Process life cycle assessment (PLCA) is widely used to quantify environmental flows associated with the manufacturing of products and other processes. As PLCA always depends on defining a system boundary, its application involves truncation errors. Different methods of estimating truncation errors are proposed in the literature; most of these are based on artificially constructed system complete counterfactuals. In this article, we review the literature on truncation errors and their estimates and systematically explore factors that influence truncation error estimates. We classify estimation approaches, together with underlying factors influencing estimation results according to where in the estimation procedure they occur. By contrasting different PLCA truncation/error modeling frameworks using the same underlying input-output (I-O) data set and varying cut-off criteria, we show that modeling choices can significantly influence estimates for PLCA truncation errors. In addition, we find that differences in I-O and process inventory databases, such as missing service sector activities, can significantly affect estimates of PLCA truncation errors. Our results expose the challenges related to explicit statements on the magnitude of PLCA truncation errors. They also indicate that increasing the strictness of cut-off criteria in PLCA has only limited influence on the resulting truncation errors. We conclude that applying an additional I-O life cycle assessment or a path exchange hybrid life cycle assessment to identify where significant contributions are located in upstream layers could significantly reduce PLCA truncation errors. Keywords:industrial ecology input-output (I-O) analysis process life cycle assessment service sectors system boundary truncation error estimate Supporting information is linked to this article on the JIE website Conflict of interest statement: The authors have no conflict to declare.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Copyright© 2019 Inter-American Development Bank. This work is licensed under a Creative Commons IGO 3.0 Attribution NonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons org/!icenses/by-nc-nd/3 0/igo/ !ega!code) and may be reproduced with attribution to the IDB and for any non-commercial purpose, as provided below. No derivative work is allowed.Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the I DB's name for any purpose other than for attribution, and the use of I DB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license.Following a peer review process, and with previous written consent by the Inter-American Development Bank (IDB), a revised version of this work may also be reproduced in any academic journal, including those indexed by the American Economic Association's EconLit, provided that the IDB is credited and that the author(s) receive no income from the publication. Therefore, the restriction to receive income from such publication shall only extend to the publication's author(s). With regard to such restriction, in case of any inconsistency between the Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives license and these statements, the latter shall prevail. Note that link provided above includes additional terms and conditions of the license. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. AbstractEnergy subsidies account for about 7% of Ecuador's yearly public spending, or two thirds of the fiscal deficit. Removing these subsidies would yield clear economic and environmental benefits and help implement climate targets set in the Paris Agreement. However, expected adverse effects on vulnerable households can make reforms politically difficult. To inform policy design, we use household survey data from Ecuador in combination with augmented input-output data to assess the distributional impacts of energy subsidy reform. We find that in absolute terms energy subsidies benefit richer households more than poor ones. Relative to household income, subsidy removal without compensation would be regressive for diesel and LPG, progressive for gasoline...

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How to share responsibility for greenhouse gas emissions between consumers and producers is a highly sensitive question in international climate policy negotiations. Traditional 'Production-Based Accounting' (PBA), which assigns responisibility to the region where emissions are released, has frequently been challenged by 'Consumption-Based Accounting' (CBA) schemes that suggest that greenhouse gas emissions generated to produce traded goods and services should be attributed to their final consumers. PBA and CBA both lack a sound foundation in economic theory as they do not consider the economic benefits accruing to producers or consumers if carbon emissions do not carry a price that reflects their social costs. We build on well-established economic theory to derive how to share responsibility for trade-related emissions between producers and consumers and apply this novel approach for the most prominent bilateral trade relationships using multi-regional input-output data. We propose an 'Economic Benefit Shared Responsibility' (EBSR) scheme, in which China is attributed significantly higher responsibility for emissions than in CBA, while lower emissions and responsibility are attributed to both the US and the EU.

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The foundations of today’s societies are provided by manufactured capital accumulation driven by investment decisions through time. Reconceiving how the manufactured assets are harnessed in the production–consumption system is at the heart of the paradigm shifts necessary for long-term sustainability. Our research integrates 50 years of economic and environmental data to provide the global legacy environmental footprint (LEF) and unveil the historical material extractions, greenhouse gas emissions, and health impacts accrued in today’s manufactured capital. We show that between 1995 and 2019, global LEF growth outpaced GDP and population growth, and the current high level of national capital stocks has been heavily relying on global supply chains in metals. The LEF shows a larger or growing gap between developed economies (DEs) and less-developed economies (LDEs) while economic returns from global asset supply chains disproportionately flow to DEs, resulting in a double burden for LDEs. Our results show that ensuring best practice in asset production while prioritizing well-being outcomes is essential in addressing global inequalities and protecting the environment. Achieving this requires a paradigm shift in sustainability science and policy, as well as in green finance decision-making, to move beyond the focus on the resource use and emissions of daily operations of the assets and instead take into account the long-term environmental footprints of capital accumulation.

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