Pub Date : 2024-05-13DOI: 10.56556/jescae.v3i2.839
Mohsin Rasheed
This study investigates the relationships between economic, environmental, and trade factors within the G7 economies from 1990 to 2022, focusing on their impacts on carbon dioxide (CO2) emissions. Analyzing data from G7 economies such as Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. The study employs multiple regression (MLR) models to examine the influence of economic and environmental factors on CO2 emissions. Additionally, factor loading analysis and structural equation modeling (SEM) is utilized to validate construct reliability and visualize complex relationships. The findings highlight positive correlations between GDP growth and employment, alongside negative correlations with income inequality. In addition, environmental challenges are evident through negative correlations with industrial and energy-related CO2 emissions. The practical implications highlight the importance for policymakers to prioritize strategies promoting economic growth, addressing income inequality, and fostering sustainable trade relationships within the G7 economies to ensure inclusive and sustainable development. This study contributes to the literature by offering comprehensive insights into the intricate dynamics between economic, environmental, and trade factors and their impacts on CO2 emissions.
{"title":"Renewable energy adoption and CO2 emissions in G7 economies: In-depth analysis of economic prosperity and trade relations","authors":"Mohsin Rasheed","doi":"10.56556/jescae.v3i2.839","DOIUrl":"https://doi.org/10.56556/jescae.v3i2.839","url":null,"abstract":"This study investigates the relationships between economic, environmental, and trade factors within the G7 economies from 1990 to 2022, focusing on their impacts on carbon dioxide (CO2) emissions. Analyzing data from G7 economies such as Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. The study employs multiple regression (MLR) models to examine the influence of economic and environmental factors on CO2 emissions. Additionally, factor loading analysis and structural equation modeling (SEM) is utilized to validate construct reliability and visualize complex relationships. The findings highlight positive correlations between GDP growth and employment, alongside negative correlations with income inequality. In addition, environmental challenges are evident through negative correlations with industrial and energy-related CO2 emissions. The practical implications highlight the importance for policymakers to prioritize strategies promoting economic growth, addressing income inequality, and fostering sustainable trade relationships within the G7 economies to ensure inclusive and sustainable development. This study contributes to the literature by offering comprehensive insights into the intricate dynamics between economic, environmental, and trade factors and their impacts on CO2 emissions.","PeriodicalId":508135,"journal":{"name":"Journal of Environmental Science and Economics","volume":"55 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140983685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-10DOI: 10.56556/jescae.v3i2.889
Rony Kumar Datta
Pursuing green growth is crucial for Bangladesh to transition from an unsustainable economic trajectory to a more sustainable and inclusive one. Bangladesh is picked for this study because of impending and severe economic and environmental threats. This study intends to review the present scenario of various environmental sustainability-related indicators in Bangladesh, focusing on renewable energy consumption, freshwater resources, water productivity, CO2 emissions, energy intensity, air pollution, and natural resource rents. The World Bank database has been utilized to obtain secondary time series data of Bangladesh spanning from 2000 to 2020. As a descriptive study, cross-sectional and observational research methods as well as descriptive statistics and figures are used to elucidate the secondary data. Data demonstrate that Bangladesh now generates 41.16% of its energy from renewables, despite considerable variability. Regardless of the variation in freshwater availability, averaging at 708.19 cubic meters per capita, efficient water productivity remains consistent, indicating a robust water management system. The country demonstrates a relatively low carbon footprint, emitting 0.35 metric tons of CO2 per capita, alongside varying energy intensity levels, highlighting the need for enhanced efficiency measures. While pervasive PM2.5 air pollution poses a significant health risk, Bangladesh's reliance on natural resource rents underscores the importance of sustainable resource management practices for long-term economic stability. The observations of this study might assist in the formulation of policies of water management systems, air pollution control initiatives, and conservation of ecology to promote Bangladesh's long-term sustainability objectives and formulate policies.
{"title":"Bangladesh towards green growth: a review of environmental sustainability indicators","authors":"Rony Kumar Datta","doi":"10.56556/jescae.v3i2.889","DOIUrl":"https://doi.org/10.56556/jescae.v3i2.889","url":null,"abstract":"Pursuing green growth is crucial for Bangladesh to transition from an unsustainable economic trajectory to a more sustainable and inclusive one. Bangladesh is picked for this study because of impending and severe economic and environmental threats. This study intends to review the present scenario of various environmental sustainability-related indicators in Bangladesh, focusing on renewable energy consumption, freshwater resources, water productivity, CO2 emissions, energy intensity, air pollution, and natural resource rents. The World Bank database has been utilized to obtain secondary time series data of Bangladesh spanning from 2000 to 2020. As a descriptive study, cross-sectional and observational research methods as well as descriptive statistics and figures are used to elucidate the secondary data. Data demonstrate that Bangladesh now generates 41.16% of its energy from renewables, despite considerable variability. Regardless of the variation in freshwater availability, averaging at 708.19 cubic meters per capita, efficient water productivity remains consistent, indicating a robust water management system. The country demonstrates a relatively low carbon footprint, emitting 0.35 metric tons of CO2 per capita, alongside varying energy intensity levels, highlighting the need for enhanced efficiency measures. While pervasive PM2.5 air pollution poses a significant health risk, Bangladesh's reliance on natural resource rents underscores the importance of sustainable resource management practices for long-term economic stability. The observations of this study might assist in the formulation of policies of water management systems, air pollution control initiatives, and conservation of ecology to promote Bangladesh's long-term sustainability objectives and formulate policies. ","PeriodicalId":508135,"journal":{"name":"Journal of Environmental Science and Economics","volume":" 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140990935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-12DOI: 10.56556/jescae.v3i1.809
S. Adebanjo, Wasiu Babajide Akintunde
The developed world, which includes the United States of America (US), constantly works to reduce carbon dioxide emissions for the benefit of its people's health while advancing technical innovation to achieve impressive economic development. This motivates this study to use artificial neural network (ANN) and the Environmental Kuznets Curve (EKC) technique to explore the relationship between technological innovation, economic development, and CO2 emissions in the US in order to add to the body of knowledge already in existence. For this study, secondary data from 1990 to 2023 was gathered from the World Bank and globaleconomy.com. The results show that, whereas the artificial neural network shows that economic development contributes more to C02 emissions, the Environmental Kuznets Curve shows that higher levels of technical innovation and economic development lower C02 emissions. Hence, in order to maintain C02 emissions at the lowest possible level and improve the nation's atmospheric conditions, the US government should guarantee sustainable policies that will promote economic development and technological innovation.
{"title":"Exploring the link between technological innovation, economic development, and CO2 emissions in the US. Application of the ANN and EKC techniques","authors":"S. Adebanjo, Wasiu Babajide Akintunde","doi":"10.56556/jescae.v3i1.809","DOIUrl":"https://doi.org/10.56556/jescae.v3i1.809","url":null,"abstract":"The developed world, which includes the United States of America (US), constantly works to reduce carbon dioxide emissions for the benefit of its people's health while advancing technical innovation to achieve impressive economic development. This motivates this study to use artificial neural network (ANN) and the Environmental Kuznets Curve (EKC) technique to explore the relationship between technological innovation, economic development, and CO2 emissions in the US in order to add to the body of knowledge already in existence. For this study, secondary data from 1990 to 2023 was gathered from the World Bank and globaleconomy.com. The results show that, whereas the artificial neural network shows that economic development contributes more to C02 emissions, the Environmental Kuznets Curve shows that higher levels of technical innovation and economic development lower C02 emissions. Hence, in order to maintain C02 emissions at the lowest possible level and improve the nation's atmospheric conditions, the US government should guarantee sustainable policies that will promote economic development and technological innovation.","PeriodicalId":508135,"journal":{"name":"Journal of Environmental Science and Economics","volume":"112 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Numerous studies have examined the potential connection between air pollution, particularly PM2.5, and the incidence of COVID-19 cases during the pandemic. While several studies have demonstrated a strong correlation, caution is advised as correlation does not imply causation. To address this concern, our two-year observational study employs a comprehensive approach that utilizes a large sample size and draws on temporal and spatial data across the United States, surpassing the limitations of previous studies restricted to specific locations. Through rigorous correlation and regression analyses, we control for potential confounding factors. Air pollution data, a crucial component of our study, has been sourced from the United States Environmental Protection Agency (EPA). Additionally, COVID-19 case data is extracted from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, providing a robust and widely recognized dataset for our analyses. Notably, a significant spatial correlation exists between COVID-19 cases and population size (r=0.98, p-value <0.01), as confirmed by multivariate regression analysis, suggesting a confounding influence of population. It is crucial to emphasize that correlation does not automatically imply a direct cause-and-effect relationship. Moreover, to minimize the impact of population, we employ rates (COVID-19 cases/population of States), demonstrating that the rate of COVID-19 cases is independent of PM2.5 and population. Additionally, the rate of COVID-19 infection is not correlated with population density, implying the population's influence on infection is more likely due to probability rather than being a direct cause. In summary, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship. In conclusion, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship.
{"title":"Correlation or Causation: Unraveling the Relationship between PM2.5 Air Pollution and COVID-19 Spread Across the United States","authors":"Mohammad Maniat, Hosein Habibi, Elham Manshoorinia, Parisa Raufi, Payam Marous, Masoud Omraninaini","doi":"10.56556/jescae.v3i1.751","DOIUrl":"https://doi.org/10.56556/jescae.v3i1.751","url":null,"abstract":"Numerous studies have examined the potential connection between air pollution, particularly PM2.5, and the incidence of COVID-19 cases during the pandemic. While several studies have demonstrated a strong correlation, caution is advised as correlation does not imply causation. To address this concern, our two-year observational study employs a comprehensive approach that utilizes a large sample size and draws on temporal and spatial data across the United States, surpassing the limitations of previous studies restricted to specific locations. Through rigorous correlation and regression analyses, we control for potential confounding factors. Air pollution data, a crucial component of our study, has been sourced from the United States Environmental Protection Agency (EPA). Additionally, COVID-19 case data is extracted from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, providing a robust and widely recognized dataset for our analyses. Notably, a significant spatial correlation exists between COVID-19 cases and population size (r=0.98, p-value <0.01), as confirmed by multivariate regression analysis, suggesting a confounding influence of population. It is crucial to emphasize that correlation does not automatically imply a direct cause-and-effect relationship. Moreover, to minimize the impact of population, we employ rates (COVID-19 cases/population of States), demonstrating that the rate of COVID-19 cases is independent of PM2.5 and population. Additionally, the rate of COVID-19 infection is not correlated with population density, implying the population's influence on infection is more likely due to probability rather than being a direct cause. In summary, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship. In conclusion, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship. ","PeriodicalId":508135,"journal":{"name":"Journal of Environmental Science and Economics","volume":"76 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-21DOI: 10.56556/jescae.v3i1.731
Abdul Rasool Khoso, Jintu Gu, Shahnaz Bhutto, Muhammad Javed Sheikh, Kainat Vighio, Arshad Ali Narejo
Pakistan, which is located in Southeast Asia, is one of the nations that is most susceptible to the effects of climate change, as seen by the increased frequency of floods and droughts. Variations in climate have a negative impact on a number of areas, such as the agricultural industry, groundwater levels, dietary resources, soil quality and organic matter content, public health, and poverty rates. This study's main goal is to evaluate the impact of climate change and the adaptations farms have made in response to variations in precipitation and temperature. Pakistani farmers have responded to climate change by implementing a variety of adaptive techniques. These tactics include changing the way that fertilizer is used, changing crop varieties, using pesticides, improving seed quality, diversifying the farm, planting shade trees, changing irrigation techniques, engaging in off-farm activities, and migrating both permanently and temporarily. As an additional adaptive step, some farmers have turned to asset sales. Additionally, research indicates that agricultural households in wetland areas experience less volatility in climate than those in arid regions.
{"title":"Climate change and its impacts in rural areas of Pakistan: a Literature review","authors":"Abdul Rasool Khoso, Jintu Gu, Shahnaz Bhutto, Muhammad Javed Sheikh, Kainat Vighio, Arshad Ali Narejo","doi":"10.56556/jescae.v3i1.731","DOIUrl":"https://doi.org/10.56556/jescae.v3i1.731","url":null,"abstract":"Pakistan, which is located in Southeast Asia, is one of the nations that is most susceptible to the effects of climate change, as seen by the increased frequency of floods and droughts. Variations in climate have a negative impact on a number of areas, such as the agricultural industry, groundwater levels, dietary resources, soil quality and organic matter content, public health, and poverty rates. This study's main goal is to evaluate the impact of climate change and the adaptations farms have made in response to variations in precipitation and temperature. Pakistani farmers have responded to climate change by implementing a variety of adaptive techniques. These tactics include changing the way that fertilizer is used, changing crop varieties, using pesticides, improving seed quality, diversifying the farm, planting shade trees, changing irrigation techniques, engaging in off-farm activities, and migrating both permanently and temporarily. As an additional adaptive step, some farmers have turned to asset sales. Additionally, research indicates that agricultural households in wetland areas experience less volatility in climate than those in arid regions.","PeriodicalId":508135,"journal":{"name":"Journal of Environmental Science and Economics","volume":"3 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139523887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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