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Record W2204468939 · doi:10.2495/sdp-v10-n4-520-527

Estimation of Co_2 emissions from tourism transport in heilongjiang province, China

2015· article· en· W2204468939 on OpenAlexvenueno aff
Zi Kang Tang, Naijia Zhang, Chengxi Shi, Kexin Bi

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2015
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
FundersMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsTourismChinaEnvironmental scienceRoad transportGreenhouse gasAir transportAgricultural economicsEnvironmental engineeringBusinessNatural resource economicsEnvironmental protectionGeographyTransport engineeringEconomicsEngineering

Abstract

fetched live from OpenAlex

The rapid development of China's tourism industry has been accompanied by an increase in CO 2 emissions, while the tourism transport accounts for a major share of CO 2 emissions.The purpose of this paper is to investigate the dynamic change of CO 2 emissions from tourism transport in Heilongjiang Province over the period 1978-2012.The results showed that the total CO 2 emissions of tourism transport rose from 13.1 × 10 4 t in 1978 to 224.17 × 10 4 t in 2012, following an average annual growth rate of 9.47%.Among four transport modes, highways transport represented the leading source of CO 2 emissions from tourism transport.CO 2 emissions from airways transport have increased dramatically and became the second largest contributor since 2003.Emissions from railway transport have remained relatively stable and that of waterways transport showed a decreased trend over the years.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.425
Threshold uncertainty score0.845

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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 designObservational
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

Citations4
Published2015
Admission routes1
Has abstractyes

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