# Natural Gas Consumption of Emerging Economies in the Industrialization Process

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## Abstract

**:**

## 1. Introduction

## 2. Variables, Data and Model Specification

#### 2.1. Definition of Variables and Data

#### 2.2. Model Specifications

## 3. Empirical Study

#### 3.1. Stationarity Test of Variables

#### 3.2. Nonlinear Test of Model

_{F}and LR statistics for linearity tests and for the tests on remaining nonlinearity. Dijk and Teräsvirta [32] pointed out that the F-version of the test has better size properties in small sample than the asymptotic ${\chi}^{2}$, while the LM and LR statistics have asymptotic ${\chi}^{2}$ distribution, so this paper only reports the LM

_{F}statistic and adopts its probability to conduct the following related statistical tests, shown in Table 3.

#### 3.3. Estimation Result of Model

#### 3.3.1. Nonlinear Characteristics Analysis

^{8.2999}= 4023.8, 8.2999 is the location parameter). This means that economic growth at the expense of huge energy consumption is also reflected in natural gas consumption. Meanwhile, it can be seen from results that natural gas consumption with low GDP per capita will increases when industrialization improves, and for emerging economies with high GDP per capita, the higher industrialization will shrink natural gas consumption. Therefore, the transformation and upgrading of industry does not only promote the economic development but also plays a role in saving energy. The positive influence of urbanization on natural gas consumption exists regardless of the country and its stage of economic development.

^{3.7662}= 43.2), nonlinearity turns from strong to weak when $ind<43.2\%$, and gradually intensifies after industrialization exceeds 43.2%, and $g(Lin{d}_{t-1})=0.5$ in the lowest point ($Lin{d}_{t-1}=\theta =3.7662$), so industrialization shows very significant incomplete symmetry and nonlinear characteristics regardless of whether industrialization is below or above 43.2%. The estimation results of Model 2 show that the coefficient of the linear part of LGDP is significantly negative, and of the nonlinear part is significantly positive, suggesting that the value of $g(Lin{d}_{t-1})$ will decreases with the speedup of industrialization in the early stages of industrialization, and the effective elasticity coefficient of LGDP (${b}_{1}+{b}_{2}g(Lin{d}_{t-1})$) falls from 0.9535 (−0.9192 + 1.8727) to 0.0172 (−0.9192 + 1.8727 × 0.5), and bounces back after the industrialization exceeds 43.2%, but fails to reach 0.9535 again. The impact of industrialization on natural gas consumption turns from negative inhibition (−0.4986) to significantly positive promotion (2.4085) in the early stages of industrial development and then starts to fall after industrialization exceed 43.2% but still keep positive in the second regime. The influence of the urbanization rate on natural gas consumption is significant only in the linear part of model, and its elasticity coefficient is 2.6672, implying that the increase of the urbanization rate can enlarge natural gas consumption, but its influence on natural gas consumption does not change obviously with the change of industrialization.

#### 3.3.2. Time-Varying Elasticity Analysis

_{t}

_{−1}as transition variable.

#### 3.3.3. Regional Difference Analysis

## 4. Conclusions and Policy Implications

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Transition function of threshold variables, list as: (

**a**) Transition function of $LGDP(t-1)$ is contained in the first panel; (

**b**) Transition function of $Lind(t-1)$ contained in the second panel; (

**c**) Transition function of $Lurb(t-1)$ contained in the third panel.

**Figure 3.**Natural gas consumption’s elasticity with respect to variables in different regimes using $ind(t-1)$ as transition variable are listed as: (

**a**) Natural gas consumption with respect to GDP per capital is contained in the first panel; (

**b**) Natural gas consumption with respect to industrialization contained in the second panel.

Variable | Lgas | LGDP | Lind | Lurb |
---|---|---|---|---|

LLC test | −6.66798 *** (0.0000) | −3.36148 *** (0.0009) | −1.72662 ** (0.0421) | −3.10495 *** (0.0010) |

ADF test | 45.8772 * (0.0533) | 36.5499 (0.2655) | 27.3674 (0.7003) | 29.4602 (0.5957) |

PP test | 94.4073 *** (0.0000) | 24.4845 (0.8264) | 74.1107 *** (0.0000) | 581.903 *** (0.0000) |

Statistics | Value of Statistic and p | |
---|---|---|

Pedroni Test | Group PP-Statistic | −6.119810 *** (0.0000) |

Group ADF-Statistic | −3.717354 *** (0.0001) | |

Kao Test | ADF | −1.479403 * (0.0695) |

Model | Model 1 | Model 2 | Model 3 | |||
---|---|---|---|---|---|---|

Transition Variables | LGDP_{t}_{−1} | Lind_{t}_{−1} | Lurb_{t}_{−1} | |||

The Number of Location Parameter | (m = 1) | (m = 2) | (m = 1) | (m = 2) | (m = 1) | (m = 2) |

${H}_{0}:r=0\text{}\mathrm{vs}.\text{}{H}_{1}:r=1$ | 56.112 *** (0.000) | 34.346 *** (0.000) | 74.022 *** (0.000) | 39.049 *** (0.000) | 45.060 *** (0.000) | 25.964 *** (0.000) |

${H}_{0}:r=1\text{}\mathrm{vs}.\text{}{H}_{1}:r=2$ | 1.544 (0.203) | 2.154 (0.047) | 2.172 *** (0.091) | 0.421 (0.865) | 1.495 (0.216) | 7.858 *** (0.000) |

${H}_{0}:r=2\text{}\mathrm{vs}.\text{}{H}_{1}:r=3$ | - | - | - | - | - | 2.085 (0.055) |

RSS | 15.897 | 15.801 | 14.192 | 13.459 | 17.368 | 14.111 |

AIC | −2.924 | −2.920 | −3.037 | −3.081 | −2.835 | −2.976 |

BIC | −2.829 | −2.814 | −2.943 | −2.975 | −2.741 | −2.800 |

_{0}succeeds, the corresponding LM

_{F}statistic has an asymptotic $F(mK,TN-N-m(K+1))$ distribution, where $r$ denotes number of transition functions, $m$ is number of location parameters, and $K$ is the number of explanatory variables, i.e., $K=3$ in our specifications. The corresponding p-value of the LM

_{F}statistic are reported in parentheses, *** denotes the 1% significance level.

Model | Model 1 | Model 2 | Model 3 | ||
---|---|---|---|---|---|

(r, m) | (1, 1) | (1, 2) | (1, 1) | ||

The linear part of the model | LGDP_{1} | b_{1} | 0.8703 ** (5.4462) | −0.9192 ** (−7.3274) | 0.3865 ** (3.6978) |

Lind_{1} | c_{1} | 3.8595 ** (11.4863) | 5.3156 ** (9.0002) | 5.5526 ** (12.7454) | |

Lurb_{1} | d_{1} | 2.6672 ** (10.6247) | 1.8781 ** (3.7367) | 7.7977 ** (10.4595) | |

The nonlinear part of the model | LGDP_{2} | b_{2} | 0.4111 ** (3.3983) | 1.8727 ** (9.3229) | −0.3137 * (−2.6414) |

Lind_{2} | c_{2} | −4.7901 ** (−11.8526) | −5.8142 ** (−8.2181) | −5.8740 ** (−12.9351) | |

Lurb_{2} | d_{2} | 2.3982 ** (5.0060) | 1.0901 (1.2916) | 4.7845 ** (9.0758) | |

Location parameter | 8.2999 | 3.7662, 3.7662 | 3.8083 | ||

Smooth parameter | 1.0424 | 6.2180 | 6.2180 |

Threshold Level | $\mathit{\theta}$ < 3.7662 | $\mathit{\theta}$ > 3.7662 |
---|---|---|

Countries | Mexico, Argentina, Brazil, Russia, Turkey, Korea, Egypt, India, Pakistan, Ukraine | Iran, Saudi Arabia, China, Indonesia, Thailand, Venezuela |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Chai, J.; Liang, T.; Zhou, X.; Ye, Y.; Xing, L.; Lai, K.K.
Natural Gas Consumption of Emerging Economies in the Industrialization Process. *Sustainability* **2016**, *8*, 1089.
https://doi.org/10.3390/su8111089

**AMA Style**

Chai J, Liang T, Zhou X, Ye Y, Xing L, Lai KK.
Natural Gas Consumption of Emerging Economies in the Industrialization Process. *Sustainability*. 2016; 8(11):1089.
https://doi.org/10.3390/su8111089

**Chicago/Turabian Style**

Chai, Jian, Ting Liang, Xiaoyang Zhou, Yunxiao Ye, Limin Xing, and Kin Keung Lai.
2016. "Natural Gas Consumption of Emerging Economies in the Industrialization Process" *Sustainability* 8, no. 11: 1089.
https://doi.org/10.3390/su8111089