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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 OpenAlexaff
Lawrence W.C. Lai, Frank T. Lorne, Stephen Davies, Kwok‐wing Chau

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.014
Scholarly communication0.0080.007
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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