Integrated assessment model

The WILIAM (“Within limits”) Integrated Assessment Model (IAM), developed in the scope of LOCOMOTION, is a model running at three geographical levels – global, European and national for the 27 EU member states and United Kingdom (UK).

The global model will consist of nine world regions, one of which is the EU-27 (at country level) and another the UK. The models run from 2005 to 2050-2100.

WILIAM model is built on the existing MEDEAS model that was developed in the context of the EU-funded MEDEAS project. For the study of the highly complex interactions between humans and their environment, the project draws on different techniques and methods, such as System Dynamics (SD) modelling with Vensim software, Input-Output Analysis (IOA), Energy Return On Investment (EROI) calculations, Life Cycle Analysis (LCA), land and carbon footprinting, microsimulation, and many others.

Integrated assessment model

Targeted at: Scientists, modelling experts.

Available from: A first version of the IAM is available here. The final versión will be available on Github in 2023.

Format: The final IAM will be available in Vensim and Python.

License: Open source license.

Support: Technical documentation will be available for download from this website.

For specific questions concerning the models, please contact

Model structure

WILIAM model is structured in seven modules and sub-modules. This structure allows for flexibly testing, improving and expanding each module without impairing the robustness of the models as a whole:

  • Economy and finance: The module covers the estimation of final demand for goods and services from around 50 different economic sectors, including households, and their linkages, based on input-output tables and multi-regional input-output models. The final energy demand is calculated for any primary energy source based on each economic sector’s energy intensity. The energy demand of the energy and non-energy economic sectors will be contrasted with different technology developments, energy availability and fossil fuels reduction scenarios. The economy is modelled following a post-Keynesian approach assuming disequilibrium (i.e. non-clearing markets), demand-led growth and supply constraints.
    The development of a financial sub-module will enable a better understanding of the constraints in the economic and energy system due to financial assets and public and private debt.
  • Renewable and non-renewable energy: The module includes the renewable and non-renewable energy resource potentials and availability taking into account biophysical and temporal constraints. In total, five final fuels are considered (electricity, heat, solids, gases and liquids) and a diversity of energy technologies are modelled. The energy yield of the different energy sources is calculated using the energy returned on energy invested (EROEI) approach. The intermittency of renewable energy sources is considered.
  • Non-fuel materials: The module takes account of the availability of materials (e.g. rare earths, lithium, silver, cobalt, indium, etc.) needed by the economy and demanded for the development of the energy infrastructures. The evolution of recycling rates per material and the energy consumption associated with the extraction of minerals is estimated. This allows the endogenous and dynamic estimation of the EROEI of each energy technology and the system as a whole. Transportation and the building sector are modelled bottom-up.
  • Energy infrastructure and technologies: Energy infrastructure includes all infrastructures needed to extract, transport and convert primary energy sources into final energy in the form of electricity, heat, liquids, gaseous or solid fuels usable for the end user. The module considers the quantity of materials required to build the infrastructures needed under the different energy transition scenarios. Demands for each material are subsequently compared with the levels of available metrics of reserves and resources.
  • Environment: The module include the carbon cycle (GHG emissions from the energy consumption associated with the economic sectors final demand, compatibility with emission reduction scenarios, estimation of tipping points probability), the water cycle and the main aspects of land competition between energy generation and other land uses, including NET. In particular, the contribution of land use changes to GHG emissions for economic purposes (known as ‘Land Use, Land Use Change and Forestry’, or LULUCF, activities), the contribution of agrofuels and the land requirements for energy production are considered.
  • Climate change: The module projects the levels of climate change as a function of the GHG emissions from human activity, which also feeds back through a damage function to capture the effect of a global temperature increase on human activity.
  • Population and society: The module focuses on the various feedbacks between energy, environment, climate change and human society and well-being. It considers how transitions in the energy system impact society in terms of inequality, migration and health, differentiating between gender and age cohorts, but also how demographic change alters energy demand. Several UN Sustainable Development Goals (SDGs) are modelled to assess the attainment of the SDGs under different scenarios.

Model novelties

Countless Integrated Assessment Models (IAMs) have been developed in the decades since the pioneering World3 model was created in the early 1970s (Meadows et al. 1972). Even though great advances have been made in the field since then, many IAMs, and especially those with greater influence over policy, share a core set of assumptions whose validity is being disputed in the scientific community, leaving scope for improvement.

WILIAM IAM strive to occupy this niche and focus on:

  • the careful modelling of the complex human-nature system that is governed by dynamic, tightly coupled, nonlinear, self-organising, adaptive and evolving feedbacks
  • the proper representation of biophysical and temporal constraints to renewable and non-renewable energy production
  • the declining Energy Return on Energy Investment (EROI) with increasing shares of renewable energy
  • the consistent integration of climate change damage feedbacks
  • the dominance of conventional economic equilibrium and optimisation approaches, which suffer significant limitations when it comes to capturing socioeconomic system dynamics and the role of macroeconomic policies for sustainability governance

The main methodological innovations are

  • The endogenous and dynamic integration of economic, financial, energy-related, social, demographic and environmental variables into the models.
  • The use of a wide array of methods, such as System Dynamics, Input-Output Analysis, Energy Return On Energy Investment (EROEI) calculations, Life Cycle Analysis (LCA), land and carbon footprinting, microsimulation, etc.;
  • The adoption of relevant functionalities from other models (World6, TIMES, LEAP, GCAM, C-Roads, …
  • The consistent quantification and representation of uncertainty in model results.


    Predominant practice LOCOMOTION ambition
    • Limited sectoral and geographical scope.
    • Nested IAMs (global→national) that integrate economic, social, financial, technological, energy resources and non-energic materials, and environmental dimensions.
    • Rather sequential structure with limited feedbacks among the represented subsystems.
    • Focus on the interactions (feedbacks) among the components of the system rather than on the detail of the components themselves.
    • Economic General Equilibrium Models.
    • Static Input-Output coefficients.
    • Limited disaggregation of economic sectors.
    • Finance not considered in model.
    • Demand-led economic module.
    • Dynamic Input-Output tables.
    • High disaggregation of economic sectors.
    • Financial submodule.
    • Absence of net energy.
    • Energy techno-optimism.
    • Lack of consistent integration of climate change damages.
    • Supply-side energy management policies.
    • No limitation to renewable energy sources.
    • Modelling dynamic EROIs for all energy sources / technologies.
    • Role of RES intermittencies and potential of technol. Change.
    • Integration of CC damage function.
    • Demand-side management policies.
    • Modelling of environmental constraints / impacts.

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