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Empirical Economics

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24-08-2017

Energy consumption promotes economic growth or economic growth causes energy use in China? A panel data analysis

Published in: Empirical Economics | Issue 3/2018

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Abstract

This study tries to examine the causal relationship between energy consumption and economic growth for twenty-nine provinces of China by employing the panel Granger causality analysis. The econometric methodology used in this paper allows us to untangle the causal nexus between energy consumption and economic growth and helps us to discriminate between competing theories on which hypothesis is applicable to China. Among the main results, it is found that there is no causality in two out of twenty-nine provinces and bidirectional causality is observed in sixteen out of twenty-nine provinces. Unidirectional causality is observed in eleven out of twenty-nine provinces of China. When bootstrap critical values are used, our empirical findings indicate that there is an unidirectional causal link running from real output to energy use for China, implying that economic growth significantly affects energy consumption, and hence, the conservation hypothesis is applicable to China.

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Variable x is said to Granger-cause variable y if y can be better predicted using the histories of both x and y than it can by using the history of y alone.

Bozoklu and Yilanci (2013) employ a Granger causality test in the frequency domain which allows them to distinguish short (temporary)- and long-run (permanent) causality.

Yalta and Cakar (2012) point out that “China is the most populous and the second largest economy in the world (International Monetary Fund 2010) with continuously increasing energy needs that cause a significant impact in energy prices and make the country a key player in the world energy markets. Moreover, China is the world’s largest emitter of greenhouse gases (GHGs), which leads to important environmental concerns, and also brings forth the question of whether sustainable growth can be achieved with energy conservation policies.”

To avoid the omitted variable bias, as shown in next section, we adopt the widely used production function approach which states that real output is a function of capital, labor and energy, all in natural logarithms.

Chang et al. (2014) also employ Emirmahmutoglu and Kose’s (2011) method to discuss the causal relationship between nuclear energy consumption and economic growth in G6 countries. We thank an anonymous referee for pointing out this paper to us.

Kónya (2006) suggests a different panel causality test which is based on the seemingly unrelated regressions (SUR) estimator proposed by Zellner (1962), and the Wald test with country-specific bootstrap critical values. Kónya’s (2006) test does not require pretesting for unit roots and cointegration apart from the lag structure. Nonetheless, this is an important problem since the unit root and the cointegration tests in general suffer from low power and different tests often lead to contradictory results. Nazlioglu et al. (2011), Chu (2012) and Chu and Chang (2012) apply Kónya’s (2006) approach to untangle the causal nexus between energy consumption and economic growth of the OECD and G-6 countries.

As compared with previous studies where the sample ends in the early 2010 (Shiu and Lam 2004; Soytas and Sari 2006), our period of analysis includes the most recent evolution of energy consumption and real income in China.

Within the energy consumption–growth literature, some studies have essentially estimated energy demand functions (e.g., Narayan and Smyth 2009; Sadorsky 2011; Belke et al. 2011; Behmiri and Manso 2012) with the inclusion of energy prices when modeling the causal relationship.

In order to save space, we refer to Pesaran et al. (2008) for the details of Swamy’s test and the estimators described in Eq. (7).

Note that the parameter restrictions (10) do not involve \(\delta \) and \(\phi \), so that the null hypothesis can be tested using a standard Wald statistic.

The 29 provinces are Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang.

Im et al. (2003) point that due to the heterogeneous nature of the alternative hypothesis in their test, caution has to be exercised when interpreting results because the null hypothesis of a unit root in each cross section may be rejected when only a fraction of the series in the panel is stationary. An additional concern here is that the presence of cross-sectional dependencies can undermine the asymptotic normality of the IPS test and lead to over-rejection of the null hypothesis of joint non-stationarity.

We code the panel Granger causality by using the Winrats software according to the MATLAB code provided by Professor Emirmahmutoglu. We thank him for providing us with his MATLAB code for reference.

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Title: Energy consumption promotes economic growth or economic growth causes energy use in China? A panel data analysis
Publication date: 24-08-2017
Published in: Empirical Economics / Issue 3/2018
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-017-1319-1

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