TY - JOUR
T1 - Projecting municipal solid waste
T2 - The case of Hong Kong SAR
AU - Chung, Shan Shan
N1 - Funding Information:
This study is supported by the 5th round of Public Policy Research Grant of the Research Grants Council, Hong Kong SAR in relation to the project coded HKBU 2001-PPR-5 .
PY - 2010/9
Y1 - 2010/9
N2 - Waste projection informs waste policy making and is an indispensable process in waste management planning. 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 study, results of previous waste projections conducted by Hong Kong's environmental authority on domestic, commercial and industrial waste growth are verified against actual waste data for determining the accuracy of these predictions. In addition, using the MSW data from 1979 to 2007 as the reference paths, two autoregression models, a factor-model based technique, were developed to simulate commercial, industrial and domestic waste disposal for the period 2008-2036 for Hong Kong SAR. While the use of multiple factor autoregression model appears to have rectified the overestimation tendency of classical linear regression model, a number of empirical and data constraints which are also typical of other factor-model based techniques are encountered.
AB - Waste projection informs waste policy making and is an indispensable process in waste management planning. 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 study, results of previous waste projections conducted by Hong Kong's environmental authority on domestic, commercial and industrial waste growth are verified against actual waste data for determining the accuracy of these predictions. In addition, using the MSW data from 1979 to 2007 as the reference paths, two autoregression models, a factor-model based technique, were developed to simulate commercial, industrial and domestic waste disposal for the period 2008-2036 for Hong Kong SAR. While the use of multiple factor autoregression model appears to have rectified the overestimation tendency of classical linear regression model, a number of empirical and data constraints which are also typical of other factor-model based techniques are encountered.
KW - Municipal solid waste
KW - Waste projection
KW - Classical linear regression
KW - Autoregression
KW - Factor models
KW - Time series models
UR - http://www.scopus.com/inward/record.url?scp=77955307581&partnerID=8YFLogxK
U2 - 10.1016/j.resconrec.2009.11.012
DO - 10.1016/j.resconrec.2009.11.012
M3 - Journal article
AN - SCOPUS:77955307581
SN - 0921-3449
VL - 54
SP - 759
EP - 768
JO - Resources, Conservation and Recycling
JF - Resources, Conservation and Recycling
IS - 11
ER -