Global Sustainability Research

Impact of tourism industry on poverty eradication in southern Nigeria

Sydney, Onuchukwu Echeta — Thu, 29 Aug 2024 00:00:00 +0000

This research work examined the impact of tourism industry on poverty eradication in southern Nigeria. Tourism industry refers to the economic sector that encompasses various activities related to the travel and leisure of people to destinations outside their usual environment. The objective of the study investigated the effect of restaurants establishment on job creation and effect of tourist attractions on increased health Care, problem of study include lack of comprehensive data, unequal distribution of benefits, inadequate infrastructure, environmental sustainability, and limited capacity building opportunities all pose significant challenges to achieving poverty reduction through tourism. The tourism industry has the potential to stimulate economic activities in various sectors such as hospitality, transportation, arts and crafts, and cultural heritage, thus providing multiple avenues for poverty reduction. The researchers made use of survey research method, where 150 questionnaires was administered to staffs and tourists at the chosen attractions. Data was analyzed descriptively using frequency distribution tables and simple percentage in order of research question. The researchers also tested hypotheses using chi- square statistical techniques. From the researcher's findings, the study revealed that tourism industry such as restaurants establishments , tourists attractions has significant positive impact on poverty eradication in southern Nigeria. This research concludes that balancing economic gains with social and environmental responsibility is essential for maximizing the positive impact of tourism on poverty eradication. The researchers recommends that Governments, businesses, and communities should collaborate to create policies that prioritize social and environmental responsibility while harnessing the economic potential of tourism for poverty alleviation, and also investing in education and skills development for local communities can enhance their capacity to participate in and benefit from the tourism sector.

Leveraging AI for Promoting Sustainable Environments in G-7: The Impact of Financial Development and Digital Economy via MMQR Approach

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

This study investigates the role of Artificial Intelligence (AI) in promoting a sustainable environment within the G-7 countries by testing the Load Capacity Curve (LCC) hypothesis. Additionally, it examines the effects of financial development, the digital economy, and urbanization on the load capacity factor using data from 2010 to 2022. The research employs cross-sectional dependence and slope homogeneity tests, revealing issues of cross-sectional dependence and heterogeneity. Panel unit root tests, both first and second generation, confirm that the variables are free from unit root problems. Furthermore, panel cointegration tests indicate that the variables are cointegrated in the long run. To assess the impact of the explanatory variables on the load capacity factor, the study utilizes the Method of Moments Quantile Regression (MMQR). The findings reveal a U-shaped relationship between income and the load capacity factor, supporting the LCC hypothesis in the G-7 region. The results also indicate that AI innovation and financial development have a significant positive correlation with the load capacity factor. In contrast, the digital economy and urbanization are found to significantly reduce the load capacity factor. Robustness checks, including the Driscoll-Kraay standard error, Augmented Mean Group, and Common Correlated Effect Mean Group estimation approaches, validate the findings obtained from the MMQR method. Moreover, the Dumitrescu-Hurlin (D-H) causality assessment is utilized to explore the causal connections between variables. The results reveal a unidirectional causal relationship between income and the load capacity factor. Additionally, bidirectional causal relationships are the remaining explanatory variables and load capacity factors.

Exploring the LCC Hypothesis in the Nordic Region: The Role of AI Innovation, Environmental Taxes, and Financial Accessibility via Panel ARDL

Tue, 03 Sep 2024 00:00:00 +0000

This study investigates the impact of artificial intelligence (AI) innovation on environmental sustainability in the Nordic region. Additionally, it tests the Load Capacity Curve (LCC) hypothesis by incorporating factors such as financial accessibility, environmental tax, and urbanization, using data spanning from 1990 to 2020. The methodology includes the Cross-Sectional Dependence test and the slope homogeneity test, revealing issues of heterogeneity and cross-sectional dependence. Furthermore, first and second-generation panel unit root assessments indicate that the variables are free from unit root problems. Panel Cointegration tests confirm that the variables are cointegrated in the long run. To analyze both short-run and long-run relationships, the study employs the Panel Autoregressive Distributed Lag (ARDL) model. The results from the Panel ARDL model support the LCC hypothesis in the Nordic region, showing a U-shaped relationship between income and load capacity factor. Moreover, AI innovation and environmental tax significantly and positively contribute to environmental sustainability in both the short and long run. In contrast, higher financial accessibility and urbanization degrade environmental sustainability over these timeframes. To validate the robustness of the Panel ARDL estimations, the study also uses Fully Modified OLS, Dynamic OLS, and Fixed Effects OLS approaches, all of which corroborate the ARDL findings. The study employs the D-H causality test to explore causal relationships among the variables. The test results reveal a unidirectional causal relationship between income and AI innovation to the load capacity factor and a bidirectional causal relationship between financial accessibility and the load capacity factor, as well as between urbanization and the load capacity factor. However, no causal relationship is found between environmental tax and the load capacity factor.

An Overview of Precision Livestock Farming (PLF) Technologies for Digitalizing Animal Husbandry toward Sustainability

Homaira Afroz Himu, Asif Raihan — Tue, 27 Aug 2024 00:00:00 +0000

As the global population continues to expand, it is imperative for livestock farming to undergo necessary adaptations in order to effectively address the escalating food demands and enhance productivity. Concurrently, it is imperative to acknowledge and tackle concerns pertaining to animal welfare, environmental sustainability, and public health. The primary aim of the article is to provide a comprehensive examination of the latest advancements in the utilization of biometric devices, big data, and blockchain technology for the purpose of digitizing animal husbandry within the context of Precision Livestock Farming (PLF). Biometric sensors are physiological devices utilized for the purpose of monitoring the health and behavioral patterns of an individual animal. This information can be utilized by farmers to do population-level analysis. Big data analytics solutions employ statistical algorithms to examine extensive and intricate data sets, detecting significant trending patterns and offering advisory recommendations for farmers' decision-making. These systems are designed to interpret and integrate data obtained from biometric sensors. Blockchain technology, when combined with sensors, facilitates the secure and convenient tracking of animal products throughout their journey from the farm to the table. This approach demonstrates efficacy in the surveillance of disease outbreaks, the prevention of economic losses, and the mitigation of food-related health pandemics. The implementation of PLF technology within the livestock industry has the potential to contribute to the attainment of sustainable development.

Obstacles and challenges of rural development in Afghanistan: examining problems and solutions: a review

Fayaz Gul Mazloum Yar, Janat Gul Zazia — Fri, 06 Sep 2024 00:00:00 +0000

This study comprehensively examines the multifaceted challenges impeding rural development in Afghanistan and proposes strategic recommendations for achieving sustainable growth. The research employs extensive document analysis, a critical review, and a literature review spanning 2020 to 2024, analyzing institutional, socio-cultural, economic, environmental, and infrastructural factors affecting rural development. The findings reveal that development is hindered by institutional obstacles such as ineffective governance, centralized planning, and a lack of coherent strategies, alongside socio-cultural issues like migration, low educational attainment, gender disparities, and economic barriers including poverty, unemployment, and limited market access. Environmental concerns such as land degradation and inadequate infrastructure further exacerbate these challenges. The study recommends a holistic approach, emphasizing decentralization, community engagement, infrastructure investment, sustainable agricultural practices, and vocational training to build local capacities. Collaborative efforts between stakeholders are essential to drive sustainable development in Afghanistan's rural areas.

The Role of Artificial Intelligence in Modern Farming System for Achieving Sustainable Agricultural Transformation in Nigeria

Aminu Adamu Ahmed, Rilwanu Sulaiman, Nasiru Adamu, Yusuf Musa — Wed, 25 Sep 2024 00:00:00 +0000

The agricultural sector in Nigeria faces pressing challenges, including food insecurity, land degradation, and climate change impacts. To address these issues, the integration of artificial intelligence (AI) in modern farming practices presents a transformative opportunity for achieving sustainable agricultural development. This paper explores the role of AI technologies in enhancing productivity, optimizing resource use, and minimizing environmental impacts within the Nigerian agricultural landscape. Through a semi-systematic literature review (SLR), the study examines the historical context of agriculture in Nigeria, current AI applications such as precision agriculture, crop monitoring, and pest detection, as well as the associated benefits of increased yield and economic returns for farmers. The semi-SLR methodology incorporates structured search strategies, established inclusion and exclusion criteria, and systematic data extraction techniques to synthesize existing knowledge and identify gaps in the current understanding of AI's impact on sustainable farming in Nigeria. The findings reveal that while AI can significantly contribute to sustainable agricultural transformation, several barriers hinder its widespread adoption, including infrastructural deficiencies, technological illiteracy, and socio-economic constraints. By analyzing these aspects, this research underscores the importance of a structured approach to literature reviews in agricultural research, ultimately aiming to inform policy and encourage the adoption of AI innovations in the sector. The findings indicate that concerted efforts from stakeholders, including policymakers, researchers, and farmers, are essential to overcome existing challenges and fully realize the potential of AI in fostering a resilient agricultural system in Nigeria.