PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 16, 20260 citationsOpen Access

Risk Assessment and Adaptation Profiling of Non-Standard LPG Installations in Light Commercial Vehicles: Insights from Kumasi, Ghana

View Full Paper
POPrince Owusu-AnsahAFAlex Justice FrimpongSWSaviour Kwame Woangbah

Key Points

  • The study aims to assess the safety and regulatory implications of Liquefied Petroleum Gas in light commercial vehicles in Ghana.
  • Conducted a multi-method quantitative approach including structured surveys and machine learning methods.
  • Employed exploratory multivariate analysis (MCA) to identify safety profiles.
  • Utilized Random Forest for classification and predictive analysis of safety practices.
  • Identified four safety profiles: At-Risk, Proactive Safety, Compliant and Equipped, and Formal and Reported.
  • Achieved a predictive accuracy of 96.5% with high sensitivity and specificity values.
  • Highlights the urgent need for regulatory frameworks and certification to enhance LPG safety.

Abstract

The rapid rise in the use of Liquefied Petroleum Gas (LPG) as an alternative vehicle fuel in Ghana presents both opportunities and risks within the national energy transition agenda. This study investigates LPG safety as well as environmental and regulatory implications using a multi-method quantitative approach that combines structured survey data, exploratory multivariate analysis (MCA), and machine learning classification (Random Forest) to uncover emerging associations and patterns in LPG safety practices. Primary data were obtained from 384 respondents, including vehicle operators, auto-technicians, regulatory officials, and LPG station attendants across five major transport zones: Kejetia, Asafo, Ahodwo, Bantama, and Suame Magazine. The MCA identified four distinct behavioural and safety profiles—At-Risk, Proactive Safety, Compliant and Equipped, and Formal and Reported—reflecting diverse compliance and risk patterns across socio-occupational groups. The Random Forest classifier achieved a predictive accuracy of 96.5% based on cross-validated performance. Sensitivity and specificity values were high, indicating reliable discrimination among incident types. To reduce the risk of overfitting, k-fold cross-validation and monitored error convergence were performed across increasing numbers of trees. While the model shows strong predictive capability, we present these results cautiously and emphasize observed associations and emerging patterns rather than definitive predictive conclusions. The findings reveal that while economic motivations underpin LPG adoption, weak institutional enforcement and widespread informal installations heighten safety vulnerabilities. Comparisons with sub-Saharan and Asian contexts underscore the need for a structured regulatory framework, mandatory certification of installers, and periodic vehicle inspections. The study contributes to the broader discourse on informal energy transitions in developing economies by demonstrating how technical and behavioural determinants interact within weak regulatory systems. Policy recommendations emphasize the integration of data-driven risk assessment tools into regulatory oversight to enhance vehicular LPG safety and sustainability.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Owusu-Ansah et al. (2026) studied this question.

synapsesocial.com/papers/69926552eb1f82dc367a1417https://doi.org/10.3390/eng7020087
Ask AI
Helpful
Bookmark
Share
View Full Paper