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Even though concerns about adverse distributional implications for the poor are one of the most important political challenges for carbon pricing, the existing literature reveals ambiguous results. For this reason, we assess the expected incidence of moderate carbon price increases for different income groups in 87 mostly low- and middle-income countries. Building on a consistent dataset and method, we find that for countries with per capita incomes of below USD 15,000 per year (at PPP-adjusted 2011 USD) carbon pricing has, on average, progressive distributional effects. We also develop a novel decomposition technique to show that distributional outcomes are primarily determined by differences among income groups in consumption patterns of energy, rather than of food, goods or services. We argue that an inverse U-shape relationship between energy expenditure shares and income explains why carbon pricing tends to be regressive in countries with relatively higher income. Since these countries are likely to have more financial resources and institutional capacities to deal with distributional issues, our findings suggest that mitigating climate change, raising domestic revenue and reducing economic inequality are not mutually exclusive, even in low- and middle-income countries.
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.