Integrated Socio-Ecological and Climate Modeling Summary PDF

**INTEGRATED SOCIO-ECOLOGICAL AND CLIMATE MODELLING SUMMARY** ============================================================= [Learning outcomes:] - **overview** of integrated social-ecological modelling approaches. - **basic understanding** of which modelling methods (top-down, bottom-up)...

**INTEGRATED SOCIO-ECOLOGICAL AND CLIMATE MODELLING SUMMARY** ============================================================= [Learning outcomes:] - **overview** of integrated social-ecological modelling approaches. - **basic understanding** of which modelling methods (top-down, bottom-up) are used to analyse dynamics of society-nature interaction. - **critically reflect on the strengths and weaknesses** of different modelling approaches [written exam:] Tutorial: - What are Models? - the framework we use to interpret data; - Why are they useful? - the purpose of modelling relationships between data is to try to predict how we can take more effective actions in future in support of some overall goal. - What are wicked problems? - Why are models needed to try and cope with such situations? One strategy of model development: - Exponential function - Logistic function - Time-delayed function - Lotka Volterra equations Rethink: What do they represent? What is included or excluded? What are advantages of in- or excluding certain variables, (means model is simple), and how do such decisions influence applications of models? Cohort-component models- what are their core differences to all above mentioned models? Social metabolism & the first law of thermodynamics (mass balance) A diagram of a diagram Description automatically generated Miso Model What is calculated within the models, endogenous and exogenous? Central concepts of system [dynamics] - Stocks (accumulation, value of the unit) - Flows (change overtime -- rate/time unit) - [Feedback loops (circular circularity)] - Interrelations Two types of feedback loop: - Reinforcing (positive) feedback loops -- example chicken and eggs - Balancing (negative) feedback loops - chicken and road crossings Complex vs complicated systems Steps of System dynamics modelling What are the units in causal loop diagram (defined by unit)? Determine what are stocks and flows? Left variables (auxiliary variables) Agent based modelling Complex vs complicated systems Social dynamics (complex systems) ABM used Standing ovation problem Cellular automata Advantages- will be a question Are able to represent spatial dynamics ABM in SES are used to evaluate outcomes on behaviour Scaling 3 types Quality Position of where one does stand up? Spatial Based on individual decisions 2 elements (identify agents, and their decision making) Identify interaction between objects and environment Interactions are represented as [decision tree] [Quantitative as well as qualitative data] Deterministic or probability based A simplified IO table: key components Refers to one year Dealing with this complex structure (economy, ecosystems) Cooking recipes for a national economy Input (production, usually in monetary terms, e.g. material extraction, Labor, profit income, taxes) Outputs: (of production, usually in monetary terms, e.g. trade flows, agriculture, mining, services, manufacturing) GDP is based on IO models to measure the value of all products in an economy Couple with physical inflows Intermediate use (supply chains) versus final demand Application of EE-MRIO: The Carbon Footprint Territorial emissions (production) Carbon Footprint: consumption Double counting and free (mathematically consistent, all emissions are allocated) Relates to the idea of system boundaries Double counting free manner  Participatory model building [Objectives:] Combinations of participatory methods Process leads to a technical simulation model Components of the PM process - PM process should consider: - Reasons and intentions of stakeholders - Reasons and intentions of modellers [Stakeholders' involvement]: Scoping & abstractions Evaluation of outputs and outcomes Data, facts, Logic, cross checking Climate modelling - Mainly physically based equations - Explicit integration step by step from now into the future "prognostic modelling" - Need a "closed system" therefore base model has to be global - Uncertainties come mainly from not resolved "parameterized" processes like cloud formations 1. **Socio Metabolic modelling & option spaces** ============================================= 1. Overview -------- - **MEFA (material and energy flow analysis) methods:** analysing resource/energy flows and stock accumulation in societal (sub)systems - **Consistency:** criteria derived from thermodynamics, in particular from the first law of thermodynamics (conservation of energy and matter) - **Triangulation** is applied in the analysis of complex systems -\> aims to improve understanding and achieve more robust or consistent results by combining at least two (or more) different approaches or data sources e.g. Triangulation in MFA: the "grazing gap" (no statistic reports amount of biomass grazed by livestock -\> different method) -\> approach is crucial to fill data gaps and reduce uncertainty - **Theoretical foundation:** **Mass balance:** Input = Net additions to stock + Output **Thermodynamic laws:** - Zeroth Law of Thermodynamics Statement: If two systems are each in thermal equilibrium with a third system, then they are in thermal equilibrium with each other. Implication: This law establishes the concept of temperature as a measurable and comparable property. - First Law of Thermodynamics (Law of Energy Conservation) Statement: Energy cannot be created or destroyed; it can only change forms. The total energy of an isolated system remains constant. Implication: This law introduces internal energy and ensures energy balance in thermodynamic processes. - Second Law of Thermodynamics Statement: In any natural thermodynamic process, the total entropy of a closed system will either increase or remain constant; it will never decrease. Key Concepts: Entropy (S): A measure of disorder or randomness in a system. Heat flows naturally from a hotter body to a colder one, not the reverse, without external work. Implications: This law explains the direction of thermodynamic processes and sets a limit on the efficiency of heat engines. - Third Law of Thermodynamics Statement: As the temperature of a system approaches absolute zero (0 K), the entropy of a perfect crystal approaches a constant minimum value (typically zero). Implication: Absolute zero is unattainable in practice, and systems become increasingly ordered as they approach this state. **Stocks** exist at a *point in time.* We are usually interested in their mass \[kg\] but other qualities can also be relevant (e.g., building volume). **Flows** are measured over a *period of time*, often one year We can be interested in material flows \[kg/yr\], substance flows (e.g. \[kgC/yr\] or energy flows \[J/yr\] **Balance in accounting** **summarizes an organisation or individual 's assets, equity and liabilities at a specific point in time.** **Material flow accounting** Inflows = outflows + additions to stock (corrected for balancing items, e.g. O2 or H2O) **Energy balance** primary energy input equals energy uses & losses (corrected for stock changes) 2. Strengths and Weaknesses ------------------------ - **Strengths** - Strength of balancing approach [provides consistency] criteria, i.e. constrains values and allows detection of measurement errors (e.g. if no plausible balance can be established) - **Important benefits** - can constrain uncertainties - diagnostic approach - can help estimating less well-known properties - can be developed into sophisticated models (e.g. BioBaM) that are complementary to other approaches (e.g. Integrated Assessment Models) - **Weaknesses** - **Neglect of Qualitative Factors:** Focuses on quantitative flows, ignoring social or ecosystem impacts. - **Limited Economic Integration:** Lacks financial or market analysis. - **Data Sensitivity:** Relies on accurate and consistent data, which may be unavailable. - **Static Analysis:** Often fails to capture dynamic, time-dependent changes. - **Simplifications**: Omits feedback loops and real-world complexities. - **Resource Intensive**: Requires significant time, data, and expertise. - **Variable Methodology:** Inconsistencies reduce comparability between studies. - **Policy Limitations:** Results aren't easily translated into actionable recommendations. **These can be mitigated by combining MEFA with other tools (LCA, system dynamics modelling) or improving its methodology.** 3. Applications ------------ **BioBAM\ **a biophysical model to evaluate systemic effects in global land use 2050 **Economic Integration**: Connects flows to market dynamics and policies. **Environmental Impact**: Evaluates emissions, resource use, and waste. **Circularity**: Supports recycling and sustainable resource use. - Guides bioeconomy policies and resource management. - Assesses environmental sustainability and innovation opportunities. **Limitations**: Data dependency and reliance on system assumptions. It works best alongside complementary tools. **The global carbon balance** 2. **System Dynamic Modeling** =========================== 4. Overview -------- 1. System A system is a group of interacting or interrelated elements that act according to a set of rules to form a unified whole. - A system, surrounded and influenced by its environment, is described by its boundaries, structure and purpose and is expressed in its functioning. - Systems are the subjects of study of systems theory and other systems sciences. - Systems have several common properties and characteristics, including structure, function(s), behaviour and interconnectivity. Systems can be... - Isolated systems - Closed systems - Open systems 2. Complicated vs complex systems **Complicated system:** ▪ Elements have a minimum degree of independence from each other change the system behaviour ▪ Reducible ▪ Predictable **Complex system:** Strong dependencies between elements Removal of an element leads to profound change in system behaviour (destruction) Not reducible - **System thinking** is a system of thinking about systems\ -\> consists of 3 items: - **elements**:\ describes the characteristics of systems thinking, such as the ability to do X - **interconnections**:\ this is the way the elements or characteristics feed into and relate to each other - **function/purpose or goal:**\ this should describe the purpose of systems thinking in a way that can be clearly understood and relates to everyday life **System Dynamic Modelling:** - developed/founded in the mid-1950s by Forester Industrial Dynamics (Forrester 1961) [Prerequisite:] -\> development of causal diagrams and policy-oriented computer simulations  **Central Concepts of SDM are:** - **Stocks** (accumulation) - **Flows** (change over time - rate/time unit) - **Feedback loops** (circular causality) - Reinforcing (positive) -\> self-reinforcing loops - variables develop in the same direction. - reinforce the behaviour of the system and - accelerate the growth or decrease - Balancing (negative) -\> self-correcting loops - variables develop in the opposite direction. - preserve or balance the system **Data requirements** - estimates from historical data (e.g. statistical data) - historical data (e.g. change in GDP) - qualitative interviews (stakeholder/expert interviews) - workshops on data collection - census data 5. **[Steps of System Dynamic Modelling:]** 3. **First: Causal Loop Diagram** - simple language to describe, analyse and communicate dynamic systems - consists of 3 components: [elements] (entities), [interrelations] incl. [polarities] (links) - determine the structure of a system and thus its basic behaviour - relationships between the elements are represented by an arrow and associated polarity - the polarity indicates how an entity behaves when the influencing entity changes 4. **Second: Stocks and flows diagram\ **Diagram of a diagram of sales Description automatically generated 5. Validation - structural tests (testing the plausibility of the model) - behavioural tests (historical fit) - sensitivity tests (Introduction of uncertainties) 6. Steps of SDM - Causal loop diagram FIRST!!! - Stock-flow relation 7. Quality of model depends on - System understanding - Availabledata - Validation procedures Modeling Philosophy & Principles -------------------------------- Applications ------------ Limitations ----------- - system understanding - available data - validation procedures 9. Strenghts  **Holistic Analysis**: Captures feedback loops, delays, and non-linear interactions.  **Visualization**: Simplifies communication with clear diagrams.  **Scenario Testing**: Enables \"what-if\" analysis for policy evaluation.  **Dynamic Insights**: Models short- and long-term behaviours.  **Interdisciplinary**: Useful across fields like ecology, economics, and healthcare.  **Decision Support**: Identifies leverage points and unintended consequences. 10. Weaknesses - **Complexity:** Requires expertise to build accurate models. - **Data Dependence:** Needs high-quality data for validation. - **Subjectivity:** Assumptions may lead to bias. - **Computational Load:** Large systems can be resource intensive. - **Simplifications:** Risks overlooking critical details. - **Validation Challenges:** Difficult to verify long-term predictions. - **Limited Individual Representation:** Focuses on system-level, not individual behaviours. **Climate Modeling** ==================== Basic climate processes **Fundamentals of NWP** (numerical weather prediction) - air pressure - air temperature - wind (3-dimensional vector) - humidity - density **7 Fundamental Equations** Continuity Equation (Conservation of Mass) 1^st^.Law of Thermodynamics (Conservation of Energy) T = Temperatur R = Gaskonstante cp= spezifische Wärme bei konstantem Druck J = Wärmefluß **Historical Review:** - 1946: weather forecast with computers - 1950: qualitative correct forecast for 24 hours - 1966 first global model - 1971 Foundation of the ECMWF (European Centre for Medium-Range Weather Forecasts) - 1979: 10 days prediction - Nowadays: 16 km grid, 91 layers, time step 12 min RCP (representative concentration pathway) scenarios given radiative forcing\ different paths to reach this radiative forcing are possible Data requirement ---------------- - Continuity Equation (Conservation of Mass) - 1\. Law of Thermodynamics (Conservation of Energy) - Equations of Motion (x,y Direction) - Equations of Motion (z Direction) = Geostrophic Equation - Ideal Gas Law - Water Vapor Balance Equation Applications ------------ possible insights generated by the model type --------------------------------------------- Limitations ----------- 4. **Agent-based modeling** ======================== 15. Overview -------- **Complicated vs complex systems** **Complicated system:** - Elements have a minimum degree of independence from each other - Removal of an element does not fundamentally change the system behaviour - Reducible - Predictable **Complex system:** - Strong dependencies between elements - Removal of an element leads to profound change in system behaviour (destruction) - Not reducible **15 major characteristics of complex social systems:** 1. Networks of heterogeneous social actors 2. emergence 3. endogeneity 4. non-linearity 5. scaling 6. different time scales 7. path dependence 8. delays and accumulation of stocks 9. adaptation, learning and exploitation 10. presence of surprising and counterintuitive behaviors 11. policy resistance 12. temporal trade off 13. resilience 14. local rationality 15. balance of power and narratives **Key features of social simulation:**\ learning, heterogeneity, incentives, networks - investigate problems in the social sciences using computer-assisted methods - societies as complex nonlinear systems - Computer models that simulate the behaviour of individual agents like humans, businesses, institutions, viruses, plants, animals etc. - [interaction] (agent-agent, agent-environment) - individual [characteristics, preferences] and [strategies] - [heterogeneous] agents! **Agents** **Elements of agent-based models** - identify objects (agents and their behaviour) - agents can be persons, households, vehicles, products and companies - identify interactions between objects - embed objects in an environment - results are the aggregated behaviour of the whole system - allows spatial localisation **Pre-assumptions** and **perceptions** determine modelling - qualitative (preferences, values or strategies) - or quantitative (indicators weighted by value judgements) outputs **Attributes** Different attributes can: - suppress or enable behavior - changing attributes can also modify behaviour - and knowledge about attributes of other agents influences behaviour of agents **Typologies** 1. **functional role** (e.g. household, farmer, politician) 2. **preferences**: the decision structure is the same in each role, but subtypes are formed by preferences 3. **behavioural mechanisms:** how agents achieve their goals, e.g. risk-averse-risk-taking, imitation, deliberation, repetition, etc. **Scaling - 3 possibilities** - **Scaling out\ **same model, extended data set, larger geographical unit\ Advantages: no new model necessary\ Disadvantages/Challenges: data availability, increasing processing time, adaptability to larger geographical units - **Scaling up**\ aggregate agents into institutions\ Advantages: little data required (compared to scaling out) -\> therefore preferred\ Disadvantages/Challenges: representability of interactions (do institutions behave like individuals?), need to remodel processes - **Nesting (Multi-Model Approach)**\ nesting of individual agents at institutional level\ visualizing interactions and reactions: governance regimes to individual behavior\ support for policy-makers\ typologisation as an important component **ABM in Social Ecology** - Characteristics of people and their interaction with the environment. - Ecosystem services are seen exclusively anthropocentrically - At the same time, the role of humans as part of the ecosystem is neglected. - Socio-ecological systems (SES) now allow empirical linking of biophysical aspects and human dimension - Integrated use of different data sources to model human behaviour **ABMs are no longer a completely new approach but still an innovative way to look at "old" problems** ABMs are often different from traditional models by being "unsimplified" - a new language for thinking about and describing - new (more complex) software skills - strategies for designing and analyzing models **Iterative Process of SES ABM** - Modelling happens iteratively - Refinements are made in each run - New data, framework conditions, ecological models, different decision-making structures, etc. are added stepwise **Cellular Automata** - rectangular grid of cells - each cell depends only on the state of the immediately surrounding cells according to some simple rules - can be used to model complex social behaviour e.g. segregation model by Schelling (1971) - easily translates into geographical maps (GIS) e.g. for land use modelling - easily allows spatial representation of social entities Modeling philosophy & principles -------------------------------- Applications ------------ 5. **Participative modelling** =========================== 1. Overview -------- **Participatory model building:** - combining participatory procedures with modelling techniques - gathering and integrating a diversity of viewpoints belonging to local, expert and specialized stakeholders - making the process of developing and providing an abstraction of reality a "collective process" - PM is conceptually broad: transfer of knowledge from one group to another, co-production of knowledge - no UNIQUE guidance for PM - needs a smart adaptability of processes **Objectives** - gaining a common understanding of a problem or issue - assisting collective decision-making processes - explaining implicit knowledge, preferences and values - improving the legitimacy of a model - reducing conflict - enhancing both individual and social learning - promoting creativity and innovation - investigating individual behaviour and collective dynamics in a controlled environment - informing and enhancing collective action. **Components of the PM process** [PM process should consider:] - reasons and intentions of stakeholders - reasons and intentions of modelers Actual sequence is adaptable: **Stakeholder participation:** Stakeholders are mainly involved in: - providing data for model and model calibration Evaluation of modelling results - evaluation of modelling outputs - public evaluation of outcomes **How are stakeholders selected to be engaged in PM?** - part of the earliest stage - participation can never be all-inclusive - self-selected or invited - recognized civil society or anyone - "Scale" of group is a challenge - balance between breadth and depth of engagement **Why might stakeholders be interested in participating?** - To make better informed decisions - To gain transparency in decision-making processes and built trust - To share expertise and gain particular insights from stakeholders - To address and resolve conflicts over resource use or environmental impacts by collaboratively developing scenarios - To empower by ensuring that e.g. traditional knowledge and cultural values are incorporated in the modelling process - To test scenarios for risk management (disaster preparedness, spread of diseases) - To understand and mitigate impacts of economically driven decisions based on a full understanding of the needs of others **Drivers behind Stakeholder engagement** - motivations and expectations are key factors - What motivates members of the public to participate? - Why do planners and policymakers want public and other stakeholder participation? - Many reasons: because stakeholders provide: - important local knowledge - fill in data and information gaps - a participatory process may help mobilize and justify funding **Human interactions - trust and confidence** - level of trust that citizens have in the institutions that are introducing a potential project is essential to the success of the participatory activities - modalities and sources of trust vary between countries, cultures, and socio-economic groups - transparency is an essential principle of acceptance - PM is a learning exercise between multiple actors **Treatment of Uncertainties in PM** - evaluation of input uncertainties (stakeholder input) - uncertainty propagation by models - model outputs and outcomes (making decisions based on products of PM processes requires an evaluation of uncertainties in the outputs) - output uncertainty analysis (making stakeholders aware of uncertainties) - adaptive strategies for predicted outcomes and scenarios (interactive development and use of scenarios) **Evaluation of input uncertainties** - Expert stakeholder assessment can be used for estimating the variance around parameters and outputs of models - There can be uncertainties in the knowledge of stakeholders - If uncertainty is large, more stakeholder inputs might be needed. **Uncertainty propagation by models** - Once uncertainties in the inputs are characterised, they need to be propagated through the model. - Web based tools are available to analyse uncertainty propagation - Quantifying expert judgements for assessing uncertainties of input data - Upscaling and downscaling spatio-temporal resolution - Uncertainty and sensitivity analysis - Communication and visualisation of results and uncertainties - When conditions allow the use of different modelling strategies (eg. SD and ABM) may help to elicit structural uncertainties **Model outputsand outcomes** - Making decisions based on products of PM processes requires an evaluation of uncertainties in the outputs - Output uncertainty analysis - Making stakeholders aware of uncertainties - Allowing stakeholders to manipulate key parameters - Using methods allowing fast prototyping and easy visualization methods to show stakeholders changes over time - Adaptive strategies for predicted outcomes and scenarios - Interactive development and use of scenarios - Scenario planning to encourage exploratory dialogue - Transparency facilitates discussion and encourages social learning **Participatory judgements, decisions and informed actions** a. being aware of human **biases** and heuristics b. **behaviors**: from individual to groups c. **ownership** **Being aware of human biases and heuristics** **Biases affect human judgement and cause tendencies:** - **To believe some people more than others** - **Make decisions in specific ways** **Well managed groups and processes are needed** **(b) Behaviours: from individuals to groups** - **behavioural and biological sciences have made significant progress over the last 40 years in helping us understand human motivation** **Many of our human behaviours and beliefs are understandable in the context of:** - **our evolutionary adaptation,** - **our cultural adaptation, traditions, and rituals,** - **our experiential learning acquired over individual lifetimes** - **sometimes our capability for structured, traceable thinking that involves abstraction, deductions, inferences, and logic** **(c) Ownership starts with the local, the present, and the demonstrable** - In the natural sciences participatory practices are often viewed as able to lead to better decision making - In the social and political sciences, participatory practices are often characterized as disillusioning - Reflexive, transparent participatory practices can help to learn to take greater ownership of their resources and environments 2. Strength and Weaknesses ----------------------- - bringing diverse stakeholders, experts and representatives into the process in order to enhance **quality** of data, results, learning and implementation - helping illustrate system dynamics - structuring communication processes for problem solving Modeling philosophy & principles -------------------------------- - gaining a common understanding of a problem or issue - assisting collective decision making processes - explicating tacit knowledge, preferences and values - improving the legitimacy of a model - reducing conflict - enhancing both individual and social learning - promoting creativity and innovation - investigating individual behaviors and collective dynamics in a controlled environment - informing and enhancing collective action - stakeholder selection - ID cooperation: qualitative and quantitative data and models - problem framing: find a topic that is interesting for all partners - Method: modeling is demanding for all partners, but - as long as you are aware, that expectations on results differ 1. strategies and measures for stakeholders and experts 2. knowledge on socio-economic and biophysical dynamics for scientists 6. **Input-Output modelling** (environmentally extended input-output models) ========================================================================= 4. Overview -------- **Questions** addressed: - How are inputs related to outputs? (e.g. products)? - How much raw materials, what amount of emissions or waste, result from one unit of product of a specific sector? - How will resource requirements or emissions change if demand changes, i.e. some products assume higher/lower level in total demand? **The economy is dual and circular:** - **Duality\ **products flow between two agents (buyer, seller)\ every product flow is accompanied by a monetary flow in in the opposite direction (duality of quantities and prices) - **Circularity\ **results from specialization/division of labour\ every seller of one specific product buys many other products\ -\> systems of interdependencies with many direct and indirect effects  **Balancing principles - strength of consistent accounts** - reconciling data on destination and origin of a flow would require 2 compatible reports on one event: that of the sender, the other of the recipient -\> error prone - [Alternative approach:]\ create a symmetric matrix where every trade partner is listed on both axes and [every flow between partners is entered only once]\ -\> ensures consistency and avoids double-counting - Ensures that inputs match outputs for every trade flow between two sectors - Ensures consistency and avoids double-counting Basis for economic statistics & models - Used to calculate GDP - Wassily Leontief, 1973 Nobel Prize in economics **Accounting conventions:** What are an economy's outputs? What are the inputs? - **Double book-keeping:**\ one activity´s outputs are another´s inputs\ entries are simultaneously inputs and outputs - **Balanced economic system:\ **total output equals total final demand\ total expenditures equal total income **Key concept simplified IO model** A diagram of a diagram Description automatically generated **Input-output tables within national accounts** - Income: GDP is equal to the gross value added - Expenditure: GDP is equal to value of total final demand expenditures (expenditures equal income) - Production: Total value of everything produced minus value of production for intermediate use. **From IO Tables to Environmental Extensions**  Strength and Weaknesses ----------------------- - static **Product mix & price heterogeneity** - [Strong (and unrealistic) assumption: each sector delivers one given mix of products and services at a uniform price] - [Results in „aggregation bias" if assumption is violated] - -\> can be reduced through finer sectoral disaggregation (data availability effort becomes limiting, as data demand rises quadratically with number of sectors) - Calculation of "mixed prices" also challenging 6. Data requirements ----------------- Modeling philosophy & Principles -------------------------------- **The Leontief inverse:** **Methodological assumptions behind Leontief inverse:** - a **linear** production function - **homogeneity** of product/sector outputs and homogenous prices for all deliveries - **Price-Quantity equivalency** = homogeneity of monetary and physical inter-industry 8. Applications ------------ Input-Output tables: - Basis for economic statistics & models - used to calculate GDP - Wassily Leontief, 1973 - nobel prize in economics  **EE-MRIO:** - **Territorial emissions**\ Emissions originating from production & consumption on a country\'s territory - **Carbon footprint**\ emissions anywhere on the planet "embodied" in products & services consumed within a country\'s national economy Conclusion ---------- - **Theoretical foundation: Balancing of inputs and outputs of sectors using matrix algebra. Representation of the economy that is consistent and free of double-counting.** - **Basis of GDP calculations** - **Describes structure of the economy and allows assessment of direct and indirect effects** - **Can allocate resource inputs and environmental effects (wastes, CO2, land requirements, etc.) to final demand, disaggregated by sectors/products → material footprints, carbon footprints, etc.** - **Considerable methodological and data problems (current research frontier in Ecological Economics and Industrial Ecology)**