Month: January 2025

Chinese Overseas Foreign Direct Investment and Income Inequality in the Developing World

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Abstract

This paper investigates the effects of foreign direct investment (FDI) by Chinese state-owned enterprises (SOEs) and privately-owned enterprises (POEs) on host-state income inequality in developing countries. Using two-stage least squares with a synthetic instrumental variable modeling technique and total and sectoral FDI data from 2003 to 2019, we find that Chinese SOEs tend to reduce host-state income inequality, while Chinese POEs generally have the opposite effect and raise inequality. We argue that the Chinese government pays greater attention to the overseas corporate conduct of SOEs than POEs out of reputational concerns, which could affect SOEs’ hiring practices in a way that reduces inequality in host countries. Conversely, POEs, who receive less Chinese government backing and carry fewer burdens of the broader state and social objectives, invest in ways that may limit capital costs, favor wage cuts, and increase inequality. Our study contributes to the corporations’ social responsibility and Chinese influence literatures.

Projected changes in climate extremes over Tanzania

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Abstract

Understanding projected changes in climate extremes at local and regional scales is critical for reducing society’s vulnerability to such extremes, as it helps to devise informed adaptation strategies and contributes to informed decision-making processes. In this paper, we analysed projected changes in climate extremes across regions in Tanzania using outputs of high-resolution regional climate models from the Coordinated Regional Climate Downscaling Experiment program (CORDEX-Africa). The indices analysed here are those recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) to characterise climate extremes over different regions. The results revealed that Tanzania would experience an increased number of warm days and nights during the present (2011–2040), mid (2041–2070), and end centuries under the RCP4.5 emission scenarios. Further, projections reveal that in future climate conditions, heavy, very heavy and exceptionally heavy rainfall events would dominate over regions along coast, central regions, northwestern parts and southwestern and northeastern highland.The number of consecutive wet days (CWDs) are likely to increase across large areas of Tanzania and more rapidily over coastal regions than that in other regions for all seasons. However, many regions in Tanzania are likely to experience an unchanged to decreasing number of consecutive dry days (CDDs). Areas along coastal regions would experience increased intensity and frequency of extreme rainfall events in the present, mid, and end centuries under the RCP4.5 emission scenario. These increases in extreme climate events are likely to pose significant damage to property, destruction of infrastructure, and other socioeconomic livelihoods for people in many regions of Tanzania. It is therefore recommended that appropriate policies are put in place to help different sectors and communities at large adapt the impacts of climate change in the future climate under RCP 4.5 scenario.

Long-term variability of extreme precipitation with WRF model at a complex terrain River Basin

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Abstract

Global warming has profound effects on precipitation patterns, leading to more frequent and extreme precipitation events over the world. These changes pose significant challenges to the sustainable development of socio-economic and ecological environments. This study evaluated the performance of the new generation of the mesoscale Weather Research and Forecasting (WRF) model in simulating long-term extreme precipitation events over the Minjiang River Basin (MRB) of China from 1981 to 2020. We calculated 12 extreme precipitation indices from the WRF simulations and compared them with observations. The spatio-temporal variations of extreme precipitation were further analyzed in terms of intensity, frequency, and duration. The results indicated that the WRF model can appropriately reproduce the spatial distribution of extreme precipitation indices with acceptable biases. The performance is significantly better for intensity and frequency indices compared to duration indices. Except for PRCPTOT and R10mm, WRF accurately captures the interannual variations of extreme precipitation. Meanwhile, the results of the pre-whitening Mann-Kendall (PWMK) test suggested that WRF can identify significant increasing trends in extreme precipitation, particularly for R95p, R99p, and R50mm. This study provides valuable insights for extreme precipitation forecasting and warning in other mountainous regions.

Projections of Future Droughts in Morocco: Key Insights from Bias-Corrected Med-CORDEX Simulations in the Haouz Region

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Abstract

Drought is one of the major challenges hindering development in semi-arid regions particularly in developping countries. Hence, this study aims to predict future climatic drought in the Haouz region of Morocco. Seven grid points of the ERA5 data were used to evaluate three regional climate models from Med-CORDEX and seven statistical bias correction techniques from the Climate Model data for hydrologic modelling of the Soil and Water Assessment Tool, using Percentage Bias values, Root Mean Square Error, correlation coefficient, and Taylor Skill Scores. For precipitation, the CNRM model corrected by the Power Transformation (PT) method proves to be the most accurate at low altitudes. At intermediate altitudes, the combination of the HadGEM model with the PT method is more adequate. Regarding maximum temperatures, the Variance Scaling (VS) and Distribution Mapping (DM) corrections applied to the CNRM and CMCC models, respectively, offer the best performance. For minimum temperatures, the CMCC-VS pair is more performant. Following these observations, a general trend towards decreasing precipitation and increasing maximum and minimum temperatures is recognized, exacerbated by altitude and RCP8.5 scenario. Trends in drought indices reveal an intensification of future climatic droughts, especially under RCP8.5 scenario. In the short term, extreme episodes occur for intermediate altitudes, particularly under RCP8.5 scenario, while for low-lying areas, an increase in moisture is noted under RCP4.5 scenario. In the mid term, an increased prevalence of mild droughts under both scenarios is noted. Over the long term, greenhouse gas emissions are amplifying severe droughts with RCP 8.5, showing a progressive worsening of climate change.

Performance Assessment of Satellite Precipitation Products over Nigeria: A Compromise Programming Approach

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Abstract

Satellite precipitation products (SPPs) remain a good alternative to gauge based data, where data can be scarce or not available at long term. However, they can be characterized by uncertainties which can emanate from complexities like sampling, algorithmic and instrumental errors; sensitivity to topography; land use and land cover, contrasts in surface temperature and emissivity and climate. Therefore, the choice of an SPP for climate and hydrological study is crucial to its outcome. This study assesses the performances of five SPPs namely, CHIRPS, CMORPH, MSWEP, PERSIANN – CDR and TAMSAT in reproducing the properties of observed precipitation for annual and seasonal periods. Five statistical metrics namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Modified Index of Agreement (MD), Cohen’s Effect Size (D) and Skill Score (SS) were used. The scores from the metrics for each SPP were aggregated using compromise programming (CP) in order to determine the best performing SPP. In addition, Boxplot and Taylor diagram were used to assess the closeness of the SPPs to the observed precipitation at both seasonal and annual timescales. The study revealed that based on the statistical performances for the annual and seasonal periods at the different stations, TAMSAT ranked as the best performing SPP 104 times over MSWEP with 101, CHIRPS with 93, CMORPH with 26 and PERSIANN-CDR with only 1. However, TAMSAT was placed second to MSWEP after the aggregation of the statistical metrics using CP. The performances of the SPPs were affected by the climatic conditions with CHIRPS having the best performance at the Sahel & Sudan zone (SSZ), TAMSAT at the Guinea zone (GZ) and MSWEP at the Rainforest & Mangrove zone (RMZ) of the country for the annual evaluation. Performances also varies during the seasonal periods. PERSIANN-CDR has been found to perform poorly for the study area in this study.

Dimension Conversion Approach for Indoor Three Dimensional Radio Environment Map design

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Abstract

This study examines the effectiveness of employing the texture-patch transformation (TPT) approach compared to two-dimensional (2D) and three-dimensional (3D) approaches for generating indoor 3D radio environment maps (REMs). Two interpolation algorithms- K-nearest neighbor (K-NN) and inverse distance weight (IDW) are used for the evaluation. It’s crucial for the dynamic generation of REMs to be fast enough to keep up with the continuously received signal strength data from sensors in real-time, especially for dynamic spectrum access in television (TV) white spaces through cognitive radio networks. This research focuses on analyzing a symmetric vertical profile of an indoor 3D RSS dataset, which serves as the basis for implementing TPT in REM design. The findings indicate that the TPT method significantly reduces computation time (CT) compared to 2D and 3D approaches for IDW and K-NN. While TPT exhibits moderate accuracy in terms of root mean square error, relative recovery error, correlation coefficient, and best-fit line analysis, it’s premature to conclude that TPT lacks good accuracy based solely on interpolation results from a 30% training dataset. TPT demonstrates a favorable trade-off between accuracy and CT. The analysis reveals that 3D K-NN yields the highest accuracy among the six algorithms tested and covers a volume of 22,525 \(m^{3}\) of TV grey space indoors. The optimal values for IDW order, K-NN parameter, and all results are determined through cross-validation. TPT approached framework could be applicable in high-speed real-time 3D dataset reconstruction or indoor 3D REM generation using 2D interpolation approache, where the dataset follows symmetrical pattern.