | 1,478,689.00 | 12.76 | 14,048.87 | 955.61 | 29.19 | 2.32 |

Standard deviation | 1,015,339.00 | 31.90 | 2128.55 | 1133.99 | 14.57 | 0.46 |

Skewness | 0.32 | 0.31 | −0.92 | 0.33 | −0.20 | 0.18 |

Kurtosis | 1.67 | 1.88 | 3.09 | 1.50 | 1.92 | 2.96 |

Jarque–Bera | 2.56 | 1.93 | 4.04 | 3.15 | 1.55 | 0.15 |

Probability | 0.27 | 0.37 | 0.13 | 0.20 | 0.45 | 0.92 |

Observations | 28 | 28 | 28 | 28 | 28 | 28 |

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**Figure 2.**Response to Cholesky One S.D. Innovations ± 2 analytic asymptotic S.E.s. Note: The line in green represents the response of the independent variables to the GDP per capita as our dependent variables. The lines in orange represent the asymptotic standard errors at the 5% level of confidence. (Source: Authors’ own calculations).

Variable | Definition | Measurement Unit | Source | Type of Variable | Acronym |
---|---|---|---|---|---|

GDP per capita | Gross domestic product divided by midyear population | Constant LCU | World Bank | Dependent variable | GDP_CAP |

Global crude oil price | Europe Brent Spot Price FOB | Dollars per Barrel | U.S. Energy Information Administration ^{1} | Independent variable | BRENT |

Gas production per capita | Annual natural gas production per person | Kilowatt-hours/year | Our World in Data ^{2} | Independent variable | GAS_CAP |

Oil production per capita | Annual crude oil production per person | Kilowatt-hours/year | Our World in Data ^{3} | Independent variable | OIL_CAP |

Trade | The sum of exports and imports of goods and services measured as a share of gross domestic product | % of GDP | World Bank | Independent variable | TRADE |

Renewable energy | Share of primary energy from renewable sources | % equivalent primary energy | Our World in Data ^{4} | Independent variable | REEN |

^{1}Retrieved from: https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RBRTE&f=A (accessed on 10 September 2023);

^{2}Retrieved from: https://ourworldindata.org/grapher/gas-prod-per-capita (accessed on 10 September 2023);

^{3}Retrieved from: https://ourworldindata.org/grapher/oil-prod-per-capita?tab=table (accessed on 10 September 2023);

^{4}Retrieved from: https://ourworldindata.org/grapher/renewable-share-energy?tab=table (accessed on 10 September 2023). Source: Authors’ work.

GDP_CAP | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | |
---|---|---|---|---|---|---|

Mean | 26,345,412.00 | 56.89 | 33,258.24 | 28,818.79 | 65.88 | 3.15 |

Median | 570,784.30 | 54.35 | 20,024.06 | 26,820.37 | 59.97 | 3.14 |

Maximum | 177,000,000.00 | 111.63 | 125,016.50 | 64,868.70 | 168.17 | 7.41 |

Minimum | 625.10 | 12.76 | 2551.87 | 955.61 | 29.19 | 0.00 |

Standard deviation | 58,031,111.00 | 31.41 | 32,002.38 | 16,864.60 | 23.19 | 2.11 |

Skewness | 1.86 | 0.31 | 1.64 | −0.04 | 1.09 | 0.09 |

Kurtosis | 4.59 | 1.88 | 4.54 | 2.14 | 4.95 | 2.05 |

Jarque–Bera | 115.10 | 11.63 | 92.94 | 5.19 | 60.25 | 6.53 |

Probability | 0.00 | 0.00 | 0.00 | 0.07 | 0.00 | 0.03 |

Observations | 168 | 168 | 168 | 168 | 168 | 168 |

Time Series/Country | Levin, Lin, Chu | Im, Pesaran, Shin | ADF-Fischer | PP-Fischer | Result | ||||
---|---|---|---|---|---|---|---|---|---|

Level | First Diff. | Level | First Diff. | Level | First Diff. | Level | First Diff. | ||

Azerbaijan | −0.42 | −4.07 * | 0.42 | −4.83 * | 12.86 | 45.56 * | 8.50 | 47.38 * | I (1) |

Iran | −0.01 | −11.05 * | −0.28 | −9.53 * | 14.28 | 93.31 * | 13.70 | 91.01 * | I (1) |

Kazakhstan | −2.61 * | −7.65 * | −0.51 | −6.77 * | 13.06 | 64.29 * | 11.48 | 63.88 * | I (1) |

Russia | −1.50 * | −8.77 * | −0.43 | −8.71 * | 12.17 | 86.15 * | 13.06 | 104.75 * | I (1) |

Turkmenistan | −0.06 | −7.20 * | 0.34 | −6.07 * | 9.03 | 58.47 * | 14.74 | 63.98 * | I (1) |

Uzbekistan | 3.05 | −5.92 * | 1.74 | −6.27 * | 15.72 | 62.57 * | 12.76 | 82.34 * | I (1) |

**Table 4.**Results of estimated coefficients from individual multiple regression (GDP_CAP as dependent variable).

Coefficients | β0 | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | BRENT (−1) | GAS_CAP (−1) | OIL_CAP (−1) | TRADE (−1) | REEN (−1) |
---|---|---|---|---|---|---|---|---|---|---|---|

Model 1 Azerbaijan | −7.75 * | −0.02 | 0.02 | 0.80 * | 0.96 * | −0.24 * | 0.09 | 0.35 * | −0.03 | −0.15 | 0.007 |

Model 2 Iran | 13.92 * | 0.06 * | 0.36 * | 0.32 * | −0.09 *** | 0.02 *** | 0.005 | −0.11 | −0.10 *** | 0.09 *** | −0.001 |

Model 3 Kazakhstan | 9.63 * | 0.14 * | 0.11 | −0.11 | −0.13 * | 0.20 *** | 0.06 | 0.19 | 0.25 | −0.13 | 0.21 ** |

Model 4 Russia | −2.47 | 0.04 | 0.38 | 0.49 *** | 0.02 | 0.08 | 0.05 | 0.26 | 0.27 | −0.07 | 0.09 |

Model 5 Turkmenistan | 9.02 * | 0.14 | 0.16 | −0.21 | −0.26 | −18.27 *** | 0.05 | −0.19 | 0.20 | 0.30 *** | −32.04 * |

Model 6 Uzbekistan | 18.19 * | 0.03 | −0.04 | −0.27 *** | 0.03 | −0.09 * | −0.08 * | 0.29 *** | −0.50 * | 0.08 ** | −0.12 * |

**Table 5.**Results of robustness and diagnostic tests from individual multiple regression (GDP_CAP as dependent variable).

Adj. R^{2} | F-Statistic | Prob. F-Statistic | S.E. of Regression | Jarque–BeraTest | Serial Correlation LM Test | Breusch–Pagan–Godfrey Test | |
---|---|---|---|---|---|---|---|

Model 1 Azerbaijan | 0.98 | 234.67 | 0.00 | 0.06 | 1.30 (0.52) | 4.23 (0.12) | 10.31 (0.41) |

Model 2 Iran | 0.97 | 127.60 | 0.00 | 0.005 | 1.22 (0.54) | 2.29 (0.31) | 10.24 (0.42) |

Model 3 Kazakhstan | 0.98 | 238.81 | 0.00 | 0.04 | 0.37 (0.82) | 4.62 (0.09) | 9.26 (0.51) |

Model 4 Russia | 0.97 | 107.28 | 0.00 | 0.04 | 0.75 (0.68) | 12.76 (0.01) | 10.05 (0.43) |

Model 5 Turkmenistan | 0.93 | 37.79 | 0.00 | 0.12 | 9.98 (0.06) | 13.35 (0.001) | 7.01 (0.72) |

Model 6 Uzbekistan | 0.99 | 612.32 | 0.00 | 0.02 | 0.69 (0.71) | 5.20 (0.07) | 12.74 (0.23) |

Variable | Levin, Lin, Chu | Im, Pesaran, Shin | ADF-Fischer | PP-Fisher | Result | ||||
---|---|---|---|---|---|---|---|---|---|

Level | First Diff. | Level | First Diff. | Level | First Diff. | Level | First Diff. | ||

GDP_CAP | 1.84 | −4.74 * | 3.13 | −4.27 * | 4.48 | 42.07 * | 3.75 | 48.02 * | I (1) |

BRENT | −0.15 | −8.50 * | 0.14 | −7.47 * | 7.42 | 70.80 * | 6.92 | 67.06 | I (1) |

GAS_CAP | 0.87 | −9.82 * | 2.45 | −9.72 | 6.88 | 94.75 * | 6.75 | 96.02 * | I (1) |

OIL_CAP | −3.07 * | −4.00 * | −1.30 | −3.79 * | 17.05 | 36.31 * | 15.86 | 44.01 * | I (1) |

TRADE | −0.17 | −8.62 * | −0.84 | −7.48 * | 14.66 | 71.69 * | 14.04 | 66.81 * | I (1) |

REEN | −1.50 | −8.81 | −2.30 * | −9.49 * | 26.64 | 94.72 * | 26.91 * | 131.43 | I (1) |

Pedroni Residual Cointegration Test | ||||
---|---|---|---|---|

1. Within-Dimension | Statistic | Weighted Statistic | Prob. Statistic | Prob. Weighted Statistic |

Panel v-Statistic | 8.93 | 2.70 | 0.00 | 0.0034 |

Panel rho-Statistic | 2.27 | 1.76 | 0.98 | 0.96 |

Panel PP-Statistic | 0.92 | 0.02 | 0.82 | 0.50 |

Panel ADF-Statistic | 0.08 | −0.86 | 0.53 | 0.19 |

2. Between-dimension | Statistics | Prob. | ||

Group rho-Statistic | 2.95 | 0.99 | ||

Group PP-Statistic | 1.18 | 0.88 | ||

Group ADF-Statistic | −0.70 | 0.23 | ||

Johansen Cointegration Test | ||||

1. Trace Test | ||||

Equation None | Trace statistic * 93.89 | 5% Critical value 95.75 | Prob. 0.06 | |

At most 1 | 63.89 | 69.81 | 0.13 | |

At most 2 | 34.67 | 47.85 | 0.46 | |

At most 3 | 17.63 | 29.79 | 0.59 | |

At most 4 | 8.01 | 15.49 | 0.46 | |

At most 5 | 1.51 | 3.84 | 0.21 | |

2. Maximum Eigenvalue Test | ||||

Equation | Max-Eigen statistic * | 5% Critical value | Prob. | |

None | 29.99 | 40.07 | 0.42 | |

At most 1 | 29.22 | 33.87 | 0.16 | |

At most 2 | 17.03 | 27.58 | 0.57 | |

At most 3 | 9.61 | 21.13 | 0.77 | |

At most 4 | 6.49 | 14.26 | 0.55 | |

At most 5 | 1.51 | 3.84 | 0.21 |

Lag/Statistics | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|

0 | −789.33 | NA | 0.02 | 13.25 | 13.39 | 13.31 |

1 | 608.67 | 2632.90 | 3.19 × 10^{−12} | −9.44 | −8.46 * | −9.04 |

2 | 672.15 | 113.21 * | 2.03 × 10^{−12} * | −9.90 * | −8.09 | −9.16 * |

3 | 697.27 | 42.28 | 2.46 × 10^{−12} | −9.72 | −7.07 | −8.64 |

4 | 720.97 | 37.53 | 3.08 × 10^{−12} | −9.51 | −6.03 | −8.10 |

5 | 746.83 | 38.34 | 3.78 × 10^{−12} | −9.34 | −5.02 | −7.59 |

6 | 771.10 | 33.58 | 4.84 × 10^{−12} | −9.15 | −3.99 | −7.05 |

7 | 804.39 | 42.71 | 5.46 × 10^{−12} | −9.10 | −3.11 | −6.62 |

8 | 836.72 | 38.25 | 6.34 × 10^{−12} | −9.04 | −2.21 | −6.27 |

Dependent Variables (ln Values) | ||||||
---|---|---|---|---|---|---|

Independent Variables (ln Values) | GDP_CAP_{it} | BRENT_{it} | GAS_CAP_{it} | OIL_CAP_{it} | TRADE_{it} | REEN_{it} |

GDP_CAP_{t−1} | 1.52 * | 0.86 | 0.56 *** | 0.25 | −0.11 | −0.14 |

GDP_CAP_{t−2} | −0.52 * | −0.86 | −0.56 *** | −0.25 | 0.10 | 0.13 |

BRENT_{t−1} | −0.01 | 0.94 * | −0.09 ** | −0.06 * | −0.01 | −0.11 |

BRENT_{t−2} | 0.002 | −0.11 | 0.07 *** | 0.03 | 0.01 | 0.11 *** |

GAS_CAP_{t−1} | −0.06 ** | 0.02 | 1.19 * | −0.007 | −0.05 | −0.03 |

GAS_CAP_{t−2} | 0.05 ** | 0.01 | −0.23 * | 0.005 | 0.02 | 0.01 |

OIL_CAP_{t−1} | 0.01 | −0.08 | 0.07 | 1.32 * | 0.06 | 0.01 |

OIL_CAP_{t−2} | −0.01 | 0.099 | −0.05 | −0.31 * | −0.06 | −0.01 |

TRADE_{t−1} | 0.009 | −0.19 | −0.19 ** | 0.01 | 1.10 * | 0.23 *** |

TRADE_{t−2} | 0.04 | 0.15 | 0.16 *** | 0.03 | −0.32 * | −0.22 *** |

REEN_{t−1} | 0.0008 | 0.08 | −0.05 | 0.005 | 0.06 | 0.87 * |

REEN_{t−2} | −0.0009 | −0.056 | 0.03 | 0.005 | −0.07 | 0.07 |

Dependent Variables (ln Values) | Adj. R^{2} | F-Statistic | Prob. F-Statistic | S.E. of Regression | Durbin–Watson Statistic |
---|---|---|---|---|---|

GDP_CAP_{it} | 0.99 | 111,779.50 | 0.00 | 0.04 | 1.95 |

BRENT_{it} | 0.76 | 43.62 | 0.00 | 0.29 | 1.90 |

GAS_CAP_{it} | 0.97 | 524.50 | 0.00 | 0.13 | 1.62 |

OIL_CAP_{it} | 0.99 | 3383.43 | 0.00 | 0.07 | 1.91 |

TRADE_{it} | 0.85 | 79.23 | 0.00 | 0.12 | 1.93 |

REEN_{it} | 0.90 | 120.77 | 0.00 | 0.20 | 1.96 |

Panel Regression Model | β_{0} | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN | Adj. R^{2} | F-Statistic | S.E. of Regression |
---|---|---|---|---|---|---|---|---|---|

Fixed Effects Model | 10.02 * | 0.40 * | 0.15 ** | 0.10 *** | −0.36 * | 0.015 | 0.99 | 4386.68 * | 0.23 |

Random Effects Model | 10.07 * | 0.40 * | 0.14 ** | 0.09 *** | −0.36 * | 0.014 | 0.66 | 68.35 * | 0.24 |

The Hausman Test | |||||||||

Test Summary | Chi-Sq.Statistic | Chi-Sqd.f. | Prob. | ||||||

Cross-section random | 11.57 | 5 | 0.0412 ** |

Null Hypthosesis (H_{0}) → Variable on the Column Does Not Cause Variable on the Line | ||||||
---|---|---|---|---|---|---|

Variable | GDP_CAP | BRENT | GAS_CAP | OIL_CAP | TRADE | REEN |

GDP_CAP | - | 5.73 * | 4.12 *** | 5.43 * | 5.10 * | 2.78 |

BRENT | 3.11 | - | 3.07 | 4.51 ** | 6.80 * | 2.41 |

GAS_CAP | 2.49 | 2.90 | - | 3.50 | 4.74 * | 1.96 |

OIL_CAP | 7.56 * | 5.71 * | 6.86 * | - | 3.23 | 1.93 |

TRADE | 3.94 *** | 1.22 | 4.99 * | 4.83 * | - | 1.20 |

REEN | 1.76 | 1.36 | 2.20 | 2.70 | 2.99 | - |

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**MDPI and ACS Style**

Vlăduţ, O.; Grigore, G.E.; Bodislav, D.A.; Staicu, G.I.; Georgescu, R.I.
Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries. *Energies* **2024**, *17*, 253.
https://doi.org/10.3390/en17010253

**AMA Style**

Vlăduţ O, Grigore GE, Bodislav DA, Staicu GI, Georgescu RI.
Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries. *Energies*. 2024; 17(1):253.
https://doi.org/10.3390/en17010253

**Chicago/Turabian Style**

Vlăduţ, Oana, George Eduard Grigore, Dumitru Alexandru Bodislav, Gabriel Ilie Staicu, and Raluca Iuliana Georgescu.
2024. "Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries" *Energies* 17, no. 1: 253.
https://doi.org/10.3390/en17010253