1) per capita energy use
人均能源消费
1.
By using nonlinear regression method,this paper analyzes per capita energy use,total energy use and per capita GDP data from 1970 to 2004 in 28 developed countries issued by Inter- national Money Fund to find evidence of an inverse U-shaped relationship between energy use and per capita GDP.
本文采用非线性回归和拟合的方法,对世界货币基金组织(International Money Fund,IMF)发布的28个发达国家从1970到2004年间的人均能源消费、能源消费总量和人均GDP数据进行回归分析,验证能源消费与收入之间是否存在倒U型曲线关系。
2) average energy source consumption per person
人均生活消费能源
3) per capita consumption
人均消费
1.
The paper applies panel data model and the per capita consumption and disposable revenue data from 1991 to 2007 to conduct comparison research to the consumption level of urban residents of 29 provinces and cities.
本文运用面板数据模型,利用了1991-2007年的人均消费和人均可支配收入的统计数据,对我国29个省市城镇居民的消费水平进行了比较研究。
2.
With the dynamic simulation of empirical data and empirical analysis,adjusting the interest taxation rate has two effects: in the short term,lowering interest taxation rate will reduce per capita consumption;in the long run,lowering interest taxation rate will raise steady-state per capita consumption.
本文将利息税引入拉姆齐模型,求出稳态人均消费路径。
4) energy consumption
能源消费
1.
Study on increase and control of carbon dioxide emission from energy consumption;
能源消费CO_2排放量的变化与控制分析
2.
Analysis of causality between energy consumption and economic growth in China;
中国能源消费与经济增长关系的因果分析
3.
Application of feedback regulation model with time delay on energy consumption system;
时滞反馈控制模型在能源消费系统中的应用
5) energy consumption
能源消费量
1.
Energy consumption prediction is made by the use of radial basis function(RBF) neural network method,and energy consumption prediction model based on RBF neural network is established.
采用径向基函数(RBF)神经网络方法进行能源消费量预测,建立了基于RBF神经网络的能源消费量预测模型。
2.
Because energy consumption system is complex and non-linear,this paper combines neural network and three models of GM(1,1),WPGM(1,1),pGM(1,1) with energy consumption data,and proposes the combination forecasting model of energy consumption.
利用我国能源消费的历史数据,采用灰色预测的GM(1,1)、无偏GM(1,1)和pGM(1,1)三种模型与BP神经网络进行优化组合,建立了灰色神经网络的能源消费量组合预测模型。
3.
To diminish the error in the non-equidistant grey model for forecasting,considering the characteristic of the sequence,this model combined neural network and three models of grey theory with energy consumption data,and proposed the combination model of energy consumption.
针对非等间距灰色系统预测中存在误差较大的问题,结合序列本身的特点,利用世界能源消费的历史数据,采用3种灰色预测模型与神经网络进行组合优化,建立了灰色神经网络的能源消费量组合预测模型。
6) per fibre consumption
人均纤维消费量
1.
In analysing the influence of economic increase on fibre demand, the relationship of per fibre consumption with GNP, and consumption structure, the gray forecasting model is established and the domestic textiles demand is forecasted.
本文在分析国民经济增长对纺织品需求的影响、人均纤维消费量与人均国民生产总值的关系、社会消费的需求格局的基础上，运用灰色系统理论建立了预测模型，对国内纺织品未来三年的需求进行了预测。
补充资料：发电能源在一次能源消费中的比重
发电能源在一次能源消费中的比重
the share of energy for electricity generation in total primary energy
fad旧n nengyuan za一y一ei nengyuan x.oofe一zhong由b lzhong发电能源在一次能派消费中的比，(theshare of energy for eleetrieity罗neratinn in totalprimary energy)是表征一个国家国民经济电气化程度的一个指标。在一次能源总消费中，发电用能源的比例越大，电力在能源系统中的地位越重要，国民经济电气化的程度就越高。由于使用电力比直接使用石油、天然气和煤炭等一次能源的效率高，且电力用途广泛，使用灵活方便，不污染环境，可靠性高，因此.世界各国的电力生产和消费以高于能源的速度增长，发电用能源在一次能源总消费t中的比例日益增大。下表列出了一些国家发电用能源占一次能源总消费的比例变化情况。一些日家发电能一占一次能派总消.一的比，(%)┌──┬───┬───┬──┬───┬──┬───┬───┐│年份│美国 │日本 │德国│加章大│法国│英国 │中国 │├──┼───┼───┼──┼───┼──┼───┼───┤│1970│28.4 │31。1 │ │43.3 │23.8│ │ │├──┼───┼───┼──┼───┼──┼───┼───┤│1980│37。65│44.9 │30.7│57.1 │36.1│39.7 │20.60 │├──┼───┼───┼──┼───┼──┼───┼───┤│1990│41.79 │50.30 │33.7│58.9 │46.0│37。6 │24.68 │├──┼───┼───┼──┼───┼──┼───┼───┤│1995│40.50 │51.50 │35.8│63.7 │57.0│36.7 │29.58 │├──┼───┼───┼──┼───┼──┼───┼───┤│1996│41。0 │50.7 │34.9│64。3 │54.2│35.4 │30.76 │└──┴───┴───┴──┴───┴──┴───┴───┘ 注:1.资料来浑日本海外电力调查会《海外电气事业统 计》和《中国电力统计资料》. 2.说明:(l)电力消费t系按电厂的发电t或净发电 t计算;(2)美国、日本、法国、英国系按供电热 效率计算煤耗，铭国、加幸大系按发电热效率计算 煤耗，中国则立接按发电煤耗计算;(3)发电能浑 消费(含火电、水电、核电等)均用上述计算的煤 耗乘以各自的总发电t。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条