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Record W4389702380 · doi:10.33423/jabe.v25i5.6586

Spillover Effects of Land Port Economy: A Case Study of Huaihua International Land Port in Hunan, China

2023· article· en· W4389702380 on OpenAlexvenueno aff
Tengteng Hou, Lu Lv, Xiaohui Shu, Robert Guang Tian

Bibliographic record

VenueJournal of Applied Business and Economics · 2023
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)Spillover effectChinaRevenueLand useBusinessNatural resource economicsEconomicsGeographyCivil engineeringMacroeconomicsFinance

Abstract

fetched live from OpenAlex

Previous studies have shown that the construction and development of international land ports have a significant economic spillover effect on the neighboring regions. The construction and development of Huaihua International Land Port inevitably exerts economic spillover effects on the adjacent regions. It is necessary to analyze and predict the trend of economic data before and after the construction of the land port in the study area to evaluate accurately the role and effect of the land port in driving the economic development of the local and neighboring regions. Then, the impact of land ports as a variable is quantified by analyzing the difference between the predicted and actual results, thus providing a basis for formulating scientific policies. Local governments’ general public budget revenue (GPBR) is an important indicator to measure the economic development of a region. The paper uses the local governments’ GPBR of Huaihua International Land Port Economic Development Zone, Hecheng District, and Zhijiang Dong Autonomous County from January 2019 to June 2023. Based on this, time series decomposition and SARIMA modeling are carried out. It analyzes the impact of Huaihua International Land Port on the region and its economic spillover effect on the surrounding regions. The results found that the impact presented by the model is consistent with the actual situation. Therefore, the evaluation results have high accuracy and reliability.

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 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 score0.336
Threshold uncertainty score0.395

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

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