Analysis of occupational accidents during the chainsaws use in Andalucía

López-Toro, Alberto Antonio; Pardo-Ferreira, María del Carmen; Martínez-Rojas, María; Carrillo-Castrillo, Jesús Antonio; Rubio-Romero, Juan Carlos

doi:10.1016/j.ssci.2021.105436

Cited by 8 publications

(1 citation statement)

References 36 publications

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“…Additionally, cross-national comparative research has been conducted [13]. Notably, a considerable segment of this research is centered on understanding accidents involving chainsaw use or motor-manual tree felling [14][15][16].…”

Section: Background and Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Hoenigsberger,

Saranti,

Jalali

et al. 2024

Applied Sciences

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Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.

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Section: Background and Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Hoenigsberger,

Saranti,

Jalali

et al. 2024

Applied Sciences

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Machine Learning and Knowledge Extraction to Support Work Safety for Smart Forest Operations

Hoenigsberger

Saranti

Angerschmid

et al. 2022

Lecture Notes in Computer Science

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Social consequences of wood-based innovations: a generic analysis of sectoral differences in Austria

Mair-Bauernfeind,

Boiger,

Asada

et al. 2024

Int J Life Cycle Assess

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Purpose To model the overall social consequences of changing wood utilization, a system perspective is needed that encompasses the entire wood utilization system in a defined region. The aim of this study was to analyze the social performance of wood-based industries in Austria using sector-specific data and to use less disaggregated data to depict social risks in the resource extraction phase. Additionally, the social consequences of innovations in terms of the social performance of a sector and potential side effects on other wood-based industries were analyzed. Methods Differences in the sectoral social performance of forestry and wood-based industries in Austria were analyzed using sectoral data for 11 different social indicators (e.g., occupational injuries, woman in managerial positions) collected at official sites in Austria. To calculate the overall social performance of the sector, sectoral data from Austria need to be combined with data from other sources representing the value chain (e.g., from resource extracting countries). This enables the social consequences of a change to be analyzed by including the social indicators in the system dynamics model WOODSIM. The WOODSIM model depicts the Austrian wood utilization system, allowing the user to model direct and indirect effects of introducing an innovation in a particular industry on the social performance of industries in the wood utilization system. Results and discussion The results show that social risks can differ depending on the sectoral context even within the same country (e.g., occupational injuries in wood harvesting compared to textile production). The most dangerous sectors (in terms of injuries) are forestry and construction (34 and 3 times higher than Austrian average, respectively). Including the risks of resource extraction affects the social performance of the industries. Surprisingly, the median for Austria is 1434 accidents per 100,000 employees, whereas it is only 592 for all countries combined. Modeling the social consequences with system dynamics reveals that some innovations can result in bigger improvements in social performance than others, mostly due to the existence of more globalized value chains. Conclusions This work illustrates the importance of including sectoral information when performing generic social life cycle assessments and models the social consequences of an innovation for the first time using system dynamics modeling. To avoid overestimating positive effects when analyzing consequences, a systems perspective must be taken. Better and more disaggregated data are needed to depict the social performance of sectors more accurately.

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Analysis of occupational accidents during the chainsaws use in Andalucía

Cited by 8 publications

References 36 publications

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Machine Learning and Knowledge Extraction to Support Work Safety for Smart Forest Operations

Social consequences of wood-based innovations: a generic analysis of sectoral differences in Austria

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