Environmentally-extended Input-output Model Research Articles

Reducing global food loss and waste could improve air quality and lower the risk of premature mortality

While the global food system substantially contributes to environmental degradation and climate change, significant amounts of lost or wasted foods along the food supply chain actively contribute to global air pollution and related health risks. In this study, we use an environmentally-extended input–output model to quantify air pollution embedded in global food loss and waste (FLW) and investigate how FLW reduction policies can mitigate air pollution linked to food consumption, decreasing associated premature mortality risks across global regions. While estimating a positive impact of FLW reduction policies on decreasing air pollution levels (from −1.5% of SO2 emissions to −10.2% of NH3 emissions) and mortality reductions (over 67 000 lives worldwide) our findings highlight that rebound effects, wherein a reallocation of consumption from food to non-food commodities, decrease health and environmental benefits by over three quarters (compared to the case with no rebound). Such rebound effects can be substantially mitigated when final consumption shifts towards less pollution-intensive products, such as service activities, rather than conforming to the current composition of non-food consumption. Our results suggest that FLW-related policies would benefit from complementary measures that incentivize sustainable non-food consumption to effectively foster the transition towards a healthier and more sustainable planet.

StatelO - Open Source Economic Input-Output Models for the 50 States of the United States of America.

Subnational input-output (IO) tables capture industry- and region-specific production, consumption, and trade of commodities and serve as a common basis for regional and multi-regional economic impact analysis. However, subnational IO tables are not made available by national statistical offices, especially in the United States (US), nor have they been estimated with transparent methods for reproducibility or updated regularly for public availability. In this article, we describe a robust StateIO modeling framework to develop state and two-region IO models for all 50 states in the US using national IO tables and state industry and trade data from reliable public sources such as the US Bureau of Economic Analysis. We develop 2012-2017 state IO models and two-region IO models at the BEA summary level. The two regions are state of interest and rest of the US. All models are validated by a series of rigorous checks to ensure the results are balanced at state and national levels. We then use these models to calculate a 2012-2017 time series of macro economic indicators and highlight results for I I states that have distinct economies with respect to size, geography, and industry structure. We also compare selected indicators to state IO models created by popular licensed and open-source software. Our StateIO modeling framework is consolidated in an open-source R package, stateior, to ensure transparency and reproducibility. Our StateIO models are US-focused, which may not be transferrable to international accounts, and form the economic base of state versions of the US environmentally-extended IO models.

Optimization based structural decomposition analysis as a tool for supporting environmental policymaking

A new tool for supporting environmental policymaking presented in this paper combines the ease of interpretation of structural decomposition analysis in the environmentally extended input-output (EEIO) model with versality of MINLP optimization. The approach allows finding those coefficients in EEIO model in the case of which a small change of their levels leads to a large reduction of industrial GHG emissions. In the illustrative empirical example the proposed approach is first implemented in GAMS and next used to identify GHG-emissions-important IO coefficients in Poland.

Environmentally-extended input-output analysis of circular economy scenarios in the Philippines

Conventional economic systems are based on the unsustainable use of natural resources. Circular Economy is a framework for socio-technical regime change that prioritizes material loops to improve resource efficiency. The framework promotes business activities that capitalize on material recirculation pathways and extended product life cycles. Many countries have begun adopting Circular Economy principles through policies, targets, and action plans, but research in this area has focused largely on developed countries and major emerging economies. The lack of scientific analysis of Circular Economy implementation in developing countries is a critical research gap with significant implications on global material flows. Fast-growing countries such as the Philippines will benefit from the regime change towards Circular Economy to resolve sustainability issues from economic and demographic trends. Policy interventions need to be calibrated to enable a smooth transition to circularity. To address this gap, this study uses a 16-sector Environmentally-Extended Input-Output model to simulate four scenarios on a) increasing product demand, b) implementing circular business models, c) implementing government regulations, and d) promoting service sectors. GDP growth, material intensity, and regulatory constraints of each scenario are analyzed. Results show a potential of up to 10.05% increase in GDP and 62.51% decrease in material footprint relative to business-as-usual. Policy implications on the Philippines' transition to a Circular Economy are discussed.

Incorporating New Technologies in EEIO Models.

We propose a methodology to add new technologies into Environmentally Extended Input–Output (EEIO) models based on a Supply and Use framework. The methodology provides for adding new industries (new technologies) and a new commodity under the assumption that the new commodity will partially substitute for a functionally-similar existing commodity of the baseline economy. The level of substitution is controlled by a percentage (%) as a variable of the model. In the Use table, a percentage of the current use of the existing commodity is transferred to the new commodity. The Supply or Make table is modified assuming that the new industries are the only ones producing the new commodity. We illustrate the method for the USEEIO model, for the addition of second generation biofuels, including naphtha, jet fuel and diesel fuel. The new industries’ inputs, outputs and value-added components needed to produce the new commodity are drawn from process-based life cycle inventories (LCIs). Process-based LCI inputs and outputs per physical functional unit are transformed to prices and assigned to commodities and environmental flow categories for the EEIO model. This methodology is designed to evaluate the environmental impacts of substituting products in the current US economy with bio-versions, produced by new technologies, that are intended to reduce negative environmental impacts. However, it can be applied for any new commodity for which the substitution assumption is reasonable.

Useeior: An Open-Source R Package for Building and Using US Environmentally-Extended Input–Output Models

useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input–output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their initial release in 2017, but users were often challenged to prepare required input data and undergo a complicated model building approach. To address these challenges, useeior was created. In useeior, economic and environmental data are conveniently retrievable for immediate use. Users can build models simply from given or user-specified model configuration and optional hybridization specifications. The assembly of economic and environmental data and matrix calculations are automatically performed. Users can export model results to desired formats. useeior is a core component of the USEEIO modeling framework. It improves transparency, efficiency, and flexibility in building USEEIO models, and was used to deliver the recent USEEIO model.

Tracing energy-water-greenhouse gas nexus in national supply chains: China 2017

The nexus of energy, water and greenhouse gas plays a vital role in resource management and response to climate change for all nations. Actions in China is decisive. In this study, the environmentally-extended input-output model and structural path analysis are combined to analyze the energy-water-greenhouse gas nexus for China 2017. The sectors of Mining and washing of coal, Production and distribution of water, Mining and washing of coal are revealed to show the highest embodied energy consumption intensity, embodied water consumption intensity, and embodied greenhouse gas emission intensity, respectively. Among all 149 sectors, some sectors are identified to be more sensitive than others: Farming, Production and supply of electricity and steam are more energy-water-greenhouse gas sensitive, and Mining and washing of coal, Extraction of crude petroleum and natural gas are more energy-greenhouse gas sensitive, and Animal husbandry, Manufacture of pharmaceutical products are more water-greenhouse gas sensitive. Structural path analysis shows that the top 30 supply chain paths contribute to 34.99% of energy consumption, to 39.18% of water consumption, and to 40.18% of greenhouse gas emissions. By studying the main paths, critical sectors of the energy-water-greenhouse gas nexus are further identified as Farming, Mining and washing of coal, Extraction of crude petroleum and natural gas, Production and supply of electricity and steam, Construction of buildings, Civil engineering. Exploring the energy-water-greenhouse gas nexus of 149 sectors could help to lock the key nodes of China's resource management and greenhouse gas emission control in the supply chains.

Land transport CO2 emissions and climate change: evidence from Cyprus

ABSTRACTThe land transport sector is one of the major emitters of CO2 and one of the hardest sectors to decarbonise. This study employs an environmentally-extended input-output model to conduct an economy-wide assessment of CO2 emissions in Cyprus, associated with a 22% increase in the final demand for the output of the sector by 2030. Model results indicate that the land transport sector creates the third highest (direct and indirect) CO2 emissions within Cyprus economy; for every 1 million euro increase in the final demand of the sector’s services and products, 407 additional tons of CO2 are emitted. Considering that temperature in Cyprus is projected to increase by up to 4.5–5°C by the end of the century, our findings highlight the importance of the land transport sector in the transition of Cyprus to a low-carbon economy and the urgency of implementing in-sector cost-effective decarbonisation strategies.

Analyzing economy-scale solid waste generation using the United States environmentally-extended input-output model

The United States Environmentally-Extended Input-Output (USEEIO) model includes commercial enterprises from 386 industrial sectors of the economy. The purpose of this work is to model the commercial generation of three streams of solid waste from USEEIO sectors: hazardous waste, non-hazardous waste excluding construction, and non-hazardous waste from construction. The waste accounts cover 536 waste materials, with commercial non-hazardous waste presently limited to municipal solid waste and construction and demolition debris. Total combined generation for all streams based on 2015 economic activity is approximately 775 million metric tons, with concrete from construction activities accounting for 44% of this mass. The chemical and plastics industries generate the most commercial hazardous waste per dollar of economic output. In most cases, waste materials such as paper, plastic, and metals are generated in greater quantities per dollar of industry output when compared to commercial construction materials and hazardous waste. When considering direct waste generation within an industry, USEEIO model rankings identified the highway and street construction and chemical manufacturing industries as potential areas to continue to pursue new innovations in material use. The rankings change when considering final consumption of goods and services, with various construction industries and state and local governments becoming more prominent. The full detailed waste models are publicly available and will be incorporated into future USEEIO releases. Quantification of waste material generation across the economy is an essential part of decision making because it will highlight areas where intervention may be beneficial.

Assessing net energy consumption of Australian economy from 2004–05 to 2014–15: Environmentally-extended input-output analysis, structural decomposition analysis, and linkage analysis

This paper provides a comprehensive analysis of Australian net energy consumption between 2004–05 and 2014–15. Results from environmentally-extended input-output (EEIO) analysis show that the Transport sector has the largest direct effect on net energy consumption in industrial sectors, which decreased by about 35% for net energy consumption per million $AUD in the period. The Export sector has the largest direct net energy consumption while Households consumption results in the largest net energy consumption embodied in different categories of Final demand. The structural decomposition analysis (SDA) decomposes the change of net energy consumption into five drivers, in which net energy intensity mainly reduces Australian net energy consumption by about 8000 Petajoules, while the level effect of Final demand increases it by about 10,000 Petajoules. Analysis of forward and backward linkages highlights the Manufacturing sector as the key industrial sector with the largest energy consumption reduction potential via minor changes in its input and Final demand. This indicates that more attention should be given to the reduction of energy demand from the consumption patterns of Households consumption, the improvement of energy intensity, and the application of cleaner technologies in the Transport and Manufacturing sectors. The Australian Environmental-Economic Accounts is combined with Australian input-output tables to construct the EEIO tables for net energy consumption. The combination of economic and environmental data sets provides a depth of understanding their potential to inform environmental policy decisions. The novelty of the research is the combination of economic and energy data sets, the application of EEIO model, the implementation of the additive SDA method, and the use of forward and backward linkages for the Australian energy system.

Exploring the economy-wide effects of agriculture on air quality and health: Evidence from Europe

Agricultural emissions strongly contribute to fine particulate matter pollution (PM2.5) and associated effects on human health. Environmentally-extended input-output models and a regional atmospheric chemistry model (WRF-Chem) were combined to conduct an economy-wide assessment of air pollution and pre-mature mortality in the European Union (EU), associated with a 20% increase in the final demand for the output of the agricultural sector. Model results revealed significant differences in air pollution originating from agricultural growth across the 28 EU countries (EU-28). The highest impact of agricultural growth on PM2.5 concentrations occur over the Northern Balkan countries (Bulgaria and Romania) and northern Italy. However, the highest excess mortality rates in the EU-28 due to changes in emissions and enhanced PM2.5 concentrations are observed in Malta, Greece, Spain and Cyprus. The least affected countries are mostly located in the northern part of Europe, with the exception of the Scandinavian Countries, which have relatively good air quality under current conditions. Our integrated modelling framework results highlight the importance of capturing both the direct and indirect air pollution emissions of economic sectors via upstream supply chains and underscore the non-linear response of surface PM2.5 levels and their health impacts to emission fluxes.

Double Progressivity of Infrastructure Development through Carbon Pricing - Insights from Nigeria

Concerns over adverse impacts on poverty and inequality discourage many lower-income countries from regulating their greenhouse gas emissions, despite being highly vulnerable to climate change. This paper analyses the distributional effects of carbon pricing and revenue recycling in basic infrastructure development in Nigeria. We assess the income impacts of a carbon tax based on microsimulations, combining an environmentally-extended input-output model with household survey data. Our results suggest double progressivity. That is, lower-income households would bear a relatively lower tax burden while enjoying greater infrastructure access gains. Hence, while carbon pricing revenues are unlikely to suffice to finance universal infrastructure access, the policy approach can serve as entry point to align environmental with sustainable development goals.

Exploring the relevance of spatial scale to life cycle inventory results using environmentally-extended input-output models of the United States

The accuracy of direct and indirect resource use and emissions of products as quantified in life cycle models depends in part upon the geographical and technological representativeness of the production models. Production conditions vary not just between nations, but also within national boundaries. Understanding the level of geographic resolution within large industrial nations needed to reach acceptable accuracy has not been well-tested across the broad spectrum of goods and services consumed. Using an aggregate 15-industry environmentally-extended input-output model of the US along with detailed interstate commodity flow data, we test the accuracy of regionalizing the national model into two-regions (state - rest of US) versus 51 regions (all US states + DC). Our findings show the two-region form predicts life cycle emissions and resources used within 10–20% of the more detailed 51-region form for most of the environmental flows studied. The two-region form is less accurate when higher variability exists in production conditions for a product.

USEEIO: A new and transparent United States environmentally-extended input-output model

National-scope environmental life cycle models of goods and services may be used for many purposes, not limited to quantifying impacts of production and consumption of nations, assessing organization-wide impacts, identifying purchasing hotspots, analyzing environmental impacts of policies, and performing streamlined life cycle assessment. USEEIO is a new environmentally-extended input-output model of the United States fit for such purposes and other sustainable materials management applications. USEEIO melds data on economic transactions between 389 industry sectors with environmental data for these sectors covering land, water, energy and mineral usage and emissions of greenhouse gases, criteria air pollutants, nutrients and toxics, to build a life cycle model of 385 US goods and services. In comparison with existing US models, USEEIO is more current with most data representing year 2013, more extensive in its coverage of resources and emissions, more deliberate and detailed in its interpretation and combination of data sources, and includes formal data quality evaluation and description. USEEIO is assembled with a new Python module called the IO Model Builder capable of assembling and calculating results of user-defined input-output models and exporting the models into LCA software. The model and data quality evaluation capabilities are demonstrated with an analysis of the environmental performance of an average hospital in the US. All USEEIO files are publicly available bringing a new level of transparency for environmentally-extended input-output models.

The carbon footprint of Greek households (1995–2012)

The goal of this paper is twofold: i) to investigate the carbon footprint of Greek households throughout the period 1995–2012, in order to identify the main socio-economic factors that affect GHG emissions, and ii) to evaluate the effectiveness of the implemented policies to tackle climate change. In this, a consumption-based emissions inventory approach is applied. The analysis is based on an environmentally-extended input-output model including direct CO2 emissions from households, indirect CO2 emissions from electricity consumption and indirect CO2 emissions from energy used in the production of goods and services purchased by households, domestic or imported. Statistical analysis and appropriate regression models were developed in order to identify the main factors influencing the carbon footprint of Greek households. The results indicate that the observed trends during the period 1995–2008 can be attributed to the effect of high economic growth. This trend is partially counterbalanced by favorable weather conditions and the implementation of greenhouse mitigation policies and measures mainly in the supply side. Since 2008 the shrinking household income is the dominant driver. In addition, the effectiveness of energy conservation policies and measures in place is rather low, while the effect of imports is limited.

Sector aggregation bias in environmentally extended input output modeling of raw material flows in Finland

This paper presents the impact of sector aggregation bias in Environmentally Extended Input Output (EEIO) models, focusing on raw material flows. Finnish industries are aggregated in different ways and causes of bias are studied. The results show that industries with high raw material use deserve special attention in EEIO models. For Finland, particularly problematic is the aggregation of biomass extractive sectors, since the relative importance of forestry causes noticeable deviations. Sources and strategies to prevent errors are described separately for biomass and mineral raw materials. A brief comparison between raw material flows and greenhouse gas emissions is also made. It is shown that aggregation of extractive sectors biases more in material flow analyses. This issue might be of significance in the near future, owing to the foreseeable changes in the European Union accounting framework.

Integrated Environmental Assessment of Future Energy Scenarios Based on Economic Equilibrium Models

The future evolution of energy supply technologies strongly depends on (and affects) the economic and environmental systems, due to the high dependency of this sector on the availability and cost of fossil fuels, especially on the small regional scale. This paper aims at presenting the modeling system and preliminary results of a research project conducted on the scale of Luxembourg to assess the environmental impact of future energy scenarios for the country, integrating outputs from partial and computable general equilibrium models within hybrid Life Cycle Assessment (LCA) frameworks. The general equilibrium model for Luxembourg, LUXGEM, is used to evaluate the economic impacts of policy decisions and other economic shocks over the time horizon 2006-2030. A techno-economic (partial equilibrium) model for Luxembourg, ETEM, is used instead to compute operation levels of various technologies to meet the demand for energy services at the least cost along the same timeline. The future energy demand and supply are made consistent by coupling ETEM with LUXGEM so as to have the same macro-economic variables and energy shares driving both models. The coupling results are then implemented within a set of Environmentally-Extended Input-Output (EE-IO) models in historical time series to test the feasibility of the integrated framework and then to assess the environmental impacts of the country. Accordingly, a disaggregated energy sector was built with the different ETEM technologies in the EE-IO to allow hybridization with Life Cycle Inventory (LCI) and enrich the process detail. The results show that the environmental impact slightly decreased overall from 2006 to 2009. Most of the impacts come from some imported commodities (natural gas, used to produce electricity, and metalliferous ores and metal scrap). The main energy production technology is the combined-cycle gas turbine plant “Twinerg”, representing almost 80% of the domestic electricity production in Luxembourg. In the hybrid EE-IO model, this technology contributes to around 7% of the total impact of the country’s net consumption. The causes of divergence between ETEM and LUXGEM are also thoroughly investigated to outline possible strategies of modeling improvements for future assessment of environmental impacts using EE-IO. Further analyses focus first on the completion of the models’ coupling and its application to the defined scenarios. Once the coupling is consistently accomplished, LUXGEM can compute the IO flows from 2010 to 2030, while the LCI processes in the hybrid system are harmonized with ETEM to represent the future domestic and imported energy technologies.

Integrated environmental assessment of future energy scenarios based on economic equilibrium models

The future evolution of energy supply technologies strongly depends on (and affects) the economic and environmental systems, due to the high dependency of this sector on the availability and cost of fossil fuels, especially on the small regional scale. This paper aims at presenting the modeling system and preliminary results of a research project conducted on the scale of Luxembourg to assess the environmental impact of future energy scenarios for the country, integrating outputs from partial and computable general equilibrium models within hybrid Life Cycle Assessment (LCA) frameworks. The general equilibrium model for Luxembourg, LUXGEM, is used to evaluate the economic impacts of policy decisions and other economic shocks over the time horizon 2006−2030. A techno-economic (partial equilibrium) model for Luxembourg, ETEM, is used instead to compute operation levels of various technologies to meet the demand for energy services at the least cost along the same timeline. The future energy demand and supply are made consistent by coupling ETEM with LUXGEM so as to have the same macro-economic variables and energy shares driving both models. The coupling results are then implemented within a set of Environmentally-Extended Input-Output (EE-IO) models in historical time series to test the feasibility of the integrated framework and then to assess the environmental impacts of the country. Accordingly, a disaggregated energy sector was built with the different ETEM technologies in the EE-IO to allow hybridization with Life Cycle Inventory (LCI) and enrich the process detail. The results show that the environmental impact slightly decreased overall from 2006 to 2009. Most of the impacts come from some imported commodities (natural gas, used to produce electricity, and metalliferous ores and metal scrap). The main energy production technology is the combined-cycle gas turbine plant “Twinerg”, representing almost 80% of the domestic electricity production in Luxembourg. In the hybrid EE-IO model, this technology contributes to around 7% of the total impact of the country’s net consumption. The causes of divergence between ETEM and LUXGEM are also thoroughly investigated to outline possible strategies of modeling improvements for future assessment of environmental impacts using EE-IO. Further analyses focus first on the completion of the models’ coupling and its application to the defined scenarios. Once the coupling is consistently accomplished, LUXGEM can compute the IO flows from 2010 to 2030, while the LCI processes in the hybrid system are harmonized with ETEM to represent the future domestic and imported energy technologies.

Estimating direct and indirect rebound effects for U.S. households with input–output analysis. Part 2: Simulation

This is the second part of a two-part paper that integrates economic and industrial ecology methods to estimate the indirect rebound effect from residential energy efficiency investments. We apply the model developed in part one to simulate the indirect rebound, given an estimate of the direct rebound, using a 2002 environmentally-extended input–output model and the 2004 Consumer Expenditure Survey (in 2002$) for the U.S. We find an indirect rebound of 5–15% in primary energy and CO2e emissions, assuming a 10% direct rebound, depending on the fuel saved with efficiency and household income. The indirect rebound can be as high as 30–40% in NOx or SO2 emissions for efficiency in natural gas services. The substitution effect modeled in part one is small in most cases, and we discuss appropriate applications for proportional or income elasticity spending assumptions. Large indirect rebound effects occur as the U.S. electric grid becomes less-carbon intensive, in households with large transportation demands, or as energy prices increase. Even in extreme cases, there is limited evidence for backfire, or a rebound effect greater than 100%. Enacting pollution taxes or auctioned permits that internalize the externalities of energy use would ensure that rebound effects unambiguously increase consumers' welfare.