The objective of this study is to explore the link between the financial sector, macroeconomic fluctuations, and sustainable energy production in South Asia. It evaluates existing research, identifies gaps, and suggests future directions. The study emphasizes the need to understand how economic activities influence the environment and how environmental factors impact economic and financial outcomes. It also examines financial market responses to understand the manifestation of economic and environmental factors, using data from Bangladesh, India, Pakistan, and Sri Lanka spanning 1987 to 2022. The results show that investment in green energy is essential for developing countries. The study suggests that the relationship between the region's stock market, GDP, carbon emissions, and green energy is complex, with a negative response to domestic credit indicating potential obstacles to investment, such as policy barriers or insufficient incentives. Mixed responses highlight the need for credit directed towards sustainable projects. Variations in responses across economic indicators may indicate policy challenges or structural issues, necessitating a reassessment of government policies to incentivize sustainable investments and promote green technology adoption. These results imply that policymakers should precisely assess the environmental ramifications of economic growth and stock market activities. This presents an opportunity to craft policies that guide investments toward cleaner technologies and industries, aligning economic growth with sustainable practices. Policymakers and financial institutions should explore ways to incentivize green energy investments and promote sustainable economic growth.

Electricity price volatility in developing economies, particularly Nigeria, presents a significant challenge to sustainable development. In this paper, we examine the macroeconomic drivers of electricity prices (ELP) in Nigeria based on annual data spanning 1980-2022 and the autoregressive distributed lag (ARDL) procedure. Our model estimates the long-run and short-run impacts of population growth (PG), economic growth (GDP), crude oil price (COPR), and electricity consumption (ELC) on electricity prices (ELP). The empirical findings reveal a positive and long-run effect of PG on ELP, indicating that rising demand from a growing population increases electricity prices. In the short-run, ELC surprisingly co-moved with ELP, which may be attributable to price-sensitive demand within specific consumer segments or periods. Furthermore, GDP and COPR exert positive effects on ELP, indicating that economic growth drives energy consumption and prices, and the cost-driven impact of fossil fuel dependence in electricity generation. These findings shed light on the complex interplay of demographic, economic, and energy market-related forces driving electricity prices in Nigeria. Therefore, the paper proposes some policy suggestions based on the empirical findings.

This study employs empirical analysis using an econometric model that examines the interdependence among environmental degradation, exports, and economic development with energy use. It also provides an environmental Kuznets curve (EKC) for selected South Asian economies utilizing time-series data. The findings reveal a long-term, stable equilibrium link between energy demand and pollution. There exists a positive relationship between structural factors and pollution. Moreover, this study constructs a model of exports and pollution from an interdependent perspective. The three perspectives are tested: the scale and structure of energy consumption considering the twin constraints of export-trade and pollution, and the scale of pollution in export-trade constraint. These results show that the increase in energy use leads to higher CO2 emissions amidst export volume. However, in the presence of income, the scale of effect lowers a little. The analysis also supports the presence of Kuznets curve for south-Asian economies. The results imply substantial scope for development in the energy use and pollution structure within South Asia's current export trade process. This development can be attained by regulating energy use and enhancing system efficacy without necessitating changes to the scale effect or structural effect.

The global agricultural supply chain grapples with inherent challenges, including opacity, inefficiency, and ethical concerns, necessitating innovative solutions, particularly within Indian Agriculture Supply Chain Management. Despite these challenges, a notable research gap emerges due to the limited exploration of adoption factors specific to the Indian agricultural context. To bridge this gap, a comprehensive investigation is initiated to delve into the nuanced dynamics of blockchain technology adoption in the Indian agricultural supply chain. The primary aim of this study is to provide insights into these adoption dynamics, employing a three-stage methodology. Starting with an extensive literature review to identify key adoption factors, the study then distributed a well-crafted questionnaire to 200 respondents, yielding 150 complete and analyzable responses. The chosen methodologies, including a one-sample t-test and exploratory factor analysis, allow for a quantitative assessment of the significance of various factors. The study's findings reveal crucial dimensions influencing blockchain adoption, such as perceived benefits, ease of use, trust, efficiency, ethical considerations, data security, integrity, regulatory compliance, and collaboration and stakeholder engagement, providing a comprehensive understanding of the adoption landscape. In conclusion, this research significantly contributes to comprehending the nuanced adoption dynamics of blockchain technology in the Indian agricultural supply chain, supported by a substantial sample size and robust methodological choices. The implications extend to stakeholders navigating the evolving agricultural supply chain, offering valuable insights for future research, policy considerations, and practical strategies, with a central focus on fostering transparency, efficiency, and ethical standards to address global agricultural supply chain challenges.

The present paper empirically examines the validity of the environmental Kuznets curve (EKC) hypothesis, which posits an inverted U-shaped relationship between CO2 emissions and economic growth, in Egypt, India, Mexico, Pakistan, Thailand, and Turkey for the period 1970-2020. The EKC validity is highly debated due to varying results across countries. This study is significant to addresses this controversy by estimating the EKC's robustness while considering the influence of financial development, globalization, and government expenditure. We employ two cointegration techniques, the autoregressive distributed lag (ARDL) and vector error correction model (VECM), also considering potential structural breaks in the data. Out key findings indicate that the EKC hypothesis is supported in India, Pakistan, and Turkey, with carbon emissions eventually declining after reaching a peak. The hypothesis is not supported in Egypt and Thailand, where emissions rise monotonically with economic growth. In Mexico, the results are mixed, with ARDL suggesting a U-shaped relationship and VECM supporting the EKC. From our empirical analysis of the EKC hypothesis, we derive the following policy implications: 1) countries should adopt comprehensive policy strategies that integrate these goals and fully consider the long-term environmental and societal impacts of their decisions; 2) countries should invest heavily in research and development to accelerate the creation of new technologies that can effectively reduce carbon emissions; and 3) countries must actively raise awareness about the environmental repercussions of economic growth.

This study delves into the complex relationship between China's international trade and economic growth, specifically focusing on emission intensity and its environmental ramifications within the context of energy efficiency. The primary objective is to provide a comprehensive understanding of how China's international trade dynamics influence economic growth, emphasizing emission intensity and employing a model that considers the nonlinear impact of trade on output. Through this model, structural breakpoints are identified, revealing distinct outcomes for China amidst positive and negative shocks from 1972 to 2021. The study uncovers the dual nature of the energy sector, acting both as a source of environmental degradation and a potential avenue for positive change through increased efficiency. Complex interplays among economic activities, inflation, and investment on carbon intensity are revealed. Additionally, the varying effects of export volume on carbon emissions intensity across lag thresholds underscore the significance of global trade dynamics for environmental sustainability. The study concludes by emphasizing the importance of balancing economic prosperity with environmental responsibility, advocating for carefully balanced policies that foster sustainable growth. Recommendations include considering the dual nature of the energy sector and implementing policies promoting increased efficiency. Policymakers are urged to adopt growth models prioritizing environmental responsibility, balancing economic activities, inflation, and investment to mitigate carbon intensity while acknowledging the diverse effects of global trade dynamics on environmental sustainability.

The presence of electrical harmonics in low voltage networks causes many problems for customers, the most important of which is the decrease in the power quality beyond the permissible limits, which leads to a decrease in voltage for long periods, a decrease in power factor and a large losses in the electrical network due to the deformation of the electric waveform of voltage and current. The aim of this paper is to design an optimal passive filter and its optimal location to suppress the harmonic distortion in the distribution system that results from nonlinear loads. This filter will use single objective functions and multi objective functions that will minimize the proposed filter cost, the real power losses, total harmonic distortion "THD" and individual harmonic distortion "IHD". This will be done under the equality constraint of power balance and some inequality constraints such as filter parameters limits, quality factor limits, voltage limits, harmonic distortion limits. The optimal passive filter parameters and location will be calculated using the modern optimization techniques such jellyfish optimization technique. IEEE 33 bus radial system will be used for demonstrating results obtained by the proposed method.

Environmental degradation is becoming a fundamental issue as it is directly associated with human lives and environmental sustainability. This research particularly highlights the significance of green transportation taxes in achieving environmental sustainability due to limited available literature considering the environmental sustainability and green transportation taxes nexus. By employing the newly developed QARDL approach, this study is unfolding the linkages of green transportation taxes on transport-based CO2 emissions for Chinese economy spanning 1992 to 2020. The results infer that green transportation taxes to enhance environmental sustainability in the long-run for the highest quantiles, i.e., 0.70 to 0.95. Though, green transportation taxes enhance environmental sustainability in all quantiles in the short-run. Based on these results, the study suggests that the Chinese government and policymakers should increase green transportation taxes that help in combating CO2 emissions, which ultimately enhances environmental sustainability.

This study delves into the intricate interconnections among the financial sector, macroeconomic dynamics, and the advancement of sustainable energy production in South Asia. Utilizing data from 1987 to 2022 in Bangladesh, India, Pakistan, and Sri Lanka, the research employs the Structural Vector Autoregressive (SVAR) method to analyze financial market responses. The findings reveal nuanced insights into the complex relationship between economic and environmental factors, fostering a comprehensive understanding of the region's intricate nexus between financial markets and sustainable energy initiatives. In the Economic Growth model, the negative coefficient for CO2 emissions underscores environmental concerns, indicating an inverse relationship with economic growth. Conversely, positive coefficients for non-fossil energy production, Gross Fixed Capital Formation, stock exchange value, and domestic credit emphasize their positive impact on economic growth. A positive GDP coefficient in the CO2 emission model signifies heightened carbon emissions with economic growth, while a negative coefficient for non-fossil energy production suggests potential environmental benefits. The nearly unitary positive coefficient for Gross Fixed Capital Formation highlights its significant role in driving carbon emissions. The Non-Fossil Energy Production model reveals an inverse relationship between GDP and clean energy, suggesting a potential reliance on fossil fuels during economic growth. Positive coefficients for CO2 emissions, Gross Fixed Capital Formation, stock exchange value, and domestic credit highlight factors influencing non-fossil energy production. Long-run stable coefficients affirm variable significance, while a negative error correction term suggests convergence in developing countries. These models offer nuanced insights into the intricate relationships among economic, environmental, and energy variables, guiding sustainable development policies in South Asia.

The primary goal of this research is to examine how different types of energy are used in South Asian countries and how they affect the environment and economic growth. From 1990 through 2021, this research employed Panel Data from multiple sources covering the South Asian countries of Pakistan, India, Bangladesh, Sri Lanka, Nepal, Bhutan, Maldives, and Afghanistan. The results are determined by using the Fixed Effect Regression (FEM) technique in both models. Several significant findings are supported by the data. First, while using renewable energy flattens carbon dioxide effusion, using non-renewable energy sources increases it. CO2 emissions are affected negatively by gross domestic product but positively by gross domestic product square. These results provide credence to the idea that South Asian countries do experience an Environment Kuznets Curve. Furthermore, foreign direct investment has a beneficial effect on CO2 emissions, lending credence to the Pollution Haven Hypothesis. Model 2 instead finds that both renewable and non-renewable energy consumption contributes to economic expansion. Gross Fixed Capital Formation and foreign direct investment also contribute to economic growth in this area. Finally, Inflation retards economic expansion.

Gravity Energy Storage (GES) systems are recently being considered as a viable solution for storing intermittent renewable energy power, specifically in high curtailment zones. While a few studies have analyzed the material costs of GES systems, there is a paucity of literature on analyzing the socioeconomic costs of GES systems. This study analyzes the location-dependent costs of GES plants using a multi-factor spatial parameterization model for evaluating the existence of a point of minimum cost in a suburban mountainous geography. A case study of 500x500 points in a 50x50km2 area in the suburban area of Fukuoka city in Japan is performed. It is found that the cost of material transportation and transmission is more dominant in determining the position of an optimal cost location than factors of excavation and land costs. The position of the minima is also related to the principal urban area in that the line connecting the Center Business District (CBD) and suburban flat areas (line 1) is where the potential minima lie. The intersection point of an orthogonal to the line connecting the CBD with a substation nearest to the flat area with line 1, is the potential zone of minima location. The findings of this study are critical for urban energy planners and reveals how socioeconomic cost factors can aid in geolocation a suitable GES installation site.

The rapid growth of renewable solar and wind energy necessitates extensive, long-term energy storage solutions to stabilize the electric grid and address the intermittent nature of these sources. In the absence of cost-effective long-duration, large-scale (LDLS) energy storage, there is a risk of increased disruptive blackouts and brownouts as intermittent energy sources approach 50% of electricity generation. Our analysis underscores volumetric energy density as a pivotal determinant in LDLS energy storage solutions, which is particularly crucial during periods of diminished renewable resource availability, such as the rainy season. Despite emphasis on conventional electric batteries and mechanical storage technologies such as compressed air and pumped hydro for LDLS, their costs pose a significant obstacle to widespread adoption, especially in emerging economies. In this study, we present a groundbreaking approach to LDLS: on-demand electricity generation from carbon-neutral renewable oil, known as "green oil," leveraging Reliance’s innovative catalytic hydrothermal liquefaction (R-CAT-HTL) technology. Our investigation evaluated two distinct sources of green oil production: organic waste streams (e.g., municipal solid waste, agricultural-waste, etc.) and on purpose production of algal biomass. We demonstrate that burning renewable green oil to produce electricity offers a cost-effective solution for large-scale (GWh) long-duration (many days) energy storage, ensuring the stability, reliability, and resiliency of the electric grid. We also demonstrate that minimum 14 days, ideally 30 days energy storage is needed for continuous uninterrupted service, especially critical for manufacturing facilities. Through a meticulous levelized cost of storage (LCOS) analysis comparing green oil with other LDLS technologies reported in literature, we demonstrate the cost effectiveness of green oil. In this model, green oil serves as stored energy, functions akin to a battery and is efficiently converted into electricity through conventional reciprocating engines or next-generation more efficient turbines. Our findings suggest that green oil derived from diverse organic sources is the most economically viable option for GWh-scale storage, particularly when storage requirements extend beyond a single day. This research thus presents a transformative cost-effective solution to the pressing challenge of LDLS energy storage, accelerating a sustainable and resilient energy future.

In developing countries, the dependence on traditional biomass for domestic energy consumption is one of the major causes of deforestation and environmental poverty. This paper investigates the impact of women’s education on the probability of using Unsustainable Biomass Cooking Technologies (UBCT) as the household main fuel of cooking instead of clean energy. Combining data from four rounds of Nigeria DHS conducted between 2003 and 2018, we take advantage of a natural experiment, the implementation of Uni-versal Primary Education policies in 1970, to obtain an exogenous variation in women’s education levels. Using an instrumental variable approach to control for the endogeneity issues, we find that women with higher levels of education are less likely to use UBCT as their primary source of cooking. This result implies that environmental policies in developing countries should rely not only on income transfers policies as suggested in the literature but also on increasing women's human capital to reduce women’s dependence on unsustainable biomass cooking practices.

Environmental concerns are increasing and business firms are inclined towards adoption of eco-friendly products and services. They are hiring employees via set of green HRM practices and struggling to become an environmentally sustainable firm. Pakistani food industry is booming day by day. However, less is known about the green HRM practices executed in the fast food industry of Pakistan. Therefore, this research is interested in examining the impact of Green recruitment and selection, green training and development and green performance and compensation management on the P-O fit, affective commitment and green recovery performance in Pakistan’s fast food industry. The moderating effects of transformational leadership will be examined between Green HRM practices and green recovery performance. A sample of 408 employees who are working at different designations were selected to accomplish the purpose of the research. These managers will be selected through convenient sampling technique. There were 21 fast food restaurants (Burger Joints) selected from all over Karachi, few of them includes; KFC, Burger Lab, Burger O Clock, Vintage, Plan B and more. The collected primary data will be tested through the application of structural equation modeling technique. Smart PLS software is used to assess the objectives. The results of the study revealed significant association between Green R&S, Green T&D and P-O fit. A non-significant association is found between Green P& C and P-O Fit. The study affirmed that Affective commitment posit a direct influence on GRP. The Green R&S and Green P&C revealed insignificant impacts on the green recovery performance within the fast food industry. The indirect association was analyzed between P&C, P-O fit, AF and GRP and revealed an insignificant result. Likewise, an indirect association is found between R/S, P-O Fit, AF and GRP. The future researchers are recommended to analysis the remaining elements of GHRM and investigate their impacts on the fast food industry of Pakistan. There is a need to create awareness regarding green HRM practices and their influence on the Green recovery performance in Pakistan. Policy makers are recommended to develop such policies that promotes GHRM in Pakistan.

The purpose of the study is to determine the relationships between the factors, attitude towards food consumption and green purchase intention in organic food markets of Pakistan. A Total of 301 sample data was analyzed by SPSS and PLS-SEM. These statistical techniques were used to test the hypotheses. The study was supported the theories include theory of Reasoned Action, Planned Behavior, and expectancy-value model of attitude theory. It also enhances the intention of purchase to buy organic food. The study found that environment, consciousness of health, and safeties of food have significantly direct and indirect influence on Intention of purchase while, taste of food, and exposure of media only have significantly indirect effect on Intention of purchase. The study recommended that retailers should advertise organic food via social media or channels as media persuasively increase the chances to grab the consumer attention to buy the product.

Technological advancements in the last few decades have created energy and cost-efficient power plants, but it is not a prominent feature in the electricity supply-chain. While many long-term and short-term energy alternatives are available, Pakistan still has a significant number of people who do not have access to electricity; as there are many areas that are neither connected to the national grid nor the provincial grids. This paper aims to find the reasons behind the energy crisis in Pakistan, keeping Islamabad as the focus of study, and to find the effect of use of renewable resources on the reduction of shortage of electricity. We will be focusing on the facts that how energy crisis has been affecting the economic growth of the country.  Our data collection will be based on primary sources and secondary sources along with which we will be conducting pilot study to check the feasibility of using the solar energy as an alternative form of electricity.

At present, global warming poses the greatest threat to the planet's ecosystem. China is the greatest CO2 emitter, expected to account for roughly 33% of overall emissions in 2021, and this has caused authorities to express significant concern in the most latest Paris accord, when they decided to cut global emissions to a particular level. In comparison, 55% of China's power in 2021 appears from coal. As a result, investigating the ecological elements that affect China's CO2 output is essential. This analysis utilizes the autoregressive distributed lag (ARDL) technique to investigate the relationships concerning fossil fuels, renewables, and economic growth from 1990 to 2021. According to the data, using fossil fuels to generate energy and increase GDP significantly increases CO2 emissions. Yet, the use of renewable energy reduces CO2 emissions both in the long run and in the short term. Overall, the outcomes imply that using renewable energy sources is key to reaching sustainable environmental goals and deters the usage of fossil fuels in the energy mix. This study suggests that China take into account empirical findings and undertake long-term initiatives to reduce carbon emissions in favor of sustainable environmental outcomes.

Renewable energy has become a crucial topic in recent years as the world is moving towards environment friendly and sustainable energy sources. The aim of this research paper is to analyze the effectiveness and efficiency of renewable energy resources such as geothermal, hydro, solar and wind energy in comparison to typical and traditional energy resources such as oil and coal. This paper focuses on the current state of renewable energy adoption, the benefits and drawbacks of all types of renewable energy resources, and the challenges faced in implementing renewable energy on a large scale. Through a comprehensive literature review, this paper discusses the methodologies and results of various studies conducted to understand the potential of renewable energy sources by secondary means. This research paper also presents an original analysis of renewable energy data to identify the most efficient and cost-effective renewable energy source. The results of this study indicate that renewable energy sources can be an effective and efficient replacement for traditional energy sources, but they face significant challenges in adoption and implementation. The study concludes with recommendations for future research and policy changes that can support the growth of renewable energy sources.