# Exploring the Nonlinear Relationship between Renewable Energy Consumption and Economic Growth in the Context of Global Climate Change

^{*}

## Abstract

**:**

## 1. Introduction

_{2}emissions and 20% renewable energy consumption by 2030.” Given such demanding goals, the following three questions should be addressed:

- Will energy transition compromise economic growth?
- Can renewable energy consumption promote energy transition and consequently impact China’s economic growth?
- What are its determinants? This topic deserves further discussion to provide a theoretical basis for China’s energy transition.

## 2. Literature Review

#### 2.1. The Increase in Renewable Energy Consumption Promotes Economic Growth

#### 2.2. The Increase in Renewable Energy Consumption Compromises Economic Growth

#### 2.3. Renewable Energy Consumption Has No Significant Impact on Economic Growth

#### 2.4. Regional Differences in the Impact of Renewable Energy Consumption on Economic Growth

## 3. Methods

#### 3.1. Theoretical Models

#### 3.2. Unit Root Test of Panel Data

- (1)
- Unit root test for homogeneous roots

- (2)
- Unit root test for heterogeneous roots

#### 3.3. Cointegration Test

#### 3.4. Panel Threshold Model

#### 3.4.1. Panel Model Construction

_{ni}is a coefficient, while ${q}_{it}$ represents the threshold variable corresponding to the three variables of energy transition, energy intensity, and new technology level selected in this study. γ is the corresponding threshold value, which divides the entire sample into two groups; the corresponding coefficients are ${\beta}_{2i}$ and ${\beta}_{3i}$. I (•) is an indicator function. ${\mu}_{i}$ and ${v}_{t}$ are the unobservable individual fixed and time effects, respectively. ${\epsilon}_{it}$ is a random disturbance term that obeys an independent and identical distribution.

#### 3.4.2. Estimation of Dynamic Panel Threshold Model

#### 3.4.3. Significance Test of Threshold Effect (Nonlinear Test)

_{0}: β

_{2}= β

_{3}, meaning that there exists a linear relationship; the alternative hypothesis is H1: β

_{2}≠ β

_{3}, which signifies the existence of a threshold effect. The test statistic is ${F}_{1}=\left({S}_{0}-{S}_{1}\widehat{\gamma}\right)/{\widehat{\sigma}}^{2}$, where ${S}_{0},{S}_{1}\widehat{\gamma}$ are the residual sums of squares under assumptions H

_{0}and H1, respectively. Hansen suggested using bootstrapping to obtain the distribution of F to obtain an effective p value. When p ≤ $\alpha $, H

_{0}is rejected, indicating the existence of a threshold value; $\alpha $ represents the significance level.

_{0}is established, β

_{2}= β

_{3}, indicating no threshold effect; the model degenerates into a linear model. If H

_{0}: β

_{2}= β

_{3}is rejected, it can be considered to have a threshold effect; then, the threshold estimate authenticity can be tested, H

_{0}: $\widehat{\gamma}$ = γ0, H

_{1}: $\widehat{\gamma}$≠ γ0. Hansen uses the maximum likelihood test threshold value, and the test statistic is $L{R}_{1}\left(\gamma \right)=\left({S}_{1}\left(\gamma \right)-{S}_{1}\widehat{\gamma}\right)/{\widehat{\sigma}}^{2}$. ${S}_{1}\left(\gamma \right),{S}_{1}\widehat{\gamma}$ are the residual sums of squares under the assumptions H

_{0}and H

_{1}, respectively. The distribution of this statistic is nonstandard. When LR

_{1}(γ0) ≤ c (α), H

_{0}is rejected, and the threshold estimator is the true value. Among them, $\mathrm{c}\left(\alpha \right)=-2\left(1-\sqrt{1-\alpha}\right)$, and $\alpha $ is the significance level.

#### 3.5. Variable Selection and Description

- (1)
- Explained variable: Economic growth (Y)

- (2)
- Explanatory variables

- (3)
- Threshold variable

**Hypothesis**

**1.**

**Hypothesis**

**2.**

**Hypothesis**

**3.**

#### 3.6. Data Description

## 4. Results and Discussion

#### 4.1. Unit Root Test

#### 4.2. Panel Cointegration Test

_{0}is rejected, implying that there exists a long-term cointegration relationship. The null hypothesis of the Kao test is that there is no cointegration relationship. We mainly observe the t-statistic and the corresponding p value in the results. The judgment method for the size of the p value is consistent with the judgment method in the unit root test. Pedroni and Kao’s panel cointegration tests are performed on the variables, and the lag order is determined using the Schwarz information criterion. Table 4 presents the results. In the Kao test, the ADF statistic passes the significance test. In the Pedroni test, all statistics pass the significance test. This result shows that the explanatory variable of environmental pollution and other explanatory variables have a long-term cointegration relationship.

#### 4.3. Estimation and Authenticity Test of Threshold Value

#### 4.4. Estimation Results of Dynamic Panel Threshold Model

- (1)
- From the perspective of linear models

- (2)
- Regression results of the threshold model

## 5. Conclusions and Recommendations

#### 5.1. Conclusions

- (1)
- Overall, the impact of renewable energy consumption on real GDP is negative, indicating that China’s current energy structure transformation strategy to increase renewable energy consumption incurs certain economic costs. However, in the long run, this negative impact will become positive as the level of technological innovation of renewable energy increases, the cost of renewable energy further decreases, and the increase in renewable energy consumption results in dynamic economies of scale and learning-by-doing effects.
- (2)
- The regression results of the threshold model reveal a significant threshold effect on energy consumption intensity, and the threshold value is approximately 3.213. When EI ≤ 3.213, renewable energy consumption has a significantly positive impact on economic growth; when EI > 3.213, the impact is significantly negative. This shows that when the energy consumption intensity is low, economic growth can be promoted; however, when the energy consumption intensity is high, the economic cost of renewable energy consumption is also high.

#### 5.2. Recommendations

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Main Opinions | Authors |
---|---|

Increased consumption of renewable energy will boost economic growth | Charfeddine and Kahia (2019) [18]; Khan et al. (2020) [19]; Topcu and Tugcu (2020) [20]; Apergis and Salim (2015) [21]; Markandya et al. (2016) [22]; Zafar et al. (2020) [23]; Odhiambo (2009) [24]; Naseri et al. (2016) [25]; Zrelli (2017) [26] |

Increased consumption of renewable energy at the expense of economic growth | Shahbaz et al. (2020) [27]; Maji et al. (2019) [28]; Han et al. (2020) [29]; Bhattacharya et al. (2016) [30]; Ocal and Aslan (2013) [31]; Qi and Li (2017) [32] |

The increase in renewable energy consumption does not have a significant impact on economic growth | Payne (2009) [33]; Menegaki (2011) [34]; Bao and Xu (2019) [35]; Chang et al. (2015) [36]; Bulut and Muratoglu (2018) [37]; Yu and Hwang (1984) [38]; Kraft and Kraft (1978) [39]; Long (1997) [40]; Yu (1992) [41] |

There are regional differences in the impact of renewable energy consumption on economic growth | Al-mulali et al. (2013) [42]; Yao and Zhang (2019) [43]; Yan et al. (2022) [44]; Qi and Li (2017) [32]; Guo and Cai (2022) [45]; Chen and Ye (2021) [46] |

Variable | Observed Value | Mean | Standard Deviation | Minimum | Maximum |
---|---|---|---|---|---|

lnY | 390 | 5.584 | 1.054 | 3.123 | 11.453 |

lnREC | 390 | 5.563 | 1.432 | 0.775 | 8.994 |

lnNREC | 390 | 7.642 | 0.963 | 3.222 | 8.052 |

lnEI | 390 | 1.785 | 2.008 | 0.143 | 22.342 |

lnET | 390 | 5.332 | 9.032 | 1.123 | 44.571 |

lnNT | 390 | 0.456 | 0.421 | 0.216 | 1.563 |

lnK | 390 | 6.432 | 1.776 | 3.664 | 9.042 |

lnL | 390 | 5.996 | 0.984 | 5.649 | 11.531 |

Variable | LLC Test | IPS Test | Fisher-ADF Test | Fisher-PP Test |
---|---|---|---|---|

t-Statistics | t-Statistics | t-Statistics | t-Statistics | |

lnY | −7.013 *** | −5.326 *** | 131.095 *** | 154.411 *** |

lnREC | −13.124 *** | −10.987 *** | 221.783 *** | 277.932 *** |

lnNREC | −8.179 *** | −3.876 *** | 78.492 *** | 144.342 *** |

lnEI | −127.063 *** | −41.562 *** | 212.875 *** | 245.872 *** |

lnET | −12.543 *** | −10.969*** | 166.324 *** | 205.772 *** |

lnNT | −10.223 *** | −8.128 *** | 168.723 *** | 211.657 *** |

lnK | −9.223 *** | −16.165 *** | 115.325 *** | 217.043 *** |

lnL | −16.127 *** | −18.033 *** | 161.287 *** | 228.619 *** |

Testing Method | Test Hypothesis | Statistics | Statistical Value |
---|---|---|---|

Kao Test | Ho: There is no cointegration relationship (ρ = 1) | ADF | −0.345 *** |

Pedroni Test | Ho: ρi = 1 H1: (ρi = ρ) < 1 | Panel v statistic | −1.347 |

Panel rho statistic | 2.568 | ||

Panel PP statistic | −0.679 *** | ||

Panel ADF statistic | −1.022 *** | ||

Ho: ρi = 1 H1: ρi < 1 | Group rho statistic | 4.134 | |

Group PP statistic | −6.142 *** | ||

Group ADF statistic | −3.889 ** |

Threshold Variable | Single Threshold Model | Double Threshold Model | Threshold Estimate | 95% Confidence Interval | ||
---|---|---|---|---|---|---|

F Value | p Value | F Value | p Value | |||

EI | 1.281 *** | 0.002 | 3.93 | 0.34 | 3.213 | [2.981, 3,442] |

ET | 5.433 *** | 0.001 | 9.442 | 0.23 | 6.456 | [3.232, 24.283] |

NT | 34.722 *** | 0.002 | 46.843 | 0.13 | 1.367 | [1.142, 1.789] |

Variable | Threshold Model | System GMM | ||
---|---|---|---|---|

EI | ET | NT | ||

$\mathrm{ln}RE{C}_{it}\xb7I\left\{{q}_{it}\le \gamma \right\}$ | 0.0234 | −0.0683 *** | −0.0187 *** | |

$\mathrm{ln}RE{C}_{it}\xb7I\left\{{q}_{it}>\gamma \right\}$ | −0.1023 | 0.026 *** | 0.0341 *** | |

$lnREC$ | −0.0485 *** | |||

$ln{Y}_{i,t-1}$ | 0.1322 *** | 0.1588 *** | 0.2384 *** | 0.0446 *** |

$lnK$ | 0.5432 *** | 0.5192 *** | 0.6534 *** | 0.1293 *** |

$lnL$ | 0.4387 ** | 0.4493 *** | 0.5128 *** | 0.3293 *** |

$lnNREC$ | 0.0763 *** | 0.0462 * | 0.0873 ** | 0.0388 ** |

Constant | −4.002 *** | −4.4422 *** | −4.7832 *** | −3.2032 *** |

Observations | 390 | 390 | 390 | 390 |

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**MDPI and ACS Style**

Feng, Y.; Zhao, T. Exploring the Nonlinear Relationship between Renewable Energy Consumption and Economic Growth in the Context of Global Climate Change. *Int. J. Environ. Res. Public Health* **2022**, *19*, 15647.
https://doi.org/10.3390/ijerph192315647

**AMA Style**

Feng Y, Zhao T. Exploring the Nonlinear Relationship between Renewable Energy Consumption and Economic Growth in the Context of Global Climate Change. *International Journal of Environmental Research and Public Health*. 2022; 19(23):15647.
https://doi.org/10.3390/ijerph192315647

**Chicago/Turabian Style**

Feng, Yuting, and Tong Zhao. 2022. "Exploring the Nonlinear Relationship between Renewable Energy Consumption and Economic Growth in the Context of Global Climate Change" *International Journal of Environmental Research and Public Health* 19, no. 23: 15647.
https://doi.org/10.3390/ijerph192315647