Application Background

In the course of economic development, energy use provides the embodied and disembodied technical progress and productivity growth (Berndt, 1990; Narayan and Wong, 2009). In fact, several studies have found positive relationships between energy consumption and economic growth (Abosedra et al., 2009; Narayan et al., 2010). However, energy use is also a major source of greenhouse gas causing environmental problems (Herring, 1999; Miketa and Mulder, 2005; Jinke et al., 2008; Sari and Soytas, 2009). Under the concern of economic growth and environmental pressure, the study of energy use, such as energy efficiency, energy intensity, and energy productivity, has become a significant research issue over the past several decades.

The energy issue is more important in mainland China, as the economy has grown aggressively in the past two decades, and China is now the second largest energy-consuming economy in the world behind the United States. In 2004, China consumed primary energy over 59 quadrillion Btu, which accounted for 13.3% of the world (EIA, 2006). Moreover, Crompton and Wu (2005) forecast that the total energy consumption in mainland China shall increase at an annual growth rate of 3.8% from 2003 to 2010. Along with this progressive demand for energy, the assessment of energy use should be taken into consideration under China‟s energy policy. Due to the above concern, the Chinese government has been actively shifting its economic development mode and reforming the economic structure since China‟s Agenda 21 was adopted in 1993. The 10^th Five-Year Plan carried out in 2001 also emphasizes improving energy efficiency and conservation. For example, energy consumption per 10,000 RMB yuan GDP in 1990 prices should be reduced to 2.2 tons of standard coal; energy conservation should be accumulated to 340 million tons of standard

coal; and the annual energy conservation ratio shall reach 4.5% by 2005. Whether or not these energy policies actually improve regional energy efficiency in mainland China remains to be examined by empirical research.

There are two well-known indicators used to study how energy inputs are efficiently used: One is energy intensity which measures the amount of energy consumption for every economic output produced in the economy, and the other is energy efficiency (or energy productivity) defined as economic output divided by energy input (Berndt, 1990; Patterson, 1996; Han et al., 2007; Nel and van Zyl, 2010). Notice that each represents identical measures from different perspectives, but we only focus on the application of the later (energy productivity) in this paper. The conventional energy efficiency index is actually the partial-factor energy productivity in which energy is the single input while substitution or complement among energy and other inputs (e.g., labor and capital stock) are neglected.

Some researchers suggest that only using partial-factor energy productivity to evaluate energy consumption may obtain a plausible result (Han et al., 2007; Hu and Wang, 2006). For example, the energy efficiency index may increase solely when energy is substituted by labor, instead of any underlying improvement in technical energy efficiency (Patterson, 1996).

Hu and Wang (2006) propose a new indicator, so-called the total-factor energy efficiency (TFEE) index defined as a ratio of the optimal-to-actual energy input, in order to compute the relative energy efficiency of each region in mainland China under a multifactor framework.

Meanwhile, they conclude that the commonly used energy efficiency index overestimates the benefit from energy consumption because of significant substitution effects among inputs.

Wei et al. (2009) later extend the work of Hu and Wang (2006) to explain what factors cause the variation in the cross-regional TFEE. Moreover, Hu and Kao (2007) and Honma and Hu (2008) also apply the concept of TFEE to investigate related issues in APEC economies and Japan‟s regions, respectively. However, the methodology used by previous studies only

focuses on computing relative energy efficiency among objects in each year such that it lacks insights with longitudinal data. Therefore, an innovative method will be proposed in this article to deal with dynamic energy productivity changes.

The main purpose of this application is to evaluate the energy productivity change of regions in mainland China with a total-factor framework during 2000-2004. In order to study the energy productivity changes, this paper introduces a total-factor energy productivity index (TFEPI) which integrates the concept of the TFEE index with the Luenberger productivity index to measure the change of total-factor energy productivity. Note that the terms, energy efficiency and energy productivity, are used interchangeably in traditional literature, while they are clearly distinguished in this paper. The term energy productivity in this study is similar to the well-known definition as a ratio of the output (GDP) to energy inputs. Nevertheless, energy efficiency is defined as using less energy input to produce the same amount output under a production frontier representing the current technology to use energy.

Hence, this study applies a non-parametric programming method, commonly known as the data envelopment analysis (DEA) approach, to compute the total-factor energy productivity change. Additionally, TFEPI can be decomposed into two components: One is the change in relative energy efficiency, indicating that an object is getting closer to or farther from its annual frontier (catch-up effect or fall-behind effect). The other is shift in the technology level of energy use, showing the shift in the production frontier under the total-factor framework. The improvement of energy technology may be because of many aspects, such as changing energy mix, innovating and diffusing energy-saving technologies, and upgrading production process and equipments (Miketa and Mulder, 2005; Ni and Johansson, 2004).

The remainder of this chapter is organized as follows. Section 3.2 interprets data

sources and variables‟ descriptions. Section 3.3 presents and discusses empirical results in the case of mainland China. Finally, section 3.4 summaries this chapter.