Method for endogenizing capital in the United States Environmentally‐Extended Input‐Output model

Each year businesses, governments, and homeowners in the United States invest around one fifth of gross domestic product into the creation of capital assets such as buildings, machinery, and software to enable production and consumption. Use of capital is typically included to some extent in environmental life cycle assessments of goods and services but is not incorporated into most environmentally extended input‐output (EEIO) models, including the US Environmental Protection Agency's USEEIO. Capital assets are typically created in years prior to their use, so a challenge lies in distributing the impacts of their creation over time. In this work, a highly detailed capital flow matrix approach is followed to distribute the use of fixed capital assets to consuming industries. Data from the US Bureau of Economic Analysis's Fixed Asset Accounts is merged with its Industry Accounts data by the creation of concordance tables. Public highways and streets are partially reallocated to industries operating vehicles. The resulting capital use matrix is later combined into a modified USEEIO. “Housing” is found to be the largest consumer of fixed assets, followed by general government, fossil fuel extraction, and financial industries involved in leasing. Construction, vehicles, and machinery are mostly used by industries in the form of fixed assets. The types of fixed assets used by industries are consistent with expectations: housing is dominated by structures, transport by equipment, and information industries by intellectual property products.     

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.     

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.     

A review of methods to trace material flows into final products in dynamic material flow analysis: Comparative application of six methods to the United States and EXIOBASE3 regions,Part 2

Modeling pathways toward sustainable production and consumption requires improved spatio-temporal and material coverage of end-use product stocks. Momentarily, studies on inflow-driven, dynamic material flow analysis (dMFA) extrapolate scarce information on material end-use shares (i.e., ratios that split economy-wide material consumption to different end-use products) for single countries and years across longer time periods and global regions. Therefore, in part 1 of this work, we reviewed five methods to derive material end-use shares which use industry shipment data in physical units and monetary input–output tables (MIOTs). Herein, we comparatively apply these methods to the United States, drawing on detailed national data, as well as the multi-regional input–output model EXIOBASE3. To better match MIOT and dMFA system definitions, we propose the end-use transfer method, which re-routes specific intermediate outputs to final demand in MIOTs. In closing, we conclude on 12 points for improved end-use shares. We find mixed results regarding the fit between end-use shares derived from industry shipments and MIOTs: for detailed national data, we find good fit for some materials (e.g., aluminum), while others deviate strongly (e.g., steel). In many cases, the temporal trend of MIOT-derived end-use shares roughly agrees with industry shipments. For EXIOBASE3, we find good fit for some countries and materials, but substantial mismatches for others. Despite mixed results, combining MIOT-based end-use shares with industry shipments and auxiliary country-level data could enable improved temporal, geographical, and end-use resolution. However, the scarcity, documentation, and quality of input data are key limitations for more accurate and detailed end-use shares. This article met the requirements for a gold-gold data openness badge described at http://jie.click/badges .      

Organization Environmental Footprint through Input‐Output Analysis: A Case Study in the Construction Sector

The implementation of global sustainability has gained worldwide attention in recent years. The Organization Environmental Footprint, which encompasses 14 impact categories, is a multicriteria measure of the environmental performance of goods and services provided by an organization from a life cycle perspective. In this article, the focus is on quantifying the Organization Environmental Footprint of a construction company in Spain. By applying an environmentally extended input‐output approach, its total footprint and impacts along the supply chain from two consecutive years were calculated. The results show that the environmental impacts from the second year of implementation were significantly higher than those from the first year. The impact category climate change was found to have experienced the greatest increase from one year to the other, with a 31% increase. This work provides an overview of 14 environmental impact categories of the company assessed, as well as recommendations for the implementation of this indicator in companies and public procurement. This approach could pave the way to shape organizations’ action plans and meet the European environmental challenges.     

Addressing the Carbon‐Crime Blind Spot: A Carbon Footprint Approach

Governments estimate the social and economic impacts of crime, but its environmental impact is largely unacknowledged. Our study addresses this by estimating the carbon footprint of crime in England and Wales and identifies the largest sources of emissions. By applying environmentally extended input‐output analysis–derived carbon emission factors to the monetized costs of crime, we estimate that crime committed in 2011 in England and Wales gave rise to over 4 million tonnes of carbon dioxide equivalents. Burglary resulted in the largest proportion of the total footprint (30%), because of the carbon associated with replacing stolen/damaged goods. Emissions arising from criminal justice system services also accounted for a large proportion (21% of all offenses; 49% of police recorded offenses). Focus on these offenses and the carbon efficiency of these services may help reduce the overall emissions that result from crime. However, cutting crime does not automatically result in a net reduction in carbon, given that we need to take account of potential rebound effects. As an example, we consider the impact of reducing domestic burglary by 5%. Calculating this is inherently uncertain given that it depends on assumptions concerning how money would be spent in the absence of crime. We find the most likely rebound effect (our medium estimate) is an increase in emissions of 2%. Despite this uncertainty concerning carbon savings, our study goes some way toward informing policy makers of the scale of the environmental consequences of crime and thus enables it to be taken into account in policy appraisals.     

Searching for Leverage to Conserve Forests: The Industrial Ecology of Wood Products in the United States

The forest and the creatures it shelters exemplify nature, and logging exemplifies the impacts of humans. In the 1990s Americans annually removed 70% more timber from the forest than in 1900. Since I900 population rose more than three times and gross domestic product (GDP) per person almost five. Despite more people, affluence, and logging, U.S. forest area remained constant. Since mid-century, standing timber volume me nearly 30%. Consumers, millers, and foresters, responding to changes in style, ethics, and technology, have contributed to these outcomes. We examine the role of each actor in the industrial ecology of forests for their leverage for sparing forests. Consumers lessened their use of wood products per GDP (Intensity of Use) during the century by 2.5% annually to offset expanding population and GDP per person, a trend that will level or lower timber consumption if population and affluence grow as expected. Millers became highly efficient at utilizing wood and recycled fiber for their material or energy, a success that limits their fcrture leverage. Foresters have leverage to grow trees faster and thus use less forest land to grow and harvest timber. Steady or declining demand for trees coupled to productive forests could spare more US. forest land for sequestering carbon, ecosystem services, and habit for nature.     

A new physical accounting model for material flows in urban systems with application to the Stockholm Royal Seaport District

Sustainable urbanization requires streamlining of resource management in urban systems which in turn requires understanding of urban metabolism (UM). Even though various methods have been applied for UM analysis, to date there is no standardized method for comprehensive accounting of material flows in urban systems. Moreover, the accounting of material flows is rarely implemented with a bottom‐up approach that can provide a thorough analysis of UM. This article presents the Urban Accounting Model (UAM) which aims to allow comprehensive accounting of urban material flows based on a bottom‐up approach. The model comprises two interlinked sub‐models. The first was developed by integrating a new physical input output table (PIOT) framework for urban systems into a three‐dimensional structure. The second comprises a set of physical accounts for systematic accounting of material flows of each economic sector in the system in order to support the compilation of the PIOTs. The functions of the UAM were explored through its application to two urban neighborhoods in the Stockholm Royal Seaport district. The application highlighted that the UAM can describe the physical interactions between the urban system and the environment or other socioeconomic systems, and capture the intersectoral flows within the system. Moreover, its accounts provide information that allow an in‐depth analysis of the metabolism of specific sectors. Overall, the UAM can function as a useful tool for UM analysis as it systematizes data collection and at the same time depicts the physical reality of the urban system.     

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.     

Assessing the Greenhouse Gas Savings Potential of Extended Producer Responsibility for Mattresses and Boxsprings in the United States

Extended producer responsibility (EPR) legislation in the United States, which currently only exists on the state level, now includes three mattress EPR acts, which intend to shift the financial and operational burden of mattress end‐of‐life (EOL) management away from local and state government. It is important to keep in mind, however, that the original objective behind EPR is to reduce the environmental life cycle impacts of products. This article therefore quantifies the greenhouse gas (GHG) savings potential of mattress and boxspring recycling and reuse in the United States and also discusses labor implications and mattress design issues. We find that all three acts are unlikely to generate redesign incentives, but are expected to dramatically increase mattress collection and recycling. The collection and recycling of all 35 million EOL mattress and boxspring units estimated to reach the end of their lives in the United States every year would generate in the order of 10,000 jobs and GHG savings between 1 and 1.5 million metric tonnes.     

A Carbon Footprint of High‐Speed Railways in China: A Case Study of the Beijing‐Shanghai Line

A carbon footprint (CF) assessment of Chinese high‐speed railways (HSRs) can help guide further development of the world's longest HSR network. In this research, a hybrid economic input‐output and life cycle assessment (EIO‐LCA) method was applied to estimate the CF of the Beijing‐Shanghai HSR line. Specific CFs were analyzed of different subsystems of the line, different stages of production, and three calculation scopes. Results showed that the annual CF of the Beijing‐Shanghai HSR is increasing, whereas the per‐passenger CF constantly declined between 2011 and 2014. Scope 1 emissions account for an average of 4% of the total annual CF, Scope 2 contribute 71%, and Scope 3 comprise 25%. Among the different stages, operation contributes the largest (71%), followed by construction (20%) and maintenance (9%). In the construction stage, the bridges have the largest CF, followed by trains, and then rails. A trade‐off exists between the increase in carbon emissions due to construction of bridges and the reduction in operation emissions affected by leveling changes in terrain. The Beijing‐Shanghai HSR line has a relatively higher per‐passenger CF than eight other HSR lines, which is largely due to China's coal‐based carbon‐intensive energy mix of electricity generation, high proportion of bridges, higher operating speed, and heavier train body. In the future, cleaner electricity supply options, more efficient raw material production, and improvement of trains are keys to reducing the CF of Chinese HSRs.     

A landscape‐based approach for assessing spatiotemporal impacts of forest biomass‐based electricity generation on the age structure of surrounding forest plantations in the Southern United States

Forest plantations support several ecosystem services including biodiversity conservation. Establishment of a forest biomass‐based industry could significantly change the age structure of forest plantations located in its vicinity and thus, could lead to a possible loss of biodiversity. Therefore, this study assesses spatiotemporal impacts of a forest biomass‐based power plant on the age structure of surrounding forest plantations at landscape level. A cellular automata approach was adopted and interactions between economic objectives of forest landowners and a power plant owner punctuated by forest growth and management characteristics were considered. These spatiotemporal impacts were jointly assessed for four separate scenarios and four different power plant capacities using appropriate landscape‐level indices. Slash pine (Pinus elliotti L.) was selected as a representative species. Results indicate that the age structure of surrounding forest plantations continuously fluctuates with respect to each year of power plant operation. However, the age structure, once disturbed, never becomes comparable to the original age structure. We also found that the mature plantations were harvested during early years of power plant operation and were never observed again for the remaining years of power plant operation. This was particularly true for high capacity power plants. Similarly, high value of selected spatial index at the end of power plant life for a high capacity power plant relative to the original low value of the same index indicates aggregation of remaining plantation ages at landscape level. Establishment of low capacity forest biomass‐based power plants and adoption of an integrated regional level planning approach could help in maintaining original age structure characteristics of surrounding forest plantations to a large extent. This might help in sustaining various ecosystem services including biodiversity conservation obtained from forest plantations in a long run.     

Marginal Models Via GLS: A Convenient Yet Neglected Tool for the Analysis of Correlated Data in the Behavioural Sciences

Behavioural research often produces data that have a complicated structure. For instance, data can represent repeated observations of the same individual and suffer from heteroscedasticity as well as other technical snags. The regression analysis of such data is often complicated by the fact that the observations (response variables) are mutually correlated. The correlation structure can be quite complex and might or might not be of direct interest to the user. In any case, one needs to take correlations into account (e.g. by means of random‐effect specification) in order to arrive at correct statistical inference (e.g. for construction of the appropriate test or confidence intervals). Over the last decade, such data have been more and more frequently analysed using repeated‐measures ANOVA and mixed‐effects models. Some researchers invoke the heavy machinery of mixed‐effects modelling to obtain the desired population‐level (marginal) inference, which can be achieved by using simpler tools – namely marginal models. This paper highlights marginal modelling (using generalized least squares [GLS] regression) as an alternative method. In various concrete situations, such marginal models can be based on fewer assumptions and directly generate estimates (population‐level parameters) which are of immediate interest to the behavioural researcher (such as population mean). Sometimes, they might be not only easier to interpret but also easier to specify than their competitors (e.g. mixed‐effects models). Using five examples from behavioural research, we demonstrate the use, advantages, limits and pitfalls of marginal and mixed‐effects models implemented within the functions of the ‘nlme’ package in R.