Search Metadata
This is the metadata for the main output directly produced by ITF Global Urban Passenger model.
–Provider–
Organization: International Transport Forum (ITF)
Contact Email: mallory.trouve@itf-oecd.org
Format: Other
–Measure–
Measure Description:
Resident and tourist passenger transport activity, systematized as the total dis-tance travelled by all people from a functionnal urban area, by trip distance category (6), by mode(18), by age category (5), by gender(2), in the city centre vs the suburbs.
Units: passenger-kilometre
–Dimension–
Resolution: 5-year step
Description:
Five year steps from 2015 to 2050 assumed to go from January 1st to December 31st.
–Dimension–
Resolution: 19 world regions [market], 187 countries [countr_code], 8971 macro Functional Urban Areas [city_name]
Description:
Markets defined at the ITF to keep homogenous socioeconomic and transport country caracteristics.
Countries as defined by ISO3 codes.
Functional Urban Areas (FUAs) defined bye the OECD-European Commission Cities in the World project, aggregated and completed with UN World Urbanization Prospects cities over 300 000 inhabitants when missing.
–Dimension–
Resolution: walk, bike, motorcycle, car, taxi, rail, metro, lrt, bus, brt, informal bus, three wheeler, scooter sharing, bike sharing, ride sharing, motorcycle sharing, car sharing, taxibus
Description:
Transport modes
–Dimension–
Resolution: 2 gender categories, 5 age categories
Description:
The 10 population categories are a combination of the 2 gender categories (female – 0 and male – 1), and 5 age categories (0-19, 20-34, 35-54, 55-69, 70+)
–Dimension–
Resolution: under 1km, 1-2.5km, 2.5-5km, 5-10km, 10-20km, over 20km
Description:
The 6 trip distance categories are each eassigned an average trip distance and the availability of a mode can be adjusted to it.
–Dimension–
Resolution: resident, tourist
Description:
Resident trips are trips made by people living in the urban area, while tourist trips are trips made by people visiting the urban area, and not living in it.
–Classifiers–
Availability: Public Data, Collaborators only, Proprietary/commercial
Kind: Model input
Sector: Transportation
Perspectives on India Energy based on Rumi (PIER) model output
–Provider–
Organization: Prayas (Energy Group) or PEG
Contact Email: energy.model@prayaspune.org
Format: Other
–Measure–
Measure Description:
Final electrical energy consumption by end-use (demand) sector
Units: GWh
–Measure–
Measure Description:
Final non-electrical energy consumption by end-use (demand) sector
Units: PJ
–Measure–
Measure Description:
Primary energy supply by energy carrier
Units: PJ
–Measure–
Measure Description:
Transfer of electricity from one geography (place of production or storage) to another (place of end-use or storage)
Units: PJ or GWh
–Measure–
Measure Description:
Transfer of non-electrical energy from one geography (place of production or storage) to another (place of end-use or storage)
Units: PJ or GWh
–Dimension–
Resolution: season, dayslice
Description:
yearly steps from 2021 to 2030 assumed to go from April 1st to March 31st.
–Dimension–
Resolution: SubGeography1, SubGeography2
Description:
All major Indian states. North-east combined into one state. Union territories combined into one state.
–Dimension–
Resolution: ConsumerType1, ConsumerType2
Description:
Consumer levels
–Dimension–
Resolution: Ref, PRS, ORS
Description:
Scenarios defined in PIER 2021
–Dimension–
Resolution: SUMMER, MONSOON, AUTUMN, WINTER, SPRING
Description:
Seasons in a year
–Dimension–
Resolution: EARLY, MORN, MID, AFTERNOON, EVENING, NIGHT
Description:
Timeslices in a day
–Dimension–
Resolution: ER, WR, NR, SR, NER
Description:
Regions
–Dimension–
Resolution: BR, JH, OD, WB
Description:
States within eastern region
–Dimension–
Resolution: CG, GJ, MP, MH, GA, UT
Description:
States within western region
–Dimension–
Resolution: DL, HR, HP, JK, PB, RJ, UP, UK
Description:
States within northern region
–Dimension–
Resolution: AP, KA, KL, TN, TS
Description:
States within southern region
–Dimension–
Resolution: AS, NE
Description:
States within north-eastern region
–Dimension–
Resolution: RURAL, URBAN
Description:
Residential consumer types:
Rural, Urban
–Dimension–
Resolution: Q1, Q2, Q3, Q4, Q5
Description:
Residential expenditure quintiles:
Quintile 1, Quintile 2, Quintile 3, Quintile 4, Quintile 5
–Dimension–
Resolution: PhysicalEnergyCarriers, NonPhysicalEnergyCarriers
Description:
Energy carriers: Physical energy carriers, Non-physical energy carriers
–Dimension–
Resolution: PhysicalPrimaryCarriers, PhysicalDerivedCarriers
Description:
Physical energy carriers
–Dimension–
Resolution: NonPhysicalPrimaryCarriers, NonPysicalDerivedCarriers
Description:
Non-physical energy carriers
–Dimension–
Resolution: COKING_COAL, STEAM_COAL, NATGAS, BIOGAS, BIOMASS, CRUDE
Description:
Physical primary energy carriers:
Coking coal, Steam coal, Natural gas, Biogas, Biomass, Crude
–Dimension–
Resolution: MS, HSD, ATF, LPG, PP_OTHER
Description:
Physical derived energy carriers
–Dimension–
Resolution: SUNLIGHT, WIND, HYDEL, ATOMIC
Description:
Non-physical primary energy carriers:
Sunlight, Wind, Hydel, Atomic
–Dimension–
Resolution: ELECTRICITY
Description:
Non-physical derived energy carriers: Electricity
–Dimension–
Resolution: D_RES, D_IND, D_AGRI, D_TRANS, D_OTHER
Description:
Demand sectors supported in PIER:
Residential, Industry, Agriculture, Transport, Other
–Dimension–
Resolution: RES_LIGHT, RES_COOL, RES_REF, RES_COOK
Description:
Energy services that are modelled bottom up:
Lighting, Space cooling, Refrigeration, Cooking
–Dimension–
Resolution: LIGHT_ELEC
Description:
Lighting service technologies: Electric lighting
–Dimension–
Resolution: FAN, COOLER, AC
Description:
Space cooling service technologies:
Fan, Cooler, Air Conditioner
–Dimension–
Resolution: FRIDGE
Description:
Refrigeration service technologies: Fridge
–Dimension–
Resolution: LPG, PNG, BIOMASS, BIOGAS, INDUCTION
Description:
Cooking service technologies:
Liquefied Petroleum Gas, Piped Natural Gas, Biomass, Biogas, Induction
–Dimension–
Resolution: INCAND, CFL, LED
Description:
Electric lighting efficiency levels:
Incandescent bulbs, Compact Fluorescent Lamps, LED lamps
–Dimension–
Resolution: 3STAR, 4STAR, 5STAR
Description:
Air-conditioner and refrigerator efficiency levels:
3-Star, 4-Star, 5-Star
–Classifiers–
Availability: Public Data, Cite and acknowledge required
Kind: Model output
Sector: Energy
This is an example of exogenous data input for the MISO2 model.
MISO2 is currently under development.
A previous model version can be found under DOI
https://doi.org/10.1016/j.ecolecon.2018.09.010.
–Provider–
Organization: Institute of Social Ecology, University of Natural Resources and Life Sciences (BOKU)
Contact Email: jan.streeck@boku.ac.at
Format: Other
–Measure–
Measure Description:
Material mass produced in a certain country and year.
Units: kilogram
–Measure–
Measure Description:
A ratio/transfer coefficient that determines which share of material production ends up in a certain end-use.
Units: percentage
–Measure–
Measure Description:
The average lifetime of a certain end-use (product group) depicting the average time which a material stays in use.
Can be used in different probability distributions to model the probability of materials flowing out of use.
Units: year
–Dimension–
–Dimension–
–Dimension–
–Classifiers–
Availability: Public Data, Collaborators only, Proprietary/commercial
Kind: Model input
JMIP model results
–Provider–
Organization: Japan Model Inter-comparision Project (JMIP)
Name: masahiro_sugiyama AT alum.mit.edu
Format: Other
–Data Link–
–Measure–
Units: EJ/yr
–Dimension–
Resolution: 5 years
–Dimension–
–Dimension–
Description:
supplementation info: https://doi.org/10.1007/s11625-021-00931-0
–Dimension–
–Dimension–
–Dimension–
Description:
description: Total final energy consumption of all fuels by all end-use sectors, excluding transmission/distribution losses.
–Classifiers–
Availability: Public Data, Cite and acknowledge required
Kind: IAM results
Sector: Energy
These data are ingested by the setup/calibration code for MESSAGEix-Transport.
This code takes an instance (“scenario”) of the MESSAGEix-GLOBIOM global model and alters it to add the detailed transport structure.
See the description `messageix-transport-core.yaml` for the data in the core model formulation. Data that exactly matches the core formulation can also be used as input; this file describes non-core data that can be transformed/used with existing code & methods.
This setup code was originally developed for use with data from the US-TIMES and MA³T models.
–Provider–
Organization: International Institute for Applied Systems Analysis (IIASA)
Contact Email: kishimot@iiasa.ac.at
Format: Other
–Measure–
Measure Description:
Count of people in a given area or category.
Units: persons
–Measure–
Measure Description:
Monetary equivalent to the intangible/non-monetary disutility (‘inconvenience’, etc.) that a person has towards using a specific technology; separate from the real, monetary costs of obtaining or using that technology.
Units: USD_2015
–Measure–
Measure Description:
Average travel speed by a particular means of transport.
Units: km/h
–Dimension–
Resolution: 5- or 10-year periods, or annual
Description:
Data at <5 year resolution can be aggregated.
–Dimension–
Resolution: country or R11 regions
Description:
Data at the country resolution can be aggregated.
–Dimension–
Resolution: 9 census regions, or total
Description:
Census divisions of the United States. This is used to map data from US-TIMES and MA³T.
–Dimension–
Resolution: 3-point scale: urban, suburban, or rural.
Description:
Urbanization or urban form of a sub-national area.
–Dimension–
Resolution: 3-point scale: early adopter, early majority, late majority
Description:
New technology adoption attitude or propensity, in particular towards new transport technologies, modes, options, etc.
–Dimension–
Resolution: 3-point scale: moderate, average, frequent
Description:
The amount of annual driving done by a person, relative to others in the population.
–Dimension–
Resolution: 27-point scale; see description
Description:
Group of people within a population that have similar transport demand/activity, behaviour, etc. characteristics. In MESSAGEix-Transport, this is operationalized as the Cartesian product of the `area_type`, `attitude`, and `driver_type` concepts/dimensions.
E.g. the label “RUEMF” means the subset of people who live in rural areas (“RU”), have early majority (“EM”) technology attitudes, and are frequent drivers (“F”).
–Dimension–
Resolution: 5-point scale: air, bus, rail, LDV, 2-3 wheelers
Description:
Mode or medium of passenger transport. In the MESSAGEix-Transport setup code, this is operationalized in a way that includes both different vehicle types (e.g. bus, LDV, 2W+3W) and the `mode` concept in the iTEM sense (e.g. “Road” for all 3 of these).
–Dimension–
Resolution: The powertrain technology of a transport vehicle.
Description:
43 distinct labels. Some of these are only valid in combination with certain other values for the `mode` dimension. In particular, the number of labels is:
– Rail: 6
– 2-3 wheelers: 2
– LDV: 12
– Aviation: 4
– Bus: 10
As well, for freight road (not included in the `mode` dimension, above), there are 9 `technology` labels.
–Classifiers–
Kind: Model input
Sector: Transportation
These are the data represented in the model core of MESSAGEix-Transport. This model uses the same, generic MESSAGE mathematical formulation as the MESSAGEix-GLOBIOM global model (without transport detail), and so this data description also applies to other instances (“scenarios”) of that model.
All of the core data are available directly as output, as well as aggregated and derived quantities.
–Provider–
Organization: International Institute for Applied Systems Analysis (IIASA)
Contact Email: kishimot@iiasa.ac.at
Format: Other
–Measure–
Measure Description:
Quantity of transport vehicles.
–Measure–
Measure Description:
For freight truck `technology` only.
–Measure–
Measure Description:
For non-LDV `mode` only. For these modes & technologies, there is no distinction by `consumer_group`.
–Measure–
Measure Description:
For LDV `technology` only. The model core optimizes the activity of each `consumer_group` by each `LDV` technology.
–Measure–
Measure Description:
Excluding aviation `mode` and associated technologies.
Units: 10^3 vehicles
–Dimension–
–Dimension–
–Classifiers–
Kind: Model Core
The Climate Policy Database is a collaborative effort to track policy adoption and identify global and national policy coverage gaps. The objective of the portal is to provide an open, collaborative platform for quick information access, policy analysis and good-practice sharing.
–Provider–
Organization: NewClimate Institute
Contact Email: policydb@newclimate.org
Format: CSV
–Data Link–
–Measure–
–Dimension–
–Classifiers–
Availability: Public Data
Kind: Climate policy data
Scenario data of the Japan model intercomparison project (JMIP) on long-term climate policy. Based on integrated assessment models and energy systems models.
–Provider–
Organization: University of Tokyo
Name: Masahiro Sugiyama
Contact Email: masahiro@ifi.u-tokyo.ac.jp
Format: CSV
–Data Link–
–Measure–
Measure Description:
Final energy consumption
Units: EJ/yr
–Dimension–
Resolution: 5-year interval
–Classifiers–
Availability: Public Data
Kind: Model output
Sector: Industry, Buildings, Transport
–Provider–
Organization: Research Institute of Innovative Technology for the Earth (RITE)
Name: Fuminori Sano; Naoko Onishi
Format: CSV
–Measure–
–Dimension–
–Classifiers–
Kind: Model input
The data set contains data on the level of societal material stocks for 18 stock-building materials (i.e. bricks, container glass, flat glass, concrete, asphalt, paper, wood, iron & steel, aluminum, copper, lead, zinc, chromium, manganese, nickel, tin, plastics, aggregates), 178 countries, 1900-2016 in 15 end-use sectors. Data uncertainty is reported as [still in development]. Data generated by model MISO2 (documentation in progress; for documentation of MISO1 see Wiedenhofer, Dominik, et al. “Integrating material stock dynamics into economy-wide material flow accounting: concepts, modelling, and global application for 1900–2050.” Ecological economics 156 (2019): 121-133.)
–Provider–
Organization: Unversity of Natural Resources and Life Sciences
Name: Dominik Wiedenhofer, Jan Streeck et al.
Contact Email: jan.streeck@boku.ac.at
Format: parquet-python
–Measure–
Measure Description:
1. count of material stock level of a given material in a given year, country and end-use sector
Units: kilotons
–Dimension–
Resolution: annual
–Dimension–
Resolution: 178 countries
–Dimension–
Resolution: engineering materials
Description:
engineering materials: bricks, container glass, flat glass, concrete, asphalt, paper, wood, iron & steel, aluminum, copper, lead, zinc, chromium, manganese, nickel, tin, plastics, aggregates
–Dimension–
Description:
tba
–Classifiers–
Availability: Public Data
Kind: Model output
Sector: Economy-wide in 15 sectors
In our Energy Sufficiency Policy Database we compile and categorise numerous sufficiency policy instruments for all sectors that were collected from various sources. With the policy database we aim at providing decision makers from politics, administrations and the civil society with a tool to plan and implement sufficiency policy measures. We also address the energy system and climate modelling community.
–Provider–
Organization: Oeko-Institut
Name: Carina Zell-Ziegler
Contact Email: c.zell-ziegler@oeko.de
Format: CSV
–Data Link–
–Measure–
Measure Description:
1. suggested or implemented policies with descriptions, literature sources and related policy strategies
2. fitting indicators to all policies to be able to measure their progress
Units: various
–Classifiers–
Availability: Public Data
Kind: policies
Sector: all
The Network for Greening the Financial System (NGFS) is a group of 65 central banks and supervisors and 83 observers committed to sharing best practices, contributing to the development of climate– and environment–related risk management in the financial sector and mobilising mainstream finance to support the transition toward a sustainable economy.
This Scenario Explorer is a web-based user interface for transition scenario results selected for the NGFS. This provides intuitive visualizations & display of timeseries data and download of the data in multiple formats. This Scenario Explorer hosts the NGFS scenarios, which were produced by NGFS Workstream 3 in partnership with an academic consortium from the Potsdam Institute for Climate Impact Research (PIK), International Institute for Applied Systems Analysis (IIASA), University of Maryland (UMD), Climate Analytics (CA), the Eidgenössische Technische Hochschule Zürich (ETH) and the National Institute of Economic and Social Research (NIESR). This work was made possible by grants from Bloomberg Philanthropies and ClimateWorks Foundation.
The bespoke scenarios developed in Phase 3 of this project are generated by state-of-the-art well-established integrated assessment models (IAMs), namely GCAM, MESSAGE-GLOBIOM and REMIND-MAgPIE. These models allow the estimation of global and regional mitigation costs, the analysis of energy system transition characteristics, the quantification of investments required to transform the energy system, and the identification of synergies and trade-off of sustainable development pathways.
–Provider–
Organization: IIASA/NGFS
Name: Bas van Ruijven
Contact Email: vruijven@iiasa.ac.at
Format: Excel
–Data Link–
–Classifiers–
Availability: Public Data
Kind: Model output
Sector: Buildings, Transport, Industry, Energy
The PFU database contains time series from 1900 through 2014 of primary energy/exergy, final energy/exergy and useful energy/exergy. The data are organized:
1. Geographically: on the lowest level by 15 countries and 5 other regions. They are aggregated into the 5 global regions in accordance with the Global Energy Assessment.
2. by energy carrier: into 12 energy carrier categories
3. by sector: industry; transport; the aggregate of commercial, residential, agriculture, public and other; and non-energy uses
4. by end-use: thermal uses (high-temperature and low-temperature), light, stationary power, transport, feedstocks and other end-uses
An update is underway.
–Provider–
Organization: IIASA
Name: Simon De Stercke
Contact Email: simon.destercke@aya.yale.edu
Format: Custom interface but data can be exported to xlsx.
–Data Link–
–Measure–
Measure Description:
Energy use
Units: TJ
–Measure–
Units: –
–Dimension–
Resolution: Residential/Commercial, Transport, Industry
–Dimension–
Resolution: annual
–Dimension–
–Dimension–
–Classifiers–
Availability: Public Data
Kind: Model input, Model input
Sector: Transport, Industry, Residential and Commercial
EUBUCCO is a scientific database of individual building footprints for 200+ million buildings across the 27 European Union countries and Switzerland, together with three main attributes – building type, height and construction year – included for respectively 45%, 74%, 24% of the buildings. EUBUCCO is composed of 50 open government datasets and OpenStreetMap that have been collected, harmonized and partly validated.
–Provider–
Organization: MCC Berlin
Name: Nikola Milojevic-Dupont, Felix Wagner, Florian Nachtigall, Felix Creutzig
Contact Email: creutzig@mcc-berlin.net
Format: CSV
–Data Link–
–Measure–
Measure Description:
Exact location of building
Units: Coordinates
–Measure–
–Measure–
–Measure–
–Classifiers–
Availability: Public Data
Kind: Official Statistics
Sector: Buildings
Other Classifier: Spatial Planning
cost and potential for all demand-side sectors
–Provider–
Organization: Jingjing Zhang
Name: Lawrence Berkeley National Laboratory
Contact Email: jingjingzhang@lbl.gov
Format: Other
–Measure–
Measure: Economic
Units: $
–Dimension–
Dimension: Space
Scope: Global
–Classifiers–
Availability: Upon Request
Kind: Model input
Sector: All
UN Habitat datasets on housing, settlements, population (development), open and green spaces, transport alternatives, prosperity, accessibility of need satisfiers, sanitation, etc., quality of life, economic indicators, spatial growth, social inclusion
–Provider–
Organization: UN Habitat
Format: Excel
–Data Link–
–Measure–
Measure: Urban indicators
Measure Description:
Please refer to the documentation: https://data.unhabitat.org/pages/datasets
–Dimension–
Dimension: Time
Scope: Global
Description:
Please refer to the documentation: https://data.unhabitat.org/pages/datasets
–Classifiers–
Availability: Public Data
Kind: Model input, Model output, Official Statistics
Sector: buildings, transport
Other Classifier: OpenStreetMap
The construction materials used in buildings have large and growing implications for global material flows and emissions. Material Intensity (MI) is a metric that measures the mass of construction materials per unit of a building’s floor area. MIs are used to model buildings’ materials and assess their resource use and environmental performance, critical to global climate commitments. However, MI data availability and quality are inconsistent, incomparable, and limited, especially for regions in the Global South. To address these challenges, we present the Regional Assessment of buildings’ Material Intensities (RASMI), a new dataset and accompanying method of comprehensive and consistent representative MI value ranges that embody the variability inherent in buildings. RASMI consists of 3072 MI ranges for 8 construction materials in 12 building structure and function types across 32 regions covering the entire world. The dataset is reproducible, traceable, and updatable, using synthetic data when required. It can be used for estimating historical and future material flows and emissions, assessing demolition waste and at-risk stocks, and evaluating urban mining potentials.
–Provider–
Name: Tomer Fishman, Alessio Mastrucci, Yoav Peled, Shoshanna Saxe, Bas van Ruijven
Contact Email: t.fishman@cml.leidenuniv.nl
Format: Excel
–Data Link–
–Data Link–
–Measure–
Measure: Material composition of buildings
–Dimension–
Dimension: Time
Scope: Global
–Classifiers–
Availability: Public Data
Kind: Data from scientific literature
Sector: Buildings
Other Classifier: Materials
This is a set of data on energy security indicators presented in the Supplementary Materials of the paper: Bento N., Grubler A., Boza-Kiss B., De Stercke S., Krey V., McCollum D., Zimm C., Alves T. (2024). Greater leverage for energy security with demand-side policies. Science, DOI: 10.1126/science.adj6150.
–Provider–
Organization: ISCTE
Name: Nuno Bento
Contact Email: nuno.bento@iscte-iul.pt
Format: Excel
–Data Link–
–Measure–
Measure: Variety of energy security indicators
Units: %
–Dimension–
Dimension: Other
Scope: 12 countries
–Classifiers–
Availability: Public Data
Kind: simulation data. Compound energy matrixes from several sources
Sector: Buildings, Transport
The data set contains data on the level of societal material stocks and flows for 20 stock-building materials (i.e. bricks, container glass, flat glass, concrete, asphalt, bitumen, paper, wood, iron & steel, aluminum, copper, lead, zinc, chromium, manganese, nickel, tin, plastics, aggregates, downcycled construction materials), 177 countries, 1900-2016 in 14 end-use product groups. Data generated by model MISO2 (preprint, in review: Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (April 15, 2024). Available at SSRN: https://ssrn.com/abstract=4794611 or http://dx.doi.org/10.2139/ssrn.4794611)
–Provider–
Organization: University of Natural Resources and Life Sciences
Name: Dominik Wiedenhofer & Jan Streeck
Contact Email: dominik.wiedenhofer@boku.ac.at
Format: CSV
–Data Link–
–Measure–
Measure: Material consumption
Measure Description:
material extraction, processing & trade of raw materials/ raw products / semi-finished products/ final products, gross additions to stock (materials in final products entering use), in-use material stocks, end-of-life waste from material stocks, processing waste, recycling and downcycling material use
Units: Mass
–Dimension–
Dimension: Space
Scope: Global
Resolution: 177 countries
–Dimension–
Dimension: Time
Scope: historical 1900-2016
Resolution: 1 year
–Dimension–
Dimension: Time
Scope: Global
–Classifiers–
Availability: Upon Request
Kind: Model output
Sector: economy-wide (14 end-use product groups)
In our Energy Sufficiency Policy Database we compile and categorise numerous sufficiency policy instruments for all sectors that were collected from various sources. With the policy database we aim at providing decision makers from politics, administrations and the civil society with a tool to plan and implement sufficiency policy measures. We also address the energy system and climate modelling community.
–Provider–
Organization: Oeko-Institut
Name: Carina Zell-Ziegler
Contact Email: c.zell-ziegler@oeko.de
Format: CSV
–Data Link–
–Measure–
Measure: Description of policy instrument
–Dimension–
Dimension: Other
Scope: Global
–Classifiers–
Availability: Public Data
Kind: Policy proposals
Sector: All sectors and cross-sectoral
The Industrial Ecology Data Commons (iedc) contains more than 200 IE-related datasets from the literature, including stocks, flows, process descriptions, IO tables, material composition of products, and many more. Launched in 2018, the iedc is continuously improved and expanded.
All data are structured according to a newly developed general data model for socioeconomic metabolism.
–Provider–
Organization: Industrial Ecology Freiburg, Faculty of Environment and Natural Resources, University of Freiburg, Germany
Name: Stefan Pauliuk
Contact Email: stefan.pauliuk@indecol.uni-freiburg.de
Format: SQLite
–Data Link–
–Measure–
Measure: More than 30 datatypes
Measure Description:
More than 30 data types: extensive (stocks, flows), intensive (coefficients), for products and for processes.
see the complete list: http://www.database.industrialecology.uni-freiburg.de/resources/IEDC_DataTypes_Overview.pdf
Units: all kinds of monetary and phyiscal units, depending on the data type
–Dimension–
Dimension: Time
Scope: Global
–Classifiers–
Availability: Public Data
Kind: Raw data, reconciled data, model output, Model input, Model output, Official Statistics
Sector: vehicles, appliances, socioeconomic drivers, economy-wide accounting, infrastructure, industry, buildings