Implementing circular economy (CE) principles is increasingly recommended as a convenient solution to meet the goals of sustainable development. New tools are required to support practitioners, decision-makers and policy-makers towards more CE practices, as well as to monitor the effects of CE adoption. Worldwide, academics, industrialists and politicians all agree on the need to use CE-related measuring instruments to manage this transition at different systemic levels. In this context, a wide range of circularity indicators (Cindicators) has been developed in recent years. Yet, as there is not one single definition of the CE concept, it is of the utmost importance to know what the available indicators measure in order to use them properly. Indeed, through a systematic literature reviewconsidering both academic and grey literature -55 sets of C-indicators, developed by scholars, consulting companies and governmental agencies, have been identified, encompassing different purposes, scopes, and potential usages. Inspired by existing taxonomies of eco-design tools and sustainability indicators, and in line with the CE characteristics, a classification of indicators aiming to assess, improve, monitor and communicate on the CE performance is proposed and discussed. In the developed taxonomy including 10 categories, C-indicators are differentiated regarding criteria such as the levels of CE implementation (e.g. micro, meso, macro), the CE loops (maintain, reuse, remanufacture, recycle), the performance (intrinsic, impacts), the perspective of circularity (actual, potential) they are taking into account, or their degree of transversality (generic, sector-specific). In addition, the database inventorying the 55 sets of Cindicators is linked to an Excel-based query tool to facilitate the selection of appropriate indicators according to the specific user's needs and requirements. This study enriches the literature by giving a first need-driven taxonomy of C-indicators, which is experienced on several use cases. It provides a synthesis and clarification to the emerging and must-needed research theme of C-indicators, and sheds some light on remaining key challenges like their effective uptake by industry. Eventually, limitations, improvement areas, as well as implications of the proposed taxonomy are intently addressed to guide future research on C-indicators and CE implementation.

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The global warming intensities of crop-based biofuels and fossil fuels differ not only in amount but also in their discharge patterns over time. Early discharges, for example, from market-mediated land use change, will have created more global warming by any time in the future than later discharges, owing to the slow decay of atmospheric CO 2 . A spreadsheet model of this process, BTIME, captures this important time pattern effect using the Bern CO 2 decay model to allow fuels to be compared for policy decisions on the basis of their real warming effects with a variety of user-supplied parameter values. The model also allows economic discounting of climate effects extended far into the future. Compared to approaches that simply sum greenhouse gas emissions over time, recognizing the physics of atmospheric CO 2 decay significantly increases the deficit relative to fossil fuel of any biofuel causing land use change.

This paper proposes a time correction factor (TCF) to properly account for the timing of land use change-derived greenhouse gas emissions in the biofuels life cycle. Land use change emissions occur at the outset of biofuel feedstock production, and are typically amortized over an assumed time horizon to assign the burdens of land use change to multiple generations of feedstock crops. Greenhouse gas intensity calculations amortize emissions by dividing them equally over a time horizon, overlooking the fact that the effect of a greenhouse gas increases with the time it remains in the atmosphere. The TCF is calculated based on the relative climate change effect of an emission occurring at the outset of biofuel feedstock cultivation versus one amortized over a time horizon. For time horizons between 10 and 50 years, the TCF varies between 1.7 and 1.8 for carbon dioxide emissions, indicating that the actual climate change effect of an emission is 70-80% higher than the effect of its amortized values. The TCF has broad relevance for correcting the treatment of emissions timing in other life cycle assessment applications, such as emissions from capital investments for production systems or manufacturing emissions for renewable energy technologies.

U.S. programs subsidize electric vehicles (EVs) in part to reduce greenhouse gas (GHG) emissions. We model a suite of life cycle GHG emissions considerations to estimate the GHG abatement potential from switching from an internal combustion engine vehicle (ICE) to an EV in the continental U.S. The GHG intensity of EVs hinges on the electricity and ambient temperature when charged and operated. Both have high spatial and temporal heterogeneity, yet are typically modeled inadequately or overlooked entirely. We calculate marginal emissions, including renewables, for electricity by region and test forecasted grid composition to estimate future performance. Location and timing of charging are important GHG determinants, but temperature effects on EV performance can be equally important. On average, EVs slightly reduce GHGs relative to ICEs, but there are many regions where EVs provide a decisive benefit and others where EVs are significantly worse. The forecasted grid shifts from coal towards renewables, improving EV performance; the GHG benefit per EV in western states is

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