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Projecting Solid Waste Arisings: the Case of Domestic Waste of Hong Kong SAR

机译:预计产生的固体废物:以香港特别行政区的生活垃圾为例

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Waste projection informs waste policy making and is an indispensable process in waste management planning. However, not only is valid waste projections difficult to make, their reliability is also difficult to prove. Between the two major methodological approaches in forecasting MSW generation, the time-series approach uses past data and their distribution to determine future waste trends. The factor model on the other hand explains and predicts waste arisings with explanatory variables such as socio-economic factors of the waste generators. This latter approach not just aims at making predictions on waste quantities, it also aims at unveiling hypothetical causal relationships between factors for the prediction of waste arisings. Thus, it is more sophisticated and intellectually sound. In this paper, results of previous waste projection studies conducted by the Hong Kong's environmental authority on domestic waste growth are verified against actual waste data for determining the accuracy of these predictions. It is then followed by the use of another factor-model based technique, autoregression, to forecast domestic waste growth for Hong Kong SAR. While the use of multiple factor autoregression model appears to rectify the over-estimation tendency of classical linear regression model, a number of empirical constraints which are also typical of other factor-model based techniques are encountered.
机译:废物预测是制定废物政策的依据,是废物管理规划中必不可少的过程。但是,不仅很难做出有效的废物预测,而且其可靠性也难以证明。在预测城市固体废弃物产生的两种主要方法学方法之间,时间序列方法使用过去的数据及其分布来确定未来的废物趋势。另一方面,因子模型通过诸如废物产生者的社会经济因素之类的解释变量来解释和预测废物的产生。后一种方法不仅旨在对废物量进行预测,而且还旨在揭示预测废物产生量的因素之间的假设因果关系。因此,它更加复杂且具有理智的声音。在本文中,根据实际环境中的废物数据验证了香港环保局先前对家庭废物增长进行的废物预测研究的结果,以确定这些预测的准确性。然后,使用另一种基于因子模型的技术,即自回归,来预测香港特别行政区的生活垃圾增长。尽管使用多因素自回归模型似乎可以纠正经典线性回归模型的过高估计趋势,但也遇到了许多经验约束,这些经验约束也是其他基于因素模型的技术所特有的。

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