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Analyzing the influence of financial technology and environmental taxation on energy transformation in BRI nations · 10.1016/j.heliyon.2024.e40635

Name: Verified Rigor Record — Analyzing the influence of financial technology and environmental taxation on energy transformation in BRI nations · 10.1016/j.heliyon.2024.e40635
Item: Analyzing the influence of financial technology and environmental taxation on energy transformation in BRI nations · 10.1016/j.heliyon.2024.e40635
Rating: 39
Author: Αlpha¹

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Αlpha¹ Editorial

This rigor review was examined and confirmed by Αlpha¹ Editorial · July 6, 2026

Αlpha¹ Rigor Review

Rigor Reviewed

CRITICAL

Critical rigor failure · Observational Study

Eight dimensions — click a segment for details

Critical

High-priority

Model-reported confidence: highReviewer agreement: highEngine: 7.6.0Source: full text

Not verified in this report

References were not verified against Crossref/OpenAlex.
No reported statistical tests were found to recompute.
No data or code availability links were detected to verify.

This Αlpha¹ Rigor Review uses AI Rigor Reviewers trained on a curated corpus of high-fidelity and retracted papers, with expert supervision and curation. It can still make mistakes; verify each finding against the source before relying on it.

Critical findings

• internal_contradiction (high, corroborated): Table 1 shows REI with Mean=5.715, Min=5.715, Max=5.715 — a constant value — but the paper later treats it as a variable in regression analyses (Table 2 shows REI correlations). A constant variable cannot be used in GMM/2SLS and would produce nonsensical results, suggesting a data error or fabrication.
• impossible_statistic (high, corroborated): EC has Mean=1.170, StdDev=1.177, Min=1.111, Max=1.335. The standard deviation (1.177) is larger than the range (0.224) and larger than the mean, which is impossible for a variable bounded between 1.111 and 1.335. This indicates a data coding or transcription error.
• internal_contradiction (high, corroborated): Table 6 reports 'Observations 1105 1333' for the two models, but the study states 148 countries over 9 years should yield 1332 observations (148*9). The 1105 in the primary model is unexplained and inconsistent with a balanced panel.
• impossible_statistic (high, corroborated): Multiple p-values reported as values >1 (e.g., 1.1111, 1.1130, 1.355, 1.373), which is impossible for a probability. This indicates a systematic formatting or encoding error that corrupts the reported statistical results.
• impossible_statistic (high, corroborated): Table 6 reports a Wald test CHI3(p-value) of '370001(1.111)', which is an implausibly large chi-square statistic for any reasonable model and the p-value is >1. This suggests corruption of the reported test statistic.
• impossible_statistic (high, corroborated): Table 7 reports p-values of '1.311', '1.033', '1.355', '1.173', '1.111', '1.330', '1.133', '1.131', '1.030', '1.113', '1.117', '1.110' for diagnostic tests, all of which are >1 and thus impossible.
• method_result_mismatch (high, corroborated): The abstract and introduction claim the study examined cruise tourism's impact on human health and ecosystems, yet no variable related to cruise tourism appears in the empirical model or any table.
• data_too_clean (high, corroborated): Table 1 variable REI has min=5.715, max=5.715, mean=5.715, and SD=1.111. A constant variable cannot have a non-zero SD; this indicates a data transcription or calculation error.
• internal_contradiction (high, corroborated): Table 2 reports correlation p-values of '1.111', which exceed the maximum possible p-value of 1, indicating a systematic reporting error.
• internal_contradiction (high, corroborated): Table 4 CD test reports p-values of 1.1111 for all tests, which is impossible for a test statistic; these values are not valid p-values.

The paper addresses a relevant question about energy transformation determinants in BRI countries using a standard econometric framework, but it is seriously undermined by multiple demonstrable errors in reported statistics (impossible p-values >1, impossible standard deviations) and unsupported claims (cruise tourism impacts never analyzed). Data and code are not available for verification, and the reporting has several transparency gaps.

Three independent AI reviewers scored all eight dimensions, supplemented by a copyedit pass and verification components (integrity, claim audit, statistics recomputation). Three dimensions (biological variables, ethical approvals, key resources) were unanimously rated not applicable. The reviewers agreed on most dimension statuses; the statistical analysis dimension was downgraded from warn to fail based on the post-hoc integrity verification uncovering demonstrable errors that the original AI reviewers did not sufficiently emphasize.

12 major claims checked against the paper's own evidence: 3 not fully backed by the presented evidence (unsupported or overstated).

19 integrity concerns flagged (10 high).

Rigor

Eight Dimensions1 pass · 4 warn

12 major claims3 unsupported7 partial2 supported

The introduction and literature review cite numerous prior works (e.g., Refs. [3,17,30,45,56]) establishing the premise that financial technology, governance, and environmental taxes influence energy transformation. The rationale is logically developed: financial systems facilitate green energy investment, governance affects policy implementation, and environmental taxes incentivize cleaner energy. Limitations of prior research (e.g., need for panel data across diverse economies) are implicitly addressed by the study's scope, though specific limitations of cited works are not explicitly detailed.

Sub-criteria

prior work citedADEQUATE

premise rationaleADEQUATE

limitations addressedINADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 85% conf

The paper adequately cites prior work on energy transformation, financial technology, governance, and environmental taxation, and provides a logical rationale for the study, though the discussion of prior limitations is somewhat generic.

Evidence

paraphrase[Section 2, Literature review]

“The literature review discusses multiple studies (e.g., Refs. [3][7][12][15][21][27]) on the roles of financial technology, governance, and environmental taxes in energy transformation.”

paraphrase[Section 1, Introduction]

“The introduction states the study addresses 'the complex challenges and dynamics of energy transformation within the Belt and Road Initiative (BRI) countries, focusing on the influence of management practices, financial technology, and environmental taxation on foreign direct investment (FDI).'”

absence[Section 1, Introduction]

The paper does not explicitly describe how limitations of prior research are addressed in the current design.

PASSReviewer 2· 90% conf

The study cites prior research on energy transformation, financial technology, governance, and environmental taxation, and provides a logical premise linking these factors to energy transitions.

Evidence

paraphrase[Sections 1 and 2]

“The paper cites multiple studies (e.g., Ref. [] on energy transition, Ref. [] on financial technology, Ref. [] on environmental taxes) to build a foundation for its research questions.”

paraphrase[Section 2 (Literature review)]

“The literature review argues that financial technology facilitates green investment, governance influences policy effectiveness, and environmental taxes promote cleaner energy, establishing a logical chain from these factors to energy transformation.”

absence[Sections 1 and 2]

Limitations of prior research are not explicitly discussed.

PASSReviewer 3· 85% conf

The paper establishes a clear scientific foundation by citing extensive prior literature on energy transformation, financial technology, governance, and environmental taxation, and it provides a logical rationale linking these factors to the study's objectives.

Evidence

paraphrase[Section 2 (Literature review)]

“The literature review cites numerous studies on finance, governance, environmental taxation, globalization, and urbanization in relation to energy transformation (e.g., Refs. [5], [8], [12], [15], [19], [22]).”

paraphrase[Section 4.4 (Discussion)]

“The discussion explicitly states H1–H5 as alternative hypotheses for each factor (Technological Transfer, governance, environmental taxation, globalization, urbanization) and indicates whether they were accepted or rejected.”

absence[Section 1 (Introduction); Section 2 (Literature review)]

Limitations of prior work are not explicitly addressed as a separate sub-section.

The study uses a balanced panel of 148 BRI countries from 2014 to 2022. It names the estimation methods (sys-GMM, 2SLS) and diagnostic tests (AR, Hansen, Sargan, weak identification). Applicable sub-criteria: randomization, blinding, replicate distinction, controls, and independent replication are not applicable for this observational econometric study. power_analysis is not reported; inclusion_exclusion is not explicitly pre-specified; outlier_handling (winsorization) is reported but only briefly; controls (baseline/ablations) are not applicable.

Sub-criteria

controlsN/A

power analysisNOT REPORTED

blinding levelsN/A

outlier handlingINADEQUATE

randomization unitN/A

inclusion exclusionNOT REPORTED

randomization methodN/A

replicate distinctionN/A

independent replicationN/A

Per reviewer (3) — the votes behind the verdict

WARNReviewer 1· 80% conf

The study uses a standard econometric panel design (sys-GMM, 2SLS) appropriate for observational macro-level data, but it does not report key design elements like sample-size justification, pre-specified inclusion/exclusion criteria, or handling of potential outliers.

Evidence

absence[Section 3, Research methodology]

No a priori power analysis or sample-size justification is reported.

direct quote[Section 3.1]

“There were 148 participants in the total specimen, with 78 participants from wealthy countries... and 72 from nations developing... The research utilized robust, well-balanced panel data covering 2014 through 2022.”

direct quote[Section 3.1]

“we employed the winsor2 cut (1–99) commands for the identical weights.”

WARNReviewer 2· 80% conf

The study applies a two-step system GMM and 2SLS to panel data, but lacks explicit description of inclusion/exclusion criteria for the sample selection, and does not address outlier handling. Power analysis is not reported.

Evidence

direct quote[Section 3.1]

“There were 148 participants in the total specimen, with 78 participants from wealthy countries in the highest and upper-moderate income groups and 72 from nations developing in the lowest and below-average income groups.”

absence[Section 3]

Power analysis is not reported.

absence[Section 3]

Outlier handling is not mentioned.

WARNReviewer 3· 85% conf

The study has a clear econometric design with two-step sys-GMM and 2SLS validation, but it lacks an explicit data source description, pre-specified inclusion/exclusion criteria, outlier handling, and power analysis.

Evidence

absence[Section 3 (Research methodology)]

No power analysis or sample size justification is reported.

absence[Section 3 (Research methodology)]

Inclusion/exclusion criteria for countries or data are not pre-specified.

direct quote[Section 3.1 (Data, variables and measurement)]

“after that, we employed the winsor2 cut (1–99) commands for the identical weights.”

The study uses aggregate national-level panel data (e.g., GDP, energy consumption, tax revenue, FDI) and does not involve any biological subjects, organisms, or cell lines. All sub-criteria (sex, age/weight, species, housing, demographics) are not applicable.

Sub-criteria

demographicsN/A

sex reportedN/A

sex justifiedN/A

age weight healthN/A

housing conditionsN/A

species strain sourceN/A

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 95% conf

The study analyzes macro-economic country-level data and does not involve biological subjects, cell lines, or animals, making all sub-criteria not applicable.

Evidence

direct quote[Section 3.1]

“This research examined the effects of green tax revenue, democracy gauge, and innovation on the energy transformation in established and emerging BRI partner nations. There has also been consideration of FDI, price increases, development, and globalization.”

N/AReviewer 2· 100% conf

The study is a macro-economic analysis of country-level data; no biological variables, subjects, or organisms are involved, so this dimension is not applicable.

Evidence

paraphrase[Sections 3.1 and 4]

“The study analyzes country-level indicators such as energy consumption, innovation index, FDI, and environmental tax revenue, with no mention of individual biological subjects.”

N/AReviewer 3· 100% conf

The study analyzes country-level economic and financial data; no biological subjects, organisms, cell lines, or human participants are involved.

Evidence

direct quote[Section 1 (Abstract); Section 3.1]

“This research examined the effects of green tax revenue, democracy gauge, and innovation on the energy transformation in established and emerging BRI partner nations.”

The paper does not report any ethics statement, but the research uses secondary panel data from 2014–2022 that is presumably publicly available (e.g., World Bank, IMF, or similar sources). There is no mention of human participants, animal experiments, or controlled-access datasets. The study type (macro-economic analysis) typically does not require ethics approval.

Sub-criteria

iacuc statementN/A

informed consentN/A

irb ethics statementN/A

regulatory complianceN/A

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 95% conf

The study uses only publicly available, aggregated national-level economic and environmental data from secondary sources, with no new data collection from human or animal subjects, so ethics approval is not applicable.

Evidence

paraphrase[Section 3.1]

“The research utilized robust, well-balanced panel data covering 2014 through 2022, comprising national-level indicators.”

absence[Whole paper]

No mention of human subjects, animal work, or ethics approval anywhere in the paper.

N/AReviewer 2· 100% conf

The study uses publicly available aggregate country-level data (macroeconomic indicators) and does not involve human or animal subjects, tissue, or non-public identifiable data, so no ethics approval is required.

Evidence

absence[Entire paper]

No ethics statement is present, but the study uses aggregate country-level data.

N/AReviewer 3· 100% conf

The study uses only publicly available, aggregated country-level economic data; no human subjects, animals, or non-public identifiable data are involved, so ethics approval is not applicable.

Evidence

paraphrase[Section 3.1 (Data, variables and measurement)]

“The study uses standard macroeconomic variables (e.g., Technological Transfer, KOF-GI, HCD, EI, REI, Innovation Index, FDI) sourced from publicly available data (World Bank, IMF).”

The analysis uses Stata-15 (identified as software) for econometric modeling but does not involve any wet-lab resources. Although software_tools_identified could apply, the Stata version is stated, meeting that criterion. However, with no other resources, the dimension is deemed not_applicable because no biological/chemical resource or investigational product is used.

Sub-criteria

mycoplasma testingN/A

reagents identifiedN/A

organisms identifiedN/A

antibodies identifiedN/A

cell line authenticationN/A

software tools identifiedADEQUATE

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 90% conf

The study uses only macroeconomic secondary data and statistical software (Stata), with no wet-lab resources, investigational products, or bespoke software tools that require identification by vendor/catalog.

Evidence

direct quote[Section 3.1]

“The acquired data has been processed and studied with Stata-15.”

absence[Whole paper]

No biological or chemical resources are used.

N/AReviewer 2· 100% conf

The study does not use any biological/chemical reagents, antibodies, cell lines, organisms, or bespoke software beyond standard statistical packages; thus no key resources to identify.

Evidence

direct quote[Section 3.1]

“The acquired data has been processed and studied with Stata-15.”

N/AReviewer 3· 100% conf

The study uses no biological or chemical resources, investigational products, or bespoke wet-lab reagents; only standard econometric software (Stata) is used.

Evidence

direct quote[Section 3.1 (Data, variables and measurement)]

“The acquired data has been processed and studied with Stata-15.”

Tests are named (two-step system GMM, 2SLS, CIPS, CADF, Westerlund cointegration). Software is identified (Stata-15). However, exact p-values are frequently reported as '∗∗∗' or '1.111' which is an implausible p-value and likely a formatting/encoding artifact. No effect sizes or confidence intervals accompany the p-values. Test assumptions (normality, equal variance for any parametric test) are not discussed; the paper uses a CD test and unit root tests but does not verify the key GMM assumptions for the error term beyond the AR and Hansen tests at the model level. Data presentation is limited to tables of coefficients with stars; no individual data points or clear definition of error bars is provided. Mathematical plausibility: The table values contain impossible numbers (e.g., p-values of 1.1111, mean EC of 1.170 with Std. Dev. 1.177 for a count or index variable that likely should be non-negative), raising concerns about reporting accuracy. This warrants a 'warn' overall.

Sub-criteria

tests namedADEQUATE

exact p valuesINADEQUATE

effect sizes ciNOT REPORTED

data presentationINADEQUATE

software identifiedADEQUATE

assumptions verifiedINADEQUATE

mathematical plausibilityWARN

Per reviewer (3) — the votes behind the verdict

WARNReviewer 1· 75% conf

Statistical tests are named (sys-GMM, 2SLS, CIPS, CADF), and software is identified (Stata-15), but exact p-values are often reported as ranges (e.g., p<0.01 or as '1.111' which appears to be a formatting error), effect sizes and confidence intervals are not reported, test assumptions are not verified, and the data presentation lacks individual data points and clear error bar definitions.

Evidence

direct quote[Table 6, Table 7]

“AR1 (p-value) −3.100 (1.1130); AR3 (p-value) −1.031 (1.353); Hansen (p-value) 10.37 (1.355)”

absence[Section 4, Tables 6-7]

No confidence intervals or effect sizes are reported.

direct quote[Table 6]

“L. Energy Consumption 1.055∗∗∗ (1.157); Technological Transfer 1.111∗∗∗ 1.137∗∗ (1.153) (1.135)”

WARNReviewer 2· 70% conf

Statistical tests are named (GMM, 2SLS), but exact p-values are reported as asterisks and threshold symbols rather than exact numbers, effect sizes are not provided, and tests for assumptions (normality, stationarity) are partially addressed via unit-root tests. Some internal inconsistencies appear in the tables.

Evidence

direct quote[Tables 5, 6, 7]

“1.055∗∗∗”

direct quote[Table 1]

“REI 1299 5.715 1.111 5.715 5.715”

direct quote[Table 2]

“p-value > 1 indicated by '1.111' and similar values in correlation table.”

WARNReviewer 3· 70% conf

Statistical tests are named and software is identified, but exact p-values are not systematically reported (many stars-only), effect sizes with confidence intervals are missing, and data presentation omits individual data points. Mathematical plausibility cannot be fully verified due to many unclear/inconsistent numbers.

Evidence

paraphrase[Tables 2, 6, 7]

“Regression tables show coefficients with asterisks (*, **, ***) but do not report the actual p-values.”

direct quote[Table 1]

“REI 1299 5.715 1.111 5.715 5.715”

paraphrase[Table 1]

“EC: Obs=1299, Mean=1.170, StdDev=1.177, Min=1.111, Max=1.335. The SD is larger than the range (~0.224), which is mathematically impossible.”

The paper includes a data availability statement: 'The authors do not have permission to share data.' This is reported_but_inadequate because it provides no concrete access route (no repository, platform, or data-availability committee). Repository_deposit and accession_numbers are not applicable for country-level panel data, but code_sharing is applicable and not reported (no mention of custom code or repository). The lone applicable criterion (data_availability_statement) is inadequate, making the overall status 'warn'.

Sub-criteria

code sharingNOT REPORTED

accession numbersN/A

repository depositN/A

data availability statementINADEQUATE

Per reviewer (3) — the votes behind the verdict

WARNReviewer 1· 90% conf

A data availability statement exists but is vague ('The authors do not have permission to share data'), lacks any repository, accession numbers, or managed-access mechanism; no code is shared.

Evidence

direct quote[Data availability statement]

“The authors do not have permission to share data.”

absence[Whole paper]

No code repository is mentioned.

FAILReviewer 2· 100% conf

The data availability statement merely states 'The authors do not have permission to share data', which is vague; no data has been deposited in a repository, and no code is shared. This fails all applicable criteria.

Evidence

direct quote[Data availability statement (end of paper)]

“The authors do not have permission to share data.”

absence[Entire paper]

No code is shared.

WARNReviewer 3· 90% conf

A data availability statement is present but is not adequate (vague statement that data cannot be shared), and no code or data has been deposited in a repository.

Evidence

direct quote[Data availability statement]

“The authors do not have permission to share data.”

Methods are described sufficiently for replication (variables, data sources, estimation strategy). A limitations section is present ('The study has several limitations, including the reliance on available data from 2014 to 2022...'). Funding and COI are declared ('The authors declare that they have no known competing financial interests...'). No reporting guideline (e.g., STROBE for observational studies) is referenced. Trial registration is not applicable. 'All outcomes reported' is difficult to assess as the study does not pre-register; the analysis appears to report the planned models. Conclusions are largely proportional but occasionally overstate causal implications from associational panel data (e.g., 'management practices... significantly influence energy transformation' is stated as a finding). Three of five applicable sub-criteria are adequate (methods_completeness, limitations_discussed, funding_coi => 60%), just at the pass threshold, but the overclaiming in conclusions and missing reporting guideline tilt to 'warn'.

Sub-criteria

funding coiADEQUATE

trial registrationN/A

reporting guidelineNOT REPORTED

methods completenessADEQUATE

all outcomes reportedINADEQUATE

limitations discussedADEQUATE

conclusions proportionalINADEQUATE

Per reviewer (3) — the votes behind the verdict

WARNReviewer 1· 80% conf

The methods section describes the econometric approach in moderate detail, limitations are discussed, and a funding/COI statement is provided, but the paper does not follow a formal reporting guideline, does not register the study (a registry is not standard for this type of macro-economic panel analysis), and the conclusions are somewhat proportional though with some over-generalization (e.g., from panel data to causal policy claims).

Evidence

direct quote[Section 5.2]

“The study has several limitations, including the reliance on available data from 2014 to 2022, which may not fully capture the local contexts and variations among BRI countries.”

direct quote[Declaration of competing interest]

“The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.”

absence[Whole paper]

No reporting guideline is referenced.

WARNReviewer 2· 70% conf

Methods are described in detail, but the paper lacks a trial registration (n/a), reporting guideline adherence, and there are apparent overclaims (e.g., linking cruise tourism to health outcomes without empirical data). Limitations are briefly discussed in Section 5.2.

Evidence

direct quote[Abstract and Introduction]

“This research examined the management, financial technology, and environmental taxation elements impacting energy transformation... Additionally, the research reviewed the impact of cruise tourism on human health and environmental ecosystems”

absence[Methods]

No reporting guideline is referenced.

WARNReviewer 3· 80% conf

Methods are described in moderate detail, limitations are discussed, conclusions are proportional, and funding/COI is declared. However, no reporting guideline is referenced, not all outcomes are transparently reported (no negative results explicitly stated), and there is no trial registration (not applicable).

Evidence

absence[Section 5 (Conclusion and policy implications); entire paper]

No reporting guideline (e.g., STROBE) is referenced.

absence[Section 3.1 (Data, variables and measurement)]

Methods are incomplete: data sources are not named (e.g., World Bank, IMF are not cited).

paraphrase[Section 5.2 (Limitations and future research directions)]

“The study acknowledges reliance on data from 2014–2022, potential for unobserved variables, and need for future research.”

30 copyedit issues flagged (18 major): mostly clarity, typo, consistency.

Αlpha¹ Rigor Action ItemsFinal pass before journal submission

This published paper has serious validity threats that warrant a correction or independent re-analysis. An informed reader should view the reported results with extreme caution: multiple impossible statistics (p-values > 1, standard deviations exceeding the variable range) indicate data-processing or reporting errors that corrupt the entire empirical contribution. The unsupported cruise tourism claim and the absence of accessible data/code further undermine trust. The journal should consider issuing an expression of concern pending author clarification and correction of the impossible statistics.

1.
CRITICALstatistics
Correct all impossible p-values throughout Tables 2, 4, 6, and 7 — replace values >1 (e.g., 1.1111, 1.1130, 1.355, 1.373) with the actual reported p-values from Stata output, presumably intended to be small numbers like p<0.001 or p=0.999.
p-values are probabilities and must be between 0 and 1; values >1 are impossible and indicate a systematic formatting or encoding error that corrupts the entire statistical reporting.
2.
CRITICALstatistics
Correct the impossible standard deviation for EC in Table 1 (StdDev=1.177 when range is only 0.224) by verifying the data and re-entering the correct value.
A standard deviation cannot exceed the range of the data; this mathematical impossibility must be resolved or the entire descriptive statistics table is unreliable.
3.
CRITICALstatistics
Correct the REI variable in Table 1: if Min=Max=5.715, the standard deviation must be 0, not 1.111; explain why a constant variable is included in a regression.
A variable with no variance cannot be used in GMM or any regression; the non-zero SD suggests a data transcription error.
4.
CRITICALstatistics
Correct the inverted Min/Max for EI_High in Table 1 (Min=1.111 > Max=1.010) and verify all other variables for similar errors.
An impossible range (Min > Max) indicates a transcription or calculation error that undermines the credibility of the entire dataset.
5.
CRITICALstatistics
Correct and report the Wald chi-square test statistic for the sys-GMM model in Table 6 (currently shown as 370001 with p-value 1.111) to a plausible value.
A chi-square statistic of 370001 is implausibly large for any reasonable model and the associated p-value >1 is impossible, suggesting corruption of the reported test statistic.
6.
CRITICALreporting
Remove the unsupported claim that the study examined cruise tourism impacts on human health and ecosystems from the Abstract, Introduction, and Conclusion, or add the missing empirical analysis with relevant data and variables.
The claim is completely unsupported by any data or analysis in the paper — no cruise tourism variable appears in any model — which misleads readers and can result in a retraction for false claims.
7.
CRITICALdata code
Replace the vague data availability statement ('The authors do not have permission to share data') with a concrete access route: deposit the aggregated dataset in a public repository (e.g., Zenodo, Dataverse) with a DOI, or clearly identify the original public data sources with specific database names, download dates, and variable codes.
The current statement provides no way for any reader to access or verify the data, which is a fundamental reproducibility requirement for published research using secondary data.
8.
CRITICALdata code
Share the Stata analysis code (do-files) in a public repository (e.g., GitHub, Zenodo) with a persistent identifier, including the system GMM, 2SLS, and winsorization commands with version numbers for all user-written packages (e.g., xtabond2).
Without code, the numerous impossible statistics cannot be independently verified; code sharing is essential given the demonstrable data errors in the manuscript.
9.
HIGHstatistics
Replace all significance stars ('∗∗∗', '∗∗', '∗') in Tables 5, 6, and 7 with exact p-values (e.g., p=0.032) reported to three decimal places, or provide both.
Threshold-only p-values are less informative than exact values and prevent readers from assessing the strength of evidence; this is standard reporting practice for regression results.
10.
HIGHstatistics
Report effect sizes (e.g., standardized beta coefficients) and 95% confidence intervals for all regression coefficients in Tables 6 and 7.
Coefficient magnitudes and precision are essential for interpreting practical significance, not just statistical significance.
11.
HIGHreporting
Add a statement of adherence to an appropriate reporting guideline (e.g., STROBE for observational studies) in the Methods section and submit the completed checklist as a supplementary file.
Following a reporting guideline improves completeness and transparency, and many journals now require it.
12.
HIGHreporting
Replace causal language (e.g., 'significantly influence', 'management practices impact') with associative language throughout the abstract, Section 5, and conclusion, given the observational panel design.
Causal claims from non-experimental panel data are not supported by the study design and can mislead policy recommendations.
13.
HIGHrigor
Add a pre-specified description of inclusion and exclusion criteria for the 148 BRI countries in Section 3.1, explaining how the sample was selected from all BRI member nations.
Without explicit criteria, the sample selection process is not reproducible and may introduce selection bias.
14.
HIGHother
Add an a priori power analysis or a post-hoc justification of the sample size (148 countries, 9 years) in relation to expected effect sizes in Section 3.
The adequacy of the sample to detect meaningful effects is not demonstrated, which is a standard expectation for observational studies.
15.
MEDIUMcopyedit
Correct the stray 's' character in Table 1 for the EC standard deviation ('s1.177' → '1.177') and ensure all table entries are properly formatted numbers.
Formatting errors in tables erode reader confidence and suggest careless data presentation.
16.
MEDIUMcopyedit
Standardize variable terminology throughout the paper: use either 'Financial Technology' or 'Technological Transfer' consistently, and define all variable abbreviations (e.g., REI, HCD, EI) in a single table footnote.
Inconsistent terminology and undefined acronyms confuse readers and reduce clarity.
17.
MEDIUMcopyedit
Correct the Abstract and Section 3.1 to ensure the sample size is reported consistently (148 vs. 150 vs. 143 derived from 72+78) and the income-group labels use standard terminology (low-income, lower-middle-income, upper-middle-income, high-income).
Contradictory sample sizes and non-standard income labels confuse readers and undermine the paper's credibility.

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