Many life cycle assessments (LCA) studies on wooden buildings show potential to decarbonise the building industry, though often neglecting to consider the systemic changes of such a shift at the building stock scale. This study applies a consequential LCA to evaluate the transition from conventional construction to increased wood-based construction in Denmark from 2022 to 2050. The assessment models a material flow analysis of the two construction scenarios, incorporating an area forecast and case buildings. By that, we assessed suppliers' capacity to likely meet the demand for wood, steel, and concrete, employed an input-output model to enhance completeness and country representativeness for other materials' markets, and considered the competition for land by indirect land use change. We implemented a dynamic IPCC-based assessment of GHG-emissions concurrently with a carbon forest model to anticipate the relationship between the delayed carbon storage resulting from using wood in buildings and forest regrowth management. The findings indicate wood construction is the most climate-friendly option for multifamily houses. In contrast, single-family houses (SFH) and office buildings (OB) exhibit the lowest climate impacts in the conventional scenario. The SFH result could be credible due to the sizable GWP impact gap between construction scenarios despite uncertainties related to the weight proportion of sedum roofs. The less conclusive OB findings relate to the substantial steel quantities in the wood case buildings, requiring further investigation. Generally, metals, cement-based- and biobased materials demonstrate the largest climate impact among the material categories. Across all three building typologies, the change to timber construction increased the impact on nature occupation (biodiversity). In conclusion, this study emphasises the need for further research on forest management model inputs, land use change approaches, potential steel suppliers' impact, and a broader array of case studies. It is because these are influential factors in facilitating informed decision-making of the increased implementation of wood in buildings. As the first study to integrate these modelling characteristics, it contributes to the research gap concerning geographical circumstances, forestry, and markets relevant to decision support for increased wood utilisation in Europe's building industry.
One Planet Foundation developed this publication in 2020, as a contribution to the UNEP project titled “Addressing Land Use Changes Leakage in Sustainable Land Use Financing and ‘Deforestation Free’ Claims” of the UNEP Life Cycle Initiative. The views expressed in this publication are those of the authors and do not necessarily reflect the views of the United Nations Environment Programme. We regret any errors or omissions that may have been unwittingly made.
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Recently, our first crowdfunded project on a comparative LCA of RSPO certified and non-certified palm oil was finalized. The study shows that RSPO certified palm oil emits 35% less GHG emissions and is associated with 20% less impacts on biodiversity compared to non-certified palm oil. These results are valid for the average of Indonesia and Malaysia in 2016. However, palm oil producers (certified or not) are not static, and neither are the effect of certification on producers as well as the criteria for being certified. E.g. it is expected that GHG emissions are further reduced in the future as a larger share of especially certified palm oil mills will install biogas capture in the palm oil mill effluent (POME) treatment. Further, new RSPO criteria on no establishment of new oil palm on peat and no deforestation of high conservation value (HCV) land are expected to lead to lower the share of oil palm cultivation on peat and to higher shares of landbank set-aside as nature conservation.
Therefore, it is highly relevant to follow the development over time.
In additional to temporal differences in results, different countries and type of growers (estates and smallholders) will also have an influence on the impact of palm oil production. It is important to trace such differences in order to learn about the potential for improvement options in different producer segments.
Palm oil using companies are currently showcasing their contribution to GHG reductions due to their commitments of buying RSPO certified palm oil. They do this by using the results of our first crowdfunded project on the impact of RSPO certified palm oil for Indonesia and Malaysia in 2016. As the impact of RSPO certified versus non-certified palm oil is not static, it is important to consistently track the developments to make correct claims.
Become a partner of this project and contribute to the development of the next life cycle assessment (LCA) comparing RSPO certified palm oil to non-certified palm oil where results are tracked over time, per country and per type of grower (estates and smallholders).
The features of the project are:
The project was officially launched on 6th November 2019 with a platform presentation at the RSPO RT2019 conference in Bangkok. A scientific paper documenting the outcomes of the study will be submitted to a peer reviewed scientific journal in 2024.
Joining the new crowdfunded project will grant you access to all data and results, and you have access to influence the scope of the project.
The price of subscription is a one-time amount at 3,500 €. The funds from new subscriptions will be used to expand the scope of the project. For additional 2,000 € the results for a specific palm oil mill and its supply-base will be calculated and provided in a small report, including a comparison with the results of the main study.
For subscription (or questions), please contact us. To go to the club click here.
The project covered:
In collaboration with DAMVAD Analytics and Goritas.
The final report (in Danish) can be read here.
Despite a mature debate on the importance of a time-dependent account of carbon fluxes in life cycle assessments (LCA) of forestry products, static accounts of fluxes are still common. Time-explicit inventory of carbon fluxes is not available to LCA practitioners, since the most commonly used life cycle inventory (LCI) databases use a static approach. Existing forest models are typically applied to specific study fields for which the detailed input parameters required are available. This paper presents a simplified parametric model to obtain a time-explicit balanced account of the carbon fluxes in a forest for use in LCA. The model was applied to the case of spruce as an example.
The model calculated endogenous and exogenous carbon fluxes in tons of carbon per hectare. It was designed to allow users to choose (a) the carbon pools to be included in the analysis (aboveground and belowground carbon pools, only aboveground carbon or only carbon in stem); (b) a linear or sigmoidal dynamic function describing biomass growth; (c) a sigmoidal, negative exponential or linear dynamic function describing independently the decomposition of aboveground and belowground biomass; and (d) the forest management features such as stand type, rotation time, thinning frequency and intensity.
The parametric model provides a time-dependent LCI of forest carbon fluxes per unit of product, taking into account the typically limited data available to LCA practitioners, while providing consistent and robust outcomes. The results obtained for the case study were validated with the more complex CO2FIX. The model ensures carbon balance within spatial and time delimitation defined by the user by accounting for the annual biomass degradation and production in each carbon pool. The inventory can be used in LCA studies and coupled with classic indicators (e.g. global warming potential) to accurately determine the climate impacts over time. The model is applicable globally and to any forest management practice.
This paper proposes a simplified and flexible forest model, which facilitates the implementation in LCA of time-dependent assessments of bio-based products.
ShareIt link: http://rdcu.be/nspw
In agriculture and forestry, an important means for mitigating impacts on biodiversity and climate change is nature conservation. However, this is seldom included in life cycle assessment (LCA) and most LCA and footprint guidelines prescribe that such off-setting shall be excluded from the system (e.g. ISO 14067; PEF guideline; ILCD guideline; PAS2050; the GHG protocol). Obviously, there are good reasons for excluding off-setting in the guidelines, however in some cases the distance between the studied product system and a mitigation option (offset) is very short, and the industry managing the product system may be the (only) one who is able to conserve high value biodiversity and carbon stock areas. This is the case of companies operating in countries where the frontier between product systems and high conservation value nature is moving.
The purpose of this paper is to describe how the most recent research within indirect land use changes (iLUC) can be used to creating a cause-effect based method for quantifying the life cycle implications of nature conservation. The application of the method is demonstrated with a case study LCA of palm oil production at United Plantations Berhad in Malaysia and Indonesia. With their recent expansion of the plantation area into Central Kalimantan Indonesia, United Plantations has voluntarily set-aside more than 8000 ha of high value conservation and high carbon stock land for permanent nature conservation. The findings are used to recommend how LCA and footprint guidelines should be revised in order to enable for the inclusion of important mitigation options.
More than 10% of global GHG emissions are related to land use changes (LUC). This is almost the same as global GHG emissions from transport and around half of global GHG emissions from electricity produced from coal. The magnitude of LUC emissions clearly indicates that excluding this from LCA is highly problematic. In addition, several LCIA methods suggest that land use related impacts are much more important than GHG emissions (Weidema 2015). This makes the exclusion of LUC from LCA even more problematic.
Often, the impacts from indirect land use change (iLUC) are lacking in LCA studies – or at the best, it is modelled without reasonable considerations on cause-effect relationships between the use of land and the induced effects. If iLUC impacts are not included properly in the LCA results, there is a great risk of producing misleading results. Therefore, there is an urgent need for a good generic way of modelling iLUC. This should not be limited to biofuels or some certain crops in a certain region. There is a need for a generic model that can be applied to all kinds of land using LCA processes (cultivation of crops, cattle grassland, forestry, and land for buildings and infrastructure).
In order to make such a model available, we established the iLUC Club in 2011, which now has more than 20 universities and companies as members. We are currently working on the fifth version of the model which makes use of global land use change matrices and satellite data. The model framework is documented in a peer reviewed scientic article: A framework for modelling indirect land use changes in life cycle assessment. The model has been compared with other iLUC models in a scientific paper, where it was ranked as the best performing with regard to several criteria. Further, we actively contribute to the ongoing scientific debate on iLUC.
The model strives towards establishing a cause-effect relationship between, on the one side:
and on the other side:
The model has been tested and applied in several studies:
Subscription to the iLUC Club gives access to:
An important open source output from the project is a file with the needed information for obtaining iLUC GHG emission data for any land use (arable, forest, grassland) in any country in the world (download file).
The current members include:
On occasion of the 10th anniversary of our engagement with iLUC (15th Nov. 2017) we held a free webinar - the recording from this webinar can be seen here (youtube video) and the slides here (pdf).
For subscription (or questions), please contact us. To go to the club click here.
New definitions are provided of intensive and extensive forestry in version 3 of the ecoinvent database. These definitions are based on explicit and easily measured indicators for the most important aspects of forestry management for biodiversity. Unfortunately, many certified forestry products come from what would be classified as intensive forestry in the ecoinvent classification. The real challenge is to develop forest management systems that have a neutral or positive biodiversity impact relative to that of plantation forestry. Such truly extensive, biodiversity-managed forestry is very challenging and not very common today. Ample options exist for increasing yields in intensive and plantation forests, which can be recommended as having lower biodiversity impact than similar products from other management systems, certified or not.
This report aims to summarize the steps, arguments and market data that are needed to identify the long term marginal supplier affected by a long-term1 change in demand for Bleached Hardwood Kraft Pulp (BHKP) and Bleached Softwood Kraft Pulp (BSKP).
Put very briefly, if the general trend in the market is expanding or stable, a long-term change in demand is assumed to affect the supplier that has the best options for expanding or renewing the production capacity. This most competitive supplier is often the one with the lowest production costs.
If the market is sharply shrinking, so that production capacity is being reduced, a change in demand is assumed to affect the least competitive supplier.
In some situations, suppliers are constrained in their ability to react to a change in demand, e.g. because of lack of available raw materials or because of production quota. In these situations, an additional assumption is needed, namely that constrained suppliers are not affected by changes in demand. This implies that the demand has to be satisfied by another un-constrained supplier.
The above assumptions are based on simple market economics.
In order to identify the most or least competitive, un-constrained supplier, it is thus necessary to have information about:
