
Reading: Good Scientific Practice in Industrial Ecology  A Factsheet. This document provides researchers and students with a condensed overview of three main aspects of good scientific practice in industrial ecology: research ethics, best practice for conducting and documenting research, and research tools.
The following topics are covered:
1) Research ethics overview. Core scientific principles and good scientific conduct.
2) Best practice for carrying out, documenting, and publishing research: including recommendations for report structure and scientific writing as well as reproducible research.
3) Some stateofthe art tools and infrastructure for IE research.
IEooc_Methods_Good_Scientific_Practice

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: (+)
NOTE: An update of the slides with minor corrections is available here:
IEooc_Methods1_Lecture1_CorrectedSlides
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 and reading 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.
IEooc_Methods1_Reading4 (related journal article)
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 and energy 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
Reading material: "Guidelines for Data Modeling and Data Integration for Material Flow Analysis and SocioMetabolic Research", document with basic standards and best practice on data formats, system definition, indicator definition, use of common classifications, uncertainty treatment and sensitivity analysis, and data traceability and provenance. These guidelines were issued by the Board of the ISIE Section on Socioeconomic Metabolism (ISIESEM), and are a standard reference for all who are in the process of publishing, documenting, or archiving MFA research, either within a software such as STAN or in a custom modelling environment. Level of difficulty: (++)
IEooc_Methods2_Reading1
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
Reading material: "Concise description of application fields for different MFA approaches and indicators", deliverable D3.2 of the EU MinFuture project. This report describes the various methods of material flow analysis (MFA) that are applied to studying raw materials stocks and flows, and it lists the definitions of and reviews the major material system indicators. It also contains various case studies illustrating MFA methods and indicators. Level of difficulty: (++)
IEooc_Methods2_Reading2
Reading material: "Compilation of uncertainty approaches and recommendations for reporting data uncertainty", deliverable D3.3 of the EU MinFuture project. This report provides a systematic way to consider uncertainty in MFA and suggests a procedure for consistently communicating the uncertainty quantification approaches used in different MFA studies. Level of difficulty: (++)
IEooc_Methods2_Reading3
Reading material: "Visualising Material Systems", deliverable D3.4 of the EU MinFuture project. This report contains a detailed overview of the different visualisation principles for MFA systems. Level of difficulty: (++)
IEooc_Methods2_Reading4
Reading material: Blog entry on "Material flow acccounting and material footprint calculation" This piece introduces the method of economywide material flow accounting and defines its central flows and indicators in the system description language of material flow analysis. Level of difficulty: (++)
IEooc_Methods2_Reading5

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
NOTE: An update of the slides with minor fixes to the notation is available here:
IEooc_Methods3_Lecture1_CorrectedSlides
Thanks to Oliver Cencic (TU Vienna) for the feedback!
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
NOTE: An update of the slides with minor fixes to the notation is available here:
IEooc_Methods3_Lecture2_CorrectedSlides
Thanks to Oliver Cencic (TU Vienna) for the feedback!
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
NOTE: An update of the slides with minor fixes to the notation and a better distinction between discrete and continuous models is available here:
IEooc_Methods3_Lecture3_CorrectedSlides
Thanks to Oliver Cencic (TU Vienna) for the feedback!
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 (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 (data file)
Jupyter notebooks containting tutorials and examples for conducting material flow analysis research with ODYM (Open Dynamic Material Systems Model), which is an open software library for dynamic material flow analysis (MFA) that contains a framework for modeling biophysical stockflow relations in socioeconomic metabolism. ODYM is available and documented in a GitHub repo. Prerequisites: Calculus. Simple differential equations. Discrete and continuous random variables. Convolution. Good programming and data visualisation skills in Python. Note that in order to run some of the tutorials, you need to download and extract the zip archive IEooc_Methods3_Software38_ODYM_Tutorial_16_Material.zip linked below. Level of difficulty: (+++)
System with two processes, two parameters, one material.
Alloying elements in recycling.
Dynamic stock modelling intro.
ODYM classification and database
Estimating the material content of the global vehicle fleet
MaTrace  Tracing material flows through different product lifecycles
IEooc_Methods3_Software38 (data file)
Journal article: "A general framework for stock dynamics of populations and built and natural environments" that introduces a general mathematical framework for dynamic stock models based on balance, intrinsic, and modelapproach equations. The framework is used to classify a variety of stock models from different disciplines and discuss their applicability. The paper also introduces a matrix equation for solving stocklifetimedriven models to determine inflows given the lifetime matrix and the evolution of the stock. Level of difficulty: (++)
IEooc_Methods3_Reading2
Excel workbook with the matrix equation implementation of stockdriven modelling for material stocks in products presented by Lauinger et al. (see IEooc_Methods3_Reading2) Full implementation is a 200x200 matrix with 200 model time steps (i.e., for a modelling period of 200 years, months, or days) for use in own case studies. Prerequisites: Dynamic stock modelling, stockdriven model, matrix algebra. Level of difficulty: (++)
IEooc_Methods3_Software9 (Excel workbook)

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.
To help you get started with openLCA, GreenDelta provides free resources, including case studies, for modeling your own LCA study on their homepage.
Video on the thinking behind LCA: Prerequisites: None. Level of difficulty: (+)
IEooc_Methods4_Video1
Video on the methodology of LCA: Prerequisites: None. Level of difficulty: (+)
IEooc_Methods4_Video2
Exercise (from application section): "Transport vs. cooling of apples: a simple life cycle perspective" Objective: To quantify the energy requirements for transport and storage/cooling. Calculate greenhouse gas emissions from these processes. Comparative calculation of the CO_2 footprints of different value chains (simple comparative life cycle assessment). Prerequisites: Quantitative systems analysis. Level of difficulty: (+)
IEooc_Application3_Exercise1a (pdf)
For this exercise a sample solution is available:
IEooc_Application3_Exercise1a_SampleSolution (xlsx)
Video lecture from the application section: Bioenergy and Biomaterials from a Life Cycle Perspective.
IEooc_Application4_Lecture9
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)
Exercise from the application sectionon the concept of payback time in life cycle thinking and on how to take into account the timing of emissions and sequestration of carbon in the calculation of the global warming potential (GWP) Goal: Get familiar with the carbon intensity of different energy carriers (orders of magnitude), understand the concept of distributing upfront emissions on the subsequently produced output, breakeven emissions, and the computation of global warming impacts of emissions from a system at different times. (‘dynamic GHG accounting’). This exercise only considers GHG. Biodiversity and economic aspects of land conversion are highly relevant but are not studied here. Level of difficulty: (+++)
IEooc_Application4_Exercise6 (pdf)
For this exercise a sample solution is available:
IEooc_Application4_Exercise6 Sample 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)
Related journal paper on the topic by Heijungs et al. (2022): "A or IA? Unifying the computational structures of process and IObased LCA for clarity and consistency.
Link to paper (open access).
Advanced Life Cycle Impact Assessment: Considering time in life cycle inventories: dynamic characterization factors for greenhouse gases. Goal: Get familiar with the global warming potential of greenhouse gases and the computation of global warming impacts of emissions from a system at different times. (‘dynamic GHG accounting’). Apply dynamic GHG accounting to different test cases. Prerequisites: Calculus, global warming potential (see IEooc_Background2_Exercise2). Level of difficulty: (+++)
IEooc_Methods4_Exercise11.
IEooc_Methods4_Exercise11 (data and workbook)
For this exercise a sample solution is available:
IEooc_Methods4_Exercise11_Solution (xlsx)
Advanced tutorials and LCA exercises with Brightway2LCA. An ecoinvent license is required:
A list of Brightway2LCA tutorials and more info on this versatile and highly computationally efficient modular and opensource LCA software in Python can be found on the Brightway2LCA homepage. Brightway2LCA is developed by Chris Mutel from PSI and other contributors.
Brightway2LCA tutorial 1: A basic tutorial for learning Brightway2LCA is available from a 2017 seminar. Level of difficulty: (+++)
Brightway2LCA seminar.
Brightway2LCA tutorial 2: A comprehensive introductory tutorial for learning Brightway2LCA was developed by Maximilian Koslowski from Uni Freiburg. Level of difficulty: (+++)
New Brightway2 tutorial  Run externally in nbviewer.

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 (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 (data file (.mat) and aggregation table (.xlsx))
Software tutorial from the application section: Efficient calculation of consumptionbased environmental accounts with MRIO. This software tutorial has three goals: 1) Learn how to break down environmental footprints into subcategories: category of consumption, region where emissions occur, industries where emissions occur, etc. 2) Learn how to extract territorial and consumptionbased emissions from footprint account, and 3) Learn how to use two of the most versatile Python functions for working with table data: numpy.reshape and numpy.einsum. This tutorial contains all the steps needed to extract footprint accounts from the EXIOBASE MRIO tables and produce overview graphs such as the ones shown in the related reading material IEooc_Application3_Reading5. Prerequisites: Good understanding of MRIO, sufficient experience in working with Python. Level of difficulty: (+++)
IEooc_Application3_Software1 (data file)

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
Reading material: "Linking economywide material flow accounting to productlevel life cycle assessment." This report first explains the methods of material flow acccounting and material footprint calculations and then defines these methods and their central flows and indicators in the system description language of material flow analysis. Finally and mainly, it explains and documents an implementation of the material footprint calculation methodology for life cycle assessment (LCA) studies. With this new characterisation method, all material inflows into LCA product systems can be converted to their respective raw material equivalents and added up to the total extracted or processed material in the supply chain of goods or services. Level of difficulty: (++)
IEooc_Methods6_Reading3
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)
Reading material: Resource tracing with input output (IO) models – an overview. This reading material explains how to trace resources through inputoutput tables. First, the differences between Leontief inputoutput (IO), Leontief price, Ghosh IO and absorbing Markov Chain models are explained. Then, it is shown how they all can be used to determine the distribution of natural resource or value added input into different final demand sectors (socalled enduse shares). This reading material is the supplement of a review, conceptual work, and empirical analysis on estimating enduse shares for material flows (how many % of total steel production go into vehicles, etc.) with monetary inputoutput tables. [Link to be added after publication.]
IEooc_Methods6_Reading4_Resource_Tracing_IO.
Tracing resources through inputoutput tables. Goal: Understand the differences between Leontief inputoutput (IO), Leontief price, Ghosh IO and absorbing Markov Chain models. Learn how they all can be used to determine the distribution of natural resource or value added input into different final demand sectors (socalled enduse shares). Apply the resulting equations to a test IO table. Prerequisites: InputOutput table and model equations, matrix algebra. Level of difficulty: (+++)
IEooc_Methods6_Exercise2.
IEooc_Methods6_Exercise2_Resource_tracing_IO_Workbook (data and workbook)
For this exercise a sample solution is available:
IEooc_Methods6_Exercise2_Resource_tracing_IO_Solution (xlsx)
Reading material (blog entry) on the material implications of lowcarbon energy supply and use. The text explains the relation between the transition to lowcarbon energy and what it means for material consumption. It argues that all forms of energy supply have major downsides, and that the high material consumption of renewables is one potential problem. It quantifies the material footprint of different technologies for the energy transition and shows that while the fossil component of the material footprint declines, the metal ore component sharply rises, largely driven by the increased copper demand of electrification of enduse sectors. See IEooc_Methods2_Reading5 for the methodology of the material footprint applied here.
IEooc_Methods6_Reading5
From the LCA section: Advanced Life Cycle Impact Assessment: Considering time in life cycle inventories: dynamic characterization factors for greenhouse gases. Goal: Get familiar with the global warming potential of greenhouse gases and the computation of global warming impacts of emissions from a system at different times. (‘dynamic GHG accounting’). Apply dynamic GHG accounting to different test cases. Prerequisites: Calculus, global warming potential (see IEooc_Background2_Exercise2). Level of difficulty: (+++)
IEooc_Methods4_Exercise11.
IEooc_Methods4_Exercise11 (data and workbook)
For this exercise a sample solution is available:
IEooc_Methods4_Exercise11_Solution (xlsx)
