Abstract Here we present a high-resolution approach for tracking material flows and energy use of products throughout their life cycles, focusing on passenger vehicles and residential buildings. We estimate future changes in material flows and operational energy use due to increased yields, light-weight designs, material substitution, increased service efficiency, extended service life, and increased reuse and recycling. Main Text comprised of 20 countries/regions six major climate-relevant materials (aluminium, cement, copper, plastics, steel, wood) for the period 2016 - 2060.

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Interesting warnings These kits are only designed for human use． Use a separate test kit for each person. You can only use each item in　the test kit once. Do not re-use the items． Do not eat or drink for at least 30 minutes before doing the test to reduce the risk of spoiling the test. What you need to do Prepare your test area and check your test kit contents Set up your test Take your swab sample Process the swab sample Read your result Report your result Safely dispose of your test kit Store the test kit at room temperature or in a cool dry place (2°C

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I received my PhD scholarship from China in July 2020 when the covid epidemic is spreading around the world. After a long holiday for me, I began my work in industrial ecology at Leiden University in February 2021. My family Seriously I am not a foodie(eat simple while working) but I Do like food and cooking. In my spare time (really scarce), I will dive into my kitchen and make pasta.

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Make Your Website Social Media Card-able on Twitter, Facebook and More See the original link We all want our websites to be shared on Twitter, Facebook and all the different channels of social media. Whether they are our own websites that have our blogs and profiles or websites we built for other people. We all value those valuable clicks, so as a means of seduction to make our links clickable, we need to make them look attractive and pretty.

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Identify climate change problems where existing gaps can be filled by machine learning. https://arxiv.org/pdf/1906.05433v2.pdf Highlights Industry ML demonstrates considerable potential for reducing industrial GHG emissions under the following circumstances. when there is enough accessible, high-quality data around specific processes or transport routes. when firms have an incentive to share their proprietary data and/or algorithms with researchers and other firms. When aspects of production or shipping can be readily fine-tuned or adjusted, and there are clear objective functions.

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