The Causality between Energy Consumption and Economic Growth for China in a Time-varying Framework.

Author:Zhang, Jin
 
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  1. INTRODUCTION

    The importance of the relationship between energy consumption and economic growth has been widely acknowledged, as evidenced by the extensive line of studies following Kraft and Kraft (1978). This issue is particularly important for China. As the largest developing country, China has recently achieved its status as the second largest economy and the largest energy user worldwide (International Energy Agency, 2010). Under the pressure to reduce Greenhouse Gases (GHG) emission, the challenge to balance between economic growth and energy consumption is even greater, especially when China is in a stage of industrialization and urbanization (Lin and Liu, 2010). This challenge calls for further understanding of the relationship between energy and growth.

    Studies on this issue for China first appeared in the 1990s (Tang and La Croix, 1993; Huang, 1993a; Chan and Lee, 1996), and have since grown steadily in number. We provide in Table 1 a brief summary of studies that have emerged since 2000 focusing on China or covering China within a group of countries. Based on varying sample periods, they examine how different types of energy consumption, from aggregate national to disaggregate regional, interact with economic growth in causality. These studies take a number of econometric approaches, with Granger causality as the dominant type, followed by innovation accounting (impulse response function or forecast error variance decomposition), mostly in an error correction model (ECM). Possibly affected by the different samples and methods, the causality findings are mixed. Earlier studies (Shiu and Lam, 2004; Wolde-Rufael, 2004; Soytas and Sari, 2006; Zou and Chau, 2006) almost unanimously suggest a unidirectional causality from energy to growth. Causality from growth to energy and bidirectional causality have also emerged more recently (Yuan et al., 2008; Zhang and Cheng, 2009; Akkemik and Goksal, 2012; Bloch et al., 2012).

    In this paper, we revisit the relationship between energy consumption and economic growth for China out of two motivations. As the first motivation, while existing studies have offered important insights into this relationship, the issue has generally been studied under the implicit assumption of a constant econometric structure, (1) giving little consideration to the possibility of structural change/break in the relationship between energy and growth. The assumption of constant econometric structure can be strong in this case. Over the recent decades, tremendous change has been witnessed in the Chinese economy, not only affecting the size of the economy, but also its structure in many important dimensions. (2) As documented in Altinay and Karagol (2004), Lee and Chang (2005), and Balcilar et al. (2010), the influence of changing economic structure and possibly even economic regimes, can result in the change of causality between energy and growth. Therefore, it is important to account for structural change when evaluating the energy-growth relationship, especially for the case of China.

    As the second motivation, we argue that beyond the bivariate causal relationship between energy consumption and economic growth, the channel of causality is also important, but has not been adequately addressed in the literature. By 'channel of causality', we mean the particular routes for the causality to take effect. For example, consider an energy--constrained economy, given a unidirectional causality from energy to growth, one way to explore the causal channel is by asking which sector of the economy energy consumption impacts most. An answer to this question will help us design an efficient industrial policy, so that energy conservation may be achieved with the least possible harm to economic growth. Clearly, the particular channel depends on the structural view we take and can be represented by the resulting structural variables we use. Two recent examples using structural variables are Liu (2009) and Feng et al. (2009). Liu (2009) studied urbanization (population structure) in relation to energy consumption and economic growth. Feng et al. (2009) studied the share of tertiary industry (economic structure) in relation to energy intensity. In fact, urbanization and industrialization are the two processes widely believed to have shaped China's growth and have fundamental effects on China's energy consumption and GHG emission. We take the research one step further by modeling both industrialization and urbanization (or to be precise, the structural shifts associated with either process) in a unified system, so to reveal their related yet differing effects on energy and growth.

    In line with related literature, we model the energy-growth causality in a Vector Autoregression (VAR) system. Industrialization and urbanization are included in the system to evaluate the channels of causality upon energy consumption and economic growth. Our VAR-based analysis departs from related studies in two aspects. First, we initiate the empirics by first testing the individual data series for structural stability, and given evidence of instability, we embed the VAR system in a rolling window framework to examine time-variation in system dynamics. Second, following Swanson and Granger (1997), we do the VAR identification using the data-driven Directed Acyclic Graphs (DAG).

    The paper proceeds as follows. Section 2 introduces the conceptual framework and data. Section 3 tests data stationarity subject to structural change. Section 4 describes the VAR-based approach for system dynamics analysis. Section 5 presents the results and discussions of system dynamics analysis. Finally, Section 6 concludes the paper.

  2. CONCEPTUAL FRAMEWORK AND DATA

    Existing studies often use an aggregate production function to provide a sensible conceptual framework for multivariate analysis of the energy-growth relationship. As one recent example, Yuan et al. (2008) consider the following three-factor production function:

    [Y.sub.t] = f([K.sub.t],[L.sub.t],[E.sub.t]) (1)

    where [Y.sub.t] [K.sub.t], [L.sub.t], [E.sub.t], represent output, capital, labor, and energy consumption respectively for time t. The conceptual production function is intended to supply a set of relevant variables, that may or may not be endogenously related, but not to dictate a specific nature/direction of causality relationship. In this paper, to incorporate the structural features of the Chinese economy, we consider an alternative framework by first noting that following the popular index decomposition approach for energy intensity (see Ma et al., 2010 for description), energy consumption can be re-expressed as a function of output and energy intensity:

    [E.sub.t] = [Y.sub.t] * [EI.sub.t] = [Y.sub.t] * ([n.summation over (i=1)] [S.sub.it], * [I.sub.it]) (2)

    where [E.sub.t] and [Y.sub.t] are as defined above, [EI.sub.t] stands for aggregated energy intensity of the economy, [S.sub.it] the output share of the ith sector, and [I.sub.it] the corresponding energy intensity of the ith sector. Unlike the production function in equation (1), [E.sub.t] is shown to be a complete decomposition consisting of [Y.sub.t], [S.sub.it], and [I.sub.it]. To focus on the structural feature of the Chinese economy, we consider explicitly in our multivariate system [S.sub.it] while we do not include [I.sub.it] directly into the system since it is inherently unobservable. This treatment essentially implies the equation below:

    [E.sub.t]/[Y.sub.t] = [EI.sub.t] = [Florin]([S.sub.1t],...,[S.sub.nt]) (3)

    If we compare this equation to equation (1), then sector energy intensity [I.sub.it] is comparable to the technology of the production and does not show up in the above energy intensity function. The benefit of this treatment is, in the following multivariate analysis, we can focus on output, energy consumption, and sector output share(s) in a compact system without having to include sector energy intensities. Further, we show in Section 5 that while [I.sub.it] is not modeled explicitly in the system, we can still draw inference on [I.sub.it] to certain degree given that equation (2) is a complete decomposition and [I.sub.it] is the only dimension not addressed directly in our empirical system.

    For the specific definition of sector output share(s), we adopt the share of nonagricultural sector in the economy to reflect the broad definition of industrialization in China. While not directly available from the equation above, urbanization, the population structure, is another structural variable widely believed to shape China's economic and social development pattern and thus is also included in the system. The joint consideration of industrialization and urbanization is designed to capture their differing and related effects on energy consumption and economic growth relationship.

    In sum, we consider a four-variable VAR system (described in detail in Section 4) for the interaction between energy consumption and economic growth. In addition to the two indispensable variables for energy consumption (EC) and economic growth (GDP), we further include the share of GDP due to nonagricultural sectors (IND, secondary and tertiary sectors combined) and the share of population living in cities and towns (URB) to represent industrialization and urbanization in the system, especially the structural shifts associated with either process. It is important to recognize that IND and URB thus defined capture the relative weights of nonagricultural GDP and urban population versus their respective counterparts (agricultural GDP and rural population), not the absolute scales. In addition, the IND defined here is a broad measure for industrialization and is used to capture the baseline feature of the Chinese economy. We later present an alternative set of analysis based on a narrow IND defined over the secondary industry only in subsection 5.6.

    Data for the four variables is sampled for the years from 1963 to 2010 and...

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