Summary of Models PDF

# Socio-Ecological Metabolic Model (SEM) - Material & Energy Flow Analysis (MEFA) - Material Input - Stock - Output (MISO) Model - Purpose: - Constrain uncertainties - diagnostic approach - Help estimating less well-known properties - Be developed into sophisticated models - Captures so...

# Socio-Ecological Metabolic Model (SEM) - Material & Energy Flow Analysis (MEFA) - Material Input - Stock - Output (MISO) Model - Purpose: - Constrain uncertainties - diagnostic approach - Help estimating less well-known properties - Be developed into sophisticated models - Captures socio-ecological metabolism based on inputs, stocks, outputs encompassing humans, livestock and artefacts; can be based on material flows or energy flows; can take a deeper dive into material processes boxes on a sectoral basis - Modelling philosophy & principles: looks into bio-physical processes within a wider biosphere; considers extraction from the biosphere and releases of outputs (waste, energy, emissions) into the biosphere; - Needs balance of accounts (economic: equity, assets, liabilities; material flows, energy flows) - Uncertainty is assessed; - Stocks exist at a point in time - Flows are measured over period of time - Requires triangulation: data triangulation, investigator triangulation, method triangulation, theory triangulation - Assumptions: - Based on 1st thermodynamic law - energy within a closed system can be converted (from mass to energy – and vice versa) but it can't disappear; - Model assumes mass balance (stocks = inputs – outputs; DMC = I + DE – Exports) - Strengths: - Conceptually simple - can measure material and energy flows on a macro-level and with sectoral disaggregation; can capture addition of stock; can craft options (e.g. for feeding world without deforestation) by showing feedback; global C-cycle and sinks; C or GHG balances; - Weaknesses: - Closing and reconciling balances can be difficult - Only covers monetarized inputs, e.g. livestock grazing accounts for substantial part of inputs, but is not captured in any mass statistics; - Data requirements: Consistent input-output data e.g. based on double-book keeping (assets, equity, ); - Applications: - Possible insights generated # System Dynamic Model (DSM) - Purpose: - For complex systems where parts are interrelated and system can't be reduced without seriously altering it; - Thus for complex systems with non-linearities, delays, feedback loops - Purpose of depicting trends and relationships (rather than exact values) - Modelling philosophy & principles: - Based on system thinking (purpose, elements, interconnecdness) - Requires definition of system (isolated, closed, open) - Requires definition of system boundaries - Based on stock (accumulation), flows (change over time) and feedback loops (reinforcing or balancing) - Model conception: - Define problem - Generate hypotheses - Model construction (system boundaries, causal loop diagram, stock and flow diagram) - Test modelled data (structural test with testing plausibility based on extreme scenarios; behavioural tests e.g. on historic observations; sensitivity test by introducing uncertainties) - Simulation - Strengths: Modelling of complex relations with feedback loops - Weaknesses: potential uncertainties about feedback loops and unknown uncertainties - Data requirements: - Historical data - Qualitative interviews - Data collection workshops - Census data, etc. - Applications: Modelling of complex relations with feedback loops - Possible insights generated: macro and meso level; # Climate Modelling - Purpose: Isolating the climate effects of individual emissions and other factors; overall predictions and forecasts about impact of climate change (radiative forcing, related global warming, seawater rise, humidity rise, ice melting, etc.) based regional and sub-regional predictions and forecasts - Representative Concentration Pathway (RCP) scenarios; - Modelling philosophy & principles: - Has strong features of system dynamic models - Has developed out of numerical weather predictions - Has made vast progress in terms of smaller grit size (horizontally) and vertical layers (in oceans and atmosphere) - Builds on laws of thermodynamic (short-wave influx, long-wave outflux, albedo, absorption, reflexion) - Builds on system dynamic models in terms of building-in feedback loops; - Started-out with less factors and added-in additional factors that can increasingly be calculated (in 50s with atmosphere/land, carbon cycle, aerosols, etc; in 60s with oceans and more aerosols, in 70s with sea ice, in 80s with upper atmosphere and ice sheets, etc.) - Triangulation and fine-tuning by comparison with historic climate data - Strengths: findings, scenarios and forecasts with various levels of certainty; ability to define the uncertainty level - Weaknesses: grits mostly too large for local forecasts and scenarios; weak data on oceans and especially on deep water; we see that in reality many feedback loops seem to have been underestimated (known and unknown uncertainties) because of lack of historic precedents within periods we can have data for; - Data requirements: - Data on temperature, wind (3 dimensional), humidity, pressure, density; - In past important areas of earth were not covered by land-based measurement - here satellites have enhanced availability of data; - Applications: Radiative Concentration Pathways (RCP) scenarios - IPCC report - Possible insights generated: global and regional climate change impact; isolation of contributions of individual factors and emissions; better understanding of various feedback loops within the overall system; # Agent-based Modelling (ABM) - Purpose: - To simulate complex systems by modeling the interactions of individual, autonomous agents - Useful for studying emergent behaviors and dynamics that arise from individual-level interactions in a system. - Modelling philosophy & principles: - Bottom-up approach: Builds system behavior from the individual agent level. - Agents have distinct attributes and decision-making rules, allowing heterogeneity. - Agents interact with one another and their environment based on predefined rules. - Focuses on local interactions leading to emergent macro-level patterns. - Incorporates adaptability: Agents can change behavior in response to their environment. - Strengths: - Captures heterogeneity among entities (e.g., people, animals, businesses). - Simulates emergent behaviors not predictable from individual components alone. - Models non-linear interactions and dynamic adaptation. - Provides insights into systems with complex agent-environment feedbacks. - Flexible in application across various fields (e.g., epidemiology, urban planning). - Weaknesses: - Computationally intensive, especially for large-scale models with many agents. - Validation of results is difficult due to emergent, unpredictable behaviors. - Results can be highly sensitive to initial assumptions or parameters. - Requires significant effort to define agent attributes and decision rules accurately. - Data requirements: - Detailed data on agent characteristics (e.g., demographic or behavioral data). - Rules of interaction (e.g., social norms, decision-making algorithms). - Environmental context or spatial data for agent interactions. - Applications: - Social science: Simulating social behaviors, norms, and group dynamics. - Epidemiology: Modeling disease spread (e.g., COVID-19). - Urban planning: Assessing traffic flows or housing dynamics. - Environmental management: Studying land use changes, resource management, or biodiversity. - Economics: Analyzing market behaviors and consumer interactions. - Possible insights generated: - Emergent patterns of behavior in complex systems. - The role of individual decision-making in influencing system outcomes. - The impact of local interactions on global system properties. # Participative Modelling (PM) - Purpose: - To engage stakeholders in the process of model design, validation, and use - Builds a shared understanding of a problem and fosters collaborative decision-making - Bridges the gap between scientific knowledge and local or experiential insights - Modelling philosophy & principles: - Inclusive approach: Combines stakeholder perspectives with expert modeling. - Iterative process: Stakeholders refine the model through feedback cycles. - Focuses on transparency, trust-building, and mutual learning. - Balances qualitative and quantitative inputs. - Recognizes power dynamics among stakeholders to ensure fair participation. - Strengths: - Enhances the legitimacy of models by involving affected communities. - Integrates diverse knowledge systems (scientific, local, and experiential). - Builds trust and collaboration among stakeholders. - Promotes learning and capacity-building for decision-making. - Facilitates conflict resolution and consensus-building. - Weaknesses: - Resource and time-intensive process. - May suffer from power imbalances or domination by certain stakeholders. - Subjective inputs can introduce biases into the model. - Requires skilled facilitation to manage group dynamics effectively. - Difficult to validate due to qualitative and experiential components. - Data requirements: - Stakeholder inputs through workshops, surveys, or interviews. - Contextual data for the issue being modeled (e.g., socio-economic or environmental data). - Background data to calibrate and validate the model. - Applications: - Environmental management: Developing conservation strategies or resource use plans. - Urban planning: Collaborative design of sustainable cities. - Public health: Engaging communities in managing health risks. - Climate adaptation: Co-creating strategies for mitigating climate impacts. - Possible insights generated: - Shared understanding of complex problems. - Real-world applicability of solutions through stakeholder validation. - Insights into power dynamics and value systems affecting decisions. # Input-Output Modelling (ΙΟ/ΕΕΙΟ) - Purpose: - To analyze the interdependencies between economic sectors by quantifying how outputs from one sector serve as inputs for another. - Provides a comprehensive view of economic activity, including production, consumption, and trade. - Modelling philosophy & principles: - Built on the Input-Output Table (IOT), which quantifies the monetary or physical flows between sectors. - Assumes linear, fixed input-output relationships (e.g., producing one unit of a product requires fixed amounts of inputs). - Reflects the Leontief Production Function, focusing on proportionality and no substitutability among inputs. - Strengths: - Provides a consistent and comprehensive framework for analyzing sectoral interdependencies. - Can track the direct and indirect effects of changes in demand on production and trade. - Widely supported by national statistical offices that produce standardized IOTs. - Useful for economic impact analysis and policy design. - Weaknesses: - Static framework: Models represent one point in time and cannot account for dynamic economic changes. - Assumption of uniformity: Treats all outputs and inputs within a sector as homogeneous, which oversimplifies reality. - No price mechanism: Does not consider changes in prices or wages in response to supply-demand shifts. - Limited to economic flows and does not include environmental or social dimensions. - Data requirements: - Input-Output Tables from national or regional statistics agencies. - Trade data for international analyses (when used in global I-O frameworks). - Applications: - Economic impact analysis: Examining the effects of policy changes or external shocks. - Trade analysis: Understanding sectoral dependencies in domestic and international trade. - Regional development: Assessing the economic impacts of infrastructure projects. - Possible insights generated: - Sectoral contributions to GDP and employment. - Identification of key sectors driving economic growth. - Interdependencies between production and consumption across sectors. # Environmentally-Extended Input-Output (EE-IO) Modeling - Purpose: - Extends traditional I-O modeling to include environmental dimensions, such as emissions, resource use, and energy consumption. - Links economic activities with their environmental impacts, enabling a comprehensive assessment of sustainability. - Modelling Philosophy & Principles: - Uses the Input-Output Table as a foundation, adding environmental data as additional rows and columns. - Tracks the flow of materials, energy, and emissions through production and consumption chains. - Captures embedded environmental impacts in goods and services, including those in international trade. - Maintains the fixed proportionality assumptions of traditional I-O modeling but includes environmental flows. - Strengths: - Comprehensive accounting: Captures direct and indirect environmental impacts throughout supply chains. - Facilitates carbon and material footprint analysis at the sectoral or national level. - Provides a clear framework for linking economic and environmental policies. - Useful for understanding the global impacts of local production and consumption decisions. - Weaknesses: - Shares many of the limitations of traditional I-O modeling, such as static assumptions and aggregation bias. - Requires detailed environmental data, which may not always be available or accurate. - Limited ability to analyze dynamic changes or feedback loops over time. - Simplifies complex production processes with fixed input-output relationships. - Data Requirements: - Input-Output Tables: The same as those used in traditional I-O modeling. - Environmental data: Includes information on emissions, resource extraction, energy use, and waste generation. - Trade data: For international extensions, especially in Multi-Regional Input-Output (MRIO) models. - Applications: - Carbon footprint analysis: Identifying emissions associated with production and consumption. - Material flow analysis: Tracing resource use across industries and supply chains. - Policy evaluation: Assessing the environmental impacts of economic policies and trade agreements. - Sustainability studies: Analyzing decoupling of economic growth from resource use. - Possible Insights Generated: - Identification of sectors with the highest environmental impact. - Quantification of emissions embedded in international trade flows. - Assessment of resource efficiency and decarbonization opportunities. - Insights into how economic growth contributes to or mitigates environmental degradation.