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Life Cycle Assessment and Long Term CO2 Reduction Estimation of Ultra Lightweight Vehicles Using CFRP Hiroaki Zushi1 , Jun Takahashi1, Kazuro Kageyama 1, Hideaki Murayama 2, Hideki Nagai3 and Jun-ichi Matsui4 1 Department of Environmental and Ocean Engineering, The University of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, JAPAN 2 HOPE-X Project Term, NASDA (National Space Development Agency of Japan) 7-44-1, Jindaiji Higashi-machi, Chofu, Tokyo 182-8522, JAPAN 3 Smart Structure Research Center, AIST (National Institute of Advanced Industrial Science and Technology), AIST Tsukuba center 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan 4 VentureLabo Co., Ltd., 1-11-1 Nishi-shimbashi, Minato-ku, Tokyo 105-0003, JAPAN Keywords: Automobile, CFRP, LCA, CO2 emission, oil consumption, fuel cell Abstract: Japanese annual oil consumption is about 300 GL (giga liter) and energy related CO2 emission is about 300 MtC (mega ton carbon).
Life cycle energy consumption and CO2 emission of lightweight automobiles Based on the analytical condition of JAMA's LCA for automobiles [4] shown in Table 1, we added the data of CFRP to calculate life cycle energy consumption and CO2 emission of the lightweight automobiles [5].
For example, in case of CFRP-GV, the reduction of annual CO2 emission is 12MtC which is about 4 % of annual Japanese total CO2 emission in 1990, and annual oil saving is 17GL which is about 6 % of annual Japanese total oil consumption.
Figure 6, which is also calculated from Fig. 4, Table 2 and Table 3, shows a prediction of the CO2 emission t-C 15,960 km Driving distance km 0 1 2 3 4 5 6 7 8 9 10 0 20000 40000 60000 80000 100000 Gasoline Vehicle CFRP Vehicle � 20% mass reduction� GV CFRP-GV Fig. 2 Influence of driving distance on life cycle CO2 emission of automobiles with and without CFRP [5] world energy related CO2 emission considering the Asian motorization.
Kageyama, ''Public Acceptance Assessment of Ultra Lightweight Vehicles using CFRP (From Cost and 3R Viewpoints)'', ibid. 0 200 400 600 800 1000 1200 1400 2000 2010 2020 2030 2040 GV CFRP-GV (40% lightweight) CFRP-FCVN (40% lightweight) CFRP-FCVG (40% lightweight) Year CFRP-GV CFRP-FCV Non-counter measure CO2 emission in the World MtC Fig. 5 Prediction of the influence of Asian motorization on the world CO2 emission from automobiles, and the effect of ultra-fuel efficient automobiles on its mitigation 0 2000 4000 6000 8000 10000 12000 14000 1980 2000 2020 2040 WORLD ( Statistics ) OECD ( Statistics ) OECD ( Kyoto ProtocolCase ) CFRP-GV ( 40%ULV ) CFRP-FCVN ( 40%ULV ) CFRP-FCVG ( 40%ULV ) Non-Counter Measure CO2 emission in the World MtC Non-Counter Measure Year CFRP-FCV CFRP-GV OECD OECD reduction effects Introduce ultra lightweight vehicles using CFRP Counter Measure Introduce fuel cell vehicles Fig. 6 Prediction of the influence

Data used in this study is extracted from [2] and [3].
PCA is based on a change in the dimensional plane of the prospected data into a new contracted data set sufficiently representative of the original larger data set [9].
This reduction in data set is always accompanied by an information loss.
As such, a successful PCA achieves the maximum data set reduction with minimum information loss.
Several data points are different then those available in [2] since data was refined and several inconsistencies corrected.

The Sup-19’s test data showed the linear relationships between the uniaxial penetration test results and the triaxial Shear test result as followed: τtriaxial＝0.82τsingle; τmax triaxial＝0.80τmax single Strength change with different temperature.
When the temperature changed, samples with PG64 asphalt and PG70 asphalt have different strength reduction status, as shown in Table 5.
Table 5 Shear strength of Sup-19 with different asphalt binder types of asphalt 40℃ 50℃ 60℃ shear strength (MPa) ultimate shear strength (MPa) shear strength (MPa) reduction proportion (％) ultimate shear strength (MPa) reduction proportion (％) shear strength (MPa) reduction proportion (％) ultimate shear strength (MPa) reduction proportion (％) PG64 0.726 0.845 0.492 32.2 0.551 34.9 0.321 55.8 0.364 56.9 PG70 1.216 1.609 0.802 34.1 0.946 41.2 0.604 50.3 0.691 57.0 It can be seen in the table 5 that the shear strength for two kinds of mixture declined by at least 50% when 60 ℃.
Though two kinds of asphalt have the similar reduction proportion at 60℃ and no apparent effect of different asphalt binder on shear strength for asphalt mixture in high temperature, however, test results had some change at 50℃.
So if using the uniaxial penetration test result as the criteria test for shearing properties, reasonable structure factor for shear strength should be considered. 2) The Sup-19’s test data showed that shear strength and ultimate shear strength with two methods have a certain linear relationship: τtriaxial＝0.82τsingle; τmax triaxial＝0.80τmax single 3) In the uniaxial penetration test, the decrease of shear strength as the temperature increase was faster than that of ultimate shear strength.

Impact tests were carried out at room temperature using the Instron SI-1M impact machine with a maximum energy of 450 J with the Instron Dynatup Impulse data acquisition system (Instron corporation, Grove City, PA, USA).
Fig. 2 shows microstructure evolved in the steel after tempforming at 600°C with a rolling reduction of 75%.
As result, laths/subgrain size and distance between HABs after tempforming with a 75% rolling reduction (Table 1) is significantly smaller than those after tempering [6,9].
Microstructure of medium-carbon steel after tempforming at 600°C with a rolling reduction of 75% (a) elongated grains in EBSD map, (b) lamellar structure with boundary M23C6 carbides.
These calculations also explain experimental data showed lacking PGY point at load-displacement curve of the TD-CVN specimen.

., aerobic/anaerobic biodegradation, cometabolism, dispersion, volatilization, oxidation, reduction, and adsorption).
Aerobic and anaerobic biodegradation are believed to be the major processes that account for both containment of the petroleum hydrocarbon plume and reduction of the contaminant concentrations.
Environmental conditions and microbial competition will ultimately determine which anaerobic biodegradation processes would dominate, but in a typical aquifer that is devoid of DO, denitrification typically occurs first, followed by iron reduction, sulfate reduction, and methanogenesis [2].
Three lines of evidence can be used to support natural attenuation of chlorinated aliphatic hydrocarbons, including: (1) Observed reduction in TCE concentrations along the groundwater flow path from the TCE spill location. (2) Documented loss of TCE mass at the field scale using chemical and geological analytical data, and a conservative tracer or a fate and transport modeling to document contaminant mass reduction and to calculate biological decay at the field scale. (3) Microbiological laboratory data that support the occurrence of biodegradation.

Introduction One of the problems experienced in power system is the large amount of data set and system complexity.
Step 2: Initialize the related parameters, such as the population sizing, the sizing of particle, the maximum number of iteration, and the power flow data i.e. linedata and busdata.
For instance, when load increased to 100MVar, the transmission loss is minimized to 138.1MW (3.91% reduction) with ten unit SVC installed at the system using EPSO technique, while, the loss is minimized to 138.2051MW (3.84% reduction) and 138.2784MW (3.79% reduction) using PSO and EP techniques, respectively.
With the same loading condition, the loss is minimized to 137.6525MW or (9.22% reduction) with three units of SVC is installed into the system using PSO technique.
Although, with the same loading condition, the loss is minimized to 138.2983MW or (12.44% reduction) with three units of SVC is installed into the system using EP technique.

The findings of this research are expected to contribute to reduction of the noise between floors in an apartment building in the future.
The noise between floors was measured by housing type in this study according to civil complaints about the noise between floors through an investigation and an analysis of research data and existing literature.
As shown in Figure 2, which depicts noise measurement data by house type, the indoor noise levels of the multiplex house were at 32.16∼{TTP}8764 41.14dB(A)Leq and at 39.01∼{TTP}8764 53.93dB(A)Lmax, and averaged at 35.36dB(A)Leq and at 44.81dB(A)Lmax, respectively, which implies that the noise level cannot be ignored(Table 1) but the noise level cannot be ignored when measured by the maximum noise level.
The noise level was higher in the multiplex house than in the apartment household, and as such another approach to living management for residents is needed other than sound insulation of the structure stipulated in the current related laws to reduce the noise level of the multiplex house, and thus diverse plans for the noise reduction need to be introduced.
[2] National Environmental Dispute Resolution Commission, Statistical Data of Environmental Dispute Cases, (2013)

(c) Demonstration of the background reduction.
The lines through the experimental data in a and c are obtained using VEPFIT.
The cyan lines through the experimental data are calculated using a linear combination of the reference spectra (see text) [22].
Hugenschmidt, Novel data analysis tool for the evaluation of Coincidence Doppler Broadening Spectra of the positron–electron annihilation line, Nucl.
Jipma, Analysis of positron profiling data by means of “Vepfit”.

Thirdly, the process of urbanization leads to the reduction of the land and causes the increase of CO2 emission.
All data are from “China Energy Statistical Yearbook”.
The computational formula is: CO2 emission=amount of energy(heat unit)×carbon content per unit calorific value×carbon oxidation rate×(44/12) Amount of energy(heat unit)= amount of energy(material unit)×reduction coefficient(energy unit calorific value) In the formula above, data of amount of energy (material unit) are from “China Energy Statistical Yearbook” This paper studies the impact of different factors till 2009 because the “China Energy Statistical Yearbook” only provides data till 2009. .
Data of calorific value are from “Major Industry And Transportation Industry Energy Statistics System in 1986” made by State Statistics Bureau and the original the state economic commission.
The GDP data in this paper are from “China Statistical Yearbook ”.

The disaster risk management is a complex social process that intervenes in the reduction and in the control of disaster risk [1].
· Risk Reduction (RR): the criterion defines the implementation of preventive measures and mitigation to reduce the risk
Four criteria (Disaster Management, Risk Identification, Risk Reduction and Financial Protection) have been identified.
Unlike other methods that use sample data, the method proposed here is based on expert opinions.
One key aspect of the approach proposed here is its hybrid nature, which combines real sample data with expert evaluations.