Economic growth, CO2 emissions, renewable waste and FDI relation in Pakistan: New evidences from 3SLS.

First attempt has been made to find the effects of foreign direct investment on environmental pollution and economic growth, in addition to finding the determinants of foreign direct investment inflows in Pakistan using the annual data set for the period of 1980-2014. Simultaneous equation model has been used to find relation between the variables of concern. Results from technique and composition effects show that increase in economic growth leads towards more pollution emissions. Scale effect shows stock of capital and labor have positive effect on the economic growth of Pakistan while pollution has negative effect on growth. In case of capital accumulation effect, economic growth and foreign direct investment have positive and significant effect on stock of capital. Although increase in economic growth increases pollution, however, economic growth declines as pollution crosses a certain limit. Foreign direct investment is also found positively related with pollution.

Comprehensive sentimental analysis of tweets towards COVID-19 in Pakistan: a study on governmental preventive measures.

Sentiments are the key factors that lead to influence our behavior. Sentiment analysis is a technique that analyzes people's behaviors, attitudes, and emotions toward a service, product, topic, or event. Since 2020, no country has remained untouched by COVID-19, and the governing bodies of most countries have been applying several anti-pandemic countermeasures to combat it. In this regard, it becomes tremendously important to analyze people's sentiments when tackling infectious diseases similar to COVID-19. The countermeasures taken by any country to control the pandemic leave a direct and crucial impact on each sector of public life, and every individual reacts to them differently. It is necessary to consider these reactions to implement appropriate messaging and decisive policies. Pakistan has done enough to control this virus's spread like every other country. This research aims to perform a sentimental analysis on the famous microblogging social platform, Twitter, to get insights into public sentiments and the attitudes displayed towards the precautionary steps taken by the Government of Pakistan in the years 2020 and 2021. These steps or countermeasures include the closure of educational institutes, suspension of flight operations, lockdown of business activities, enforcement of several standard operating procedures (SOPs), and the commencement of the vaccination program. We implemented four approaches for the analysis, including the Valence Aware Dictionary and sEntiment Reasoner (VADER), TextBlob, Flair, and Bidirectional Encoder Representations from Transformers (BERT). The first two techniques are lexicon-based. Flair is a pre-trained embedding-based approach, whereas BERT is a transformer-based model. BERT was fine-tuned and trained on a labeled dataset, achieving a validation accuracy of 92%. We observed that the polarity score kept varying from month to month in both years for all countermeasures. This score was analyzed with real-time events occurring in the country, which helped understand the public's sentiment and led to the possible formation of a notable conclusion. All implemented approaches showed independent performances. However, we noticed from the classification results of both TextBlob and the fine-tuned BERT model that neutral sentiment was dominant in the data, followed by positive sentiment.

Farmers' perception and adaptations to climate change: findings from three agro-ecological zones of Punjab, Pakistan.

The main objective of this study was to capture farmers' perceptions and adaptations to climate change in agriculture sector. Along with this, it also identified farmers' adaptations to perceived climate change. Binary logit models were applied on data of 386 farmers, collected from three different agro-ecological zones of Punjab, Pakistan, to present a comprehensive analysis of different adaptation strategies missing in the existing literature. The coefficients of a binary logit model only explain the direction of change; therefore, to see the magnitude of change, marginal effects were also estimated. Findings revealed that farmers perceived climate change and opted different adaptation strategies. Results of binary logit models described age, education, farming experience, landholding, access to climate information, access to credit facilities, and access to extension services as important determinants of adaptation. This research also found lack of access to climate information, lack of irrigation resources, and knowledge about appropriate adaptations as key constraints in adaptation process. This situation can be improved by enhancing institutional support and capacity. It is suggested that improved agricultural education with better access to climate information and extension services affects the farmers' well-being directly and hence is good for the economy of Pakistan.

Linking environment, malaria, and agricultural returns: a labor time use analysis at different stages of production using 3SLS.

The present study was designed to investigate the effects of disease on time spent by family and hired labor on farm activities. The effect of illness on cost incurred on farm activities and revenue earned from agriculture has also been examined in detail. The reason behind choosing malaria is because of its strong association with the quality of surrounding environment especially in the case of farm workers who are compelled to work in the environmental conditions quite suitable for the transmission of malaria. The effects of health shocks due to malaria are segregated according to three stages of production: land preparation, field management, and harvesting stages. Simultaneous equation model was employed using cross-sectional data collected from 252 farm workers through a pre-tested questionnaire. Farmers' living environment was found to be contributing in the spread of disease. Results also show that malaria affects labor time at harvesting stage as it is more labor-intensive stage of production. We find that malaria significantly affects the health of farm workers and their families forcing farm families to substitute family labor with hired labor. Further, the cost incurred on cure of disease significantly adds to the cost on agricultural production. This consequently leads to a substantial reduction in revenue. The effect of the cost incurred on prevention and cure of disease was also found negative on revenue. The study suggests that, in order to make farmers more productive, there should be malaria interventions specifically targeting the health of farmers. It is also suggested that, for successful malaria interventions especially in irrigated areas, the magnitude of the disease on different stages of production should be given due consideration.