Exploring Ecourbanism: A Whole-Systems Approach to Healthier Cities, People, and Environments
Bibliographic record
Abstract
The World Health Organization’s Ottawa Charter (1986) stated, “health promotion is not just the responsibility of the health sector”, advocating for reciprocal maintenance to care for each other, our communities, and our natural environment. Biodiversity is fundamental to human health and wellbeing, and to the resilient functioning of ecosystems at all scales. Humans have co-evolved with microbes, which are an essential part of our own functionality, and research shows the impact of a biodiverse human microbiome on physical and mental health. This diversity is acquired through environmental exposures throughout life, termed the exposome, with childhood being an essential formative period, but modern urban living separates us from the nature contacts we need. Today, non-communicable diseases are the leading cause of death and long-term disability, claiming over 35 million lives annually. The United Nations Environment Programme’s Global Environment Outlook GEO6 (2019) calls for urgent transformational change to address this. Cities have a global reach, affecting climate change, biodiversity loss, and pollution, which together present a health emergency to people and the planet. Because nature works from the bottom up, ecourbanism advocates for a regenerative approach to natural capital investment that enhances the delivery of resilient ecosystem goods and services fostering community wellbeing. Designing convivial spaces interwoven with nature fosters cultural ecosystem services, aiding community-building, combating loneliness, and promoting physical activity to enable human flourishing. Addressing these issues requires a multi-scale approach, from macro to micro levels, and this paper aims to demonstrate how ecourbanism, as a whole-systems approach to planning and design, can create salutogenic, resilient urban environments. A profound ‘deep green’ transformation is essential to promote both human and environmental health. Urban areas, which generate 80% of global GDP, must reinvest in environmental quality to boost health, productivity, and resilience. Ecourbanism envisions greener urban areas, where blue and green infrastructure augment or replace traditional grey infrastructure. Urban landscapes should become more productive, needing new frontline workers tending to the living environment, who complement public health and healthcare professionals. Upstreaming healthcare measures in this way delivers multiple co-benefits, including soil improvements, reducing embodied carbon, increasing carbon sequestration and storage, water and thermal regulation, urban food production, and biodiversity. 2030 is a key milestone for delivery of the seventeen Sustainable Development Goals, The United Nations’ Decade on Ecosystem Restoration, and major carbon emission cuts. It is imperative that we act now to transform our urban environments and ensure a healthier, more sustainable future for all.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".