Changes of waste generation in Australia: insights from structural decomposition analysis

Published on Jan 1, 2019in Waste Management5.43
· DOI :10.1016/j.wasman.2018.11.004
He He2
Estimated H-index: 2
(UniSA: University of South Australia),
Christian John Reynolds10
Estimated H-index: 10
(University of Sheffield)
+ 2 AuthorsJohn Boland23
Estimated H-index: 23
(UniSA: University of South Australia)
Abstract Waste generation is linked to consumption both in households (Final demand) and in the supply chain. Gaining understanding into the driving forces that change of waste generation in the supply chain can contribute to solving issues of waste management. The environmentally-extend input-output model is an effective tool with which to investigate the relationship between economic activities and waste generation. In this paper structural decomposition analysis (SDA) is employed to analyse the determinants of changes of waste generation in Australian economy from 2007–2008 to 2013–2014. Empirical results indicate that the major determinant for the increase of waste generation was change in Final demand’s overall level of economic activity. Changes in the production mix of Final demand (mix effect) was responsible for a decrease of waste generation in Australian economy during the period. The Manufacturing sector was found to have the highest waste generation intensity. Meaning that each million $AUD output of the Manufacturing sector resulted in the most amount of waste generation. In addition, technological change has contributed the largest waste generation effect for the Construction sector in 2011–2012. These findings suggest that Final demand, technological changes and sectoral changes are identified as the drivers of Australian waste generation historically. To reduce waste generation, policy must be targeted at altering behaviour of consumption and waste generation, and increasing innovation of new ecological technologies for Australian industry.
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#1Jing Wei (BFU: Beijing Forestry University)H-Index: 2
#2Kai Huang (BFU: Beijing Forestry University)H-Index: 5
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#2A Al Tabbaa (University of Cambridge)H-Index: 36
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#1Christian John Reynolds (USYD: University of Sydney)H-Index: 10
#2Julia Piantadosi (UniSA: University of South Australia)H-Index: 12
Last.John Boland (UniSA: University of South Australia)H-Index: 23
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