Journal of Environmental Science and Economics

Leveraging AI for a Greener Future: Exploring the Economic and Financial Impacts on Sustainable Environment in the United States

Sun, 25 Aug 2024 00:00:00 +0000

In response to increasing environmental challenges, the United States has deliberately adopted technical advancements to promote sustainable development. This includes efforts to decrease pollution, improve energy efficiency, and encourage the use of environmentally friendly technology in different industries. This study investigates the role of Artificial Intelligence (AI) technology in promoting environmental sustainability in the United States from 1990 to 2019. It also examines the impacts of financial development, ICT use, and economic growth on the Load Capacity Factor (LCF). Various unit root tests revealed no unit root issues and mixed integration orders among variables. The Autoregressive Distributive Lag (ARDL) model explored cointegration, indicating long-run relationships among the variables. The ARDL findings confirm the Load Capacity Curve hypothesis for the United States, with AI technology and ICT use positively correlating with LCF in both the short and long run. Conversely, financial development and population growth significantly reduce LCF. Robustness checks using FMOLS, DOLS, and CCR estimation approaches align with the ARDL results. Granger causality tests reveal unidirectional causality from economic growth, AI, financial development, and ICT use to LCF and bidirectional causality between population and LCF. Diagnostic tests confirm the results are free from heterogeneity, serial correlation, and specification errors. This study underscores the importance of AI and ICT in enhancing environmental sustainability while highlighting the adverse impacts of financial development and population growth on LCF.

Climate Change Intersecting Socio-economic Vulnerabilities of Kalash Indigenous Community in Nothern Pakistan

Zafar Khan, Itbar khan, Uzma Kamal — Tue, 27 Aug 2024 00:00:00 +0000

Climate Change has a significant effect on all walks of life or human activities across the world. However, indigenous communities in different parts of the world are more susceptible to the worst effects of climate change due to their dependency on natural resources. Climate change directly affects natural resources such as forests, water, grazing land, bio-diversity and traditional foods of indigenous communities. This study explores how climate change overlaps or intersects the socio-economic, and bio-cultural marginality of the Kalash indigenous community, living in northern Pakistan. The qualitative research paradigm was used to explore how climate change overlaps or intersects the socio-economic marginality of the Kalash Indigenous community. It reveals that climate change has significantly affected natural resources such as water, biodiversity, forests and crops of Kalash's indigenous community. Climate-induced natural disasters affected their livelihood resources and compelled them to migrate or be displaced from their native town. It reveals that climate change also affects women's marginality in Kalash's indigenous community. It also reveals that climate change overlaps and intersects with the socio-economic marginality of Indigenous communities and policymakers should give priority to indigenous communities who have a dependency on natural resources to protect them from the worst effect of climate change across the world.

Analyzing the Nexus between AI Innovation and Ecological Footprint in Nordic Region: Impact of Banking Development and Stock Market Capitalization using Panel ARDL method

Sun, 01 Sep 2024 00:00:00 +0000

This study investigates the impact of Artificial Intelligence (AI) innovation on the ecological footprint in the Nordic region from 1990 to 2020, alongside the effects of banking development, stock market capitalization, economic growth, and urbanization. Utilizing the STIRPAT model, the study incorporates cross-sectional dependence and slope homogeneity tests, revealing issues of heterogeneity and cross-sectional dependence. The analysis employs both first and second-generation panel unit root tests, confirming that the variables are free from unit root problems. Panel cointegration tests demonstrate that the variables are cointegrated in the long run. To explore the short- and long-term relationships, the study utilizes the Panel Autoregressive Distributed Lag (ARDL) model. The Panel ARDL results indicate that economic growth, stock market capitalization, and urbanization positively correlate with the ecological footprint in both the short and long run. Conversely, AI innovation and banking development negatively correlate with the ecological footprint. To validate the Panel ARDL estimations, robustness checks are performed using Fully Modified OLS, Dynamic OLS, and Fixed Effects with OLS, all of which support the initial findings. Furthermore, the study employs the D-H causality test to identify causal relationships. The results show a unidirectional causal relationship between AI innovation, stock market capitalization, urbanization, and the ecological footprint. In contrast, a bidirectional causal relationship exists between economic growth and the ecological footprint, as well as between banking development and the ecological footprint.

The effects of trade openness on CO2 emissions in Sub-Saharan Africa: fresh evidence from new measure

Mohamadou Oumarou, Mohammadou Nourou — Mon, 02 Sep 2024 00:00:00 +0000

This study assesses the effects of trade openness on carbon dioxide (CO₂) emissions in Sub-Saharan Africa (SSA). In contrast to previous studies, and in order to make a significant contribution to the empirical literature on the subject, we capture trade openness through a new and innovative approach that takes into account not only the share of a country’s trade in its gross domestic product but also the size of its trade in world trade. In addition, this study also stands out for its consideration of trade openness in different sectors of the economy (primary, secondary and tertiary). For the econometric strategies, the study used data from 38 SSA countries between 2002 and 2022 and estimated the effects by the Generalized Method of Moments (GMM) system and the double ordinary least squares method. The main results show that in SSA: trade openness contributes to rising CO₂emissions. In addition, trade in the primary (agriculture), secondary (industry) and tertiary (services) sectors contributes to the increase in CO₂ emissions. The models used are controlled by several variables. The results show that the renewable energy consumption is a key driver of environmental quality, which seems to reduce CO₂emissions. On the other hand, human capital, population growth and the quality of institutions increase CO₂ emissions. Furthermore, the interaction between openness and institutional quality has a negative impact on CO₂emissions. Therefore, in order to reduce CO₂ emissions, SSA needs to put the environment on the agenda of future trade negotiations; to implement policies and strategies that guarantee growth without abandoning the environment.

Assessing the Impact of Private Investment in AI and Financial Globalization on Load Capacity Factor: Evidence from United States

Wed, 11 Sep 2024 00:00:00 +0000

The need for sustainable solutions has increased globally as a result of the growing environmental problems brought about by urbanization and industrialization. Given this, private investment in artificial intelligence (AI) has become a viable means of promoting environmental sustainability, mainly because of AI's capacity to minimize ecological footprints and maximize resource utilization. This research investigates the role of private investment in AI in promoting environmental sustainability in the United States from 1990 to 2019. It also analyzes the impact of financial globalization, technological innovation, and urbanization by testing the Load Capacity Curve (LCC) hypothesis. The research utilizes stationarity tests, which indicate that the variables are free from unit root problems and exhibit mixed orders of integration. Using the Autoregressive Distributive Lag (ARDL) Model bound test, the analysis finds that the variables are cointegrated in the long run. The short-run and long-run estimations of the ARDL model confirm the existence of the LCC hypothesis in the United States, revealing a U-shaped association between income and load capacity factor. The findings show that private investment in AI has a significant positive correlation with the load capacity factor, thus promoting environmental sustainability. Conversely, technological innovation and financial globalization exhibit a negative correlation with the load capacity factor in both the short and long run. To validate the ARDL estimation approach, the study employs Fully Modified OLS, Dynamic OLS, and Canonical Correlation Regression estimation methods, all of which support the ARDL outcomes. Additionally, the Granger Causality test reveals a unidirectional causal connection from private investment in AI, financial globalization, economic growth, technological innovation, and urbanization to the load capacity factor.

Population Growth and Resource Scarcity: Implications for Conflict and Cooperation in Taraba State, Nigeria

Andeskebtso Yohanna Adaki — Sun, 29 Sep 2024 00:00:00 +0000

Population growth has profound effects on resource availability and social dynamics in many regions across the globe. In Taraba State, Nigeria, the rapid increase in population has intensified resource scarcity, leading to heightened conflicts among communities, particularly those reliant on land and water for their livelihoods. This study examines how population growth intersects with resource depletion, conflict, and cooperation in Taraba State. A mixed-methods approach was employed, involving the analysis of questionnaire data from 294 respondents and qualitative insights from interviews with 10 local leaders, agriculturalists, and herders. The findings reveal that population growth significantly exacerbates the depletion of essential resources, such as land and water, with 72.8% of respondents indicating that population increases directly impact resource availability. This scarcity has led to a 78.3% occurrence of conflicts in areas experiencing severe resource depletion. The study also shows that resource scarcity forces communities into competition, often escalating tensions into violent disputes. However, the research accentuates the critical role of traditional governance structures, which were acknowledged by 64.3% of the respondents as key to fostering cooperation and mitigating conflicts in the state. The study concludes that integrating traditional governance systems with formal frameworks is essential for sustainable resource management and conflict reduction in Taraba State. Policymakers should prioritize these strategies to address the challenges posed by rapid population growth and resource scarcity, ensuring the long-term stability and well-being of communities in the state.