Australian waste plastics flows

Plastics waste is a significant issue in Australia, with plastics waste generation continuing to grow. Central to idea of a ‘circular economy’ is that waste materials flow back into production, to be remade into new products. However, in Australia these flows are not visible due to limitations in published datasets. In this study, we simulated waste flows from consumption, through waste treatment and back into new production. This approach reveals how both plastics waste and recycled plastics become embodied in final consumption. The analysis shows there is currently no evidence that plastics waste generation has decoupled from either population or GDP growth. In fact, the recycling rate has declined in relative terms, potentially caused by recycling infrastructure limitations and an overall decline in Australian manufacturing.

Plastics waste data was extracted from the National Waste Database [1], however this was reshaped to make it easier to use. The reshaped data can be downloaded here: https://zenodo.org/records/12059380.

 
 

It can be seen from the figure that recycling tonnage decreases relative to total waste generation, where the ratio is calculated as recycling/total waste generation (in tonnes). One cause of this is likely the processing recycling capacity limitation in Australia [2]. The National Plastics Plan and the Recycling Modernisation Fund aim to improve this situation through the expansion of recycling infrastructure [3].Also shown in the figure 5 the decline in manufacturing’s contribution to total GDP (manufacturing gross value added/GDP), an ongoing trend in Australia for the past few decades [4]. Unfortunately, the number of local plastic resin manufacturers has decreased in recent years, with only HDPE, LDPE and PP still produced in Australia [5]. Growing Australia’s manufacturing and remanufacturing sector is part of the federal government’s Modern Manufacturing Strategy [6]. The existence of local manufacturing industries who demand recycled materials can give an advantage to local recyclers and secondary producers, for example through lower relative transport costs.

 
 

One perspective of the circular economy is that of a network, where producers and consumers are in relationship with one another, along with other actors such as waste treatment and recycling service providers. Network analysis can be employed to investigate circular economy indicators, for example the strength of interconnections between different regions and the sparsity of recycling infrastructure distribution. Network analysis has been previously used to optimise the placement of waste treatment infrastructure and production facilities. Networks are also used to define social and business relationships between actors in the circular economy, for example waste producing firms who may exchange waste products or cooperate on new product designs. The network map below was constructed to show the relationships between LGAs, population centres and waste recycling services.

This research was recently published in Resources, Conservation and Recycling.

[1] https://www.dcceew.gov.au/environment/protection/waste/national-waste-reports/2022

[2] Harford, N. and French, J. (2022). Australian Recycling Infrastructure, Capacity and Readiness (Plastic and Paper). Australian Council of Recycling; Equilibrium. https://www.acor.org.au/uploads/2/1/5/4/21549240/220623_acor_infrastructure_readiness_report_june_2022_- _updated.pdf

[3] DAWE (2021). National Plastics Plan. Department of Agriculture, Water and the Environment, Canberra. https://www.agriculture.gov.au/sites/default/files/documents/national-plastics-plan-2021.pdf.

[4] Langcake, S. (2016). Conditions in the Manufacturing Sector. Reserve Bank of Australia. https://www.rba.gov.au/publications/bulletin/2016/jun/4.html.

[5] O’Farrell, K., Harney, F., and Stovell, L. (2022). Australian Plastics Flows and Fates Study 2020-21 – National Report. Department of Climate Change, Energy, the Environment and Water; Blue Environment Pty Ltd. https://www.dcceew.gov.au/environment/protection/waste/publications/australian-plastic-flows-and-fates-report-2020-21.

[6] Tomaras, J. (2020). Waste management and recycling in Budget Review 2020–21. Department of Parliamentary Services, Australian Parliament. https://parlinfo.aph.gov.au/parlInfo/download/library/prspub/7622081/upload_binary/7622081.pdf.

Primary industries of NZ

 

Sheep and wool growing at Farewell Spit

Sheep and wool growing at Farewell Spit

Mussel farming in the Pelorus Sound

Mussel farming in the Pelorus Sound

Mussel farming in the Pelorus Sound

Pine plantations on the shores of the Pelorus Sound

Pine plantations on the shores of the Pelorus Sound

 

Transparency, forced labour and sector heterogeneity

 
 

Under the Modern Slavery Act of 2018 [1] Australian businesses must report their exposure to modern slavery in their supply chains. However, discovering whether modern slavery exists in complex and global supply chains is difficult, even when businesses are well-motivated. Modern slavery can come in many forms. For example, debt bondage or bonded-labour, can trap workers in coercive situations until a debt is repaid. Workers may also be subjected to wage exploitation, where wages are severely underpaid [2]. Furthermore, the victims of human trafficking may be forced to work and have their passport or visa withheld.

Recently, attempts have been made to trace modern slavery in supply chains using input-output analysis [3,4]. Input-output analysis (IOA) is a powerful technique for tracing impacts along supply chains and has been used to successfully calculate carbon footprints. However, these supply chain analyses may be somewhat fraught when used to trace modern slavery.

By its’ nature, modern slavery is illicit and occurs in the shadows. This means that modern slavery data is scant, incomplete and often published with high uncertainty. Modern slavery auditors can conduct factory inspections to check for slavery-like conditions, however it’s not clear how results from specific factories can be used to make generalisation about whole sectors or industries. Other datasets and indicators are potentially correlated with modern slavery, for example skill, income, access to education and employment opportunities, access to trade unions and freedom to associate. Unfortunately, using inequality as an indicator risks painting all nations with high inequality, often in the global south, as complicit in modern slavery.

One assumption of IOA theory is that the firms within a sector are generally similar or homogeneous with respect to their direct intensity qj. That is, the direct intensity qj of an individual firm is similar to the group of firms comprising the sector. For the case of GHG impacts, we expect firms within a sector to have roughly comparable qj values since emissions generation is strongly influenced by technology and thermodynamic limits.

However for impacts that are sparsely distributed among firms, such as modern slavery, this assumption of sector homogeneity breaks down. Most firms will contribute zero to the sector's total impact, with perhaps only several firms contributing non-zero intensities. The implication of this is that the majority of the sector's output is not associated with modern slavery and the majority of the firms in the sector are not responsible for it. This makes it difficult to interpret footprint results implicating this sector in modern slavery.

The issue of sector heterogeneity can also be seen in the light of aggregation error. Aggregation error occurs when dissimilar firms are aggregated into the same sector within an IO model. With more information we could disaggregate the 'sparse polluters' from the rest of the sector. In fact, statistical offices and IOT compilers will create new sectors when technology and production recipes vary greatly within existing sectors, for example splitting coal-fired and hydroelectric from all electricity generation. However there is not enough information to perform this disaggregation in the case of modern slavery.

Despite these challenges, there are still tools available to improve supply chain transparency. For example, the freight and logistics industries have developed a number of technologies [5] to improve transparency in cold-chain logistics to prevent food wastage, reduce supply chain disruptions and to provide proof of cold-chain breaches. Unfortunately freight transport can also be a conduit for human trafficking and facilitate modern slavery. Transparency of container access can help mitigate this risk, as well as other issues such as product loss through pilfering. Another aspect to transparency is who reaps the rewards of our consumption. In some cases the lower order production layers, such as retailing, can gobble up a large fraction of total product cost and the workers at higher production layers remain lowly paid [6].

[1] Modern Slavery Act: https://www.legislation.gov.au/Details/C2018A00153

[2] https://www.governmentnews.com.au/migrants-trapped-in-slave-like-conditions-at-aussie-farms/

[3] Shilling et al., 2021, Modern slavery footprints in global supply chains, Journal of Industrial Ecology

[4] https://www.sydney.edu.au/science/our-research/research-areas/physics/big-data-combatting-modern-slavery.html

[5] Such as: https://opensc.org/, https://www.cargoai.co/, https://www.enkibox.com.au/

[6] https://theconversation.com/it-would-cost-you-20-cents-more-per-t-shirt-to-pay-an-indian-worker-a-living-wage-88309

Supply chain abstraction and reality

An abstraction layer is a thinking tool that enables us to temporarily ignore some parts of a problem. Thinking in terms of abstraction layers allows an engineer or scientist or any problem solver to focus on a specific aspect of a problem, as the wider environment and associated complexity is temporarily ignored. The language of abstraction layers is common in software engineering and computer science, where many libraries, frameworks and protocols sit atop one another [1].

One example of an abstraction is buying our food from the supermarket. All of the underlying physical reality and complexity of growing food, keeping it fresh and transporting it from the farm to market is more-or-less abstracted away from the consumer. However at some point, these abstractions begin to break down or to “leak”, in fact it has been posited that all abstractions end up leaking to some degree [2]. The supermarket abstraction begins to leak, for example, when a global pandemic hits food supply chains and consumers find the supermarket shelves empty. When this happens the consumer is forced to consider; where does all this food come from anyway, and should I grow my own food? The leak in the abstraction has laid reality bare.

The science of sustainability often involves attempts to reveal the physical reality that has been obscured by abstraction. Here the abstraction is created by our industrialised and consumerist society, whereby the consumer is disconnected from the manufacture of goods and services, and the entailing environmental degradation [3]. This disconnection comes about due to the complex and interconnected nature of global production and trade, as well as the pattern of shifting or ‘externalising’ pollution to other countries or regions – where the impacts are out of sight [4].

There are modelling techniques available that can unravel the complex web of inputs and impacts along global supply chains [5]. These techniques attempt to account for all the processes that contribute to the goods and services we consume. This allows environmental impacts to be traced to specific consumption activities, despite the impacts and consumption events being separated in time and space.

Using these models allows us to peel-back the production layers and traverse the supply chain, hopefully revealing more of the underlying physical reality. This is demonstrated in the fictitious strawberry jam supply chain shown above. Each step away from the consumer upstream reveals another production layer and set of inputs. These techniques rely on large environmental economic models which knit together the world’s production, trade, consumption and environmental data [6]. The models give us insights into the far fringes of our supply chains, however represent reality only in aggregate – individual supply chains may deviate from those in the model.

Other tools exist to follow goods along supply chains in real-time, and have applications in the freight transport, fishing, cold-chain and food industries [7]. These tools come under the broad heading of supply-chain transparency and chain-of-custody techniques [8]. These techniques differ from the supply chain models in that they usually track an individual product, rather than attempting to account for the innumerable contributing components to a complex product.

These tools are all attempts to reconstruct the past of a product and provide consumers better information about the real costs and environmental impacts of their purchases. However, perhaps this strategy has limits: we may not be able to unravel the full impacts of our actions in the world and therefore should tread as lightly as possible.

[1] See for example, https://en.wikipedia.org/wiki/Fundamental_theorem_of_software_engineering

[2] https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-abstractions/

[3] Erb at al., 2009, Embodied HANPP: Mapping the spatial disconnect between global biomass production and consumption, Ecological Economics

[4] Peters, G et al., 2011, Growth in emission transfers via international trade from 1990 to 2008, PNAS

[5] Techniques such as Input-output Analysis and Life Cycle Assessment.

[6] These databases include EXIOBASE, GLORIA, EORA and others.

[7] See for example: https://www.enkibox.com.au/, https://opensc.org/.

[8] https://hbr.org/2019/08/what-supply-chain-transparency-really-means

This post first appeared on the Shrunk Labs blog: https://shrunk.ai/blog/f/supply-chain-abstraction-and-reality

Supply chain tools for modern slavery

In 2018, Australia passed into the law the Modern Slavery Act, requiring companies to investigate and report on modern slavery in their supply chains. [1]

The OAASIS [2] project (the Open Analysis of Slavery in Supply Chains) aims to harness the power of input-output analysis to assist companies and governments to understand how slavery is embodied in the supply chains of goods and services that Australian's consume. OAASIS hopes also to build tools that help companies comply with the Act.

Part of this work is to assemble and compile a collection of tools that can be used to calculate modern slavery footprints. For example the make-labour-satellite python repo (https://github.com/modern-slavery-open-lib/make-labour-satellite) reads in several modern-slavery and 'problematic labour' datasets and casts them to the GLORIA MRIO [3] classification - enabling them to be used with GLORIA to calculate footprints [4]. The first datasets we have added is from Shilling (2021) [5] and also the ILO [6].

If you would like to be involved, or know of some good labour data, please reach out!

[1]: Modern Slavery Act: https://www.legislation.gov.au/Details/C2018A00153

[2]: The OAASIS project: https://www.sydney.edu.au/science/our-research/research-areas/physics/big-data-combatting-modern-slavery.html

[3]: GLORIA MRIO: https://ielab.info/analyse/gloria

[4]: The data is converted to the GLORIA classfication using the mapping operation: x'(1.p) = x(1.n) Msr(n.m) Msr(m.p)

[5]: Shilling et al., 2021, Modern slavery footprints in global supply chains, Journal of Industrial Ecology, https://doi.org/10.1111/jiec.13169

[6]: International Labour Organization: https://www.ilo.org/global/statistics-and-databases/lang--en/index.htm

Urban waste

 
 

Biodiversity impacts and nested trade models

Usually MRIO models are either global or subnational, with global models containing international trade relations between nations and subnational models containing trade between regions within a country. However neither of these models detail the relationships between local centers of production and global consumption. Specifying these relations requires a “nested” model, where the subnational models is nested with a global model.

Recently we constructed a nested MRIO model, featuring extensive subnational and global trade detail, and information on imports and exports between subnational Australian regions and global regions. Our aim was to show how a nested MRIO model offers a more accurate quantification of localized impacts than use of global-only models. To this end, we traced Australian beef along international trade routes, destined for consumption in the USA.

We linked the nested MRIO model with data on biodiversity threats from the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, specifically “Animalia” kingdom that are listed as “Critically Endangered”, “Endangered”, and “Vulnerable”, and that are threatened by livestock farming (by both smallholders and agro-industry) in all Australian regions. We highlight the distribution of threats obtained from the nested MRIO model to show the power of this nesting technique in capturing localized impacts, specifically in the Northern regions of Australia, which export beef to the USA.

For more detail see: https://pubs.acs.org/doi/10.1021/acs.est.1c08804

 

Distribution of biodiversity threats in Australia, driven by beef consumption in the USA: based on the global model.

Distribution of biodiversity threats in Australia, driven by beef consumption in the USA: based on a nested model

 

UN Comtrade and MRIO integration

 
 

I recently worked on a pipeline to pre-process the UN Comtrade dataset before it is incorporated into the GLORIA MRIO. GLORIA (Global Resource Input-Output Assessment) is a multi-regional input-output (MRIO) database that was built by the University of Sydney using the IELab infrastructure for the UN International Resource Panel (UN IRP). GLORIA has 164 regions, 120 sectors per region, forms a continuous time series for 1990-2020 and is one of the largest and most up-to-date published MRIO databases (at the time of writing).

While the Comtrade1 is an excellent source of bilateral trade data, there are issues with this dataset that must be resolved before the data can be incorporated into a MRIO framework. These issues include:

  • Comtrade contains 2 perspectives on the same trade, representing both the importer and exporter’s reports. However the values of these data points do not always agree, and in some cases one is zero and the other is not.
  • UN SNA MA trade data provides an additional trade dataset, and is taken to be the point-of-truth. SNA MA details total imports and exports by country, however these totals do not agree with the combined totals from the Comtrade and Services trade databases. UN SN MA sets total import and exports by country but not bilateral trading pairs.
  • Trade in certain commodities contains gaps (zero entries) in the timeseries, where such a gap is unlikely to occur in reality.

The raw UN Comtrade and Services trade data are unpacked into a sparse tensor H, with elements hivrst, and where r and s are the origin and destination countries respectively. t indexes the year dimension. Commodities and services categories are indexed by i, using the custom classification HSCPC (containing > 6,357 distinct categories). HSCPC is the union of the HS (commodities) and CPC (services) classification systems. The index v denotes valuation, where imports (v = 1) are valued as CIF (purchaser’s prices) and exports (v = 2) are valued as FOB (producer or basic prices).

A reconciliation algorithm is then implemented to resolve the errors and inconsistencies in the Comtrade database. A RAS procedure involving a series of scaling operations is used; the sparse tensor data structure makes this scaling relatively easy to implement. Smoothing is also implemented to fill zero entries.

1 The term ‘Comtrade’ is used lazily here to refer to both the UN’s Comtrade and Services trade databases.

Still waters run deep

Recently Shrunk set out on a journey to build an open, affordable and flexible method for minimising energy related emissions for industrial, residential and remote applications. Deepwater is a microgrid optimisation service, enabling the ideal design and control of on-site renewable generation, storage and local energy consumers. 

Deepwater allows sites to actively minimise (or avoid entirely) their exposure to energy grid related emissions, reliability and cost structures. Deepwater creates a custom-built predictive control strategy, tuned specifically for local conditions and profiles of operation that are unique to each facility. The control is designed and optimised to achieve desired site-specific objectives such as reductions in emissions, cost or enhancing energy resilience ensuring complete flexibility for clients.

Deepwater is interoperable with existing systems installed at your site, both physical infrastructure and any digital monitoring and control platforms. Deepwater is technologically agnostic and simply drives the most value and best environmental outcome out of the existing infrastructure, without locking you in a walled-garden. Participating in the service only takes a few simple steps.

Deepwater is currently in alpha-testing, and we welcome anyone interested in partnering with us to develop it further or you would like to discuss further please get in touch  or visit www.deepwater.studio.

 
 

Plastics recycling and industrial metabolism

Recently, Shrunk had the opportunity to visit a plastics recycling facility on the outskirts of Melbourne. The size and scale of the operation was overwhelming and we thank our hosts for the amazing tour!

The facility takes a mixed-plastics waste stream and, after a complex series of operations, produces recycled plastic. A network of conveyor belts carries the plastic waste through each processing stage, with specialised robots operating within protective cages to separate each plastic type [1]. The waste is cleaned and decontaminated before high-temperature processes melt and reform the material into recycled plastic pellets and flakes. This finished product is ready to be used as an input into new production. 

This facility is an excellent example of the processing level and technology required if we are to move from our linear 'take-make-waste' economy towards something more circular [2,3]. In a ‘circular economy’, materials are continuously cycled within the system, which minimises the extraction of virgin material and minimises material sent as waste to landfill. 

While impressive, this complex process of separating and remaking plastics is not ‘free’, the process of sorting and remelting can be energy intensive. One of the reasons sorting and separating consumes energy is that we are working against the mixing entropy [4]. The ‘mixed-plastics’ waste stream is in a disordered state, in the thermodynamic sense, whereas the sorted and separated result is highly ordered. We need to do work on the system to undo the mixing, this work requires energy. Further, because the waste stream can be very contaminated (e.g. drink bottle labels and organics are forms of contamination), a certain portion of the waste stream is essentially un-sortable and is diverted to landfill.

During our tour we noticed the large number of HDPE and PET plastic containers that had arrived at the facility – milk bottles, laundry detergent containers and shampoo bottles. An alternative to sorting and remelting these containers is to design them for reuse, rather than recycling. Reuse can be designed in a number of ways, including: refillable by bulk container (customer brings own container to store), returnable packaging (customer returns and retailer or manufacturer cleans and reuses), reusable transit packaging (pallets or boxes used for shipping are returned to the freight company) [5]. These designs are not new, though currently not prevalent. Reuse can come with its own challenges however, for example the energy and water consumption used during food container cleaning can be significant. These issues highlight the importance of the produce design phase in reducing environmental impacts from material use.

Resources

 [1] Plastic ID codes: https://chemistryaustralia.org.au/Content/PIC.aspx

[2] https://www.gsb.stanford.edu/insights/replacing-take-make-waste-model-sustainable-supply-chains

[3] Circular Economy Victoria: https://www.cev.org.au/

[4] Gutowski & Dahmus, 2005, Mixing Entropy and Product Recycling, https://web.mit.edu/ebm/www/Publications/Gutowski_ISEE_2005.pdf

[5]  Coelho, PM, 2021, Sustainability of reusable packaging–Current situation and trends, Resources, Conservation & Recycling: X

Cross-posted from the Shrunk Labs blog: https://shrunk.ai/blog/f/waste-plastics-and-the-circular-economy

 
 

A Triple Bottom Line Analysis of Global Consumption

Recently I had a chapter published in a new book: A triple bottom line analysis of global consumption. This is an atlas of each nation’s consumption emissions. The chapter was written prior to the pandemic, so perhaps the results are a little dated now - but hopefully still interesting. Here is an excerpt from the book chapter:

 

Contrary to rising physical impacts such as emissions, the emissions intensity of economic productivity (t-CO2/$ GDP) has declined by one third (Figure 1). The emission intensity improvements suggest progress in reducing the climate impact of the economy. However, the intensity improvements are largely due to an increase in GDP, rather than reduced emissions. Seventy percent of the increase in GDP comes from services where energy intensity is a less robust measures of progress. Nevertheless, increased renewable electricity generation [i], and a proportional decline in domestic manufacturing [ii] have ensured that emissions intensity is less than it might have been. Despite the reductions in emissions intensity, there has not been an absolute reduction in production emissions.

 
 

Figure 1: Emissions intensity (t-CO2/$), real GDP (constant price 2010 USD), production emissions (t-CO2), population and manufacturing GVA as a proportion of total industry GVA. All timeseries indexed to a 2000 baseline.

 
 

Figure 2 shows how the Australian economy exhibits a tight coupling between growth in affluence, consumption-based emissions and material extraction, along with rising rates of household debt and an almost stable human development measure [iii]. The structure of this developed economy requires rising emissions and material flows, relies on household debt to fund it and provides few social rewards on average for the financial tension and physical impacts it causes. To achieve the promise of decoupling environmental impacts from affluence, the goals and philosophies of modern nation-state economies require fundamental restructuring.

 
 

Figure 2: GDP per capita, Human Development Index (HDI), household income-to-debt ratio, consumption-based emissions and consumption-based material extraction. All timeseries are indexed to a 2000 baseline.

 

[i] United Nations Development Programme (UNDP), http://hdr.undp.org/en/countries/profiles/AUS

[ii] Department of the Environment and Energy, 2018, Australian Energy Statistics, Commonwealth of Australia

[iii] Australian Bureau of Statistics, 2019, 5204.0 - Australian System of National Accounts, 2017-18, Commonwealth of Australia

Metal shop

 
 

Global freight network

Recently, I worked on a project to map global freight transport emissions. The transportation of freight by land, sea and air underpins the global trade in physical commodities. Greenhouse gas emissions from freight transportation are a significant component of global emissions, however the inclusion of freight transport in emissions accounts and environmental impact studies is often incomplete. Both data availability and the difficulty in allocating freight emissions to specific commodity trades contributes to this.

It is not possible to connect every origin-destination country pair directly using every mode, as some routes are infeasible. For example, a country may not have a seaport and therefore cannot trade directly by sea, or there may be no land border between two countries meaning direct road or rail connections are impossible. Furthermore, freight statistics contain the transport mode as it appears to the reporting country, i.e. the mode used to cross that country’s border. However, this record may represent only the first hop (for exports) or the final hop (for imports) of a multi-hop journey. For example, where a commodity is reported as arriving by sea from a partner country, it may have first travelled by road to an intermediate country with a sea port and then onward to the destination country by ship. To handle all these cases, multi-hop trade routes can be modelled between trading partners.

To perform this modelling of trade routes, an adjacency matrix is created that defines feasible direct connections between countries. A number of data sources are used to determine feasible direct trade, by mode, and the distance each route traverses. For sea freight, the WFP Geonode dataset of world sea ports is used to determine whether both origin and destination countries have sea ports and the sea distance between them given by the CERDI database. The CERDI database considers actual shipping routes between countries and shortcuts between landmasses, for example through the Panama Canal. For rail and road freight, countries must share a land border for overland routes to be feasible (with some exceptions, such as Denmark-Sweden, and Singapore-Malaysia). For rail freight adjacency, the additional condition is imposed that both origin and destination countries have internal rail networks. For air, road and rail freight the distance between the origin and destination capital cities is used (calculated using the Haversine distance between two points on a sphere). We assume a path exists between all countries with sea ports.

The adjacency matrix is available here: https://github.com/spottedquoll/cargo-journeys

Resources
WFP 2017, Global ports Geonode, World Food Programme, United Nations
Bertoli, S., Goujon, M., and Santoni, O. 2016, The CERDI-seadistance database
ITF, 2013, Key Transport Statistics, 2012 Data, International Transport Forum
World Bank, 2019, World Bank Open Data

 
 

Cranes, skyhooks and iterative design

Daniel Dennett’s cranes vs skyhooks is a thinking tool for incremental improvement and iterative design. In construction, smaller cranes are used to build larger cranes, which in turn build complicated structures. This is analogous to evolutionary processes, where complexity is achieved incrementally out of seemingly simple components. Complicated designs can be traced backwards to simpler designs in the past. Skyhooks, on the other hand, attempt to explain things in the world without referring to an incremental pathway, such as magic and intelligent design. 

Dennett explains that cranes do lifting in design space, which roughly means that cranes are the mechanisms by which designs improve. Design space is the set of all design parameters within a particular domain. For example in biology, design space could include frog legs, human ears, hummingbird wings etc. In this case, the crane, or series of cranes, is evolution by natural selection. In industrial design the design space might be the allowed materials, weight and shape of a new product. Note that there doesn’t need to be an actual designer, so the design space of the universe might include things like, in the words of Maria Popova, the “the rings of Saturn, …, each idea Einstein ever had, …, the whiskers of Montaigne’s cat”.  

How does this affect our thinking and innovation process at Shrunk? Technology innovation may resemble magic or skyhooks at first, however it more likely resulted from many small incremental improvements. Therefore we try to imagine the incremental path between our ideas and working solutions - does such an incremental path exist? - which steps already exist and which must be invented? By practising frequent software releases and showing our work to anyone who will listen, we overcome the fear of shipping. We love experimentation and prototypes, and while sometimes (often) these fail, the process always informs the next design.

Cross-posted from the Shrunk Labs blog: https://shrunk.ai/blog/f/cranes-skyhooks-and-iterative-design

Australian circular economy atlas

The National Waste Database is a repository for Australia's solid waste data. This collection of waste data is useful however has some issues: The timeseries is not complete, as some years are missing. Allocation to industries is very coarse, there are only 3 waste generating entities: construction and demolition, commercial and industrial, and municipal (households). Further, not all reporting regions (States and Territories) provide data at the same resolution of material type.

We have created an open source dataset in an attempt to solve some of these issues. Missing years are filled using linear interpolation. The regional resolution is disaggregated to SA2 regions using the ABS Business Register. Municipal (households) waste is split into SA2s from state totals using population. The ABS Waste Account is used to establish a relationship between waste types and generating sectors.

The data is published in both sparse tensor flat file formats. The dataset dimensions are:
- years: 2007 - 2019,
- regions: 2310 SA2 (2016) ASGS regions,
- entities: 115 generating entities; 114 SUPG (supply-use product group) industries + 1 households,
- waste types: 69 waste material types,
- treatments: 5 waste treatment methods


The dataset is available here for download: https://zenodo.org/record/5646740, and is made available under a Creative Commons Attribution 4.0 International License.