AI News, Artificial Intelligence for Computational Sustainability: A Lab Companion/Machine Learning for Prediction/Regression and Ecological Footprints

Artificial Intelligence for Computational Sustainability: A Lab Companion/Machine Learning for Prediction/Regression and Ecological Footprints

Global Footprint Network, 2012) is the amount of land (e.g., in hectares) that is needed to sustain indefinitely, without degradation, a process or entity, ranging in scale from (manufacture, use, and disposal of) individual artifacts to cities, nations and the world’s human population.

shown however that different calculators can give very different estimates of CO2 for the same individual, and this lack of consistency, and an accompanying lack of transparency in calculator calculations, can damage public perceptions of the reliability of these calculators, and therefore diminish the effectiveness of these tools for purposes of behavior and policy change.

2) With a data set, where each datum is a feature vector composed of lifestyle (observational data) values and calculator (model) values, students would run machine learning experiments, using these procedures or any of many possible variants.

Issues of dynamical systems, knowledge representation, other areas of computation and environmental science, all make ecological footprint estimation a rich area for AI and domain science research and education that far exceeds the limits of machine learning exercises that have just been outlined.

Life Cycle Databases

A summary of each source - including data formats, accessibility, content, and transparency information - is available by clicking on the database link.

Data should also be evaluated using the data quality indicators described in the Corporate Value Chain (Scope 3) Accounting and Reporting Standard (chapter 7) and the Product Life Cycle Accounting and Reporting Standard (chapter 8) and selecting the highest quality data available in the context of business objectives and the principles of relevance, completeness, consistency, transparency, and accuracy.

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