TY - JOUR
T1 - Comparative life cycle assessment and costing of an autonomous lawn mowing system with human-operated alternatives
T2 - implication for sustainable design improvements
AU - Saidani, Michael
AU - Pan, Zhonghao
AU - Kim, Harrison
AU - Wattonville, Jason
AU - Greenlee, Andrew
AU - Shannon, Troy
AU - Yannou, Bernard
AU - Leroy, Yann
AU - Cluzel, François
N1 - Funding Information:
This work was supported by the John Deere. This material is partially based upon the work supported by Deere and Company. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsor.
Funding Information:
This material is partially based upon the work supported by Deere and Company. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsor.
Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - Due to recent technological advancements, automation and autonomous solutions are gaining increasing popularity. Yet, a lack of in-depth investigations is noticed on the potential environmental benefits and economic repercussions of implementing autonomous systems. The present study aims to fill part of this gap by quantifying the environmental and economic sustainability of a robotic lawn mower, in comparison with human-operated counterparts. Combining life cycle assessment and life cycle costing methodologies, and by defining adequate functional units, building simulation models, and collecting life cycle inventory data, a systematic comparative study between autonomous and conventional lawn mowers is performed on their environmental and economic impacts. Through this multi-indicator analysis, environmental and economic trade-offs between the autonomous and conventional mowing solutions are quantitatively discussed for key relevant usage scenarios, from mowing an average residential yard to maintaining larger fields like a football stadium or a schoolyard. Concretely, sensitivity analyses on key parameters influencing the performance of the autonomous mower have been conducted to evaluate the environmental and economic benefits of an augmented robotic mower. While optimising the path planning of the current robotic mower would lead to the most substantial savings, improvements on the battery performance, cutting width, and speed of the autonomous solution appear as other promising areas for future work.
AB - Due to recent technological advancements, automation and autonomous solutions are gaining increasing popularity. Yet, a lack of in-depth investigations is noticed on the potential environmental benefits and economic repercussions of implementing autonomous systems. The present study aims to fill part of this gap by quantifying the environmental and economic sustainability of a robotic lawn mower, in comparison with human-operated counterparts. Combining life cycle assessment and life cycle costing methodologies, and by defining adequate functional units, building simulation models, and collecting life cycle inventory data, a systematic comparative study between autonomous and conventional lawn mowers is performed on their environmental and economic impacts. Through this multi-indicator analysis, environmental and economic trade-offs between the autonomous and conventional mowing solutions are quantitatively discussed for key relevant usage scenarios, from mowing an average residential yard to maintaining larger fields like a football stadium or a schoolyard. Concretely, sensitivity analyses on key parameters influencing the performance of the autonomous mower have been conducted to evaluate the environmental and economic benefits of an augmented robotic mower. While optimising the path planning of the current robotic mower would lead to the most substantial savings, improvements on the battery performance, cutting width, and speed of the autonomous solution appear as other promising areas for future work.
KW - Autonomous systems
KW - design improvement
KW - economic analysis
KW - environmental evaluation
KW - sensitivity analysis
KW - sustainability
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U2 - 10.1080/19397038.2021.1919785
DO - 10.1080/19397038.2021.1919785
M3 - Article
AN - SCOPUS:85105213276
SN - 1939-7038
VL - 14
SP - 704
EP - 724
JO - International Journal of Sustainable Engineering
JF - International Journal of Sustainable Engineering
IS - 4
ER -