Research On Energy Efficiency And Consumption In China

Despite of China's per capita energy equivalent to merely 45% of the world average resource possession, our energy talent must require lower energy's increase ratio to support an annual 7%-8% economic growth rate by the end of the middle period of this century. However, our energy utilization ratio calculated on the basis of foreign exchange rate is not high, and our energy consumption per unit GDP is 4.17 times more than OECD member countries in 2007. Based on the facts mentioned, this paper attempts to study the change of China's energy efficiency together with its influencing elements from two angles, one of total factor energy efficiency (TFEE) with the environmental elements reckoned, and the other of energy intensity. Finally, it analyzes China's total energy consumption in the future as well as its trace concerning energy consumption per unit GDP.The approach of BCC model under DEA is employed to work out TFEE of all provincial districts, in which input elements cover energy consumption, sown area of agricultural produce, capital stock, human resource, and output involves GDP and the inverse of environmental pollution index calculated by entropy approach. As to the nation-wide average TFEE, the East ranks number one followed by the Northeast, the Central, and the East in turn in the twelve years ranging from 1995 to 2006. Besides, it is shown that there exists convergence in every area by using convergence method. At last, Tobit regression method is introduced to demonstrate the impact of individual element on TFEE. As a result, such aspects as the heavy industry and ordinary industries, energy structure, resource talent are negatively correlated with TFEE. On the contrary, there is a positive relationship between TEEE with the elements including the increment ratio between the tertiary industry ratio, technical progress, opening-up extent, employment growth rate.As far as the technical progress is mentioned, both hard progress covering scientific technical inventions and so-called soft one such as system innovation and management reform are involved. To start with, in this part the technical progress of every provincial area is decomposed into scientific technical growth indicators representing hard technical progress, and pure technical efficiency indicators and scale efficiency indicators characterized by soft technical progress based on total factor productivity indicator, i.e. Malmqusit indicator with energy input and environmental pollution output covered. Furthermore, the panel measuring analytic method is employed to illustrate how every component involved in technical progress has an effect on energy efficiency. Research shows that with a view to contribution rate of efficiency improvement the scientific technical progress ranks the first followed by the pure technical efficiency and scale efficiency which are almost the same, and that the Northeast and the Central performs better than the East and the West in terms of energy efficiency improvement. The long-run equilibrium relationship between China's energy intensity and three variables of industry structure, technical progress and opening-up extent is analyzed via the time-sequence co-integration analytic method that generates the co-integration equation related to China's energy intensity as well as the error-correction model followed by impulse-response function analysis and variance decomposition. The conclusions are that the China's energy intensity will decline 0.1033, 0.0922, and 0.1488 unit standard coal per thousand yuan as to a 1% increase of the tertiary industry ratio, a10-billion-yuan growth of R&D technical knowledge stock, and a a10-billion-yuan increment of the gap between GDP and GNP respectively.The China's energy intensity and the dynamic relationship between its influencing factors are highlighted by application of panel data co-integration analytical method, which is divided into the nation area and four principal economic districts for unit-root test and co-integration test each. It's decided that LIEC from 1995 to 2006 is decided as a dependent variable, and that LSS from fiscal spending, LFTD, LPTI serve as variables representing technical progress, opening-up degree, and industry structure separately. The panel data unit-root test and co-integration test demonstrate a long-term co-integration relationship between LIEC, LPTI, LSS, and LFTD in the nation and in the four major economic areas. In addition, the regression analysis discloses the facts as follows: the Northeast achieved the highest contribution rate of industry structure change related to energy intensity reduction, then the East and the Central at the lowest level; when contribution rate concerning technical progress as to energy intensity decline is mentioned, the East is the first, the West the next , and the Central the last; the Central ranks the number one followed by the West and the Northeast in terms of the contribution rate of opening-up degree as to energy intensity reduction.The variations of total energy intensity result from three areas including industry structure adjustment, technical progress and opening-up, and total household energy and its utilization. This part, therefore, takes advantage of the modified pull-type approach to decompose the change of energy intensity during 1985-2007 into structural adjustment, efficiency, and household energy consumption factors, which verifies the contribution made by the efficiency factor to reduction of total energy intensity and at the same time explains the reasons why the total energy intensity soared abnormally in China during 2002-2005.In this part, the author adopts grey correlation analytic approach and chooses among a variety of variables these factors highly related with China's energy consumption including the scientific research spending from governmental finance, the ratio of the tertiary industry, and the foreign trade dependent degree that symbolize technical progress, industry structure, and opening-up extent respectively. By making use of co-integration analysis, this thesis discusses the co-integration relationship between five factors such as China's energy consumption, GDP, and technical progress and then sets up an error-correction model. The conclusion presents -0.0072 denoting the technical progress elasticity, -0.7245 the industry structural change elasticity, 0.5791 the opening-up extent elasticity of the energy consumption in long run.The last part develops a nonlinear system dynamics model for energy consumption and GDP, which turns out that GDP growth accelerates the total energy consumption when the energy consumption per unit GDP is less than 2.507, and also carries out a dynamic simulation concerning total energy consumption and unit GDP by using Matlab tools. Then the scenarios simulation approach is introduced to discuss different GDP growth targets as to energy consumption per unit GDP and the decline of energy consumption per unit GDP for each sector.