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A significant corridor under the "Belt and Road Initiative" is announced by the government of China for numerous transport and energy projects. China focuses greatly on the sustainability of these projects, in accordance with Green Investment Principles. For this, environmental, social, governance, and technological considerations are taken into account. This paper offers a novel assessment of the impact of green sustainability under BRI and the current emission scenario in Pakistan. The question that needs to be addressed here is whether the projects under Belt and Road Initiative are as environment friendly as they claim. In the first phase of this study, we review theories establishing evidence based on existing literature to determine the influence of Belt and Road on economic development. In the next phase, we use Environment Kuznets Curve and apply estimates from Johansson's Cointegration Test to quantify the present environmental scenario of Pakistan and probable environmental risk portrayed by the China-Pakistan Economic Corridor (CPEC). This paper helps in determining the relationship between carbon emission and three major variables including energy consumption, economic growth, and foreign direct investment (FDI). Cointegration analysis is applied on time series data comprising of a 40 years' period from 1979 to 2019. The paper finds that (i) the belt and road corridors could substantially improve trade, foreign investment, and living conditions for citizens in participating countries; (ii) moreover, this study confirms the presence of one cointegrated equation suggesting the presence of a long term relationship between the CO2 and independent variables and a short run association running from EnC to CO2. The study also proposes a Green BRI Model which formulates a body that devises environmental performance standards, reviews procedures, and ensures that they get implemented on all CPEC projects. The Belt and Road Initiative increases emissions among participating countries. China and corridor economies need to adopt strict policy reforms that increase transparency, expand trade, improve debt sustainability, and mitigate environmental, social, and corruption risks. [ABSTRACT FROM AUTHOR]

Extreme value theory (EVT) is useful for modeling the impact of crashes or situations of extreme stress on investor portfolios. EVT is mostly utilized in financial modeling, risk management, insurance, and hydrology. The price of gold fluctuates considerably over time, and this introduces a risk on its own. The goal of this study is to analyze the risk of gold investment by applying the EVT to historical daily data for extreme daily losses and gains in the price of gold. We used daily gold prices in the Pakistan Bullion Market from August 1, 2011 to July 30, 2021. This paper covers two methods such as Block Maxima (BM) and Peak Over Threshold (POT) modeling. The risk measures which are adopted in this paper are Value at Risk (VaR) and Expected Shortfall (ES). The point and interval estimates of VaR and ES are obtained by fitting the Generalized Pareto (GPA) distribution. Moreover, in this paper, return-level forecasting is also included for the next 5 and 10 years by analyzing the Generalized Extreme Value (GEV) distribution. [ABSTRACT FROM AUTHOR]

The unequal water distribution in the universe has resulted in more than 2 billion people living in water-stressed areas. Globally, withdrawals of water resources are still below the critical level. Pakistan is also affected by this serious problem. In the context of environmental evaluation, irrigation scheduling, and water resource management, evapotranspiration predictions are crucial. The evapotranspiration estimation methods may need to be evaluated daily or monthly to understand the climate change effects in the local areas. This paper investigates spatial-temporal interpolation of evapotranspiration data from 41 Pakistani meteorological stations. To estimate evapotranspiration, we have used the average time series data between 2006 and 2015 on temperature, relative humidity, wind speed, and solar radiation. We developed a new modified Hargreaves equation to estimate evapotranspiration and evaluate the performance of different models. We found that our modified Hargreaves model could perform better than the original Hargreaves model. To analyze trends in different seasons, such as rainy, dry, and annual, FAO-56 Penman-Monteith, Blaney-Criddle, and Hargreaves-Samani models are used. We used Ordinary Kriging for Functional Data to map and predict evapotranspiration spatially and temporally in various locations. [ABSTRACT FROM AUTHOR]

The production data of mineral resources are noisy, nonstationary, and nonlinear. Therefore, some techniques are required to address the problem of nonstationarity and complexity of noises in it. In this paper, two hybrid models (EMD-CEEMDAN-EBT-MM and WA-CEEMDAN-EBT-MM) flourish to improve mineral production prediction. First, we use empirical mode decomposition (EMD) and wavelet analysis (WA) to denoise the data. Second, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition (CEEMDAN) are used for the decomposition of nonstationary data into intrinsic mode function (IMF). Then, empirical Bayesian threshold (EBT) is applied on noise dominant IMFs to consolidate noises, which are further used as input in the data-driven model. Next, other noise-free IMFs are used in the stochastic model as input for the prediction of minerals. At last, the predicted IMFs are ensemble for final prediction. The proposed strategy is exemplified using Pakistan's four major mineral resources. To measure the prediction performance of all the models, three methods, that is, mean relative error, mean square error, and mean absolute percentage error, are used. Our proposed framework WA-CEEMDAN-EBT-MM has shown improvement with minimum mean absolute percentage error value compared to other existing models in prediction accuracy for all four minerals. Therefore, our proposed strategy can predict the noisy and nonstationary time-series data with an efficient mechanism. Hence, it will be helpful to the policymakers for making policies and planning in mineral resource management. [ABSTRACT FROM AUTHOR]

In this paper, we set forth a framework for solving a multiattribute group decision-making (MAGDM) problem, namely, the selection of a firm for participation in a Saudi oil refinery project in Pakistan. This project will prove a key success factor for the economic growth of Pakistan due to its enormous economic impact on the energy sector, industrial development, commerce, transportation, and so on. This multiplicity justifies that several intricate components comprising both intrinsic and external attributes should be adequately evaluated for the selection of such a firm, that is, the formulation of this question as a MAGDM problem. Nonbinary evaluation with two-dimensional ambiguity and uncertainty in the parameters are general concerns in modern literature, and they fit into this problem. Within this context, one of the most superior and amenable theories (complex spherical fuzzy N -soft sets, henceforth C S F N S f S s) shall be used to formulate a new comprehensive method, known as complex spherical fuzzy N -soft-VIKOR (C S F N S f -VIKOR) method. According to the general spirit of the benchmark technique, the normalized Euclidean distances and the weights of the attributes are jointly handled, and as consequence, two main features ("maximum group utility" and "minimum individual regret") are acquired. The coefficient strategy with reference to group utility measure and individual regret measure of opponents are employed for the compromise measure. Armed with this novel tool, we single out the most feasible firm according to the preference order of the alternatives examined by the decision-makers on the subject of linear normalized weights of experts and attributes. Furthermore, a comparative analysis justifies the C S F -VIKOR method, and some results prove its capabilities and validity. Moreover, a sensitivity test certifies the stability of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate estimation of the mining process is vital for the optimal allocation of mineral resources. The development of any country is precisely connected with the management of mineral resources. Therefore, the forecasting of mineral resources contributed much to management, planning, and a maximum allocation of mineral resources. However, it is challenging because of its multiscale variability, nonlinearity, nonstationarity, and high irregularity. In this paper, we proposed two revised hybrid methods to address these issues to predict mineral resources. Our methods are based on denoising, decomposition, prediction, and ensemble principles that are applied to the production of mineral resource time-series data. The performance of the proposed methods is compared with the existing traditional one-stage model (without denoised and decomposition strategies) and two-stage hybrid models (based on denoised strategy), and three-stage hybrid models (with denoised and decomposition strategies). The performance of these methods is evaluated using mean relative error (MRE), mean absolute error (MAE), and mean square error (MSE) as evaluation measures for the production of four principle mineral resources of Pakistan. It is concluded that the proposed framework for the prediction of mineral resources indicated better performance as compared to other existing one-stage, two-stage, and three-stage models. Furthermore, the prediction accuracy of the revised hybrid model is improved by reducing the complexity of the production of mineral resource time-series data. [ABSTRACT FROM AUTHOR]

The problem of energy crisis and environmental pollution has been mitigated by the generation and use of solar power; however, the choice of locations for solar power plants is a difficult task because the decision-making process includes political, socio-economic, and environmental aspects. Thus, several adverse consequences have been created by the choice of suboptimal locations. The objective of this paper is to address the integrated qualitative and quantitative multicriteria decision-making framework for the selection of solar power plant locations. Neutrosophic sets (NSs) are the latest extension of the ordinary fuzzy sets. The main characteristic of the neutrosophic sets is satisfying the condition that the sum of the truth, indeterminacy, and falsity grades must be at least zero and at most three. In this research, we establish novel operational laws based on the Yager t-norm and t-conorm under neutrosophic environments (NE). Furthermore, based on these Yager operational laws, we develop a list of novel aggregation operators under NE. In addition, we design an algorithm to tackle the uncertainty to investigating the best solar power plant selection in five potential locations in Pakistan. A numerical example of solar power plant location problem is considered to show the supremacy and effectiveness of the proposed study. Also, a detailed comparison is constructed to evaluate the performance and validity of the established technique. [ABSTRACT FROM AUTHOR]

The extreme value theory (EVT) has been used to model and measure the distribution of extreme minima of Byco Petroleum in the Pakistan stock market over the period from 2005 to 2012. This paper covers the investigation of distributions that are mostly used in finance including the generalized extreme value (GEV), generalized logistics (GL), and generalized Pareto (GPA) distribution. L-moment ratio diagram is being used to find the appropriate distributions among the distributions. L-moment diagram depicts that GEV and GL distributions are suitable to represent the extremes of Byco Petroleum Pakistan Limited. Thereafter, the probability weighted moment (PWM) method has been used in order to estimate the parameters of probability distributions. Furthermore, Anderson–Darling (AD) goodness-of-fit test is employed to test the goodness of fit among GEV and GL distributions, and it is clear from the results that the GL distribution is more reliable and applicable for extreme minima of Byco Petroleum Company in the Pakistan stock exchange market. EVT and traditional methods are used for value-at-risk (VaR) analysis. The analysis indicates that EVT methods are more suitable for risk measurement in comparison with traditional methods. [ABSTRACT FROM AUTHOR]

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan's COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection. [ABSTRACT FROM AUTHOR]

Immunization is a precautionary measure that helps to stop diseases before their occurrence. Vaccine-preventable diseases are a primary cause of death among children under the age of five in many developing nations. The purpose of this study is to investigate the immunization status and associated demographic characteristics among children aged 12–23 months in Punjab, Pakistan. The study used the data from the Multiple Indicator Cluster Survey (MICS) for Punjab, Pakistan. Data were collected from caregivers using interviewer-administered questionnaires. To summarize the data, descriptive statistics are computed, and logistic regression is used to identify the significant factors that are responsible for complete immunization among the children in Punjab. Odds ratios, 95% CI, and Chi-square statistics were computed to identify the factors associated with no or partial immunization. The prevalence of complete immunization coverage was 89.1%. Women in the rich wealth quantile had the highest odds of completing the immunization for their children (AOR = 2.314; 95% CI: 1.642–3.261) compared to those who are poor. Those in rural areas were more likely to fully vaccinate their children (AOR = 1.54; 95% CI: 1.232–1.925) compared to those in urban areas. Those in the highest level of the educational group (AOR = 2.639; 95% CI: 1.800–3.87) are more likely to complete vaccination for their children compared to those with no formal education. However, female children are less likely to complete immunization compared to male children (AOR = 0.813; 95% CI: 0.687–0.963). The immunization status of children shows a significant association with maternal education, wealth status, and area of residence. [ABSTRACT FROM AUTHOR]

In the context of advanced prosthetic and orthotic medical devices, funding is a major challenge, especially in low-income countries. This research aims to investigate the best funding solution for such medical devices to offer disabled people a better quality of life and improve their health and economic situation. This is the first time such methods are being used in a study based on related research involving a fuzzy analytic hierarchy process (FAHP)/VIekriterijumsko KOmpromisno Rangiranje (VIKOR) model to find the best funding source. A survey questionnaire created by the author and completed by Pakistan subject experts was used to analyze data via a weightage value ranking system as an assessment tool. Prioritization of funding alternatives was based on main criteria and subcriteria. Limitations include sample number of study participants, amount of data collected, number of models used, and time constraints. Potential funding sources were ranked based on a VIKOR weightage value model using a hybrid decision-making process that showed results as "private insurance" having a weightage value of (0.1956) as being the best funding source since they are having the minimum weightage and then next from best to least they were "need-based" with a weightage value of (0.2510), "self-pay" with a weightage value of (0.7970), and finally "government-based" having a weightage value of (0.9636). Implications of this study conclude that the proposed model efficiently finds the best funding sources for advanced prosthetic and orthotic (P&O) medical devices in low-income countries. Executing this model in low-income countries such as Pakistan can help solve P&O medical device coverage challenges and offer disabled people better opportunities to work and contribute to their quality of life and economy. [ABSTRACT FROM AUTHOR]

Software quality plays an important role in the easy and quick adoption of a software product by the end users. Agile methods have proven to play an effective role in ensuring software quality; however, the public sector is hesitant in its adoption. This study evaluates the adoption of agile methods in the public sector in-house software development and capitalization of potential benefits. A quantitative approach is used with 28 survey questionnaires based on budget, time, quick debugging and correction, maintenance, easy testing, and installation as software quality components to assess the employability of agile methods. The questionnaire was served to 216 information technology (IT) professionals (project managers: 6%, developers: 61%, quality assurance (QA) leads: 8%, and testers: 25%) from the public sector companies having experience in software development using agile and waterfall methods. The quality components and hypotheses are evaluated using the T-test and chi-square test, respectively, with a 95% confidence interval. The results highlight the benefits of using agile methodologies for software quality components in the public sector. Additionally, the findings demonstrate how agile software development approaches significantly affect the quality of software products and successful delivery within budget and deadline. [ABSTRACT FROM AUTHOR]

Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of the data series and moving average technical analysis are used in both models. Explanatory factors were used as indicators, and the prediction performance was assessed using a variety of commonly known statistical metrics. These statistical metrics suggested the presence of conditional heteroscedasticity. Thus, the process turns to capture the volatility effect of conditional heteroscedasticity through GARCH modeling. It may be inferred based on the results of tentative models; that the ARCH model outperforms the GARCH model in terms of predicting the USD/PKR exchange rate. [ABSTRACT FROM AUTHOR]

In this study, a new partial randomized response model (RRM) has been proposed for estimating the population mean of two quantitative sensitive variables simultaneously. The utility of proposed model under stratification is also explored. The efficiency comparisons of the proposed model under simple and stratified random sampling are carried out numerically. A real data set was collected through direct questioning, proposed partial RRM and competitor randomized device from the students of statistics and animal sciences departments of Quaid-I-Azam University Islamabad, Pakistan. The performance of the proposed partial RRM is better than competitor RRM under simple and stratified random sampling. [ABSTRACT FROM AUTHOR]

Diseases like hepatitis remained a major health concern, especially in developing countries. The awareness and knowledge about such diseases are of prime importance. The analysis of socioeconomic factors associated with the tendency of awareness and knowledge about the said diseases is fundamental. However, in developing countries like Pakistan, very few studies have considered such investigations using nationally representative data. In addition, a careful review of the literature suggests that no studies have analyzed the trends in awareness and knowledge about said disease with respect to time using nationally representative datasets. Furthermore, the existing literature regarding these studies has utilized the classical methods for the analysis. We have considered a detailed study for analyzing the trends in awareness and knowledge about the said disease in the general population of Pakistan from 2012 to 2018, using nationally representative data collected through Pakistan Demographic and Health Surveys. In addition, we have considered the Bayesian methods for the analysis and performance of the proposed Bayes methods that have been compared with the frequently used classical methods. The results indicated that the proposed Bayesian multiple logistic regression models performed better as compared to classical multiple logistic regression models (CMLRMs). This is due to fact that widths of 95% CIs were smaller for Bayesian multiple logistic regression models (BMLRM), as compared to classical multiple logistic regression models. The findings of the study suggest that there are severe disparities (with respect to different socioeconomic groups) in the knowledge and awareness of respondents for hepatitis. The levels of knowledge and awareness about the said disease are drastically low for respondents living in rural areas, having lower levels of education and wealth. These disparities seem to persist, as the corresponding odds have not changed much during the period 2012 to 2018. The policy-maker should plan and implement the strategies to reduce the observed disparities for different sectors of society. [ABSTRACT FROM AUTHOR]

The agriculture sector plays a vibrant role in the economic prosperity of advanced and developing countries. It is a crucial source of revenue for the majority of the population. Nevertheless, unfortunately, in Pakistan, the share of the agricultural sector in Gross Domestic Product (GDP) is gradually declining. Therefore, comprehensive strategies and actions need to be developed and implement to enhance the agricultural productivity of Pakistan. In this study, an attempt has been made to examine the crop yield revenue of Punjab, Pakistan, by ranking the districts according to their contribution to the agricultural GDP of Pakistan's economy. A Multi-Criteria Decision Making (MCDM) technique, namely, characteristic objects method (COMET), which is entirely free of the rank reversal paradox, is used for this purpose. However, to make a fair comparison, in this research, a comprehensive framework is proposed to normalize the crop yield revenue of Punjab under probabilistic nature. The proposed framework is applied to various districts of Punjab, Pakistan, from 1992 to 2019. It is concluded that Jhang, Faisalabad, and Rahim Yar Khan (RYK) are the highest-ranked districts, while Nankana Sahib, Rawalpindi, and Islamabad are the lowest-ranked districts of Punjab, Pakistan, according to their contribution to the agricultural GDP of Pakistan's economy. Outcomes associated with this research would be helpful to build precise and accurate budget allocation policies. [ABSTRACT FROM AUTHOR]

Many underdeveloped countries are facing acute shortage of electric power and short term measures are important to consider to address the problems of power outage, power plant failures, and disaster areas. Distributed generation (DG) is a promising approach for such cases as it allows quick on-site installation and generation of electric power. Injection of DG can improve the system voltage profile and also reduce the system's total power losses. However, the placement and sizing of the DG unit is an optimization problem in the radial distribution system. As a test case, this study examines voltage profile improvement and system power losses for an 11 KV residential feeder at the Abdul Rehman Baba grid station in Pakistan, which is modelled using the Electrical Transient Analyzer Program (ETAP). For various scenarios, several tests are conducted to assess the effects of DG on the distribution system. The results show that proper design considerations of size and location of a DG, to be inserted in to the system, lead to significant reduction in power losses and improvement in voltage profile and thus improvement in the overall efficiency of the power system. The projections of this work can be used to optimize the expansion of a power system and tackling different issues related to voltage profile in distribution sector worldwide. [ABSTRACT FROM AUTHOR]

The textile and clothing (T&C) sector plays an important role in the growth and industrial development of emerging economies. Work stress is a real concern for organizations with no immunity for the T&C sector. It hampers individual as well as organizational work performance that ultimately places a significant hurdle in achieving competitiveness in the market. This study aims at exploring the antecedents and consequences of work stress and how different causes of stress influence perceived stress among the employees of the textile and clothing organizations in Pakistan. Data were collected from 1470 employees of 24 organizations. Cohen's Perceived Stress Scale-10 (PSS-10) and two researcher-developed instruments (34 items and 22 items each) were used for the data collection. Information gathered was analyzed by PLS-SEM (partial least squares structural equation modelling) technique. Statistically significant results were found for the antecedents, namely, work-family conflict, role clarity, work characteristics, social working environment, managers' support at work, and training and career development, with work-family conflict as the most significant, and for the consequences, namely, psychological, emotional, physical, performance, and behavioral with psychological effect as the most significant. The findings of the study could help managers, designers, planners, and ergonomists in making more informed and proactive decisions while attempting to prevent or control the stress phenomenon in the T&C sector. Moreover, the study highlights the need for gaining more in-depth insight into the work-stress phenomenon proactively so that the effectiveness of intervention strategies might be assured. [ABSTRACT FROM AUTHOR]

Consistent estimation techniques need to be implemented to obtain robust empirical outcomes which help policymakers formulating public policies. Therefore, we implement the least squares (LS) and the high breakdown robust least trimmed squares (LTS) regression techniques, while using econometric regression model based on a growth equation for the two countries, namely, India and Pakistan. We used secondary annual time series data which covers a long period of 41 years. The adequacy of the time series econometric model was checked through cointegration analysis and found that there is no spurious regression. Classical and robust procedures were employed for the estimation of the parameters. The empirical results reveal that the overall fit of the model improves in case of LTS technique, while the significance of the predictors changes significantly in cases of both countries due to the removal of outliers from the data. Thus, empirical findings exhibit that the results, obtained through LTS, are better than LS techniques. [ABSTRACT FROM AUTHOR]

Pakistan is one of those countries that are heavily dependent on hydrocarbon fuel for energy production. This results in severe CO2 emissions that lead to climate change. Although renewable energy resources such as wind are available in abundance, they have not been fully utilized and so energy crises in Pakistan increase every year. This study focuses on using wind energy as an alternative to thermal power sources as the main source of power generation in Pakistan. This research also helps in designing a project management tool to prioritize sources of power generation, including both renewables and nonrenewables, while also considering projects' technical, environmental, social and economic criteria. An Analytical Hierarchy Process has been used to analyze the four main criteria and 12 subcriteria. The analysis revealed that wind energy is the best source among all options for power generation source, followed by solar. Oil, gas, and coal, the other three alternatives, were well below in a comparative analysis. Gas achieves overall better performance if only economic criteria are considered, but due to shortfalls in availability and diminishing reserves, this source is also not feasible. Relying on wind power as the only energy source will not be technically possible, but it can become a leading energy source inside Pakistan. This will reduce the burden on thermal energy sources and make the country more energy-secure, rather than relying heavily on imported oil and gas, as it currently does. Wind power generation capacity in Pakistan is increasing, so that the cumulative capacity is around 2118 MW installed and commissioned by the end of 2018. [ABSTRACT FROM AUTHOR]

Innovative capacity has a decisive and crucial role in determining who is prospering in the global arena. Innovation is crucial for value creation, high productivity, economic growth, employment, and sustainable development that benefit the consumers, firms, businesses, and economy as a whole. Several institutional, political, and social conditions lead to different responses of innovation. For instance, low voice and accountability may lead to low institutional setting, cronyism, and favoritism in allocation of resources, which can harm innovation. Similarly, the terrorist attack of 9/11 caused horrific scales of destructions, which caught the countries on various fronts such as economic, political, and technological. Further, sound infrastructure and savings rates are essential to enhance innovation. Applying the ARDL approach to cointegration, this study investigates the impact of voice and accountability, terrorism, infrastructure, and savings on innovation in Pakistan. The empirical evidence shows that low voice and accountability and terrorism have adverse impacts on innovation in the short- and long-run periods. Further, findings reveal that inadequate infrastructures and low savings adversely affect the innovation in both periods. Various robust checks such as Maki cointegration, Johansen cointegration test, fully modified ordinary least square, dynamic ordinary least square, and canonical cointegration regression corroborate the results. The findings of the study would be of substantial implications for government and policymakers. [ABSTRACT FROM AUTHOR]

Equity premium is a vital number in finance to be considered for making fund allocation and investment decisions. This study explores the relationship between (controllable) determinants of firm-level equity premium in the context of Pakistan stock market. We use a sample of 306 firms' annual data, from 01/2001 to 6/2017, using a two-stage least-squares method to estimate our model. During the selected sample period, the average market premium of the Pakistan stock exchange (KSE100 Index) was twenty percent. The average equity premium of individual firms was only eight percent. Company fundamentals are considered determinants of firm-level equity premiums. Panel data econometrics techniques were used to estimate the modified version of the multifactor model for the Pakistan Stock Exchange. It is found that the market premium, return on equity, dividend payout ratio, accounts receivable, and firm size significantly and positively affect the firm-level equity premium. However, increase in the debt-to-equity ratio and quick ratio negatively affects that premium. The company fundamental variables are controllable for the firms and can be improved by company management to encourage investors and maximize shareholder wealth. [ABSTRACT FROM AUTHOR]