“Someone’s sitting in the shade today because someone planted a tree a long time ago” —Warren Buffett. Research aim Building a detailed, spatially explicit, internationally trade linked GMRIO of food and energy systems at a ~5arc min(9km) resolution. Exploring current environmental and social footprints of these systems and the threats or opportunities presented by EU net-zero strategies. Incorporating net-zero pathways from the literature into the model from both production and consumption perspectives, and connecting land suitability maps for future renewable energy, mining, and agriculture potentials.

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Published:22 December 2020 Authors: Yuli Shan, Jiamin Ou, Daoping Wang, Zhao Zeng, Shaohui Zhang, Dabo Guan & Klaus Hubacek https://www.nature.com/articles/s41558-020-00977-5 Scenarios of global lockdown and effects on CO2 emissions Economic impacts model ARIO model：Adaptive Regional Input-Output Model can be used to analyse the disaster-induced influence on regional economy. The Leontief production function does not allow for substitution between inputs. Scenarios of lockdown Lockdown periods Strictness of the first lockdown period Strictness of future lockdown periods Labour loss dominates the economic loss in the model.

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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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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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