The China-Pakistan Economic Corridor (CPEC) is a journey towards economic integration of Eurasia. The CPEC contains US$62 billion investment projects on energy, infrastructure, and other development projects in Pakistan. However, CPEC could enhance climate change vulnerabilities for the faltering economy of Pakistan due to its three possible environmental risks and repercussions. Its major environmental concern is related to energy projects as three quarters of the newly planned energy will be generated from traditional coal-fired power plants. Traditional coal power plants are the major contributors to CO 2 emissions and smog, which ultimately lead to global warming and climate change. Its second important environmental concern is linked with massive tree cutting for the construction of various road networks from Kashghar, China, to Gwadar, Pakistan. Tree cutting leads to enormous concentration of CO 2 emissions along the road networks. Vehicle trafficking is its third important environmental threat. Karakorum highway is expected to carry up to 7000 trucks per day that will release up to 36.5 million tons of CO 2 . Despite all the environmental risks, the CPEC enables Pakistan to manage energy crisis and upgrade aging infrastructure. However, if appropriate remedial measures are not taken to diminish environmental risks, Pakistan will be among major contributors to CO 2 emissions, and its rank will be worsen in global climate risk index, after completion of this project. Therefore, it is very crucial to assess possible environmental impacts of CPEC projects regarding energy, infrastructure, and transportation. Furthermore, scientists from both countries should collaborate to manage the environmental repercussions of CPEC projects.

The COVID 19 pandemic has had tremendous economic impacts and continues to wreak havoc around the world. This research work has been conducted to analyze the macroeconomic effects of the COVID 19 in the context of Pakistan. The impact ECON Supply Chain (IESC) Computable general equilibrium (CGE) Model which was formulated by Walmsley and Minor (2016) has been employed so that the supply chain effects of many of Pakistan’s government policies in response to the coronavirus pandemic can be assessed. An 8% shock was given to 11 sectors of the economy and a 5% shock was given to electricity. Lastly, the impact of these shocks on all 31 sectors of the economy that are included in the model was assessed. Results discovered that there was a decline in real GDP, real exports, real imports, and per capita utility from private expenditure, meanwhile, terms of trade and regional household income increased. This study also illuminated that during pandemic goods market prices increased for 16 sectors while supply price of commodities decreased for 15 sectors. Based on the empirical findings, some relevant policy implications are suggested to overcome the pandemic.

In the current times, there is high demand for artificial intelligence (AI) techniques to be integration with real-time collection, wireless infrastructure, as well as processing in terms of end-user devices. It is now remarkable to make use of AI for detection as well as prediction of pandemics that are extremely large in nature. Coronavirus pandemic of 2019 (COVID-19) began in Wuhan, China and caused the deaths of 175,694 deaths around the world, while the number of active patients stands at 254,4792 patients around the world. In Pakistan, from January 2020 March 2021, there have been 658,132 positive cases, 603,512 recovered cases of COVID-19 with 16,208 deaths, reported by world health organization. Nonetheless, the quick and exponential increase in COVID-19 patients has made it necessary that quick and efficient predictions be made in terms of the possible outcomes with respect to the patient for the sake of suitable treatment by making use of AI techniques. A fine-tuned random forest model has been proposed by this paper, which has been given a boost by AdaBoost algorithm. The COVID-19 patient’s health, geographical area, gender, and marital status are used for the prediction of severity in terms of cases as well as possible outcomes, either recovery or no recovery (i.e. death). The model is 90% accurate and has a 0.76 F1 Score on the set of data used. Analysis of data shows a positive correlation with respect to the gender of patient, and death. It also shows that most of the patients had ages between twenty years and seventy years.