Physical Input-output Model Research Articles

A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China

Effective management of an urban solid waste system (USWS) is crucial for balancing the tradeoff between economic development and environment protection. A factorial ecological-extended physical input-output model (FE-PIOM) was developed for identifying an optimal urban solid waste path in an USWS. The FE-PIOM integrates physical input-output model (PIOM), ecological network analysis (ENA), and fractional factorial analysis (FFA) into a general framework. The FE-PIOM can analyze waste production flows and ecological relationships among sectors, quantify key factor interactions on USWS performance, and finally provide a sound waste production control path. The FE-PIOM is applied to managing the USWS of Fujian Province in China. The major findings are: (i) waste is mainly generated from primary manufacturing (PM) and advanced manufacturing (AM), accounting for 30% and 38% of the total amount; (ii) AM is the biggest sector that controls the productions of other sectors (weight is from 35% to 50%); (iii) the USWS is mutualistic, where direct consumption coefficients of AM and PM are key factors that have negative effects on solid waste production intensity; (iv) the commodity consumption of AM and PM from other sectors, as well as economic activities of CON, TRA and OTH, should both decrease by 20%, which would be beneficial to the sustainability of the USWS.

Energy conversion of urban wastes in China: Insights into potentials and disparities of regional energy and environmental benefits

With the rapid economic growth and urbanization, China is suffering from serious challenges on energy security and the problems regarding waste treatment and emissions mitigation. Converting urban wastes into energy has been recognized as a promising way to achieve circular economy. In this study, combustible waste, food waste, industrial organic wastewater, and breeding-farm manure are considered to be utilized for energy recovery through waste-to-energy (WtE) technologies. Accordingly, four WtE sectors for power generation are formed and introduced into the socioeconomic activities. A methodological framework is established by combining econometrics, physical input-output model and baseline method of clean development mechanism to evaluate the energy and environmental benefits in China’s 31 provincial regions during the period 2016–2025. The results reveal that the regions with more waste generation and power generation are Guangdong (11.82 billion kWh in 2025) and Jiangsu (11.43 billion kWh in 2025). Hebei has the largest accumulative mitigation potentials for the emissions of greenhouse gases, sulfur dioxide, nitrogen oxides, and soot and dust (577.57 Mt carbon dioxide equivalent, 1.79 Mt, 0.89 Mt and 16.22 Mt, respectively), followed by Guangdong and Zhejiang. Less developed regions in northwestern China such as Gansu, Qinghai and Ningxia have less energy recovery and mitigation potentials. Meanwhile, the changes in industrial structure contribute to more mitigations in the sectors of power and heat, coal mining, and oil and gas extraction. The quantification of the energy and environmental benefits and revelation of the features and disparities of waste utilization for energy recovery across regions can provide insights and managerial implications for better policy-making regarding regional waste management.

Uncovering urban food-energy-water nexus based on physical input-output analysis: The case of the Detroit Metropolitan Area

Secure and efficient supply for the food, energy and water resources is essential for sustainable urban development. Due to the close interaction of food, energy and water systems, it is necessary to analyze food-energy-water nexus from an integrated perspective. Taking the Detroit Metropolitan Area as a case, this study first constructs a food-energy-water physical input-output model to quantify food, energy and water flows. Then, structural path analysis is adopted to identify critical supply chain paths driven by the final demand of key sectors. Quantitative results of food-energy-water flows show that major inputs of food and energy in the Detroit Metropolitan Area are from outside through imports, while water use is predominately extracted from local sources. Local consumption activities for the food, energy, and water systems are mainly concentrated downstream of the supply chain. Structural path analysis results show that intermediate processes use relatively large amounts of food, energy and water, and should be more concerned. Also, identifying sectors involving multiple systems, such as Food Processing, Domestic Consumption, Solid Waste Management, Wastewater Treatment, and Residual Processing, can promote co-benefit opportunities. This holistic view on urban FEW nexus presented in this study can facilitate better decisions and help avoid unintended consequences.

Structural analysis of material flows in China based on physical and monetary input-output models

The physical input-output (PIO) model has different product flow scopes from the monetary input-output (MIO) model. Using the PIO and MIO models to analyze the structure of material flows can identify new hotspots for policy decisions over each other. However, PIO models are not well-studied in existing studies on the structure of China's material flows. This study uses both the PIO and MIO models to analyze consumption-based material flows of Chinese sectors and critical supply chain paths leading to China's material flows. Results show that the PIO and MIO models validate each other by simultaneously identifying critical sectors and important final demand categories. However, product prices and the treatment of services lead to different findings based on the MIO and PIO models. Compared with the PIO model, the MIO model underestimates the contribution of the construction sector to China's material flows, but overestimates that of the equipment and machinery and other services sectors. The PIO model reveals the higher importance of rural household consumption, while the MIO model identifies more critical supply chain paths associated with services. The PIO and MIO models can complement each other in the implementation and assessment of environmental measures (e.g., production efficiency improvement).

Qualitative and quantitative modelling to build a conceptual framework to identify energy saving options: case study of a wire producing company

Energy management systems are considered as appropriate instruments to identify and realize energy efficiency potentials in industrial plants. Studies point out a largely untapped potential for energy efficiency in industry through the adoption of systematic energy management. We suggest introducing qualitative and quantitative modelling tools into energy management to facilitate a focused process to set priorities and identify effective measures. This paper describes the case of voestalpine Austria Draht GmbH, an Austrian wire rolling mill. voestalpine Austria Draht GmbH at present is implementing a knowledge based energy management system.Within the work described in this paper, first an overview of energy flows using Sankey diagrams was elaborated. Then a qualitative systems analysis was developed for the plant and a gap analysis done for priority areas. Subsequently physical input–output models for several energy intensive units were prepared. The paper describes the models for the cooling water system and product cooling. This study revealed a significant potential to save electricity by optimizing the control of cooling water pumps along the milling train and to recover heat from the final products. 2 GWh of electricity can be saved annually.The biggest single energy loss in the mill is the heat from air cooling the products on hook conveyors. These heat flows contain about 30–50 % of the energy input for heating up the billets. Following a theoretical analysis of the potential, a demonstration plant was developed and pilot tests were run to characterize the realistic potential of heat recovery on the hook conveyor. Based on the test results, feasibility studies for large scale plants were carried out. As a result, 0.8 GWh of heat can be recovered annually.The method was found useful because it helped to structure the existing information with a reasonable effort mostly by the operators themselves in their work and to build a conceptual model of energy flows and energy efficiency of the total plant.

Investigating Reasons for Differences in the Results of Environmental, Physical, and Hybrid Input‐Output Models

SummaryIt is vital to find reasons for differences in the results of environmental input‐output (EIO), physical input‐output (PIO), and hybrid input‐output (HIO) models for industrial and environmental policy analysis. Using EIO, PIO, and HIO models, China's industrial metabolism is calculated. Four reasons were found to account for differences in the results of analysis using EIO, PIO, and HIO models: the manner in which they deal with residential consumption, service sectors, and waste recycling, and the assumption of unique sector prices. The HIO model, which treats residential consumption as sectors of the intermediate delivery matrix, is preferred to the EIO and PIO models for analyzing industrial and environmental policies. Moreover, waste recycling in five sectors—agriculture; the manufacture of paper, printing, and articles for culture, education, and sports activities; the manufacture of nonmetallic mineral products; smelting and pressing of metals; and construction—should be comprehensively considered when using the HIO model to study problems related to these five sectors. Improvements in the EIO, PIO, and HIO models and future work are also discussed.

Sustainable urban materials management for air pollutants mitigation based on urban physical input–output model

Sustainable urban materials management is significant for environmental sustainability. This study identifies key economic sectors for sustainable urban materials management using urban physical input–output (PIOT) table and hierarchical cluster analysis method to mitigate urban air pollutants (comprising CO2, SO2 and NOx) and examines uncertainties. Suzhou city in China is taken as an example. Key economic sectors in Suzhou considering direct amount are production and supply of electric and heat power, smelting and pressing of ferrous metals, services, other manufacturing, textile, non-metallic mineral products and chemistry, while key economic sectors considering accumulative amount are agriculture, construction, production and supply of electric and heat power, services and smelting and pressing of ferrous metals. Uncertainties for sustainable urban materials management are consistent with the development stage of the economy. Thus, when constructing urban PIOT for a particular city based on monetary input–output table (MIOT) of another city or province, the development stage of the city or province whose MIOT is used should be similar to that of the particular city. If not, lower and upper limits for the development stage should be set to investigate uncertainties.

Environmental implications of electricity generation in an integrated long-term planning framework

Long-term, integrated modelling of electricity generation capacity and its operational requirements is important for coherent energy policy because of the multi-decadal lifespan of such plant, and because related fuel requirements, water consumption, and greenhouse gas emissions impact other sectors. This paper describes a physical model of electricity generation designed for long-term simulations, including the reproduction of decades of historical data, and its application to key policy questions. The simulation uses a dynamic, physical input–output model for the basic material and energy industries, embedded in an economy-wide stocks and flows framework (SFF). The historical reproduction, illustrated for the state of Victoria, is based on integrating bottom-up technical data on physical processes as inputs, and validating output against top-down aggregate data. We show that while greenhouse gas emissions would decrease significantly with the aggressive introduction of cleaner generation technology, long-term reductions are difficult to maintain against ongoing growth in consumption. Additionally, our sensitivity analysis identifies the importance of rapid action due to the age structure of operating power stations. We also identify significant imposts of alternative power generation on land and water resources, though a business-as-usual scenario would impose greater losses of water resources.

Comparing urban solid waste recycling from the viewpoint of urban metabolism based on physical input–output model: A case of Suzhou in China

Investigating impacts of urban solid waste recycling on urban metabolism contributes to sustainable urban solid waste management and urban sustainability. Using a physical input–output model and scenario analysis, urban metabolism of Suzhou in 2015 is predicted and impacts of four categories of solid waste recycling on urban metabolism are illustrated: scrap tire recycling, food waste recycling, fly ash recycling and sludge recycling. Sludge recycling has positive effects on reducing all material flows. Thus, sludge recycling for biogas is regarded as an accepted method. Moreover, technical levels of scrap tire recycling and food waste recycling should be improved to produce positive effects on reducing more material flows. Fly ash recycling for cement production has negative effects on reducing all material flows except solid wastes. Thus, other fly ash utilization methods should be exploited. In addition, the utilization and treatment of secondary wastes from food waste recycling and sludge recycling should be concerned.

An improved input–output model for energy analysis: A case study of Suzhou

There are some disadvantages in current input–output models for energy. This study proposes a methodology named hybrid physical input–output model for energy analysis (HPIOMEA) to study energy metabolism, taking Suzhou in China as an example. The HPIOMEA calculates energy resources in both energetic and mass units and air pollutants in mass units simultaneously from the perspective of energy balance and mass balance, which is beyond the reach of current input–output tables for energy. In addition, it can validate the rationality of the table compilation and energy projection, and illustrate the direct and accumulative effects of energy and air pollutants. The HPIOMEA reflects the physical reality of energy metabolism much better. In addition, future work on energy analysis is proposed.

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.

Modeling Basic Industries in the Australian Stocks and Flows Framework

The Australian stocks and flows framework (ASFF) is a tool for establishing a coherent historical picture of the Australian physical economy and for testing long‐term future scenarios (up to 2050 or even 2100). These scenarios can be used to investigate the long‐term physical consequences of current and future choices affecting the physical dimensions of sustainability. In this article we describe the methodology for and construction of a key component of ASFF: a dynamic physical input‐output model of material flows in the basic industries.The materials model in ASFF describes physical flows and their transformation by industrial processes. The model's structure permits scenario analysis of long‐term technological change by permitting time‐varying input‐output coefficients and vintage models of capital stocks. As a consequence, the model contains a large number of parameters, which can be left at default settings or adjusted as the modeler sees fit, in order to simulate the widest possible range of physically realizable scenarios. The materials model is built using a methodology that integrates bottom‐up process analysis with top‐down statistics on material and energy flows. We present some examples showing how the materials model has been implemented to model Australian heavy industries. Several possibilities for further developing the materials model are also described.

04/01582 The ecological footprint: a non-monetary metric of human consumption applied to North America: Senbel, M. et al. Global Environmental Change, 2003, 13, (2), 83–100

In the last few years some studies have been presented, which link land use accounting and input–output analysis (based on monetary input–output tables (MIOTs)) for the calculation of direct and indirect land appropriation of production and consumption activities. The compilation of the first comprehensive physical input–output tables (PIOTs) for some western European countries in the 1990s opened new possibilities for linking physical accounting and input–output analysis. Using a physical multiplier for this kind of calculation is more appropriate, as the most land intensive sectors are also the sectors with the highest amounts of material flows. Physical input–output analysis illustrates land appropriation in relation to material flows of each of the sectors, which is more appropriate from the point of view of environmental pressures than land appropriation in relation to monetary flows of a MIOT. Physical input–output analysis has so far not been applied for any land-related studies. Based on a physical input–output model of the EU-15, physical input–output analysis is applied in this paper, in order to calculate direct and indirect land requirements for the production of exports from EU-15 to the rest of the world.

Applying physical input–output analysis to estimate land appropriation (ecological footprints) of international trade activities

In the last few years some studies have been presented, which link land use accounting and input–output analysis (based on monetary input–output tables (MIOTs)) for the calculation of direct and indirect land appropriation of production and consumption activities. The compilation of the first comprehensive physical input–output tables (PIOTs) for some western European countries in the 1990s opened new possibilities for linking physical accounting and input–output analysis. Using a physical multiplier for this kind of calculation is more appropriate, as the most land intensive sectors are also the sectors with the highest amounts of material flows. Physical input–output analysis illustrates land appropriation in relation to material flows of each of the sectors, which is more appropriate from the point of view of environmental pressures than land appropriation in relation to monetary flows of a MIOT. Physical input–output analysis has so far not been applied for any land-related studies. Based on a physical input–output model of the EU-15, physical input–output analysis is applied in this paper, in order to calculate direct and indirect land requirements for the production of exports from EU-15 to the rest of the world.