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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]

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]

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]

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]

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]