Green growth dynamics: unraveling the complex role of financial development and natural resources in shaping renewable energy in Sub-Saharan Africa
Alhasan Osman; Mohd Afjal; Majed Alharthi; Mohamed Elheddad; Nassima Djellouli; He Zhang · 2025 · Humanities and Social Sciences Communications
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract This study examines the complex interplay between financial development, natural resources, and renewable energy consumption in Sub-Saharan Africa from 2000 to 2020, highlighting the pivotal role of financial strategies in enhancing sustainable energy practices. Employing an array of analytical techniques, including panel-corrected standard errors (PCSE), fixed effects, random effects models, and panel-fixed quantile regression, we delve into the nuanced relationships among these critical variables. The findings reveal that while natural resources and financial development generally e
Abstract by Alhasan Osman; Mohd Afjal; Majed Alharthi; Mohamed Elheddad; Nassima Djellouli; He Zhang, Humanities and Social Sciences Communications (2025) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
The Oregon Experiment — Effects of Medicaid on Clinical Outcomes
Negative / Null Result ReportMicrocredit in Theory and Practice: Using Randomized Credit Scoring for Impact Evaluation
Negative / Null Result ReportThe Cost of Carbon: Capital Market Effects of the Proposed Emission Trading Scheme (ETS)
Negative / Null Result ReportPushing on a string: US monetary policy is less powerful in recessions ∗
Negative / Null Result ReportThe Evidence on Globalisation
Negative / Null Result ReportStatistical tests for power-law cross-correlated processes
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1057/s41599-025-04364-3