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Record W4400830128 · doi:10.15396/eres2024-149

Comparative study of European and North American institutional frameworks concerning the fight against climate change and the biodiversity protection.

2024· article· en· W4400830128 on OpenAlexaboutno aff
Andrée De Serres, Sylla Maldini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityClimate changeEnvironmental resource managementPolitical scienceEnvironmental planningBusinessEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

This study is based on the analysis of the evolution of institutional and regulatory frameworks of four territorial jurisdictions concerning the fight against climate change and biodiversity protection in the real estate sector: European Union, United-Kingdom, United States, and Canada.When it comes to the fight against climate change, obligations for new construction and major renovations are growing. It is possible to observe the inclusion of an approach considering the life cycle of buildings, the monitoring of construction waste, or even the use of less polluting materials and energy consumptions limits with ultra-efficient buildings or even energy production. Existing buildings for their part are not left out, since they will still constitute a large majority of the real estate stock of the legal frameworks studied. Although the room for maneuver is less, the obligations mainly focus on the energy performance dimensions and requirements increasingly target owners by penalizing them in the event of non-compliance with the consumption thresholds per m2 or ft2 that have been imposed. These sanctions may result in the impossibility of renting non-compliant spaces. All these elements are accompanied by tax incentives or assistance to stimulate the achievement of these thresholds.The biodiversity protection is less established than the fight against climate change, because the government concerns about their ecosystemic impacts have emerged more recently. Nevertheless, considerable dynamism exists to frame this theme. In fact, certain frameworks are already requiring for new constructions such as the need to consider as a priority the realization of dense developments on brownfields sites in urban areas in order to limit sprawl, integrate natural elements within the building to ensure that real estate development does not result in a loss of biodiversity or the obligation to prioritize the in-depth renovation of a building before destroying it to build a new one and justifying why, if applicable. Existing buildings are not left out since obligations relating to greening rates are already effective in some jurisdictions and are on the shelf for others.Finally, in terms of data disclosure, most regulatory frameworks have obligations relating to organizations trading on public stock markets. However, some of them have obligations aiming at private organizations. For the moment the thresholds (of turnover, number of employees and number of assets under management) target large organizations, but these thresholds will fall year after year to include more companies. Jurisdictions which have not yet established obligations in this regard are working on similar requirements. These requirements or draft laws are based on benchmarks such as the TCFD, the ISSB or even EFRAG.All these elements in terms of existing or currently developing obligations complicate the development and ownership of real estate assets. It is a sector in which it becomes necessary to collaborate with partners of choice and to develop knowledge and know-how internally to manage existing stock in order to limit the holding of non-compliant properties (by transferring them or undertaking upgrading work). Monitoring and analyzing the regulatory framework is also essential to anticipate the obligations that could arise and ensure that the long-term value of the assets is preserved as well as the value creation model, because the maintenance or retrofit works on the assets as well as the collection of quality data that can be disclosed generates significant costs.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.195
GPT teacher head0.264
Teacher spread0.069 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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