This study intends to explore the impact of oscillations in the exchange rate on commodity-wise trade flows between Pakistan and its major trading partner China, while employing the annual data for 1982–2017. Applying the ARDL bounds testing approach, this study confirms that 94% of the selected exporting and 86% of the chosen importing industries possess “cointegration”. Further, the findings reveal that 81%, in the short run, and 52%, in the long run, of exporting industries respond to the volatile exchange rate. Moreover, the volatility affects 77%, in the short run, while 65%, in the long term, of the importing industries. Fascinatingly, the findings also indicate that a major shareholder exporting industry coded as 651 (“Textile yarn and thread” with 57% share) gets benefitted from the volatile exchange rate.

No abstract

The current study endeavors to explore the effects of oscillations in the exchange rate on the household aggregate consumption of developed, emerging, and developing economies, employing the panel data from 1995 to 2017. To select an appropriate panel data estimation technique, we apply Brush-Pagan & Hausman Tests for each set of chosen economies. Further, our study deduces that, in the case of developed economies, the oscillations in the exchange rate, significantly, affect the domestic consumption, supporting Alexander’s (1952) conjecture. However, in the case of emerging and developing economies, aggregate consumption does not respond to the exchange rate volatility.

Water, being the basic requirement of life, is important to all living organism, human health and food production. A positive correlation between economic growth and rate of water utilisation has also been observed in a growth model with water as a productive input for private producers [Barbier (2004)]. In addition, high per-capita consumption (PCC) of water is regarded as an indicator of the level of economic development where per-capita water consumption is defined as the average of water consumed by a person in a day. The declining availability of water supply, mainly due to global climate change, is one of the important issues faced by many developing countries at the present time. It is estimated that nearly two third of nations across the globe will experience water stress by 2025.1 Thus, the safety and availability of clean water is an on-going concern within the global village

Geographically distributed data centers are used as backbone infrastructure to meet rapidly increasing service demands of computations and data storage in cloud computing. This increase results in high energy consumption, increased operational expenditures, and high carbon footprint which are becoming points of great concern for service providers. In this research work, we present a simulation framework named GreenCloudNet++ for simulation and evaluation of energy‐efficient, green‐aware, and secure job scheduling mechanisms for geographically distributed data centers. GreenCloudNet++ has been developed as an extension to CloudNetSim++, which is designed to simulate distributed data center architectures connected with high speed networks. The functionality of CloudNetSim++ is extended by adding hierarchical job scheduling mechanism, hierarchical statistics collection mechanism and integration mechanism for green energy. Proposed model considers availability of green energy at each data center and maximizes its utilization. It considers the amount of underutilized computational resources at individual data centers while assigning jobs to the data centers, which helps to achieve better server consolidation resulting in better energy efficiency. Proposed model also relies on network load inside each data center which helps avoiding hotspots.