Static Life Cycle Assessment Research Articles

Dynamic environmental payback of concrete due to carbonation over centuries

This research introduces a dynamic life cycle assessment (LCA) based carbonation impact calculator designed to enhance the environmental evaluation of cement-based construction products. The research emphasizes the limitations of static LCAs which fail to capture the time-dependent nature of carbon sequestration by carbonation.We provide an easy-to-use spreadsheet-based LCA carbonation model. The model is available in the supplementary information, and includes a suite of changeable parameters for exploring the effect of alternative environmental conditions and concrete block composition on carbonation. The tool enables use of both a static and dynamic LCA method to calculate the production emissions and carbonation sequestration of a concrete block over a 1000-year time horizon.Carbonation can partially mitigate initial production emissions and adjust radiative forcing over long periods. Using a static attributional LCA approach, carbonation sequesters 6 % of the CO2 generated from its production emissions. We describe the ratio of carbonation to production emissions as the partial “carbonation payback”, and with dynamic LCA show the variation of this ratio over time. Considering time by applying the dynamic LCA approach, we find this partial “carbonation payback” is split between uptake during the 60-year service life (0.13 kg CO2) and the 940-year end of life period (0.12 kg CO2) in our baseline case. Further scenario analyses illustrate the significant variability in carbonation payback, driven by environmental factors, cement composition, and the use of supplementary cementitious materials.The results highlight the critical role of modelling choices in estimating the carbonation payback. The carbonation calculator developed in this study offers a sophisticated yet user-friendly tool, providing both researchers and practitioners with the ability to dynamically model the sequestration potential of concrete, thereby promoting more sustainable construction practices.

Developing a dynamic life cycle assessment framework for buildings through integrating building information modeling and building energy modeling program

The construction and building sector is one of the largest contributors to the global carbon emissions. Therefore, it is imperative to accurately assess the carbon emissions of buildings throughout the life cycle. Many studies conducted life cycle assessment (LCA) of buildings to evaluate carbon emissions. However, due to the lack of dynamic data, most studies adopted the static LCA methodology, which neglected the dynamic variations during life cycle stages of a building. Unlike previous studies that collected static data from questionnaires and documents, the present study aims to establish a novel dynamic life cycle assessment (D-LCA) framework for buildings by incorporating the building information modeling (BIM) and the building energy modeling program (BEMP) into the static LCA. First, a static LCA is established as the baseline scenario that covers the “cradle-to-grave” life cycle stages. A BIM model is established using Revit to obtain the inventory of building materials. The Designer Simulation Toolkit (DeST) is used as a BEMP to simulate the operating energy consumption of the studied building, taking into account changes in energy mix, climate change, and occupant behavior. At the same time, the DeST results are further used as a data input for dynamic scenarios. The D-LCA framework is applied to a high-rise commercial building in China. This study found that the difference between static and dynamic scenarios was up to 66.7 %, mainly reflected in the dynamic energy consumption during the operation phase, indicating the inaccuracy of traditional static LCA. Therefore, a D-LCA by integrating BIM and BEMP can facilitate dynamic modeling and improve the accuracy and reliability of LCA for buildings.

Evaluating cotton apparel with dynamic life cycle assessment: The climate benefits of temporary biogenic carbon storage

Static life cycle assessment (LCA) methodologies fail to consider the temporal profiles of system inputs and outputs (including emission timing), such that they underestimate the benefits of temporarily stored biogenic carbon in bioproducts, such as cotton. This research focuses on greenhouse gas emission timing and applies dynamic emission accounting to the life cycle of cotton woven pants. The significance of temporary biogenic carbon storage and emission timing is illustrated by converting the 2017 Cotton Incorporated static LCA to a dynamic model using the Dynamic Carbon Footprinter (baseline scenario). A reduction in cumulative radiative forcing for dynamic relative to static modeling of 22%, 5%, and 2% are observed at 10-years, 30-years, and 100-years, respectively. Alternative scenarios analyzed include converting cotton woven pants at end of life to bioenergy, to compost, or to building insulation, an alternative cotton production scenario using regenerative agricultural practices, and two pants extended lifetime scenarios. The regenerative agricultural practice scenario provides reductions in cumulative impacts compared to the baseline scenario of 96%, 69%, and 105% after 10, 30, and 100-years, respectively. A 3x extension in the lifetime of pants provides a benefit in reduced cumulative impacts of 31%, 40%, and 41%, after 10, 30, and 100-years, respectively. This case study with cotton demonstrates that dynamic LCA is a useful tool for assessing the benefits of biobased products, and it allows for more nuanced analysis of reductions in climate impacts in both the short- and long-term time horizons.

Prospects of Digital Twin for Dynamic Life Cycle Assessment of Smart Manufacturing Systems

Smart manufacturing systems are poised to revolutionize industrial processes by leveraging advanced technologies for increased efficiency and productivity. However, alongside these advancements, there is a growing imperative to address environmental sustainability concerns. Conventional static life cycle assessment (LCA) methods often provide valuable insights into the environmental impacts of such manufacturing systems but often fall short in capturing real-time data and dynamic system interactions. Further, using the digital twin technology, physical assets can be virtually replicated in order to monitor, evaluate, and improve the particular manufacturing system. The dynamic properties can be effectively brought to LCA investigations by utilizing this technique. This paper explores the prospects of integrating digital twin technology for facilitating the dynamic LCA to enable comprehensive and timely environmental performance evaluation of smart manufacturing systems. We discuss the concepts, technological components, and potential applications of digital twin-enabled dynamic LCA, along with challenges and future research directions.

Development of the virtual battery concept in the paper industry: Applying a dynamic life cycle assessment approach

Energy-intensive industries face the challenge of reducing carbon emissions while remaining competitive. Key measures include fossil fuel substitution, energy efficiency, and integration of renewable energy sources, but their fluctuating production profile makes them difficult to integrate and require industries to adapt their current consumption and production patterns to become more flexible.In this study, the virtual battery concept (VBC), exemplarily demonstrated for a specific site in the paper industry, is introduced and evaluated from an environmental, energetic, and techno-economic perspective. A mathematical optimization model of the class mixed integer linear programming (MILP) was applied to express the industrial site as VBC and derive the operational data basis for the subsequent life cycle assessment (LCA). A dynamic LCA approach is presented to allow the consideration and assessment of the temporal behavior of energy load profiles and their associated environmental implications.The results showed that compared to a static LCA, the dynamic approach leads up to 42 % lower greenhouse gas (GHG) emissions for 1 MJ of public electricity. The global warming potential (GWP) of the total energy supply chain was reduced by 40 % for electricity and 8 % for heat, respectively. By means of a scenario analysis, the GWP to produce one ton of paper was reduced up to 33 % compared to the business-as-usual (BAU) case to 672 kgCO2eq.r in the best case. However, the total primary energy demand (PED) increased by 34 %, but the fossil PED was reduced by 32 % and the renewable PED increased by 157 %. The renewable PED share covered up to 67 % of the total PED. The techno-economic analysis revealed total annual cost savings of up to 44 % to 64.6 € per ton of paper. Environmental costs were estimated to range from 79.6 to 88.9 € per ton of paper.The VBC is considered a promising approach to utilize regional renewable excess electricity effectively, reduce fossil-based energy generation, increase grid stability, and to avoid costly grid infrastructure investments in future. In principle, the VBC is site-independent and replicable to other industries but needs to be evaluated site specifically according to certain process characteristics and requirements.

California's harvested wood products: A time-dependent assessment of life cycle greenhouse gas emissions

Following life-cycle assessment (LCA) methodology, this study presents a state-level estimation of embodied carbon of wood products harvested in 2019 from California and subsequently processed, manufactured, transported, used, and disposed at the end-of-life (EoL). In a conventional static approach to LCA, all GHG emissions were aggregated and considered to occur at year 0 of the given time horizon (500 years in this study) and used a static characterization factor (CF). In dynamic LCA, GHG emissions occurring in different years were considered, and their global warming impact (GWI) was determined using a time-dependent CF over the selected time horizon of 500 years. Four scenarios were developed to examine the impact of EoL choices on GWI. It was found that dynamic GWI for all scenarios ranged from 0.27 to 0.93 million tonne CO₂e, which were 45–73 % lower than those estimated with static LCA approach, indicating that the static LCA approach could lead to an underestimation of the benefits of substituting wood for non-wood products, compared to those based on dynamic LCA approach. This analysis also demonstrated that the choice of EoL treatment option is a key factor affecting the estimated GWI as it directly determines the annual emission of GHGs released into atmosphere and subsequently their warming effect depending on the time harvested wood products (HWPs) spend in the horizon of assessment. Overall, the dynamic LCA performed in this study enabled more robust interpretations of embodied carbon by including temporal boundaries associated with the HWPs life cycle.

Dynamic life cycle assessment of geothermal heat production from medium enthalpy hydrothermal resources

Geothermal energy is a renewable energy source with large unexploited potential. Medium enthalpy deep geothermal resources are commonly used in Europe to provide heat. Life Cycle Assessment (LCA) studies on such applications are scarce and their majority follows a static approach. We describe a two-step dynamic LCA framework for deep geothermal heating applications to more accurately estimate their environmental performance. A semi-dynamic approach considers the temporal evolution of the processes and a fully-dynamic approach also applies dynamic impact assessment methods. We investigate a deep geothermal heating plant located in Northern Belgium for which site-specific data are available. Compared to a static LCA, the dynamic methods find a 50–129% higher global warming impact. Analogous differences are found for eleven other impacts. Regardless, the global warming impact remains lower than for natural gas heating. Large impact variations are also observed when the average European electricity mix is considered to supply the plant, indicating that LCA studies on pumped geothermal heating plants that neglect the time parameter could be largely misestimating the impacts. The dynamic LCA also calculates the impact evolution through time. We find that the continuation of the plant operation after a time period might not lead to considerable impact reduction. Such information is hidden in a static approach and could be used for the optimization of geothermal development strategies. Dynamic methods also facilitate the design of targeted impact mitigation strategies and the comparison between alternative heating systems. We recommend the application of dynamic LCA on other types of geothermal energy plants and other energy-related applications.

Dynamic LCA of the increased use of wood in buildings and its consequences: Integration of CO2 sequestration and material substitutions

Wood products can have a lower impact on climate change (CC) than other building materials, and their carbon content can transform buildings into temporary carbon sinks. However, the large-scale use of wood products in buildings requires a better understanding of their consequences to support CC policies. The consequential life cycle inventory of such products contains elementary flows with values sometimes greater sometimes less than zero. Therefore, their static assessment did not show the influence of carbon uptake and release, either during or after the temporal boundary of the system. The originality lies in the combination of the dynamic modeling of tree harvesting and growth, temporary carbon storage, end-of-life (EOL) strategies, material substitution, and characterization factors in a consequential life cycle assessment (LCA). This combination highlights the extent to which material substitution, EOL strategies, and carbon sequestration can be decisive. In addition, it addresses the static and dynamic measurements of radiative forcing (W.m−2) and the carbon dioxide (CO2) equivalent (CO2eq.). In this case study, material substitution contributed the most to result, followed by post-harvest CO2 sequestration. In terms of metrics, the conventional static LCA overestimates the long-term cumulative impact on CC (tonne CO2eq.) without providing information on the short-term impact of the case study. In addition, the assessment of such a dynamic consequential life cycle inventory (LCI) with a dynamic metric provides more specific information, regardless of the temporal boundary. However, the absolute dynamic metrics (W.m−2) should support the relative dynamic metric (tonne CO2eq.1st year) to avoid misleading conclusions.

A dynamic calculation model of the carbon footprint in the life cycle of hospital building: a case study in China

PurposeThe calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.Design/methodology/approachOn the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.FindingsThe results applied to the case show that the CO2 emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO2 emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO2 reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.Originality/valueThis research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

Dynamic Versus Static Life Cycle Assessment of Energy Renovation for Residential Buildings

Currently, a life cycle assessment is mostly used in a static way to assess the environmental impacts of the energy renovation of buildings. However, various aspects of energy renovation vary in time. This paper reports the development of a framework for a dynamic life cycle assessment and its application to assess the energy renovation of buildings. To investigate whether a dynamic approach leads to different decisions than a static approach, several renovation options of a residential house were compared. To identify the main drivers of the impact and to support decision-making for renovation, a shift of the reference study period—as defined in EN 15643-1 and EN 15978—is proposed (from construction to renovation). Interventions related to the energy renovation are modelled as current events, while interventions and processes that happen afterwards are modelled as future events, including dynamic parameters, considering changes in the operational energy use, changes in the energy mix, and future (cleaner) production processes. For a specific case study building, the dynamic approach resulted in a lower environmental impact than the static approach. However, the dynamic approach did not result in other renovation recommendations, except when a dynamic parameter for electricity production was included.

From carbon neutral to climate neutral: Dynamic life cycle assessment for wood‐based panels produced in China

AbstractThe forestry sector is crucial in supporting climate change mitigation, where the mitigation potential is assessed by combining forest carbon analysis and wood product life cycle assessment (LCA). Static LCA (sLCA) is the approach commonly used in national forestry mitigation models worldwide. Static GHG effects are calculated as a running total of emissions and removals, which are often used to imply climate effects. Also, carbon neutrality, a state when the GHG effects equal zero, is used to imply neutral climate effects. However, until carbon neutrality is achieved, the increased emissions contribute to climate warming. Dynamic LCA (dLCA) is an improved method to estimate climate effects by considering the atmospheric dynamics and heat trapping capacity of different GHGs. Climate neutrality is a state when the warming effects caused by increased emissions are fully compensated by warming reduction contributed by removals. We applied dLCA and sLCA to China‐made wood‐based panels produced from 1990 to 2018 by harvesting poplar plantations. Our results suggested that, compared to dLCA results, static GHG effects largely underestimated climate warming effects or overestimated mitigation contributions. Also, decades or longer was required to achieve climate neutrality following carbon neutrality, if achievable. So, within a given timeframe, a forestry mitigation activity can achieve carbon neutrality but increase climate warming, hindering the goal of limiting global temperature rise that was set in the 2015 Paris Agreement. Thus, to assess climate warming effects, using dLCA in addition to GHG effects is essential for forestry mitigation analysis.

Projected environmental benefits of replacing beef with microbial protein.

Ruminant meat provides valuable protein to humans, but livestock production has many negative environmental impacts, especially in terms of deforestation, greenhouse gas emissions, water use and eutrophication1. In addition to a dietary shift towards plant-based diets2, imitation products, including plant-based meat, cultured meat and fermentation-derived microbial protein (MP), have been proposed as means to reduce the externalities of livestock production3-7. Life cycle assessment (LCA) studies have estimated substantial environmental benefits of MP, produced in bioreactors using sugar as feedstock, especially compared to ruminant meat3,7. Here we present an analysis of MP as substitute for ruminant meat in forward-looking global land-use scenarios towards 2050. Our study complements LCA studies by estimating the environmental benefits of MP within a future socio-economic pathway. Our model projections show that substituting 20% of per-capita ruminant meat consumption with MP globally by 2050 (on a protein basis) offsets future increases in global pasture area, cutting annual deforestation and related CO2 emissions roughly in half, while also lowering methane emissions. However, further upscaling of MP, under the assumption of given consumer acceptance, results in a non-linear saturation effect on reduced deforestation and related CO2 emissions-an effect that cannot be captured with the method of static LCA.

Temporal Hotspot Identification using Dynamic Life Cycle Inventory: Which are the Critical Time-spans within the Product Life Cycle?

Dynamic Life Cycle Assessment (LCA) accounts for the temporal variation of the LCA model. Due to the relative lack of temporally-differentiated characterization factors for the Life Cycle Impact Assessment stage, the most common way to perform dynamic LCA is to limit the dynamic component to the Life Cycle Inventory (LCI) stage. In the manufacturing context, this would translate into a shop-floor connected through Internet of Things (IoT) technologies, to continuously track inventory variables such as technological and environmental flows as well as throughput rate. LCA impacts could then be assessed for each time-step, following their temporal distribution, which would have been overseen with a traditional static LCA. Hotspot analysis can be used to analyze the most relevant drivers of LCA impacts. It can be repeated for each impact category, is embedded in commercial LCA software and, can be done at life cycle stage, process, technological and elementary flow levels. We define temporal hotspots as critical time-spans, responsible for a significant share of LCA impacts. Here we show how to identify temporal hotspots in the context of a dynamic LCI. This would allow for the assessment and management of a new level of differentiation for the hotspot analysis, the temporal one. Moreover, traditional levels of hotspot identification (e.g. process or flow) can be used in an assessment focused on the critical time-spans. To guide readers through the implementation of the methodology, we test it on a hypothetical case study. The methodology could inform decision makers looking for detailed data-driven solutions to reduce LCA impacts of those life cycle stages for which dynamic LCI data is available. Since dynamic LCI data rarely covers the full life cycle, a hotspot analysis with static data is still needed, as a preliminary assessment.

Environmental impacts of the future German energy system from integrated energy systems optimization and dynamic life cycle assessment

Mitigating climate change requires a fundamental transformation of our energy systems. This transformation should not shift burdens to other environmental impacts. Current energy models account for environmental impacts using Life Cycle Inventories (LCIs) that typically rely on historic processes. Thus, the LCIs are static and do not reflect improvements in underlying background processes, e.g., in the energy supply. Dynamic Life Cycle Assessment (LCA) incorporates changes in the LCI and allows for a consistent assessment of future energy systems. We integrate dynamic LCA in a national energy system optimization and discuss the differences between employing static and dynamic LCA in energy system optimization and assessment. Dynamic LCA leads to lower environmental impacts in most categories (e.g., climate change: -18%) and is required for a quantitative environmental assessment. However, our analysis shows that static LCA is sufficient to identify general trends in energy system optimization and assessment for Germany till 2050.

A dynamic approach for life cycle global warming impact assessment of machine tool considering time effect

PurposeMachine tools are the equipment used for the cutting and shaping of materials, like metals, which generate greenhouse gas (GHG) emissions across their life cycles due to material use and energy consumption. The life cycle emissions of machine tools are distributed over time and may vary with technology advancement. This paper aims to incorporate these temporal factors into the global warming impact (GWI) assessment of machine tools and further reveal their influences on the results.MethodIncorporating emission time into the GWI assessment of machine tools is based on the following dynamic life cycle assessment (LCA) framework. First, compute temporally differentiated GHGs of machine tools based on the activity-based modeling. And then, use time-dependent characterization factors (CFs), which are developed based on the radiative forcing concept, to assess their GWI. By using this framework, a dynamic life cycle GWI assessment of machine tool is conducted using two gear hobbing machines. Both the emission time and the potential changes of life cycle emissions due to the improvement of electricity mix and the variation of machine tool use modes are considered.Results and discussionThe results demonstrated that when the emission time was considered, both machines offered 3% of reduction of GWI, compared with their static results, respectively. Further reductions were found for the two machines, when the electricity improvement and the changes of the machine tool use modes were considered. All the differences between the static and the dynamic environmental impact results become smaller with the extension of the time horizons (THs) that accounted for the evaluation.Conclusions and recommendationsThe conventional static LCA has the potential to overestimate the real GWI of machine tools. It is more important to account for the emission time in GWI assessment at shorter THs or for a longer lifetime of machine tools. This work offers a method to dynamically assess the real GWI of machine tools. The proposed method helps to make robust decision-making for environmentally friendly machine tool selection and support sustainable production.

Dynamic and consequential LCA aspects in multi-objective optimisation for NZEB design

Multi-objective optimisation coupled with building energy simulation (BES) and life cycle assessment (LCA) models is a promising method to eco-design net-zero energy buildings (NZEBs) in line with sustainable objectives such as UN SDG’s goals 7, 11, 12 and 13. This paper presents a method of building multi-objective optimisation based on NSGA-II coupled with the BES model COMFIE and the building LCA tool EQUER to identify NZEB designs that minimise construction costs and GHG emissions. A dynamic electricity mix model was implemented in LCA to evaluate more precisely time-related impacts of heating and solar photovoltaic production. Three different LCA approaches defining the multi-objective optimisation problem were compared: static LCA (considering an average annual electricity mix), dynamic attributionnal LCA (average hourly mix) and dynamic consequential LCA (marginal hourly mix). Results show minor differences in optimums quality between static and dynamic attributionnal approaches but important differences in optimums design parameters between attributionnal and consequential approaches. The influence of the LCA approach on multi-objective optimisation results emphasises the need to specify guidelines for practitioners about the choice of the LCA approach.

Impact of GHGs temporal dynamics on the GWP assessment of building materials: A case study on bio-based and non-bio-based walls

In a static Life Cycle Assessment (LCA), the global warming potential (GWP) is calculated assuming Green House Gas (GHG) impact to be independent of their emission or uptake timing.This study investigates if this approach is adequate to fully capture the global warming impact (GWI) of building materials. Static LCA (sLCA) was compared to dynamic LCA (dLCA) on two case studies, a conventional wall made of concrete and mineral wool, and a bio-based wall made of wood and straw.The main results are:●sLCA do not allow to evaluate the real GWI of building materials. This might mislead the comparison of building materials.●GWP indicator might be estimated at 100- and 500-year-TH to better support mitigation in the building sector;●The relative metric in kgCO2 equivalent misleads conclusions. Absolute global warming indicators calculated with dLCA might be fairer to compare building materials’ GWI;●sLCA with at 100 years GWP indicator and a relative metric in kgCO2e, which is the approach currently used in the French building sector, disadvantages bio-based solutions compared to conventional ones;●dLCA applied to an alternative functional unit — maintaining a housing function during several centuries — demonstrates that temporary carbon storage induced by bio-based materials do not lead to dramatic carbon release for future generations.

Dynamic Benchmarking of Building Strategies for a Circular Economy

Increasing building demands from a growing world population puts enormous pressure on natural resources. Management of resource consumption and environmental impacts is therefore vital to secure contemporary and future well-being and progress. Circular Economy (CE) is perceived as an industrial economy model potentially minimizing resource consumption, waste production and environmental impacts by the means of increased material circularity e.g. reuse. In order to promote CE in buildings, there is a need for benchmarks to support building designers in choosing environmentally viable solutions. Although life cycle assessment (LCA) help policy makers and building practitioners to define such benchmarks, benchmark studies often rely on static LCA approaches. Hence, uncertain and unknown dynamic changes during a buildings’ long service life influencing the performance of long term sustainable building design principles are not accounted for. Through a literature review the paper at hand identified dynamic technological progress such as resource and energy consumption, energy grid mix, waste management, design and innovation and production efficiency as potentially essential to include when defining realistic CE building strategy benchmarks. How these dynamic factors may affect LCA results were demonstrated through a case study of a concrete column based on a range of possible scenarios. This included estimated future projections and the uncertainty relating to prospective assessments resulting in an output in the form of a span of possible future developments and environmental impacts instead of a single output. Based on the literature review and case study, main challenges of incorporating dynamism within building LCA benchmarking were identified.

Dynamic assessment elements and their prospective solutions in dynamic life cycle assessment of buildings

Life cycle assessment (LCA) is a mature and widely used tool for quantifying environmental performance in the building sector. Temporal variations in building characteristics over their long life cycles significantly influence LCA results. Therefore, in recent years, dynamic LCA (DLCA) has become an emerging research field. However, DLCA research is still in its infancy, as demonstrated by the lack of systematic analysis regarding the dynamic aspects that should be considered. By examining the data transformation pathway in accordance with the standard LCA framework, this study formalizes four identified dynamic assessment elements (DAEs), namely dynamic consumption, dynamic basic inventory datasets, dynamic characterization factors, and dynamic weighting factors. A DLCA framework for buildings is proposed to enable the incorporation of DAEs into the static LCA framework. Furthermore, to better support the DLCA applications, viable prospective solutions and the availability of related data regarding the four DAEs are discussed. A case study shows that the assessment results are sensitive to the DAEs at varying degrees, and the DLCA model potentially offers a more realistic evaluation result. This study is expected to serve as a reference and provide guidelines for developing further in-depth DLCA models and methods that consider complex temporal variations.

Dynamic Life Cycle Assessments of a Conventional Green Building and a Net Zero Energy Building: Exploration of Static, Dynamic, Attributional, and Consequential Electricity Grid Models.

Our study assesses the differences between regional average- and marginal-electricity generation mixes as well as the variability between predicted and observed energy consumption of a "conventional green" Leadership in Energy and Environmental Design (LEED) building and a Net-Zero Energy Living Building (NZEB). The aim of our study was to evaluate the importance of using temporally resolved building-level data while capturing the dynamic effects a changing electrical grid has on the life cycle impacts of buildings. Two static and four dynamic life cycle assessment (LCA) models were evaluated for both buildings. Both buildings' results show that the most appropriate models ( hybrid consequential for the LEED Gold building, hourly consequential for the NZEB) significantly modified the use-phase global warming potential (GWP) impacts relative to the design static LCA (49% greater impact for the LEED Gold building; 45% greater reduction for the NZEB). In other words, a "standard" LCA would underestimate the use phase impacts of the LEED Gold building and the benefits of the NZEB in the GWP category. Although the results in this paper are specific to two case study buildings, the methods developed are scalable and can be implemented more widely to improve building life cycle impact estimates.