Physical Input-output Tables Research Articles

Socioeconomic metabolism: Drivers of resource productivity

The use of natural resources drives socioeconomic metabolism (SEM) and has been rising as economies develop, but not all countries present the same trends. Understanding how key aspects of the SEM relate to resource use and productivity is critical for promoting sustainable development.This paper analyses 15 years of the SEM dynamics in four European countries by quantifying gross domestic product (GDP) and resource use per capita. The monetary and material flows of Estonia, Croatia, Finland, and the United Kingdom were quantified in monetary and physical input-output tables, enabling the characterization of the flows between the economic sectors and of their cross-correlation indicators, namely through resource productivity.Key findings included the observation of a correlation between high productivity and more developed economies, where infrastructure has been developed for high-productivity services, the effects of economic structure on resource productivity, and on how the SEM developed after an economic recession.

How sustainable development goals have transformed our world? Evolution of the ecological networks of the Italian economy

This article focuses on modelling the structure of the Italian economy and its evolution from 2015 to 2019 using Physical Input-Output tables to understand its functioning in terms of biophysical and monetary exchanges. We considered energy extracted from natural resources within the country and embodied energy from natural resources extracted abroad. The model includes final demand (government, household, and gross capital formation) as endogenous factors. The results show that Agriculture, Advanced Manufacturing, and Tertiary industry are the main sectors consuming energy when considering the direct energy required to produce one unit of economic output. However, when assessing embodied energy, Tertiary Industry emerges as the leading consumer of energy. The findings demonstrate how the services sectors are highly dependant on the flow of energy-intensive resources. Ecological Network Analysis (ENA) was applied to identify the main sectors driving the Italian economy and their ability to obtain energy inflows from other sectors. The study found that the Tertiary industry, Final demand, and Manufacturing sectors were the main drivers of the Italian economy and had a higher ability to obtain energy inflows. The results also showed that exploitative or control relations dominated interactions between different economic sectors. Our results highlight that the dematerialization process at national level comes at environmental costs elsewhere.

Urban Metabolism Characterization from the National to the Regional Scale: A Case Study of Lisbon

Cities are complex systems with a key role in economic development and in improving the environmental sustainability of nations. Therefore, a comprehensive understanding of urban systems is fundamental to designing relevant policies and initiatives. Urban metabolism characterization may provide a holistic framework for analyzing urban systems in the context of the broader national metabolism. This paper provides a methodology to characterize the metabolism of an urban area, such as the Lisbon metropolitan area, taking as a source of data the socioeconomic metabolism of the country. The national metabolism is established by computing physical input-output tables. The downscaling to the urban level is accomplished with a variety of sector-specific parameters, from which an urban mass balance is established for a set of economic activities. The results obtained for the case study of Portugal and the city of Lisbon suggest that the share of services and the type of manufacturing industries in the economic structure of Lisbon, together with the higher resource productivity in the most representative economic sectors, are key aspects of the metabolism that contribute to Lisbon having higher overall resource productivity. The representation of the metabolism flows revealed key differences between the supply chains of Lisbon and the national flows, as well as a strong dependence of the Lisbon economy on material flows from other regions.

Supply-side drivers of phosphorus emissions from phosphorus supply chains in China

ABSTRACT Human activities interfere with natural Phosphorus (P) cycles by introducing increased levels of P emissions to air, land, and water. A supply-side analysis of P supply chains and associated P emissions can provide insights into underlying economic activities and transitions responsible for human-induced P emissions. Taking the mainland China as the case, this study constructs time-series physical input-output tables to describe P supply chains during 1949–2012. Subsequently, it identifies critical products and influencing factors of P supply chains enabling P emissions to the environment (including air, land, and water) from the supply perspective. The results show that phosphate rock, an important initial supplier of P from natural environment to China’s P supply chain, was responsible for 86% of P emissions in 2012. Moreover, food crops and livestock are important initial suppliers of P from soil to China’s P supply chain, through cultivation and pasturing, respectively. From 1949 to 2012, the change in primary input level was the largest driver of P emission increments, followed by changes in population, emission intensity, and primary input structure. On the contrary, changes in production structure reduced P emissions. These findings could support supply-side policy decisions on P emission control.

The PIOLab: Building global physical input–output tables in a virtual laboratory

Informed environmental-economic policy decisions require a solid understanding of the economy's biophysical basis. Global physical input–output tables (gPIOTs) collate a vast array of information on the world economy's physical structure and its interdependence with the environment, which can help to monitor progress toward a sustainable circular economy. However, building gPIOTs requires dealing with mismatched and incomplete primary data with high uncertainties, which makes it a time-consuming and labor-intensive endeavor. We address this challenge by introducing the PIOLab: A virtual laboratory for building gPIOTs. This represents the newest branch of the industrial ecology virtual laboratory (IELab) concept, a cloud-computing platform and collaborative research environment through which participants can pool resources to assemble individual input–output tables that target specific research questions. To overcome the lack of primary data, the PIOLab builds extensively upon secondary data derived from a variety of models commonly used in industrial ecology. We use the case of global iron-steel supply chains to describe the architecture of the PIOLab and highlight its analytical capabilities. A major strength of the gPIOT is its ability to provide mass-balanced indicators on both apparent/direct and embodied/indirect flows, for regions and disaggregated economic sectors. We present the first gPIOTs for 10 years (2008–2017), covering 32 regions, 30 processes, and 39 types of iron/steel flows. Diagnostic tests of the data reconciliation show a good level of adherence between raw data and the values realized in the gPIOT. We conclude with elaborating on how the PIOLab will be extended to cover other materials and energy flows. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

PIOT‐Hub ‐ A collaborative cloud tool for generation of physical input–output tables using mechanistic engineering models

AbstractMapping material flows in an economy is crucial to identifying strategies for resource management toward lowering the waste and environmental impacts of society, a key objective of research in industrial ecology. However, constructing models for mapping material flows at a sectoral level, such as in physical input–output tables (PIOTs) at highly disaggregated levels, is tedious and relies on a large amount of empirical data. To overcome this challenge, a novel collaborative cloud platform PIOT‐Hub is developed in this work. This platform utilizes a Python‐based simulation system for extracting material flow data from mechanistic models, thus semi‐automating the generation of PIOTs. The simulation system implements a bottom‐up approach of utilizing scaled engineering models to generate physical supply tables (PSTs) and physical use tables (PUTs) which are converted to PIOTs (described in (Vunnava & Singh, 2021)). Mechanistic models can be uploaded by users for sectors on PIOT‐Hub to develop PIOTs for any region. Both models and resulting PST/PUT/PIOTs can be shared with other users utilizing the collaborative platform. The automation and sharing features provided by PIOT‐Hub will help to significantly reduce the time required to develop PIOT and improve the reproducibility/continuity of PIOT generation, thus allowing the study of the changing nature of material flows in regional economy. In this paper, we describe the simulation system MFDES and PIOT‐Hub architecture/functionality through a demo example for creating PIOT in agro‐based sectors for Illinois. Future work includes scaling up the cloud infrastructure for large scale PIOT generation and enhancing the tool compatibility for different sectors in economy.

Thematic exploration of sectoral and cross-cutting challenges to circular economy implementation

Circular economy (CE) offers a pathway towards sustainable, closed-loop resource systems, but widespread adoption across industrial sectors is limited by fragmented knowledge and varied implementation approaches. This article reviews sector-specific challenges and opportunities associated with implementing and measuring the benefits of CE strategies. Literature mapping highlights progress towards CE implementation in food, chemicals, metals, consumer electronics, and building and infrastructure sectors, and towards measuring CE outcomes via systems analysis methods like life cycle assessment (LCA) and material flow analysis (MFA). However, key challenges were also identified that point to future research and demonstration needs. First, research on CE adoption typically exists as case studies that are closely linked to a sector. But literature has not effectively synthesized knowledge gained across domains, particularly understanding underlying barriers to CE and where they occur in product life cycles. Second, research on CE outcomes often applies well-established methods without adapting for unique attributes of CE systems. A key opportunity is in integrative methodological advances, such as expanded use of consequential LCA, development of physical Input–Output tables, and integrating MFA with dynamical models. Finally, regardless of sector, new CE business models are seen as a critical enabler to realize success, but theoretical frameworks in literature are not well-tested in practice. The review also highlights opportunities to harness other emerging trends, such as big data, to provide better information for system modelers and decision-oriented insight to guide CE stakeholders.

Comparative analysis of metals use in the United States economy

Building a circular economy requires knowledge of physical material flows and stocks. One approach for obtaining data on the intersectoral exchanges of materials in an economy is with physical input-output tables (PIOTs). Using PIOTs of eleven alloying metals (aluminum, vanadium, chromium, manganese, iron, cobalt, nickel, copper, niobium, molybdenum, tungsten) for the entire United States economy in 2007, we apply network-based metrics and visualizations to identify key sectors and compare different PIOTs with each other. Some 40–45% of all intersectoral trade contains the major metals aluminum, copper, and iron, while this number ranges between only 11–15% for minor metals (e.g., cobalt, vanadium, niobium, molybdenum, tungsten). The majority of sectors rely on products containing the major metals, reflecting widespread use of those products in our modern economy. Network size provides an indication of supply chain steps required to move from metal production to finished product manufacturing. Supply chains for the minor metals require an average of 5–8 steps, while those of major metals involve 3 steps on average. Cobalt is used extensively to illustrate these results because its status as a “technology-critical material” demonstrates how these analytical approaches can reveal sector usage and dependency for a metal of potential supply concern. We conclude by presenting automobile supply chain networks and discuss the position of the automobile production sector in the US economy. The analytical and visualization approaches presented result in an improved understanding of metal flows and can help to better communicate underlying data, e.g., in a policy context.

Dynamic input-output analysis for energy metabolism system in the Province of Guangdong, China

Abstract The development of human society is inseparable from energy. The exploration of urban energy metabolism plays an essential role in improving sustainable development. Combing input-output analysis with ecological network analysis help academics to shed light into the complicated system interactions and interior energy flows. In this study, using Guangdong as a case study, the Energy Ecological Network model is developed to account for the intensity of the embodied energy consumption using monetary input-output tables from 2000, 2002, 2005, 2007, 2010, and 2012. Sectors and energy flows are treated as nodes and paths to compile the corresponding physical input-output tables, which can facilitate a more comprehensive and balanced understanding of urban energy consumption by integrating various accounting perspectives. In detail, network control analysis is extended to reveal inter relationships and relative contribution rate of each sector. Network utility analysis gives an overall consideration of the dynamic changes in energy metabolism relations from multiple perspective. Furthermore, the modified robustness method penetrates into how each sector affects the stability of the system. The results show that the energy metabolic level in Guangdong is relatively low and indirect flows are the key to improving the system efficiency. The advanced manufacturing (AM) sector relied on other sectors in energy trade and have limited reciprocal relationships in the study period. Therefore, it is urgent to adjust the external structure and internal circulation of AM sector. The comprehensive dynamic analysis will give a scientific support to guide the development of energy reform in an attempt to promoting healthier development of energy metabolism system.

Review: Sustainable Urban Metabolism, by Paulo Ferrão and John E. Fernández

Book Review| September 01 2015 Review: Sustainable Urban Metabolism, by Paulo Ferrão and John E. Fernández Paulo Ferrão and John E. FernándezSustainable Urban MetabolismCambridge, Mass.: MIT Press, 2013, 244 pp., 19 tables, 37 b/w illus. $35, ISBN 9780262019361 Jonathan Massey Jonathan Massey 1California College of the Arts Search for other works by this author on: This Site PubMed Google Scholar Journal of the Society of Architectural Historians (2015) 74 (3): 378–379. https://doi.org/10.1525/jsah.2015.74.3.378 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Jonathan Massey; Review: Sustainable Urban Metabolism, by Paulo Ferrão and John E. Fernández. Journal of the Society of Architectural Historians 1 September 2015; 74 (3): 378–379. doi: https://doi.org/10.1525/jsah.2015.74.3.378 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentJournal of the Society of Architectural Historians Search What knowledge and analytical framework should inform the actions of policy makers, urban managers, and city dwellers in the Anthropocene, the era in which humans have transformed the earth’s atmosphere, oceans, and climate? According to Paulo Ferrão and John E. Fernández, one strategy for reformatting urban analysis and governance in the Anthropocene is to analyze urban processes using equations like those reproduced above, which describe greenhouse gas emissions for a process (87), the material composition of a product (172), and the material intensity of an economic activity (177). Understanding how cities function and how they can function sustainably, the authors explain, requires analytical tools and categories bearing a delirious array of acronyms: economic input-output methods (EIO), ecological footprint analysis (EO), life-cycle assessment (LCA), and material flow analysis (MFA)—which encompasses DMI, TMR, HF, DPO, DMO, TDO, TMO, DMC, TMC, NAS, and PTB—as well as physical input-output tables (PIOT), not to mention... You do not currently have access to this content.

ANALYSING THE STRUCTURE OF THE ECONOMY USING PHYSICAL INPUT–OUTPUT TABLES

Analyses using physical input–output tables (PIOTs) are key to understanding the physical metabolism of economies, since they relate production to the generation of emissions and use of resources. Two methods have been developed to calculate the primary resources and emissions associated with a given final demand. However, one of these alters the PIOT, revealing different technical coefficients and Leontief inverse matrices. Which method should be used for structural analysis? In this paper, I compare both methods, explain the structural differences between them and illustrate the latter through a backward linkage analysis. I find that only one method is suited to the analysis of the physical structure of the economy, since it comprehends both the production of goods and associated emissions. The method is identified as a new model capable of tracing by-products as final outputs. Finally, I generalise both methods to analyse PIOTs including several emission types.

Analysis of urban metabolic processes based on input-output method: model development and a case study for Beijing

Discovering ways in which to increase the sustainability of the metabolic processes involved in urbanization has become an urgent task for urban design and management in China. As cities are analogous to living organisms, the disorders of their metabolic processes can be regarded as the cause of “urban disease”. Therefore, identification of these causes through metabolic process analysis and ecological element distribution through the urban ecosystem’s compartments will be helpful. By using Beijing as an example, we have compiled monetary input-output tables from 1997, 2000, 2002, 2005, and 2007 and calculated the intensities of the embodied ecological elements to compile the corresponding implied physical input-output tables. We then divided Beijing’s economy into 32 compartments and analyzed the direct and indirect ecological intensities embodied in the flows of ecological elements through urban metabolic processes. Based on the combination of input-output tables and ecological network analysis, the description of multiple ecological elements transferred among Beijing’s industrial compartments and their distribution has been refined. This hybrid approach can provide a more scientific basis for management of urban resource flows. In addition, the data obtained from distribution characteristics of ecological elements may provide a basic data platform for exploring the metabolic mechanism of Beijing.

Ecological network analysis of an urban metabolic system based on input–output tables: Model development and case study for Beijing

If cities are considered as “superorganisms”, then disorders of their metabolic processes cause something analogous to an “urban disease”. It is therefore helpful to identify the causes of such disorders by analyzing the inner mechanisms that control urban metabolic processes. Combining input–output analysis with ecological network analysis lets researchers study the functional relationships and hierarchy of the urban metabolic processes, thereby providing direct support for the analysis of urban disease. In this paper, using Beijing as an example, we develop a model of an urban metabolic system that accounts for the intensity of the embodied ecological elements using monetary input–output tables from 1997, 2000, 2002, 2005, and 2007, and use this data to compile the corresponding physical input–output tables. This approach described the various flows of ecological elements through urban metabolic processes and let us build an ecological network model with 32 components. Then, using two methods from ecological network analysis (flow analysis and utility analysis), we quantitatively analyzed the physical input–output relationships among urban components, determined the ecological hierarchy of the components of the metabolic system, and determined the distribution of advantage-dominated and disadvantage-dominated relationships, thereby providing scientific support to guide restructuring of the urban metabolic system in an effort to prevent or cure urban “diseases”.

Data Acquisition for Applying Physical Input-Output Tables in Chinese Cities

Summary The physical input-output table (PIOT) is a useful tool for analyzing the environmental sustainability of cities. Taking Chinese statistical sources as an example in this study, we discuss data acquisition methods for applying the PIOT to cities. We propose several methods and present a case study of Suzhou City to illustrate the proposed methods. These methods can provide foundations for constructing the PIOT of cities in other countries.

An assessment of greenhouse gas emissions and material flows caused by the Finnish economy using the ENVIMAT model

An environmentally extended input–output (EE-IO) analysis – environmental impacts of material flows caused by the Finnish economy – was carried out in order to improve data on production and consumption in Finland. The study resulted in the ENVIMAT model, which can be used to analyze the relationship between material flows, environmental impacts and the economy. The model is based on monetary and physical input–output tables and an environmental life-cycle impact assessment. This article summarizes the main methodological aspects and findings regarding the material flows and climate impacts caused by the Finnish economy in 2002 and 2005. The Finnish model has relatively detailed input data with 150 industries and 918 products and the data on imports was assessed according to a mixed approach with the help of life-cycle inventory data. The results of the model showed that the Finnish economy uses imported material resources as much as domestic resources. Life-cycle greenhouse gas (GHG) emissions caused by imports were equivalent to 70–80% of domestic emissions. The GHG emissions embodied in imports (emissions abroad) and exports (emissions within Finland) were of the same magnitude. The analysis showed that the service sector accounted for 44% of GHG emissions caused by the domestic final use of products. Analysis of the results also showed that the indicator of total material requirement (TMR) should not be used for environmental impact comparisons of products and services. In the future, the aim is to use the ENVIMAT model for assessing temporal changes in the economy; for monitoring sustainable development; for planning climate change mitigation; and for identifying important factors in the economy and assessing their impacts.

A tool for strategic biophysical assessment of a national economy – The Australian stocks and flows framework

The Australian Stocks and Flows Framework (ASFF) was developed to assess the biophysical longevity of the Australian economy, with top-down coverage of the whole physical economy based on bottom-up process-based detail. The ASFF employs mass-balance identities associated with stock and flow dynamics throughout the national economy and associated interaction with the environment. We show that the ASFF shares common features with complementary approaches, including Mass Flow Analysis, Physical Input–Output Tables, and Life Cycle Analysis, but is distinctly different from these because the biophysical processes throughout the economy and environment are represented explicitly. The detailed physical processes modelled have a strong empirical basis, being calibrated with six or more decades of historical data. Given the coverage of the entire economy in physical terms, it provides for many subject specific analyses such as water, energy, climate change, etc, which can also be assessed in integrated analysis of scenarios to 2100 in order to highlight conflicts, trade-offs, and synergies. The ASFF can be applied and adapted to represent specific interests in more detail and context, as demonstrated by multiple applications of the ASFF. Overall, it is designed to explore the possible trajectories of the national economic system over the long term within irrefutable biophysical constraints, and thereby inform development of appropriate policy. The open biophysical nature of the ASFF is intended for exploration and learning, rather than being normative or policy prescriptive.

Applying physical input–output tables of energy to estimate the energy ecological footprint (EEF) of Galicia (NW Spain)

Nowadays, the achievement of sustainable development constitutes an important constraint in the design of energy policies, being necessary the development of reliable indicators to obtain helpful information about the use of energy resources. The ecological footprint (EF) provides a referential framework for the analysis of human demand for bioproductivity, including energy issues. In this article, the theoretical bases of the footprint analysis are described by applying input–output tables of energy to estimate the Galician energy ecological footprint (EEF). It is concluded that the location of highly polluting industries in Galicia makes the Galician EEF quite higher than more developed regions of Spain. The relevance of the outer component of the Galician EEF is also studied. First, available information seems to indicate that the energy incorporated to the trading of manufactured goods would notably increase the Galician consumption of energy. On the other hand, the inclusion of electricity trade in the EEF analysis, including an adjustment, following the same philosophy as with manufactured goods is proposed. This adjustment would substantially reduce the Galician EEF, as the exported electricity widely exceeds the imported one.

MFA model to assess economic and environmental consequences of food production and consumption

Agri-food sector is a crucially important part of the society, because it is a major factor affecting public health and welfare and it also contributes – directly and indirectly – both to the environment and to the national gross product and employment. Improving sustainability of the agri-food sector implies production of nutritionally better food by using fewer inputs and by reducing environmental burden. In reducing environmental burden, it is essential to restrict the material throughput, to identify the hot spots and direct the measures to them. Improving performance of the food sector requires that the benefits and inputs be quantified in an unambiguous way and that the inputs are estimated for the whole production chain. A comprehensive view of the whole system is necessary. Here, the material flow approach (MFA) has been used to describe the Finnish food flux. The quantitative numerical data have been derived from the farm models' data base, and the data have been adjusted so as to comply with the production and consumption statistics of Finland. Using the compiled data an extended input–output model has been constructed. The model allows for the evaluation of some of the economic and environmental consequences, when the structure of food production and the patterns of food consumption are changed. The consequences can be traced within agriculture, within the food sector as a whole, or at the level of the nation-wide economy. In combination with other information the model, thus, serves as a practical tool for planning. The paper at hand gives an overview of the data base and the basic calculation principles of the model. The usability of the model is demonstrated with results from modelling examples, in which the share of organic production or the share of vegetarian food in the average Finnish diet has been increased. The possibilities and restrictions of the approach as well as some of the needs for further development are discussed. The study is the first step in developing MFA methods to analyse and to monitor the material flows of the Finnish food flux. The results have been used also in compiling the Finnish physical input–output tables. The study, thus, contributes to the overall development of the materials flow accounting.

Constructing physical input–output tables for environmental modeling and accounting: Framework and illustrations

A physical input–output table (PIOT) provides a framework in which all the physical flows associated with an economy can be recorded. This makes it a valuable tool for environmental–economic modeling and accounting. During the 1990's PIOTs were constructed for a number of countries. Subsequently, the PIOT and related physical supply and use tables have been taken up in the System of Environmental and Economic Accounting [UN (United Nations), 2003. System of Environmental and Economic Accounting. United Nations, New York]. This paper reviews PIOTs for the Netherlands, Germany, Denmark, Italy, Finland and the European Union. These studies applied the ‘basic’ and ‘extended’ PIOT frameworks. This paper elaborates these frameworks with packaging, residuals (wastes and emissions), recycling and stock changes, in order to create a ‘full’ PIOT. The production process of the extended and full PIOT is split into structural and auxiliary production processes. Modeling applications and ways of deriving environmentally relevant information from the full PIOT are illustrated using a numerical example.

Physical and monetary input–output analysis: What makes the difference?

A recent paper in which embodied land appropriation of exports was calculated using a physical input–output model (Ecological Economics 44 (2003) 137–151) initiated a discussion in this journal concerning the conceptual differences between input–output models using a coefficient matrix based on physical input–output tables (PIOTs) in a single unit of mass and input–output models using a coefficient matrix based on monetary input–output tables (MIOTs) extended by a coefficient vector of physical factor inputs per unit of output. In this contribution we argue that the conceptual core of the discrepancies found when comparing outcomes obtained using physical vs. monetary input–output models lies in the assumptions regarding unit prices and not in the treatment of waste as has been claimed (Ecological Economics 48 (2004) 9–17). We first show that a basic static input–output model with the coefficient matrix derived from a monetary input–output table is equivalent to one where the coefficient matrix is derived from an input–output table in physical units provided that the assumption of unique sectoral prices is satisfied. We then illustrate that the input–output tables that were used in the original publication do not satisfy the assumption of homogenous sectoral prices, even after the inconsistent treatment of waste in the PIOT is corrected. We show that substantially different results from the physical and the monetary models in fact remain. Finally, we identify and discuss possible reasons for the observed differences in sectoral prices faced by different purchasing sectors and draw conclusions for the future development of applied physical input–output analysis.