Like other developing countries, Pakistan faces one of the most serious challenges of how to mitigate carbon emissions while achieving sustainable development. Although, it is widely accepted that the rising trend of carbon emissions and the resulting negative effects of climate change on human activities have emerged as major issues in recent years, the environmental effectiveness needed to clean the environment and promote sustainability is often overlooked. Using the PLSM 2018–2019 survey, this study attempts to examine the household sector’s renewable and non-renewable energy usage magnitude, and the share of renewable and non-renewable energy in Pakistan. Furthermore, this study examines the impact of income, household size, biomass, non-renewable energy, and clean energy on carbon emissions using the STIRPAT model. It is obvious from the empirical findings that the coefficient of income is positive, whereas the coefficient of income square is negative and statistically significant, which indicates that carbon emissions in the household sector increase at lower income levels, while decreasing as income increases. The household size shows that the population has a positive impact on carbon emissions. The impact of biomass, non-renewable, and clean energy is particularly appealing, as the household sector consumes more biomass and non-renewable energy, which stimulates carbon emissions to rise. In the rural sector, clean energy has a negative but statistically insignificant impact on carbon emissions, showing a greater reliance on biomass and non-renewable energy consumption. Lastly, it is suggested that reducing the use of non-renewable energy in the household sector while increasing the use of green energy could be a policy option for making the environment clean and sustainable.

The incredible increase in carbon emissions is a major global concern. Thus, academicians and policymakers at COP26 are continuously urging to devise strategies to reduce carbon and other greenhouse gas emissions. The transportation sector is a major contributor to greenhouse gas emissions in developing countries. Therefore, this study projected an increase in fossil fuel demand for transportation and corresponding carbon dioxide emission in Pakistan from 2018 to 2030 by employing the Grey model and using annual data from 2010 to 2018. Furthermore, the determinant of fossil fuel demand is modeled using an environmental sustainability model such as stochastic regression IPAT that links environmental impact as a product of population, affluence, and technology on annual time series data spanning from 1990 to 2019. The projected values of oil demand and carbon emissions reveal an increasing trend, with average annual growth rates of 12.68% and 11.45%, respectively. The fully modified ordinary least squares (FM-OLS) findings confirmed the environmental Kuznets hypothesis. The increase in population growth emerged as the major driver for oil demand and carbon dioxide emissions, while technological advancement can reduce oil demand and corresponding carbon emissions. This study urges Pakistan to switch from oil to gas and other green energies by encouraging hybrid vehicles, as the number of vehicles on the road positively impacts the transport sector’s oil demand. Moreover, increasing economic growth and controlling the population growth rate by discouraging more children can be a valid policy for reducing oil demand and corresponding carbon emissions.

Poverty is defined as a lack of basic necessities for survival. The current study examines the socio-economic factors that contribute to poverty in five of the district of Lasbela's nine tehsils, namely (Bela, Dureji, Hub, Sonmiani, and Uthal. Lasbela is Balochistan's tenth most deprived district, located near Karachi. The data for this study is cross-sectional and contributed from the project's 2014 Base Line Survey (BLS, 2014). The Logit model was used to evaluate the variables under study in order to achieve the study goal. According to our empirical findings, the average daily income in this district was less than $1.50. The marginal effects of all five tehsils are comparatively the same for all the variables used in the model. Furthermore, the empirical findings show that socioeconomic factors such as household head education, dependency ratio, and household size have a strong and significant impact on poverty across tehsils, whereas economic factors such as personal expenses and financial aid have a significant impact on poverty reduction in district Lasbela. Furthermore, the study's overall conclusion shows that multidimensional poverty exists in the district, with tehsil Dureji being the most impoverished, followed by Bela, Uthal, Sonmiani, and Hub. Some lucrative measures are suggested in connection to poverty reduction: First, access to free and accessible education in rural areas through a mobile education service. Secondly, the construction of roads and small dams is necessary to be established. Third, the implementation of a public-private partnership (PPP) programme for ventures, for promoting technological, digital, vocational, and modern education training in the first phase, and easy access to soft loans through microfinance banks in the second phase for small/startup businesses.

Aim of the Study: The main aim of this research paper is to analyze the behavior of households' budget expenditures on education and health in the province of Balochistan. There are several reasons for our study to select Balochistan, but the most important reason is that Balochistan is the largest province of Pakistan. Methodology: For this quantitative research, the descriptive statistic, Pearson correlation and Working-Leser model. Findings: Amongst health categories, households spend lion share of their budget on medicine and education categories, they spend more on school/ college/ university fees. The Pearson correlation result suggests that budget allocation to health is strongly negatively correlated with logarithm income while obtaining a positive correlation between budget allocation to education and logarithm income. Correlations between household size and budget allocation to health and education were negative while a positive correlation between budget allocation to health and employee, and between budget allocation to education and employee. The econometric result suggests that income, household size and employment status are the key drivers for determining household budget allocation to education and health. Conclusion: The rise in employment and controlling policy of household size could be a valid policy options while the income elasticity is suggesting that rise in income could also be a valid policy option in case of Balochistan for rising budget allocation to health and education.