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Innovative planning for sustainable development: A win-win approach through conversion of negative into positive externalities

2025· article· en· W4411164901 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLand Use Policy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Planning and Valuation
Canadian institutionsNew York Institute of Technology
Fundersnot available
KeywordsExternalitySustainable developmentBusinessWin-win gameEnvironmental economicsIndustrial organizationEnvironmental planningEconomicsMicroeconomicsPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

Predicated on the theoretical foundations of Coasian and Schumpeterian economics, this analytical work adopts a broad view of planning as resource management. It involves the zoning of land or sea with an aim to contribute to sustainability by justifying a micro policy model of sustainable development, unambiguously defined as win-win development via technological and institutional innovations. The paper positions a micro model within the literature on the win-win approach to sustainable development, and clarifies the economic relationship between transaction cost reduction and sustainable development. The paper elucidates the relevance of planning policies to sustainable development through successful innovations in production, explains the general economic role of town planning as a matter of public policies that address boundary-specific and cross-boundary issues in promoting sustainable development, and presents some specific planning policies that reduce transaction costs and promote sustainable development. The prospect of planning for sustainable development is discussed.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.305
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it