Input-output Model Research Articles

A high resolution input–output model to assess the economic impact of floods

A high resolution input–output model to assess the economic impact of floods

Urban-Rural Carbon Emission Differences in China’s Urbanization: Spatial Effects and Influencing Factors

Coping with climate crisis and promoting urbanization are important parts of the Sustainable Development Goals (SDGs), thus controlling carbon emissions in the urbanization process becomes the crux of achieving the SDGs, and the increase of carbon emissions due to urban-rural differences and intra-provincial rural-urban migration are important and neglected parts of the study on carbon emissions during the urbanization process. Utilizing China 2017 cross-sectional data and environmentally extended multi-regional input-output model (EEMRIO) to account for China’s urban-rural household consumption carbon emission differences (HCCEDs) and carbon emission increments (CEIs) due to intra-provincial rural-urban migration, this research integrates spatial lag model (SLM) and threshold regression (TR) model to investigate the spatial effects, individual differences and influencing factors of HCCED in the context of urbanization. The results indicate that HCCEDs are higher in the north and lower in the south, with significant spatial aggregation effects. Household consumption expenditure ratio (q = 2.03) becomes the determining factors in moderating the influence degree of other factors, with consumption difference and demographic difference emerging as the pivotal exacerbating and inhibiting factors. The results of this study will provide data, methodological basis and policy recommendations for the development of new urbanization and low-carbon cities, facilitating further investigation the driving mechanisms of HCCEDs.

Towards Environmental Sustainability: An Input–Output Analysis to Measure Industry-Level Carbon Dioxide Emissions in Egypt

Egypt’s average share of global carbon dioxide emissions has been rising from mid-1990s to date. This motivates the present study to identify industries that drive carbon dioxide emissions (as direct emitters and as total emitters with high emission multiplier effects). Environmental input–output analysis is applied to Egypt’s 2017–2018 input–output table to measure sectoral emissions. The industries identified as high emitters are linked to Egypt’s achievement of Sustainable Development Goals, namely, Goals 7, 8, 9, 12, and 13. The findings indicate that ten industries qualify as environmentally degrading (dirty), having the highest emission multiplier effects (in descending order): electricity, gas, and water; non-metallic mineral products; basic metals; rubber and plastic products; chemicals and chemical products; paper and paper products; food products; hotels and restaurants; transportation and storage; and textiles. Eight of these industries also have high output multiplier effects. This underscores that although potential investment in and the growth of these industries will generate output multiplier effects, they will also be coupled with emission multiplier effects. Five other industries had high emission multipliers, as follows: water and sewerage; beverages; coke and refined petroleum products; extraction of crude petroleum; and mining of metal ores. The growth of these industries would not be in favor of the achievement of SDGs. Policy measures are recommended.

Triple bottom line analysis of the decline in foreign tourist demand in Japan due to the COVID‐19 pandemic: Counterfactual structural decomposition approach

AbstractThis study developed a consumption‐endogenous input–output analysis framework and a novel counterfactual structural decomposition analysis (SDA) framework to assess the economic, social, and environmental effects of the 2020 decline in foreign tourists due to the COVID‐19 pandemic. The results show a pandemic‐induced loss of 33 million tourists, resulting in 3.44 trillion JPY in value‐added losses, employment declines for 868,976 people, and environmental benefits of an 11.6 Mt‐CO2 emissions reduction. Sectors hit the hardest include hotels, eating and drinking services, and wholesale and retail trade. The deterioration revealed a 100% reduction in value added from foreign visitors, whereas shifts in production contributed to a 3.6% increase in CO2 emissions. This study recommends government intervention, focusing on stipends for sectors with the highest value‐added losses and employment declines, as well as support funds for sectors that reduce environmental impact.

Sectoral Interdependencies and Multipliers in Nigeria: An Input-Output Approach

This study examines the sectoral contributions to Nigeria's Gross Domestic Product (GDP) and intersectoral interdependencies using Input-Output (I-O) analysis. Results as obtained from the 2022 Eurostat database revealed significant value added/sectoral contributions, agriculture ($6.512B, 15.1%), fishing ($4.529B, 1.1%), food/beverages ($8.446B, 19.6%), and mining/quarrying ($14.659B, 34.1%). Intersectoral interdependencies highlighted key intersectoral linkages between agriculture and food/beverages, mining/quarrying and other industries, and fishing and food/beverages. Results showed that agriculture's output (74.58%) is primarily consumed by households and used by the food and beverages sector; output from mining/quarrying (85.73%) is consumed mainly by industries (manufacturing/construction), and output from the fishing (81.68%) sector is mainly consumed by households and used by the food and beverages sector. The Type I and Type II multipliers assessed the economic ripple effects of changes in industry demand. The findings indicate substantial indirect effects, with output multipliers ranging from 1.726% (food/beverages), 2.118% (fishing), 1.803% (mining/quarrying), and 1.758% (agriculture). Income multipliers range from income multipliers: to $2.053B (food/beverages), $3.290B (fishing), $2.540B (agriculture), and $3.01B (mining/quarrying). Fishing and mining/quarrying sectors exhibited the highest total multiplier effects, emphasizing their potential for driving economic growth. The study confirms the robustness of the results using I-O analysis, validating the initial findings. Policy implications include targeted investments in strategic sectors such as agriculture, fishing, food/beverages, and mining/quarrying, which would stimulate economic growth and diversification. Also, resource allocation and diversification strategies to enhance intersectoral linkages would maximize economic benefits and reduce dependence on individual sectors.

Global Value Chain Embedding and Total Factor Productivity in Carbon Emission Reduction: A Multi-Country Analysis of the Paper Industry

Against the backdrop of carbon reduction and low-carbon economic development, this study takes the global paper industry chain as its research subject and employs the SBM-GML model and input–output model to measure the low-carbon TFP and value chain embedding levels of 42 major global economies from 2001 to 2021. Using fixed-effects and threshold effect models, the study examines both the overall and stage-specific impacts of value chain embedding on low-carbon TFP. The results reveal that between 2001 and 2021, while the low-carbon TFP of major countries in the global paper industry chain steadily increased, significant disparities persisted among them. Most countries experienced low-carbon TFP growth driven by technological progress, whereas only a few high low-carbon TFP nations also demonstrated upward trends in technical efficiency. The division of labour position in the value chain significantly promoted low-carbon TFP at a ratio of 1:0.26. In contrast, the low-carbon TFP effects of participation displayed a nonlinear impact with a threshold of 0.21: it exerted an insignificant inhibitory effect in the initial stage but, upon crossing the threshold, resulted in a significant positive effect with a ratio of 1:0.13. These findings highlight the importance of optimizing GVC strategies to achieve sustainable industrial transformation and growth.

Upstreamness and downstreamness in input–output analysis from local and aggregate information

Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.

Research on the Measurement of Digitalization on the Integration of China’s Manufacturing Industry into the RCEP Regional Value Chain

In this paper, the industry classification of digital economy is determined, the comprehensive index framework of digital economy development level in China is constructed, and the input-output analysis method and index method are used to measure the digital investment status of manufacturing industry in China. At the same time, based on the realistic demand that China’s manufacturing industry needs to climb the regional value chain, this paper constructs the forward participation index, backward participation index, regional value chain position index, and regional value chain Balassa, and analyzes the current situation of China’s manufacturing industry in RCEP region. Construct a semi-parametric additive model to explore the impact of digital investment on China’s manufacturing industry’s participation in the RCEP regional value chain. In addition, this paper uses the community analysis method to build the RCEP regional manufacturing digital input trade network, and comprehensively evaluates the trade network pattern of digital input from multiple dimensions. The results show that: (1) the digital investment in China’s manufacturing industry is increasing year by year. (2) The indicators of manufacturing industry’s participation in RCEP regional value chain show a fluctuating situation, which is generally consistent with China’s economic development. (3) Within a certain range, digital investment will promote the participation of China’s manufacturing industry in the RCEP regional value chain. China is gradually becoming an important participant in RCEP regional trade. According to the corresponding measurement results, it provides policy suggestions for the deep integration of China’s manufacturing industry and digital economy.

Comparison of greenhouse gas emissions associated with the construction of timber, concrete, and steel check dams in Akita, Japan: An input-output analysis.

To mitigate global warming, replacing concrete and steel with timber as the primary construction material for construction projects, such as check dams, is being promoted in Japan and other countries. Timber check dams have more limited installation sites than concrete or steel dams because of installation conditions such as locations less susceptible to debris flows and locations where there is constant running water. However, even when the installation conditions are met, engineers and contractors are reluctant to select timber as a construction material because of its high construction cost. In this study, an input-output table was used to compare the greenhouse gas (GHG) emissions associated with the construction of a timber check dam at the design stage with those associated with the construction of concrete and steel check dams to quantitatively evaluate the added value of timber utilization (in addition to its construction cost). The results revealed that replacing concrete and steel check dams with timber check dams could reduce GHG emissions by 61% and 34%, respectively. This study demonstrated the possibility of evaluating the GHG emissions associated with a construction project at the design stage. Moreover, it highlights the importance of considering the GHG emissions associated with construction materials when selecting the most appropriate materials for public works projects.

Inequitable distribution of risks associated with occupational heat exposure driven by trade

The exposure to extreme heat at workplaces poses substantial threat to human effort and manual labour. This becomes more prominent due to the global dispersion of labour-intensive production activities via trade. We combine a climate model with an input–output model to quantify the risks associated with trade-related occupational extreme heat exposure. Here we show an 89% surge in trade-related labour exposure to extreme heat, escalating from 221.5 to 419.0 billion person-hours between 1995 and 2020. Lower-middle-income and low-income economies constituted 53.7% and 18.3% of global exposure but only 5.7% and 1.0% of global labour compensation. In countries highly susceptible to extreme heat conditions, workers perform tasks in heated conditions for up to about 50% of their working hours. The disproportionate trade effects in redistributing global benefits and costs leads to the inequality in heat exposure between developed and developing economies. In striving for equitable and safe work conditions, workers vulnerable to heat extremes in developing economies should be protected by climate adaptation infrastructure, given their critical roles in the global production system.

Examining safety conditions in the construction sector across Chinese provinces: an input-output analysis

Examining safety conditions in the construction sector across Chinese provinces: an input-output analysis

Economic consequences of eliminating gender discrimination in the labor market

Theoretical and empirical literature on labor market discrimination is extensive on showing persistent wage gaps across genders at individual level. However, at the aggregate level, the economic consequences of gender discrimination remain unclear. In this article, economic consequences of gender discrimination are estimated through interacting a wage decomposition model (individual) and an input–output model (aggregated level). Using the decomposition’s results, it was possible to calculate individual wage adjustments, so that all individuals are remunerated according to their observable characteristics as well as the group of non-discriminated individuals. In turn, these estimates were used to simulate changes on labor nominal costs (price effect) and consumption (income effect). Our results indicate that the income effect generated through consumption overcomes the price effect, raising production (0.90%), welfare (2.70%) and employment (0.94%). Nonetheless, the results are very heterogeneous across sectors and households.

Unveiling China's household CO2 emissions with disaggregated energy sectors: An affinity-propagation multi-regional input-output model

Although much research has focused on CO2 emissions driven by household consumption, significant challenges remain in capturing complex regional variations and the indirect contributions of disaggregated energy sectors and different income groups. In this study, an affinity-propagation multi-regional input-output (AP-MRIO) model is developed through incorporating Dagum Gini coefficient (DGC) and affinity propagation (AP) clustering within a multi-regional input-output (MRIO) modeling framework. AP-MRIO not only traces CO2 emissions from China's provincial household consumption, particularly within disaggregated energy sectors, but also reveals the interaction between sector emissions and income levels. Results obtained disclose that (i) within energy sectors, thermal power, electric power distribution, and petroleum are major emitters (accounting for 68.7%, 17.5%, and 6.6%, respectively); in comparison, CO2 emissions from renewable energy sectors (hydropower, nuclear, wind, and solar) are lower (2.7%); (ii) urban middle- and high-income households contribute significantly to CO2 emissions (57.1%), and notable carbon inequalities exist both within and between regions for energy sectors; (iii) some provinces (e.g., Inner Mongolia, Liaoning, and Heilongjiang) should prioritize reducing per capita emissions from the non-renewable energy sectors; other provinces (e.g., Ningxia, Guangdong, and Hunan) should further promote the development of renewable energy and focus on emissions embodied in the use of intermediate products.

Carbon footprint heterogeneity across elderly groups in China

Abstract Assessing the elderly’s environmental impact is crucial as aging deepens. This paper incorporates age heterogeneity and urban-rural heterogeneity within the elderly into the study of aging’s impact on the environment, dividing the elderly population into four subgroups. Utilizing an environmentally extended multi-region input-output(EE-MRIO) model and data from 12,000 households, the carbon footprints of four subgroups was calculated. Findings reveal significant urban-rural and age-related heterogeneities in carbon emissions among those aged 60 and above. Urban elderly have higher per capita carbon footprints than rural elderly, and ‘the younger elderly’ have higher footprints than ‘the oldest old’. This heterogeneity extends to carbon footprint’s distribution: Urban elderly lean towards enjoyable consumption, while rural elderly prioritize survival. ‘The younger elderly’ have a greater carbon footprint from transport. This paper nests Chinese and global EE-MRIO, showing that global economies are more impacted by China’s elderly demand than mainland China itself. Furthermore, urban-rural structure has a greater impact on global carbon emissions compared to age structures.

A multi-objective optimization approach for interprovincial carbon emission reduction in China: Considering industrial structure and ownership attributes.

Balancing economic growth with carbon emission control requires the coordination of policy preferences for economic and carbon reduction goals, as well as the low-carbon transformation of industrial and ownership structures. This study employs a multi-objective input-output optimization model to investigate the trade-offs among maximizing GDP growth, minimizing carbon emissions, and minimizing energy consumption within the planning period of 2017-2035 in China. The model accounts for the heterogeneity of industrial structures and ownership attributes, examining both national aggregates and the adjustment paths and ownership trends across different provinces. The findings reveal: (1) Sectors with high output and low energy consumption are growing, while high pollution and high energy-consuming sectors are declining, driven primarily by technological levels, followed by policy and development preferences. (2) Domestic capital expanding into high technology and capital-intensive sectors, HMT capital favoring the economically developed high technology regions, and foreign investment declining. (3) Regional adjustment in high-value or high-energy-consumption sectors are more pronounced in central and western provinces and less economically developed regions, while industries that are neither high-energy-consumption nor high-value show greater increases in resource-rich provinces with better infrastructure.

The impact of importing on the CO2 intensity of the global and national construction industries.

CO2 intensity is a valuable indicator for investigating the amount of CO2 emissions prevented by importing relative to the economic costs the importer pays for international trade; this plays a significant role in mitigating climate change and promoting environmental sustainability. This study explores the effect of importing on the CO2 intensity of the construction industry by establishing an analytical framework that divides the total CO2 intensity into aggregate CO2 intensity in imported products (AII) and aggregate CO2 intensity in domestic products (AID), and targets the gap between AII and AID to reveal the effects of importing. A multi-regional input-output model was adopted to evaluate the AII and AID of the construction industries in 15 economies for 2014 and then the LMDI method was used to decompose the gap between AII and AID from the perspective of the source industry. The results indicated that importing showed an increased effect of 3.11 ton/USD on the CO2 intensity of the construction industry globally. At a national scale, importing increased the CO2 intensity of the construction industries in most countries (13/15). In terms of decomposition analysis, the aggregate CO2 intensity effect decreased AII by 23.15 ton/USD and the aggregate structure effect increased AII by 151.51 ton/USD. For most economies, the aggregate structure effect, especially when sourcing from the electricity industry and the mining industry, mainly caused AII to be higher than AID and resulted in a pulling effect of importing on the CO2 intensity of the construction industry. On this basis, adjusting industrial source weights in importing is significant for achieving the CO2 intensity mitigation targets of the global and national construction industries.

Bridging environmentally extended input-output models and complex network analysis: A bibliometric analysis of trends and opportunities

Bridging environmentally extended input-output models and complex network analysis: A bibliometric analysis of trends and opportunities

Modeling regional private income and its embedded carbon emissions: sources, flows and inequalities.

Composing regional total income jointly with government income, private income represents levels of development and affluence from the household perspective. Considering the need for fair carbon emission reduction responsibility distributions among regions with divergent income levels, private income-embedded emission (PIEE) and the inter-regional inequalities remain to be explored. Combining input-output analysis and the Gini coefficient, this study traces the sources and disposals of regional private income in China, as well as their embedded carbon emission flow, and quantifies the distribution and inequality of PIEE across industrial sectors and provincial regions. Results indicate that the sectors of power and heat, smelting and pressing of metals, and manufacture of non-metallic mineral products contribute the most to PIEE. Hebei and Inner Mongolia have larger PIEE, while emissions embedded in private income disposals are primarily driven by consumption of products from agriculture, light industry, and services. Regions clustering at the beginning (Beijing, Shanghai, Tianjin) of the C-Lorenz curve of PIEE account for 19.3% of private income, while 8.2% in PIEE, with the percentages for those clustering at the end (Inner Mongolia, Ningxia, Xinjiang) as 10.0% and 28.5%. The findings provide insights into strategizing emission reduction along the entire value flow and coordinating carbon emissions, income and emission reduction responsibility.

Economic crises and energy use: An input–output analysis of Catalonia's 2008–2014 financial crisis

AbstractThe impact of economic crises on an economy's energy consumption, considering its sectorial interactions, remains an unexplored area. For this article, we investigated the structural sectorial relationships in energy terms before, during, and after Catalonia's 2008–2014 financial crisis, using environmentally extended input–output analysis. We used three input–output tables from 2005, 2011, and 2014, for which we constructed and employed five vectors representing sectorial energy consumption (“natural gas,” “coal,” “petroleum,” “electricity,” and “biomass and waste”) for 41 economic sectors. We studied the evolution of backward and forward linkage coefficients in terms of energy, as well as key sectors over this period. Our findings reveal that most sectors, particularly energy‐intensive ones, experienced a reduction in both backward and forward linkages. However, the relative importance of sectors in the Catalan economy remained relatively stable over the years, indicating a certain level of persistence in this indicator throughout the period, despite the economic crisis.

A 2023. ÉVI WKF KARATE VILÁGBAJNOKSÁG NÉZŐINEK FOGYASZTÁSI SZOKÁSAI ÉS A NEMZETGAZDASÁGI HATÁSOK VIZSGÁLATA

Sporting event tourism is a rapidly expanding segment of sports tourism (DREYER, 2002; WEED, 2006; BÁNHIDI, 2015; BORBÉLY-MÜLLER, 2015; LAFLIN, 2018). Alongside the growing number of events, domestic and international spectators have also increased significantly (LAFLIN 2018, LACZKÓ-STOCKER, 2020). Hungary hosted the Karate World Championship for the first time in karate history. This study aimed to analyze the consumption habits of domestic and international spectators attending the event. A questionnaire survey of 806 visitors was conducted, and the data was analyzed using SPSS. Economic impacts were assessed using input-output analysis based on the Sectoral Balance of Relations. Results indicated that the event's tourism appeal was primarily local and regional, with 78.7% of domestic respondents traveling less than 50 km. Domestic spectators attended an average of 2.76 competition days during the six-day event, while international visitors spent 4.42 nights in Hungary. The average total expenditures were HUF 43,075.26 for domestic spectators and HUF 471,741 for international spectators. The event contributed HUF 1.55 billion to Hungary's GDP. The World Championship reinforced Hungary's status in sports tourism, showcasing the economic and social benefits of hosting international events. Positive feedback from spectators affirmed the successful organization and highlighted the continued viability of such events in Hungary.