Demographic urbanization caused great changes in scale of residents' consumption and residents' lifestyle and then impacted changes of regional household energy consumption. This paper expanded Logarithmic Mea...Demographic urbanization caused great changes in scale of residents' consumption and residents' lifestyle and then impacted changes of regional household energy consumption. This paper expanded Logarithmic Mean Decomposition Index method through introducing variables of urbanization and residential consumption into the model. It also analyzed the influences of six factors as energy structure, energy intensity, population scale, urbanization, residential consumption, and consumption inhibit on regional household energy consumption. Results showed that in 2003-2012, impact of urbanization on regional household energy consumption of Chinese three areas was significantly higher than population size. The "population gathered in eastern region" phenomenon caused eastern region getting the largest population scale effect. Driving force of residential consumption on regional household energy consumption was much higher than the other five effects. Due to the comparative advantage of residential consumption compared with government consumption, investment, and net export, the decrease of consumption ratio promoted the growth of regional household energy consumption. Energy intensity in Chinese three regions kept reducing in 2003-2012. The progress of energy utilization technology slowed the growth of regional household energy consumption, and energy intensity effect was most significant in the central region.展开更多
The paper intends to analyze economic factors that influence electricity consumption in the OECD economies. A special interest in this context is given to spillover effects of trade on electricity consumption. For thi...The paper intends to analyze economic factors that influence electricity consumption in the OECD economies. A special interest in this context is given to spillover effects of trade on electricity consumption. For this purpose, a model is constructed that using a dynamic panel study approach. The model is estimated in a GMM framework in which a dynamic procedure is conducted along the balanced growth path for electricity consumption in each economy. In advance, the long run dynamic behavior of prices, GDP, and trade induced spillover variables is determined. In a further step, the short run dynamic mechanism is pursued by estimating the partial adjustment dynamic coefficient on the target level of electricity consumption. The analysis is conducted for industrial, as well as residential electricity consumption. Alternatively, the same procedure is estimated by the application of a fixed period model. The model provides a benchmark tool for electricity policy decisions and for electricity consumption projections.展开更多
基金supported by Funding of National Natural Science Foundation of China"Research on environmental risk assessment and management of the avoidance project based on perspective of public perception,""Research on the evolution mechanism of the avoidance cluster behavior by considering of endogenous information under the internet environment"[Grant Numbers 71671080,7157109]Funding of National Natural Science Youth Foundation of China"The formation,evolution and conflict coordination of the avoidance behavior"[Grant Number 71301070]+1 种基金Funding of National Statistical Science Research Project"Energy statistics and its balance sheet in China based on perspective of energy quality"[Grant Number 2016LZ36]Funding of Science Foundation of Huainan Normal University"Benefit evaluation of coal mining subsidence area comprehensive management based on external perspective"[Grant Number 2016xj07zd]
文摘Demographic urbanization caused great changes in scale of residents' consumption and residents' lifestyle and then impacted changes of regional household energy consumption. This paper expanded Logarithmic Mean Decomposition Index method through introducing variables of urbanization and residential consumption into the model. It also analyzed the influences of six factors as energy structure, energy intensity, population scale, urbanization, residential consumption, and consumption inhibit on regional household energy consumption. Results showed that in 2003-2012, impact of urbanization on regional household energy consumption of Chinese three areas was significantly higher than population size. The "population gathered in eastern region" phenomenon caused eastern region getting the largest population scale effect. Driving force of residential consumption on regional household energy consumption was much higher than the other five effects. Due to the comparative advantage of residential consumption compared with government consumption, investment, and net export, the decrease of consumption ratio promoted the growth of regional household energy consumption. Energy intensity in Chinese three regions kept reducing in 2003-2012. The progress of energy utilization technology slowed the growth of regional household energy consumption, and energy intensity effect was most significant in the central region.
文摘The paper intends to analyze economic factors that influence electricity consumption in the OECD economies. A special interest in this context is given to spillover effects of trade on electricity consumption. For this purpose, a model is constructed that using a dynamic panel study approach. The model is estimated in a GMM framework in which a dynamic procedure is conducted along the balanced growth path for electricity consumption in each economy. In advance, the long run dynamic behavior of prices, GDP, and trade induced spillover variables is determined. In a further step, the short run dynamic mechanism is pursued by estimating the partial adjustment dynamic coefficient on the target level of electricity consumption. The analysis is conducted for industrial, as well as residential electricity consumption. Alternatively, the same procedure is estimated by the application of a fixed period model. The model provides a benchmark tool for electricity policy decisions and for electricity consumption projections.
