| With the advancement of industrialization and urbanization, China’s energyconsumption shows a tendency of rising sharply. The contradiction betweeninternational oil prices and the supply and demand of energy makes energy constraint“bottlenecks†increasingly highlighted. At the same time, environment pollutioncaused by low energy efficiency is more shocking. Therefore, how to improve energyefficiency has been a strategic choice for China crack energy dilemma. Comparedwith the international industrial powers, China’s industrial energy consumption is40%higher than average. There is a lot of room for the energy efficiency to improve. Asindustrial processes in different districts are in different stages at home, energy usagelevels appear big differences. Looking into and comparing the energy usage levels indifferent districts, and excavating the crucial affecting factors have significance foradvancing China’s energy economic transformation and sustainable developmentunder normal economic.This paper bases on30provinces’ panel data in1997-2012of China, uses thesuper SBM-DEA model which contains the unexpected output to measure and valuateChina’s regional industrial total factor energy efficiency and energy saving andemission reduction. And from the aspect of space, the convergence analysis modeland the spatial panel data model are used to analyze the convergence of the energyefficiency of industrial total factor regional differences and the main influence factors.From the aspect of time, variance decomposition and effects of pulse function areapplied to study the contribution and impact effects of the influencing factors. Maincontributions are as follows:(1) The total factor energy efficiency analysis framework based on the scenetheory is put forward. Carbon dioxide emissions are regarded as unexpectedoutput and bring it into the framework. This thesis obtains the super SBM-DEAmodel to measure Regional Industrial Energy Efficiency in China and valuateenergy saving and emission reduction.This thesis expands the original total factor energy efficiency and the total factor energy efficiency framework should not be confined to this district’s input and output,the influence of all kinds of factors should also be taken into consideration andcultural values of zoology and environment protection should be emphasized.Therefore，this paper consider carbon dioxide emissions as unexpected output andbring it into total factor energy efficiency analysis framework which is based on thetheory of the scene. It obtains the super SBM-DEA model to measure and valuateChina’s regional industrial total factor energy efficiency and energy saving andemission reduction. Despite the overestimation of total factor energy efficiency ofeach province, the results show that total-factor energy efficiency that containsunexpected output in eastern coastal area, the middle part and the west are decreasing,the energy saving potential in the west, the middle part, the east and the northeast oldindustrial base are decreasing, the energy conserving potential of the west, the middlepart, the eastern northeast old industrial base and the east are decreasing.(2) The convergence model of Regional Industrial Energy Efficiency inChina are established to analyze the spatial differences of total factor space ofenergy efficiency in China’s four big economy areas.By establishing four types of convergence model of energy efficiency the Chinaregional industrial total factor, including convergence, absolute convergence,relative convergence and club convergence models, we analyze the differences oftotal factor space of energy efficiency in China’s four big economy areas as the eastcoast, northeast old industrial base, the middle part and the west. The results showthat the differences of industrial energy in four big areas will not decline as time goeson, the industrial energy efficiency of each province in the eastern coastal areas vergesto its homeostasis, the differences among them will last but incline; the industrialenergy efficiency of each province in the western coastal areas also verges to itshomeostasis, the differences among them will last but not incline; In central area, theindustrial energy efficiency provinces with similar internal features verges to acommon homeostasis, but the homeostasis of provinces with different internalfeatures has a differentiation and this differentiation will last and not shrink. Northeast China old industrial base presents wind-down situation, but it will present itshomeostasis.(3) The affecting factors system of Regional Industrial Energy Efficiency inChina is built and the relationship of the affecting factors and the industrial totalfactor energy efficiency is analyzed by establishing regional industrial energyefficiency of spatial lag panel data model that contains regional fixed effects.On the basis of the existing domestic research，four indicators are added. Theyare the adjustment of industrial structure, urbanization rate, trade imports and tradeexports. A system of the affecting factors is created on China’s regional industrial totalfactor energy efficiency, and establishes regional industrial energy efficiency ofspatial lag panel data model that contains regional fixed effects based on30regionalpanel data, then analyze the impact of these factors on energy efficiency effects.Results show that there is significant positive correlation regional industrial energyefficiency, different economic development policy of different parts of China led to ahigh in east and low in west situation of China’s regional industrial energy efficiencybenchmark level in geographic space. Energy prices, industrial structure, foreigndirect investment and trade export volume have a negative influence on energyefficiency. Industrial structure adjustment, per capita GDP and trade imports have apositive influence on energy efficiency. Urbanization rate, energy consumptionstructure and technological progress have no significant impact on energy efficiency.(4) PVAR models of factors and energy efficiency are set up to analyze thecontribution and impact effects of affecting factors on the industrial total factorin energy efficiency.In all,10PVAR models of factors and energy efficiency are designed. Besides,variance decomposition and impulse response function are adopted to analyze thecontribution rate of affecting factors to regional industrial total factor energyefficiency change and the impact effects of influencing changes to regional industrialtotal factor effect of energy efficiency changes. Results show that the contribution ofindustrial structure adjustment, energy prices, per capita industrial output, technological progress, industrial structure, trade imports, trade exports, urbanizationrate, energy consumption structure, and foreign direct investment on the regionalindustrial total factor energy efficiency changes are decreasing. Energy prices has aafter the first is negative impact on the industrial total factor energy efficiency. Energyconsumption structure has a after the first is negative impact on the industrial totalfactor energy efficiency. Per capita industrial output value has the positive andnegative shocks impact of industrial total factor energy efficiency. Foreign directinvestment, urbanization rate, industrial structure, technological progress, industrialstructure adjustment, trade imports and trade exports always have a negative impactof industrial total factor energy efficiency.The total factor energy efficiency analysis framework based on the scene theoryis put forward. The energy efficiency problem is studied from the view of macrosignificance of the ideology instead of the nature quality. SBM-DEA model,convergence analysis, spatial autocorrelation panel data model and effects of pulsefunction are used in this paper. Through empirical research of30provinces of Chinaon the time-space effect of China’s regional industrial total factor of energy efficiency,the existing energy efficiency problem research possesses more systematicness andcompleteness. The results provide referenced theories and methodology support forthe government to make polices to improve energy efficiency. |
PDF Full Text Request