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’.
Objective. The National Institute for Land and Infrastructure Management (NILIM), in cooperation with the Japan Society of Civil Engineers (JSCE), developed a Life Cycle Assessment (LCA) method for infrastructures from FY2008 to FY2010. The objective of this LCA method is to quantitatively evaluate and calculate the environmental impact of infrastructure development with regard to factors such as CO2 emission, waste disposal, and use of natural resources. In order to apply CO2 emission data estimated with this LCA method to decision-making, we must first determine the difference between the predicted value (at the design stage) and the definitive value (after the completion of construction). Results and Discussion. The difference between the predicted value and the definitive value is due to (1) changes in the types and quantities of construction material and the machinery specified in the design documents, and (2) updating the environmental load units during the construction process. To examine (1), CO2 emissions were calculated on the basis of seven design documents. These documents focused on roads, bridges, tunnels, etc. The results of the calculations showed the following: 1) the predictive value is likely to range between 76% and 164%; 2) the main factors responsible for fluctuations in the predictive value are transportation distance and type of construction machinery; 3) excluding the influence of transportation distance and type of construction machinery, the predictive value is likely to range between 87% and 108%. Regarding (2), we think that the value of the environmental load units of concrete, iron, and asphalt may vary drastically. Conclusions. This study showed that differences exist between predicted values and definitive values in the LCA method for infrastructures. When making decisions based on a predicted value, we must take this difference sufficiently into account.
In this paper, we focused on BTL (Biomass-to-Liquid) fuel for trucks which is synthesized from woody biomass feedstock, and discussed the alternative potential of BTL for the purpose of mitigation of CO2 emission in a transportation sector. Here, we estimated the specific CO2 emission of BTL fuel and the energy intensity of BTL production system on basis of "Well-to-Wheel" concept, so-called LCA methodology. So far, there are some examples of the life cycle inventory in the transportation sector. However, many of those are based on the literature review. Based on the results of basic experiments, demo-plant operation and test-run of truck, the data of the energy production phase ("Well-to-Tank") and the fuel consumption of target truck ("Tank-to-Wheel"), which is extremely important, were used in our estimation. Here, in order to examine the possibility of low-emission fuel promotion as alternative diesel, we focused on following four Bio-fuels; Bio-H2, Bio-DME (Di-Methyl Ether), Bio-MeOH, and Bio-FTD (Fischer-Tropsch Diesel). Considering the energy density of biomass feedstock in the domestic area, we selected BLUE Tower gasification process (BT process, standard plant scale: 15 t/d) which will be close to the practice stage. Also, the characteristic of syngas through BT process has the concentration ratio of H2:CO=2:1, which would be suitable for synthesizing BTL fuels. Through our life cycle stages, we estimated CO2 emission [g-CO2/km] in 4-ton trucks case. The performance data such as fuel consumption was measured in the driving test on JE05 mode which is based on the change of speed in urban area, adopted as the official mode to measure exhaust gas emissions and fuel consumption of a heavy duty car on a chassis dynamometer in Japan. Note that the fuel consumption data of H2 and MeOH trucks were assumed due to the results of past, present and/or overseas R&D activities. Compared with the CO2 emission of conventional fuel (Diesel: 443.43 g-CO2/km), the mitigation benefit was obtained under the minimum emissions conditions (i.e. H2: 266.53 g-CO2/km, MeOH: 391.95 g-CO2/km, DME: 274.72 g-CO2/km, FTD: 291.28 g-CO2/km).
Objective. Our life style is based on the large consumption of energy, materials and natural resources through the use of many products and services. In order to take a responsible action toward the environment in this life style, it is essential to realize the visible and invisible environmental loads of our daily lives. The concept of life cycle thinking should play an important role in environmental education（EE）in helping students to realize their own environmental loads with their lives' activities. We already reported a life cycle thinking-based EE program for high school students focusing on industrial products. In this work, we have developed a new program for EE based on our direct and indirect water use from a life cycle perspective.
Results and Discussion. We defined two educational objectives for the design of the program. One is to aid students to rethink water use in daily lives on the basis of the recognition of the influence of our direct and indirect water use on the environment and the other is to support them to applying the learning of this program to the reconsideration of other lives' activities. The developed program comprises four parts: the introduction, lecture based part, group work and conclusion. The introduction provides students with raising an awareness about our mass-consumption life style in terms of life cycle thinking. After the introduction, the students learn fundamental issues related with direct and indirect water use through our daily lives in the lecture based part. The students then participate in a group work to consider a decrease of water use in their circumstances. Finally, an opportunity to rethink our life style based on the preceding activities is given as the conclusion of the program. The developed program was implemented in a high school in Kanagawa at Jan. 2012. A questionnaire survey was conducted after the implementation and many positive responses consistent with the educational objectives were obtained.