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

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.

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.

Effects of Spatial Scale on Life Cycle Inventory Results

Efforts to compile life cycle inventory (LCI) data at more geographically refined scales or resolutions are growing. However, it remains poorly understood as to how the choice of spatial scale may affect LCI results. Here, we examine this question using U.S. corn as a case study. We compile corn production data at two spatial scales, state and county, and compare how their LCI results may differ for state and national level analyses. For greenhouse gas (GHG) emissions, estimates at the two scales are similar (<20% of difference) for most state-level analyses and are basically the same (<5%) for national level analysis. For blue water consumption, estimates at the two scales differ more. Our results suggest that state-level analyses may be an adequate spatial scale for national level GHG analysis and for most state-level GHG analyses of U.S. corn, but may fall short for water consumption, because of its large spatial variability. On the other hand, although county-based LCIs may be considered more accurate, they require substantially more effort to compile. Overall, our study suggests that the goal of a study, data requirements, and spatial variability are important factors to consider when deciding the appropriate spatial scale or pursuing more refined scales.

Spatial characteristics and driving factors of global energy-related sulfur oxides emissions transferring via international trade

Using the logarithmic mean divisia index decomposition methods within the multi-region input-output analytical framework, this paper investigates global energy-related sulfur oxides emissions transferring via trade, so as to reveal spatial characteristics of the pollutant emissions flows, and explores driving factors of the changes of sulfur dioxide emissions embodied in trade (SEET) for 39 major countries for the period 1995-2011. One important finding from this study is that the global SEET mainly flew from developing countries like China to highly developed economies like the U.S., the EU, and Japan. However, of particular concern is that for some countries like Canada and Australia with ample resources and wealthy regions, they had been gradually becoming the net sulfur dioxide emissions exporters in global trade since 1995. Another important finding is that economic development had played a significant role in promoting the global SEET growth, and the expanse of population scale had a slight and positive driving effect on increasing the sulfur oxides emissions embodied in trade for a large proportion of 39 countries, but some coping strategies like improving energy intensity, increasing the proportion of clean energy in the total energy consumption, and optimizing industrial structure could effectively lower the sulfur oxides emissions embodied in trade in a group of 39 countries.

From Water-Use to Water-Scarcity Footprinting in Environmentally Extended Input-Output Analysis

Environmentally extended input-output analysis (EEIOA) supports environmental policy by quantifying how demand for goods and services leads to resource use and emissions across the economy. However, some types of resource use and emissions require spatially explicit impact assessment for meaningful interpretation, which is not possible in conventional EEIOA. For example, water use in locations of scarcity and of abundance are not environmentally equivalent. Opportunities for spatially explicit impact assessment in conventional EEIOA are limited because official input-output tables tend to be produced at the scale of political units, which are not usually well-aligned with environmentally relevant spatial units. In this study, spatially explicit water-scarcity factors and a spatially disaggregated Australian water-use account were used to develop water-scarcity extensions that were coupled with a multiregional input-output model (MRIO). The results link demand for agricultural commodities to the problem of water scarcity in Australia and globally. Important differences were observed between the water-use and water-scarcity footprint results as well as the relative importance of direct and indirect water use, with significant implications for sustainable production and consumption-related policies. The approach presented here is suggested as a feasible general approach for incorporating spatially explicit impact assessments in EEIOA.