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KnowSDGs

Methodology

Mapping models to SDGs aims to facilitate and improve the use of models for sustainability assessment of EU policies in the SDGs framework. It helps policy makers to identify the appropriate models for the definition and the assessment of specific policy options.

The mapping considers all the models present in MIDAS (the Modelling Inventory and Knowledge Management System of the European Commission), which are currently run and maintained at the Joint Research Centre of the European Commission (JRC-EC). The model description was analysed for each of model, focusing on the aim, the content, the main output, the policy role and the impact areas that the model can help to assess. The links between the models and the SDGs were identified by experts through textual analysis, and confirmed by the modelers. The overall structure of the SDGs, organized by goals, targets and indicators, guides the mapping, considering both UN and ESTAT indicators, in order to capture the global framework of the SDGs.

For every goal, the mapping provides the list of models that can contribute to it and explaining how, which targets they address, and if they are able to measure in a quantitative way the achievement of the goals. It also offers technical information about every model, on model type, modelling approach, spatial and temporal dimensions, models’ outputs and how they can be linked to EU/UN SDGs indicators.

The mapping is based on the information available in MIDAS and collected through workshops, questionnaires and surveys, which involved more than a hundred modelers and experts across the JRC.


Purpose of the model

Short-term forecasting:
A model that focuses on predicting the near future (up to 5 years) based on the past and present data, most commonly using current trend analysis, i.e. a continuation of present methods extrapolated into the future.
Medium- to long-term exploring (baselines) or scenario analysis:
A model that focuses on making projections in medium (5-15 years) or long-term (15 years<). Baseline is a description of what may happen under a specific set of assumptions, which at the time of making the projections were judged plausible, and which provides a base for comparing the policy options. Scenario analysis is a process of examining and evaluating possible events, policy options that could take place in the future by considering various feasible outcomes.
Medium- to long-term backcasting:
A model that focuses on planning in a medium (5-15 years) or long-term (15 years<). Is a method that starts with defining a desirable future and then works backwards to identify policies and programs that will connect that specified future to the present.

Model type

Top-down models:
Based on a top-down analytical approach, which starts with the big picture and it breaks down from there into smaller segments. Top-down ‘macro framework’ models are likely to be more useful for undertaking system-level or economy-wide scenario analysis driven by the national long-term goals and targets, and for exploring trade-offs and synergies among sectors.
Bottom-up models:
Based on a bottom-up approach, which is the piecing together of systems to give rise to more complex systems. Bottom-up sectoral models could support more detailed option-level impact analysis of concrete interventions, technologies and investments.
Computable general equilibrium (CGE) models:
Allow for consistent comparative analysis by ensuring that the economic system and individual markets remain in general equilibrium in the long term. They are typically used to capture one off and long-term effects from policy "shocks". They are able to produce disaggregated results as such models only require one (base) year of data. They provide detailed information on the policy impact of a particular variable of interest. Many CGE models suffer from a lack of historical validation. Some types of CGE are also used for forecasting and scenario building.
Input-output models:
Offer an alternative approach to large-scale economic modelling. This is typically used for short-term analysis of supply chains and how industries are related. The models are based around economic input-output tables which indicate the values of purchases between economic sectors in a particular year. Input output tables are usually available at the national level though they can be aggregated to regional and European levels. Results are easy to interpret and few resources are necessary, but the models are simple, rely heavily on assumptions and can only be used for static analysis as the model doesn’t take into account changes over time.
Econometric models:
These models are typically used to capture medium/long-term effects from shocks and for forecasting. Modelled relationships are econometrically estimated using historical detailed time-series data rather than economic theory. Models can capture the process of dynamic adjustment and structural changes if these are not too substantial. Such models are not generally suitable for short-term analysis (but can in some cases span different time frames). They are also premised on the assumption that historical relations will still be valid in the future.
Partial equilibrium (PE) models:
Single Sector Models or System Models typically used in the detailed analysis of a specific economic sector (such as energy supply) or a combination of related economic sectors (such as the interaction of energy supply, and a number of energy demand sectors) over short/medium/long term. They can provide a high degree of disaggregation within the sector(s) covered. Models are unable to capture the interactions with other sectors and the effects in other markets but remain in equilibrium within the sectors in question. Factors related to issues outside of the sectors in question must be supplied exogenously and interaction/feedback to the rest of the economy is ignored.
Micro-simulation models:
Typically used for analyses at a detailed disaggregated level over the short term these usually focus on individuals, households or firms (e.g. tax effect on income distribution) although they can be provide insights at a higher level of aggregation. Models require very detailed disaggregated data and may not therefore be able to cover all actors of interest or all resource flows nor important general equilibrium feedback effects.
System dynamics models:
Mathematical models designed to understand the behaviour of complex systems over time (using stocks, flows, internal feedback loops, table functions and time delays).
Multi-agent models:
Computerized systems composed of multiple interacting agents.
Simulation models:
Type of mathematical models which combine both mathematical and logical concepts, that try to emulate a real-life system through use of computer software.
Optimisation models:
Type of mathematical models that attempts to optimize (maximize or minimize) an objective function without violating resource constraints; also known as mathematical programming.
Process (or Physics)-based models:
Type of models which focus on simulating detailed physical or biological processes that explicitly describe system behaviour.
Statistical models:
Mathematical models that describe relationships between two or more variables in the form of mathematical equations.

Modelling approach

The way the data-generating process is being modelled in a model.

Deterministic:
A model in which outcomes are precisely determined through known relationships among states and events, without any room for random variation. It tells us that something can be predicted exactly, i.e. a given input will always produce the same output.

Stochastic:
A model that represents a situation where uncertainty is present. Is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. It describes a system whose changes in time are described by its past plus probabilities for successive changes.

Probabilistic:
A model based on the theory of probability, the fact that randomness (uncertainty) plays a role in predicting future events. The potential outcomes are determined from the same past probability distribution and each outcome is completely independent of each other, without any memory of the past.



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