https://hdl.handle.net/11436/912 Thu, 09 Jul 2026 18:22:14 GMT 2026-07-09T18:22:14Z https://hdl.handle.net/11436/11022 Digital and precision farming, emissions trade-offs, and food crop yields in Pakistan Nabi, Agha Amad; Anser, Muhammad Khalid; Asif, Muhammad; Zaman, Khalid Climate change, low productivity, and environmental degradation are jeopardizing Pakistan's agricultural sector, whose sustainability and resilience can be potentially improved using agricultural technology (AgriTech). This study examines the relationship between digital technology, precision farming, methane (CH4) and nitrous oxide (N2O) emissions, and Pakistan's grain crop yields to determine how modern technology impacts ecologically responsible farming. The study used Autoregressive Distributed Lag bounds testing to explore how data analytics, modern farming technologies, and agricultural value-added (AGRI) affect grain crop yields in the short and long run. Long-and short-term crop yields were reduced by AGRI. Data analytics could only produce short-term advantages, but precision agriculture tools and digital technologies assisted in enhancing yields significantly. CH4 and N2O emissions were significantly associated with yield growth, suggesting efficiency trade-offs. This study found that digital technology is an intensive farming method, resulting in higher yields linked to higher input consumption and emissions. The technology also enabled precision agriculture to increase productivity with lower environmental impacts. Taken together, the findings of the current study collectively underline the need to merge smart farming technologies with environmentally friendly methods to boost Pakistan's agricultural productivity and sustainability. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/11436/11022 2025-01-01T00:00:00Z https://hdl.handle.net/11436/10972 The digital solution: how artificial intelligence is revolutionizing energy poverty alleviation Tianyi, Zhang; Zhou, Qili; Ali, Sajid; Nazar, Raima; Anser, Muhammad Khalid Artificial intelligence has become a powerful catalyst in addressing the challenge of energy poverty, particularly in emerging economies where reliable and cost-effective energy access remains a significant challenge. This research examines the asymmetric impact of artificial intelligence on energy poverty by employing a ‘Quantile-on-Quantile’ methodology, which captures complex and asymmetric connections across multiple quantiles of the variables. Using annual data from 2005 to 2023 for ten emerging economies—India, Brazil, South Africa, Mexico, Indonesia, Bangladesh, Nigeria, Pakistan, Thailand, and the Philippines—the analysis reveals a strong inverse relationship between artificial intelligence and energy poverty. The findings indicate that higher levels of artificial intelligence integration significantly reduce energy poverty, though variations exist across quantiles and countries. In particular, significant reductions in energy poverty are observed at higher quantiles of artificial intelligence in India, Brazil, South Africa, and Bangladesh. However, mixed effects are observed in Nigeria and Pakistan due to the limited availability of artificial intelligence infrastructure. These findings suggest that artificial intelligence-driven technologies such as smart grids, predictive maintenance, and intelligent energy distribution systems play a crucial role in improving energy access. The study concludes that policymakers must adopt artificial intelligence-tailored energy strategies, prioritizing investments in artificial intelligence applications that target infrastructural gaps and regional disparities. Artificial intelligence should be embedded in national energy frameworks to ensure inclusive, efficient, and equitable energy transitions in energy-poor regions. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/11436/10972 2025-01-01T00:00:00Z https://hdl.handle.net/11436/10958 Emotional intelligence, support, and organizational culture’s impact on decision-making: mediation and moderation analysis in academia Munir, Saqib; Anser, Muhammad Khalid; Shah, Syed Tahir Hussain; Islam, Talat; Zaman, Khalid Academic decision-making is composite, involving a number of players with various points of view and frequently conflicting interests. This study explored the relationship between emotional intelligence competence, organizational support, complexity of the academic environment, and role seniority on decision-making efficiency in academia, with a focus on the moderating-mediating role of organizational culture and job satisfaction. Conducted in Pakistan, the study targeted a diverse sample of 1227 individuals from various academic institutions, including deans, department heads, administrative staff, faculty members, research scholars, and postgraduate students, selected using a stratified random sampling method. This study proposes a comprehensive model for higher education decision-making dynamics using behavioural and institutional components, particularly in developing countries like Pakistan. The findings suggested that emotional intelligence competence, organizational support, complexity of the academic environment, and role seniority all have significant direct effects on academia decision-making efficiency (ADME). Furthermore, job satisfaction of university staff was found to mediate the relationship between these variables and ADME, while organizational culture of the university moderated the effects of the independent variables on ADME. The findings provide a framework for development of an effective decision-making culture in academia, highlighting the need for institutions to prioritize the well-being and satisfaction of their staff in order to optimize decision-making processes. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/11436/10958 2025-01-01T00:00:00Z https://hdl.handle.net/11436/10938 To quit or not to quit? estimating the causal effects of Türkiye’s indoor smoking ban on the intentions to quit smoking Değerli, Hakan; Acar, Yasin; Ankara, Hasan Giray Background: Smoking bans aim to reduce tobacco use, but their long-term effectiveness remains uncertain. This study investigates short- and long-term causal effects of Türkiye’s indoor smoking ban policy on the intentions to quit smoking among adults. Methods: Using three waves of data (2008, 2012, and 2016) from the Global Adult Tobacco Survey (GATS), we analyse the changes in intentions to quit smoking before and after the implementation of indoor smoking ban policy in July 2009. The analysis includes data from 2008 as the pre-policy period, 2012 as the short post-policy period, and 2016 as a long post-policy period, to observe both initial and sustained effects. Results: Results indicate a modest positive effect shortly after the ban’s implementation, with a 2% increase in the log odds of intention to quit smoking in 2012 among the individuals exposed to the ban. However, by 2016, the effect appears to vanish, with a 11% decrease in the log odds of intention to quit smoking among those exposed to the ban over a prolonged period. Conclusions: Overall, the study suggests that while the smoking ban initially encouraged quitting intentions, its effect did not sustain over time, emphasizing the need for the controls of society compliance and of policy implementations. Wed, 01 Jan 2025 00:00:00 GMT https://hdl.handle.net/11436/10938 2025-01-01T00:00:00Z