Water content in trade: a regional analysis for Morocco

Haddad, Eduardo Amaral; Mengoub, Fatima Ezzahra; Vale, Vinícius de Almeida

doi:10.1080/09535314.2020.1756228

Cited by 23 publications

(22 citation statements)

References 33 publications

(27 reference statements)

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“…6 The interregional input-output system was estimated using the Interregional Input-Output Adjustment System (IIOAS) method. This approach has been applied for distinct interregional systems: an inter-island model for the Azores (Haddad et al, 2015) and interregional models for Brazil (Haddad et al, 2017), Colombia (Haddad et al, 2018), Egypt (Haddad et al, 2016), Greece (Haddad, Cotarelli, et al, 2020), Lebanon (Haddad et al, 2014), Mexico and Morocco (Haddad, Mengoub, et al, 2020). 7 Following the Brazilian National Accounting System sectoral classification.…”

Section: Notesmentioning

confidence: 99%

Structural propagation of pandemic shocks: an input–output analysis of the economic costs of COVID-19

Haddad

Perobelli

Araújo

et al. 2020

Spatial Economic Analysis

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Section: Notesmentioning

confidence: 99%

Structural propagation of pandemic shocks: an input–output analysis of the economic costs of COVID-19

Haddad

Perobelli

Araújo

et al. 2020

Spatial Economic Analysis

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“…Compared with traditional trade measures, TiVA provides a more detailed picture of not only direct but also indirect interactions between regions and regional and international partners (Meng, Fang, Guo, & Zhang, 2017; Meng, Wang, & Koopman, 2013). Following Haddad, Mengoub, et al (2020), let us consider a single economy with n subnational regions, s industries, 5 and foreign destinations m = { RoW }. The national gross output can be expressed as follows:

boldx = {(I - A)}^{- bold1} boldy = boldLy .

The actual DVA of a subnational region n = 1 needed to meet their final demand is given by the following:

{GDP}_{1} = v_{1} {(I - A)}^{- bold1} boldy bold-italici,

where v 1 is a row matrix with the first element equal to the ratio between value‐added and gross output in industries of region 1 and zeros elsewhere like

v_{1} = ([], \overset{{boldv}_{bold1}}{true} 0 \dots 0)

, and i is a summation vector of ones.…”

Section: Methodsmentioning

confidence: 99%

“…By not adopting a global multiregional database, we assume that the imports generate regional value-added with the same local production technology, which seems produced in Brazil. This allows us to incorporate foreign markets from the perspective of purchase and value-added sales (Haddad, Mengoub, and Vale, 2020).…”

Section: Measurement Of the Impact On Bilateral Tiva Based On Hemmentioning

confidence: 99%

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The subnational supply chain and the COVID‐19 pandemic: Short‐term impacts on the Brazilian regional economy

Sanguinet

Alvim

Atienza

et al. 2021

Regional Science Policy & Practice

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To control the transmission of the coronavirus disease (COVID-19), Brazilian local governments have adopted partial lockdown measures in economic sectors, thereby triggering transmission shocks along input-output supply chains. The national internal market and territorial disparities favor the formation of subnational production networks within borders, thus increasing the potential effects of lockdown measures on regional integration production networks. Therefore, this study makes hypothetical simulations of COVID-19 mitigation policy decisions to understand the regional impacts on integration in supply chains, considering both domestic and global value chains. The generalized hypothetical extraction method is applied to a Brazilian interregional input-output model with 68 industries and 27 regions, imputing partial removals on intermediate consumption and final demand. The results suggest that richer subnational areas, mainly São Paulo and Rio de Janeiro, are proportionally more impacted by COVID-19 trade shocks. However, the poorer peripheries are doubly affected, either by the foreign shock, which would damage their economic structure, or by the retraction of the subnational demand from core states. The findings highlight that economic shocks are spatially distributed through different industrial structures, thus stressing the need to avoid "one size fits all" regional policies to mitigate the potential negative effects on exposed regions. KEYWORDS Regional effects. Hypothetical extraction method. Domestic value chains. COVID-19 outbreak. JEL CLASSIFICATION R12. R10.

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“…For example, it is expected that some African areas might face a lack of rainfed maize yields, represented by a decrease of 25% in 2050, compared to 2000. In addition, it was also estimated that agricultural withdrawal would attend 80% by 2020 in Morocco [20].…”

Section: Agriculture Challengesmentioning

confidence: 99%

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

2022

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Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights.

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Water content in trade: a regional analysis for Morocco

Cited by 23 publications

References 33 publications

Structural propagation of pandemic shocks: an input–output analysis of the economic costs of COVID-19

Structural propagation of pandemic shocks: an input–output analysis of the economic costs of COVID-19

The subnational supply chain and the COVID‐19 pandemic: Short‐term impacts on the Brazilian regional economy

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

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