eScholar-UMT
eScholar is the institutional repository for research conducted at UMT and maintains a large collection of theses, dissertations and projects produced by UMT graduates as part of their respective degree programs. It includes (but not limited to):
- PhD/MS Theses
- Graduate Program Research Projects
- Undergraduate Program Reports and Final Year Projects
- Full-text articles/research work of faculty and students
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Development of ḥalāl industry in Pakistan
(UMT,Lahore, 2025) HASSAN ASHRAF USMANI
Islam provides a comprehensive framework for life, encompassing principles related to lifestyle, dietary practices, pharmaceuticals, and healthcare. In recent years, Pakistan has seen significant trends in the production and consumption of Ḥalāl products, driven by technological advancements and globalization. Despite this fact, Pakistan—home to a 96.2% Muslim population—has not established itself as a significant player in the global Ḥalāl industry. It is expected that with a predominantly Muslim population comprising manufacturers, traders, and consumers, Pakistan is positioned to serve as a role model for the global Ḥalāl industry. This study examined the structural composition of Pakistan’s Ḥalāl industry, including federal and provincial authorities, certification bodies, and academic institutions, to identify the critical barriers impeding its growth. A qualitative research methodology was employed, utilizing reviews of legal frameworks, news reports, and the official websites of key stakeholders. Additionally, semi-structured interviews were conducted to provide deeper insights into the industry's challenges. The findings revealed that contradictions such as overlapping roles and conflicting rules in the operational frameworks of government-owned or supported institutions emerged as significant challenges, followed by resource limitations and operational inefficiencies that hampered progress. Moreover, unresolved Shariah-related issues and the absence of academic degree programs specific to the Ḥalāl industry were identified as critical barriers. This study recommends fostering harmony and collaboration among government-backed institutions and encouraging their focus on regulatory roles rather than direct involvement in certification processes. Furthermore, the establishment of the Higher Education Commission (HEC)-approved degree programs and the promotion of industry-relevant Shariah research are essential measures to drive innovation and growth in Pakistan’s Ḥalāl sector.
The role of climate policy uncertainty in forecasting cryptocurrency volatility
(UMT,Lahore, 2026) FATIMA KHALID
While the environmental imprint of virtual cryptocurrency markets has amplified regulatory careful consideration, specifically of energy-intensive proof-of-work assets, the mounting unpredictability of climate policy may have an effect on market perceptions and stability. The current study explores the influence of CPU on cryptocurrency volatility and inter connectedness, with a special prominence on the distinction between brown or emission intensive and green or emission-efficient cryptocurrencies. The current study employed daily return data on six major cryptocurrencies, namely Bitcoin, Ethereum, XRP, Binance Coin, Dogecoin, and Litecoin, along with monthly CPU index from September 2019 to December 2024. Interconnectedness and spillovers in the markets are modeled in a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model estimated by the Kalman filter. The generalized forecast error variance decompositions derived from the Kalman filter are used to derive the dynamic interconnectedness indices. For further analysis of the mixed-frequency setting, the GARCH-MIDAS model is employed, decomposing volatility into short-run and long-run components and allowing CPU to influence volatility persistence. The results exhibit significant heterogeneity in CPU sensitivity. The emission-intensive cryptos, especially Bitcoin and Dogecoin, are highly sensitive to CPU, demonstrating elevated volatility during uncertain regulatory conditions. The emission-efficient assets, XRP and Binance Coin, are less sensitive to CPU, reflecting their resilience. Ethereum has moderate sensitivity due to the transitional period. The spillover result illustrates the high connection amongst the cryptos, and the primary shock leaders are Bitcoin and Ethereum. Overall, the results suggest that CPU is a central factor influencing volatility and risk across cryptocurrency markets.
Exploring the effects of air pollution and health expenditure on infant mortality rates in Pakistan
(UMT,Lahore, 2015) Ishaq Bin Zaighum
The current research is an attempt to examine some of the important factors that influence infant mortality rates in Pakistan. The two major factors include air pollution measured by carbon dioxide (𝐶𝑂2) emissions and expenditure on health as share of GDP. Other variables include food production index, percentage of newborns protected against tetanus, and ratio of rural population to urban population. The series are checked for their normality and stationarity. The technique of Autoregressive Distributed Lag (ARDL) bounds testing approach is applied. After the co-integration is established among the variables, short run and long run coefficients are estimated and the model passed through all the diagnostic checks successfully. It was found that air pollution and health expenditure are directly related with the infant mortality rate while other three variables are inversely related with the infant mortality rate. All variables except ‘Food Production Index’ are significant in both the short run and long run. Granger causality test suggested that air pollution, food production and health expenditure granger cause infant mortality rates.
Impact of Artificial Intelligence (AI) on the labor market in developing countries
(UMT,Lahore, 2024) Momina Humayun
This study investigates the interplay between artificial intelligence (AI), technological adoption, and economic variables, focusing on their implications for unemployment and economic growth. Employing the Autoregressive Distributed Lag (ARDL) and Dynamic Ordinary Least Squares (DOLS) methodologies, the study examines macroeconomic data to uncover long-term determinants of unemployment. AI and machine learning (ML) adoption demonstrate a positive association with reduced unemployment, reflecting their potential to generate new job opportunities. Conversely, increased digitization through data science (DS) correlates with decreased unemployment, underscoring the importance of digital skills in modern economies. The key insights of this study shed light on the perspectives to existing literature, revealing complex interactions between AI adoption, economic growth, and labor markets. While supporting traditional economic theories on technological impacts, such as skill-based technological change (SBTC), the findings also challenge some prevailing notions. Overall, this research underscores the multifaceted nature of AI's economic impact, emphasizing the outcomes of the labor market in the developing countries. It further suggests some more avenues for future research by considering dynamics of technological adoption and economic outcomes.
Nexus of public expenditures and economic growth
(UMT,Lahore, 2024) Israr Ullah
Many This thesis delves into the intricate relationship between public expenditures and economic growth across 50 developing Asian countries from 2007 to 2019, employing the Feasible Generalized Least Squares (FGLS) method. Against the backdrop of rapid economic transformations and policy interventions in the region during this period, understanding the dynamics of public spending and its impact on economic development is crucial for informed policymaking. By examining a diverse sample of developing Asian nations, this study employs robust econometric techniques to analyze the multifaceted interplay between different categories of public expenditures and their implications for economic growth. Infrastructure development, education, healthcare, and defense spending are among the key areas scrutinized to uncover their respective impacts on economic performance. Utilizing the FGLS method allows for the mitigation of potential issues such as heteroscedasticity and serial correlation, ensuring the reliability of the statistical analyses conducted. The findings of this research contribute significantly to the existing body of knowledge on economic development strategies in Asia, offering nuanced insights into the effectiveness of public expenditure allocations in fostering sustainable and inclusive growth.