Renewable energy electricity, environmental taxes, and sustainable development: empirical evidence from E7 economies.

Since globalization has increased both production and population, it has also increased environmental damage. This is why the development of renewable energy sources is crucial to the survival of humanity and the planet itself. Business patterns across the various nations, however, have changed significantly over time. This study examines how environmental taxes and renewable energy electricity affect renewable energy consumption in emerging seven economies by using panel dataset over the period of 1990 to 2020. Control variables include economic growth, carbon emissions, and environmental innovation. The results confirmed the presence of the long-run co-integration association, the existence of slope coefficient heterogeneity, and the dependency of cross sections using several panel data methods. Since the data was not normally distributed, a new technique known as method of moments quantile regression (MMQR) was applied in this study. The projected results contend that the major factors of renewable energy consumption are renewable energy output, environmental taxation, economic growth, and carbon emissions. However, eco-friendly innovations drastically cut back on the need for renewable energy. Bootstrap quantile regression verifies the results' reliability, and the panel Granger causality test corroborates that the listed factors have a bidirectional causal relationship with renewable energy usage. Furthermore, this research recommends boosting spending on renewable electricity, the environmental tax sector, and ecological innovation in order to expand the use of renewable energy.

Estimation of poverty bounds for Pakistan using synthetic panel data.

Poverty is a big threat to prosperity in developing countries like Pakistan. Alleviating poverty needs concerted efforts including how to measure and analyze poverty. Therefore, this paper employs synthetic panel technique and uses repeated cross-sections household survey dataset (Household Integrated and Economic Survey (HIES)) of Pakistan for 2010-11 and 2015-16, to derive poverty bounds for Pakistan. The findings of the paper suggest that 17% of population still remains in poverty in 2015-16 as they were in 2010-11. They don't move in or out of poverty. In the same periods 19% population affected by poverty. The 2.5% poor's of 2010-11 moves out of poverty in 2015-16. This constitutes the first attempt to provide an insight into poverty dynamics in Pakistan using the available survey data.

Influencing factors of consumers' buying intention of solar energy: a structural equation modeling approach.

Due to China's massive usage of fossil fuels, climate change concerns have become serious challenges to the country's sustainable development. Despite the fact that China has effectively employed solar technology to address these problems, there is a paucity of research examining consumers' intention to adopt solar energy in the rural region of China. This study intends to fill this gap in the literature by studying consumers' buying intentions for solar energy in rural China for household purposes. Additionally, the study added to the theory of planned behavior by adding three new variables, namely, environmental knowledge, environmental concern, and beliefs about the benefits of solar energy. Primary data were collected from 847 respondents in Hebei Province using a comprehensive questionnaire survey. Structural equation modeling was employed to examine the data. Empirical results revealed that attitude, environmental knowledge, subjective norm, perceived behavioral control, and beliefs about the benefits of solar energy positively influence buying intention of solar energy. On the contrary, environmental concern had no significant effect on buying intention for solar energy. Study outcomes emphasize the critical significance of changing societal norms, boosting consumer awareness, redesigning regulatory mechanisms, and stressing the benefits provided by solar power through coherent and persistent efforts while simultaneously enhancing environmental sustainability practices.

Comparison of Different Nutritional Screening Approaches and the Determinants of Malnutrition in Under-Five Children in a Marginalized District of Punjab Province, Pakistan.

OBJECTIVES: This research measures the occurrence of malnutrition amongst under-five children in the Rahimyar Khan district of Southern Punjab in Pakistan. Employing different anthropometric measurement approaches such as (1) conventional indices (HAZ, WAZ, and WHZ), (2) CIAF, (3) BMI-for-age, and (4) MUAC, we compare their estimated results and examine the relationship between socioeconomic determinants and different anthropometric indicators. METHODS: The study employs a proportional purposive random sampling method to collect data from 384 rural households in the community-based study using a self-administered survey and following the Lady Health Workers (LHWs) registered records. The nutritional status of 517 under-five children is measured with references to WHO (2009) child growth standards. Furthermore, the investigation used the model of binary logistic regression to measure the impact of socioeconomic factors on child malnutrition. RESULTS: Compared with other approaches, the CIAF identifies more malnourished children (63%). The results of binary logistic regression illustrate that all the explanatory variables indicate a more significant empirical association with CIAF than conventional indices, BMI-for-age, and MUAC. CONCLUSION: CIAF is a more reliable tool for assessing child nutrition because it not only demonstrates more accurate estimates of malnutrition but also recognizes children with multiple anthropometric failures.

Distance to Healthcare Facility and Lady Health Workers' Visits Reduce Malnutrition in under Five Children: A Case Study of a Disadvantaged Rural District in Pakistan.

This study accesses the impact of lady health worker (LHWs) visits in the community and distance to a healthcare facility on the nutritional status of under-five children. Additionally, it explores the perceptions and attitudes of the community about the performance of LHWs. A self-administered instrument was applied to gather data on different parameters, such as children's height, age, weight, and socioeconomic status from 384 rural households in a marginalized district of Punjab province with the help of a purposive random sampling technique. The binary logistic regression model was employed for the computation of the probability of malnutrition. The prevalences of stunting, underweight children, and wasting in the district were 34.8%, 46.1%, and 15.5%, respectively. The logistic results illustrate that those households in which LHW visits occur regularly within 15 days (OR = 0.28 with 95% CI: 0.09-0.82) have a lower probability of malnutrition prevalence among their children. The distance to the health facility shows that the odds of malnutrition were higher from 3-4 Kilometers (Km) (OR = 2.61, 95% CI: 0.85-8.14), and odds were also higher for the ≥5 km category (OR = 2.88, 95% CI: 0.94-8.82). Children from richer families had lower chances of being malnourished (OR = 0.28, 95% CI: 0.07-1.14). Furthermore, the respondents show a positive attitude towards LHWs. They have given the first rank to their performance being beneficial to mothers and childcare, especially on checkups and safe deliveries, while they have shown negative responses and given lower ranks to their performance due to irregular visits (6th rank) and poor community awareness (7th rank). We conclude that LHWs' regular visits to targeted households and less distance to healthcare facilities reduce the malnutrition risk in under-five children.

Modeling COVID-19 Impact on Consumption and Mobility in Europe: A Legacy Toward Sustainable Business Performance.

This article has explored the impact of coronavirus disease 2019 (COVID-19)-induced decline in consumer durables and mobility on nitrogen dioxide (NO2) emission in Europe by providing empirical and graphical justifications based on consumer price index (CPI) and gross domestic product (GDP) deflator indexes. The empirical estimations show that carbon dioxide (CO2) and NOx emission along with other greenhouse gases drastically decreased in the wake of COVID-19-induced lockdowns and decrease in the demand of consumer goods in Europe. This means that COVID-19 improved environment in the European region. However, high cost (e.g., unemployment, loss of life, and social segregation) makes COVID-19 an unstable solution to environmental woes where positive impact of COVID-19 on environment achieved in short run cannot be guaranteed in the long run. Besides environment, COVID-19 drastically curtailed economic activities and exposed them to the risk of economic crisis particularly in case of Europe.

Research Methods in a Multinational Business Environment and Implications for Capital Formation: Application of Cross-Sectional Autoregressive Distributed Lag Methods.

We explore whether foreign direct investment outflows augment or obstruct public or private capital in developing countries by decomposing domestic capital into private and public capital. While developed countries are the primary source of foreign direct investment outflows (FDIOs), developing economies have become the primary source of FDIO over the past 30 years. We apply cross-sectional autoregressive distributed lag (CS-ARDL) methods to overcome the issue of endogeneity and cross-sectional dependency in our dataset. This study analyzes the interaction effects of foreign direct investment and institutional quality (IQ) in promoting aggregate domestic capital formation in developing countries. Our empirical results show that FDI outflows augment private capital formation and additionally, IQ also upsurges private capital formation. Conversely, as per results, FDI outflows obstruct public capital formation, and IQ crowds out public capital formation significantly while private capital crowds out FDI inflows. As per result estimations, we notice that FDIO crowds in private capital formation, thus we conclude that the private sector controls the majority of the sectors for developing countries and the role of the public sector is quite minimal. We conclude that private and public capital possess different attributes; thus clubbing them together might result in aggregation bias. Our result estimations provide several useful policy implications.

"Does Institutional Quality, Natural Resources, Globalization, and Renewable Energy Contribute to Environmental Pollution in China? Role of Financialization".

Our study explores the impact of financialization on carbon emissions by utilizing diverse financialization proxies, particularly for China. We examine the impact of financialization, institutional quality, globalization, natural resources, trade openness, and renewable and nonrenewable energy consumption on environmental pollution over the period 1996-2017 by utilizing dynamic autoregressive distributed lag (ARDL) simulations. The empirical findings of the study indicate that institutional quality, trade, globalization, natural resources, and renewable energy consumption significantly decrease environmental pollution in the long run, while foreign direct investment and financialization have neutral effects on carbon emissions. Our findings demonstrate that a 1% increase in institutional quality, trade, IFDI, renewable energy, and globalization leads to a decrease in CO2 emissions by 0.198, 0.016, 0.075, 0.010, and 0.072%, respectively. Even though financialization indexes contributed insignificantly to environmental degradation, other explanatory variables significantly affected carbon emissions through indirect effects of financialization. Financialization indexes behave in a similar context, and these proxy indicators are good parameters to understand the complex nature of financialization. Moreover, in order to achieve low carbon emissions and sustainable development, countries need viable financial institutions that focus on green growth by promoting clean production process strategies to ensure the reduction of CO2 emissions.

Prevalence of child malnutrition and household socioeconomic deprivation: A case study of marginalized district in Punjab, Pakistan.

Better socioeconomic status and well-being in households decrease malnutrition and health risks in children. The objective of the present study is to assess the current nutritional status of pre-school children and to correlate the prevalence of malnutrition with Household Deprivation Status (HDS) in one of the deprived districts of the Punjab province in Pakistan. Using primary data collected from 384 households through a proportional purposive random sampling technique, this study calculates the z-scores of weight-for-age (WAZ), weight-for-height (WHZ), and height-for-age (HAZ). The study has used a cut-off point which is -2 standard deviations below the median of the WHO/NCHS reference population for each anthropometric indicator. The results indicate that the underweight, stunting, and wasting prevalence rates are 46.1%, 34.83%, and 15.49% respectively in district Rahimyar Khan. Also, the expected tendency of malnutrition is worst for HDS-1 and HDS-2 which are the most deprived segments of the population. As the household shifts from HDS-1 to HDS-2 and further to HDS-3, the rates of stunting (HAZ) and underweight (WAZ) decreases but wasting (WHZ) does not. The study concludes that the high prevalence of malnutrition in the district is correlated with overall socio-economic deprivation.