| 摘要 | ABSTRACT
1. Research Purpose
Policy measures targeting energy efficiency have gained importance due to the rapid increase in energy demand driven by emerging countries including China and India. The main purpose of these measures is to reduce energy consumption by promoting more efficient use of energy. Multiple policies can be used to achieve the same goal, but some may be more costly to implement and maintain than others. The cost/benefit ratio of the measures under consideration is important, especially when operating with a limited budget. A unified methodological standard is essential in order to compare the results of different policies and prioritize the most effective measures.
As the number of energy efficiency policies grows, the need will become more pressing to develop a consolidated system of analysis that can be applied to the policies�� achievements. To construct a more efficient system, we need to perform a thorough investigation not only of the existing analytical tools and but also of the relevant databases.
Towards these ends, this study firstly aims to conduct a survey of the existing literature for analytical systems that are used in various countries to evaluate their energy efficiency policies in industry and transportation sectors. The properties of various analytical models are presented and compared so that we can make more evident which design is appropriate for our purpose. The scope of this survey is not limited to tools that only relate to energy efficiency, but includes those applied to energy issues in industry and transportation sectors in general. Second, we apply the Divisia Index approach and an econometric method (the simultaneous equation model) to energy consumption in the industry and transportation sectors and interpret the results. Finally, we present the importance of constructing an integrated database as well as an analytical system with which to extend the capacity of research in this area.
2. Research Results and Policy Suggestions
Besides a Divisia index analysis and econometric approaches, this study examines several other analytical tools including (1) CGE (Computable General Equilibrium) models (2) the MARKAL (MARKet ALlocation) model (3) Integrated Assessment Models: MESSAGE(Model for Energy Supply Systems And their General Environmental impact) and AIM(Asia-Pacific Integrated Model) (4) NEMS(National Energy Modeling System).
Next, as mentioned above, we employ the Divisia index method and the simultaneous equation model to analyze the impact of energy efficiency policies on energy intensity in the industrial sector and the rebound effect of fuel consumption in the transportation sector, respectively. First the changes in the energy intensity in the manufacturing industry over the data period(2000-2011) are divided into 5 factors; structure changes, changes in the energy intensity for each business line, productivity fluctuations, variations in energy mix, and energy efficiency changes. When we include the consumption of non-energy use, energy efficiency itself increased by 3.1% during 2000-2011, which contributes to the improvement in the overall energy intensity of the manufacturing industry. With the consumption of non-energy use excluded we obtained similar results, and energy efficiency contributed 29.5% to the energy intensity improvements, which is greater than the effect of structural change(22.3%).
The second analysis is of fuel consumption in the transportation sector. It is possible that the consumers who newly purchase highly fuel efficient vehicles increase their driving mileage due to lower driving costs than before(the ��rebound effect��). If such a tendency prevails then it may substantially offset the effect of a policy such as Corporate Average Fuel Efficiency Standards(CAFE). In order to achieve a precise analysis, we need to estimate the magnitude of the rebound effect. By applying a simultaneous equation model to a panel dataset collected over the period 2003-2011, the rebound effect is estimated around 36%for a decrease in driving costs of 1%. Also, consumers�� perceptions of the ��appropriate�� level of average fuel efficiency of cars decreased until 2009, but rose rapidly afterwards towards the average fuel efficiency standard. The perceived appropriate fuel efficiency level for consumers is still lower than the standard.
In order to perform a comprehensive analysis of policy impact, extensive micro-level data are required. Data on activities and market structure that affect energy consumption are crucial to improve the precision of researches in related areas. Currently there exists no main body that collects the relevant data and provides an integrated database. In line with the growing importance of energy efficiency measures, we need to prepare a long-term plan to develop and administer an integrated database as well as an appropriate evaluation system. |
