A PESTLE analysis of the trucking industry: key insights and implications
DOI: 10.1080/23311975.2024.2409335
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This review article addresses the lack of holistic research regarding the multifaceted challenges and future driving forces impacting the trucking industry. While heavy truck technology is advancing rapidly through electric vehicles, autonomous systems, and artificial intelligence, the sector simultaneously faces critical issues such as driver shortages, safety concerns, fuel costs, and environmental contamination. The authors aim to provide a comprehensive understanding of these dynamics to guide strategic decision-making and proactive adaptation within the industry. To achieve this, the researchers conducted a systematic literature analysis using the PRISMA methodology, searching the Web of Science and Scopus databases for records published between January 2018 and July 2024. From an initial pool of 108 selected records, 66 articles were analyzed using the PESTLE framework, which examines Political, Economic, Social, Technological, Legal, and Environmental factors. The study focuses exclusively on road transport via heavy vehicles, excluding rail, maritime, and air transportation. The analysis revealed that 87% of the reviewed investigations focused on technological, social, and environmental factors, while only 13% addressed other topics. Technological findings highlight the significant impact of autonomous trucks (ATs), which offer potential reductions in fuel consumption and emissions but face adoption barriers related to infrastructure, battery life, and driver displacement. Medium and large companies showed greater receptivity to ATs, whereas long-distance operators were more resistant. Electric trucks present challenges regarding charging infrastructure and grid capacity, though they offer opportunities to reduce greenhouse gas emissions. Artificial intelligence and machine learning applications were found to improve routing efficiency, accident prediction, and customer profit margin forecasting. Social factors emphasized safety, with studies indicating that connected vehicles and revised rest-break regulations can reduce crash frequencies. However, social acceptance of automation remains divided, with drivers expressing concerns about job loss and fairness. Environmental and political-legal factors underscored the need for policies supporting alternative energy sources and emission reductions, noting that such policies often face resistance from industry stakeholders. The significance of this study lies in its identification of key barriers and opportunities for the trucking industry’s transition toward sustainability and automation. The findings suggest that successful adoption of emerging technologies requires not only technological advancement but also supportive infrastructure, policy frameworks, and strategies to address social concerns such as employment and safety. By synthesizing current research across multiple dimensions, the paper provides a structured overview of the industry’s operating environment, offering valuable insights for policymakers, fleet managers, and researchers to anticipate changes and plan for future developments.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | DOAJ | — | — | 1 | 2026-06-25 |
| archive | success | openalex | — | — | 4 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.