• Energy Research

AbstractDespite being renewable, photovoltaic energy is not burden‐free, since energy and materials are necessary to manufacture, maintain, dismantle, and recycle photovoltaic systems. Over its life cycle, the assessed carbon footprint of silicon‐based photovoltaic energy published in the literature often ranges from 40 to 110 gCO2eq/kWh. However, most of these estimations rely on life cycle inventory (LCI) data that represent the early‐stage performance of the photovoltaic industry. Indeed, collecting LCI data is time‐consuming and practitioners often reuse existing outdated data, which becomes problematic as the photovoltaic industry has been rapidly and significantly evolving. This analysis relies on the parametrization of existing LCI data to better account for the progress already accomplished by the photovoltaic industry. A Life Cycle Assessment (LCA) model, called PARASOL_LCA, is thus developed. The results of the analysis highlight that the use of outdated LCI data leads to an overestimation of environmental impacts of photovoltaic energy by a factor of 2 or even more for the best current available technologies. The analysis also shows that PARASOL_LCA, with its numerous parameters, can also serve to assess the environmental performance of prospective photovoltaic technologies and to identify impact reduction levers through sensitivity analysis.

The invasive brown seaweed Sargassum muticum (Yendo) exhibits a significant content of phenolic compounds, polysaccharides and fucoxanthin, with potential biological activities. In this study, four valorization strategies for S. muticum biomass were compared under a life cycle perspective. Depending on the alternative, three products were obtained: sodium alginate, antioxidant extract and fucoxanthin-containing extract. Regardless of the approach, the combined extraction of alginate and antioxidant from wet algae constituted the most efficient scenario. Among the stages, supercritical extraction of fucoxanthin and non-isothermal autohydrolysis were identified as the major environmental burdens due to electricity consumption. Although changes in product distribution fairly affected the environmental impacts of the scenarios, the single extraction of antioxidant fraction and the integral valorization to obtain fucoxanthin, alginate and antioxidant were only competitive when considering a functional unit based on the value of the products through an economic allocation approach instead of the amount of valorized algae.

Buildings are responsible for 40% of total final energy consumptions in Europe. Numerous bottom-up models were recently developed to support local authorities in assessing the energy consumption of large building stocks and reduction potentials. However, current models rarely consider uncertainty associated to building usage and characteristics within the stock, resulting in potentially biased results. This study presents a generic model simplification approach using uncertainty propagation and stochastic sensitivity analysis to derive fast simplified (surrogate) models to estimate the current building stock energy use for improved urban planning. The methodology includes an engineering-based energy model as input to global sensitivity analysis (GSA) using the elementary effects (EE) screening and Sobol’ method for key parameters identification and regression analysis to derive simplified models for entire building stocks. The application to the housing stock of Esch-sur-Alzette (Luxembourg) showed that the parameters explaining most of the variability in final energy use for heating and domestic hot water are floor area, set-point temperature, external walls U-values, windows and heating system type. Results of the simplified models were validated against measured data and confirmed the validity of the approach for a simple yet robust assessment of the building stock energy use considering uncertainty and variability.

In the energy transition context, private decision-makers and policymakers must choose between different energy scenarios for process improvement and territorial planning. Hence, implications of the development of these technologies need to be objectively analysed to ensure their alignment with energy transition objectives. Life Cycle Analysis is nowadays one of the most appropriate methods to assess impacts related to products and services. While LCA application for environmental assessment is already consolidated, social aspects of sustainability are not systematically evaluated yet. For application to social impacts, the UNEP/SETAC Guidelines propose a framework to conduct Social-LCA. However, its application to specific systems is still under development. Most frequently, social issues included in sustainability analyses on electricity generation sector concern number of employment or health and safety issues. Moreover, few studies in the literature present a rigorous approach to account for social impacts affecting different stakeholder categories, especially at territorial level. Offshore wind farm development by industrial requires accounting for many criteria related to territories’ issues and social acceptance.This study presents a S-LCA method based on industrial perception for defining the priority social aspects and related stakeholders in the context of offshore wind farms. It is conducted in the framework of project which relies on close collaboration with key industrial partners. Since LCA results are especially suitable for comparative purposes, the S-LCA method must be designed by considering the social issues not only at offshore wind farm scale, but also at power generation sectorial scale. The identified ranking of social issues will be used to identify where the S-LCA study will focus on in the next steps.

AbstractSolar photovoltaics (PV) is the second largest source of new capacity among renewable energies. The worldwide capacity encompassed 135 GW in 2013 and is estimated to increase to 1721 GW in 2030 and 4674 GW in 2050, according to a prospective high‐renewable scenario. To achieve this production level while minimizing environmental impacts, decision makers must have access to environmental performance data that reflect their high spatial variability accurately. We propose ENVI‐PV (http://viewer.webservice‐energy.org/project_iea), a new interactive tool that provides maps and screening level data, based on weighted average supply chains, for the environmental performance of common PV technologies. Environmental impacts of PV systems are evaluated according to a life cycle assessment approach. ENVI‐PV was developed using a state‐of‐the‐art interoperable and open standard Web Service framework from the Open Geospatial Consortium (OGC). It combines the latest life cycle inventories, published in 2015 by the International Energy Agency (IEA) under the Photovoltaic Power Systems Program (PVPS) Task 12, and some inventories previously published from Ecoinvent v2.2 database with solar irradiation estimates computed from the worldwide NASA SSE database. ENVI‐PV is the first tool to propose a worldwide coverage of environmental performance of PV systems using a multi‐criteria assessment. The user can compare the PV environmental performance to the environmental footprint of country electricity mixes. ENVI‐PV is designed as an environmental interactive tool to generate PV technological options and evaluate their performance in different spatial and techno‐economic contexts. Its potential applications are illustrated in this paper with several examples. Copyright © 2016 John Wiley & Sons, Ltd.

AbstractMicroalgae can serve as a highly productive biological feedstock for fuels and chemicals. The lipid fraction has been the primary target of research, but numerous assessments have found that valorization of co‐products is essential to achieve economic and environmental goals. The relative proportion of co‐products depends on the biomolecular composition of algae at the time of harvesting. In the present study, the productivity of lipid, starch, and protein fractions were shown through growth experiments to vary widely with species, feeding regime, and harvesting period. Four algae species were cultivated under nitrogen‐replete and ‐deplete conditions and analyzed at 3‐day intervals. Dynamic growth results were then used for life cycle assessment using the US Department of Energy's GREET model to determine optimal growth scenarios that minimize life cycle greenhouse gas (GHG) emissions, eutrophication, and cumulative energy demand (CED), while aiming for an energy return on investment (EROI) greater than unity. Per kg of biodiesel produced, C. sorokiniana in N‐replete conditions harvested at 12 days was most favorable for GHG emissions and CED, despite having a lipid content of <20%. N. oculata in N‐deplete conditions with a 12‐day harvesting period had the lowest life cycle eutrophication impacts, driven by efficient nutrient cycling and valorization of microalgal protein and anaerobic digester residue co‐products. Results indicate that growth cycle times that maximize a single fraction do not necessarily result in the most favorable environmental performance on a life cycle basis, underscoring the importance of designing biorefinery systems that ‐simultaneously optimize for lipid and non‐lipid fractions. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd

With the massive growth of the global capacity of photovoltaics (PV) over the last decade, the PV waste is expected to increase dramatically in the near future. Having potential to reduce the use of raw materials and preserve natural resources, PV recycling is attracting more and more attention. This being said, the environmental impacts over the life cycle of PV technologies, including the end-of-life (EoL) stage, should be evaluated carefully. Life cycle assessment (LCA) is currently the most common methodology to assess the potential environmental impacts of a product throughout its entire life cycle. However, the modelling of recycling in LCA has always been a challenge and no consensus has yet been reached, since the treatment of recycling does not only involve an EoL management of waste, but also the production of recycled material. Perovskite on silicon tandem is a widely investigated emerging PV technology having the potential to overcome the power conversion efficiency (PCE) limit of the single-junction crystalline silicon technology. The EoL modelling seems more challenging in the case of emerging technologies for which the EoL is more uncertain than for established technologies. In this article, six common and important approaches of EoL modelling in LCA were applied to future perovskite/silicon tandem modules to analyze the effect of the different EoL modelling approaches on the LCA results. The aim was to identify the most suitable methodological approaches to account for recycling, when modelling the life cycle of PV modules. The environmental performance of perovskite/silicon tandem modules was assessed over their life cycle and expressed in terms of impacts per m2of module. After testing the six EoL modelling approaches and comparing the LCA results, the EoL modelling choice was found to lead to non-negligible differences. For example, in terms of climate change, the impact of the tandem modules ranges from 45 to 59 kg CO2-eq/m2. Among the six EoL modelling options, the approaches of simple cut-off and cut-off with economic allocation are more oriented towards the promotion of high rates of recycled material integrated as an input to the assessed product among industrial actors, while the approach of closed-loop allocation provides incentives to maximize the ratio of recycling at the EoL, regardless the initial ratio of recycled content within the product. Some approaches such as the circular footprint formula (CFF) tend to provide both incentives to increase the content of recycled input material in the manufacturing of the product and the recycling ratio at the EoL of such product. After applying the different alternatives, a set of recommendations to select the relevant EoL modelling approaches are provided: 1) the CFF is recommended as a representative approach due to its wide applicability, tending to provide an intermediate result and reflecting the characteristics of materials; 2) sensitivity analysis should be applied to check the robustness of the results, 3) the cut-off approach and the closed-loop allocation should be used at least for the sensitivity analysis.