مجموع عوامل رشد بهره وری انرژی، پیشرفت فنی و تغییر کارایی: مطالعه تجربی از چین

This article introduces total-factor energy productivity change index (TFEPI) based on the concept of total-factor energy efficiency and the Luenberger productivity index to evaluate the energy productivity change of regions in China with a total-factor framework. Moreover, the TFEPI can be decomposed into change in energy efficiency and shift in the energy use technology. According to the computation results, China’s energy productivity was decreasing by 1.4% per year during 2000–2004. The average total-factor energy efficiency improves about 0.6% per year, while total-factor energy technical change declines progressively 2% annually. The factors affecting TFEPI are also examined: (1) The east area has a higher TFEPI than the central and west area; (2) increasing the development status and electricity share of energy consumption will improve the region’s TFEPI performance, while increasing the proportion of GDP generated by the secondary industry deteriorates TFEPI of a region.

In the course of economic development, energy use provides the embodied and disembodied technical progress and productivity growth [1] and [2]. In fact, several studies have found positive relationships between energy consumption and economic growth [3] and [4]. However, energy use is also a major source of greenhouse gas causing environmental problems [5], [6], [7] and [8]. 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 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 [9]. Moreover, Crompton and Wu [10] forecast that the total energy consumption in 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 10th 5-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 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 [1], [11], [12] and [13]. 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 [12] and [14]. For example, the energy efficiency index may increase solely when energy is substituted by labor, instead of any underlying improvement in technical energy efficiency [11]. Hu and Wang [14] 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 China under a multi-factor 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. [15] later extend the work of Hu and Wang [14] to explain what factors cause the variation in the cross-regional TFEE. Moreover, Hu and Kao [16] and Honma and Hu [17] 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 paper to deal with dynamic energy productivity changes. The main purpose of this article is to evaluate the energy productivity change of regions in 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. The Luenberger productivity index introduced by Chambers et al. [18], as a difference of directional distance function, measures whether total-factor productivity changes from the base period to the next period. As shown by Luenberger [19] and Chambers et al. [20], the directional distance function provides a flexible method to calculate both input contractions and output expansions. According to the flexibility of directional distance function, some researchers have considered that the Luenberger productivity index is more appropriate than the well-known Malmquist productivity index [21] and [22]. Moreover, Chambers et al. [18] illustrates that the Luenberger productivity index can be decomposed into efficiency and technical changes. Hence, our 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 [6] and [23]. Comparing to traditional parametric methods (such as the Cobb–Douglas function and translog production function), the advantage of using the DEA method is that this method avoids model misspecification [21] and [24]. Moreover, the DEA-Luenberger index can easily compute total-factor productivity change, efficiency change, and technical change. Since the DEA-Luenberger index cannot analyze the change in single factor productivity under total factor concern, the TFEPI is introduced to deal with this issue in this article. The remainder of this paper is organized as follows. Section 2 introduces the proposed total-factor energy productivity index using the DEA approach. Section 3 interprets data sources and variables’ descriptions. Section 4 presents and discusses empirical results in the case of China. Finally, Section 5 concludes this paper.

Conventional energy indices, such as energy efficiency and energy intensity, can be used to evaluate how energy inputs are efficiently utilized. However, these indicators neglect the substitution among energy consumption and other factors so that the results obtained from conventional energy indicators overestimate or underestimate the actual state. This paper proposes the total-factor energy productivity index (TFEPI) to assess energy productivity growth for regions in China. TFEPI constructs a multiple-input framework that avoids single-input bias since energy is not the only input to produce economic output. The DEA approach based on the Luenberger index and relative TFEE is applied to conduct a total-factor energy productivity index in this study. The TFEPI proposed in this paper is a dynamic indictor to measure the total-factor energy productivity growth by getting rid of the substitution and complement effects among all inputs. It helps provide more insights about efficiency changes as well as technology changes in energy use. This paper reports the results of an empirical study of regional productivity growth in China. Accordingly, China’s average total-factor energy productivity was decreasing by 1.4% per year during 2000–2004, especially in the period 2001–2002 (−3.2%). However, the traditional energy productivity index reveals that China’s energy productivity change was only decreasing 0.5% annually during the research period. This comparative result shows that the traditional energy productivity index overestimates the energy productivity change if energy is taken as the single input. At the regional level, seven of 29 regions enhance their total-factor energy productivity. The TFEPI not only evaluates total-factor energy productivity change, but also appraises change in relative energy efficiency (catching up effect) and shift in the technology of energy use (innovation effect) by decomposing TFEPI. The finding from a change in relative energy efficiency shows that the whole country’s average total-factor energy efficiency improves about 0.6% per year and two periods (2001–2003) present negative growth. This indicates that the relative energy efficiency gap between all regions has gradually condensed since 2000. Nevertheless, the results of total-factor energy technical change illustrate that the technology of use energy declines progressively at 2.0% per year during 2000–2004. Over the 5 years, none of all regions in China shows a positive total-factor energy technical change. We conclude that energy productivity decline in China is attributable to negative technical growth and not relative efficiency change. What causes regional total-factor productivity inequality and decline in China are important issues in future work. In the present study we only examine the effect of a region’s development status, economic structure, and energy mix on total-factor energy productivity change, but these effects cannot completely explain the situation of energy productivity in China. Some research studies based on cross-country or cross-region studies suggest that relative energy price may be the key determinants of energy productivity growth [6] and [34]. For example, the oil price shows a discrepancy between regions in China, because local governments still have some authority to set the selling price. Hence, the difference in pricing among regions could result in some regions with higher prices (such as Shanghai) having an incentive to improve energy productivity. It recommends that additional research focus on the components of the total-factor energy productivity index to draw more precise conclusions about specific effects on energy productivity growth among regions in China. Moreover, it may be of interest for future studies to discuss the contribution of each input variables toward total-factor productivity growth. Hence, additional research would usefully extend the present TFEPI to investigate how the productivity of other input variables change.