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Investment decisions within the organizations are critical for firm’s profitability and also have a long-term impact because many of these decisions are irreversible. Firms with limited funds can achieve impressive returns if their investments are well-planned. Therefore, the research objective is to determine the impact of investment decisions on firm profitability (earning per share) of non-financial sector (Communication, media industry, energy sector etc.) of Pakistan. For this purpose, data was collected for the period of 2011-2021 from the annual reports of non-financial companies which were listed in 100 index Pakistan Stock exchange. The quantitative research approach, longitudinal research design and panel data approach has been used in the study. The panel data results indicates that working capital management (WCM) indicators namely inventory turnover ratio (ITO), average collection period (ACP), average payment period (APP) and cash conversions cycle (CCC) have positive and significant effect on earnings per share. In the same vein, capital expenditure also have positive and significant effect on earnings per share and also has indirect effect through the mediating effect of sales growth which indicates partial mediation because the coefficient after mediation was lower as compared to the direct effect. Based on the findings, the research has contributed a body of literature in the extant literature along with the significant and mediating effect. Therefore, this study could be considered to be a pioneer study that could help the researchers to conduct their research in future. This study could also help to the managers and decisions makers to know about the importance of investment decisions to increase firm profitability.

The research objective was to determine the impact of capital expenditure on textile firm’s profitability in Pakistan. For this purpose, study utilizes panel data from the annual reports of firms operating in the textile sector of Pakistan. Quantitative research approach and longitudinal research design was applied. The panel data results indicates that the impact of capital expenditures on firm profitability is significant and positive. The findings suggest that increasing capital expenditures can lead to higher profitability for textile firms in Pakistan. This implies that textile firms in Pakistan should prioritize investments in capital expenditures in order to improve their financial performance. The findings provided by this research could be valuable for textile firms in Pakistan as they seek to make strategic investment decisions. Additionally, policymakers and investors in the textile sector can use the results of this study to inform their decision-making processes and optimize investment strategies. The study's findings could also contribute to a better understanding of the relationship between capital expenditures and firm profitability in the textile sector of Pakistan. The research could also helped to the researchers to conduct their research in future.

Named entity recognition (NER) is a task of proper noun identification from natural language text and classification into various types such as location, person, and organization. Due to NER's applications in different natural language processing (NLP) tasks, numerous NER approaches and benchmark datasets have been proposed. However, developing NER techniques for low-resource languages is still limited due to the absence of substantial training datasets. Punjabi is a classic example of low resource language. Although various researchers have worked on Punjabi, they focused on the Gurmukhi script. To overcome the challenges in developing NER for the Shahmukhi script, we present an improved technique for Punjabi NER for the Shahmukhi script in this paper. We firstly extend the existing dataset by adding new NER classes by leveraging a novel Pool of Words data augmentation strategy. Our extended dataset has 11,31,509 tokens and 1,25,789 labeled entities with more named entities (NEs) than the older dataset. In the next step, we fine-tuned a transformer model known as Bidirectional Encoder Representations from Transformers (BERT) for the NER task. We performed experiments using the proposed approach on a new and older dataset version, showing that our method achieved competitive results.

The developing world is facing multidirectional challenges of socioeconomics in the era of Sustainable Development Goals (SDGs-2030). Fiscal indicators like fiscal imbalance and debt financing are the key hurdles in financing sustainable environment projects. This study attempted to probe the impact of the fiscal indicators on the environmental conditions of the South Asian region. For a comprehensive analysis, the study utilized a dataset of all eight countries of South Asia for 19 years. Per capita greenhouse gas emission has been used as a dependent indicator, while fiscal imbalance anddebt to gross domestic product (GDP) have been taken as independent indicators along with energy consumption, energy intensity and GDP growth rate. The generalized least squares and quantile autoregressive distributive lag methods have been adopted from econometric for this empirical analysis. Both ways confirm that fiscal imbalance and debt financing play a significant role in greenhouse gas emissions accumulation in the entire region of South Asia. Energy consumption and the cost of producing energy (energy intensity) are the other sources of greenhouse gas emissions accumulation in the area. This study suggested that developing economies of South Asia may not be able to fulfill their commitments regarding SDGs-2030 if their issues of fiscal imbalance and debt financing have not been treated initially.

The well-functioning domestic institutions are very important for the global value chains to have its positive impact on environment. This article provides an empirical assessment of the impact of domestic institutional arrangements along the way to participate in global value chains on environmental performance index for sustainability goals for 41 Asian countries over 2001–2018 period. Most recent empirical studies assumes that causality runs to environmental measures through trade and governance but inverse relationship is also feasible and none of the previous studies have discussed about it. Using instrumental variable strategy to closing these gaps, we analyse the mechanism of direct and indirect impact of participation in global value chains on environmental performance index. Our results show that environmental performance index is negatively affected by participation in global value chains and this relationship overturns when participation in global value chains is accompanied by governance facilitation. Further, the findings suggest that governance-augmented participation in global value chains is a tool of environmental sustainability.

Electricity is one of the primary consumer utilities, while its efficiency is always questionable. This study investigated institutional efficiency with reforms to enhance electricity performance in South and East Asian countries. For this purpose, the study used a panel dataset of eight South and East Asian countries from 1995 to 2018. A two-stage least square and Bayesian Vector Autoregression (VAR) test was applied to estimate the results. According to the results, a fragile institutional framework is an essential factor that snubs the positive influence of any electricity reforms in the region. The findings also show that improvement in economic growth and the utilization of renewable energy rather than fossil fuels are significant factors that can improve institutional efficiency and ultimately highlight the positive role of electricity reforms. This study highlights possible reforms in electricity sectors, and institutional (direct or indirect) efficiency could be an essential factor in designing a policy framework to enhance the region’s electricity performance.

Like many developing countries, Pakistan has limited energy production and governmental funding to support its development. This research focuses on energy efficiency levels and governmental funds distribution among four provinces in Pakistan. Based on balanced panel data from 2006 to 2016, this study develops a novel Zero-Sum Gain (ZSG) DEA approach to simultaneously assess energy efficiency levels and reallocate limited financial resources among provinces. Under the assumption of constant outputs, energy production and governmental funds are considered as input variables, while GDP and population as output variables. Results indicate that efficiency levels of Pakistan provinces range between 0.62 to 0.88, thus suggesting room for improvement in funding allocation.

PurposeIn this paper, the authors investigate that the increasing level of fossil fuel combustion in the industrial sector has been considered the prime cause for the emissions of greenhouse gas. Meanwhile, the research focusing on the impact of fossil fuel consumption on the emission of CO2 is limited for the developing countries containing Vietnam. This study applied the autoregressive distributed lag (ARDL) approach with structural breaks presence, and the Bayer–Hanck combined cointegration method to observe the rationality of the environmental Kuznets curve (EKC) hypothesis in the dynamic relationship between the industrialization and carbon dioxide (CO2) emission in Vietnam, capturing the role of foreign direct investment (FDI) inflows and the fossil fuel consumption over the period of 1975–2019. The outcomes revealed the confirmation of cointegration among the variables and both short and long-run regression parameters indicated the evidence for the presence of a U-shaped association between the level of industrial growth and CO2 emission that is further confirmed by employing the Lind and Mehlum U-test for robustness purpose. The results of Granger causality discovered a unidirectional causality from FDI and fossil fuel consumption to CO2 emission in the short run. For the policy points, this study suggests the use of efficient and low carbon-emitting technologies.Design/methodology/approachIn order to test for consistency and robustness of the cointegration analysis, this study also applied the ARDL bound testing method to find out long-run association among variables with the existence of the structural break in the dataset. The ARDL method was preferred to other traditional cointegration models; because of the smaller dataset, the results obtained from the ARDL method are efficient and consistent and equally appropriate for I(1) and I(0) variables.FindingsThe short-run and long-run causal associations among variables have been observed by employing the error correction term (ECT) augmented Granger-causality test that revealed the presence of the long-run causality among variables only when the CO2 emission is employed as a dependent variable. The outcomes for short-run causality indicated the presence of unidirectional causality between consumption of fossil fuel and CO2 emission, where the fossil fuel consumptions Granger-cause CO2 emission. Industrial growth has also been found to have an impact on fossil fuel consumptions, however not the opposite. This advocates that the policies aimed at reducing the fossil fuel consumptions would not be harmful to industrial growth as other energy efficient and cleaner technology could be implemented by the firms to substitute the fossil fuel usage.Originality/valueThe study explored the dynamic relationship among FDI, consumption of fossil fuel, industrial growth and the CO2 emission in Vietnam for the time period 1975–2019. The newly established Bayer–Hanck joint cointegration method and the ARDL bound testing were employed by taking into account the structural breaks in the dataset.

Information retrieval is acquiring particular information from large resources and presenting it according to the user’s need. The incredible increase in information resources on the Internet formulates the information retrieval procedure, a monotonous and complicated task for users. Due to over access of information, better methodology is required to retrieve the most appropriate information from different sources. The most important information retrieval methods include the probabilistic, fuzzy set, vector space, and boolean models. Each of these models usually are used for evaluating the connection between the question and the retrievable documents. These methods are based on the keyword and use lists of keywords to evaluate the information material. In this paper, we present a survey of these models so that their working methodology and limitations are discussed. This is an important understanding because it makes possible to select an information retrieval technique based on the basic requirements. The survey results showed that the existing model for knowledge recovery is somewhere short of what was planned. We have also discussed different areas of IR application where these models could be used.

The technological innovation and strict environmental protocols in the highly developed regions have become the primary sources for foreign direct investment to move in the pollution haven economies. In this regard, this study attempted to identify the role of foreign direct investment (FDI) in the developing economies of the Brazil, Russia, India, China, and South Africa (BRICS) region. For this reason, a dataset was obtained between 1995 and 2019. Chudik and Pesaran's (2015) latest dynamic common correlated effects (DCCE) technique is used because of its new features when integrating the problems of heterogeneity and structural breaks into panel data that are general and do not encompass much recent research in this context. According to the empirical outcomes, foreign direct investment is a source of pollution haven in this region. However, the moderating effect of institutional quality on foreign direct investment has been found negative for ecological footprint. It also found the threshold point where the foreign direct investment effect becomes negative on ecological footprint. Based on these empirical results, this research suggests that foreign direct investment strategy should be maintained in the presence of good institutional efficiency as it enhances the environment and promotes economic development.

This paper presents a technique for optical recognition of Urdu characters using template matching based on a probabilistic N-Gram language model. Dataset used has the collection of both printed and typed text. This model is able to perform three types of segmentations including line, ligature and character using horizontal projection, connected component labeling, corners and pointers techniques, respectively. A separate stochastic lexicon is built from a collected corpus, which contains the probability values of grams. By using template matching and the N-Gram language model, our study predicts complete segmented words with the promising result, particularly in case of bigrams. It outperforms three out of four existing models with an accuracy rate of 97.33%. Results achieved on our test dataset are encouraging in one perspective but provide direction to work for further improvement in this model.

This work highlights the impact of renewable and non-renewable energy, capital formation, and economic growth on CO2 emissions in the economies that are emerging due to China’s Silk Road Economic Belt (SREB) initiative. In this context, data for 24 SREB economies from 1995 to 2014 are analyzed through the Autoregressive Distributed Lag (ARDL) approach. The results suggest that using a high share of renewable energy to execute economic activities and improve the level of capital formation significantly decreases the rate of CO2 emissions in both the long term and short term in the considered SREB countries. The presence of the Environmental Kuznets Curve and the existence of an inverted U-shaped relationship between economic growth and CO2 emissions are also determined. The negative impact of Gross Capital Formation (GCF) and the negligible effects of trade openness on CO2 discharge are also observed. The negative effect of GCF on carbon emissions suggests either that the SREB economies are investing in low-carbon economic activities or simply that those assets are being produced using cleaner energy sources. It is suggested that these countries should pay more attention to renewable energy resources to improve the environment and maximize the economic benefits of the SREB initiative.

Shariah governance is a central feature and the second layer of corporate governance for Islamic banks (IBs) and Islamic financial institutions (IFIs). Shariah governance is unique to IBs and IFIs due to their possession of Shariah Supervisory Boards (SSB). SSBs serve to ensure that the management of Islamic banking institutions complies with shariah principles. Shariah governance is not only designed to raise the confidence of investors and the public in terms of authenticity and compliance to Islamic banking practices, but also to minimise the fiduciary and reputational risks of Islamic banking institutions. Due to the importance of shariah governance and the role of SSBs in IBs, this research investigates the role of SSBs in influencing the financial performance of IBs with the moderation role of ownership structure in Pakistan. By using nine years of data (2009-2017) pertaining to three Islamic banks in Pakistan, we found that shariah supervisory board reputations, expertise, cross membership, change in composition, shariah qualification, and ownership have significant moderation relationships with the financial performance of IBs. This study is an attempt to provide a deeper understanding of the role of owners and Shariah Supervisory Boards in enhancing the financial performance of Islamic banks for both researchers and policymakers.

Formulating a robust low-carbon strategy has now been critical to achieving sustainable economic development amid broad carbon emissions across the BRIC region thanks to rapid industrialization and international trade growth. Developing such a strategy requires measuring actual sustainability performance from the energy, economy and ecological perspectives in this region. Therefore, this study explores the relationship among BRIC countries’ energy, economic and environmental sustainability performance by applying a common weight data envelopment analysis (DEA)-like composite indicator approach. The indicators encompass a comprehensive, latest, appropriate and applicable set of indicators. The results reveal that China and Brazil outperform against other underlined countries of the region in achieving sustainability with an overall index score of 0.96. India stands in ranking on second with a steady and unchanged score in the region, followed by South Africa and Russia, standing in ranking on fourth and fifth, respectively, in this study. Inclusive scores of both countries indicate the actual and robust possible potential for better sustainability performances in the region. This study proposes a policy by lifting the cross-border trade for renewable energy to achieve long-term environmental sustainability in the BRICS region.

This study evaluates the sustainable power plant cost in the outlook of global power plant efficiency to reduce fossil fuel dependency and greenhouse gas emissions. For this purpose, the Global Change Assessment Model (GCAM) applied for conducting the cost assessment of power zone technologies for all principal electricity generation. This study considers the characteristics of essential factors (cement, price of resources, possible increases in employees, and metals) that affect costs. This study suggests that the cost of electricity-generating technologies significantly affects growth efficiency, reduction in production cost, and improving environmental conditions. It also suggests that the cost of electricity-generating technologies, combined with technology mixture, is the key factor behind replacing existing technology in the electricity sector. EPRI cost assessments expanded by around 30% and 50% during 2015-2016. Factors like competition amongst power plant owners, the ambiguous marketplace, production procedures, and lack of environment-related strategies have resulted in massive environmental degradation in developing economies like Pakistan. Based on empirical findings, this study recommends designing efficient technologies, which would reduce power plant costs and ensure vertical prospects related to environmental conditions in the future.

The unconventional energy sources like hydrogen energy have tremendous potential of filling the gap between economic growth and clean energy consumption. A little intention has been made in this regard in the developing economies like Pakistan. This study develops a fusibility analysis to highlight the potential of hydrogen energy source in Pakistan. For this purpose, this study used a hybrid mathematical model that combines the range of wind speed with the log law to push wind power's potential to generate wind hydrogen in Pakistan. The study results indicate that Pakistan has an excellent source to generate hydrogen energy through wind power stations. According to the outcomes, Nooriabad can produce 303.66 million RE/kWh per year through wind energy sources. According to the results, the rest of the seven wind generation sites also can generate enough hydrogen energy. This study also concluded that hydrogen energy has enough sources to meet the demand for light-duty vehicles in Pakistan.

Climate change challenges associated with global warming are an increasing concern for developing countries. Pakistan is experiencing severe energy crises, and global warming effects have considerably changed its climate, especially the temperature, which has risen significantly. Renewable energy is uninterrupted, clean, and strengthens energy security. This study employed a linear programming model to assess the potential of renewable hydrogen power and its impact on electricity prices, measured the possible demand of wind-generated renewable hydrogen for light-duty vehicles. The findings demonstrate that Pakistan has sufficient wind-generated renewable power potential, and its light-duty vehicles could shift to renewable energy. The findings of this research recommend a policy shift to light-duty hydrogen-based vehicles to eliminate fossil fuel imports and ensure green supplies. With a shift to hydrogen-based vehicles, the government could ensure maximum revenue and green supplies. The study provides valuable guidelines for decision-makers.

Economic integration in the form of Belt and Road Initiative project opens many opportunities and hazards, especially of the participating nations’ environment. The current study attempted to empirically test the economic and energy usage (renewable and non-renewable) impact on some selected countries of belt and road projects. For this purpose, the panel data set of twenty-four emerging economies of belt and road projects was selected from 1995 to 2014. The autoregressive distributed lags technique of econometric applied to determine the effect of renewable and non-renewable energy, GDP and GDP2 for EKC, and gross fixed capital formation on carbon emission in the selected countries of Belt and Road Initiative project. The outcomes of this study confirm the existence of EKC in these underlined countries. Here, fossil fuel-based energy consumption is a source of environmental degradation, while renewable and clean energy usage can help sustain environmental conditions without affecting economic growth progress. Capital fixed formation in these economies can enhance economic growth and help to sustainable environmental conditions in the belt and road countries. Thus, based on these empirical outcomes, this study suggests economic and financial assistance in green renewable energy sources and clean technological innovation to enhance economic benefits of Belt and Road Initiative project without compromising the environmental conditions of the region.

Education is a basic need of humanity and key element for the development of a nation. The population is increasing day by day and the main problem of education particularly in primary education is the increasing of number of students in the schools. So, the enrolment size of schools is increasing consistently. The effect of school enrolment size on the student’s achievement has been a major factor for the failure of the education system especially primary education. The purpose of this research is to discover the possible relations between the school enrolment size and student’s achievements. The data of four primary schools namely GPS Gaggu, GPS Samina, GPS Tagiani and GPS Yaroo has been selected to check the effect of school enrolment size on the student’s achievements. The results of the study indicate that as the enrolment size increased in the school, the achievements of the student’s decreased and vice versa.

Assessment of energy and environmental issues has received a special focus due to global warming and climate vulnerabilities. Energy, environment and economic development simultaneously yielded a combined effect, which is favorable from the economic point of view while unfavorable for environmental aspects. It is necessary to measure the energy, economic and environmental performance with appropriate methods. Therefore, this study measures the energy security and environmental efficiency. Data used in this study were collected from the official website of International Energy Agency (IEA), British Petroleum (BP) and World Bank. We employ a non-radial DEA technology for assessing energy and environmental efficiency for OECD countries. Results reveal that the values of energy intensity range from 0.93 to 0.30, while GHG emission per capita values range from 24.61 to 5.28. As far as energy efficiency is concerned, Iceland and Greece have the highest value (0.99) of energy efficiency, whereas Ireland has the lowest value (0.34) of energy efficiency. Further analysis shows that Austria, Australia, Portugal and Spain have values between 0.70 and 0.80 for energy intensity, whereas UK, Sweden, Germany and the USA have values of energy intensity, between 0.40 and 0.50. The study proposes some valuable policy implications for policy and decision-makers to make the environment more sustainable.

Renewable energy decreases the threat of energy insecurity. Solar energy systems are proving to be an easy and cheaper solution to energy crisis worldwide. Developing countries including Pakistan have augmented efforts to boost the current solar capacity. The identification of desired solar power point plant fabrication requires robust analysis of several factors. Adequate research has not been done on the site selection process for solar projects in Pakistan. This study identifies and proposes multiple point states having solar energy potential. Based on the multiple main and sub-criteria obtained from the literature, a mathematical formulation has been used to install the solar projects at rural areas of Pakistan. An empirical assessment of the off-grid rural solar power projects in Pakistan has also been conducted in this case study. The results reveal that mass, money supply and ratio are important, while the transmission on the matrix, the cost of land and the sun were generated. The study found the district of Barkhan (R2) of Baluchistan to be the most ideal site, followed by Jacobabad (R3) and Mastung (R3). With respect to economic viability, this study found that solar energy systems provide electricity which is much cheaper than conventional energy supplied in the country. This study could be useful reference for South Asian countries including India, Bangladesh, and Afghanistan, which are currently electrifying remote and rural areas through solar energy system.

Wind energy is seen as an important energy to sustainably meet the energy needs of Ghana. However, the industry in Ghana is yet to take off due to policy uncertainty and regulatory costs. The paper analyzed the key determinants and how they interact to impact the scaling up of wind energy in Ghana, using time series data, the vector auto regression (VAR) model from 2013 to 2019.There were four endogenous variables, grouped under policy, population growth, wind capacity, and electrification rate. The findings revealed the dynamic behavior of the variables from the VAR to a strongly significant positive correlation to deploying wind energy in Ghana. The impulse response functions (IRFs) equally exhibited a positive impact long-run trajectory growth of the variables after a shock to the system. The response of the first lags had differences of log policy and that of the log of GDP produced a curious result from the shock by taking a steady positive growth path in the short run and nosedived to a negative pathway in the long run. On the other hand, the interaction of the first differences of the lags of log wind capacity and log policy is quite instructive, as the headwind produced a negative relationship in the short run and to a positive growth path in the long run. This was anticipated, as the wind capacity installation of Ghana is expected to increase in the long run, when pipeline projects materialize.