对经济活动与货物运输关系中一些关键假设的回顾

IF 0.3 4区 工程技术 Q4 ECONOMICS International Journal of Transport Economics Pub Date : 2004-06-01 DOI:10.1400/16913
O. Kveiborg, M. Fosgerau
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引用次数: 29

摘要

在预测货运增长时,往往需要若干换算因素,这取决于预测的详细程度。在这里,我们研究将不同行业的固定价格生产转化为不同商品生产并进一步转化为权重的转换因素。描述这些转换的数据很难获得,建模者只能求助于各种特别的假设。我们获得了1981年至1992年期间的一组数据,详细说明了根据丹麦国民核算按工业和商品按固定价格和按吨分列的生产情况。有了这些数据,我们就能够检验一些通常被做出的假设。因此,我们的研究结果对未来的货运建模练习具有启示意义,特别是有必要收集哪些数据以及有必要寻求明确建模的关系。我们发现有必要考虑跨行业生产构成的变化,但每个行业内的商品组合可以被安全地视为常数。不断变化的价值密度占运输增长的近三分之一;然而,这是由于第一年的数据。否则,对于我们的数据,可以将值密度视为常数。最后,我们发现使用导入或导出数据来估算值密度会导致不可接受的大误差。(一)
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A REVIEW OF SOME CRITICAL ASSUMPTIONS IN THE RELATIONSHIP BETWEEN ECONOMIC ACTIVITY AND FREIGHT TRANSPORT
A number of conversion factors are often needed when projecting freight transport growth, depending on the level of detail of the projection. Here we investigate conversion factors that convert production in fixed prices in different industries into production of different commodities and further into weight terms. Data to describe these conversions are hard to come by and modellers have been left to resort to various ad hoc assumptions. We have obtained a data set covering the period from 1981 to 1992 detailing production by industry and commodity both in fixed prices and in tons based on the Danish national accounts. With these data we are able to check some of the assumptions that have commonly been made. Our findings thus have implications for future freight modelling exercises, in particular for what data it is necessary to collect and what relationships it is necessary to seek to model explicitly. We find that it is necessary to account for changing composition of production across industries, but that the commodity mix within each industry safely can be regarded as constant. Changing value densities account for almost a third of transport growth; however, this is attributable to the first year of data. Otherwise, value densities could be regarded as constant with our data. Finally, we find that using import or export data to impute value densities induces unacceptably large errors. (A)
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