CALCULATING ENERGY-RELATED CO2 EMISSIONS EMBODIED IN INTERNATIONAL TRADE USING A GLOBAL INPUT–OUTPUT MODEL

The Global Resource Accounting Model (GRAM) is an environmentally-extended multi-regional input–output model, covering 48 sectors in 53 countries and two regions. Next to CO₂ emissions, GRAM also includes different resource categories. Using GRAM, we are able to estimate the amount of carbon emissions embodied in international trade for each year between 1995 and 2005. These results include all origins and destinations of emissions, so that emissions can be allocated to countries consuming the products that embody these emissions. Net-CO₂ imports of OECD countries increased by 80% between 1995 and 2005. These findings become particularly relevant, as the externalisation of environmental burden through international trade might be an effective strategy for industrialised countries to maintain high environmental quality within their own borders, while externalising the negative environmental consequences of their consumption processes to other parts of the world. This paper focuses on the methodological aspects and data requirements of the model, and shows results for selected countries and aggregated regions.     

To RAS or not to RAS? What is the difference in outcomes in multi-regional input–output models?

The global resource accounting model (GRAM), which is based on OECD input–output and bilateral trade data, is a multi-regional input–output model covering 53 countries and 2 regions. What differentiates GRAM from other state-of-the-art models in this field is that it does not use a matrix balancing technique, such as RAS, after the initial construction of the global intermediate coefficient and final demand matrices. Instead, it reproduces prescribed intermediate and final demand, and determines value added residually. This choice was made to alter the original data as little as possible and keep the calculations traceable. This simpler solution technique might, however, yield different results. This paper aims at identifying the difference between the current solution of GRAM and the solution of a RASed version of GRAM, thus contributing to the assessment of currently used methodologies in this research field. The short conclusion is that, even though some differences during the calculations are present, the calculated output (production) matrix does not differ substantially. The results show that larger differences are brought about by poor assumptions regarding missing or conflicting data rather than by applying or not applying a RAS procedure to the constructed global matrices.     

Alternative Approaches of Physical Input–Output Analysis to Estimate Primary Material Inputs of Production and Consumption Activities

In the last few years, a number of studies have been presented that link material flow accounting and input–output analysis (based on monetary input–output tables) for the calculation of direct and indirect resource inputs for production and consumption activities. The compilation of the first physical input–output tables for some European countries in the 1990s opened new possibilities for linking physical accounting and input– output analysis. Physical input–output analysis has so far only been applied for selected materials, but it has not been used for comprehensive assessments of material requirements of economic activities. In this paper, possibilities and limits of this new input–output approach are clarified. We present and discuss a procedure similar to monetary input– output analysis and develop an alternative approach to account for primary inputs and waste otherwise not included in the analysis. Based on aggregated input–output tables for Germany, we present numerical examples intended to compare the alternative approaches of physical input–output analysis.     

HARMONISING NATIONAL INPUT—OUTPUT TABLES FOR CONSUMPTION-BASED ACCOUNTING — EXPERIENCES FROM EXIOPOL

Environmentally extended, multi-regional, input–output (MRIO) databases have emerged to fulfil the need for mapping the impacts of globalisation, following resource-intensive supply chains crossing country borders. EXIOBASE is one such data set designed for use in analysis relevant to resource use and European Union policy. It provides the most detailed harmonised sector classification in any MRIO and integrates data from a wide range of sources. We review the necessary steps in order to harmonise source data in MRIO databases, and describe methods to increase the product and industry detail of aggregate supply and use tables (SUTs) in order to provide a homogenous classification across countries that allows resource-specific modelling. We cover mathematical programming approaches used to reconcile data sets, and investigate some implications of reverse engineering symmetric input–output tables and disaggregating the SUTs. We focus particularly on the footprint multiplier at the product level, where policy formation is targeted.     

INVESTIGATING ALTERNATIVE APPROACHES TO HARMONISE MULTI-REGIONAL INPUT–OUTPUT DATA

Over recent years a small number of global multi-regional input–output (MRIO) databases were developed to describe the entire global economy at high sector detail. We investigate the differences that arise out of applying different construction procedures for two global MRIO databases: The EXIOBASE database, developed as part of the EU FP6 & 7 programs and the Eora database developed at the University of Sydney. The procedures used in EXIOBASE involve a high degree of interrogation and adjustment throughout the construction of the data set, whilst the Eora MRIO relies on single-step mathematical programming techniques and high-performance computing. We unravel the effect of the different approaches taken to develop the databases by undertaking a number of combinatorial experiments in which we exchange parts of the construction process between the EXIOBASE and Eora build pipelines. We conclude that Eora's highly automated data reconciliation approach produces MRIO databases that are of comparable quality to those constructed with EXIOBASE's multi-step approach. However, the reliability and robustness of the resulting MRIO database largely depend on the level of detail and reliability of the underlying raw data.     

Alternative Estimates of Real Value Added by Industry for Canada

This paper presents a few of the estimates of the real value added of Canadian industries, as obtained through the application of a new deflation methodology. These estimates, which cover the period 1961–91, are compared with the standard estimates based on the double-deflation method. The results show that the estimates based on these alternative methodologies differ substantially, so that the choice between them is not a question of theoretical nicety, but does really matter.     

THE ‘REST OF THE WORLD’ – ESTIMATING THE ECONOMIC STRUCTURE OF MISSING REGIONS IN GLOBAL MULTI-REGIONAL INPUT–OUTPUT TABLES

Incomplete data for the economic structure of numerous countries hamper the compilation of global multi-regional input–output (MRIO) tables. By themselves, most of these countries are of only limited importance for the global economy and incumbent environmental issues. Hence, in most recent global MRIO tables these countries are either roughly estimated or summarised in one rest of the world (RoW) region. Combining a wide range of countries, this RoW region may play a significant role in global economic and environmental accounts. We conceptualise the importance of RoW in several environmental footprint accounts and present algorithms to estimate the structure of RoW. The approach utilises the information of the economic structure within known parts of the MRIO table to estimate the unknown structure. Using this method, global warming potential and employment footprints remain stable irrespective of the chosen initial estimates, whereas natural land use footprints and individual product impacts vary significantly.     

CONSTRUCTION OF MULTI-REGIONAL INPUT–OUTPUT TABLES USING THE CHARM METHOD

Subnational multi-regional input–output tables (IOT) are important tools for studying interregional socio-economic and/or environmental interrelations that help to address a wide range of current societal, ecological and economic challenges. However, the lack of subnational input–output data is a major obstacle which leads to a wide use of non-survey methods. Like other non-survey methods, the cross-hauling adjusted regionalization method (CHARM) was originally developed for the construction of single-regional IOT. In this paper, we extend CHARM to the case of bi- and multi-regional IOT. We find that the original CHARM formula has two limitations that are also of great importance for the single-regional case: First, cross-hauling in interregional trade is implicitly set to zero and, second, accounting balances may be violated owing to structural differences between the regional and national economies. We present a modified formula addressing these issues and examine its performance in terms of a case study.     

EVALUATING UNCERTAINTY IN RISK-BASED INTERDEPENDENCY MODELING WITH INTERVAL ARITHMETIC

Several sources of uncertainty exist in the effort to quantify the efficacy of preparedness decision-making in interdependent systems. For the Inoperability Input–Output Model (IIM), a risk-based extension of the traditional Leontief model, which describes the propagation of inoperability throughout interconnected economic sectors, uncertainty is manifested in parameters describing the strength of interdependencies among sectors and in parameters describing the adverse impacts of a disruptive event, among others. As the model is used to evaluate preparedness options to reduce the impact of these disruptive events, such uncertainty can impact decision-making efforts. This paper introduces interval arithmetic as an approach for dealing with uncertainties in the IIM when probability distributions are not known and only variable bounds are available. Illustrative examples highlight the use of the approach as well as a means to improve the evaluation and comparison of risk management strategies in interdependent systems when only intervals are known.     

REGIONAL WATER FOOTPRINTS OF THE YANGTZE RIVER: AN INTERREGIONAL INPUT–OUTPUT APPROACH

Recently, researchers have applied the multi-regional input–output (MRIO) approach to water footprint (WF) analysis. The concept of interregional input–output (R-MRIO) was developed to analyse regional issues. Researchers have concentrated on the development of global or international input–output (N-MRIO) tables. Using the N-MRIO and the R-MRIO approach allows the study of global and regional issues, respectively. The WF is an indicator influenced by trade among nations and regions. However, the treatment of imports in an R-MRIO approach differs in whether international imports are separated or combined. We evaluate the effects of the difference between these models and discuss policy implications for the Yangtze River, China. The WF calculated using the combined type model is 11% larger than that by the separated type model. This difference can be ascribed to international imports, mainly internal consumption and interregional trade. We find that this difference affects social equity in water-abundant areas.     

Global Impacts of the Automotive Supply Chain Disruption Following the Japanese Earthquake of 2011

This paper provides an input–output method to estimate worldwide economic impacts generated by supply chain disruptions. The method is used to analyse global economic effects due to the disruptions in the automotive industry that followed the Japanese earthquake and the consequent tsunami and nuclear crisis of March 2011. By combining a mixed multi-regional input–output model, the World Input–Output Database and data at the factory level, the study quantifies the economic impacts of the disruptions broken down by country and industry. The results show that the global economic effect (in terms of value added) of this disruption amounted to US$139 billion. The most affected (groups of) countries were Japan (39%), the USA (25%), China (8%) and the European Union (7%). The most strongly affected industries were transport equipment (37%), other business activities (10%), basic and fabricated metals (8%), wholesale trade (7%) and financial intermediation (4%).     

Accounting for value added embodied in trade and consumption: an intercomparison of global multiregional input–output databases

Global multiregional input–output (MRIO) tables constitute detailed accounts of the economic activity worldwide. Global trade models based on MRIO tables are being used to calculate important economic and environmental indicators such as value added in trade or the carbon footprint of nations. Such applications are highly relevant in international trade and climate policy negotiations, and consequently MRIO model results are being scrutinized for their accuracy and reproducibility. We investigate the variation in results from three major MRIO databases by comparing underlying economic data and territorial and consumption-based results across databases. Although global value-added accounts were similar across databases, we find some significant differences at the level of individual countries and sectors. Model disagreement was relatively stable from the territorial to the consumption perspective. Pairwise matrix comparison statistics indicated that the Global Trade Analysis Project and World Input-Output Database MRIO tables were overall more similar to each other than either was to the Eora database.     

USING TIME-SERIES TO MEASURE UNCERTAINTY IN ENVIRONMENTAL INPUT–OUTPUT ANALYSIS

Environmental Input-Output Analysis (EIOA) is a tool for environmental analysis of broad classes of sectoral activities, taking into account indirect effects in other sectors in the supply chain. The core of EIOA is an input–output table (IOT) and national accounting matrix including environmental accounts (NAMEA) for a fixed base-year. We evaluate the uncertainty in EIOA using a time series of current-price IOT and NAMEA for 13 years from 1990 to 2002. We find annual variations in the current-price IOT and NAMEA, which may represent either realistic changes in production or measurement error. We assume the changes are errors and apply a regression analysis to remove the trends from the underlying data and estimate the uncertainty in the raw IOT. We then calculate the emissions for various final users and sectors to estimate the uncertainties from typical EIOA investigations. Using Monte Carlo analysis, we then investigate how well the variations in the current-price IOT and NAMEA over time may represent uncertainties. The results of this work have several implications for both statistical offices and the analyst. Statistical offices can provide details on data sources, methodologies, and estimates of annual variations. Analysts can incorporate this uncertainty information to understand the implications of uncertainty on their calculations and ultimately the policy recommendations derived from their studies.