• Energy Research

Abstract The targeted transition towards an electricity system with low or even negative greenhouse gas emissions affords a chance to address other environmental concerns as well, but may potentially have to adjust to the limited availability of assorted non-fossil resources. Life cycle assessment (LCA) is widely recognized as a method appropriate to assess and compare product systems taking into account a wide range of environmental impacts. Yet, LCA could not inform the latest assessment of co-benefits and trade-offs of climate change mitigation by the Intergovernmental Panel on Climate Change due to the lack of comparative assessments of different electricity generation technologies addressing a wide range of environmental impacts and using a consistent set of methods. This paper contributes to filling this gap. A consistent set of life cycle inventories of a wide range of electricity generation technologies is assessed using the Recipe midpoint methods. The life-cycle inventory modeling addresses the production and deployment of the technologies in nine different regions. The analysis shows that even though low-carbon power requires a larger amount of metals than conventional fossil power, renewable and nuclear power leads to a reduction of a wide range of environmental impacts, while CO 2 capture and storage leads to increased non-GHG impacts. Biomass has relatively modest co-benefits, if at all. The manufacturing of low-carbon technologies is important compared to their operation, indicating that it is important to choose the most desirable technologies from the outset.

Abstract The targeted transition towards an electricity system with low or even negative greenhouse gas emissions affords a chance to address other environmental concerns as well, but may potentially have to adjust to the limited availability of assorted non-fossil resources. Life cycle assessment (LCA) is widely recognized as a method appropriate to assess and compare product systems taking into account a wide range of environmental impacts. Yet, LCA could not inform the latest assessment of co-benefits and trade-offs of climate change mitigation by the Intergovernmental Panel on Climate Change due to the lack of comparative assessments of different electricity generation technologies addressing a wide range of environmental impacts and using a consistent set of methods. This paper contributes to filling this gap. A consistent set of life cycle inventories of a wide range of electricity generation technologies is assessed using the Recipe midpoint methods. The life-cycle inventory modeling addresses the production and deployment of the technologies in nine different regions. The analysis shows that even though low-carbon power requires a larger amount of metals than conventional fossil power, renewable and nuclear power leads to a reduction of a wide range of environmental impacts, while CO 2 capture and storage leads to increased non-GHG impacts. Biomass has relatively modest co-benefits, if at all. The manufacturing of low-carbon technologies is important compared to their operation, indicating that it is important to choose the most desirable technologies from the outset.

An in-depth review of five major integrated assessment models from an industrial ecology perspective reveals differences between the fields regarding the modelling of linkages in the industrial system. Technology-rich integrated assessment models (IAMs) address possible technology mixes and future costs of climate change mitigation by generating scenarios for the future industrial system. Industrial ecology (IE) focuses on the empirical analysis of this system. We conduct an in-depth review of five major IAMs from an IE perspective and reveal differences between the two fields regarding the modelling of linkages in the industrial system, focussing on AIM/CGE, GCAM, IMAGE, MESSAGE, and REMIND. IAMs ignore material cycles and recycling, incoherently describe the life-cycle impacts of technology, and miss linkages regarding buildings and infrastructure. Adding IE system linkages to IAMs adds new constraints and allows for studying new mitigation options, both of which may lead to more robust and policy-relevant mitigation scenarios.

An in-depth review of five major integrated assessment models from an industrial ecology perspective reveals differences between the fields regarding the modelling of linkages in the industrial system. Technology-rich integrated assessment models (IAMs) address possible technology mixes and future costs of climate change mitigation by generating scenarios for the future industrial system. Industrial ecology (IE) focuses on the empirical analysis of this system. We conduct an in-depth review of five major IAMs from an IE perspective and reveal differences between the two fields regarding the modelling of linkages in the industrial system, focussing on AIM/CGE, GCAM, IMAGE, MESSAGE, and REMIND. IAMs ignore material cycles and recycling, incoherently describe the life-cycle impacts of technology, and miss linkages regarding buildings and infrastructure. Adding IE system linkages to IAMs adds new constraints and allows for studying new mitigation options, both of which may lead to more robust and policy-relevant mitigation scenarios.

Abstract Cumulative energy demand (CED) estimates from life cycle assessments (LCAs) are increasingly used to determine energy return on investment (EROI), but the difference in indicators can lead to a misclassification of energy flows in the assessment. The core idea of EROI is to measure the relation of energy diverted from society to make energy available to society. CED, on the other hand, includes forms of energy that are not appropriated by society, such as fugitive methane emissions from oil wells as well as losses of heating value of coal during transport and storage. Such energy forms should be excluded from EROI; failure to do so leads to results that are inconsistent with the intention of EROI and potentially misleading. We demonstrate how this problem is at least partially rectifiable by adopting consistent energy accounting, but also note that among the energy flows not appropriated by society occurring in CED, not all flows can easily be removed. Further, we point to inconsistencies in heating value assumptions in a widely used database that have misled analysts. Finally, we argue that the differential weighting of primary energy forms in published CED-based EROI work is unsubstantiated and should be reconsidered.

Abstract Cumulative energy demand (CED) estimates from life cycle assessments (LCAs) are increasingly used to determine energy return on investment (EROI), but the difference in indicators can lead to a misclassification of energy flows in the assessment. The core idea of EROI is to measure the relation of energy diverted from society to make energy available to society. CED, on the other hand, includes forms of energy that are not appropriated by society, such as fugitive methane emissions from oil wells as well as losses of heating value of coal during transport and storage. Such energy forms should be excluded from EROI; failure to do so leads to results that are inconsistent with the intention of EROI and potentially misleading. We demonstrate how this problem is at least partially rectifiable by adopting consistent energy accounting, but also note that among the energy flows not appropriated by society occurring in CED, not all flows can easily be removed. Further, we point to inconsistencies in heating value assumptions in a widely used database that have misled analysts. Finally, we argue that the differential weighting of primary energy forms in published CED-based EROI work is unsubstantiated and should be reconsidered.

Abstract The fields of life cycle assessment (LCA) and integrated assessment (IA) modelling today have similar interests in assessing macro-level transformation pathways with a broad view of environmental concerns. Prevailing IA models lack a life cycle perspective, while LCA has traditionally been static- and micro-oriented. We develop a general method for deriving coefficients from detailed, bottom-up LCA suitable for application in IA models, thus allowing IA analysts to explore the life cycle impacts of technology and scenario alternatives. The method decomposes LCA coefficients into life cycle phases and energy carrier use by industries, thus facilitating attribution of life cycle effects to appropriate years, and consistent and comprehensive use of IA model-specific scenario data when the LCA coefficients are applied in IA scenario modelling. We demonstrate the application of the method for global electricity supply to 2050 and provide numerical results (as supplementary material) for future use by IA analysts.