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Record W4282979776 · doi:10.1080/1331677x.2022.2081865

Role of public and private investments for green economic recovery in the post-COVID-19

2022· article· en· W4282979776 on OpenAlexaboutno aff
Xiaoqing Dai, Fangping Rao, Zhen Liu, Muhammad Mohsin, Farhad Taghizadeh–Hesary

Bibliographic record

VenueEconomic Research-Ekonomska Istraživanja · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
FundersMinistry of Education, Culture, Sports, Science and Technology
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BusinessEconomic recoveryEconomicsVirologyMedicineOutbreakInfectious disease (medical specialty)MacroeconomicsDisease

Abstract

fetched live from OpenAlex

This study evaluates the outlook of government expenditure through public and private financing for the green economic revitalization after COVID-19 in Canada. The various econometric estimations are used to measure the impact of government expenditure on green economic recovery. The implementation of public investment is explicitly associated with private funding. The results suggest that the government policy incentives and non-government financing influence fossil fuel energy sources proportions on non-government investment, which is additional than the feed-in tariffs. According to fixed effects results, the distribution of fossil fuel energy sources is an essential obstacle in solar energy investment. In contrast, the presence of varied types of renewable energy encourages non-government climate investment. Throughout the study period after the breakout of the pandemic phase, neither fossil fuel energy sources nor economic policy is marginally efficient. The different macroeconomic programs in green economic recovery might be ideal for attaining the needed impact. The critical policy conclusion of the results of this research is that an influential role of the public and private investment may be part of an optimal firm innovation plan for green economic recovery in the post-COVID-19 period.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.295
Teacher spread0.204 · 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.

Study designTheoretical or conceptual
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

Citations15
Published2022
Admission routes1
Has abstractyes

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