Research group at the Faculty of Environment and Natural Resources
Background 1: Conceptual Foundations  
Introductory text about systems thinking: "Quantitative Analysis of Industrial Systems: Intellectual Framing". The text (16 pages) gives a brief introduction to systems thinking, contains a general theory for analysing coupled humanenvironment systems, and provides an explanation of what industrial ecology exactly is. IEooc_Background1_Reading1 Introductory video: 17 min video lecture about industrial ecology: IEooc_Background1_Lecture1 (part of an online course on Systems Ecology by Complexity Labs.) Classical reading: "The Economics of the Coming Spaceship Earth", by Kenneth E Boulding (1968): IEooc_Background1_Reading2 Classical reading: "Strategies for Manufacturing", by Frosch and Gallopoulus (1989): IEooc_Background1_Reading3 

Background 2: Climate, sustainability, and the contribution of industrial ecology  
Video lecture on the big picture: Climate change: IEooc_Background2_Lecture1 Video lecture on the big picture: Sustainability: IEooc_Background2_Lecture2 Video lecture on the big picture: The scaling behaviour of cities: IEooc_Background2_Lecture3 Exercise: Systems thinking for renewable energy. Learn about the main types of renewable energy, the main barriers for their implementation, and the system linkages that determine their future contribution to climate change mitigation by reading the relevant chapter of the IPCC 5th Assessment Report. Prerequisites: None. Level of difficulty: (+) IEooc_Background2_Exercise1. Chapter 7 of part III of the IPCC 4th Assessment report is the reading material for this exercise: Reading material: Chapter 7 of part III of the IPCC 4th assessment report (pdf) For this exercise a sample solution is available: IEooc_Background2_Exercise1_Solution (pdf) 

Background 3: Open science for sustainability  
Reading: Proposal for higher data transparency in industrial ecology: With the growth of the field of industrial ecology (IE), research and results have increased significantly leading to a desire for better utilization of the accumulated data in more sophisticated analyses. This implies the need for greater transparency, accessibility, and reusability of IE data, paralleling the considerable momentum throughout the sciences. It is argued that increased transparency, accessibility, and reusability of IE data will enhance IE research by enabling more detailed and reproducible research, and also facilitate metaanalyses. Two initial actions intended to advance these goals are presented: IEooc_Background3_Reading1 Reading: Guidelines for software development for industrial ecology: IEooc_Background3_Reading2 Webinar on open LCA software: IEooc_Background3_Lecture1 Reading: Input from other communities: A comment on transparency in energy system modelling: IEooc_Background3_Reading3 
Methodology 1: Basics of industrial ecology data and accounting.  
Video lecture on the basic principles of industrial ecology data modelling and accounting: material and energy flow analysis: IEooc_Methods1_Lecture1 In this lecture the definitions and basic methodology for material and energy flow accounting are presented, including the basic elements of the quantitative system definition, the process balancing equations, indicator elements, units of measurement, multilayer system descriptions, and a number of examples. Prerequisites: No advanced math is required at this stage. Level of difficulty: (+) Exercise: Locating data in a system definition and indicator development. Learn how to establish a system definition to allocate quantitative information that is given as text. Define and calculate indicators based on the system definition. Prerequisites: No advanced math is required at this stage. Level of difficulty: (+) IEooc_Methods1_Exercise1. For this exercise a sample solution is available: IEooc_Methods1_Exercise1_Solution (pdf) Reading: The supporting documents of the material and energy flow analysis software STAN are a good reference for building proper system definitions and for data modelling in material and energy flow analysis and industrial in general. An overview of the different documents can be found here. Recommended STAN reading 1: Glossary of basic systems analysis terms: IEooc_Methods1_Reading1 Recommended STAN reading 2: Principles of establishing a system definition: IEooc_Methods1_Reading2 Reading: A more theoretical paper explains the underlying system structure of material and energy flow analysis, life cycle assessment, and inputoutput analysis: Level of difficulty: (++) IEooc_Methods1_Reading3 Related video lecture (17 minutes) IEooc_Methods1_Lecture2 Video lecture on a general data model for socioeconomic metabolism: IEooc_Methods1_Lecture2 In this lecture, a general data model for locating data in the systems context is presented. It allows researchers to format data describing stocks, flows, material composition of products, lifetimes, prices, life cycle inventories, IO tables, etc. in a common structure. The data model can be used to build databases that combine data that are commonly associated with specific methods, but which are of use to many researchers. It can also be used to develop data sharing infrastructure for research groups, institutions, and the entire community. Prerequisites: No advanced math is required at this stage. Level of difficulty: (++) Exercise: Basic data reconciliation. You will learn about the principles of data reconciliation and apply data reconciliation to a simple system. You will make use of the mass balance to formulate constraints and to determine nonmeasured variables. You will understand the basics of the maximum entropy principle. Note: For this exercise a copy of "Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data" by Shankar Narasimhan and Cornelius Jordache, ISBN: 9780884152552, is required. Prerequisites: Linear programming and its application in Excel. Level of difficulty: (++) IEooc_Methods1_Exercise2 (pdf). IEooc_Methods1_Exercise2 (data). For this exercise a sample solution is available: IEooc_Methods1_Exercise2_Solution (xlsx) 

Methodology 2: Basics of material flow analysis.  
Video lecture on MFA system models and their analytical and numerical solution. Prerequisites: Matrix algebra and its implementation in Excel. Level of difficulty: (++) IEooc_Methods2_Lecture1 Video lecture on data uncertainty and sensitivity of results in MFA system models. Prerequisites: Calculus. Random variables, discrete and continuous probability distributions. Level of difficulty: (+++) IEooc_Methods2_Lecture2 Exercise: Cement production, efficiency strategies and related indicators: The goal of this exercise is to consolidate your understanding of basic quantitative system analysis. Also, to get some detailed knowledge about energy use and greenhouse gas emissions of the cement industry. Prerequisites: No advanced math required. Level of difficulty: (++) IEooc_Methods2_Exercise1. For this exercise a sample solution is available: IEooc_Methods2_Exercise1_Solution (pdf) IEooc_Methods2_Exercise1_Solution (xlsx) Exercise: Recycling systems: Efficiency strategies and uncertainty propagation: From a systems perspective, you will gain basic insights into material cycles and recycling systems using the example of beverage cans in Germany. You will conduct a sensitivity analysis, error propagation and calculation of result elasticities. Prerequisites: Calculus. Random variables and analytical error propagation. Level of difficulty: (+++) IEooc_Methods2_Exercise2. For this exercise a sample solution is available: IEooc_Methods2_Exercise2_Solution (pdf) Check also this exercise from the application section, which contains a MonteCarlo Simulation: "Inclusion of Consumption of carbon intensive materials in emissions trading. You will gain a basic systems understanding of material markets, learn about the material content of merchandise groups, error propagation, and the application of MonteCarloSimulation in material flow analysis." Prerequisites: Calculus. Random variables, discrete and continuous probability distributions, MonteCarloSimulation. Level of difficulty: (+++) IEooc_Application3_Exercise1 (pdf) IEooc_Application3_Exercise1 (data and workbook) For this exercise a sample solution is available: IEooc_Application3_Exercise1_Solution (pdf) and IEooc_Application3_Exercise1_Solution (xlsx) Video lecture on the concept 'urban metabolism' and how it can be useful to local governments. Urban metabolism studies help cities and city regions assess current resource use and identify pathways for improvement. (from UN Environment): IEooc_Methods2_Lecture3 

Methodology 3: Dynamic Material Flow Analysis.  
Video lecture introducing the basic principles of dynamic material flow analysis, the main data sources for dynamic MFA models, some examples of dynamic MFA, and the most important approaches to solving mathematical models of dynamic MFA systems: Prerequisites: Calculus. Linear difference equations, simple differential equations. Level of difficulty: (+++) IEooc_Methods3_Lecture1 Video lecture on dynamic stock models. The following concepts are introduced and explained: Population balance models, the leaching model, impulse response functions, agecohorts, and the lifetime model. Prerequisites: Calculus. Simple differential equations. Discrete and continuous random variables. Convolution. Level of difficulty: (+++) IEooc_Methods3_Lecture2 Video lecture on inflowdriven and stockdriven modelling: With inflowdriven modelling stocks can be determined from historic inflows using a convolution operation. With stockdriven modelling the inflow can be determined from a given stock scenario using inverse convolution. Prerequisites: Calculus. Simple differential equations. Discrete and continuous random variables. Convolution. Level of difficulty: (+++) IEooc_Methods3_Lecture3 Exercise: "Dynamic model of the German steel cycle, 18002008." The goals of this exercise are twofold: first, to develop a systems understanding regarding the development of flows and stocks in material cycles, using the example of the steel cycle in Germany. Second, to estimate steel stocks using dynamic stock modelling. Prerequisites: Calculus. Simple differential equations. Discrete and continuous random variables. Convolution. Level of difficulty: (+++) IEooc_Methods3_Exercise1 (pdf) IEooc_Methods3_Exercise1 (Data, xlsx) For this exercise a sample solution is available: IEooc_Methods3_Exercise1_Solution (pdf) and IEooc_Methods3_Exercise1_Solution (xlsx) Blog entry: "The lifetime of materials in the technosphere" introducing a simple dynamic MFA model of a material cycle to study the dispersion of materials in the technosphere. Prerequisites: Analytical solution of MFA systems, geometric series. Level of difficulty: (++) IEooc_Methods3_Reading1 Exercise on estimating the number of life cycles of metals: Goal of this exercise is to develop and solve a basic model of the recycling loop, to define and calculate the lifetime of a material in the technosphere and the average number of life cycles. Prerequisites: Analytical solution of MFA systems, geometric series. Level of difficulty: (++) IEooc_Methods3_Exercise2. For this exercise a sample solution is available: IEooc_Methods3_Exercise2_Solution (pdf) Jupyter notebook with a tutorial on inflowdriven and stockdriven modelling, using the dynamic_stock_model class in Python and the Chinese steel stock as an example: In this workbook it is shown how inflowdriven and stockdriven modelling can be implemented in Python using the dynamic_stock_model class. Prerequisites: Calculus. Simple differential equations. Discrete and continuous random variables. Convolution. Basic programming and data visualisation in Python. Level of difficulty: (+++) IEooc_Methods3_Software1 (Jupyter notebook) (Save link as .ipynb file!) IEooc_Methods3_Software1 (data file) Jupyter notebook with a tutorial on stockdriven modelling for material stocks in products, using the dynamic_stock_model class in Python and the global passenger vehicle fleet as an example: In this workbook it is shown how stockdriven modelling can be implemented in Python using the dynamic_stock_model class and applied to calculate the material flows and stocks in the products that we use. Prerequisites: Calculus. Simple differential equations. Discrete and continuous random variables. Convolution. Basic programming and data visualisation in Python. Level of difficulty: (+++) IEooc_Methods3_Software2 (Jupyter notebook) (Save link as .ipynb file!) IEooc_Methods3_Software2 (data file) 

Methodology 4: Life cycle assessment.  
For LCA some very good open teaching material exists. The list of open teaching material of the International Life Cycle Academy (ILCA) provides an overview of the available open content. In particular, the LCA text book is highly recommendable. It is developed by colleagues from Carnegie Mellon University in Pittsburgh. The UN Environment Life Cycle Initiative also provides LCA training material on its homepage. Video lecture on the computational structure of LCA: In this lecture the maths of LCA are explained, following the Leontief inputoutput model. First, the processes and flows that are modeled and calculated are defined and located in the system. Then, the different calculation steps are explained step by step. Prerequisites: Matrix algebra. Level of difficulty: (+++) IEooc_Methods4_Lecture1 Basic LCA exercises, no LCA software and database required: LCA basics: Simple comparative LCA: Practice systems thinking and quantitative systems analysis, work with system definitions, apply life cycle thinking to electric vehicles and electric transportation. Prerequisites: No advanced math required. Level of difficulty: (+) IEooc_Methods4_Exercise1. For this exercise a sample solution is available: IEooc_Methods4_Exercise1_Solution (pdf) LCA basics: Processbased LCA: Practice systems thinking and quantitative systems analysis, work with system definitions, apply life cycle thinking to solar power by conducting a quick processbased LCA of PV module production. Prerequisites: No advanced math required. Level of difficulty: (+) IEooc_Methods4_Exercise2. IEooc_Methods4_Exercise2 (data and workbook) For this exercise a sample solution is available: IEooc_Methods4_Exercise2_Solution (xlsx) LCA basics: Matrix algebra and the LCA master equation: Apply the life cycle perspective, understand the computational structure of LCA, understand and implement basic matrix algebra operations on paper. Prerequisites: Matrix algebra. Level of difficulty: (++) IEooc_Methods4_Exercise3. For this exercise a sample solution is available: IEooc_Methods4_Exercise3_Solution (xlsx) LCA basics: LCA with matrix algebra in Excel: Understand the computational structure of LCA, understand and implement basic matrix algebra operations in Excel. Prerequisites: Matrix algebra. Level of difficulty: (++) IEooc_Methods4_Exercise4. IEooc_Methods4_Exercise4 (data and workbook) For this exercise a sample solution is available: IEooc_Methods4_Exercise4_Solution (xlsx) LCA basics: Life Cycle Impact Assessment: Practice life cycle thinking, work with the LCIA method LC impact, calculate regional endpoint indicators, understand and implement basic matrix algebra operations. Prerequisites: Matrix algebra. Level of difficulty: (++) IEooc_Methods4_Exercise5. IEooc_Methods4_Exercise5 (data and workbook) For this exercise a sample solution is available: IEooc_Methods4_Exercise5_Solution (pdf) IEooc_Methods4_Exercise5_Solution (xlsx) Advanced LCA exercises with openLCA. An ecoinvent license is required: A list of openLCA tutorials and info videos can be found on GreenDelta's Youtube channel. Getting started with openLCA: The goal of this tutorial is to install and learn how to use the openLCA software for life cycle assessments using ecoinvent v3.2 and several impact assessment methods. The use of parameters, choice of electricity mix, sensitivity analysis, export of data, and a small test case are described. Level of difficulty: (++) IEooc_Methods4_Exercise6. Modifying processes in openLCA: Copy processes, modify processes, change the electricity source, and conduct a comparative LCA of different steel recycling routes. Level of difficulty: (++) IEooc_Methods4_Exercise7. For this exercise a sample solution is available: IEooc_Methods4_Exercise7_Solution (pdf) Allocation and recycling in ecoinvent: Learn how waste treatment, recycling, and allocation are handled in ecoinvent and openLCA. Level of difficulty: (+++) IEooc_Methods4_Exercise8. For this exercise a sample solution is available: IEooc_Methods4_Exercise8_Solution (pdf) Other advanced LCA exercises: Reading exercise on a comparative LCA of electric and conventional passenger vehicles: Understand the content and policy relevance of a recent LCA research article on electric transportation. IEooc_Methods4_Exercise9. Material for reading exercise: IEooc_Methods4_Exercise9_Reading. The matrix method for LCA: Equivalence of two approaches: Learn more about the two matrix approaches to LCA: The Heijungs and Suh (2002) technology matrix and the Leontief inputoutput model. Show that both approaches are equivalent. Level of difficulty: (+++) IEooc_Methods4_Exercise10. For this exercise a sample solution is available: IEooc_Methods4_Exercise10_MatrixMethods_Solution (pdf) IEooc_Methods4_Exercise10_MatrixMethods_Solution (xlsx) 

Methodology 5: Inputoutput analysis.  
Lecture on the basics of inputoutput analysis, IO tables and the Leontief IO model, part I: Prerequisites: Matrix algebra. Level of difficulty: (++) IEooc_Methods5_Lecture1_Part1 Lecture on the basics of inputoutput analysis, IO tables and the Leontief IO model, part II: Prerequisites: Matrix algebra. Level of difficulty: (++) IEooc_Methods5_Lecture1_Part2 Lecture on the basics of inputoutput analysis, IO tables and the Leontief IO model, part III: Prerequisites: Matrix algebra. Level of difficulty: (++) IEooc_Methods5_Lecture1_Part3 Lecture on the basics of inputoutput analysis, IO tables and the Leontief IO model, part IV: Prerequisites: Matrix algebra. Level of difficulty: (++) IEooc_Methods5_Lecture1_Part4 Exercise on IO basics: This is an introductory exercise to IO analysis, covering the mathematical basics of IO modelling and the system structure of IO models. Prerequisites: Matrix algebra on paper and Excel. Level of difficulty: (+++) IEooc_Methods5_Exercise1. IEooc_Methods5_Exercise1 (data and workbook) For this exercise a sample solution is available: IEooc_Methods5_Exercise1_Solution (pdf) IEooc_Methods5_Exercise1_Solution (xlsx) Lecture on multiregional inputoutput analysis. Prerequisites: Matrix algebra on paper and Excel. Level of difficulty: (+++) IEooc_Methods5_Lecture2 Exercise: "Multiregional inputoutput analysis (Excelbased)." This exercise contains a simple application of the MRIO analysis: construction of supply chains, carbon footprint calculations of final consumers in the EU, investigation of fine particulate matter and mercury emissions along the supply chain. Prerequisites: Matrix algebra on paper and Excel. Level of difficulty: (+++) IEooc_Methods5_Exercise2 (pdf) IEooc_Methods5_Exercise2 (data and workbook) For this exercise a sample solution is available: IEooc_Methods5_Exercise2_Solution (pdf) and IEooc_Methods5_Exercise2_Solution (xls) Jupyter notebook with a tutorial for calculating consumptionbased emissions and breaking them down into products, region, and industry. Prerequisites: Matrix algebra, basic Python programming. Level of difficulty: (+++) IEooc_Methods5_Software1 (Jupyter notebook) (Save link as .ipynb file!) IEooc_Methods5_Software1 (data file) Jupyter notebook with functions and a tutorial for aggregating MRIO results along the products, region, and industry dimensions. A 163 products x 48 regions x 163 industries footprint result is aggregated to 11 product groups, six regions, and five industrial sectors. Prerequisites: Matrix algebra, Python programming. Level of difficulty: (+++) IEooc_Methods5_Software2 (Jupyter notebook) (Save link as .ipynb file!) IEooc_Methods5_Software2 (data file (.mat) and aggregation table (.xlsx)) 

Methodology 6: Method integration.  
Reading material (blog entry) on the differences between processbased LCA and monetary MRIO. Knowing about these differences is important when comparing MRIO and LCA results and when combining the two methods. IEooc_Methods6_Reading1 Reading material (book chapter) on prospective (forwardlooking) assessment of sustainable development strategies using industrial ecology tools. In this text the general principles of prospective modeling are lined out and the current development status of two prospective model types is described: extended dynamic material flow analysis and THEMIS (TechnologyHybridized EnvironmentalEconomic Model with Integrated Scenarios). These models combine the high level of technological detail known from lifecycle assessment (LCA) and material flow analysis (MFA) with the comprehensiveness of, respectively, dynamic stock models and input/output analysis (I/O). These models are dynamic; they build future scenarios with a time horizon until 2050 and beyond. They were applied to study the potential effect of a wide spectrum of sustainable development strategies, including renewable energy supply, home weatherization, material efficiency, and lightweighting. IEooc_Methods6_Reading2 Exercise: "Passenger vehicle lightweighting. A quantitative analysis of the coupling between the transportation and material production sectors. Application of material flow analysis and life cycle assessment in a common framework." Estimate the systemwide impact of a climate change mitigation strategy in a specific sector. Learn about lightweighting of vehicle as a strategy to reduce GHG emissions on the medium scale. Prerequisites: No advanced math required. Level of difficulty: (+) IEooc_Methods6_Exercise1 (pdf) For this exercise a sample solution is available: IEooc_Methods6_Exercise1_Solution (pdf) and IEooc_Methods6_Exercise1_Solution (xlsx) 
Application 1: Sociometabolic regimes and transitions  
Webinar about sociometabolic regimes, from the ISIE webinar series on socioeconomic metabolism: IEooc_Application1_Lecture1 Exercise on land constraints in agricultural societies: Develop a simple engineering model, learn about the physical distance and population constraints in the agricultural society, and estimate the area yield of modern renewable energy technologies. Prerequisites: Calculus. Level of difficulty: (+++) IEooc_Application1_Exercise1. For this exercise a sample solution is available: IEooc_Application1_Exercise1_Solution (pdf) and IEooc_Application1_Exercise1_Solution (xlsx) Exercise on decoupling emissions from societal development at the large scale  The IPAT equation: Understand how population, affluence, and a cap for a certain emission to the environment determine how industry must decouple from that emission. Learn about and apply the IPAT equation and link it to global climate and development targets. Prerequisites: Exponential function. Level of difficulty: (++) IEooc_Application1_Exercise2. For this exercise a sample solution is available: IEooc_Application1_Exercise2_Solution (pdf) 

Application 2: Circular economy  
Introductory blog entry: Circular economy: Breakthrough or distraction? IEooc_Application2_Reading1 Core reading: "Critical appraisal of the circular economy standard BS 8001:2017 and a dashboard of quantitative system indicators for its implementation in organizations": IEooc_Application2_Reading2 Blog entry about circular economy and inuse stocks: In this piece the role played by inuse stocks of products, buildings, and infrastructure in closing material cycles (or the 'circular economy transition') is highlighted. IEooc_Application2_Reading3 Exercise on estimating the number of life cycles of metals (from methods section): Goal of this exercise is to develop and solve a basic model of the recycling loop, to define and calculate the lifetime of a material in the technosphere and the average number of life cycles. Prerequisites: Analytical solution of MFA systems, geometric series. Level of difficulty: (++) IEooc_Methods3_Exercise2. For this exercise a sample solution is available: IEooc_Methods3_Exercise2_Solution (pdf) 

Application 3: Supply chain studies  
Research article about environmental footprints of households by regions: This study develops an inventory of carbon footprints associated with household consumption for 177 regions in 27 EU countries, thus, making a key contribution for the incorporation of consumptionbased accounting into local decisionmaking. IEooc_Application3_Reading1 Blog entry about the current limits and possible extensions of emissions trading: A new policy proposal recommends charging consumers of emissions intensive materials such as steel and aluminium for the carbon emissions of material production. The proposal was developed to be considered for implementation in Phase IV of the EU Emissions Trading System commencing in 2021. Material flow cost accounting was applied to quantify the distribution of the carbon charge across commodity groups and to estimate the resulting price changes. IEooc_Application3_Reading2 Related Policy paper about "Inclusion of Consumption of carbon intensive materials in emissions trading": IEooc_Application3_Reading3 Related assessment of "Inclusion of Consumption of carbon intensive materials in emissions trading" using material flow cost accounting: IEooc_Application3_Reading4 Exercise: "Inclusion of Consumption of carbon intensive materials in emissions trading. You will gain a basic systems understanding of material markets, learn about the material content of merchandise groups, error propagation, and the application of MonteCarloSimulation in material flow analysis." Prerequisites: Calculus. Random variables, discrete and continuous probability distributions, MonteCarloSimulation. Level of difficulty: (+++) IEooc_Application3_Exercise1 (pdf) IEooc_Application3_Exercise1 (data and workbook) For this exercise a sample solution is available: IEooc_Application3_Exercise1_Solution (pdf) and IEooc_Application3_Exercise1_Solution (xlsx) 