Do International Fragmentation of Production and Global Value Chains Participation Affect the Long-run Economic Growth?

This article aims to investigate whether the international fragmentation of production and the global value chains (hereafter GVCs) participation affects the economic growth for a set of 40 advanced and emerging economies. It considers four aspects related to the type of participation and position in GVCs captured by different value-added measures: (a) vertical specialisation index; (b) GVC participation index; (c) GVC position index in low-tech sectors; and (d) GVC position index in high-tech sectors. A panel autoregressive distributed lag (PARDL) model is pioneeringly employed to capture the long-term relationship between economic growth and our four measures for annual value-added data from 1995 to 2011, provided by the World Input–Output Tables (WIOT). The main long-run results indicate that (a) higher levels of international fragmentation of production and GVCs’ participation ensure higher GDP per capita growth rates; (b) the fragmentation and GVCs’ participation are more important to GDP growth than the gross exports as a percentage of GDP; (c) GVCs’ participation index, which considers both the ‘forward’ and ‘backward’ participation, is less important than the vertical specialisation, measured by the foreign intermediate imports; and (d the countries engaged in upstream positions in low-technology GVCs were positively and significantly benefitted in terms of growth. JEL Codes: F14, F43

Satellite–Derived Topography and Morphometry for VHR Coastal Habitat Mapping: The Pleiades–1 Tri–Stereo Enhancement

The evolution of the coastal fringe is closely linked to the impact of climate change, specifically increases in sea level and storm intensity. The anthropic pressure that is inflicted on these fragile environments strengthens the risk. Therefore, numerous research projects look into the possibility of monitoring and understanding the coastal environment in order to better identify its dynamics and adaptation to the major changes that are currently taking place in the landscape. This new study aims to improve the habitat mapping/classification at Very High Resolution (VHR) using Pleiades–1–derived topography, its morphometric by–products, and Pleiades–1–derived imageries. A tri–stereo dataset was acquired and processed by image pairing to obtain nine digital surface models (DSM) that were 0.50 m pixel size using the free software RSP (RPC Stereo Processor) and that were calibrated and validated with the 2018–LiDAR dataset that was available for the study area: the Emerald Coast in Brittany (France). Four morphometric predictors that were derived from the best of the nine generated DSMs were calculated via a freely available software (SAGA GIS): slope, aspect, topographic position index (TPI), and TPI–based landform classification (TPILC). A maximum likelihood classification of the area was calculated using nine classes: the salt marsh, dune, rock, urban, field, forest, beach, road, and seawater classes. With an RMSE of 4 m, the DSM#2–3_1 (from images #2 and #3 with one ground control point) outperformed the other DSMs. The classification results that were computed from the DSM#2–3_1 demonstrate the importance of the contribution of the morphometric predictors that were added to the reference Red–Green–Blue (RGB, 76.37% in overall accuracy, OA). The best combination of TPILC that was added to the RGB + DSM provided a gain of 13% in the OA, reaching 89.37%. These findings will help scientists and managers who are tasked with coastal risks at VHR.

Assessing Participation and Position of Vietnam in Global Value Chains

Vietnam’s policy of openness to trade and investment has made it integrated very quickly with the global production networks, also known as global value chains, which has brought many visible fortunes to it in terms of socio-economic achievements. To have a deeper insight into the prospects and constraints of its integration into global value chains, and also to assess its degree of integration, this paper has employed Koopman et al. (2010) approach to measure the participation index, and position index. The analysis shows that the participation of Vietnam in global value chains has increased significantly in the last two decades based on strengthening FDI-led exports of Vietnam. It also shows that most of the exporting industries are located in the middle-stream in the value curve and are net buyers of intermediate products for exports, which infers the presence of predominant I2E practices in Vietnam. The paper also identifies and assesses the risk I2E practices are prone to. Scenario analysis suggests that Vietnam shall focus on upskilling of its labour force and developing indigenous industrial base. In the meantime, domestic firms shall be encouraged to collaborate with foreign firms and densify into global value chains.

Fault Detection by Signal Reconstruction in Nuclear Power Plants

In this work, the recently developed auto associative bilateral kernel regression (AABKR) method for on-line condition monitoring of systems, structures, and components (SSCs) during transient process operation of a nuclear power plant (NPP) is improved. The advancement enhances the capability of reconstructing abnormal signals to the values expected in normal conditions during both transient and steady-state process operations. The modification introduced to the method is based on the adoption of two new approaches using dynamic time warping (DTW) for the identification of the time position index (the position of the nearest vector within the historical data vectors to the current on-line query measurement) used by the weighted-distance algorithm that captures temporal dependences in the data. Applications are provided to a steady-state numerical process and a case study concerning sensor signals collected from a reactor coolant system (RCS) during start-up operation of a NPP. The results demonstrate the effectiveness of the proposed method for fault detection during steady-state and transient operations.

Mapping of shallow water bathymetry and reef geomorphology using Sentinel-2 satellite imagery in Genteng Besar and Genteng Kecil Island, Kepulauan Seribu

Information on seafloor characteristics is one of the essential variables in coastal management and marine ecosystems. Application methods in remote sensing technology to study about characteristics of shallow waters have continuously been done. This research consists of two parts: an estimation of depth using Sentinel 2B satellite imagery with the Lyzenga algorithm and geomorphological classification of the benthic area using the Benthic Terrain Modeler (BTM) approach. BTM is a method to analyze benthic habitat and shallow water geomorphology. Integrated Depth data were analyzed using BTM to obtain bathymetric position index (BPI), slope, and classification of reef geomorphological structures. The resulting BPI value range is directly proportional to the given spatial area (scale factor). The slope is ranged between 0.01° – 19.24°, while optimum depth estimation is applicable until 10-meter. The values of BPI and slope were used as variables to classify the geomorphology of shallow water benthic areas based on the previous classification dictionary. Six geomorphological classes resulting from this study are Back Reef, Deep Depression, Depression, Lower Bank Shelf, Mid-Slope Ridges, and Reef Crest.

High-resolution snow depth prediction using Random Forest algorithm with topographic parameters and an ecosystem map: a case study in the Greiner Watershed, Nunavut

Increased surface temperatures (0.7℃ per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties (depth and density) at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. As arctic snow is strongly influenced by vegetation, an ecotype map at 10 m resolution was added to a method with the Random Forest (RF) algorithm previously developed for alpine environments and applied here over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up-wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Ecotypes with taller vegetation with moister soils were found to have deeper snow because of the trapping effect. Using feature importance with RF, snow depth distributions were predicted from topographic and ecosystem parameters with a root mean square error = 8 cm (23%) (R² = 0.79) at 10 m resolution for an arctic watershed (1 500 km²) in western Nunavut, Canada.

Landscape Mapping, Ichnological and Benthic Foraminifera Trends in a Deep-Water Gateway, Discovery Gap, NE Atlantic

Multidisciplinary studies have allowed us to describe the abiotic landscapes and, thus, reveal the ichnological and benthic foraminifera trends in a deep-water gateway. Mesoscale landscape mapping is presented based on the bathymetric position index, substrate types and near-bottom water temperature. Four sediment cores, retrieved from the entrance, centre and exit of the gap, were subject to computed tomography, ichnological and benthic foraminifera studies. A high diversity of abiotic landscapes in the relatively small area of Discovery Gap is detected and its landscape is characterized by 23 landscape types. The most heterogeneous abiotic factor is a topography that is associated with sediment patchiness and substrate variability. The ichnological and tomographical studies of the sediment cores demonstrate lateral and temporal differences in the macrobenthic tracemaker behaviour. The ichnofossils assemblage of the sediment core can be assigned to the Zoophycos ichnofacies with a higher presence of Zoophycos in the entrance site of the gap and during glacial intervals. Higher benthic foraminifera diversity and species richness during the Holocene are also registered in the southern part of the gap compared to the northern part. The spatial and temporal differences in macro-benthos behavior and benthic foraminifera distribution in the deep-water gateway are proposed to relate to the topographical variations of the Antarctic Bottom Water and its influence on the hydrodynamic regime, nutrient transport, etc.

Insights for Landfill Site Selection Using GIS: A Case Study in the Tanjero River Basin, Kurdistan Region, Iraq

The increasing world population and the growing quantity of solid waste have become a challenging problem facing governments and policy makers because of the scarcity of suitable sites for new landfills and the negative perception of these sites by the people. This study aims to evaluate the performance of different Multi-Criteria Decision-Analysis (MCDA) approaches using remote sensing and Geographic Information System (GIS) data for identifying suitable landfill sites (LFSs). We evaluated the methodologies used by various investigators and selected appropriate ones as suitable sites for Municipal Solid Waste (MSW) landfill in the Tanjero River Basin (TRB) in the Iraqi Kurdistan region. We applied Boolean Overlay (BO), Weighted Sum Method (WSM), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP), and Technique for Order Performance by Similarity to an Ideal Solution (TOPSIS) to allow combined use of 15 thematic layers as predictive factors (PFs). In this study, we applied the Topographic Position Index (TPI) for the first time to select MSW LFSs. Almost all methods showed reliable results and we identified eight suitable sites situated in the western part of the TRB having total area of ~18.35 km2. The best accuracy was achieved using the AHP approach. This paper emphasizes that the approach of the used method is useful for selecting LFSs in other areas, which are located in similar environments.

Influence of Neighborhood Competition on Douglas-Fir Growth Is Not Altered by Local Environmental Conditions and Climate

To evaluate impacts of competitive conditions in tree neighborhoods on growth responses as influenced by moisture availability and local environmental conditions, we sampled 102 codominant 40- to 70-year-old coastal Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) in regions with relatively high and low moisture availability. We quantified local environmental conditions (topographic position index, heat load, and soil depth), and the annual basal area increment and climate moisture deficits during two growth periods: a five-year period prior to commercial thinning and years 6 to 10 after thinning. In both regions and growth periods, tree growth was higher for trees growing in local neighborhoods with lower competition. The density/growth relationships differed by region and by growth period in the moist regions, but they were not influenced by climate moisture deficit. Furthermore, including topographic position index, heat load, or soil depth did not improve model support. Our results highlight the importance of managing local competition and indicate that environmental factors such as soil depth, heat load, and topography may be less likely to warrant consideration when developing thinning prescriptions. This could allow foresters to accommodate other ecosystem services when designing density management treatments, at least within typical growing conditions. Study Implications: Concerns about climate change have led to questions whether existing management practices, such as current thinning prescriptions, need to be modified to ensure sustainable provision of ecosystem services. Our results highlight the prominent role of local competitive conditions and indicate that fine-scale differences in topography and soils within our study region are not useful criteria for modifying thinning prescriptions to alter how trees responds to climate conditions, at least under typical growing conditions. Thus, foresters can focus their prescriptions on other aspects when developing thinning or other partial harvesting operations, such as timber production or wildlife habitat.

A Two-Sided Lockdown? Social Class Variations in the Implementation of Homeschooling During the COVID-19 Lockdown

The COVID-19 pandemic deeply affected how schools and families functioned through most of 2020. In particular, school closures meant parents took on a more central role in their children’s learning. This study analyzed social class variations in the quantity and quality of homeschooling during the lockdown. Through an online questionnaire, 360 parents reported (1) their digital equipment and use, (2) the perceptions of their ability to homeschool their children, (3) how they handled homeschooling and (4) the extent to which they supported other activities considered more or less “profitable” from an educational point of view (e.g., reading, watching television). A social position index was used as a proxy of social class. The results indicated that all parents were highly involved in setting up homeschooling and that the lower the parents’ social position, the more they spent time homeschooling their children. However, in line with the digital divide literature, the lower the parents’ social position, the lower the digital equipment and the less the parents felt capable of homeschooling. Finally, the higher the social position of the families, the more children spent time doing activities considered to be “educationally profitable,” and the less they spent time doing “unprofitable activities.” Thus, even if all parents were highly involved in homeschooling, higher social position parents were better equipped both materially and psychologically to face the challenge of homeschooling. The long-term impact of these processes on the perpetuation of social class inequalities are discussed.