Resources, Conservation & Recycling

Research data

Code and data used in: Davenport, M.L., Qi, D. & Roe, B.E. (2019). "Food-related routines, product characteristics, and household food waste in the United States: A refrigerator-based assessment ". Submitted to Resources, Conservation and Recycling. 1. Analysis Stata (.do) file 2. State of the American Refrigerator Survey: Refrigerator Inventory Data

Material flow analysis for lithium in the United States from 1910 to 2016. The dataset contains information on extraction and trade of: lithium minerals and brines, lithium chemicals (intermediate products), lithium products (end use sector), accumulated in-use lithium, and lithium waste.

Surveyed data from China urban residents on their environmental attitude, personal norm, perceived efficacy, and behavior, together with their encountering behavioral cost components.

A questionnaire was applied to collect data in a chemical industry. The respondents included a population of managers, operational and administrative support workers. The following research questions arise: • How do you predict the energy saving behavior in an industrial installation, considering not only the intention to act? • Which latent variables could predict behavior toward energy saving using structural equation modeling better? • Which external factors such as stress, ergonomics, and complexity could influence energy saving behavior?

Results of a stratified door-to-door solid waste characterization performed in the city of Tandil, Buenos Aires (Argentina).

Data for: Environmental knowledge, collaborative economy and responsible consumption in the context of second-hand perinatal and infant clothes in Spain

Content of Supplementary Materials 1 Sheet A Prerequisites and assumptions 2 Sheet B Material Flow Analysis (MFA) 3 Sheet C Life cycle inventory 4 Sheet D Life cycle impact assessment 5 Sheet E Sensitivity analysis-LCA 6 Sheet F Economic performance 7 Sheet G Integrated analysis (w-LCC) 8 Sheet H Externality costs 9 Sheet I Supplementary tables and figures

Please note that uploaded files are actually on line supplementary material, which could not be attached elsewhere

The provided data set provides the basis for the scenario modeling for the European automotive sector for a set of scenarios for the time period 2010 - 2050. The python code reads in scenario files, and allows to choose specific parameters, such as recycling rate and reuse rate for specific components to model the material flows over time and calculate the Relative Statistical Entropy values of the system stages of the material flow system that is provided in the paper.