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Record W4388105694 · doi:10.18280/ijsdp.181007

The Impact of the Belt and Road Initiative on the Capital Market of Chinese Listed Companies: From the Perspective of Sustainable Development

2023· article· en· W4388105694 on OpenAlexvenueno aff
Gongwen Xu, Dhakir Abbas Ali, Amiya Bhaumik

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Environment
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Sustainable developmentBusinessMarket developmentCapital marketCapital (architecture)FinanceNatural resource economicsEnvironmental planningMarket economyEconomicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

This paper primarily investigates the microeconomic impacts of the Belt and Road Initiative.Utilizing the principle of natural experimentation and a sustainable development perspective, we employ the Difference in Differences model with Multiple Time Periods to analyze the influence of the Belt and Road Initiative on corporate capital market performance.The study reveals that, in the capital market, firms directly investing in Belt and Road countries tend to outperform their counterparts investing in non-Belt and Road countries.Specifically, these firms demonstrate enhanced stock liquidity, a finding that maintains robustness upon further analysis.Delving deeper, the research uncovers that non-state-owned enterprises, as compared to state-owned enterprises, display more significant capital market performance when they directly invest in Belt and Road countries.In other words, the improvement in stock liquidity is more pronounced for these firms.In a world increasingly focused on sustainable development, this study can effectively guide investment strategies and contribute to the formulation of Chinese policies.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.295
Teacher spread0.274 · 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 designQualitative
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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