Trends of Trade Disputes During the WTO Regime

Patterns and trends of trade disputes reveal vital information about the users of the Dispute Settlement Understanding (DSU) of the World Trade Organization (WTO). Despite WTO has detailed guidelines regarding how the multilateral trade should be practiced by the Member Countries, the DSU is the ultimate Agreement that promises fair justice against unfair trade practices. Analyses of trade disputes show that the developed countries use the Dispute Settlement Mechanism (DSM) more than the developing or the newly industrialized countries. The rate of participation of the least-developed countries (LDC) in the dispute settlement process is particularly very low. The direction of disputes shows that the disputes are mostly targeted to the developed countries. All categories of countries- i.e. developed, developing, newly industrialized and transitional economies- lodged disputes against the developed countries more frequently compared to the disputes they lodged against other categories of countries. This indicates that developed countries are targeted in the trade disputes. However, the rate of winning disputes for the developed countries is also higher than those of the other categories. This indicates that despite the developed countries are targeted in the disputes, they manage to survive quite successfully.

Evaluation and Damage Detection of Highway Bridges with Distinct Vulnerabilities

Bridge failures over the past few decades have shown conventional bridge monitoring is insufficient to effectively evaluate the safety of this important piece of infrastructure. Therefore, new methods for bridge monitoring and special considerations in bridge design are needed to ensure the health of these structures as they continue to age and prevent the possibility of catastrophic collapses. The objective of this research is to explore new means for detecting damage in bridge members during normal operations that are both accurate and affordable at the same time. However, to make any damage detection method effective and efficient, the behavior of intact and damaged bridges needs to be investigated, preferably using simple analytical models. Therefore, to achieve the objective of this research, a two-fold investigation was performed. One was to study the bridge behavior subjected to various damage scenarios and identify possible failure mechanisms. Achieving this objective leads to a method for bridge evaluation after damage and determines its level of vulnerability to such damage; in other words, it defines the redundancy and reliability of the structure. The other was to develop an effective non-destructive method for damage detection based on the bridge behavior after the damage. vii Two types of bridges were selected and studied for this purpose, twin steel box girder bridges (TSBG) that are classified as fracture critical and prefabricated bridge systems containing cast-in-place joints. These bridges are designed with distinct vulnerabilities that make them susceptible to certain types of damages. The results of the current study confirmed that concrete deck failure is the dominant failure mode of the TSBG bridge after the occurrence of a fracture in one of the girders. Therefore, an improved simple and unified yield line analysis method was developed to determine the bridge deck capacity. An extensive analytical evaluation and availability of a simple model for load-carrying capacity developed in this study facilitated a comprehensive and coherent reliability approach to assess the safety of TSBG bridges after the complete fracture of one steel girder. Although the results of this research cannot readily be generalized for all TSBG bridges without further evaluation, this study shows that simply supported twin steel box girder bridges could indeed be safe and potentially removed from the fracture critical list. Moreover, the TSBG bridge dynamic analysis after damage showed that bridge frequencies are sufficiently sensitive for identifying partial or full-depth girder fracture in the simple span bridges. However, these significant damages may cause very small changes in the natural frequencies of continuous span bridges. The results show a significant change in the vibration mode shapes after damage in both simple and continuous span bridges. The mode shapes are sensitive enough to detect damage at the inflicted locations, in most cases providing better resolution when compared to the frequency changes. Investigation on the performance of the full-depth precast-prestressed voided slab bridge shows the vulnerability of such bridge decks to damage at the deck longitudinal joints. Using the FE analysis and load testing results, a new damage detection method for viii structural health monitoring of bridges with precast deck panels was also introduced. This method, applicable to all bridges with modular precast deck units, can effectively identify locations and significance of potential deck joint damage based on the measured changes in bridge response and model updating. A damage detection software tool was also developed in this case that is patent pending

Impacts of the changing climate on agricultural productivity and food security: Evidence from Ethiopia

This study investigates the influence of climate change on agriculture productivity and food security in the context of Ethiopia. We use 2011–2020 state level data set of four major seasonal crops of Cash and Food in Ethiopia, namely, barley, wheat, maize, and sorghum. Methodologically, we apply the productivity function and the Ricardian approaches in the modelling for simulating the association of climate change with agriculture productivity. This study documents the interconnectedness among changes in climate, security of food and agriculture, indicating how the prior changes bring the latter kind of alterations. In general, agriculture in Ethiopia is prone to changes in climate and variations in the levels of precipitation, posing threats to food security of the rural population. The specific findings of this study highlight sorghum and barley as the majorly impacted stable crops through changes in meteorology. Furthermore, the study shows that barley production has vital contribution to causing insecurity of food in Ethiopia. The study ends with recommending some policy prescriptions and adaptation methods for mitigating the detrimental effects of climate change on production of agriculture and security of food in Ethiopia

The impact of oil and global markets on Saudi stock market predictability: A machine learning approach

This study investigates the predictability power of oil prices and six international stock markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock market using five Machine Learning (ML) techniques and the Generalized Method of Moments (GMM). Our analysis reveals that prior to the 2006 collapse, oil exerted the least influence on the Saudi market, while the UK and Japan were the most influential stock markets. However, after the collapse, oil became the most influential factor, highlighting the strong dependence of Saudi Arabia's economic structure on oil production. This finding is particularly noteworthy given Saudi Arabia's efforts to reduce its reliance on oil through Vision 2030. We further demonstrate that China's influence on the Saudi market increased significantly after the 2006 collapse, surpassing that of the UK. This is attributable to the substantial trade between China, Japan, and Saudi Arabia, as well as the rise in Saudi foreign direct investment in China, and the decline in such investment in the UK post-collapse. Our results carry important implications for stock market investors and policymakers alike. We suggest that policymakers in Saudi Arabia should continue to diversify their economy away from oil and strengthen economic ties with emerging markets, particularly China, to reduce their vulnerability to oil price fluctuations and ensure sustainable economic growth

Dynamic interlinkages between carbon risk and volatility of green and renewable energy: A TVP-VAR analysis

Our paper applies a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to investigate interlinkages among carbon emissions futures and the volatility of the renewable energy sector. The findings show that the system-wide dynamic connectedness realized a peak in early 2020 in the wake of the COVID-19 crisis. Net total directional connectedness proves that carbon emissions futures and wind energy play the roles of both net transmitters and net receivers of shocks in both periods – before and after the pandemic. The findings of this paper can support policy formulations to avoid rapid fluctuations in carbon prices, make the carbon price table, and limit the negative effect of carbon risk on the energy market, while promoting the protection of systemic financial risks in the renewable energy sector and ensuring a green energy supply