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Relationship between CO2 emissions, tourism receipt, energy use and international trade in Pakistan
The current study explores the potential impact of economic growth, tourism receipt, energy consumption and trade openness on CO2 in Pakistan over the period of 1980-2017. The study adopted the Autoregressive Distributed Lagged (ARDL) model to investigate the short and long-run estimates simultaneously. The study further applied Granger causality to find out the direction of causalities. To arrive at long-run robust estimates, the study employed the Dynamic Ordinary Least Squares (DOLS) model. Last but not least, the current study also used an innovative accounting approach i.e. Variance decomposition and Impulse production function. The results found that economic growth has a significant impact on CO2 emission and negative and highly significant impact on tourism receipt while emission, energy consumption and international trade are also the main determinants of tourism in Pakistan. The study found unidirectional causality from GDP, tourism receipt, energy consumption and trade openness towards CO2 emission. The outcomes of ARDL model are also supported by the DOLS results. The innovative accounting approach further strengthens the results of the study. In a nutshell, overall results indicate that tourist receipts, CO2 emission, energy consumption, and trade openness are interlinked. The findings of the current study thus suggest that the government should encourage investment in the industry\u27s services sector to enhance its efficiency. In addition, it will also need to ensure that the services sector contributes far more to the GDP than to the manufacturing sector. The results demonstrate investments should be diverted towards the services sector on a broader range as less-polluting services industries (tourism as one of the main sectors) are more feasible than polluting capital industries to invest in
Ubiquitous health profile (UHPr): a big data curation platform for supporting health data interoperability
The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed.N/