Over the years, the household sector has become an important energy consumer and the main source of greenhouse gas (GHG) emissions. The rural household sector has significant potential for emission reduction due to its heavy reliance on traditional fuels and technologies. A great number of academic studies have been undertaken to analyze patterns of household energy and their determinants around the globe, particularly in developing countries. However, little is known about the association between household dynamics and patterns of energy (biomass vs. non-renewable) use. This study aims to analyze the relationship between different household dynamics, such as household size, income, climate, availability of resources, markets, awareness, consumption of energy, and carbon emissions. The study uses the STIRPAT model to investigate the impact of income, household size, housing dimensions, clean energy, and market accessibility on energy consumption. The findings of the study reveal that biomass energy accounts for the majority of household energy consumption and dung has the highest share in total household energy consumption (39.11%) The consumption of biomass increased with the size of the household and decreased with the level of income. A 1 kgoe increase in biomass consumption resulted in a 15.355 kg increase in CO2 emissions; on the other hand, a 1 kgoe increase in non-renewable-energy consumption resulted in just a 0.8675 kg increase in CO2 emissions. The coefficients of housing unit size, distance from the LPG market, and livestock were the primary determinants for choosing any fuel. Having knowledge of modern cookstoves, clean energy, and the environmental impact of fuels reduced the consumption of both energy sources. Furthermore, it was found that households with a greater reliance on biomass emitted higher quantities of carbon compared to those with a low reliance on biomass. Based on the results of the study, it can be stated that a reduction in the use of biomass and non-renewable energy is possible with adequate interventions and knowledge.

Environmentalists are more concerned with the environment in this age of industrialization, and they are continually interested in researching factors that can facilitate the transition towards sustainability. This study applies an econometric technique called the panel Generalized Method of Moments generalized moments to analyze green finance and renewable energy’s impact on CO2 emissions from 2010 to 2019. According to the findings, green finance has a significant negative and positive impact on carbon emissions and green economic recovery. In addition, the results showed that logistics operations use energy and fossil fuel, and the findings also showed that the amount of fossil fuel and non-green energy sources creates a significant harmful effect on the environmental sustainability, in addition to having a negative impact on economic growth. Inadequate transportation-related infrastructure and logistics services are other significant contributors to CO2 and overall emissions of greenhouse gases. According to the findings, sustainable energy development can be advanced by fostering the growth of green finance. This can be accomplished by employing a variety of metrics that pertain to the three dimensions of economic development, financial development, and environmental development.

The essential purpose of this research is to investigate the relationship between foreign assistance and economic growth. It also analyzes the role corruption might play in influencing this relationship in aid-recipient countries that have similar real gross domestic product (GDP) per capita, but different development patterns. An analysis is performed on specific samples from 2000 to 2019. The model is split into three sections for this purpose: i.e., all developing economies, sub-Saharan Africa (SSA), and the most corrupt countries from regions other than SSA. The difference generalized method of moments (GMM) panel framework is used for empirical analysis. The study concludes that foreign aid does not result in encouraging and significant changes in overall economic growth in developing economies. By contrast, corruption has a powerful impact on foreign aid effectiveness. It is also observed from the analysis that SSA economies receive high levels of foreign assistance, but still cannot extract maximum benefit due to various economic and social problems. Furthermore, foreign assistance effectiveness is almost insignificant in most corrupt economies from other regions.

This paper tries to evaluate the economic importance of foreign inflows in determining the real effective exchange rate. Monetary policy plays a substantial role in determining the stability of prices, trade and foreign inflows like foreign direct investment, personal remittances, and foreign aid. In this study, the causal relationship is analyzed among policy variables and control variables. The study used secondary time series data from 1960-to 2020. Augmented Dickey Fuller (ADF) and Philips Perron (PP) unit root tests are used to check the stationary of the variables. Results showed that all variables are stationary at the level I (0) and the first difference I (1). The auto regressive distributive lag (ARDL) approach and Granger Causality approach is discussed to find cointegration and causality respectively. ARDL Bound test reveals the cointegration existence among the variables. ARDL results suggest that foreign inflows (Foreign Direct Investment, Foreign Aid, Personal Remittances), Trade and Inflation showed a significant relationship with Real Effective Exchange Rate in the long run. Granger Causality suggested the existence of Causality among Foreign Aid and Real Effective Exchange Rate, Foreign Direct Investment and Foreign Aid, Trade and Remittances. The results of the study are found in contradiction with the law of one price.

Over the past decades, emerging stock markets have started to significantly contribute to economic growth through mobilizing long-term capital by pooling funds, facilitating savings and investments into profitable projects and improving corporate governance structure. A plethora of empirical studies is devoted to investigate the determinants of different capital markets but due to highly controversial and inconclusive findings about macroeconomic determinants, this study contributes to the body of existing literature by empirically investigating the macroeconomic forces that drive the stock market development of Pakistan from 1980 to 2019. By applying Ng-Perron and Zivot-Andrews unit root tests (to determine the integrating orders of variables) and Autoregressive Distributed Lag (ARDL) bounds testing approach, our results confirm cointegration among variables and exhibit the significant positive impact of economic growth and banking sector development on stock market development and negative affect of inflation, foreign direct investment and trade openness on it in long run. At the same time, the short run results show a significant relationship of economic growth, inflation and foreign direct investment with stock market development. Our study has some important policy implications.