Promise of a green economic recovery post-Covid: trojan horse or turning point?

Industrial Ecolog

Differentiated responsibilities of US citizens in the country’s sustainable dietary transition

Industrial Ecolog

Mapping potential conflicts between global agriculture and terrestrial conservation

Industrial EcologyEnvironmental Biolog

Locating pressures on water, energy and land resources across global supply chains

Industrial Ecolog

Lessons from micro- and macro-modelling linkages: example of linking LCA/DMFA and CGE model for developing Circular Economy scenarios

This report aims to provide a primer for CGE practitioners to understand the potential to link micro (i.e., Life Cycle Assessment (LCA) and Dynamic Material Flow Analysis (DMFA)) and CGE models to model circular economy strategies. The report offers an overview of the state-of-the-art of LCA/DMFA and CGE model linkages (including 8 archetypes), key lessons from LCA/DMFA studies to macro-modelling approaches, and an example of substituting internal combustion engine vehicles with electric vehicles, considering the circularity potential of Li-ion batteries to illustrate the application of micro- and macro-modelling linkages towards circular economy scenarios.</p

Sustainable agrifood systems for a post-growth world

Industrial Ecolog

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press