Universität HamburgZentrum für Marine und Atmosphärische Wissenschaften

Forschungsstelle Nachhaltige Umweltentwicklung

Universität Hamburg

 | suche | sitemap | kontakt | impressum | old pages
English version
home > Models, Tools, Data
FNU Universität Hamburg
Grindelberg 5
D-20144 Hamburg
Tel.: +49 40 42838-6593

Models, Tools, Data

Models

CHIMP

The Canberra-Hamburg Integrated Model of Population is a demographic model to be used in association with a model for long-term projections of economic growth.

EUFASOM

The European Forest and Agricultural Sector Optimization Model (EUFASOM) is a partial equilibrium, bottom-up land use model. 

HABITAT

The HABITAT model uses mixed integer programming to find cost-effective allocations of European wetland reserves. The model minimizes the cost of European wetland biodiversity targets by jointly examining up to 70 species contained in up to 3000 spatial units across 37 countries.

FUND

The Climate Framework of Uncertainty, Negotiation and Distribution is an integrated assessment model of climate change. Spanning the whole problem from demographyto atmospheric chemistry and back, and covering the whole world and the nexttwo centuries, FUND evaluates the impacts of climate change and internationalgreenhouse gas emission reduction policies and identifies policy strategiesthat are either efficient or cost-effective from either an individual or acollective viewpoint. This page gives more information on the model, andpresents several versions for downloading.

GTAP-EF/W

This variant of GTAP-E is a computable general equilibrium model for studying the economicimpacts of climate change and water resources.

HTM

The Hamburg Tourism Model is a simulation model of tourism flows to and from 207 countries. It analyses scenarios of population and economic growth as well as climate change.

Fisheries

The two independently developed models depict cod fisheries in the Baltic Sea and Cod and Capelin Fisheries in the Barents Sea.

Further models

  • BALMOREL
    Baltic model of regional electricity liberalisation<!--[if !supportEmptyParas]-->
  • DEMETER
    Gerlagh and vdZwaan’s integrated assessment model of climate change
  • DICE
    Nordhaus’ family of integrated climate-economy models
  • GEMINI-E3
    Computable general equilibrium model, energy-economy-environment
  • Ginsburgh & Keyzer
    A selection of CGE models implemented in GAMS
  • GTEM
    Global trade and environment model, ABARE
  • MERGE
    Model for evaluating the regional and global effects of greenhouse gas reduction policies by Alan Manne and Rich Richels
  • NICCS
    A simple climate model by Georg Hooss

Tools

Data

Population density and Income density in 2000 on a 100 km by 100 km grid.

National data

This is a data set with data on climate, coast, culture, economy, education, geography, health, infrastructure, politics, population, research and development, risk and insurance for all countries in the world. Most of these data can be found on the internet, but for some you would have to look hard. They are here conveniently grouped together. Jackie Hamilton, Oliver Hansen, Karim El Haw, Ina Theil and Richard Tol constructed this database. If you have any data that you feel need to be added, please send an email. This data is updated on an irregular basis. The latest update is from November 7, 2002.

We are working on gridded data, but there is not much to show except for these maps. These maps can’t beat Nordhaus’ globe.

Desalination costs

It includes the plant information of five main desalting processes and in particular the costs. This data was used in

Y. Zhou and R.S.J. Tol (2004), ‘The Implications of Desalination to WaterResources in China – An Economic Perspective’, Desalination, 164(3), 225-240;
Y. Zhou and R.S.J. Tol (2005), ‘Evaluating the Costs of Desalination and WaterTransport’,Water Resources Research, 41 (3), W03002.

Coastal Households in Tanzania

This data was used in

Sesabo, J.K, H. Lang and R.S.J. Tol (2006), Perceived Attitude and MarineProtected Areas (MPAs) establishment: Why households’characteristics matters in Coastal resources conservation initiatives inTanzania, FNU-99, Hamburg University and Centre for Marine and Atmospheric Science, Hamburg;
Sesabo, J.K and R.S.J. Tol (2005), Technical Efficiency and Small-scale Fishing Households inTanzanian coastal Villages: An Empirical Analysis, FNU-95, HamburgUniversity and Centre for Marine and Atmospheric Science, Hamburg;
Sesabo, J.K.and R.S.J. Tol (2005), Factor affecting IncomeStrategies among households in Tanzanian Coastal Villages: Implication for Development-Conservation Initiatives, FNU-70, Hamburg University andCentre for Marine and Atmospheric Science, Hamburg

Further Data

© Forschungsstelle Nachhaltige Umweltentwicklung, Universität Hamburg
home > Models, Tools, Data