| 英文摘要 |
The 2 main objectives of the project include firstly the consolidation of model and the various parameters used in the estimation of both the manufacturing and the pharmaceutical wastes and secondly calculating the wastes produced with the corrected model and parameters. Information on the various methods used for waste estimation in different countries were firstly collected by the research team with extra attention paid to the problems encountered by the existing estimation model to serve as the foundation for revision of both the model and the parameters used. Due to the multi-disciplinary nature of waste estimation, experts from the industry, academic and various government agencies were invited to discuss the existing estimation procedure and the areas for model improvement, which includes the emission intensity, calculation unit, reported data, proper treatment rate, control factors and the estimation scope. In order to ensure to accuracy of the reported data, manual matching was used to examine the approximately 50 thousand reported listing and it was confirmed that as much as 23% and 39% of industrial and pharmaceutical listing respectively were wrong. The corrected data was then passed to the waste control centre for database correction. The estimation of industrial and pharmaceutical wastes produced in year 2008 was performed with the revised parameters and estimation scope. Estimation of wastes produced by the above mentioned industries for the period of 2005~2007 were also performed as demanded by the Department of Budget, Accounting and Statistics of the Executive Yuan, to assist the revision of the green GDP accounting. Trend analysis was performed to the estimation results and signs of abnormality were shown in the industrial general waste for the period between 2006~2008 and industrial hazardous waste for year 2007. Verification with the waste control centre confirmed that there were indeed errors in the reported data used for estimation and re-estimations were performed with the corrected data.
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