Infrastructural ecology as a planning paradigm: Two case studies
Bibliographic record
Abstract
Moving beyond conventional mono-sectoral planning and management of urban systems, 'infrastructural ecology' advances a multi-objective, holistic design approach.Planned integration across the sectors of energy, water, sanitation and waste allows for reciprocal exchanges across two or more systems, leveraging synergies and providing multiple co-benefits.By reducing overall throughput of matter, eliminating wastes and avoiding carbon-intensive technologies, this paradigm offers a model for critical services provision for the next 2 billion people in emerging economies -both those moving to cities and particularly those who remain in rural poverty.Two exemplary cases, one in India, another in Brazil, reveal the efficacy of renewable power produced by cooperative, cross-sector initiatives.The first, Omnigrid Micropower Co., Pvt., Ltd. (OMC) realized a workable bottom line for solarpowered generation that serves some of India's poorest, rural citizens when combined with the power demand from the telecommunications sector.OMC's remote small to mid-size solar power plants today serve nearby telecom tower base stations and deliver community energy needs through mini-grids and adapted power equipment that eliminates expensive wiring for household service.These installations not only electrify villages, they provide permanent jobs.In the second case, Itaipu Binacional, the entity behind the world's largest generator of renewable power, the 8-km (5-mi)-wide 14 GW Itaipu hydroelectric dam, had sustained degradation of water quality in its reservoir from the area's agricultural waste.It partnered with farmers to develop an Agroenergy Condominium that used distributed biodigesters to process the waste from local corn production and farmer's herds, producing biogas sufficient to energize 2,200 households while yielding high quality fertilizer.The Agroenergy Condominium and OMC's cross-sector solution are both examples of strategic investments addressing energy poverty, improving quality of life, and increasing economic productivity while keeping carbon contributions level.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".