Construction of a multi-regional input-output table for Nagoya metropolitan area, Japan

Yamada, Mitsuo

doi:10.1186/s40008-015-0022-7

Cited by 20 publications

(18 citation statements)

References 13 publications

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“…Normally, sample data would be required to estimate these parameters, but trade data between cities were not available. Therefore, we followed the approach of Nakano and Nishimura () and Yamada () and used transaction data from the 2012 China MRIO table as a proxy for sample data for each sector. Because our estimates are for the Hebei cities, we chose the transaction data between Hebei and the surrounding provinces or megacities (e.g., Beijing, Tianjin, Shanxi, and Shandong).…”

Section: Methodsmentioning

confidence: 99%

“…Multiregion input–output models (MRIOs) have been widely recognized as good assessment tools that can track the supply chain and capture regional homogeneity as well as heterogeneity, thus accurately reflecting environmental and socioeconomic implications (Bachmann, Roorda, & Kennedy, ; Dietzenbacher, Lenzen, et al., ; Miller & Blair, ; Wang, ). Over the recent decade, many efforts have been made to develop MRIO databases at international level: EORA (Lenzen, Kanemoto, Moran, & Geschke, ; Lenzen, Moran, Kanemoto, & Geschke, ), WIOD (Dietzenbacher, Los, et al., ), GTAP‐MRIOT (Peters, Andrew, & Lennox, ), and Asian International Input–Output Table produced by IDE‐JETRO (Meng, Zhang, & Inomata, ); or the national level such as China (Liu, Li, Liu, Tang, & Guan, ; Mi et al., ; Zhang, Shi, & Zhao, ), the UK (Yu, Hubacek, Feng, & Guan, ), Japan (Nakano & Nishimura, ; Yamada, ), and Australia (Lenzen et al., ). Although these databases have helped gain a comprehensive understanding of regional disparities and spillover effects in interregional and international trade, none of current MRIOs (national or provincial) offer insights in assessment at the local scale, particularly at the city level.…”

Section: Introductionmentioning

confidence: 99%

“…Although the importance of high‐resolution MRIOs has been well acknowledged, compiling MRIOs can be time consuming, involving an exhaustive and costly amount of data (Wiedmann, Wilting, Lenzen, Lutter, & Palm, ); thus, this process is generally limited by the availability of comprehensive and consistent survey data, especially at the city level (Lenzen et al., ; Lin, Hu, Cui, Kang, & Ramaswami, ; Minx et al., ). Therefore, nonsurvey and partial‐survey methods that have more reasonable data requirements have frequently been used for IO table construction (Kowalewksi, ; Yamada, ). Nonsurvey methods refer to modeling techniques using input–output (IO) table and trade flows estimation to develop a targeted IO table, whereas partial‐survey methods use some survey‐based data as a baseline or constraint for the targeted IO table.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Linking city‐level input–output table to urban energy footprint: Construction framework and application

Zheng

Meng

et al. 2019

J of Industrial Ecology

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Multiregion input-output (MRIO) models have become increasingly important in economic and environmental analysis. However, the current resolution of most MRIO models fails to capture the heterogeneity between subregions, especially in cities. The lack of city-level MRIO tables has impeded the accomplishment of city-level studies and hampered the understanding of the relationship between urban growth and consumption, and teleconnections to other regions. In this paper, we propose a partial survey-based multiple-layer framework for MRIO table compilation of a Chinese province that distinguishes city-based regions. This framework can effectively address a large number of data processes and retain consistency between layers. Using the framework, we first compile a nested Hebei-China city-level MRIO table and then apply city-level energy footprint accounting of the North China urban agglomeration. Our results present the critical role of Hebei cities in energy supply in 2012 and quantify energy use embodied in goods for the domestic trade. Tangshan, Shijiazhuang, and Handan are distinctive cities in the energy supply chain of other regions, for both less developed and developed regions. This multiple-layer framework represents a feasible approach for developing subregional-level MRIO models and offers the possibility to analyze global trade at the subregional level with limited data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Linking city‐level input–output table to urban energy footprint: Construction framework and application

Zheng

Meng

et al. 2019

J of Industrial Ecology

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“…It is well known that the MRIO table is mostly based on two types, Isard-type and Chenery-Moses-type [29,30]. These two types contrast in the assumption of the interregional trade coefficient.…”

Section: Economic Modelmentioning

confidence: 99%

Estimating Tsunami Economic Losses of Okinawa Island with Multi-Regional-Input-Output Modeling

et al. 2019

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Understanding the impacts of tsunamis, especially in terms of damage and losses, is important for disaster mitigation and management. The aim of this study is to present our estimations of the potential losses from tsunami damage on Okinawa Island. We combine the use of a tsunami hazard map and our proposed economic loss model to estimate the potential losses that would be sustained by Okinawa Island in the event of a tsunami. First, to produce the tsunami hazard map, we calculated tsunami flow characteristics using the mathematical model TUNAMI-N2 and incorporating 6 earthquake fault scenarios around the study area. The earthquake scenarios are based on historical records along the Ryukyu Trench and the Okinawa. The resulting inundation map is overlaid with economic land use type and topography maps to identify vulnerable regions, which are then employed to compute potential economic losses. Second, we used our proposed economic model for this study area to calculate the potential losses that would be sustained in these vulnerable regions. Our economic model extends the multi-regional-input-output (MRIO) model, where the economic values of industrial sectors are scaled to correlate with land use and topography types (coastal and inland areas) to calculate losses through the Chenery–Moses estimation method. Direct losses can be estimated from the total input of the MRIO table, while indirect losses are computed from the direct losses and interaction parameter of the MRIO table. The interaction parameter is formed by linear programming and calculated using the Leontief methodology. Our results show that the maximum total damaged area under the 6 earthquake scenarios is approximately 30 km2. Inundation ranging from 2.0 to 5.0 m in depth covers the largest area of approximately 10 km2 and is followed by areas with inundation depths of 1.0–2.0 m and >5.0 m. Our findings show that direct losses will occur, while indirect losses are only approximately 56% that of direct losses. This approach could be applied to other areas and tsunami scenarios, which will aid disaster management and adaptation policies.

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“…The results provided the superiority of the RAS for updating the input-output coefficients, especially in the short term. The RAS method is effective in revising input-output tables [33][34][35][36][37], and with its help, dynamic changes in the WF can be examined via IOA. Numerous socio-economic factors affect water demand and can be identified through dynamic change analysis [38].…”

Section: Study Areamentioning

confidence: 99%

Water Footprint Calculation on the Basis of Input–Output Analysis and a Biproportional Algorithm: A Case Study for the Yellow River Basin, China

Yin

Wang

Yan

2016

Water

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Abstract:In the Yellow River basin, China, ecosystems suffer from the overexploitation and utilization of water resources, resulting in various environmental impacts. Consideration must be given to both human and ecosystem water requirements in water resources management. A water footprint (WF) is a tool for estimating industrial, agricultural, commercial and household water requirements and for examining the impact of consumption on water resources. The study attempts to establish an approach to analyse the dynamic processes and driving forces that result in certain WFs. Using input-output tables for provinces and municipalities, we calculate water use coefficients, the total WF and the net external WF of consumption in China's Yellow River Basin. A biproportional algorithm is employed to revise the input-output tables for analysing the temporal dynamics of the WF. The factor analysis and linear regression were used to analyse the main influencing factors of WF. Results indicate that the coefficient for water use by primary industries is highest and that coefficients for provincial water use differ significantly. Second, household consumption and residuals from capital accumulation constituted approximately half of the total WF of the Yellow River basin in 2002 and also differed significantly among provinces. Third, the ratio of the net external WF to the total WF increased, and the ratio of final consumption to the total WF declined during the period examined. Fourth, output by secondary industries correlated most strongly with the WF, followed by area under irrigation, per capita meat consumption, water consumption per 10,000-yuan increase in added value and population.

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Construction of a multi-regional input-output table for Nagoya metropolitan area, Japan

Cited by 20 publications

References 13 publications

Linking city‐level input–output table to urban energy footprint: Construction framework and application

Linking city‐level input–output table to urban energy footprint: Construction framework and application

Estimating Tsunami Economic Losses of Okinawa Island with Multi-Regional-Input-Output Modeling

Water Footprint Calculation on the Basis of Input–Output Analysis and a Biproportional Algorithm: A Case Study for the Yellow River Basin, China

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