# 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

- Kum, H.; Ocal, O.; Aslan, A. The relationship among natural gas energy consumption, capital and economic growth: Bootstrap-corrected causality tests from G-7 countries. Renew. Sustain. Energy Rev.
**2012**, 16, 2361–2365. [Google Scholar] [CrossRef] - Apergis, N.; Payne, J.E. Energy consumption and economic growth: Evidence from the Commonwealth of Independent States. Energy Econ.
**2009**, 31, 641–647. [Google Scholar] [CrossRef] - Shahbaz, M.; Lean, H.H.; Farooq, A. Natural gas consumption and economic growth in Pakistan. Renew. Sustain. Energy Rev.
**2013**, 18, 87–94. [Google Scholar] [CrossRef] [Green Version] - Işik, C. Natural gas consumption and economic growth in Turkey: A bound test approach. Energy Syst.
**2010**, 1, 441–456. [Google Scholar] [CrossRef] - Wei, B.R.; Yagita, H.; Inaba, A.; Sagisaka, M. Urbanization impaction energy demand and CO
_{2}emission in China. J. Chongqing Univ. Eng.**2003**, 5, 46–50. [Google Scholar] - Parikh, J.; Shukla, V. Urbanization, energy use, and greenhouse effects in economic development: Results from a cross-national study of developing countries. Glob. Environ. Chang.
**1995**, 5, 87–103. [Google Scholar] [CrossRef] - Imai, H. The effect of urbanization on energy consumption. J. Popul. Probl.
**1997**, 53, 43–49. [Google Scholar] - Jones, D.W. Urbanization and energy. Encycl. Energy
**2004**, 6, 329–335. [Google Scholar] - Lariviere, I.; Lafrance, G. Modelling the electricity consumption of cities: Effect of urban density. Energy Econ.
**1999**, 21, 53–66. [Google Scholar] [CrossRef] - Ewing, R.; Rong, F. The impact of urban form on U.S. residential energy use. Hous. Policy Debate
**2008**, 19, 1–30. [Google Scholar] [CrossRef] - Zamani, M. Energy consumption and economic activities in Iran. Energy Econ.
**2007**, 29, 1135–1140. [Google Scholar] [CrossRef] - Jiang, Z.; Lin, B. China’s energy demand and its characteristics in the industrialization and urbanization process. Energy Policy
**2012**, 49, 608–615. [Google Scholar] - Wang, T.; Lin, B. China’s natural gas consumption and subsidies—From a sector perspective. Energy Policy
**2014**, 65, 541–551. [Google Scholar] [CrossRef] - Kani, A.H.; Abbasspourb, M.; Abedi, Z. Estimation of demand function for natural gas in Iran: Evidences based on smooth transition regression models. Energy Model.
**2014**, 36, 341–347. [Google Scholar] [CrossRef] - Aslan, A. Does natural gas consumption follow a nonlinear path over time? Evidence from 50 US States. Renew. Sustain. Energy Rev.
**2011**, 15, 4466–4469. [Google Scholar] [CrossRef] - Fallahi, F. Causal relationship between energy consumption (EC) and GDP: A Markov switching (MS) causality. Energy
**2011**, 36, 4165–4170. [Google Scholar] [CrossRef] - Moral-Carcedo, J.; Vicens-Otero, J. Modelling the non-linear response of Spanish electricity demand to temperature variations. Energy Econ.
**2005**, 27, 477–494. [Google Scholar] [CrossRef] - Zhao, J.; Fan, J. Empirical research on the inherent relationship between economy growth and energy consumption in China. Econ. Res.
**2007**, 8, 31–42. (In Chinese) [Google Scholar] - He, X.; Pan, H. Nonlinear relationship between energy consumption and economic growth: Evidence from PSTR approach. China Popul. Resour. Environ.
**2013**, 23, 84–89. (In Chinese) [Google Scholar] - Lee, C.C.; Chiu, Y.B. Electricity demand elasticities and temperature: Evidence from panel smooth transition regression with instrumental variable approach. Energy Econ.
**2011**, 33, 896–902. [Google Scholar] [CrossRef] - Bessec, M.; Fouquau, J. The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach. Energy Econ.
**2008**, 30, 2705–2721. [Google Scholar] [CrossRef] - Sadorsky, P. Renewable energy consumption and income in emerging economies. Energy Policy
**2009**, 37, 4021–4028. [Google Scholar] [CrossRef] - Sadorsky, P. The impact of financial development on energy consumption in emerging economies. Energy Policy
**2010**, 38, 2528–2535. [Google Scholar] [CrossRef] - Asif, M.; Muneer, T. Energy supply, its demand and security issues for developed and emerging economies. Renew. Sustain. Energy Rev.
**2007**, 11, 1388–1413. [Google Scholar] [CrossRef] - Liu, Y.; Chen, S.; Zhang, Y.; Zou, X.; Wang, Y. Energy Kuznets curve: Evidence from developed countries. Chin. J. Manag. Sci.
**2008**, 16, 648–653. (In Chinese) [Google Scholar] - Richmond, A.K.; Kaufmann, R.K. Is there a turning point in the relationship between income and energy use and/or carbon emissions? Ecol. Econ.
**2006**, 56, 176–189. [Google Scholar] [CrossRef] - Duarte, R.; Pinilla, V.; Serrano, A. Is there an environmental Kuznets curve for water use? A panel smooth transition regression approach. Econ. Model.
**2013**, 31, 518–527. [Google Scholar] [CrossRef] - Jones, D.W. How urbanization affects energy-use in developing countries. Energy Policy
**1991**, 19, 621–629. [Google Scholar] [CrossRef] - Northam, R.M. Urban Geography; John Wiley & Sons: Hoboken, NJ, USA, 1979. [Google Scholar]
- González, A.; Teräsvirta, T.; Dijk, D. Panel Smooth Transition Regression Models; University of technology Sydney: Brisbane, Australia, 2005. [Google Scholar]
- Fouquau, J.; Hurlin, C.; Rabaud, I. The Feldstein–Horioka puzzle: A panel smooth transition regression approach. Econ. Model.
**2008**, 25, 284–299. [Google Scholar] [CrossRef] - Dijk, D.; Teräsvirta, T.; Franses, P.H. Smooth transition autoregressive models—A survey of recent developments. Econ. Rev.
**2002**, 21, 1–47. [Google Scholar] [CrossRef] - Granger, C.W.J.; Terasvirta, T. Modelling Non-Linear Economic Relationships; Oxford University Press: Oxford, UK, 1993. [Google Scholar]
- Teräsvirta, T. Specification, estimation, and evaluation of smooth transition autoregressive models. J. Am. Stat. Assoc.
**1994**, 89, 208–218. [Google Scholar] - Hansen, B.E. Threshold effects in non-dynamic panels: Estimation, testing, and inference. J. Econom.
**1999**, 93, 345–368. [Google Scholar] [CrossRef] - Gao, T. Panel data model. In Econometric Analysis Method and Modeling; Tsinghua University Press: Beijing, China, 2009. (In Chinese) [Google Scholar]
- Colletaz, G.; Hurlin, C. Threshold Effects of the Public Capital Productivity: An International Panel Smooth Transition Approach. Available online: https://halshs.archives-ouvertes.fr/halshs-00008056/document (accessed on 24 October 2016).
- Hoskisson, R.E.; Eden, L.; Chung, M.L. Strategy in Emerging Economies. Acad. Manag. J.
**2000**, 43, 249–267. [Google Scholar] [CrossRef] - Liu, X. Analysis for economic growth and energy. Appl. Stat. Manag.
**2006**, 4, 443–447. (In Chinese) [Google Scholar] - Sun, H.; Cheng, J. China energy demand forecast and analysis in the process of industrialization and urbanization. China Popul. Resour. Environ.
**2011**, 7, 7–12. (In Chinese) [Google Scholar] - Tian, C.; Hao, Y. Compare and review of emerging economies and their difference. Comp. Econ. Soc. Syst.
**2011**, 5, 118–125. (In Chinese) [Google Scholar]

**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