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Towards carbon neutrality: Developing an assessment framework for villages

2025· article· en· W7105797063 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

VenueHabitat International · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsAgriculture and Agri-Food Canada
FundersChinese Academy of ForestryNational Natural Science Foundation of China
KeywordsCarbon neutralityCarbon fibersGreenhouse gasCarbon sinkClimate changeSustainabilityRural areaNeutrality

Abstract

fetched live from OpenAlex

Addressing climate change requires both global cooperation and local action. Mountainous rural areas, with their significant carbon sink potential, play a crucial yet under-explored role in achieving carbon neutrality. However, it is not clear how far these villages are from carbon neutrality. This study proposes a novel carbon neutrality assessment framework specifically designed for mountainous villages, integrating carbon emission reduction, sink, and community engagement to assess and guide local carbon neutrality efforts. Case studies in Baizhang Town and its six surrounding villages showed that the town and four villages achieved carbon neutrality, illustrating the framework's effectiveness in reducing emissions and enhancing sink. Theoretical analysis further supports the framework by providing insights into its applicability and scalability for rural communities. The framework offers a practical tool for local communities to manage carbon emissions and implement sustainable practices. This framework can be adapted to other rural areas, offering a model for carbon neutrality efforts across diverse mountainous regions.

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 score0.041
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.340
Teacher spread0.320 · 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