Environmental impacts of wooden, plastic, and wood-polymer composite pallet: a life cycle assessment approach

Purpose Waste recycling is one of the essential tools for the European Union’s transition towards a circular economy. One of the possibilities for recycling wood and plastic waste is to utilise it to produce composite product. This study analyses the environmental impacts of producing composite pallets made of wood and plastic waste from construction and demolition activities in Finland. It also compares these impacts with conventional wooden and plastic pallets made of virgin materials. Methods Two different life cycle assessment methods were used: attributional life cycle assessment and consequential life cycle assessment. In both of the life cycle assessment studies, 1000 trips were considered as the functional unit. Furthermore, end-of-life allocation formula such as 0:100 with a credit system had been used in this study. This study also used sensitivity analysis and normalisation calculation to determine the best performing pallet. Result and discussion In the attributional cradle-to-grave life cycle assessment, wood-polymer composite pallets had the lowest environmental impact in abiotic depletion potential (fossil), acidification potential, eutrophication potential, global warming potential (including biogenic carbon), global warming potential (including biogenic carbon) with indirect land-use change, and ozone depletion potential. In contrast, wooden pallets showed the lowest impact on global warming potential (excluding biogenic carbon). In the consequential life cycle assessment, wood-polymer composite pallets showed the best environmental impact in all impact categories. In both attributional and consequential life cycle assessments, plastic pallet had the maximum impact. The sensitivity analysis and normalisation calculation showed that wood-polymer composite pallets can be a better choice over plastic and wooden pallet. Conclusions The overall results of the pallets depends on the methodological approach of the LCA. However, it can be concluded that the wood-polymer composite pallet can be a better choice over the plastic pallet and, in most cases, over the wooden pallet. This study will be of use to the pallet industry and relevant stakeholders.

Attributional & Consequential Life Cycle Assessment: Definitions, Conceptual Characteristics and Modelling Restrictions

To assess the potential environmental impact of human/industrial systems, life cycle assessment (LCA) is a very common method. There are two prominent types of LCA, namely attributional (ALCA) and consequential (CLCA). A lot of literature covers these approaches, but a general consensus on what they represent and an overview of all their differences seems lacking, nor has every prominent feature been fully explored. The two main objectives of this article are: (1) to argue for and select definitions for each concept and (2) specify all conceptual characteristics (including translation into modelling restrictions), re-evaluating and going beyond findings in the state of the art. For the first objective, mainly because the validity of interpretation of a term is also a matter of consensus, we argue the selection of definitions present in the 2011 UNEP-SETAC report. ALCA attributes a share of the potential environmental impact of the world to a product life cycle, while CLCA assesses the environmental consequences of a decision (e.g., increase of product demand). Regarding the second objective, the product system in ALCA constitutes all processes that are linked by physical, energy flows or services. Because of the requirement of additivity for ALCA, a double-counting check needs to be executed, modelling is restricted (e.g., guaranteed through linearity) and partitioning of multifunctional processes is systematically needed (for evaluation per single product). The latter matters also hold in a similar manner for the impact assessment, which is commonly overlooked. CLCA, is completely consequential and there is no limitation regarding what a modelling framework should entail, with the coverage of co-products through substitution being just one approach and not the only one (e.g., additional consumption is possible). Both ALCA and CLCA can be considered over any time span (past, present & future) and either using a reference environment or different scenarios. Furthermore, both ALCA and CLCA could be specific for average or marginal (small) products or decisions, and further datasets. These findings also hold for life cycle sustainability assessment.

Quantification of Non-linearities in the Consequential Life Cycle Assessment of the Use Phase of Battery Electric Vehicles

The diffusion of Battery Electric Vehicles (BEVs) is projected to influence the electricity grid operation, potentially offering opportunities for load-shifting policies aimed at higher integration of renewable energy technologies in the electricity system. Moreover, the examined literature emphasizes electricity as a relevant driver of BEVs Life Cycle Assessment (LCA) results. To evaluate LCA impacts associated to future BEVs diffusion scenarios in Italy, we adopt the Consequential Life Cycle Assessment (CLCA) methodology. LCA conventionally assumes a proportional relation between environmental impact indicators and the functional unit. However, such relation may not be representative if the electricity system is significantly affected by the large-scale diffusion of BEVs. Our study couples the conventional CLCA methodology with the EnergyPLAN model through three different approaches, which progressively include BEV-specific dynamics, to capture correlations between additional BEVs fleets and the electricity grid operation, that affectthe mix of electricity consumed in the use phase by BEVs, in Italy in 2030. Here we show that if renewables capacity is not additionally installed in response to additional BEVs electricity demand, the marginal Climate change total indicator of BEVs may increase up to ~40%, with respect to a business-as-usual scenario. Moreover, we quantitatively support the literature indications on how to properly estimate BEVs LCA impacts. Indeed, we weight electricity LCA impacts on hourly BEV charge profiles, finding that this approach best captures BEVs interdependence with the electricity system. At low BEVs diffusion, this approach clearly shows the potential BEVs capability to increase exploitation of renewable energy, whereas at high BEVs diffusion, it fully highlights potential responses of fossil fuel power plants to additional electricity demand. Due to these dynamics, we find that linearly scaling the business-as-usual scenario results would lead to an underestimation of 12.45 Mton CO2-eq of the total impacts of an additional BEVs fleet, under a 100% BEV diffusion scenario. Our methodology could be replicated with different energy system models, or at various geographical scales. Our framework could be coupled with comprehensive assessments of transport systems, to further provide robustness to policymakers by including non-linearities in the mix of electricity consumed during the use phase of BEVs.