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While previous research has focused on developing prospective LCI databases that build upon projections from integrated assessment models (IAMs), until now only attributional databases have been developed. To construct consequential LCI databases, a novel approach is required that can be applied consistently on a large scale. To this end, the heuristic approach from Bo Weidema was selected as a basis for this study. This approach has been validated before with historical data and was adapted in this study to identify the marginal suppliers in a prospective context. The different steps within the approach were analyzed and alternative techniques for each step within the heuristic method were proposed. The techniques were tested out on the future electricity sector using projections from two IAMs (IMAGE and REMIND). Results showed how sensitive the results are to which technique is selected in each step. The most sensitive step is the selection of the time interval, with even small changes resulting in a noticeable difference. In addition the results also showed a substantial difference between the IAM projections. The relevance and goals of the alternative techniques for each step were discussed to guide users on forming the heuristic method for their study.

Large-scale offshore wind energy developments represent a major player in the energy transition but are likely to have (negative or positive) impacts on marine biodiversity. Wind turbine foundations and sour protection often replace soft sediment with hard substrates, creating artificial reefs for sessile dwellers. Offshore wind farm (OWF) furthermore leads to a decrease in (and even a cessation of) bottom trawling, as this activity is prohibited in many OWFs. The long-term cumulative impacts of these changes on marine biodiversity remain largely unknown. This study integrates such impacts into characterization factors for life cycle assessment based on the North Sea and illustrates its application. Our results suggest that there are no net adverse impacts during OWF operation on benthic communities inhabiting the original sand bottom within OWFs. Artificial reefs could lead to a doubling of species richness and a two-order-of-magnitude increase of species abundance. Seabed occupation will also incur in minor biodiversity losses in the soft sediment. Our results were not conclusive concerning the trawling avoidance benefits. The developed characterization factors quantifying biodiversity-related impacts from OWF operation provide a stepping stone toward a better representation of biodiversity in life cycle assessment.

Purpose Much progress has recently been made in modelling future background systems for LCA by including future scenario data, e.g. from Integrated Assessment Models (IAMs), into life cycle inventory (LCI) databases. A key problem is, however, that this yields potentially dozens of scenario LCI databases (i.e. LCI databases that represent different scenarios and reference years), instead of a single background database, which is very impractical for LCA modelling purposes. This paper proposes an approach to overcome this problem. Methods The approach consists of transforming all scenario LCI databases into a single superstructure database and an associated scenario difference file. The superstructure database is also a regular LCI database, but is constructed to contain all unique exchanges (elementary and intermediate flows) and processes that exist across all scenario LCI databases. The scenario difference file stores the differences between all scenarios and can be used to turn the superstructure into a specific scenario LCI database. This is very fast as it can be done in memory during LCA calculations. Results and discussion A key advantage of the superstructure approach is that a single LCI database can be used to represent different background systems. Therefore, the practitioner does not need to re-link a foreground system to multiple LCI databases, which is work-intensive and invites modelling errors. LCA results for all scenarios and reference years can be calculated automatically. We also illustrate how the superstructure approach has been implemented in the Activity Browser open source LCA software. Although this paper introduces the superstructure approach for background scenarios, it can also be used to model foreground scenarios, and even, as implemented in the Activity Browser, combinations of background and foreground scenarios. Finally, we briefly discuss further challenges that need to be addressed for a more widespread use of background scenarios in LCA. Conclusions The superstructure approach presents a practical solution for making the use of future background scenarios more wide-spread and, therefore, to overcome the problem of performing prospective LCA with temporally inconsistent foreground and background systems. The implementation in the Activity Browser makes the approach available for anyone and may serve as inspiration for other LCA software to implement the superstructure approach or a similar concept. While this may be an important technical milestone, additional coordination between data providers, scenario generators, LCA practitioners, and software developers will be required to further facilitate the use of background scenarios in prospective LCA studies.

The environmental benefits of low-carbon technologies, such as photovoltaic modules, have been under debate because their large-scale deployment will require a drastic increase in metal production. This is of concern because higher metal demand may induce ore grade decline and can thereby further intensify the environmental footprint of metal supply. To account for this interlinkage known as the “energy-resource nexus”, energy and metal supply scenarios need to be assessed in conjunction. We investigate the trends of future impacts of metal supplies and low-carbon technologies, considering both metal and electricity supply scenarios. We develop metal supply scenarios for copper, nickel, zinc, and lead, extending previous work. Our scenarios consider developments such as ore grade decline, energy-efficiency improvements, and secondary production shares. We also include two future electricity supply scenarios from the IMAGE model using a recently published methodology. Both scenarios are incorporated into the background database of ecoinvent to realize an integrated modeling approach, that is, future metal supply chains make use of future electricity and vice versa. We find that impacts of the modeled metal supplies and low-carbon technologies may decrease in the future. Key drivers for impact reductions are the electricity transition and increasing secondary production shares. Considering both metal and electricity scenarios has proven valuable because they drive impact reductions in different categories, namely human toxicity (up to −43%) and climate change (up to −63%), respectively. Thus, compensating for lower ore grades and reducing impacts beyond climate change requires both greener electricity and also sustainable metal supply. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

Life cycle assessment (LCA) and environmentally extended input output analysis (EEIOA) are two widely used approaches to assess the environmental impacts of products and services with the aim of providing decision support. Here, we compare carbon footprint (CF) results for products and services in the ecoinvent 3.4 cut-off and the hybrid version of EXIOBASE. While we find that there is good agreement for certain sectors, more than half of the matched products differ by more than a factor 2. Best fits are observed in the energy, manufacturing, and agricultural sectors, although deviations are substantial for renewable energy. Poorer fits are observed for waste treatment and mining sectors. Both databases have a limited differentiation in the service sector. Differences can, to some degree, be explained by methodological differences, such as system boundaries and approaches used to resolve multi-functionality, and data differences. The common finding that, due to incomplete economic coverage (truncation error), LCA-based CFs should be lower than EEIOA-based CFs, could not be confirmed. The comparison of CFs from LCA and EEIOA databases can provide additional insights into the uncertainties of CF results, which is important knowledge when guiding decision makers. An approach that uses the coefficient of variation to identify strategic database improvement potentials is also presented and highlights several product groups that could deserve additional attention in both databases. Further strategic database improvements are crucial to reduce uncertainties and increase the robustness of decision support that the industrial ecology community can provide for the economic transformations ahead of us.

To conserve resources and enhance the environmental performance, China has launched the "Zero waste" concept, focused on reutilization of solid waste and recovery of materials, including copper. Although several studies have assessed the copper demand and recycling, there is a lack of understanding on how different waste management options would potentially reduce primary copper demand and associated environmental impacts in China in the context of energy transition. This study addresses this gap in view of a transition to low-carbon energy system and the optimization of copper waste management combining MFA and LCA approaches. Six types of waste streams (C&DW, ELV, WEEE, IEW, MSW, ICW) are investigated in relation to various "Zero waste" strategies including reduction, reuse (repair, remanufacturing or refurbishment), recycling and transition from informal to formal waste management. Under present Chinese policies, reuse and recycling of copper containing products will lead to a somewhat lower dependency on primary copper in 2100 (11187Gg), as well as lower total GHG emissions (64869 Gg CO2-eq.) and cumulative energy demand (1.18x10 boolean AND 12 MJ). Maximizing such "Zero waste" options may lead to a further reduction, resulting in 65% potential reduction of primary copper demand, around 55% potential reduction of total GHG emissions and total cumulative energy demand in 2100. Several policy actions are proposed to provide insights into future waste management in China as well as some of the challenges involved.

Life cycle assessment (LCA) models and databases have increased in size, resolution, and complexity, requiring analysts to rely on an ever-increasing number of uncertain model inputs. Such increased complexity calls for systematic approaches to assessing the uncertainty of the output results of LCA models and the sensitivity of LCA model outputs to the model's uncertain inputs. In this contribution, we provide a theoretical basis and present a practical software implementation that combines uncertainty analysis and moment-independent global sensitivity analysis, which can be readily applied to full-scale LCA models. We implemented our approach in the Activity-Browser open source LCA software and it is made available for use in LCA studies. We demonstrate the approach and software implementation with a case study of crystalline silicon photovoltaics.

The building sector plays a key role in energy transition and carbon reduction while capturing the dynamic characteristics (e.g. materials, energy performance, and environmental impact) of building stock is a great challenge during the gradual process. This study presents a bottom-up dynamic building stock model that links dynamic material flow analysis with building energy modeling. The environmental impact of material and energy requirements is assessed by considering future electricity mix. The model is applied to evaluate the pathways to the climate-neutral energy supply of residential building stock in the Netherlands by 2050. Results show that space heating demand decreases by about 2/3 by 2050, while the energy for hot water increases to 92% of space heating demand. 80% of public grid electricity for appliances and lighting can be potentially substituted if rooftop photovoltaic (PV) systems are installed on 50% of renovated buildings and all the new buildings. Greenhouse gas (GHG) emissions of operational energy are reduced by approximately 60–90%, depending on the electricity mix. Annual GHG emissions from material production are not as important as those related to operational energy. Insulation materials account for a large proportion of the carbon footprint of material production. The model has a high spatial and temporal resolution and can be linked with local energy source availability (e.g. buildings or neighborhoods) to provide more accurate support for policymaking.

Polyhydroxyalkanoates (PHA) production from waste streams using mixed microbial cultures (MMC) can unlock the potential of PHA to substitute oil-based plastics. However, these processes are still at low technology readiness level (4–6). Demonstrating a better environmental performance would boost their deployment at industrial scale. Hence, including environmental guidance during their development, when there are still opportunities for major alterations, is essential. To the best of our knowledge, this work elucidates for the first time how waste-to-PHA biorefineries could develop in the future by combining prospective LCA with scenario methodology and where the attention of stakeholders should be focused. Four future scenarios were derived considering both surrounding (e.g., scale, environmental or bioeconomy policies) and technological parameters (e.g., acidification yield, PHA content in biomass or recovery yield). Those scenarios derived under ambitious environmental and bioeconomy policies shop up to 50% lower environmental impacts than those under business-as-usual policies. These differences are caused by the different background processes’ environmental burdens (e.g., electricity mix with low renewable energies share) and the higher consumption of chemicals and utilities. However, the environmental impacts caused by lower yields can be partially mitigated by valorizing the intermediate waste streams into biogas. Sensitivity analysis results pointed out recovery yield and PHA content as the parameters that influence most the environmental performance, being responsible for up to 60% of variance in environmental performance. These parameters determine the chemicals and utilities consumption in PHA downstream processing, which is confirmed as the main environmental hotspot. This work goes beyond previous LCA studies on PHA production and quantifies the influence of different parameters on the environmental performance SI

The life cycle assessment framework was adapted to the territorial level (the "territorial LCA") to assess the environmental impacts and services of land-use planning scenarios. Given the various geographical conditions of the territory, the potential alternatives of land-use scenarios could be enormous. To prevent the iterative process of proposing and comparing alternative scenarios, this work aims to move one step further to automatically generate optimal planning scenarios by linking the novel territorial LCA with multi-objective optimization (MOO). A fuzzy optimization approach is adopted to deal with the trade-offs among objectives and to generate optimized scenarios, minimizing the environmental damages and maximizing the satisfaction level of the desired land-use functions subjected to constraints such as area availability and demand. Geographical Information System (GIS) is employed to manipulate geographic datasets for spatial assessment. An illustrative case study tests the novel integrated method (the territorial LCA, MOO, and GIS) on its ability to propose optimal land-use planning for bioenergy production in a region in Belgium. The study results reveal the competition of land uses for different energy products, the trade-offs among impact categories, and potential impacts on other territories if implementing optimal land planning for the territory under study. The optimization outcomes can help decision-making on the optimal locations for different crop types (i.e., miscanthus, willow, and maize in the case study) and utilizations (i.e., electricity, heat, biogas, and bioethanol in this study) complying with the objectives and constraints. This integrated tool holds the potential to assist policymakers when deciding on how to use the territory facing the global context of increasing demands for multiple uses of bio-based products, such as for food, feed, fuel, fiber, and chemicals. Limitations of the current method and its potential for real-world applications are discussed, such as expanding the scope to include life cycle sustainability assessment and taking farmers' behavior and crop rotation into account.

Circular business models (CBMs) and their potential environmental benefits have been widely assessed by using life cycle assessment (LCA). However, most LCA studies consider static systems and assume instant and full technology adoption, limiting the analysis of the implications of circular transitions. Considering technology diffusion in LCA models may bring a better understanding of the environmental implications of the adoption of CBMs. Nevertheless, diffusion is also related to stock dynamics, which are difficult to represent in classic LCA models. To overcome these issues, we propose a modeling framework that integrates three modeling families to assess the environmental impacts and material implications of the adoption of CBMs: diffusion of innovations, product stock dynamics, and LCA. We present a method of application and illustrate it with a theoretical case study. This framework might be useful in the socio-economic analysis of systems transitioning to CBMs, especially in systems that involve long-lived products.

Summary The present article aims to determine the current carbon footprint (CF) of Zernez, a Swiss mountain village, and to identify reduction potentials of greenhouse gas (GHG) emissions. For this purpose, material and energy flows were assessed mainly based on detailed household surveys, interviews, and energy bills, but also by means of other information sources, for example, national statistics, traffic censuses, and literature values. To set up the GHG balance, special attention was paid to the consistent definition of system boundaries by adopting two fundamentally different perspectives: purely geographical accounting (PGA) and the consumption-based footprint (CBF) method. Each of these two perspectives total approximately 10 tonnes of carbon dioxide equivalents per capita per year. The PGA revealed that 70% of the direct emissions in Zernez are caused by agricultural activities, whereas no consumption area dominated the consumption-induced CF. For the identification of targeted measures, both perspectives were considered in a complementary manner. The building stock and its underlying energy supply system showed a GHG reduction potential of 80%. The building sector was thus detected as a reasonable first step for the municipality to adopt GHG mitigation strategies. In the case of Zernez, building-stock-related measures are predicted to decrease the current CF by 13% (CBF) and 17% (PGA), respectively.

Sustainable use of wood may contribute to coping with energy and material resource challenges. The goal of this study is to increase knowledge of the environmental effects of wood use by analyzing the complete value chain of all wooden goods produced or consumed in Switzerland. We start from a material flow analysis of current wood use in Switzerland. Environmental impacts related to the material flows are evaluated using life cycle assessment–based environmental indicators. Regarding climate change, we find an overall average benefit of 0.5 tonnes carbon dioxide equivalent per cubic meter of wood used. High environmental benefits are often achieved when replacing conventional heat production and energy‐consuming materials in construction and furniture. The environmental performance of wood is, however, highly dependent on its use and environmental indicators. To exploit the mitigation potential of wood, we recommend to (1) apply its use where there are high substitution benefits like the replacement of fossil fuels for energy or energy‐intensive building materials, (2) take appropriate measures to minimize negative effects like particulate matter emissions, and (3) keep a systems perspective to weigh effects like substitution and cascading against each other in a comprehensive manner. The results can provide guidance for further in‐depth studies and prospective analyses of wood‐use scenarios.

Purpose Good background data are an important requirement in LCA. Practitioners generally make use of LCI databases for such data, and the ecoinvent database is the largest transparent unit-process LCI database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the database, set the foundation for a truly global database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.

High spatial resolution is critical for a building stock energy model to identify spatial hotspots and provide targeted recommendations for reducing regional energy consumption. However, input uncertainties due to lacking high-resolution spatial data (e.g. building information and occupant behavior) can cause great discrepancies between modeled and actual energy consumption. We present a modeling framework that can act as a blueprint model for most European countries based on geo- referenced data, building archetypes, and public algorithms. Further sophistication is added in a step-wise approach, including the shift from average to hourly weather data, refurbishment, and occupants’ heating schedules. The model is demonstrated for the city of Leiden, the Netherlands, and the simulated results are spatially validated against the measured natural gas consumption reported at postcode level. Results show that when these factors are considered, the model can provide a good estimate of the energy consumption at the city scale (overestimated by 6%). At postcode level, nearly 83% of the absolute differences between modeled and measured natural gas consumption are within one standard deviation (±25 kWh/m2a, about 30% of the mean measured natural gas consumption). Further research and data would be required to provide reliable results at the level of individual buildings, e.g. information on refurbishment and occupant behavior. The model is well suited to identify spatial hotspots of residential energy consumption and could thus provide a practical basis (e.g. maps) for targeted measures to mitigate climate change.

Car-sharing platforms provide access to a shared rather than a private fleet of automobiles distributed in the region. Participation in such services induces changes in mobility behaviour as well as vehicle ownership patterns that could have positive environmental impacts. This study contributes to the understanding of the total mobility-related greenhouse gas emissions reduction related to business-to-consumer car-sharing participation. A comprehensive model which takes into account distances travelled annually by the major urban transport modes as well as their life-cycle emissions factors is proposed, and the before-and-after analysis is conducted for an average car-sharing member in three geographical cases (Netherlands, San Francisco, Calgary). In addition to non-operational emissions for all the transport modes involved, this approach considers the rebound effects associated with the modal shift effect (substituting driving distances with alternative modes) and the lifetime shift effect for the shared automobiles, phenomena which have been barely analysed in the previous studies. As a result, in contrast to the previous impact assessments in the field, a significantly more modest reduction of the annual total mobility-related life-cycle greenhouse gas emissions caused by car-sharing participation has been estimated, 3–18% for three geographical case studies investigated (versus up to 67% estimated previously). This suggests the significance of the newly considered effects and provides with the practical implications for improved assessments in the future.

This study assesses the environmentally optimal wood utilisation patterns under varying wood cascading options, using the example of Switzerland. Cascading is the use of the same wood unit in multiple, successive product cycles. To consider aspects relevant at the system level (e.g. stocks/flows, demand/supply constraints) as well as at the product level (e.g. process inventories), we present a model that combines material flow analysis (MFA), life cycle assessment (LCA) and mathematical optimisation to identify environmentally optimal wood use scenarios concerning climate change and particulate matter formation. We separately include the temporal dynamics of biogenic carbon flows, i.e. carbon uptake, storage and subsequent release, which may have a considerable influence on the climate change performance of wood products. Results indicate that multiple cascading (mC) of wood can decrease environmental impacts: total systemic impact reductions over the modelled 200-year time horizon compared to single cascading (i.e. all waste wood is directly incinerated), are between 35–59 Mt CO2-eq. and 43–63 kt PM10-eq. Driving factors for the environmental impact of future wood use scenarios are: waste wood processing efficiency, wood storage effects (in case of biogenic carbon accounting), and available cascading options. Particularly, high quality wood cascade of wooden beams is a promising recycling path for reducing environmental impacts. We conclude that by implementing wood cascading, future Swiss wood utilisation can be further improved in terms of environmental impact. The tool combination of dynamic MFA, LCA and optimisation proved to be suitable to identify environmentally optimal scenarios for a complex value chain.

Summary Industrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input-output models and supply and use tables, and the dynamic_stock_model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research.

Cascading, the use of the same unit of a resource in multiple successional applications, is considered as a viable means to improve the efficiency of resource utilization and to decrease environmental impacts. Wood, as a regrowing but nevertheless limited and increasingly in demand resource, can be used in cascades, thereby increasing the potential efficiency per unit of wood. This study aims to assess the influence of cascading wood utilization on optimizing the overall environmental impact of wood utilization. By combining a material flow model of existing wood applications both for materials provision and energy production - with an algebraic optimization tool, the effects of the use of wood in cascades can be modelled and quantified based on life cycle impact assessment results for all production processes. To identify the most efficient wood allocation, the effects of a potential substitution of non-wood products were taken into account in a part of the model runs. The considered environmental indicators were global warming potential, particulate matter formation, land occupation and an aggregated single score indicator. We found that optimizing either the overall global warming potential or the value of the single score indicator of the system leads to a simultaneous relative decrease of all other considered environmental impacts. The relative differences between the impacts of the model run with and without the possibility of a cascading use of wood were 7% for global warming potential and the single score indicator, despite cascading only influencing a small part of the overall system, namely wood panel production. Cascading led to savings of up to 14% of the annual primary wood supply of the study area. We conclude that cascading can improve the overall performance of a wood utilization system. (C) 2015 Elsevier Ltd. All rights reserved.

The optimal size and location of bioenergy plants with regards to environmental and economic performance are assessed with a spatially explicit value chain model of the production of synthetic natural gas (SNG) from wood. It consists of several individual models for the availability, harvest, transportation, conversion of wood to SNG, electricity and heat, and the use of these products to substitute non-renewable energy services. An optimization strategy is used to choose the optimal technology configuration for plant sizes from 5 to 200 MW and different locations for any desired weighting between the environmental performance based on life cycle assessment (LCA) and the economic performance. While the economic optima are found at plant sizes between 100 and 200 MW, the environmental optima are found in the range of 5e40 MW. This trade-off can be minimized at plant sizes above 25 MW according to the presented model. The most important driver of the environmental performance is the efficient substitution of non-renewable energy, which is a site-specific factor. In comparison to this, spatial factors such as wood availability, harvest, and transportation, have a smaller influence on the environmental performance, at least for a country of the size of Switzerland. The main drivers of the economic performance are the revenues from the sale of the SNG plant’s products and the SNG production costs, but transportation and wood costs also play a role.

Purpose: The environmental performance of products or services is often a result of a number of key decisions that shape their life cycles (e.g., techology choices). This paper introduces a modular LCA approach that is capable of reducing the effort involved in performing scenario analyses and optimization when several key choices along a product’s value chain lead to many alternative life cycles. Methods: The main idea is that the value chain of a product can be divided into interconnected but exchangeable modules, which together represent a full life cycle. A module is comprised of unit processes from the practitioner’s LCI database. The inputs, outputs, and system boundaries of each module can be tailored to the context of the studied system. Alternatives arise whenever multiple modules produce substitutable products. Unlike in conventional LCI databases, no copies are necessary to represent the same process with different inputs. A module-product matrix is used to store this information. It can be used as a basis for an automated scenario analysis of all alternatives or as an input to an optimization model. Results and discussion: Our approach is illustrated in two case studies: (1) Passenger car fuel choices are modeled by 15 modules representing 33 alternative value chains for diesel, petrol, natural gas and electric cars. The automated comparison of LCA results indicates that electric mobility is often the preferable option from a climate perspective, but impacts depend strongly on the electricity source. (2) A dynamic optimization model including stocks is built from eight modules to analyze the optimal use of wood for material and energy applications. Results indicate that although direct substitution benefits are higher for energy applications, cascading use of wood can maximize environmental performance over the entire life cycle. Conclusions: The modular LCA approach permits an efficient modeling and comparison of alternative product life cycles, enabling practitioners to focus on key decisions. It can be applied to exploit a potential that is hidden in LCI databases, which is that they contain many specific inventories but not all useful combinations in the context of scenario analyses. The user-defined level of abstraction that is introduced through modules can be helpful in the communication of LCA results. The modular approach also facilitates the integration of LCA and optimization as well as other industrial ecology methods. An open source software is provided to enable others to apply and further develop our implementation of a modular LCA approach., The International Journal of Life Cycle Assessment, 21 (4), ISSN:0948-3349, ISSN:1614-7502

The optimal use of forest energy wood, industrial wood residues, waste wood, agricultural residues, animal manure, biowaste, and sewage sludge in 2010 and 2030 was assessed for Europe. An energy system model was developed comprising 13 principal fossil technologies for the production of heat, electricity, and transport and 173 bioenergy conversion routes. The net environmental benefits of substituting fossil energy with bioenergy were calculated for all approximately 1500 combinations based on life cycle assessment (LCA) results. An optimization model determines the best use of biomass for different environmental indicators within the quantified EU-27 context of biomass availability and fossil energy utilization. Key factors determining the optimal use of biomass are the conversion efficiencies of bioenergy technologies and the kind and quantity of fossil energy technologies that can be substituted. Provided that heat can be used efficiently, optimizations for different environmental indicators almost always indicate that woody biomass is best used for combined heat and power generation, if coal, oil, or fuel oil based technologies can be substituted. The benefits of its conversion to SNG or ethanol are significantly lower. For non-woody biomass electricity generation, transportation, and heating yield almost comparable benefits as long as high conversion efficiencies and optimal substitutions are assured. The shares of fossil heat, electricity, and transportation that could be replaced with bioenergy are also provided.

The conversion of wood to synthetic natural gas (SNG) via gasification and catalytic methanation is a renewable close to commercialization technology that could substitute fossil fuels and alleviate global warming. In order to assure that it is beneficial from the environmental perspective, a cradle to grave life cycle assessment (LCA) of SNG from a first-of-its-kind polygeneration unit for heating, electricity generation, and transportation was conducted. These SNG systems were compared to fossil and conventional wood reference systems and environmental benefits from their substitution evaluated. Finally, we conduct sensitivity analysis for expected technological improvements and factors that could decrease environmental performance. It is shown that substituting fossil technologies with SNG systems is environmentally beneficial with regard to global warming and for selected technologies also with regard to aggregated environmental impacts. On the condition that process heat is used efficiently, technological improvements such as increased efficiency and denitrification could further increase this advantage. On the other hand, lower GHG emissions and aggregated impacts are partly compensated by other environmental effects, e.g. eutrophication, ecotoxicity, and respiratory disease caused by inorganics. Since more efficient alternatives exist for the generation of heat and electricity from wood, it is argued that SNG is best used for transportation. In the light of a growing demand for renewable transportation fuels and commercial scale technological development being only in its initial stage, the production of SNG from wood seems to be a promising technology for the near future.

This paper analyzes the sustainable domestic biomass potential for bioenergy in Switzerland. Relevant biomass resources were selected based on expert interviews and literature analyses. A definition of technical and sustainable biomass potentials was developed. The technical and sustainable biomass potentials were then assessed based on technical and sustainability constraints. The sustainable potentials were further subdivided into the already energetically-used potential and the remaining biomass potential. Data was collected from the literature and supplementary interviews with field experts. Finally, the primary energy potential from biomass was calculated and compared to the current Swiss energy demand. We show that there is currently no sustainable potential for agricultural biomass, such as energy crops, crop residues and grass. On the other hand, there is a substantial potential from woody biomass, manure and waste biomass. The main constraints that limit the sustainable biomass potential are competing material utilizations, economic factors as well as the Swiss biofuels policy. Currently, 3.6% of Switzerland's energy demand is met by biomass resources, whereas the remaining potential could provide an additional 3.3%. Hence, with respect to a sustainable energy supply, bioenergy in Switzerland could cover a total share of 7%.

One of the simple, robust and scalable methods for purification of plasmid DNA is by membrane filtration. Utilizing manipulation of pH, ionic strength, and membrane pore size, the process is designed to completely retain the plasmid DNA. The performed study shows that despite electrostatic repulsion and a significant size difference between plasmid and membrane pore, the circular double-stranded DNA plasmid is able to penetrate through UF membranes. Experiments demonstrate that the tertiary structure of DNA changes easily and is dramatically affected by variations in ionic strength and pH. The penetration is probably due to stretching of the plasmid by hydrodynamic shear. In contrast to previous reports, the transmitted DNA copies kept their integrity and infectious ability.

The goal of this study was to identify drivers of environmental impact and quantify their influence on the environmental performance of wooden and massive residential and office buildings. We performed a life cycle assessment and used thermal simulation to quantify operational energy demand and to account for differences in thermal inertia of building mass. Twenty-eight input parameters, affecting operation, design, material, and exogenic building properties were sampled in a Monte Carlo analysis. To determine sensitivity, we calculated the correlation between each parameter and the resulting life cycle inventory and impact assessment scores. Parameters affecting operational energy demand and energy conversion are the most influential for the building’s total environmental performance. For climate change, electricity mix, ventilation rate, heating system, and construction material rank the highest. Thermal inertia results in an average 2–6% difference in heat demand. Nonrenewable cumulative energy demand of wooden buildings is 18% lower, compared to a massive variant. Total cumulative energy demand is comparable. The median climate change impact is 25% lower, including end-of-life material credits and 22% lower, when credits are excluded. The findings are valid for small offices and residential buildings in Switzerland and regions with similar building culture, construction material production, and climate., Environmental Science & Technology, 49 (16), ISSN:0013-936X, ISSN:1520-5851

This paper presents a regionalized LCA-based multiobjective optimization model of building energy demand and supply for the case of a Swiss municipality for the minimization of greenhouse gas emissions and particulate matter formation. The results show that the environmental improvement potential is very large: in the optimal case, greenhouse gas emissions from energy supply could be reduced by more than 75% and particulate emissions by over 50% in the municipality. This scenario supposes a drastic shift of heat supply systems from a fossil fuel dominated portfolio to a portfolio consisting of mainly heat pump and woodchip incineration systems. In addition to a change in heat supply technologies, roofs, windows and walls would need to be refurbished in more than 65% of the municipality's buildings. The full potential of the environmental impact reductions will hardly be achieved in reality, particularly in the short term, for example, because of financial constraints and social acceptance, which were not taken into account in this study. Nevertheless, the results of the optimization model can help policy makers to identify the most effective measures for improvement at the decision making level, for example, at the building level for refurbishment and selection of heating systems or at the municipal level for designing district heating networks. Therefore, this work represents a starting point for designing effective incentives to reduce the environmental impact of buildings. While the results of the optimization model are specific to the municipality studied, the model could readily be adapted to other regions.

This study discusses the potential of applying agent-based modeling (ABM) to wood markets. A corresponding model of the wood market of a Swiss canton, consisting of a coupled roundwood and wood fuel market, is presented. The model includes wood-producing agents, such as public foresters and private forest owners, roundwood-consuming agents, such as sawmills, different classes of wood fuel consumers, and in-between wood traders. Other important model elements include agent interaction and negotiation, execution and scheduling structures, and agent adaptation mechanisms. Two sets of scenarios demonstrate the model's power for scenario exploration. The first set of scenarios analyzes the effects of an excess and scarce supply of wood on both markets. The second set looks for the optimal number of roundwood agents in the market from the perspective of the various stakeholders involved. Taking a more in-depth view of important design decisions and their pros and cons, this study argues that ABM offers new opportunities for the explorative study of wood markets as a result of these markets' special characteristics.

Life cycle assessment of wood energy: factors for high ecological benefits Wood energy is increasingly used to replace non-renewable energy sources. Energy wood is a limited resource and should therefore be used wisely not only to maximize the economic but also the environmental benefits associated with its use. This article assesses the environmental burdens associated with wood energy (for heat, electricity and transportation) and the benefits that may arise when non-renewable energy technologies are substituted. It is shown that from a global warming perspective the use of wood energy seems almost always beneficial, but this effect may be significantly reduced if biogenic CO2 is taken into account. The method of ecological scarcity on the other hand, which considers several additional environmental dimensions and combines these into a single score, shows significantly lower environmental benefits of wood in comparison with non- renewable energy. One of the principal reasons for this are air emissions associated with wood energy such as particulate matter, nitrogen oxides (NOx) and volatile organic carbon (VOC). Considerable environmental benefits can be achieved in both cases if the following three key factors are respected: 1) a wise choice of the substituted technology and the underlying energy carrier, 2) a high efficiency in the conversion from energy contained in biomass to final energy, and 3) the implementation of measures to reduce air emissions such as particle filters and, if possible, more advanced flue gas cleaning. The article then discusses the limits of the comparison and selected issues within the life cycle assessment methodology that need further development.

Procedure to develop an agent-based model of a Swiss forest wood fuel market It is helpful to develop agent-based model based on a structured procedure. The chosen traditional software engineering approach is mainly composed of five phases: 1) the analysis of the observed system; 2) the model development; 3) the detailed design; 4) the actual software implementation; and 5) the model's evaluation. The different phases are briefly described by the respective goals, tasks and expected results. This contribution shows the development of a Swiss forest wood fuel market with specific examples for each phase. The essential work steps and intermediate results are demonstrated from the start to a fully developed agent-based model. Finally it is demonstrated how to carry through scenario analyses for the modeled forest wood fuel market. The simulation example shows first that, as is to be expected, a reduction of the wood supply leads to increasing forest wood fuel prices. Second, it shows how the decision behavior of wood suppliers can additionally modify the price of forest wood fuel in a situation of scarce supply, whereas this effect is smaller in a situation of balanced supply and demand.

Bioenergy from woodfuel has a considerable potential to substitute fossil fuels and alleviate global warming. One issue so far not systematically addressed is the question of the optimal size of bioenergy plants with regards to environmental and economic performance. The aim of this work is to fill this gap by modeling the entire production chain of wood and its conversion to bioenergy in a synthetic natural gas plant both with respect to economic and environmental performance. Several spatially explicit submodels for the availability, harvest, transportation and conversion of wood were built and joined in a multi-objective optimization model to determine optimal plant sizes for any desired weighting of environmental impacts and profits. We find a trade-off between environmental and economic optimal plant sizes. While the economic optima range between 75 – 200 MW, the environmental optima are with 10 – 40 MW significantly smaller. Moreover, the economic optima are highly location specific and tend to be smaller if the biomass resource in the geographic region of the plant is scarcer. The results are robust with regards to the effect on global warming as well as with respect to the aggregated environmental impact assessment methods Ecoindicator ’99 and Ecological Scarcity 2006.

The quantities of e-waste are expected to increase sharply in Chile. The purpose of this paper is to provide a quantitative data basis on generated e-waste quantities. A material flow analysis was carried out assessing the generation of e-waste from computer equipment (desktop and laptop PCs as well as CRT and LCD-monitors). Import and sales data were collected from the Chilean Customs database as well as from publications by the International Data Corporation. A survey was conducted to determine consumers' choices with respect to storage, re-use and disposal of computer equipment. The generation of e-waste was assessed in a baseline as well as upper and lower scenarios until 2020. The results for the baseline scenario show that about 10,000 and 20,000 tons of computer waste may be generated in the years 2010 and 2020, respectively. The cumulative e-waste generation will be four to five times higher in the upcoming decade (2010-2019) than during the current decade (2000-2009). By 2020, the shares of LCD-monitors and laptops will increase more rapidly replacing other e-waste including the CRT-monitors. The model also shows the principal flows of computer equipment from production and sale to recycling and disposal. The re-use of computer equipment plays an important role in Chile. An appropriate recycling scheme will have to be introduced to provide adequate solutions for the growing rate of e-waste generation.

Purpose The objective of this paper was to provide LCA practitioners with recommendations and a framework for upscaling emerging technologies by reviewing upscaling methods applied so far in ex ante life cycle assessment (LCA). Methods Web of Science was searched for articles published between 1990 and 2019 (April) using different variations of the term “ex ante LCA” as keywords. Suitable studies were reviewed to understand the key characteristics and main methodological principles of upscaling methods. Results and discussion A total of 18 studies were selected for literature review. Review results showed that most studies reported what a hypothetical upscaled technology would look like in the future. All studies described how they estimated data; they applied different data estimation methods, using process simulation, manual calculations, molecular structure models (MSMs) and proxies. Since the review results showed that most ex ante LCA studies followed similar upscaling steps, we developed a framework for the upscaling of emerging technologies in ex ante LCA consisting of three main steps: (1) projected technology scenario definition, (2) preparation of a projected LCA flowchart, and (3) projected data estimation. Finally, a decision tree was developed based on the review results that provides recommendations for LCA practitioners regarding the upscaling procedure in ex ante LCA. Conclusions Our findings can be useful for LCA practitioners aiming at upscaling in ex ante LCA. We provide an overview of upscaling methods used in ex ante LCA and introduce a framework describing the steps involved in the upscaling process and a decision tree recommending an up-scaling procedure. The results show that in theory all data estimation methods described in this paper can be applied to estimate material flows, energy flows, and elementary flows (emissions and natural resource use). Finally, since different kinds of expertise are required for upscaling in ex ante LCA, we recommend that technology experts from different fields are involved in performing ex ante LCA, e.g., technology developers, LCA practitioners, and engineers.

Purpose: Version 3 of ecoinvent includes more data, new modeling principles, and, for the first time, several system models: the “Allocation, cut-off by classification” (Cut-off) system model, which replicates the modeling principles of version 2, and two newly introduced models called “Allocation at the point of substitution” (APOS) and “Consequential” (Wernet et al. 2016). The aim of this paper is to analyze and explain the differences in life cycle impact assessment (LCIA) results of the v3.1 Cut-off system model in comparison to v2.2 as well as the APOS and Consequential system models. Methods: In order to do this, functionally equivalent datasets were matched across database versions and LCIA results compared to each other. In addition, the contribution of specific sectors was analyzed. The importance of new and updated data as well as new modeling principles is illustrated through examples. Results and discussion: Differences were observed in between all database versions using the impact assessment methods Global Warming Potential (GWP100a), ReCiPe Endpoint (H/A), and Ecological Scarcity 2006 (ES’06). The highest differences were found for the comparison of the v3.1 Cut-off and v2.2. At average, LCIA results increased by 6, 8, and 17 % and showed a median dataset deviation of 13, 13, and 21 % for GWP, ReCiPe, and ES’06, respectively. These changes are due to the simultaneous update and addition of new data as well as through the introduction of global coverage and spatially consistent linking of activities throughout the database. As a consequence, supply chains are now globally better represented than in version 2 and lead, e.g., in the electricity sector, to more realistic life cycle inventory (LCI) background data. LCIA results of the Cut-off and APOS models are similar and differ mainly for recycling materials and wastes. In contrast, LCIA results of the Consequential version differ notably from the attributional system models, which is to be expected due to fundamentally different modeling principles. The use of marginal instead of average suppliers in markets, i.e., consumption mixes, is the main driver for result differences. Conclusions: LCIA results continue to change as LCI databases evolve, which is confirmed by a historical comparison of v1.3 and v2.2. Version 3 features more up-to-date background data as well as global supply chains and should, therefore, be used instead of previous versions. Continuous efforts will be required to decrease the contribution of Rest-of-the-World (RoW) productions and thereby improve the global coverage of supply chains., The International Journal of Life Cycle Assessment, 21 (9), ISSN:0948-3349, ISSN:1614-7502