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Conference paper
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Circular economy incognito: mapping circular activities and employment in Victoria, Australia

Publisher
Circular economy Recycling Sustainable economics Victoria
Description

Circular economy (CE) policymaking is becoming increasingly popular amongst various levels of governments in Australia and is beginning to engender urban planning changes. Many of these policies focus on supporting larger-scale recycling activities and waste management sectors with an emphasis on maximising the ecological benefits of a CE transition. However, there is little understanding of how key circular activities contribute to diverse and equitable economies and employment opportunities. This is a critical, yet underappreciated facet of facilitating the circular economy transition, given that a socioeconomic restructuring of this magnitude, will still depend on the viability of socially beneficial labour opportunities. Currently many circular economy policies focus on the proportion of materials circulated versus wasted and the technologies and innovation involved in improving this.

As a result there are critical gaps with understanding the diversity of employment available in circular activities, where they are and how accessible they may be to people of varying skills, ages, incomes and cultural and linguistically diverse groups. Understanding the nature of these jobs and their location patterns will contribute significantly to key knowledge gaps in urban planning and policymaking to facilitate an equitable circular economy transition. Our study addresses this gap by analysing the spatial patterns of growth and decline for key circular activity sectors between 2011-2016 using Victoria, Australia as a case study. The study involves a structural analysis to classify ‘circular activities’ using ANZSIC industry codes (4-digit) (ABS, 2019) and draws from primarily 2016 ABS Census data (using TableBuilder) to identify key characteristics of the workforce, including skills required of common occupations, qualifications, age, cultural and linguistic diversity, wages and income. The study also involves quantitative spatial analyses including a location quotient analysis and shift-share analysis.

 

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