Nature-based solutions in Australia: a systematic quantitative literature review of terms, application and policy relevance
Bibliographic record
Abstract
Abstract Nature-based Solutions (NbS) are emerging as an approach to sustainable environmental management and addressing environmental and social issues in ways that benefit human well-being and biodiversity. NbS have been applied to social-environmental challenges such as climate change and urbanization, but with diverse conceptualisations and applications that may impact their effectiveness and broader uptake. Much of the literature and implementation of NbS has emerged from Europe and though NbS use is rising in Australia, the context is unclear. This systematic quantitative literature review aims to understand Nature-based Solutions in an Australian context. Here we explore the meaning and practical uses of NbS in Australia, through three research questions: In Australia, 1) what is meant by the term ‘nature-based solutions’? 2) what socio-ecological challenges do NbS aim to address and how? 3) are there gaps in NbS research and policy application that are hindering uptake of NbS approaches? We show that in Australia, local governments are using NbS in urban planning to address the compounding challenges brought on by climate change in the human-environment interfaces. However, there is no consensus on NbS definitions and approaches, research is focussed on urban areas and problems, and NbS implementation follows a bottom-up, localised pattern without an integrated policy framework. Based on these findings, we provide recommendations for improving the implementation of NbS in Australia including: 1) a consistency of NbS definition and awareness of NbS approaches; 2) interdisciplinary and interdepartmental collaboration on NbS methods and effectiveness and; 3) an integrated policy framework supporting NbS nationwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.049 | 0.043 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".