The Money View and Marx’ s Theory of Money and Credit. Revised Version1
By Karen Helveg Petersen2
February 2021
1 Revised version. The first draft was prepared for the AHE Conference (webinar) on July 17, 2020. 2 Ph.D. Independent researcher, author of Rent Capitalism: Economic Theory and Global Reality. Copenhagen: Frydenlund 2017 (in Danish).
Mail: zkarenhelveg@gmail.com.
Paper prepared for presentation at the AHE 22nd Annual Conference, July 17, 2020 and at the Marx Now Conference 2020, October 9, 2020
This chapter discusses how the widespread adoption of blockchain technology (distributed ledgers) might contribute to solving a larger class of economic problems related to systemic risk, specifically the degree of systemic risk in financial networks (ongoing credit relationships between parties). The chapter introduces economic network theory, drawing from König and Battiston (2009). Then, Part I develops payment network analysis (analyzing immediate cash transfers) in the classical payment network setting (Fedwire (Soramäki 2007)) synthesized with the cryptocurrency environment (Bitcoin (Maesa 2017), Monero (Miller 2017), and Ripple (Moreno-Sanchez et al. 2018)). The key finding is that the replication of network statistical behavior in cryptographic networks indicates the robust (not merely anecdotal) adoption of blockchain systems. Part II addresses balance sheet network analysis (ongoing obligations over time), first from the classical sense of central bank balance sheet network analysis developed by Castrén (2009, 2013), Gai and Kapadia (2010), and Chan-Lau (2010), and then proposes how blockchain economic networks might help solve systemic risk problems. The chapter concludes with the potential economic and social benefits of blockchain economic networks, particularly as a new technological affordance is created, algorithmic trust, to support financial systems.
Business Transformation through Blockchain: Volume I
Editors Horst Treiblmaier, Roman Beck Publisher Springer, 2018 ISBN 3319989111, 9783319989112 Length 290 pages
A systems thinking approach to reimagining innovation models: The example of clean hydrogen
Sue McAvoy1 | Cristyn Meath2 Agnes Toth-Peter2 |
Ninad Jagdish3. Jurij Karlovsek4.
1Centre for the Business and Economics of Health (CBEH), Faculty of Business, Economics and Law, The University of Queensland, Brisbane, Queensland, Australia
2Australian Institute for Business and Economics, The University of Queensland, Brisbane, Queensland, Australia
3School of Civil Engineering, The University of Queensland, Brisbane, Queensland, Australia
4BTN Pty Ltd, Singapore, Singapore
Correspondence
Sue McAvoy, Centre for the Business and Economics of Health (CBEH) and the School of Business, The University of Queensland, St Lucia, Brisbane, Queensland 4072, Australia.
МІНІСТЕРСТВО ОСВІТИ І НАУКИ УКРАЇНИ МАРІУПОЛЬСЬКИЙ ДЕРЖАВНИЙ УНІВЕРСИТЕТ ЕКОНОМІКО-ПРАВОВИЙ ФАКУЛЬТЕТ КАФЕДРА ЕКОНОМІКИ ТА МІЖНАРОДНИХ ЕКОНОМІЧНИХ ВІДНОСИН
До захисту допустити: Зав. кафедри «__»________2024 р.
Кваліфікаційна робота за освітнім ступенем «Магістр» на тему: «Соціально-економічний розвиток України в контексті поглиблення євроінтеграційних процесів»
International Society for the Systems Sciences (ISSS) in 1988
American Society for Cybernetics
Key Scholars
Ervin Laszlo
Norbert Wiener
Ludwig von Bertalanffy
George J. Klir
Howard Pattee
Jay Forrester
George Richardson
Fritjof Capra
James Grier Miller
Gregory Bateson
Niklas Luhmann
Heinz von Foerster
Archie J. Bahm
Kenneth Boulding
W. Ross Ashby
C. W. Churchman
Mario Bunge
Herbert A. Simon
Robert Rosen
Stafford Beer
Anatol Rapoport
Ralph Gerard
Russell Ackoff
Erich Jantsch
Ralph Abraham
Stuart Kauffman
Louis Kauffman
Humberto Maturana
Alfred North Whitehead
Paul A. Weiss
Kurt Lewin
Roy R. Grinker
William Gray
Nicolas Rizzo
Karl Menninger
Silvano Arieti
Peter Senge
FIVE TYPES OF SYSTEMS PHILOSOPHY
Source: FIVE TYPES OF SYSTEMS PHILOSOPHY
Atomism
Holism
Emergentism
Structuralism
Organicism
Source: FIVE TYPES OF SYSTEMS PHILOSOPHY
Bunge’s three types of systems philosophies are expanded to five: atomism (the world is an aggregate of elements, without wholes; to be understood by analysis), holism (ultimate reality is a whole without parts, except as illusory manifestations; apprehended intuitively), emergentism (parts exist together and their relations, connections, and organized interaction constitute wholes that continue to depend upon them for their existence and nature; understood first analytically and then synthetically), structuralism (the universe is a whole within which all systems and their processes exist as depending parts; understanding can be aided by creative deduction), and organicism (every existing system has both parts and whole, and is part of a larger whole, etc.; understanding the nature of whole-part polarities is a clue to understanding the nature of systems. How these five types correlated with theories of conceptual systems and methodologies is also sketched.
Source: Five systems concepts of society
Bunge’s three “concepts of society” exemplify three types of systems philosophy. This article criticizes Bunge’s analysis as minimally inadequate by expanding his range to five concepts of society which exemplify five kinds of systems philosophy: individualism, emergentism, organicism, structuralism, and holism. Emphasis is given to stages in the development of emergentism, including cybernetics (four stages), systems theory (eight stages), and holonism, and then to opposing structuralism (four examples). Organicism as a type of systems philosophy and concept of society is constructed by incorporating the constructive claims of both emergentism and structuralism and by overcoming oppositions to them systematically.
Source: Holons: Three conceptions
Recent advances in systems theory have required a new term, ‘holon’ (a whole of parts functioning as part of a larger whole). These advances are complicated by differing interpretations provided by three competing kinds of general systems theory: Emergentism, structuralism and organicism. For emergentism, use of the term signifies a shift in emphasis from focusing on the dynamic equilibrium between a whole and its parts to that between the whole and the larger whole of which it is a part. For structuralism, the term serves in explaining subsystem adaptation to environmental and hierarchical constraints and determinations by invariant principles. By incorporating ideas from both emergentism and structuralism into its more intricate interpretations, the author claims that organicism presents a more adequate conception of the nature of holons—now regarded as essential to general systems thinking.
Source: Comparing civilizations as systems
Comparison of Western, Indian and Chinese civilizations as cultural systems exhibiting persisting ideals constituting important structural differences reveals that two taproots of Western civilization (the Hebraic stressing will and the Greek stressing reason) as characteristics essential to the nature of the world and man, are opposed in Hindu culture idealizing Nirguna Brahman as complete absence of both will (desire) and reason (distinctions) and yogic endeavor designed to eliminate both from persons, are partially integrated as complementary opposites in Chinese taoistic yin-yang ideals about both the universe and man. Opportunities for further research comparing cultural systems seem unlimited.
Source: Systemism: the alternative to individualism and holism
Systems Philosophy
Source: Systems Philosophy
Source: Systems Philosophy
Source: Systems Philosophy
Source: Systems Philosophy
Source: Systems Philosophy
Source: Systems Philosophy
Source: Systems Philosophy
Source: Systems Philosophy
Source: Introduction to Systems Philosophy
First Published in 1972, Introduction to Systems Philosophy presents Ervin Laszlo’s first comprehensive volume on the subject. It argues for a systematic and constructive inquiry into natural phenomenon on the assumption of general order in nature. Laszlo says systems philosophy reintegrates the concept of enduring universals with transient processes within a non-bifurcated, hierarchically differentiated realm of invariant systems, as the ultimate actualities of self-structuring nature. He brings themes like the promise of systems philosophy; theory of natural systems; empirical interpretations of physical, biological, and social systems; frameworks for philosophy of mind, philosophy of nature, ontology, epistemology, metaphysics and normative ethics, to showcase the timeliness and necessity of a return from analytic to synthetic philosophy. This book is an essential read for any scholar and researcher of philosophy, philosophy of science and systems theory.
Source: General Systems Theory: Foundations, Development, Applications
Source: General Systems Theory: Foundations, Development, Applications
Source: General Systems Theory: Foundations, Development, Applications
Source: Systems Theory as the Foundation for Understanding Systems
Source: Systems Theory as the Foundation for Understanding Systems
Source: Systems Theory as the Foundation for Understanding Systems
Source: Feedback Thought in Social Science and Systems Theory
On the Philosophical Ontology for a General System Theory
CUI Weicheng Key Laboratory of Coastal Environment and Resources of Zhejiang Province (KLaCER) School of Engineering, Westlake University, Hangzhou, China
Philosophy Study, June 2021, Vol. 11, No. 6, 443-458
In Systems: New Paradigms for the Human Sciences edited by Gabriel Altmann and Walter A. Koch, 337-349. Berlin, New York: De Gruyter, 1998. https://doi.org/10.1515/9783110801194.337
Mario Bunge: A Centenary Festschrift
Michael R. Matthews
Springer International Publishing, Aug 1, 2019 – 827 pages
Systemism: the alternative to individualism and holism
Mario Bunge
The Journal of Socio-Economics Volume 29, Issue 2, 2000, Pages 147-157
Emergence and Evidence: A Close Look at Bunge’s Philosophy of Medicine
Rainer J. Klement 1,* and Prasanta S. Bandyopadhyay 2
1Department of Radiation Oncology, Leopoldina Hospital Schweinfurt, Robert-Koch-Straße 10, 97422 Schweinfurt, Germany 2 Department of History & Philosophy, Montana State University, Bozeman, MT, 59717, USA * Correspondence: rainer_klement@gmx.de; Tel.: +49-9721-7202761
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Billingham J. 2014b. In search of GST. Position paper for the 17th Conversation of the International Federation for Systems Research on the subject of ‘Philosophical Foundations for the Modern Systems Movement’, St. Magdalena, Linz, Austria, 27 April – 2 May 2014. (pp. 1– 4).
Billingham J. 2015. GST* as the unifying theory of the Systems Sciences. In Systems Philosophy and Its Relevance to Systems Engineering, D Rousseau, J Wilby, J Billingham, S Blachfellner (eds.). Workshop held on 11 July 2015 at the International Symposium of the International Council on Systems Engineering (INCOSE) in Seattle, Washington, USA. Available at https://sites.google.com/site/syssciwg2015iw15/systems-science-workshop-at-is15
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Keeping materials longer in the economy through reuse, re-purposing or recycling could reduce 33 per cent of the carbon dioxide emissions embedded in products.
Circularity requires a significant bridge between trade in goods and trade in services.
Increased recycling could reduce demand for primary resources, leading to both risks and opportunities in developing countries dependent on the extraction of natural resources.
CIRCULAR ECONOMY: THE NEW NORMAL?
Linear production is a familiar cycle. Resources are extracted and transformed into goods and services, sold and used, after which they are scrapped. This model has underpinned the expansion of the global economy since the industrial revolution.
It has linked material prosperity to the extraction of resources, yet has often overlooked the undue pressures placed on the environment and has rarely considered the cost of handling, scrapping and disposing of used materials, some of which are hazardous to human health. As the global population increases, incomes rise and nations strive to eradicate poverty, demand for goods and services will necessarily grow. The aim of achieving Sustainable Development Goal 12 on responsible consumption and production requires changing the linear production model. The concept of a circular economy and practice therefore merits close attention, as it can open new opportunities for trade and job creation, contribute to climate change mitigation and help reduce the costs of cleaning and scrapping in both developed and developing countries.
A circular economy entails markets that give incentives to reusing products, rather than scrapping them and then extracting new resources. In such an economy, all forms of waste, such as clothes, scrap metal and obsolete electronics, are returned to the economy or used more efficiently. This can provide a way to not only protect the environment, but use natural resources more wisely, develop new sectors, create jobs and develop new capabilities.
Each year, 1.3 billion tons of garbage are produced by 3 billion urban residents.1 This is the end point of a linear economic flow that starts with manufacturing, which uses 54 per cent of the world’s delivered energy, especially in energy-intensive industries such as petrochemicals, cement, metals and paper.2 Each year, 322 million tons of plastic, 240 million tons of paper and 59 million tons of aluminium are produced in the world, much of which goes to export markets and is not recycled.3
A rusty container or an obsolete mobile telephone are only two examples of the many products that end up being discarded, along with their transistors, metal structures and complex plastics. Each component requires a great deal of energy, time, land and capital to be produced and, even as the products become obsolete, their components often do not. The potential value of metals and plastics currently lost in electronic waste may be €55 billion annually.4
As the supply of recycled, reused and re-manufactured products increases, such products are maintained for longer in the economy, avoiding their loss to landfills. Food losses could be halved through food- sharing and discounting models that reduce fresh food waste. Access to efficient home appliances could be increased through leasing instead of sales. Organic waste could be recovered or transformed into high-value protein through the production of insect larvae.
Benefits such as these could be gained by both developed and developing countries; the potential economic gains are estimated at over $1 trillion per year in material cost savings.5 Several economies are already exploring circular strategies, including Brazil, China, India, Kenya, the Lao People’s Democratic Republic, Morocco, South Africa, Turkey, Uruguay, VietNam and the European Union.6 India and the European Union stand to gain savings of $624 billion and €320 billion, respectively.7
The effects of increased recycling on global value chains are an important area for research. For example, a circular model for metals implies an increase in the re-purposing, reuse and recycling of such materials. This can transform end points of the value chain, such as junkyards and dumping sites for metals, into new reprocessing hubs that supply metals to markets. This growth trend in recycling markets may be desirable from an environmental perspective, yet could reduce demand for primary resources, requiring an adjustment in employment, logistics and scal structures in countries dependent on the extraction of natural resources.8 At the same time, growth in the recycling, re-purposing and reuse of materials could support the emergence of regional reprocessing and recycling hubs and open new opportunities for the commodities and manufacturing sectors. Greater circularity could reduce the depreciation of physical capital in the economy, increasing overall wealth in societies. The specific benefits that developing countries could obtain by adopting formal circular economy strategies is a new subject for research, and further studies and data are needed.
Circularity can change trade patterns and improve the utilization of idle capacity
Circular models could help countries grow with resources already available in their territories. This may imply a reduction in international trade, yet the 140 million people joining the middle class each year guarantee growth in overall trade.9 Such growth may occur not in goods but in services such as access-over-ownership models.10 In addition, increased circularity can change production patterns, improving asset utilization rates and producing value chains based on recycling and re-manufacturing centres close to where products are used. This could lead to fewer transport-related losses, quicker turnarounds between orders and deliveries, lower levels of carbon dioxide emissions and the creation of jobs that cannot be offshored.
Some countries have trade surpluses in physical goods and others in immaterial services. Trade therefore results in a net transfer of materials from one region to another as seen, for example, in trade patterns between China and the United States. The United States imports many goods from China but does not export nearly as many finished goods in return. However, nearly 3,700 containers of recyclables per day are exported to China; in 2016, such exports amounted to 16.2 million tons of scrap metal, paper and plastics worth $5.2 billion.11
Key Terms:
Circular Economy
Cradle to Cradle
Closed Supply Chains
Industrial Ecology
Reverse Ecology
Blue Economy
Regenerative Design
Performance Economy
Natural Capitalism
Bio-mimicry
Doughnut Economics
From Input to the European Commission from European EPAs about monitoring progress of the transition towards a circular economy in the European Union
Material flow analysis (MFA) is a systematic assessment of the flows and stocks of materials within a system defined in space and time. It connects the sources, the pathways, and the intermediate and final sinks of a material. Because of the law of the conservation of matter, the results of an MFA can be controlled by a simple material balance comparing all inputs, stocks, and outputs of a process. It is this distinct characteristic of MFA that makes the method attractive as a decision-support tool in resource management, waste management, and environmental management.
An MFA delivers a complete and consistent set of information about all flows and stocks of a particular material within a system. Through balancing inputs and outputs, the flows of wastes and environmental loadings become visible, and their sources can be identified. The depletion or accumulation of material stocks is identified early enough either to take countermeasures or to promote further buildup and future utilization. Moreover, minor changes that are too small to be measured in short time scales but that could slowly lead to long-term damage also become evident.
Anthropogenic systems consist of more than material flows and stocks (Figure 1.1). Energy, space, information, and socioeconomic issues must also be included if the anthroposphere is to be managed in a responsible way. MFA can be performed without considering these aspects, but in most cases, these other factors are needed to interpret and make use of the MFA results. Thus, MFA is frequently coupled with the analysis of energy, economy, urban planning, and the like.
In the 20th century, MFA concepts have emerged in various fields of study at different times. Before the term MFA had been invented, and before its comprehensive methodology had been developed, many researchers used the law of conservation of matter to balance processes. In process and chemical engineering, it was common practice to analyze and balance inputs and outputs of chemical reactions. In the economics field, Leontief introduced input–output tables in the 1930s, thus laying the base for widespread application of input–output methods to solve economic problems. The first studies in the fields of resource conservation and environmental management appeared in the 1970s. The two original areas of application were (1) the metabolism of cities and (2) the analysis of pollutant pathways in regions such as watersheds or urban areas. In the following decades, MFA became a widespread tool in many fields, including process control, waste and wastewater treatment, agricultural nutrient management, water-quality management, resource conservation and recovery, product design, life cycle assessment (LCA), and others.
Substance Flow Analysis
From Feasibility assessment of using the substance flow analysis methodology for chemicals information at macro level
SFA is used for tracing the flow of a selected chemical (or group of substances) through a defined system. SFA is a specific type of MFA tool, dealing only with the analysis of flows of chemicals of special interest (Udo de Haes et al., 1997). SFA can be defined as a detailed level application of the basic MFA concept tracing the flow of selected chemical substances or compounds — e.g. heavy metals (mercury (Hg), lead (Pb), etc.), nitrogen (N), phosphorous (P), persistent organic substances, such as PCBs, etc. — through society.
An SFA identifies these entry points and quantifies how much of and where the selected substance is released. Policy measures may address these entry points, e.g. by end‐of‐pipe technologies. Its general aim is to identify the most effective intervention points for policies of pollution prevention. According to Femia and Moll (2005), SFA aims to answer the following questions:
• Where and how much of substance X flows through a given system?
• How much of substance X flows to wastes?
• Where do flows of substance X end up?
• How much of substance X is stored in durable goods?
• Where could substance X be more efficiently utilised in technical processes?
• What are the options for substituting the harmful substance?
• Where do substances end up once they are released into the natural environment?
When an SFA is to be carried out, it involves the identification and collection of data on the one hand, and modelling on the other. According to van der Voet et al. (OECD, 2000), there are three possible ways to ‘model’ the system:
Accounting (or bookkeeping) The input for such a system is the data that can be obtained from trade and production statistics. If necessary, further detailed data can be recovered on the contents of the specific substances in those recorded goods and materials. Emissions and environmental fluxes or concentration monitoring can be used for assessing the environmental flows. The accounting overview may also serve as an identification system for missing or inaccurate data.
Missing amounts can be estimated by applying the mass balance principle. In this way, inflows and outflows are balanced for every node, as well as for the system as a whole, unless accumulation within the system can be proven. This technique is most commonly used in material flow studies, and can be viewed as a form of descriptive statistics. There are, however, some examples of case studies that specifically address societal stocks, and use these as indicator for possible environmental problems in the future (OECD, 2000).
Static modelling is the process whereby the network of flow nodes is translated into a mathematical ‘language’, i.e. a set of linear equations, describing the flows and accumulations as inter‐dependent. Emission factors and distribution factors over the various outputs for the economic processes and partition coefficients for the environmental compartments can be used as variables in the equations. A limited amount of substance flow accounting data is also required for a solution of the linear equations. However, the modelling outcome is determined largely by the substance distribution patterns.
Static modelling can be extended by including a so‐called origin analysis in which the origins of one specific problematic flow can be traced on several levels. Three levels may be distinguished:
• direct causes derived directly from the nodes balance (e.g one of the direct causes of cadmium (Cd) load in soil is atmospheric deposition);
• economic sectors (or environmental policy target groups) directly responsible for the problem. This is identified by following the path back from node to node to the point of emission (e.g. waste incineration is one of the economic sectors responsible for the cadmium load in soil);
• ultimate origins found by following the path back to the system boundaries (e.g. the extraction, transport, processing and trade of zinc (Zn) ore is one of the ultimate origins of the cadmium load in soil).
Furthermore, the effectiveness of abatement measures can be assessed with static modelling by recording timelines on substances (OECD, 2000).
Dynamic modelling is different to the static SFA model, as it includes substance stocks accumulated in society as well as in various materials and products in households and across the built‐up environments.
For SFA, stocks play an important role in the prediction of future emissions and waste flows of products with a long life span. For example, in the case of societal stocks of PVC, policy makers need to be supplied with information about future PVC outflows. Today’s stocks become tomorrow’s emissions and waste flows. Studies have been carried out on the analysis of accumulated stocks of metals and other persistent toxics in the societal system. Such build‐ups can serve as an ‘early warning’ signal for future emissions and their potential effects, as one day these stocks may become obsolete and recognisably dangerous, e.g. as in the case of asbestos, CFCs, PCBs and mercury in chlor‐alkali cells. As the stocks are discarded, they end up as waste, emissions, factors of risks to environment and population. In some cases, this delay between inflow and outflow can be very long indeed.
Stocks of products no longer in use, but not yet discarded, are also important. These stocks could include: old radios, computers and/or other electronic equipment stored in basements or attics, out‐of‐use pipes still in the ground, obsolete stocks of chemicals no longer produced but still stored, such as lead paints and pesticides. These ‘hibernating stocks’ are likely to be very large, according to OECD estimates (2000). Estimating future emissions is a crucial issue if environmental policy makers are to anticipate problems and take timely, effective action. In order to do this, stocks cannot be ignored. Therefore, when using MFA or SFA models for forecasting, stocks should play a vital part. Flows and stocks interact with each other. Stocks grow when the inflows exceed the outflows of a (sub)‐system and certain outflows of a (sub)‐system are disproportional to the stocks.
For this dynamic model, additional information is needed for the time dimension of the variables, e.g. the life span of applications in the economy; the half life of compounds; the retention time in environmental compartments and so forth. Calculations can be made not only on the ‘intrinsic’ effectiveness of packages of measures, but also on their anticipated effects in a specific year in the future. They can also be made on the time
it takes for such measures to become effective. A dynamic model is therefore most suitable for scenario analysis, provided that the required data are available or can be estimated with adequate accuracy (OECD, 2000).
Life Cycle Analysis (LCA)
What is Life Cycle Assessment (LCA)?
As environmental awareness increases, industries and businesses are assessing how their activities affect the environment. Society has become concerned about the issues of natural resource depletion and environmental degradation. Many businesses have responded to this awareness by providing “greener” products and using “greener” processes. The environmental performance of products and processes has become a key issue, which is why some companies are investigating ways to minimize their effects on the environment. Many companies have found it advantageous to explore ways of moving beyond compliance using pollution prevention strategies and environmental management systems to improve their environmental performance. One such tool is LCA. This concept considers the entire life cycle of a product (Curran 1996).
Life cycle assessment is a “cradle-to-grave” approach for assessing industrial systems. “Cradle-to-grave” begins with the gathering of raw materials from the earth to create the product and ends at the point when all materials are returned to the earth. LCA evaluates all stages of a product’s life from the perspective that they are interdependent, meaning that one operation leads to the next. LCA enables the estimation of the cumulative environmental impacts resulting from all stages in the product life cycle, often including impacts not considered in more traditional analyses (e.g., raw material extraction, material transportation, ultimate product disposal, etc.). By including the impacts throughout the product life cycle, LCA provides a comprehensive view of the environmental aspects of the product or process and a more accurate picture of the true environmental trade-offs in product and process selection.
The term “life cycle” refers to the major activities in the course of the product’s life-span from its manufacture, use, and maintenance, to its final disposal, including the raw material acquisition required to manufacture the product. Exhibit 1-1 illustrates the possible life cycle stages that can be considered in an LCA and the typical inputs/outputs measured.
Methods of LCA
Process LCA
Economic Input Output LCA
Hybrid Approach
From Life cycle analysis (LCA) and sustainability assessment
Material Input Output Network Analysis
PIOT (Physical Input Output Tables)
MIOT (Monetary Input Output Tables)
WIOT (Waste Input Output Tables
MRIO (Multi Regional Input Output)
SUT (Supply and Use Tables)
From Industrial ecology and input-output economics: An introduction
Although it was the pioneering contributions by Duchin (1990, 1992) that explicitly made the link between input–output economics and industrial ecology, developments in input– output economics had previously touched upon the core concept of industrial ecology.
Wassily Leontief himself incorporated key ideas of industrial ecology into an input– output framework. Leontief (1970) and Leontief and Ford (1972) proposed a model where the generation and the abatement of pollution are explicitly dealt with within an extended IO framework. This model, which combines both physical and monetary units in a single coefficient matrix, shows how pollutants generated by industries are treated by so-called ‘pollution abatement sectors.’ Although the model has been a subject of longstanding methodological discussions (Flick, 1974; Leontief, 1974; Lee, 1982), its structure captures the essence of industrial ecology concerns: abatement of environmental problems by exploiting inter-industry interactions. As a general framework, we believe that the model by Leontief (1970) and Leontief and Ford (1972) deserves credit as an archetype of the various models that have become widely referred to in the field of industrial ecology during the last decade, including mixed-unit IO, waste IO and hybrid Life Cycle Assessment (LCA) models (Duchin, 1990; Konijn et al., 1997; Joshi, 1999; Nakamura and Kondo, 2002; Kagawa et al., 2004; Suh, 2004b). Notably, Duchin (1990) deals with the conversion of wastes to useful products, which is precisely the aim of industrial ecology, and subsequently, as part of a study funded by the first AT&T industrial ecology fellowship program, with the recovery of plastic wastes in particular (Duchin and Lange, 1998). Duchin (1992) clarifies the quantity-price relationships in an input–output model (a theme to which she has repeatedly returned) and draws its implications for industrial ecology, which has traditionally been concerned exclusively with physical quantities.
Duchin and Lange (1994) evaluated the feasibility of the recommendations of the Brundtland Report for achieving sustainable development. For that, they developed an input–output model of the global economy with multiple regions and analyzed the consequences of the Brundtland assumptions about economic development and technological change for future material use and waste generation. Despite substantial improvements in material efficiency and pollution reduction, they found that these could not offset the impact of population growth and the improved standards of living endorsed by the authors of the Brundtland Report.
Another pioneering study that greatly influenced current industrial ecology research was described by Ayres and Kneese (1969) and Kneese et al. (1970), who applied the massbalance principle to the basic input–output structure, enabling a quantitative analysis of resource use and material flows of an economic system. The contribution by Ayres and Kneese is considered the first attempt to describe the metabolic structure of an economy in terms of mass flows (see Ayres, 1989; Haberl, 2001).
Since the 1990s, new work in the areas of economy-wide research about material flows, sometimes based on Physical Input–Output Tables (PIOTs), has propelled this line of research forward in at least four distinct directions: (1) systems conceptualization (Duchin, 1992; Duchin, 2005a); (2) development of methodology (Konijn et al., 1997; Nakamura and Kondo, 2002; Hoekstra, 2003; Suh, 2004c; Giljum et al., 2004; Giljum and Hubacek, 2004; Dietzenbacher, 2005; Dietzenbacher et al., 2005; Weisz and Duchin, 2005); (3) compilation of data (Kratterl and Kratena, 1990; Kratena et al., 1992; Pedersen, 1999; Ariyoshi and Moriguchi, 2003; Bringezu et al., 2003; Stahmer et al., 2003); and (4) applications (Duchin, 1990; Duchin and Lange, 1994, 1998; Hubacek and Giljum, 2003; Kagawa et al., 2004). PIOTs generally use a single unit of mass to describe physical flows among industrial sectors of a national economy. In principle, such PIOTs are capable of satisfying both column-wise and row-wise mass balances, providing a basis for locating materials within a national economy.3 A notable variation in this tradition, although it had long been used in input–output economic studies starting with the work of Leontief, is the mixed-unit IO table. Konijn et al. (1997) analyzed a number of metal flows in the Netherlands using a mixed-unit IO table, and Hoekstra (2003) further improved both the accounting framework and data. Unlike the original PIOTs, mixed-unit IOTs do not assure the existence of column-wise mass-balance, but they make it possible to address more complex questions. Lennox et al. (2004) present the Australian Stocks and Flows Framework (ASFF), where a dynamic IO model is implemented on the basis of a hybrid input–output table. These studies constitute an important pillar of industrial ecology that is generally referred to as Material Flow Analysis (MFA).4
Although the emphasis in industrial ecology has arguably been more on the materials side, energy issues are without doubt also among its major concerns. In this regard, energy input–output analysis must be considered another important pillar for the conceptual basis of ‘industrial energy metabolism.’ The oil shock in the 1970s stimulated extensive research on the structure of energy use, and various studies quantifying the energy associated with individual products were carried out (Berry and Fels, 1973; Chapman, 1974). Wright (1974) utilized Input–Output Analysis (IOA) for energy analysis, which previously had been dominated by process-based analysis (see also Hannon, 1974; Bullard and Herendeen, 1975; Bullard et al., 1978). The two schools of energy analysis, namely process analysis and IO energy analysis, were merged by Bullard and Pillarti (1976) into hybrid energy analysis (see also van Engelenburg et al., 1994; Wilting,1996). Another notable contribution to the area of energy analysis was made by Cleveland et al. (1984), who present a comprehensive analysis, using the US input–output tables, quantifying the interconnection of energy and economic activities from a biophysical standpoint (see Cleveland, 1999; Haberl, 2001; Kagawa and Inamura, 2004). These studies shed light on how an economy is structured by means of energy flows and informs certain approaches to studying climate change (see for example Proops et al., 1993; Wier et al., 2001).
What generally escapes attention in both input–output economics and industrial ecology, despite its relevance for both, is the field of Ecological Network Analysis (ENA). Since Lotka (1925) and Lindeman (1942), material flows and energy flows have been among the central issues in ecology. It was Hannon (1973) who first introduced concepts from input–output economics to analyze the structure of energy utilization in an ecosystem. Using an input–output framework, the complex interactions between trophic levels or ecosystem compartments can be modeled, taking all direct and indirect relationships between components into account. Hannon’s approach was adopted, modified and re-introduced by various ecologists. Finn (1976, 1977), among others, developed a set of analytical measures to characterize the structure of an ecosystem using a rather extensive reformulation of the approach proposed by Hannon (1973). Another important development in the tradition of ENA is so-called environ analysis. Patten (1982) proposed the term ‘environ’ to refer to the relative interdependency between ecosystem components in terms of nutrient or energy flows. Results of environ analysis are generally presented as a comprehensive network flow diagram, which shows the relative magnitudes of material or energy flows between the ecosystem components through direct and indirect relationships (Levine, 1980; Patten, 1982). Ulanowicz and colleagues have broadened the scope of materials and energy flow analysis both conceptually and empirically (Szyrmer and Ulanowicz, 1987). Recently Bailey et al. (2004a, b) made use of the ENA tradition to analyze the flows of several metals through the US economy. Suh (2005) discusses the relationship between ENA and IOA and shows that Patten’s environ analysis is similar to Structural Path Analysis (SPA), and that the ENA framework tends to converge toward the Ghoshian framework rather than the Leontief framework although using a different formalism (Defourny and Thorbecke, 1984; Ghosh, 1958).
From Materials and energy flows in industry and ecosystem netwoks : life cycle assessment, input-output analysis, material flow analysis, ecological network flow analysis, and their combinations for industrial ecology
From Regional distribution and losses of end-of-life steel throughout multiple product life cycles—Insights from the global multiregional MaTrace model
From Feasibility assessment of using the substance flow analysis methodology for chemicals information at macro level
Sankey Diagram
From Hybrid Sankey diagrams: Visual analysis of multidimensional data forunderstanding resource use
Sankey diagrams are used to visualise flows of energy, materials or other resources in a variety of applications. Schmidt (2008a) reviewed the history and uses of these diagrams. Originally, they were used to show flows of energy, first in steam engines, more recently for modern systems such as power plants (e.g. Giuffrida et al., 2011) and also to give a big-picture view of global energy use (Cullen and Allwood, 2010). As well as energy, Sankey diagrams are widely used to show flows of resources (Schmidt, 2008a). Recent examples in this journal include global flows of tungsten (Leal-Ayala et al., 2015), biomass in Austria (Kalt, 2015), and the life-cycle of car components (Diener and Tillman, 2016). More widely, they have been used to show global production and use of steel and aluminium (Cullen et al., 2012; Cullen and Allwood, 2013), and flows of natural resources such as water (Curmi et al., 2013). In all of these cases, the essential features are: (1) the diagram represents physical flows, related to a given functional unit or period of time; and (2) the magnitude of flows is shown by the link1 widths, which are proportional to an extensive property of the flow such as mass or energy (Schmidt, 2008b). Creating these diagrams is supported by software tools such as e!Sankey (ifu Hamburg, 2017), and several Life Cycle Assessment (LCA) and Material Flow Analysis (MFA) packages include features to create Sankey diagrams.
From Hybrid Sankey diagrams: Visual analysis of multidimensional data for understanding resource use
Visualization of energy, cash and material flows with a Sankey diagram
The most popular software for creating Sankey diagrams. Visualize the cash, material & energy flow or value streams in your company or along the supply chain. Share these appealing diagrams in reports or presentations.
Materials and energy flows in industry and ecosystem netwoks : life cycle assessment, input-output analysis, material flow analysis, ecological network flow analysis, and their combinations for industrial ecology
Viewpoints which Matter 