Model
A modeling framework for medium to long-term energy system planning, energy policy analysis, and scenario development
Water Security (WAT)
Integrated Biosphere Futures (IBF)
Energy, Climate, and Environment (ECE)
Sustainable Service Systems (S3)
Pollution Management (PM)
Integrated Assessment and Climate Change (IACC)
Transformative Institutional and Social Solutions (TISS)
Multidimensional Demographic Modeling (MDM)
Austria
Brazil
China
Germany
Italy
Norway
Dataset
This Scenario Explorer presents an ensemble of quantitative, model-based climate change mitigation pathways underpinning the Special Report on Global Warming of 1.5°C (SR15) by the Intergovernmental Panel on Climate Change (IPCC) published in 2018. The ensemble was also used and extended in the IPCC's Special Report on Climate Change and Land (SRCCL, 2019).
Tool
The concept of nexus thinking has gained traction amongst the applied research community to examine cross-sector linkages between land, water, and energy strategies. A nexus approach identifies the interactions among sectors to better understand the synergies and trade-offs involved in meeting future resource demands in a sustainable way.
Dataset
The Horizon 2020 project ENGAGE quantifies avoided climate change impacts through analysis of the exposure and associated costs for individual sectors and regions to climate change at different levels of and timing for global peak temperature. A particular focus is placed on quantifying the benefits (or trade-offs) of climate policies on biodiversity, food, poverty, water, air quality, health, and employment, particularly for vulnerable populations.
Model
The "IIASA Logistic Substitution Model II" (LSM2) is a software tool to estimate the parameters of technological growth and substitution processes. Applications include marketing research, scenario development, historical transition analyses, as well as meta-analysis across many individual technologies.
Model
Pathfinder is designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: (1) the capacity to be calibrated using Bayesian inference, (2) the capacity to be coupled with integrated assessment models (IAMs), and (3) the capacity to explore a very large number of climate scenarios to narrow down those compatible with limiting climate impacts.
Model
MAGICC is a reduced complexity Earth system model that has been widely used in climate science for over three decades, most notably in multiple IPCC reports. It is most often used in a probabilistic setup, providing information not only about our best-estimate of future climate change but also the uncertainty that arises from interactions between the Earth system’s many components. MAGICC is also as the climate component in multiple integrated assessment models (IAMs).
Dataset
A host of scientific chemistry and climate model experiments explore responses of the global atmosphere and climate systems to possible future changes in emissions of air pollutants and greenhouse gases.
The Pollution Management research group (formerly AIR program) has used its GAINS model to develop a set of global emission fields of nine substances that provide consistent sets of future sectoral emissions for well-specified assumptions on economic development and the effectiveness of dedicated emission control policies.