# Are There Spillovers from China on the Global Energy-Growth Nexus? Evidence from Four World Regions

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

_{2}emission, and trade openness, among others. For example, Shahbaz and Lean (2012) found that financial development, economic growth, industrialisation, and urbanisation increase energy consumption for the Tunisian economy. Islam et al.’s (2013) results suggest that energy consumption is influenced by economic growth, financial development, and population level for Malaysia. Mirza and Kanwal (2017) found bidirectional causality between energy consumption, economic growth, and CO

_{2}emissions for Pakistan. Recently, globalisation caught researchers’ attention. Bidirectional causality between energy consumption and economic growth, energy consumption and trade openness, and economic growth and trade openness was found by Shahbaz et al. (2013) in Indonesia. Marques et al. (2017a) examined the relationships between energy consumption, economic growth, and globalisation for 43 countries and found that globalisation drives both energy consumption and economic growth.

## 3. Data and Methodology

## 4. Results

## 5. Discussion

## 6. Conclusions

## Supplementary Materials

Supplementary File 1## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Primary energy consumption in percentage of world total primary energy consumption (BP 2017).

America | Europe and Central Asia | Asia Pacific | Africa and the Middle East | China | ||||||
---|---|---|---|---|---|---|---|---|---|---|

Statistic | LY | LE | LY | LE | LY | LE | LY | LE | LY | LE |

Mean | 30.026 | 21.743 | 30.328 | 21.762 | 29.614 | 20.899 | 28.425 | 20.038 | 27.912 | 20.559 |

Median | 30.258 | 21.747 | 30.326 | 21.772 | 29.699 | 20.942 | 28.329 | 20.090 | 27.896 | 20.535 |

Maximum | 30.834 | 21.983 | 30.758 | 21.889 | 30.348 | 21.639 | 29.238 | 21.012 | 29.911 | 21.849 |

Minimum | 29.531 | 21.395 | 29.765 | 21.471 | 28.694 | 20.029 | 27.576 | 18.730 | 26.069 | 19.144 |

SD | 0.393 | 0.184 | 0.289 | 0.092 | 0.488 | 0.493 | 0.447 | 0.666 | 1.209 | 0.797 |

Skewness | −0.174 | −0.178 | −0.187 | −1.391 | −0.298 | −0.131 | 0.297 | −0.346 | 0.077 | 0.146 |

Kurtosis | 1.781 | 1.584 | 1.900 | 5.016 | 1.859 | 1.656 | 2.074 | 2.051 | 1.723 | 1.901 |

Jarque-Bera | 3.147 | 4.172 | 2.643 | 23.115 | 3.247 | 3.675 | 2.369 | 2.703 | 3.243 | 2.529 |

Probability | 0.207 | 0.124 | 0.267 | 0.000 | 0.197 | 0.159 | 0.306 | 0.259 | 0.198 | 0.282 |

Observations | 47 | 47 | 47 | 47 | 47 | 47 | 47 | 47 | 47 | 47 |

Regions/Variables | ADF | PP | KPSS | ||||||
---|---|---|---|---|---|---|---|---|---|

(a) | (b) | (c) | (a) | (b) | (c) | (a) | (b) | ||

America | LY | −2.018 | −2.565 | 10.764 | −1.434 | −3.293 ** | 9.180 | 0.193 ** | 0.892 *** |

LE | −1.647 | −1.721 | 3.873 | −1.820 | −1.721 | 3.873 | 0.111 | 0.878 *** | |

DLY | −5.064 *** | −4.660 *** | −2.303 ** | −4.944 *** | −4.371 *** | −2.004 ** | 0.097 | 0.397 * | |

DLE | −5.607 *** | −5.498 *** | −4.536 *** | −5.542 *** | −5.421*** | −4.456 *** | 0.067 | 0.231 | |

Europe and Central Asia | LY | −3.063 | −1.619 | 3.483 | −2.567 | −2.138 | 7.187 | 0.132 * | 0.890 *** |

LE | −3.037 | −2.910 * | 1.861 | −2.807 | −3.804 *** | 1.211 | 0.169 ** | 0.378 * | |

DLY | −4.856 *** | −4.647 *** | −2.719 *** | −4.710 *** | −4.533 *** | −2.637 *** | 0.057 | 0.316 | |

DLE | −4.697 *** | −4.202 *** | −4.105 *** | −4.711 *** | −4.136 *** | −4.012 *** | 0.134 * | 0.450 * | |

Asia Pacific | LY | −1.326 | −3.2 | 4.257 | −1.341 | −3.283 ** | 11.262 | 0.223 *** | 0.886 *** |

LE | −0.965 | −1.826 | 11.728 | −1.132 | −1.826 | 10.278 | 0.149 ** | 0.891*** | |

DLY | −5.659 *** | −4.903 *** | −1.497 | −5.571 *** | −4.903 *** | −1.470 | 0.073 | 0.643 ** | |

DLE | −5.581 *** | −5.427 *** | −1.675 * | −5.552 *** | −5.423 *** | −2.109 ** | 0.099 | 0.308 | |

Africa and the Middle East | LY | −1.484 | −0.053 | 3.468 | −1.904 | −0.586 | 5.233 | 0.166 ** | 0.878 *** |

LE | −2.066 | −4.329 *** | 1.218 | −1.817 | −4.516 *** | 8.332 | 0.202 ** | 0.881 *** | |

DLY | −4.884 *** | −4.949 *** | −3.169 *** | −4.844 *** | −4.922 *** | −2.955 *** | 0.129 * | 0.122 | |

DLE | −2.455 | −1.696 | −1.348 | −8.059 *** | −6.517 *** | −1.956 ** | 0.122 * | 0.605 ** | |

China | LYCHN | −3.618 ** | 0.225 | 3.310 | −3.146 | 1.023 | 16.542 | 0.141 * | 0.888 *** |

LECHN | −2.636 | −0.052 | 2.945 | −2.174 | −0.824 | 6.960 | 0.119 * | 0.883 *** | |

DLYCHN | −3.305 * | −3.405 ** | −1.100 | −4.052 ** | −4.012 *** | −0.844 | 0.128 ** | 0.234 | |

DLECHN | −3.947 ** | −4.027 *** | −2.620 *** | −4.013 ** | −4.089 *** | −2.620 *** | 0.083 | 0.088 |

**Notes**: (a) denotes the test statistic with the trend and constant; (b) denotes the test statistic with constant; (c) denotes the test statistic without trend and constant. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

Regions | LY | LE | DLY | DLE | |
---|---|---|---|---|---|

America | T-statistic | −5.360 *** | −2.910 | −5.881 *** | −7.508 *** |

Specification | (1) | (3) | (4) | (1) | |

Break | 2008 | 1992 | 2007 | 1983 | |

Europe and Central Asia | T-statistic | −4.124 | −3.647 | −5.862 *** | −5.569 *** |

Specification | (1) | (1) | (4) | (3) | |

Break | 2003 | 1991 | 2009 | 1999 | |

Asia Pacific | T-statistic | −5.473 ** | −3.707 | −6.449 *** | −6.902 *** |

Specification | (1) | (3) | (3) | (3) | |

Break | 1989 | 1987 | 2009 | 1982 | |

Africa and Middle East | T-statistic | −5.313 ** | −4.585 ** | −7.170 *** | −9.942 *** |

Specification | (3) | (2) | (1) | (4) | |

Break | 1981 | 1984 | 1985 | 1985 | |

China | T-statistic | −3.680 | −2.978 | −4.702 ** | −6.013 *** |

Specification | (2) | (1) | (2) | (1) | |

Break | 2015 | 2003 | 1982 | 2002 |

**Notes**: Trend specification/break specification: (1) trend and intercept/trend and intercept; (2) trend and intercept/trend only; (3) trend and intercept/intercept only; (4) intercept only/intercept only. *** and ** denote statistical significance at the 1% and 5% levels, respectively.

A-Y | A-E | ECA-Y | ECA-E | AP-Y | AP-E | AME-Y | AME-E | |
---|---|---|---|---|---|---|---|---|

ARS | 0.826 | 0.846 | 0.819 | 0.789 | 0.734 | 0.791 | 0.766 | 0.717 |

SER | 0.007 | 0.009 | 0.007 | 0.011 | 0.009 | 0.009 | 0.016 | 0.013 |

JB | 0.145 | 1.995 | 0.984 | 1.134 | 1.115 | 1.279 | 1.152 | 2.265 |

LM | 0.324 | 2.952 | 2.903 | 1.548 | 2.462 | 2.924 | 2.000 | 0.311 |

ARCH | 0.413 | 0.263 | 0.012 | 0.109 | 0.090 | 0.482 | 2.051 | 0.003 |

RESET | 0.555 | 0.061 | 0.178 | 0.583 | 0.383 | 1.120 | 0.029 | 0.374 |

**Notes**: Diagnostic tests results are based on F-statistics. ARS means adjusted R-squared. SER means standard error of regression. JB means Jarque-Bera normality test. LM means Breusch-Godfrey serial correlation LM test. ARCH means ARCH test. Reset means Ramsey RESET test.

**Table 5.**Quandt-Andrews breakpoint test and Bai-Perron global $l$ breaks vs. none multiple breakpoint test.

Quandt-Andrews Breakpoint Tests | |||

Model | Maximum Likelihood-ratio (LR) F-statistics | Maximum Wald F-statistic | Break date |

A-Y | - | - | - |

A-E | 5.560 *** | 33.359 *** | 1987 |

ECA-Y | 3.252 * | 19.509 * | 2009 |

ECA-E | 4.565 *** | 22.823 *** | 1989 |

AP-Y | 3.555 ** | 21.329 ** | 1992 |

AP-E | - | - | - |

AME-Y | - | - | - |

AME-E | 8.449 *** | 42.244 *** | 1981 |

Bai-Perron Global Break vs. None Multiple Breakpoint Tests | |||

Model | Scaled F-statistic | Estimated break dates | |

A-Y | - | - | |

A-E | 60.434 ** (5 breaks) | 1979; 1985; 1994; 2002; 2011 | |

ECA-Y | 190.412 ** (5 breaks) | 1980; 1988; 1995; 2001; 2011 | |

ECA-E | 76.507 ** (5 breaks) | 1977; 1988; 1994; 2000; 2010 | |

AP-Y | 126.500 ** (5 breaks) | 1978; 1984; 1990; 2000; 2009 | |

AP-E | - | - | |

AME-Y | - | - | |

AME-E | 50.038 ** (2 breaks) | 1980; 1988 |

**Notes:*****, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

Date | A-Y | A-E | ECA-Y | ECA-E | AP-Y | AP-E | AME-Y | AME-E |
---|---|---|---|---|---|---|---|---|

1979 | 0.644 | 1.087 | 1.065 | 0.445 | 2.312 * | 2.455 ** | 2.665 ** | 2.770 ** |

1980 | 0.616 | 1.190 | 1.196 | 0.446 | 3.528 *** | 5.482 *** | 2.571 ** | 3.158 ** |

1981 | 3.392 *** | 1.189 | 1.322 | 0.775 | 2.706 ** | 4.929 *** | 3.379 *** | 8.449 *** |

1989 | 1.660 | 1.627 | 1.567 | 4.565 *** | 2.850 ** | 5.351 *** | 2.513 ** | 3.047 ** |

1990 | 1.697 | 1.489 | 0.328 | 2.422 * | 2.978 ** | 5699 *** | 2.982 ** | 2.593 ** |

1991 | 1.801 | 1.692 | 0.224 | 2.265 * | 3.280 ** | 4.920 *** | 1.989 * | 2.064 * |

**Notes:**F-statistic values. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

A-Y | A-E | ECA-Y | ECA-E | AP-Y | AP-E | AME-Y | AME-E | |

Constant | −1.565 ** | 2.515 *** | 3.029 *** | - | 2.121 *** | −6.001 *** | 4.656 *** | - |

Trend | - | - | - | - | - | −0.009 ** | - | - |

DLY | - | 0.969 *** | - | 0.946 *** | - | 0.796 *** | - | 0.275 *** |

DLE | 0.699 *** | - | 0.594 *** | - | 0.582 *** | - | 0.378 ** | - |

DLYCHN | - | - | −0.069 | - | - | 0.191 *** | −0.463 *** | 0.252 ** |

DLECHN | - | - | −0.041 | 0.112 ** | - | - | 0.066 | - |

LY(-1) | - | 0.101 | - | 0.071 ** | - | 0.286 *** | - | 0.054 *** |

LE(-1) | 0.265 *** | - | 0.093 *** | - | 0.062 | - | −0.121 ** | - |

LYCHN(-1) | - | - | 0.028 * | - | - | 0.102 *** | 0.063 ** | 0.019 |

LECHN(-1) | - | - | 0.052 ** | 0.015 | - | - | 0.191 *** | - |

LYCHN | 0 | 0.002 | - | −0.031 * | −0.015 | - | - | - |

LECHN | 0.011 | 0 | - | - | 0.059 *** | −0.051 ** | - | −0.007 |

ECM | −0.146 *** | −0.261 *** | −0.226 *** | −0.074 * | −0.142 ** | −0.194 *** | −0.276 *** | −0.097 *** |

Time Dummies | ||||||||

America | ||||||||

SD8716 | −0.028 *** | 0.029 *** | - | - | - | - | - | - |

SD8316 | 0.025 *** | - | - | - | - | - | - | - |

SD8016 | - | −0.029 *** | - | - | - | - | - | - |

ID2012 | - | −0.022 ** | - | - | - | - | - | - |

A-Y | A-E | ECA-Y | ECA-E | AP-Y | AP-E | AME-Y | AME-E | |

Time Dummies | ||||||||

Europe and Central Asia | ||||||||

SD0916 | - | - | - | −0.014 * | - | - | - | - |

SD0816 | - | - | −0.039 *** | - | - | - | - | - |

SD7516 | - | - | −0.016 ** | - | - | - | - | - |

ID1994 | - | - | - | −0.033 *** | - | - | - | - |

Asia Pacific | ||||||||

SD8016 | - | - | - | - | 0.025 *** | −0.043 *** | - | - |

ID2009 | - | - | - | - | −0.032 *** | - | - | - |

ID1975 | - | - | - | - | −0.035 *** | - | - | |

Africa and Middle East | ||||||||

SD0816 | - | - | - | - | - | - | 0.039 *** | - |

SD8116 | - | - | - | - | - | - | −0.033 * | - |

SD7616 | - | - | - | - | - | - | - | 0.051 *** |

ID1993 | - | - | - | - | - | - | - | −0.042 *** |

ID1980 | - | - | - | - | - | - | - | −0.056 *** |

ID1979 | - | - | - | - | - | - | - | 0.034 ** |

**Notes**: SD means shift dummy and ID means impulse dummy. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

A-Y | A-E | ECA-Y | ECA-E | AP-Y | AP-E | AME-Y | AME-E | |
---|---|---|---|---|---|---|---|---|

F-statistic | 5.662 *** | 7.162 *** | 10.890 *** | 12.217 *** | 7.822 *** | 9.207 *** | 20.897 *** | 20.965 *** |

**Notes**: k = 3, where k represents the number of independent variables in the equation estimated. Critical values were obtained from Pesaran et al. (2001). Critical values for no intercept and no trend for the bottom and top are, respectively, 3.42 and 4.84, for 1%; 2.45 and 3.63, for 5%; and 2.01 and 3.10, for 10%. Critical values for unrestricted intercept and no trend for the bottom and top are, respectively, 4.29 and 5.61, for 1%; 3.23 and 4.35, for 5%; and 2.72 and 3.77, for 10%. Critical values for unrestricted intercept and restricted trend for the bottom and top are, respectively, 4.3 and 5.23, for 1%; 3.38 and 4.23, for 5%; and 2.97 and 3.74, for 10%. *** denotes statistical significance at the 1% level.

A-Y | A-E | ECA-Y | ECA-E | AP-Y | AP-E | AME-Y | AME-E | |
---|---|---|---|---|---|---|---|---|

Short-run | ||||||||

DLY | - | 0.969 *** | - | 0.946 ** | - | 0.796 *** | - | 0.275 *** |

DLE | 0.699 *** | - | 0.594 *** | - | 0.582 *** | - | 0.378 *** | - |

DLYCHN | - | - | −0.069 | - | - | - | −0.463 *** | 0.252 ** |

DLECHN | - | - | −0.041 | 0.11 ** | - | - | 0.066 | - |

Long-run | ||||||||

LY | - | 0.388 *** | - | 0.955 *** | 1.477 *** | - | 0.557 *** | |

LE | 1.820 *** | - | 0.412 *** | - | 0.439 | - | −0.438 * | - |

LYCHN | 0.003 | 0.009 | 0.124 ** | −0.421 ** | −0.107 | 0.528 *** | −0.229 ** | 0.191 |

LECHN | 0.072 | 0.002 | 0.229 ** | 0.208 | 0.412 ** | −0.044 *** | 0.692 *** | −0.067 |

**Notes:*****, **, and * denotes statistical significance at the 1%, 5%, and 10% levels, respectively.

Americas | Europe and Central Asia | Asia Pacific | Africa and the Middle East | |||||
---|---|---|---|---|---|---|---|---|

LY | LE | LY | LE | LY | LE | LY | LE | |

LY | - | 12.956 *** | - | 6.784 ** | - | 7.508 ** | - | 8.468 ** |

LE | 6.083 ** | - | 8.667 *** | - | 3.311 | - | 11.965 *** | - |

LYCHN | 1.761 | 0.526 | 0.568 | 9.441 *** | 6.866 ** | 0.548 | 0.482 | 2.972 |

LECHN | 20.876 *** | 7.174 ** | 16.223 *** | 28.373 *** | 20.246 *** | 11.053 *** | 1.061 | 13.565 *** |

**Notes:***** and ** denote statistical significance at the 1% and 5% levels, respectively.

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**MDPI and ACS Style**

Marques, L.M.; Fuinhas, J.A.; Marques, A.C.
Are There Spillovers from China on the Global Energy-Growth Nexus? Evidence from Four World Regions. *Economies* **2019**, *7*, 59.
https://doi.org/10.3390/economies7020059

**AMA Style**

Marques LM, Fuinhas JA, Marques AC.
Are There Spillovers from China on the Global Energy-Growth Nexus? Evidence from Four World Regions. *Economies*. 2019; 7(2):59.
https://doi.org/10.3390/economies7020059

**Chicago/Turabian Style**

Marques, Luís Miguel, José Alberto Fuinhas, and António Cardoso Marques.
2019. "Are There Spillovers from China on the Global Energy-Growth Nexus? Evidence from Four World Regions" *Economies* 7, no. 2: 59.
https://doi.org/10.3390/economies7020059