Poster Session 1


Davaasuren DashzevegVilas SalokheErik Bohez and Peeyush Soni

LCA of Greenhouse Grown Tomatoes in Thailand

ABSTRACT. This study was carried out with the LCA of greenhouse grown tomatoes in Thailand. The functional unit of this study was 1 kg of tomato and the system boundary was cradle to gate of greenhouse tomato fruits (seedling process to packaging process), which includes seedling, growing, harvesting and packaging process.

The third order of LCA was applied in this study by using SimaPro 7.1 software program. The Eco-Indicator 99 H/H was selected as method for evaluating environmental impacts of greenhouse grown tomatoes. LCIA based on the characterization and single score elements. The selected impact categories of Eco-Indicator 99 method are carcinogens, respiratory organic and inorganics, climate change, radiation, ozone layer, acidification and ecotoxicity, land use, mineral, fossil fuels.

Limitations of this study are transportation processes of products to the market and food storage of tomato products, neither of which were included in this study. Moreover, no waste scenario analysis of product system was studied.


Yurong ZhangYuanfeng Wang and Minghui Liu

Effect of steam curing on environmental impact of fly ash concrete

ABSTRACT. Steam curing can enhance the early strength of concrete, but may cause a certain loss of long-term strength; fly ash can increase the strength of concrete with age. Therefore, in this paper, the effect of steam curing on compressive of fly ash concrete was investigated by experiments, and then the environmental impact of fly ash concrete was calculated by life cycle assessment. The results showed that high volume fly ash concrete had better adaptability to steaming curing; environmental impacts tended to decrease with the increase in the addition of fly ash, whether under normal curing regime or under steaming curing regime; the difference of each environmental impact per strength between the two curing regimes became smaller with the increase in the addition of fly ash.


Yuchi Sun and Adam Brandt

Life Cycle Exergy Analysis on a Carbon Capture Enabled Power Plant

ABSTRACT. Installing carbon capture lines in power plants, which provides zero and even negative net emissions, is considered to be a vital part of GHG emission mitigation strategy. However, retrofitting coal- fired power plants in this way will significantly decrease power output and increase material cost.

Exergy, a thermal metric that denotes the potential to drive changes, is a good measurement to quantify the life cycle change incurred by installing amine absorption equipment. This study builds two power plant models based on NETL baseline case 9 and case 10, with an emphasis on material cost. Major equipment of the amine absorption line, such as absorbers, strippers, blowers, compressors, pumps, heat exchangers, pipeline, etc. are modeled in detail. A life cycle exergy calculation method based on the Ecoinvent database is subsequently used to calculate the life cycle exergy from these two power plant models.

It is discovered that lifetime exergy efficiency will decrease significantly from 26.52% to 19.04% between the two cases. Most of the exergy change is due to the increased coal consumption, while the installment of carbon capture equipment only account for 0.015% of the exergy input change.

From a life cycle point of view, the amine absorption equipment itself, while large in size, is not very demanding in exergy input. The decreased electricity output, which mainly goes to fuel the additional reboiler heat duty, should require more attention.


Deidre WolffAidan Duffy and Geoff Hammond

Integrated Quantitative Uncertainty Analysis in LCA studies- A Case Study Analysis

ABSTRACT. Although the importance of uncertainty analysis in Life Cycle Assessment studies is well-known, a methodological procedure to identify, integrate and quantify the error propagation throughout the analysis is still lacking. This study presents an uncertainty classification methodology, along with an integrated, quantitative analysis as a best practice example for uncertainty quantification.

Three case studies have been chosen to apply the uncertainty methodology; the first being a simple product system-an electric kettle, the second a more complex product system-an apartment complex, and lastly, a complex fluctuating market- the production of electricity in Ireland. These three LCA case studies require various amounts of data, making uncertainty quantification more complex. Comparisons of the application of the uncertainty analysis methodology to each case study are made.


Ikechukwu NwaneshiuduIndroneil Ganguly and Francessa Pierobon

Environmental Assessment of Mild Bisulfite (MBS) Pretreatment of Forest Residues into Ethanol for Biofuel Production

ABSTRACT. BACKGROUND: Ethanol production via biochemical conversion of cellulosic forest residuals requires critical conversion steps; including biomass collection and pre-processing, pretreatment, enzyme production, enzymatic hydrolysis, and fermentation. Mild Bisulfite (MBS) pretreatment (a variant of SPORL pulping) is an emerging option for the breakdown and subsequent processing of biomass towards fermentable sugars. An environment assessment of the entire process (biomass-to-ethanol) using this technique is critical to discern its future sustainability in the ever-changing biofuels landscape. CONCLUSIONS: The results reveal that global warming and eutrophication were greatly impacted by enzyme production and the preparation/transport of biomass within the MBS process towards cellulosic sugar. This work, which singly highlights the impacts of ethanol production from cellulosic materials, will be critical in informing the growing biofuels literature.


Laurence Wright and Paul Wright

Think Global, Drink Local: An LCA of Microbrewing

ABSTRACT. The brewing industry has experienced a renaissance in recent decades, with the number of micro- and craft- brewers increasing rapidly. Campaigns and initiatives, for example, the UK based CAMRA (Campaign for Real Ale) ‘LocAle’ scheme (highlighting local and ‘green’ produce), have helped drive the increasing demand. Whilst exact definitions for “micro-” or “craft-“ breweries vary, typically they are small scale independently owned breweries using traditional brewing methods. Typically they adopt a different approach to marketing and distribution in order to compete in an extremely competitive market, with an emphasis on flavour, quality, and individuality. Despite the relative simplicity – inputs of malt, hops, water, yeast and energy – of the craft beer manufacturing process opportunities exist for brewers to enhance sustainability – environmentally, socially and economically. To realise these opportunities breweries must first examine the supply-chain, manufacturing, and distribution processes to establish points of impact, compare alternative possibilities. This poster will present the results of an applied research project investigating the environmental sustainability of the micro-brewing industry in the UK. The geographic and economic extent of the micro-brewing industry in the UK is first explored. Subsequently LCA results from case-study micro-breweries are used to compare the full range and temporal/spatial extent of environmental impacts resulting from the micro-brewing process.


Amir Behzad BazrgarAfshin SoltaniAlireza KoochekiEbrahim Zeinali and Alireza Ghaemi

Comparative life cycle assessment of traditional and mechanized sugar beet production in Iran

ABSTRACT. Agricultural production is not very well investigated in Iran environmentally and LCA has attracted very little attention. The aim of this study was introducing LCA and its application as well as assessment of sugar beet life cycle in different cropping systems in Iran. For this purpose the cradle to gate LCI was developed from 93 sugar beet farms grouped into mechanized, semi-mechanized and traditional cropping systems in the east of Iran. Field operations and agricultural machinery data were obtained from Ecoinvent [1] and adjusted to Iran condition before use. Modeling of mixed electricity production was carried out using Simapro 7.3.0. [2]. Different techniques were used to calculate field emissions [3]. Heavy metal emissions to water and soil were assessed by a simple annual input output balance by SALCA-heavy metal [4]. Phosphorous based emissions due to the application of fertilizers were calculated according to Prasuhn [5]. The dynamics of available nitrogen in the soil were modelled using SUNDIAL [6] and nitrous oxide emissions were estimated using an adapted IPCC method (4). Nitrogen oxides emissions and release of carbon dioxide after urea application were estimated from the emissions of N2O according to Nemecek and Kagi [7]. EPD, V 1.03 system of SEMC [8] used for LCIA. The results showed 489 kg CO2-equivalent, 83 mg CFC-11 -equivalent, 0.33 kg C2H4-equivalent, 2.2 kg SO2-equivalent, 0.64 kg PO43–equivalent, 7987 MJ-equivalent in global warming, ozone layer depletion, photochemical oxidation, acidification, eutrophication, non-renewable energy demand impacts categories per metric ton of sugar beets respectively. environmental impacts of all mechanized sugar beet farms in all impact categories was lower than the total average of different systems while these impacts was larger than average in 57 and 33 percent of traditional and semi-mechanized farms respectively. Contribution analysis showed that irrigation, direct emissions from field and production of chemical fertilizers had the highest contribution on environmental impacts of sugar beet farms and should be reviewed to improve environmental performance of sugar beet production in Iran. Results indicated that environmental impacts for one ton sugar beet production in Iran were between 2 and 40 times more than the Swiss condition. Use of LCA is hindered by the lack of LCI data on agricultural inputs in countries such as Iran. This study is an attempt to develop a comprehensive life cycle inventory and show its use on LCA of sugar beet production in Iran. These results also show the possibility of reducing environmental impacts to enhance both environmental and economical sustainability in Iranian sugar beet production system.


1. Ecoinvent Centre, 2010. Ecoinvent data v1.3, Final reports ecoinvent 2006 No. 1–15, Swiss Centre for Life Cycle Inventories, Dübendorf, 2006, CD-ROM. 2. PRé Consultants, 2011. SimaPro Database Manual-Methods library. Available online at 3. Bazrgar, A. B., Soltani, A., Koocheki, A., Zeinali, E., and Ghaemi,A., 2011. Environmental emissions profile of different sugar beet cropping systems in East of Iran, African Journal of Agricultural Research Vol. 6(29), pp. 6246-6255 4. Nemecek, T., Erzinger, S., 2005. Modeling Representative life cycle inventories for Swiss arable crops. Int. J. LCA 10(1) 1-9 5. Prasuhan, V., 2006. Erfassung der PO4-Austrage fur die Okobilanzierung SALCA Phosphor. Agroscope Rekenholz Tanikon ART, 20p. Online at 6. Smith, J.U., Bradbury, N.J., Addiscott, T.M,. 1996. SUNDIAL: a PC-based system for simulating nitrogen dynamics in arable land. Agron. J. 88, 38–43. 7. Nemecek, T., Kagi, T., 2007. Life Cycle Inventories of Swiss and European Agricultural Production Systems. Final report ecoinvent V2.0 NO. 15a. Agroscope ReckenholzTaenikon Research Station ART, Swiss Centre for Life Cycle Inventories, Zurich and Dubendorf, CH. 8. Swedish Environmental Management Council (SEMC), 2008. Introduction, intended uses and key programme elements for Environmental Product Declarations, EPD, available in:

Special Note: Presenter is from Department of Agronomy, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran


Runsheng Song and Sangwon Suh

Strategies for predictive chemical LCIs using Artificial Neural Network

ABSTRACT. Life Cycle Assessment (LCA) can provide critical insights during the design of a chemical to minimize its life cycle impacts. However, developing a life cycle inventory (LCI) of a chemical is time-consuming, while over 15,000 new chemicals are newly registered every day. Therefore, a method that enables a rapid analysis of life cycle environmental implications of a chemical at an early stage of its development when data is limited is needed. To bridge the data gap, we present here our work of estimating chemical’s life cycle inventory using machine learning techniques to based on its chemical structure.

The possibility of a using statistical model as an alternative approach to developing a complete LCI has been proved by some studies1. Here we extend the literature in two ways: (1) expanding the number of predictable flows in LCI and improved its accuracy using Artificial Neural Networks (ANNs); (2) targeting direct inputs rather than LCI or LCIA to minimize regional biases. ANNs is a nonlinearity regression model which has been applied in many areas. In this study, the model will be trained using existing chemical LCI data in the Ecoinvent database and corresponding chemical descriptors will be generated. First, we examine the feasibility of ANN in predicting elementary flows in chemical manufacturing using larger training datasets. Elementary flows in the training set may contain regional biases, distorting the input and output relationships inherent to the chemical structure. Therefore, second, we conduct a contribution analysis to identify significant intermediate flows in chemical manufacturing, and apply ANNs to predict the volume of such flows.

The results show strong correlations between chemical structures and LCI and/or LCIA results. For example, the model that predicts Cumulative Energy Demand (CED) has Median Relative Error (MRE) of about 30% on the test data set. More than 80% of the models have a MRE lower than 80%. However, to avoid the regional bias in predicting elementary flows, significant products (direct inputs to make a chemical) have been identified and a predictive model will be applied on those products at the next step. Uncertainty will also be incorporated into the model.

Using these techniques, LCIs and LCIAs of chemicals can be predicted using only the information on chemical structures, in the absence of any better quality data at hand. The techniques will be incorporated into the Chemical Life Cycle Collaborate (CLiCC) tool, which will be an online, open-access platform for rapid LCA of chemicals.

1. Wernet, Gregor, et al. “Bridging data gaps in environmental assessments: Modeling impacts of fine and basic chemical production.” Green Chemistry11.11 (2009): 1826-1831.


Jessica PerkinsSangwon SuhArturo Keller and Stefano Cucurachi

Improving the Practicability of LCA through Iterative Stakeholder Engagment

ABSTRACT. LCA research and use in industry are growing[1]; however, a prevalent shortcoming of LCA is the practicability to inform decision makers in policy, industry and at the consumer level[2].

The objective of the Chemical Life Cycle Collaborative (CLiCC) is to use predictive tools to provide a full LCA when large data gaps are prohibitive to understanding the impact of a chemical. The CLiCC tool will be an open-access, rapid-response method for generating LCA results for chemicals. To improve the practicability, CLiCC is engaging potential users through an iterative communication process, and the tool will be pilot tested with CLiCC’s industry and regulatory collaborators.

The first step in improving the usability of the CLiCC tool was to identify the stakeholders. These include ten industry partners from upstream and downstream companies in various sectors, as well as US EPA and California DTSC[3]. A project launch workshop and interviews helped establish a list of the tool’s potential applications, each with a profile characterizing the user. This information is guiding development of CLiCC’s individual modules (inventory estimation, release, fate & transport) as well as the overall tool architecture. Quarterly updates and additional interviews have identified other areas for development, including stochastic representation of uncertainty and provision of supporting data sources for individual tool outputs. Early-stage pilot tests of individual modules are identifying methods to improve usability that otherwise may not have been discovered until the tool’s development was complete.

As LCA becomes a more mainstream decision-making tool, it is critical to develop methods to improve usability. The approach adopted by CLiCC reduces the distance between LCA developers and decision-makers. Presented here are current findings from CLiCC’s stakeholder engagement process and an outline of how this knowledge transfer guides the continual development of the CLiCC tool to improve its practicability.

Masanet, E.; Chang, Y. Who Cares About Life Cycle Assessment? Journal of Industrial Ecology 2014, V18, 787–791.

Zamagni, A.; Masoni, P.; Buttol, P.; Raggi, A.; Buonamici, R. Finding Life Cycle Assessment Research Direction with the Aid of Meta-Analysis. Journal of Industrial Ecology 2012, V16, S39-S52.

Department of Toxic Substance Control (DTSC)


Yuwei Qin and Sangwon Suh

Reducing the computation time of LCI Uncertainty Assessment

ABSTRACT. Adequate quantitative uncertainty analysis in LCA is still an unresolved issue. However, the latest version of the Ecoinvent database v3.1 does not include quantitative uncertainty values at the LCI level due to the high computational power required.

We use Monte Carlo simulation (MCS) to quantify stochastic uncertainty of unit process data. While MCS does not need a complex mathematical framework, attributing uncertainty ranges to unit process data from the Ecoinvent v3.1 requires intensive computation and large memory space. This work solves the challenge of using MCS for LCI uncertainty propagation involving large-scale unit process data and intensive computational requirements. Our study shows that parallel programming with large memory can reduce the computation time by a factor of 1000.

After a sufficiently high number of simulations is completed, the output of the uncertainty analysis of LCI data is stored in the similar format as Ecoinvent v2 with stochastic values that describe the probability distribution generated by the MCS.

This development of an LCI database with quantitative uncertainty information can support LCA users to generate further uncertainty analysis on LCA and aid in making informed sustainable decisions.


Lukasz LelekJoanna Kulczycka and Anna Lewandowska


ABSTRACT. This paper explores the possibility of Life Cycle Assessment (LCA) application as a tool supporting Strategic Environmental Assessment (SEA). Different scenarios of electricity generation structure according to Polish Energy Policy by 2030 were assessed and quantified (comparing the current energy generation structure with various approaches to diversification of the electricity generation structure such as increase use of renewable energy sources, introduction of nuclear energy). The environmental performance of these scenarios was compared using LCA, which takes into account direct and indirect environmental impact, i.e. from minerals extraction to waste management. Using a life cycle perspectives is important when comparing different energy generation structures, particularly in the case of Poland where the current structure of energy generation is based mainly on hard coal and brown coal (approx. 90%). To compare the total cradle-to-gate environmental impact the functional unit was defined as 1TJ of electricity generated, based on release of final energy from a power plant. The Impact2002+ v2.05 method was chosen, as it proposes a feasible implementation of a combined midpoint/damage category method, for example including both global warming (midpoint) and climate change (damage). The inventory data were collected from Polish statistics and the Ecoinvent database (v.2.2) The LCA results show that the highest environmental impact of electricity generation in Poland is related mainly with emission to the air reflected in global warming and respiratory inorganic impact categories. The significant decrease in global warming (more than 20% lower in comparison to reference scenario 2006) could be achieved in 2020 when coal will be replaced by renewable (19,3% in total structure) and nuclear energy (6,7%). Diversification in energy production can result in fulfilling the Polish obligation connected with the CQ2eq reduction per the Kyoto protocol. By introducing LCA it was proved that overall environmental impact of energy sector will be also reduced. It was found that LCA methodology can support the assessment of different scenarios presented in planning documents by determining ecological effects based on quantified impact.


Burcin Atilgan and Adisa Azapagic

Life Cycle Assessment of Electricity Scenarios to 2050: The Case of Turkey

ABSTRACT. Being a party to the Kyoto Protocol, Turkey is keen to reduce the GHG and other emissions. It is, therefore, important that Turkey identifies possible future electricity options, if climate change and other environmental impacts are to be curbed. This work focuses on electricity and considers 15 scenarios up to 2050 in Turkey including business as usual (BAU) and different carbon reduction targets scenarios to estimate the related life cycle environmental impacts. All life cycle stages are considered, including extraction, processing and transport of fuels and raw materials, plant construction, operation and decommissioning. 14 technologies are considered including fossil-fuel technologies with and without CCS, nuclear and a range of renewable options. For most technologies, future technological improvements have been taken into account, based on projections by various sources. The findings indicate that continuing with BAU would increase up to three times the current annual life cycle GHG emissions. Switching from the current mix to renewables (with a contribution up to 79%) and nuclear (up to 30%) would lead to a reduction of seven impacts such as acidification and eutrophication compared to the current situation. This is despite the fact that the electricity demand is assumed to increase four-fold, from the current 211 TWh to 852 TWh in 2050. The results indicate that per kWh of electricity generated, the electricity mixes assumed in all the scenarios are environmentally more sustainable than at present, including BAU; this is due to technology learning curves. The only exception to this is depletion of elements which increases up to four times on today’s value because of the need to build new plants. However, due to a large increase in demand over the years, the total impacts increase up to 70 times. Therefore, technological improvements on their own are not sufficient and must be coupled with reducing the demand for a more sustainable future electricity system in Turkey. This the first attempt at assessing the life cycle environmental impacts of future scenarios for Turkey aiming to inform on the impacts and the hotspots to help improve the environmental performance of the electricity sector in the future.