Growth Sources of Green Economy and Energy Consumption in China: New Evidence Accounting for Heterogeneous Regimes.

AuthorLiu, Guanchun

    Since the reform and opening up in 1978, China has experienced rapid growth for nearly three decades with an average rate 10%, and especially the amount of energy consumption has been rising dramatically. As illustrated in Figure 1, energy consumption in China increases from 1039 million tons of coal equivalent (Mtec) to 3460 Mtec over 1990-2016, suggesting an annual growth rate of 4.84%. Particularly, its growth mode can be divided in to three phases: (i) one is the subperiod 1990-2001, in which energy consumption grows much slowly; (ii) another is the subperiod 2002-2012, where energy consumption manifests a sharp growth pattern; (iii) the third subperiod is 2013-2016, in which energy consumption maintains almost stable, and even drops after 2015. At present, China has been the largest carbon emitter and energy consumer in the worldwide (Lin and Du, 2015), contributing to 22.9% of global energy consumption and 27.3% of global carbon emissions (Zhang et al., 2017).

    Owing to the fact that the process of industrialization and urbanization in China is still on the way, it is easy to detect that its energy consumption will grow steadily in the next few decades (Lin and Du, 2015). To address the serious problems of natural resources constraints and environmental pollutions and realize the mode of green growth that performs with lower energy consumption and carbon emission, China's central government targets to reduce energy intensity by 20%, 16% and 15% during the 11th (2006-2010), 12th (2011-2015) and 13th (2016-2020) Five-Year Plan periods (Zhang and Hao, 2017), and reduce the concentration of P[M.sub.2.5] in 2017 by 25% than 2012 (Zhang et al., 2017). From Figure 1, we can see that energy intensity decreases from 5.51 (unit: million tons of coal equivalent/ten billion RMB, Mtec/Tbr) to 0.46 from 1990 to 2016, indicating an annual slowdown rate of 8.91%. In particular, energy intensity drops slower as economy develops, which may suggest that the reliance of economic growth on energy consumption declines.

    However, China has entered a 'new normal' stage since 2012, at which the growth rate continuously drops and performs 6.9% in 2016. Specifically, Barro (2016) argues that the per capita growth rate in China is likely to fall soon from around 8% per year to a range of 3-4%, and then manifests an "L-style" pattern. Further, great transformations happened in China's industrial structure, and the relative importance of the tertiary industry keeps increasing. Under the new background, it is essential to reestimate the sources of green economy in China, and especially investigate whether the importance of energy consumption varies across industries and years. Answering the issue not only provides some explanations for why China's growth rate falls after the year 2012, but also is of great significance for its sustainable growth and energy consumption reduction in the next periods.

    To understand the growth sources of green economy, one important branch is directly to decompose economic growth into different components with theoretical frameworks, such as the Solow model and data envelopment analysis (e.g., Wang and Yao, 2003; Lee and Chang, 2008; Wang and Feng, 2015; Feng et al., 2017b; Liu et al., 2018; Lee and Lee, 2019). Nevertheless, they assume that there is a universal production technology for all economies, which would bring about some estimation bias and misleading results with the existence of growth regime heterogeneity (e.g., Solow, 1994; Brock and Durlauf, 2001; Orea and Kumbhakar, 2004; Greene, 2005; Bos et al., 2010). In this paper, we develop an advanced structural methodology combined the Solow framework with heterogeneous regimes, and then decomposes the growth rates of different industries in China into green total factor productivity growth and factor endowment growth (including labor, physical capital, and energy consumption). It is worth noting that, we especially take energy consumption that is ignored in most previous studies into the decomposition framework to reflect resources and environmental costs. Thus, green total factor productivity growth refers to the improvement of production efficiency at the expense of energy consumption, while factor endowment growth denotes the quantity growth of production factors that include energy consumption.

    Specifically, we relax the traditional assumption that all the provinces have the same production technology in China (e.g., Wang and Yao, 2003; Wang and Feng, 2015; Liu et al., 2018). To allow for heterogeneous green growth regimes, following some recent works (e.g., Orea and Kumbhakar, 2004; Greene, 2005; Bos et al., 2010; Liu et al., 2019), a flexible finite mixture model is introduced into the estimation of Cobb-Douglas production function. While most previous studies a priori sort provinces into different groups using geography and income, we classify provinces based on the similarity of the conditional distribution of real GDP. Specifically, the number of multiple green growth regimes is endogenously determined and regime-specific output elasticities for production factors are estimated. Meanwhile, the posterior probabilities that each province belongs to one of the regimes in a particular period are obtained from a multinomial logit sorting regression controlling for several determinants of growth regime. What's more, using regime-specific output elasticities and time-varying probabilities, heterogeneous green growth regimes across Chinese provinces are constructed. Then, we extend the Solow decomposition framework accounting for regime heterogeneity to obtain the green growth sources of different industries, and especially explore the importance of energy consumption.

    Based on a panel data of three industries for 29 Chinese provinces over 2000-2015, our empirical analysis proceeds with four main questions: (i) investigating whether the same industry across provinces follows a universal green growth path; (ii) estimating the green growth sources of different industries; (iii) assessing the importance of energy consumption across industries, and (iv) exploring the decomposition bias in traditional methods that do not account for regime heterogeneity. To preview our results, a finite mixture model with three regimes is best to describe the green production technology of each industry for Chinese provinces. Specifically, some provinces switch their regimes over time, while the others maintain the same regime. Furthermore, when accounting for regime heterogeneity in the Solow decomposition framework, we observe that the contribution rate of factor endowment (green total factor productivity) is overestimated (underestimated) in traditional methods. With respect to the role of energy consumption, it is overestimated in traditional methods for the primary and tertiary industries, but underestimated for the secondary industry. More importantly, the reliance of China's over economy as well as the secondary and tertiary industries on energy consumption tends to decline during the period 2000-2015.

    Our work is related to a number of different literatures. First, this paper contributes to the empirical researches on the growth sources of Chinese green economy. We extend the data sample to 2015 that includes the 'new normal' stage in China after 2012, whereas most similar studies generally examine the question before 2000 and especially ignore energy consumption in the production function (e.g., Chow, 1993; Borensztein and Ostry, 1996; Wang and Yao, 2003; Wu, 2003). Second, this paper connects to the empirical studies on accounting for regime heterogeneity. Differing from traditional works (e.g., Shen and Lee, 2006; Liu et al., 2017), we overcome the issue using a finite mixture model based on the similarity of the conditional distribution of real GDP. Third, this paper relates to the empirical literature that applies the latent class models or finite mixture models. Following Bos et al. (2010) and Liu et al. (2019), we permit Chinese provinces to switch regime over time, while most previous researches assume a constant growth regime for each economy (e.g., Alfo et al., 2008; Owen, et al., 2009; Flachaire et al., 2014). Fourth, this paper contributes to the empirical works using the Solow decomposition framework. While most previous studies neglect the existence of regime heterogeneity (e.g., Chow, 1993; Borensztein and Ostry, 1996; Wang and Yao, 2003; Wu, 2003; Liu et al., 2018), we account for heterogeneous regimes and analyze the decomposition bias of traditional methods.

    The remainder of this paper is organized as follows. Section 2 discusses the necessity of regime heterogeneity. Section 3 presents the traditional Solow decomposition framework and an extended version that accounts for heterogeneous regimes on the basis of finite mixture models, as well as the econometric specification for parameter estimation. Section 4 describes data and variable definitions. Section 5 provides the existence of heterogeneous green growth regimes, the decomposition results for growth sources across industries in China, and the bias in traditional methods. Section 6 concludes the paper and provides some policy suggestions.


    2.1 The environment-growth nexus

    Differing from the traditional views that ignore the roles of environment, more and more attentions are paid to explore the relationship between the environment and economic growth. Specifically, several growth models that incorporate environmental factors are developed to understand the environment-growth nexus. Hassler et al. (2012) build a green growth model with fossil fuel energy where the input is fixed in the short run but can be changed over time by directed input-augmenting technical change, and suggest that the economy would direct more efforts at energy-saving facing with higher oil price. Acemoglu et al. (2012) establish a two-sector growth model of directed...

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