industrial-ecology

There are 15 repositories under industrial-ecology topic.

  • IndEcol/Dashboard

    A collection of open source projects relevant for industrial ecology practitioners, hosted on GitHub and beyond

    14654039

  • MaximeAgez/pylcaio

    A Python class to hybridize lifecycle assessment (LCA) and environmentally extended input-output (EEIO) databases.

    Language:Python38247

  • IndEcol/IE_data_commons

    Code and documentation for a commons of structured industrial ecology data

    Language:Python2311252

  • CIRAIG/OpenIO-Canada

    Module to create symmetric Environmentally Extended Input-Output tables for Canada.

    Language:Python206296

  • nheeren/material_intensity_db

    Material intensity database for research on infrastructure systems

    Language:TeX2010310

  • IndEcol/OpenScience

    Public repository documenting the development of open science procedures and structures for industrial ecology, loosely connected to the Data Transparency Task Force (DTTF) of the International Society for Industrial Ecology (ISIE)

    131092

  • CIRAIG/IE_ML_mapping

    Module to automate mapping of classifications based on machine learning word association.

    Language:Python6110

  • jbnsn/A-most-simple-implementation-of-Kitzes-2013-in-Python

    A most simple implementation of Kitzes (2013) in Python.

    Language:Python4200

  • IndEcol/IEDC_tools

    A collection of tools to interact with the Industrial Ecology Data Commons project

    Language:Python3851

  • jbnsn/Disaggregating-input-output-models-with-incomplete-information

    Implementation of "Disaggregating input-output models with incomplete information" by Lindner et al. (2012) in Python.

    Language:Python3102

  • majeau-bettez/LiSET

    Clustering tools for the Lifecycle Screening of Emerging Technology (LiSET) framework

    Language:Python3201

  • andrea-ballatore/litter-dynamics

    Urban litter, such as cans, packaging, and cigarettes, has significant impacts and yet little is known about its spatio-temporal distribution, with little available data. In contexts of data scarcity, crowdsourcing provides a low-cost approach to collecting a large amount of geo-referenced data. We consider 1.7 million litter observations in the Netherlands, collected by the crowdmapping project Litterati. First, we analyze the biases of this data at the province and municipality level. Second, in a local case study with high-quality data (the city of Purmerend), we investigate the spatial distribution of urban litter and the points of interest that attract it. This study’s findings can support both the crowdmapping process, steering volunteers efforts, and policy-making to tackle litter at the urban level.

    1201

  • jbnsn/RMRIO-database-py-import

    Python import of the RMRIO-database from the *.mat files

    Language:Python1100

  • CIRAIG/Regioinvent

    Python class regionalizing processes from the ecoinvent database using trade date from the UN COMTRADE database and common sense.

    Language:Python0121

  • jbnsn/Import-EXIOBASE-3rx-in-Python

    Import the EXIOBASE 3rx database in Python from a *.mat file

    Language:Python0110