Multi-regional Input-output Tables Research Articles

Disruptions in global value chains due to COVID-19: stylized facts and policy lessons

This paper provides an early assessment of global value chains (GVCs) amid the disruptive effects of COVID-19 on world trade. Using the Asian Development Bank’s updated Multiregional Input-Output Table, key indicators were estimated to identify important stylized facts about the contraction of GVC activities in 2020. Econometric models were also estimated to analyze the disruptive effect of COVID-19 outbreaks and stringent containment measures on GVC trade. The input-output analysis confirms that all major economic sectors suffered large losses, especially services. However, the bulk of the decline in overall GVC trade can still be traced to lower backward transactions in manufacturing. On the aggregate level, stronger backward GVC participation was associated with relatively milder contraction while the opposite was observed for forward participation. The regressions showed that positive growth of GVC trade was less likely in sectors with relatively larger exposure to foreign downstream shocks. Further, the combined effects of stringent containment measures and severe COVID-19 outbreaks also reduced the probability of growth in both backward and forward GVC transactions. These findings indicate that on top of foreign suppliers’ internal disruptions (foreign supply shock), weak global consumption (foreign demand shock) and local producers’ domestic sourcing problems (local supply shock) contributed to the steep contraction of GVCs in 2020. Against this background, the major challenges to robust recovery were also identified. These include the downside risks of a prolonged pandemic, the resurgence of protectionist tendencies, the strength of global demand, the reconfiguration of broken supply chains, and the ability of countries to coordinate their actions especially with respect to vaccination.

Is virtual water trade beneficial for the water-deficient regions? New evidences from the Yellow River Basin, China

Study regionThe Yellow River Basin (a water-deficient region) in China. Study focusTo establish whether the virtual water trade (VWT) is beneficial for water-deficient regions we conducted an integrated assessment of the local and distant VWT of the Yellow River Basin (YRB). We adopted a modified input–output analysis model and applied multiregional input–output tables of 31 provinces in China (2017) to estimate the dependence of the YRB on water from other regions and to determine the VWT status. We calculated the total water consumption coefficient of 29 sectors in the YRB, investigated the water-use intensity of various sectors, and analyzed the water footprint and per capita usage of each province. Furthermore, we revealed the VWT among downstream, midstream, and upstream regions of the YRB. New hydrological insights for the regionThis study revealed that virtual water flow is unidirectional from downstream and midstream regions to upstream regions, and that the trade structures among these three regions of the YRB differ notably, which has rarely been addressed in previous literature. Furthermore, the VWT exacerbates water resource shortages in the YRB. The irrational industrial structure is the main reason for the need for transfer of water resources. This information could inform the development of effective policies both for alleviating regional water shortages and for developing a viable virtual water strategy for the YRB.

Identifying the critical paths and sectors for carbon transfers driven by global consumption in 2015

Climate change caused by carbon emission threatens human well-being, and global trade drives the transfer of carbon emission between consumers and producers. Thus, quantifying global carbon transfer paths is important. Based on multi-regional input–output tables, we used structural path analysis to decompose the carbon emission processes embodied in trade to analyze the tier structure at multiple scales, and traced critical carbon transfer paths to dissect their distributions. We developed a sectoral importance indicator to consider both the amount of carbon transfer and frequency of sectors to identify key sectors. The first four tiers dominated the embodied carbon emission (>70%) at global, national, sectoral, and national-sectoral scales. Among the critical paths, basic industries (Electricity, Gas, and Water; Petroleum, Chemical and Non-Metallic Mineral Products; Transport), Construction and Services were responsible for the most embodied carbon emission, with the former having prominent direct emission, and the latter sectors having prominent carbon transfers from upstream sectors. The Construction and Electrical and Machinery in China and Services in the United States were key final production sectors; Electricity, Gas, and Water in China and the United States were key primary and final production sectors, and Petroleum and Chemical and Non-Metallic Mineral Products in China was a key primary production and reprocessing sector. Identification of key paths and sectors provides scientific guidance for formulating and adjusting global emission reduction policies.

The impact of the rise of emerging economies on global industrial CO2 emissions: Evidence from emerging economies in Regional Comprehensive Economic Partnership

The rise of emerging economies has changed the production structure of the global industrial chain, which in turn has had a significant impact on global industrial CO2 emissions. As the most representative countries among the emerging economies, the developing countries in the Regional Comprehensive Economic Partnership (RCEP) have become the leading supplier for global industrial chain over the past decade. To study the impact of the rise of emerging countries in RCEP on global industrial CO2 emissions, we proposed a new structural decomposition analysis method using the global environmentally extended multi-regional input–output table. A quantitative analysis using a multi-regional input–output table shows that if the emission intensities remain unchanged in 2010, the increase in these countries’ proportion of supply of final demand and supply of intermediate input resulted in an increase of 2,068 and 87 million tons (Mt) in global CO2 emissions, respectively. The counterfactual analysis shows that China and the Association of Southeast Asia's (ASEAN) participation in the global value chains would increase global CO2 emissions in 2010–2015, but the emissions from Chinese energy and raw material production has gradually slowed or reversed. Simultaneously, the emissions embodied in global industrial chains from ASEAN have increased. Emerging economies should pursue a low-carbon way in consumption patterns and production technologies, and global mitigation efforts should prioritize support to the emerging emitters with green technology.

A hybrid multi-regional input-output model of China: Integrating the physical agricultural biomass and food system into the monetary supply chain

Lacking systematic supply-use information of agricultural biomass and food products within China makes the existing provincial environmental pressure assessments (e.g., water consumption) either not detailed enough (e.g., by the input-output table-based approach) or not comprehensive enough (e.g., by the process-based approach). This study develops a symmetric inter-provincial multi-regional input-output (MRIO) model that hybridizes the physical food and agricultural biomass system with the monetary supply chain of China. First, we construct the inter-provincial supply, use, and input-output tables in physical units of 84 agriculture, food and forestry products. These physical supply/use/MRIO tables systematically capture agri-food product flows, at an unprecedented level of product detail, along domestic supply chains within China. Then we integrate the physical MRIO table of agri-food products into the monetary all-sector MRIO table to construct a symmetric hybrid MRIO table of China. The application of our hybrid MRIO model to the case of provincial blue water footprint assessments reveals that the hybrid model enhances both the monetary MRIO table-based approach and the process-based approach from different aspects. For instance, the hybrid MRIO model can reduce the uncertainty of monetary MRIO modeling due to the aggregation of products with different environmental properties into homogeneous sectors. Lastly, uncertainty analysis is implemented to quantify the main sources of uncertainties, and understand the reliability of our new hybrid MRIO model for future sustainable development design.

Multi-Regional Input–Output Tables for Macroeconomic Simulations in Poland’s Regions

The capacity of multi-regional input–output tables (MRIOTs) to investigate linkages across structures and subnational regions within a country makes them a useful and solid tool of various types of simulations. This study develops MRIOTs for the Polish regions that can be subsequently employed to macroeconomic simulations, including assessing internal policy impulse responses or testing the regional economic resilience to external shocks. For this purpose, we build three macro-regions, discuss how their structural features validate their use to study differentials in economic policy effects or regional economic resilience, and finally, describe the process of constructing the MRIOTs.

Eco-Efficiency of the food and beverage industry from the perspective of sensitive indicators of the water-energy-food nexus

The water-energy-food (WEF) nexus has motivated several studies, opening space to provide robust support for the decision-making process. Certain studies seek to understand the interdependence of these three resources in a quantitative and/or qualitative manner, usually using total water, energy, and food indicators. However, in times of social, economic, and environmental crisis, national policies must be optimized, and hence it is necessary to evaluate marginal and critical indicators of each resource, i.e., sensitive indicators. Based on these indicators, we propose a methodology for assessing ecological efficiency (eco-efficiency) in industries using a multiregional input-output table, Data Envelopment Analysis, and the Malmquist index, with the main objective of internalizing normally overlooked impacts in the national policymaking process. For this, we present a quantitative assessment of the WEF nexus in the Brazilian food and beverage industry, since it is one of the pillars of the country's economy and has several positive and negative effects related to the WEF nexus, as well as a growth trend to meet rising food demand. From 2015 to 2019, this industry eco-efficiency was evaluated in terms of water stress, fossil energy consumption, and financial compensation to low-skilled workers. The main findings show that the Fish industry has the highest average eco-efficiency, with sustainable gains over the period, while the Meat cattle industry has the lowest average eco-efficiency. As a result, the Fish industry places less strain on these resources to generate the same socioeconomic benefits as others, resulting in less food and water insecurity, and should be prioritized in Brazil's national policies regarding industry expansion. We conclude that this methodology can help integrate WEF resources into national policy decision-making processes, allowing the identification of negative impacts that should be avoided or mitigated, as well as positive impacts that should be encouraged.

A global overview of developments of urban and rural household GHG footprints from 2005 to 2015

Household greenhouse-gas footprints (HGFs) are an important source of global emissions but can vary widely between urban and rural areas. These differences are important during the ongoing rapid, global, urbanization process. We provide a global overview of HGFs considering this urban-rural divide. We include 16 global regions, representing 80% of HGFs and analyze the drivers of urban and rural HGFs between 2005 and 2015. We do this by linking multi-regional input-output (MRIO) tables with household consumption surveys (HCSs) from 43 regions. Urban HGFs from high-income regions continue to dominate, at 75% of total HGFs over 2010–2015. However, we find a significant increase of rural HGFs (at 1% yr−1), reflecting a convergent trend between urban and rural HGFs. High-income regions were responsible for the majority of urban HGFs (USA: 27.8% and EU: 18.7% in 2015), primarily from transport and services, while rural HGFs were predominately driven in emerging regions (China: 24% and India: 21.8% in 2015) mainly driven by food and housing. We find that improving emission intensities do not offset the increase in HGFs from increasing consumption and population during the period. A broad transition of expenditure from food to housing in rural areas and to transport in urban areas highlights the importance of reducing the emission intensities of food, housing, and transportation. Counterintuitively, urbanization increased HGFs in emerging regions, resulting in a >1% increase in China, Indonesia, India and Mexico over the period, due to large migrations of people moving from rural to urban areas.

Filter methods for MRIO tables: an evaluation

Researchers who deal with network analysis based on multi-regional input–output (MRIO) tables cannot avoid the intensively discussed issue of filtering, which means identification of the most important and significant trade connections. The question of what is an appropriate filter method remains. This paper expands the existing discussion and brings new insight based on the evaluation of existing filter methods for MRIO tables. Six filter methods from the prevailing literature are identified as relevant and tested on the published MRIO tables: EORA26 and EXIOBASE. The results are verified by a case study. The evaluation shows that the Tolerable Limit approach and filter based on the Weaver–Thomas Index are the most restrictive. The Leontief filter and the filter based on holistic accuracy can be partially recommended. The filter on absolute trade values and average transactions can be recommended as ‘good’ methods.

Investigating ASEAN’s Participation in Global Value Chains: Production Fragmentation and Regional Integration

This paper focuses on the Association of Southeast Asian Nations (ASEAN)—a major final assembler in production—where studies and evidence on the role of the region in global value chains are limited. We seek to provide new evidence regarding the extent and patterns of international fragmentation in ASEAN. To do so, we derive the foreign value-added shares of final products for all global value chains of ASEAN. Using the Asian Development Bank’s multiregional input–output tables for 2000–2017, we document a series of stylized facts. The results show declining foreign value-added shares in ASEAN. Regional economic integration within ASEAN has increased, while value-added contributions vary widely across its members. We find evidence of increasing value-added contributions from emerging economies to ASEAN, whereas the contributions from advanced economies have declined.

Risk assessment for adaptation to climate change in the international supply chain

Climate change affects people's daily lives and industrial activities by increasing the frequency of extreme weather events, such as typhoons and heatwaves, raising the sea-level, and changing rainfall patterns. These effects expand across countries and industries through disruptions in supply chains. In this study, a method of identifying high-risk processes in the supply chain due to climate-related disasters is developed, along with climate-related disaster risk factors (CDRF) for each country and each type of climate-related disaster. As a case study, the automotive supply chains of the USA, China, Japan, and Germany were assessed using a multi-regional input-output table. The results show that, in the USA, Japanese, and German automotive industries, the climate-related disaster risk to workers outside the country is higher than that to workers within the country. However, the economic climate-related disaster risk is higher in the home country. In China's automotive industry, the risks to both workers and the economy were higher for the home country because the high level of involvement within the country. High-income countries should support climate change adaptation in low- and lower-middle income countries. These policies are also important for improving the stability of their own industrial base.

Balance between poverty alleviation and air pollutant reduction in China

Key targets of the sustainable development goals might be in contradiction to each other. For example, poverty alleviation may exacerbate air pollution by increasing production and associated emissions. This paper investigates the potential impacts of achieving different poverty eradication goals on typical air pollutants in China by capturing household consumption patterns for different income groups and locations, and linking it to China’s multi-regional input-output table and various scenarios. We find that ending extreme poverty, i.e. lifting people above the poverty line of USD 1.90 a day in 2011 purchasing power parity (PPP), increases China’s household emissions by only less than 0.6%. The contribution increases to 2.4%–4.4% when adopting the USD 3.20 PPP poverty line for lower-middle-income countries. Technical improvements in economic sectors can easily offset poverty-alleviation-induced emissions in both scenarios. Nevertheless, when moving all impoverished residents below the USD 5.50 PPP poverty line for upper-middle-income countries, household emissions in China would increase significantly by 18.5%–22.3%. Counteracting these additional emissions would require national emission intensity in production to decrease by 23.7% for SO2, 13.6% for NO x , 82.1% for PM2.5, and 58.0% for PM10. Required synergies between poverty alleviation and emission reduction call for changes in household lifestyles and production.

Credit Chain and Sectoral Co-movement: A Multi-Region Investigation

This paper empirically examines how sectoral co-movement are correlated with trade credit usage in a multi-region setting. Extending the models in Shea (2002) and Raddatz (2010), we develop a framework that captures the impact of trade credit usage on co-movement between sectors within a country and cross countries separately. Using the Multi-Regional Input-Output Table developed by Asian Development bank, we assemble a dataset consisting of 14 manufacturing industries for 53 economies. We provide empirical evidence that trade credit linkage is an influential channel for both the domestic and cross-border shocks to propagate and to create a more profound impact in industries around the globe. We find the impact of domestic credit chain on sectoral co-movement is twice as strong as the international ones. We further examine the time trend of this relationship, and find that from 2000 to 2018, the positive relationship between the intensity of trade credit usage and sectoral correlation decreases. We posit that this could be due to a more diversified global trade pattern changes during these two decades.

A scenario-based method for projecting multi-regional input–output tables

Multi-regional input–output (MRIO) data are a powerful tool to analyze complex interdependencies in the international trade and supply network. Their field of application is however limited by the fact that MRIO datasets are only available for past years whereas the structure of the international trade network has been found to change profoundly over time. We here propose the SPIN method, a simple and flexible algorithm that can project MRIO tables into the future based on transparent scenarios of how gross domestic product and trade relations may evolve in that time. By combining well-established input–output techniques, namely the Leontief quantity model and an RAS-type algorithm, our method provides a straightforward mean to convert quantitative scenarios of the world economy into consistent MRIO tables. We illustrate the functioning of the SPIN method by projecting the evolution of the trade network after the 2008 financial crisis under different alternative scenarios of recovery.

China's carbon emissions from the electricity sector: Spatial characteristics and interregional transfer.

As a major carbon emitter, the electricity sector is crucial to the realization of China's emission reduction objectives. Existing studies focus mostly on the influencing factors, emission efficiency and low carbon development of carbon emissions in the electricity sector. Missing from the literature is an analysis of spatial characteristics of carbon emissions and the embodied carbon emission transfer caused by the separation of electricity production and consumption, which is the basis for assigning the responsibility for emission reduction. Thirty provinces in China were taken as research objects, and Moran's I index was adopted to analyze the spatial characteristics of the electricity sector's carbon emissions and carbon emission intensity. Based on multiregional input-output tables, we compared the transfer situation of China's provincial electricity carbon emissions in 2010 and 2015. The results demonstrate that, from 2010 to 2015, the electricity carbon emissions in 20 provinces increased, whereas the carbon emission intensity in 21 provinces decreased. Carbon emissions and carbon emission intensity of electricity in most provinces demonstrate positive spatial clustering characteristics. The total amount of carbon emission transfer in the electricity sector increased from 421.22 million tons in 2010 to 581.369 million tons in 2015, the number of net transfers out of areas increased from 13 to 15, and the number of net transfers into areas decreased from 16 to 15. The active degree of carbon emission transfer reveals the eastern region > the central region > the western region. Different emission reduction policies should be formulated based on the difference in resource endowment between the north and south. Provinces that transferred out large amounts of electricity carbon emissions should take greater responsibility for emission reduction. Integr Environ Assess Manag 2022;18:258-273. © 2021 SETAC.

Critical provincial transmission sectors for carbon dioxide emissions in China

The identification of critical sectors at the provincial level is important for achieving China's CO2 mitigation target. Previous studies have mainly adopted production-based or consumption-based methods to identify critical sectors on two sides of the supply chain. Some sectors located in the intermediate parts of the supply chains are commonly ignored but are critical transmission centers for CO2 emissions. In this study, we use a betweenness-based method based on multi-regional input-output tables to identify critical provincial transmission sectors for CO2 emissions in China in 2007, 2010, 2012, and 2015. The results show that some critical provincial transmission sectors are overlooked by production-based or consumption-based methods but transmit vast amounts of CO2 emissions in the intermediate supply chains, such as the Metal products in Hebei, and the Smelting and pressing of ferrous and nonferrous metals in Gansu. Moreover, critical provincial transmission sectors vary among different provinces and exhibit different patterns in different years. Most critical provincial transmission sectors were located in the northern, central and eastern coastal regions in 2007 and 2010, then shifted to the western region in 2012. In 2015, these sectors were mainly located in the central and eastern coastal regions. Analyzing the spatiotemporal variations in critical provincial transmission sectors can contribute to region-specific policy formulation for CO2 emission mitigation. Improving the intermediate input efficiency may also contribute to CO2 emission mitigation, such as tax breaks or subsidies in critical provincial transmission sectors. It is important to consider betweenness-based results as a complement to policy decisions.

Entropy-based Chinese city-level MRIO table framework

Cities are pivotal hubs of socioeconomic activities, and consumption in cities contributes to global environmental pressures. Compiling city-level multi-regional input-output (MRIO) tables is challenging due to the scarcity of city-level data. Here we propose an entropy-based framework to construct city-level MRIO tables. We demonstrate the new construction method and present an analysis of the carbon footprint of cities in China's Hebei province. A sensitivity analysis is conducted by introducing a weight reflecting the heterogeneity between city and province data, as an important source of uncertainty is the degree to which cities and provinces have an identical ratio of intermediate demand to total demand. We compare consumption-based emissions generated from the new MRIO to results of the MRIO based on individual city input-output tables. The findings reveal a large discrepancy in consumption-based emissions between the two MRIO tables but this is due to conflicting benchmark data used in the two tables.

Dynamics of deforestation worldwide: A structural decomposition analysis of agricultural land use in South America

Deforestation, mainly caused by the conversion of forest land to agriculture, threatens the achievement of multiple goals across the 2030 Agenda. This environmental issue is particularly marked in the area formed by Argentina, Brazil, and Paraguay (ABP region), where a net forest loss of more than 5,5 million hectares per year and a simultaneous net gain in agricultural land of almost 3 million hectares were registered during 2000–2015. To identify the main contributors to the growth in agricultural land use driving regional deforestation, a Structural Decomposition Analysis (SDA) is applied on multiregional input-output tables. Results suggest that changes detected within ABP were mainly influenced by shifts in domestic demand and exacerbated by the influence of Brazil within the Mercosur trade agreement. Outside ABP, results show that consumption per capita and population expansion in developed and developing economies (the EU28, the US, and China) are major drivers of regional deforestation. Although globalization led to a surge in the ABP’s land displaced to other countries, our results indicate that outsourced agricultural activities did not affect the growth in ABP’s agricultural land use. There is thus a need of designing mitigation measures with a global sense that also addresses co-responsibility mechanisms among countries in the region.

Creating multi‐scale nested MRIO tables for linking localized impacts to global consumption drivers

AbstractIndustrial Ecology Virtual Laboratories (IELabs) enable the construction of national‐to‐local‐scale multi‐regional input–output (MRIO) models. These IELabs have been proven to be especially important for analyzing research questions that warrant sub‐national spatial detail. The field of industrial ecology has clearly progressed from the time of national‐only input–output tables. Here, we present a newly developed tool called NLab—“nested IELab”—that nests sub‐national MRIO tables within global country‐scale MRIOs. This capability allows for the investigation of interactions between sub‐national production and consumption systems, with global systems interlinked via international trade. We provide a technical and mathematical roadmap for construction of nested input–output tables in the NLab, and demonstrate this capability through a real‐world assessment of the Western Australian wine industry. Our results suggest that nested MRIO tables provide an added layer of detail at a regional level, when undertaking consumption‐based footprint assessments, leading to improved assessment of quantification of regional impacts. The NLab presented in this work provides tools for analysis of complex trade linkages between industries at various scales, which has the further potential to open avenues for policy‐makers to analyze the implications of local decisions at a global level, and vice versa.

Regional and sectoral structures of the Chinese economy: A network perspective from multi-regional input–output tables

A multi-regional input–output table (MRIOT) containing the transactions among the region-sectors in an economy defines a weighted and directed network. Using network analysis tools, we analyze the regional and sectoral structure of the Chinese economy with the province-sector MRIOTs of China in 2007 and 2012. Global analyses are done with network topology measures. Growth-driving province-sector clusters are identified with community detection methods. Influential province-sectors are ranked by weighted PageRank scores. The results have revealed a few interesting and telling insights. The level of inter-province activities increased with the rapid growth of the national economy, but not as fast as that of intra-province economic activities. Regional community structures were deeply associated with geographical factors. The community heterogeneity across the regions was high and the regional fragmentation increased during the study period. Quantified metrics assessing the relative importance of the province-sectors in the national economy echo the national and regional economic development policies to a certain extent.