COMPARATIVE EVALUATION OF MRIO DATABASES

This editorial is the introduction to a special issue of Economics Systems Research on the topic of intercomparison of multi-regional input–output (MRIO) databases and analyses. It explains the rationale for dedicating an issue of this journal to this area of research. Then the six papers chosen for this issue are introduced. This is followed by a concluding section outlining future directions for developers and users of MRIO databases.     

A STRUCTURAL DECOMPOSITION APPROACH TO COMPARING MRIO DATABASES

The construction of multi-regional input–output tables is complex, and databases produced using different approaches lead to different analytical outcomes. We outline a decomposition methodology for investigating the variations that exist when using different multiregional input–output (MRIO) systems to calculate a region's consumption-based account. Structural decomposition analysis attributes the change in emissions to a set of dependent determinants, such as technical coefficients, the Leontief inverse and final demands. We apply our methodology to three MRIO databases – Eora, GTAP and WIOD. Findings reveal that the variation between Eora and GTAP can be attributed to differences in the Leontief inverse and emissions’ data, whereas the variation between Eora and WIOD is due to differences in final demand and the Leontief inverse. For the majority of regions, GTAP and WIOD produce similar results. The approach in this study could help move MRIO databases from the academic arena to a useful policy instrument.     

SENSITIVITY AND UNCERTAINTY ANALYSIS IN MRIO MODELLING; SOME EMPIRICAL RESULTS WITH REGARD TO THE DUTCH CARBON FOOTPRINT

Environmental multi-regional input–output (MRIO) models require large amounts of data that all have their specific uncertainties. This paper presents a sensitivity and uncertainty analysis in order to gain an understanding of the directions in which efforts should be made to reduce these uncertainties. The analyses were carried out for an MRIO model to calculate the Dutch carbon footprint. A sensitivity analysis of the technical coefficients showed that changes in the coefficients in the domestic blocks and in the Dutch import blocks had the largest effects on the calculated footprint. The uncertainty analysis consisting of a Monte Carlo simulation based on probability distributions around the model coefficients showed a relatively low degree of uncertainty in the total Dutch carbon footprint; uncertainties in the carbon emissions allocated to regions, sectors and products were larger. Both analyses showed that, in certain cases, it is justified to apply a partial MRIO analysis.     

A METHOD TO CREATE CARBON FOOTPRINT ESTIMATES CONSISTENT WITH NATIONAL ACCOUNTS

Although multiregional input–output (MRIO) databases use data from national statistical offices, the reconciliation of various data sources results in significantly altered country data. This makes it problematic to use MRIO-based footprints for national policy-making. This paper develops a potential solution using the Netherlands as case study. The method ensures that the footprint is derived from an MRIO dataset (in our case the World Input–Output Database (WIOD)) that is made consistent with Dutch National accounts data. Furthermore, usage of microdata allows us to separate re-exports at the company level. The adjustment results in a foreign footprint in 2009 that is 22% lower than the original WIOD estimates and a significantly altered country allocation. We demonstrate that already in the data preparation phase due to the treatment of re-exports and margins, large differences arise with Dutch national statistics, which may help explain the variation in footprint estimates across MRIO databases.     

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.     

CARBON EMISSION ACCOUNTING IN MRIO MODELS: THE TERRITORY VS. THE RESIDENCE PRINCIPLE

Consumption-based CO₂ emissions, which are commonly calculated by means of environmentally extended input–output analysis, are gaining wider recognition as a way to complement territorial emission inventories. Although their use has increased significantly in the last years, insufficient attention has been paid to the methodological soundness of the underlying environmental extension. This should follow the internationally agreed accounting rules of the System of Environmental-Economic Accounting, which addresses the activities undertaken by the residents of a country, independent from where these take place. Nonetheless, some footprint calculations use extensions that account for all the activities within the territory, which leads to methodological inconsistencies. Thus, this article introduces the most relevant conceptual differences between these accounting frameworks and shows the magnitude of the gap between them building on the data generated for the EXIOBASE model. It concludes that the differences are high for many countries and their magnitude is increasing over time.     

CONVERGENCE BETWEEN THE EORA,WIOD, EXIOBASE,AND OPENEU'S CONSUMPTION-BASED CARBON ACCOUNTS

In this paper, we take an overview of several of the biggest independently constructed global multi-regional input–output (MRIO) databases and ask how reliable and consonant these databases are. The key question is whether MRIO accounts are robust enough for setting environmental policies. This paper compares the results of four global MRIOs: Eora, WIOD, EXIOBASE, and the GTAP-based OpenEU databases, and investigates how much each diverges from the multi-model mean. We also use Monte Carlo analysis to conduct sensitivity analysis of the robustness of each accounts’ results and we test to see how much variation in the environmental satellite account, rather than the economic structure itself, causes divergence in results. After harmonising the satellite account, we found that carbon footprint results for most major economies disagree by<10% between MRIOs. Confidence estimates are necessary if MRIO methods and consumption-based accounting are to be used in environmental policy-making at the national level.     

Explaining value chain differences in MRIO databases through structural path decomposition

Many multiregional input–output (MRIO) databases are used to calculate consumption-based accounts. Results feature in climate policy discussion on emissions reduction responsibilities; yet studies show that outcomes produced by each database differ. This paper compares the emissions associated with value chains from Eora, EXIOBASE, GTAP and WIOD. Structural path analysis identifies the largest paths in each database and the differences in common paths are calculated. For the top 100 value chain paths that contain the largest difference, structural path decomposition is used to identify the contribution each part of the value chain makes towards the difference. The results identify and quantify key flows that are the cause of difference in the databases. From these, we can conclude that key MRIO database construction decisions, such as using the residence or territorial principle for emissions allocation and whether energy spends are reallocated based on physical data, are the major causes of differences.     

SUPPLY-USE FRAMEWORK FOR INTERNATIONAL ENVIRONMENTAL POLICY ANALYSIS

The technical variation between countries in the production of goods and services, in terms of not only input coefficients, but also emission coefficients, creates scope for international trade to reduce environmental pressures. For this purpose we extend the theory of trade and the environment as to accommodate technical variation between countries in production and emissions. We use and steer close to the extended input and output tables, which include emission data. By treating environmental standards analogous to capital and labor capacity constraints, the aggregation problem for economic and environmental measures gets the same format as the well-understood aggregation problem for labor and capital. In a pilot application we determine the gains to free trade in products and emission permits.     

ECONOMETRIC ANALYSIS OF EUROPEAN CARBON DIOXIDE EMISSIONS BASED ON RECTANGULAR SUPPLY-USE TABLES

This paper formalises the so-called Supply-Use Based Econometric (SUBE) approach that allows for the introduction of econometric analysis in the calculation of backward input–output multipliers of the Leontief-type quantity model, using rectangular supply and use tables. The SUBE approach does not require any kind of inverse matrix and incorporates the traditional approach (with square supply-use tables) as a particular case. The empirical analysis shows that the SUBE carbon dioxide multipliers for the EU27 are considerably lower than those obtained by the traditional Leontief inverse. In an application of the SUBE approach, the European economy appears to emit about 10% less carbon dioxide than in a situation in which it would not import any intermediate inputs from outside the EU27.     

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.     

THE BIAS OF THE MULTIPLIER MATRIX WHEN SUPPLY AND USE TABLES ARE STOCHASTIC

The literature on stochastic input–output (I–O) analysis has paid considerable attention to the bias in the Leontief inverse. This paper extends previous studies by assuming supply and use tables (SUTs rather than I–O tables or input coefficients matrices) to be stochastic. This is a natural starting point because SUTs have become the basic data sources for I–O applications. In a Monte Carlo simulation experiment, a given SUT is randomized in two different ways and the effects are determined for eight different multiplier matrices. The analysis is carried out for Spain, Italy, the Netherlands, Germany and Finland, using their SUTs for 2006. The findings indicate that, in general, biases are statistically significant but negligibly small. This corroborates earlier findings obtained for stochastic I–O tables.     

THE POWER OF VISIBLE HANDS: AN ENVIRONMENTAL STRUCTURAL DECOMPOSITION ANALYSIS CONSIDERING THE PEOPLE'S DAILY EFFECT

As an organ of the Central Committee of the Communist Party of China, the People's Daily (PD) has a critical influence on China's policies and economy. This study examines the impact of the PD on China's economic structural changes and industry environmental performance. To separate the ‘PD effect’ from other effects in total sectoral CO₂ emission changes, we propose a new variation of structural decomposition analysis using the frequencies of keywords employed in the PD. We use data from the PD for 2001 to 2011 and the Chinese input–output tables for 2002, 2005, 2007, and 2010. The results show that, on average, the PD effect explains about 11% of changes in total sectoral CO₂ emissions. Specifically, the PD has a relatively strong impact on the mining and utility sectors, while its impact on the manufacturing sectors is relatively weak.     

DEVELOPMENT OF REGIONAL SOCIAL ACCOUNTING MATRICES WITH DETAILED AGRICULTURAL LAND RENT DATA AND IMPROVED VALUE-ADDED COMPONENTS FOR THE USA

We describe a method for creating social accounting matrices (SAMs) with detailed agricultural land rent data for any arbitrary subset of the 48 contiguous states in the USA. Data on land use and land rents from various public sources is merged with national accounts data. The method reorganizes the rental income of persons concept present in national accounts to payments to conventional primary factors of production. This method also reallocates portions of the indirect business tax account to the appropriate sales and import tax accounts. SAMs created using this method should be useful inputs into input–output or computable general equilibrium models explicitly representing a heterogeneous land market and analyzing the economic effects of agricultural, bioenergy, water and climate policies on land-use change, land rents, agricultural commodity markets, trade and households’ welfare. The method's implementation is freely available, enabling others to rapidly create SAMs with their own desired region and sector aggregations.