Environmental Footprints and Scenario Analysis for Assessing the Impacts of the Agri‐Food Industry on a Regional Economy: A Case Study in Spain

The study of the environmental footprints of various sectors and industries is increasingly demanded by institutions and by society. In this context, the regional perspective is becoming particularly important, and even more so in countries such as Spain, where the autonomous communities have the primary responsibility for implementing measures to combat environmental degradation and promote sustainable development, in coordination with national strategies. Taking as a case study a Spanish region, Aragon, and significant economic sectors, including agriculture and the food industry, the aim of this work is twofold. First, we calculate the associated environmental footprints (of emissions and water) from the dual perspectives of production (local impacts) and consumption (final destination of the goods produced by the agri‐food industry). Second, through a scenarios analysis, based on a general equilibrium model designed and calibrated specifically for the region, we evaluate the environmental implications of changes in the agri‐food industry (changes in the export and import pattern, as well as in consumer behavior). This model provides a flexible approximation to the environmental impacts, controlling for a wider range of behavioral and economic interactions. Our results indicate that the agri‐food industry has a significant impact on the environment, especially on water resources, which must be responsibly managed in order to maintain the differential advantage that a regional economy can have, compared to other territories.     

Combining Multiregional Input‐Output Analysis with a World Trade Model for Evaluating Scenarios for Sustainable Use of Global Resources,Part I: Conceptual Framework

Consumption in a particular country often entails resource extraction, production, and environmental degradation in remote locations. This fact has stimulated a growing body of empirical analysis using input‐output (I‐O) databases and techniques to reveal and quantify the underlying linkages. Two lines of research rooted in I‐O economics, multiregional input‐output (MRIO) analysis and I‐O modeling of the world economy, describe and analyze these relationships, the first for the past, increasingly in the form of footprints and the underlying pathways, and the latter under alternative scenarios about possible courses of action in the future. The article shows how organizing such scenario outcomes into an MRIO database can extend the reach of MRIO analysis to the future while simultaneously supplementing the capabilities of the world trade modeling framework. We describe the compilation of an MRIO database from the results of scenario analysis using the world trade model (WTM) in a companion article (Part II, Implementation); the subsequent application of MRIO techniques to this database permits the evaluation of prospects for the future. We also address several overlooked challenges, namely, the need to include factor endowments and distances between potential trade partners in an MRIO database, the representation of sectors providing transport of internationally traded goods, and the manipulation of mixed physical and money units when both quantities and prices are endogenous.     

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

It 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.     

Effects of Using Heterogeneous Prices on the Allocation of Impacts from Electricity Use: A Mixed‐Unit Input‐Output Approach

Economic input‐output life cycle assessment (IO‐LCA) models allow for quick estimation of economy‐wide greenhouse gas (GHG) emissions associated with goods and services. IO‐LCA models are usually built using economic accounts and differ from most process‐based models in their use of economic transactions, rather than physical flows, as the drivers of supply‐chain GHG emissions. GHG emissions estimates associated with input supply chains are influenced by the price paid by consumers when the relative prices between individual consumers are different. We investigate the significance of the allocation of GHG emissions based on monetary versus physical units by carrying out a case study of the U.S. electricity sector. We create parallel monetary and mixed‐unit IO‐LCA models using the 2007 Benchmark Accounts of the U.S. economy and sector specific prices for different end users of electricity. This approach is well suited for electricity generation because electricity consumption contributes a significant share of emissions for most processes, and the range of prices paid by electricity consumers allows us to explore the effects of price on allocation of emissions. We find that, in general, monetary input‐output models assign fewer emissions per kilowatt to electricity used by industrial sectors than to electricity used by households and service sectors, attributable to the relatively higher prices paid by households and service sectors. This fact introduces a challenging question of what is the best basis for allocating the emissions from electricity generation given the different uses of electricity by consumers and the wide variability of electricity pricing.     

Input‐Output‐based Life Cycle Inventory

An input‐output‐based life cycle inventory (IO‐based LCI) is grounded on economic environmental input‐output analysis (IO analysis). It is a fast and low‐budget method for generating LCI data sets, and is used to close data gaps in life cycle assessment (LCA). Due to the fact that its methodological basis differs from that of process‐based inventory, its application in LCA is a matter of controversy. We developed a German IO‐based approach to derive IO‐based LCI data sets that is based on the German IO accounts and on the German environmental accounts, which provide data for the sector‐specific direct emissions of seven airborne compounds. The method to calculate German IO‐based LCI data sets for building products is explained in detail. The appropriateness of employing IO‐based LCI for German buildings is analyzed by using process‐based LCI data from the Swiss Ecoinvent database to validate the calculated IO‐based LCI data. The extent of the deviations between process‐based LCI and IO‐based LCI varies considerably for the airborne emissions we investigated. We carried out a systematic evaluation of the possible reasons for this deviation. This analysis shows that the sector‐specific effects (aggregation of sectors) and the quality of primary data for emissions from national inventory reporting (NIR) are the main reasons for the deviations. As a rule, IO‐based LCI data sets seem to underestimate specific emissions while overestimating sector‐specific aspects.     

Embodied Resource Flows in a Global Economy

This article presents a methodology for identifying critical links in global resource supply chains by tracking resources from their extraction in one region of the world economy through their embodiment in intermediate products in the same and other regions to eventual embodiment in final goods. We build on previous work that applied an absorbing Markov chain (AMC) to results obtained using an input‐output (IO) model of a single region to define a resource‐specific network within that economy. In the absence of model calculations, the AMC can also be applied to standard IO data for a past year. This article first generalizes the analytic framework from a single region to the important case of the global resource‐specific network. This network typically includes cycling of embodied resources between sectors not only within each economy, but also among regions, as subsequent rounds of intermediate products are traded. Next, we refine that analysis to exhibit a crucial subnetwork, the resource end‐use network, which only tracks the portion of the resource that ends up embodied in a specific final product in a given region. Finally, we develop techniques to distinguish key branches of these networks and provide detailed insights about the structure of global resource dependence. A numerical example is applied to results of scenario analysis using an IO model of the world economy. Two alternative scenarios are compared. In each scenario, embodied resources are carried over specific branches of a global network in three regions using three resources to produce four goods.     

Towards a Circular Economy in Australian Agri‐food Industry: An Application of Input‐Output Oriented Approaches for Analyzing Resource Efficiency and Competitiveness Potential

The food industry in Australia (agriculture and manufacturing) plays a fundamental role in contributing to socioeconomic sectors nationally. However, alongside the benefits, the industry also produces environmental burdens associated with the production of food. Sectorally, agriculture is the largest consumer of water. Additionally, land degradation, greenhouse gas emissions, energy consumption, and waste generation are considered the main environmental impacts caused by the industry. The research project aims to evaluate the eco‐efficiency performance of various subsectors in the Australian agri‐food systems through the use of input‐output–oriented approaches of data envelopment analysis and material flow analysis. This helps in establishing environmental and economic indicators for the industry. The results have shown inefficiencies during the life cycle of food production in Australia. Following the principles of industrial ecology, the study recommends the implementation of sustainable processes to increase efficiency, diminish undesirable outputs, and decrease the use of nonrenewable inputs within the production cycle. Broadly, the research outcomes are useful to inform decision makers about the advantages of moving from a traditional linear system to a circular production system, where a sustainable and efficient circular economy could be created in the Australian food industry.     

Combining industrial ecology tools to assess potential greenhouse gas reductions of a circular economy: Method development and application to Switzerland

Industrial ecology (IE) methodologies, such as input/output or material flow analysis and life cycle assessment (LCA), are often used for the environmental evaluation of circular economy strategies. Up to now, an approach that utilizes these methods in a systematic, integrated framework for a holistic assessment of a geographic region's sustainable circular economy potential has been lacking. The approach developed in this study (IE4CE approach) combines IE methodologies to determine the environmental impact mitigation potential of circular economy strategies for a defined geographic region. The approach foresees five steps. First, input/output analysis helps identify sectors with high environmental impacts. Second, a refined analysis is conducted using material flow and LCA. In step 3, circular strategies are used for scenario design and evaluated in step 4. In step 5, the assessment results are compiled and compared across sectors. The approach was applied to a case study of Switzerland, analyzing 8 sectors and more than 30 scenarios in depth. Carbon capture and storage (CCS) from waste incineration, biogas and cement production, food waste prevention in households, hospitality and production, and the increased recycling of plastics had the highest mitigation potential. Most of the scenarios do not influence each other. One exception is the CCS scenarios: waste avoidance scenarios decrease the reduction potential of CCS. A combination of scenarios from different sectors, including their impact on the CCS scenario potential, led to an environmental impact mitigation potential of 11.9 Mt CO₂-eq for 2050, which equals 14% of Switzerland's current consumption-based impacts.     

Dynamic Models of Fixed Capital Stocks and Their Application in Industrial Ecology

Industrial assets or fixed capital stocks are at the core of the transition to a low‐carbon economy. They represent substantial accumulations of capital, bulk materials, and critical metals. Their lifetime determines the potential for material recycling and how fast they can be replaced by new, more efficient facilities. Their efficiency determines the coupling between useful output and energy and material throughput. A sound understanding of the economic and physical properties of fixed capital stocks is essential to anticipating the long‐term environmental and economic consequences of the new energy future. We identify substantial overlap in the way stocks are modeled in national accounting, dynamic material flow analysis, dynamic input‐output (I/O) analysis, and life cycle assessment (LCA) and we merge these concepts into a common framework for modeling fixed capital stocks. We demonstrate the usefulness of the framework for simultaneous accounting of capital and material stocks and for consequential LCA. We apply the framework to design a demand‐driven dynamic I/O model with dynamic capital stocks, and we synthesize both the marginal and attributional matrix of technical coefficients (A‐matrix) from detailed process inventories of fixed assets of different age cohorts and technologies. The stock modeling framework allows researchers to identify and exploit synergies between different model families under the umbrella of socioeconomic metabolism.     

A cost–benefit analysis of the environmental taxation policy in China: A frontier analysis‐based environmentally extended input–output optimization method

China's high‐speed economic development and reliance on overconsumption of natural resources have led to serious environmental pollution. Environmental taxation is seen as an effective economic tool to help mitigate air pollution. In order to assess the effects of different scenarios of environmental taxation policies, we propose a frontier‐based environmentally extended input–output optimization model with explicit emission abatement sectors to reflect the inputs and benefits of abatement. Frontier analysis ensures policy scenarios are assessed under the same technical efficiency benchmark, while input–output analysis depicts the wide range of economic transactions among sectors of an economy. Four scenarios are considered in this study, which are increasing specific tax rates of SO₂, NO_x, and soot and dust separately and increasing all three tax rates simultaneously. Our estimation results show that: raising tax rates of SO₂, NO_x, and soot and dust simultaneously would have the highest emission reduction effects, with the SO₂ tax rate making the greatest contribution to emission reduction. Raising the soot and dust tax rate is the most environmentally friendly strategy due to its highest abatement to welfare through avoided health costs. The combination of frontier analysis and input–output analysis provides policy makers a comprehensive and sectoral approach to assess costs and benefits of environmental taxation.     

Identifying Lowest‐Emission Choices and Environmental Pareto Frontiers for Wastewater Treatment Wastewater Treatment Input‐Output Model based Linear Programming

This article proposes a linear programming model that is based on the wastewater treatment input‐output model (W²IO) to identify the lowest‐emission choice among alternative feasible options for wastewater treatment; this model can be considered as an application of the waste input‐output linear programming model (WIO‐LP) to wastewater issues. Using the data of the Tokyo metropolitan W²IO table, I apply this model to obtain the optimal wastewater treatment options under alternative scenarios. The Pareto frontiers of environmental loads are derived to show the trade‐off relationships among various types of environmental load and the effect of the introduction of high‐temperature incineration of dewatered sludge on the generation of environmental loads. The main conclusion of the study is that when all three types of environmental load (landfill level, global warming potential, and chemical oxygen demand) are considered, the introduction of high‐temperature incineration causes the widening of the Pareto frontier of environmental loads and also causes it to move closer to the origin.     

Unraveling the Nexus: Exploring the Pathways to Combined Resource Use

In response to the unprecedented decline in global natural resource endowments, the so‐called nexus framework is gaining increasing influence on resource management practices. In this research, we approach the resource nexus through the concept of nexus pathways. Nexus pathways are configurations that resource flows follow along supply chains leading to the combined use of two or more resources. Three general types of pathways are identified: direct (on‐site use), dependent (one‐way supply chains), and interdependent (supply‐chain feedbacks). We quantify and compare each pathway by means of multiregional input‐output analysis and structural path analysis, and apply this approach to a comparative case study on the water‐energy nexus (WEN) in the United States and China. Interdependencies or feedbacks are generally thought to be relevant for the WEN, especially between water and energy sectors. Our economy‐wide analysis for both countries indicates, however, that feedbacks neither play an important role in the WEN nor substantially take place between water and energy sectors. The most important feedbacks contribute to less than 1% of total resource use, and these take place mostly between manufacturing sectors. Overall, the studied WEN is mostly driven by dependent pathways and, to a lesser degree, direct resource use. Comparative differences between the two countries are largely explained by differences in economic structure, technology, and resource endowments. Our findings call into question current research and policy focus and suggest greater attention to less complex, but more determining, pathways leading to absolute resource use.     

Unintentional Flow of Alloying Elements in Steel during Recycling of End‐of‐Life Vehicles

Alloying elements in steel add a wide range of valuable properties to steel materials that are indispensable for the global economy. However, they are likely to be effectively irretrievably blended into the steel when recycled because of (among other issues) the lack of information about the composition of the scrap. This results in the alloying elements dissipating in slag during steelmaking and/or becoming contaminants in secondary steel. We used the waste input‐output material flow analysis model to quantify the unintentional flows of alloying elements (i.e., chromium, nickel, and molybdenum) that occur in steel materials and that result from mixing during end‐of‐life (EOL) processes. The model can be used to predict in detail the flows of ferrous materials in various phases, including the recycling phase by extending steel, alloying element source, and iron and steel scrap sectors. Application of the model to Japanese data indicates the critical importance of the recycling of EOL vehicles (ELVs) in Japan because passenger cars are the final destination of the largest share of these alloying elements. However, the contents of alloying elements are rarely considered in current ELV recycling. Consequently, the present study demonstrates that considerable amounts of alloying elements, which correspond to 7% to 8% of the annual consumption in electric arc furnace (EAF) steelmaking, are unintentionally introduced into EAFs. This result suggests the importance of quality‐based scrap recycling for efficient management of alloying elements.     

A Hybrid‐Unit Energy Input‐Output Model to Evaluate Embodied Energy and Life Cycle Emissions for China's Economy

We develop a hybrid‐unit energy input‐output (I/O) model with a disaggregated electricity sector for China. The model replaces primary energy rows in monetary value, namely, coal, gas, crude oil, and renewable energy, with physical flow units in order to overcome errors associated with the proportionality assumption in environmental I/O analysis models. Model development and data use are explained and compared with other approaches in the field of environmental life cycle assessment. The model is applied to evaluate the primary energy embodied in economic output to meet Chinese final consumption for the year 2007. Direct and indirect carbon dioxide emissions intensities are determined. We find that different final demand categories pose distinctive requirements on the primary energy mix. Also, a considerable amount of energy is embodied in the supply chain of secondary industries. Embodied energy and emissions are crucial to consider for policy development in China based on consumption, rather than production. Consumption‐based policies will likely play a more important role in China when per capita income levels have reached those of western countries.     

Truncation Error Estimates in Process Life Cycle Assessment Using Input‐Output Analysis

Process life cycle assessment (PLCA) is widely used to quantify environmental flows associated with the manufacturing of products and other processes. As PLCA always depends on defining a system boundary, its application involves truncation errors. Different methods of estimating truncation errors are proposed in the literature; most of these are based on artificially constructed system complete counterfactuals. In this article, we review the literature on truncation errors and their estimates and systematically explore factors that influence truncation error estimates. We classify estimation approaches, together with underlying factors influencing estimation results according to where in the estimation procedure they occur. By contrasting different PLCA truncation/error modeling frameworks using the same underlying input‐output (I‐O) data set and varying cut‐off criteria, we show that modeling choices can significantly influence estimates for PLCA truncation errors. In addition, we find that differences in I‐O and process inventory databases, such as missing service sector activities, can significantly affect estimates of PLCA truncation errors. Our results expose the challenges related to explicit statements on the magnitude of PLCA truncation errors. They also indicate that increasing the strictness of cut‐off criteria in PLCA has only limited influence on the resulting truncation errors. We conclude that applying an additional I‐O life cycle assessment or a path exchange hybrid life cycle assessment to identify where significant contributions are located in upstream layers could significantly reduce PLCA truncation errors.     

Considering Variations in Waste Composition during Waste Input‐Output Modeling

Concentrations of pollutants vary in wastes from different sources. However, existing waste input‐output (WIO) models do not take these differing concentrations into account. This article proposes a new category of model, which we are calling a waste input‐output model at the substance level (WIOS model). The WIOS model considers variations in waste composition. These variations potentially affect the life cycle inventory of the waste treatment stage. The proposed model is expected to produce more accurate results than existing WIO models that do not consider variations in the composition of wastes. In addition, the proposed model provides a method to trace substances undergoing waste treatment. In this article, use of the WIOS model is illustrated by simulating the overall environmental loads of total organic carbon from wastewater treatment at a facility in Germany. The results show that variations in the composition of wastes entering treatment significantly affect the modeled estimates of total environmental loads caused by wastewater treatment. In addition, the results of the proposed model are different from results given by existing hybrid input‐output WIO models that do not consider variations in the composition of wastewater as it undergoes treatment.     

An Australian Multi‐Regional Waste Supply‐Use Framework

The production of waste creates both direct and indirect environmental impacts. A range of strategies are available to reduce the generation of waste by industry and households, and to select waste treatment approaches that minimize environmental harm. However, evaluating these strategies requires reliable and detailed data on waste production and treatment. Unfortunately, published Australian waste data are typically highly aggregated, published by a variety of entities in different formats, and do not form a complete time‐series. We demonstrate a technique for constructing a multi‐regional waste supply‐use (MRWSU) framework for Australia using information from numerous waste data sources. This is the first MRWSU framework to be constructed (to the authors' knowledge) and the first sub‐national waste input‐output framework to be constructed for Australia. We construct the framework using the Industrial Ecology Virtual Laboratory (IELab), a cloud‐hosted computational platform for building Australian multi‐regional input‐output tables. The structure of the framework complies with the System of Environmental‐Economic Accounting (SEEA). We demonstrate the use of the MRWSU framework by calculating waste footprints that enumerate the full supply chain waste production for Australian consumers.     

The Waste Footprint of French Households in 2020: A Comparison of Scenarios of Consumption Growth Using Input‐Output Analysis

This study aims at quantifying and analyzing the waste footprint of French household consumption in 2020 with respect to different scenarios of economic growth. Three models are jointly used: (1) a multiregional unilateral input‐output model extended to waste, to quantify waste generation from economic activities induced by household consumption; (2) a coefficient‐based model dedicated to quantifying postconsumer waste as a function of household consumption; and (3) the New Econometric Model of Evaluation by Sectorial Interdependency and Supply (NEMESIS), a macroeconometric model used to elaborate different scenarios of growth in household consumption in the period 2008–2020. Three scenarios consider changes primarily in terms of household consumption volume, while one scenario additionally considers changes in the composition of consumption according to the past‐30‐year trend. First, this study suggests that if the trend in changes of composition is maintained, it will lead, by 2020, to a “relative” decoupling between French household consumption and waste footprint with respect to dry recyclables, mixed wastes, and organic wastes and to an “absolute” decoupling with respect to mineral wastes. Second, this study provides a mapping of the changes in French household waste footprints from 2008 to 2020 as a function of scenarios, with indications of where these changes would actually occur in the economy (waste from economic activities or postconsumer waste) and geographically (in France or abroad). In particular, for most of the scenarios considered, changes in French household consumption from 2008 to 2020 primarily induce changes in organic and mineral waste generation abroad rather than in France.     

Environmental Impacts of Products:Policy Relevant Information and Data Challenges

The research and analysis presented in this special issue shows that the same limited number of consumption categories are consistently revealed to be responsible for the largest share of environmental impact: mobility (automobile and air transport), food (meat, poultry, fish, and dairy followed by plant‐based food), and residential energy use in the house (heating, cooling, electrical appliances, and lighting). It appears that differences in impact per euro between the product groupings are relatively limited, so it is essential to reduce the life‐cycle impacts of products as such, rather than to shift expenditures to less impact‐intensive product groupings. Furthermore, the effectiveness of expenditure on material products to improve quality of life leaves much room for improvement. Environmentally extended input‐output (EEIO) tables probably form, in this field, the most appropriate information support tool for priority setting, prospective assessment of options, scenario analysis, and monitoring. A clear benefit would result from integrating the input–output (IO) tables in the report to Eurostat of the 25 individual countries that make up the European Union (EU), with other officially available information on emissions and resources use, into a 60‐sector EEIO table for the EU. This, obviously, would be the first step toward more detailed tables. Three strategies are suggested to realize the additional, desirable detail of 150 sectors or more, each achievable at an increasing time horizon and with increasing effort: (1) developing further the current CEDA EU25 table; (2) building a truly European detailed input–output table accepting the restrictions of existing data gathering procedures; and (3) as (2), but developing new, dedicated data gathering and classification procedures. In all cases, a key issue is harmonizing classification systems for industry sectors, consumer expenditure categories, and product classifications (as in import/export statistics) in such a way that data sets may adequately be linked to input–output tables.