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Vol. 9 (2013) No. 1 p. 2-12




Objective. There are two kinds of limitations in the current input-output table (IOT) from the point of view of environmental impact assessment caused by road transport. Firstly, only two sectors are defined for freight transport on road in the IOT. Secondly, only monetary transaction data are available and quantity transaction data are not provided for road transport in the current IOT statistics. The coarse resolution of road transport sectors could be an uncertainty factor for estimating embodied environmental burden intensities using the IOT especially in the case of the environmental emissions where automobile is the major source. Moreover, the coarse resolution and monetary unit could become the barrier in hybrid analysis when one wants to introduce process-analysis data including road transport information into the IOT. In this paper, we calculated the embodied intensities of road transport indices for 403 sectors using the new approach for extending the IOT in order to solve these problems.
Results and Discussion. Firstly, we compared transport cost and freight ton-kilometers for 36 items in 2005 by combing the IOT and the road transport statistics. While most items locate around the average unit value of transport cost, some items such as daily commodity shows the different trends. The possible reasons are the variation of unit values of transport cost among the items and the limitation of combining the different statistics. The former indicates the necessity of quantity description in the road transport sectors. Secondly, we estimated 403 sectoral freight ton-kilometers and vehicle-kilometers and compared them against the sectoral transport cost. Most sectors are located within factor 2 about the ratio between transport index and transport cost. The correlation between sectoral freight ton-kilometers and vehicle-kilometers are high, though some sectors are not in the trend in reflection of the variation about truck types and superimposed loads. Finally, we calculated the embodied intensities of freight ton-kilometers and vehicle-kilometers for 403 sectors using the proposed approach, namely ‘Annex Table of First Order Induction Method’, and compared them against the results by the normal method. The rank correlation factor is high as 0.95 for the embodied freight ton-kilometers intensities, while the values of the intensities of the primary industry products and its processed products are nearly double by the proposed approach. On the other hand, the rank correlation factor decreases to 0.91 for the embodied vehicle-kilometers intensities. It is found that the embodied intensities of heavy industries such as steel and construction industry become smaller and that the intensities of food goods become larger by the proposed approach.
Conclusions. The current work indicated that extension of the IOT about road transport is significant for improvement of accuracy of embodied environmental burden intensities because the calculated embodied intensities were different from that by the normal method. Moreover it showed that the embodied intensities of vehicle-kilometers are not necessarily proportional to that of freight ton-kilometers by disaggregation into four types of trucks and by consideration of superimposed load. Our estimation of 403 sectoral freight ton-kilometers and vehicle-kilometers by truck types are useful not only for ‘Annex Table of First Order Induction Method’, which is conducted in this paper, but also for the other typical extension method such as ‘monetary-quantity hybrid extension method’ and ‘sector disaggregation method’.

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