Energy balance and CO2 emission in mechanized biomass harvesting in pine stands under thinning

Rodrigues, Carla Krulikowski; Lopes, Eduardo da Silva; Silva, Dimas A.; Filho, Afonso Figueiredo; Pelissari, Allan Libanio

doi:10.1590/0001-3765201920180839

An. Acad. Bras. Ciênc.

2019

DOI: 10.1590/0001-3765201920180839

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Energy balance and CO2 emission in mechanized biomass harvesting in pine stands under thinning

Carla Krulikowski Rodrigues

Eduardo da Silva Lopes

Dimas A. Silva

et al.

Abstract: How to cite: rODrIgUeS CK, LOPeS eS, SILVA DA, FIgUeIreDO FILHO A AND PeLISSArI AL. 2019. energy balance and CO 2 emission in mechanized biomass harvesting in pine stands under thinning. An Acad Bras Cienc 91: e20180839.

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Fuel Consumption and CO2 Emissions in Fully Mechanized Cut-to-Length (CTL) Harvesting Operations of Industrial Roundwood: A Review

Kärhä,

Eliasson,

Kühmaier

et al. 2024

Curr. For. Rep.

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Purpose of Review The aim of this literature review was to bring together the most relevant and recent research information on the fuel consumption and CO2 emissions caused by the fully mechanized cut-to-length (CTL) harvesting machinery when applied to industrial roundwood. A specific aim of this review was to describe the effect of different independent variables on fuel consumption in fully mechanized CTL wood-harvesting operations. Recent Findings The review showed that the engine power of CTL forest machines accounts for most of the variance in the hourly fuel consumption of both harvesters and forwarders. We underline that the cubic-metre-based fuel consumption of CTL forest machines is correlated to the same factors that affect work productivity. Among all influencing factors, the average stem size, removal intensity and silvicultural treatment have the strongest effect on the fuel consumption per m3 incurred with felling-processing, whereas forwarding distance, removal intensity and payload size are the main drivers of fuel consumption per m3 as incurred with extraction. Further influencing factors are soil type (mineral soil or peatland), use of tracks, assortment type and machine size. Together with those factors, the role of the machine operator remains crucial and is dependent on two separate skills: the capacity to achieve high productivity, and that to apply fuel-saving driving techniques. Summary The easiest way to reduce the carbon footprint of CTL harvesting machines is to increase the productivity of the harvesting work, for example by giving machine operator-specific training to utilize more efficient work methods and economic energy-efficient driving techniques. Furthermore, several other measures to reduce the carbon footprint of CTL harvesting operations were discussed in this review. Finally, we recommend that all essential variables that have a significant impact on the productivity of harvesting work, fuel consumption and CO2 emissions are reported in study papers in the future.

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Fuel Consumption and CO2 Emissions in Fully Mechanized Cut-to-Length (CTL) Harvesting Operations of Industrial Roundwood: A Review

Kärhä,

Eliasson,

Kühmaier

et al. 2024

Curr. For. Rep.

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Stochastic model to aid decision making on investments in renewable energy generation: Portfolio diffusion and investor risk aversion

Freitas¹,

Vogel

Korzenowski

et al. 2020

Renewable Energy

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Energy balance and CO2 emission in mechanized biomass harvesting in pine stands under thinning

Abstract: How to cite: rODrIgUeS CK, LOPeS eS, SILVA DA, FIgUeIreDO FILHO A AND PeLISSArI AL. 2019. energy balance and CO 2 emission in mechanized biomass harvesting in pine stands under thinning. An Acad Bras Cienc 91: e20180839.

Cited by 2 publications

References 30 publications

Fuel Consumption and CO2 Emissions in Fully Mechanized Cut-to-Length (CTL) Harvesting Operations of Industrial Roundwood: A Review

Fuel Consumption and CO2 Emissions in Fully Mechanized Cut-to-Length (CTL) Harvesting Operations of Industrial Roundwood: A Review

Stochastic model to aid decision making on investments in renewable energy generation: Portfolio diffusion and investor risk aversion

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