Inform Policy Decisions Research Articles

Evaluating teacher grading during the pandemic: An analysis of how teacher grades in 2020 compared to exam grades during ‘normal times’

The aim was to evaluate teacher grades awarded in 2020 after GCSE and A-level exams were cancelled due to the pandemic. We identified if there were changes in the relationships between student, school and subject-level features and grades awarded by teacher judgement in 2020 when compared to pre-pandemic years.
 A multi-level modelling analysis was conducted using the GRADE dataset which links data on pupil characteristics with results data and university application data. The model outputs identifying the relationship between student, school and subject characteristics and GCSE and A-level results were compared between 2020 and normal exam years to identify any notable changes.
 A further study focussed on whether university application patterns had an influence on teacher judgements at A-level, since teachers may have known students’ application decisions when deciding on grades. For this analysis university tariff and the degree subject applied to by each student were key variables.
 Analysis showed that across both GCSEs and A-levels there was an average increase in results by around half a grade in 2020. There was also less variation in grades in 2020. However, the majority of relationships between grades and other features studied had not substantially changed.
 Prior attainment was by far the strongest predictor of grades, but this relationship was slightly stronger in 2020 compared to previous years.
 Subjects with more non-exam assessment tended to be those with the largest increase in grades, as well as ‘facilitating’ subjects at A level. There was also a decrease in the attainment gap between male and female students at A level.
 The follow up study indicated that teachers were not influenced by student’s application decisions when allocating grades.
 Findings suggest that teacher grades did not introduce any substantial sources of bias or different patterns of grading in 2020. This research helped to inform policy decisions around national assessment in England in 2021, as well as contributing to the broader understanding of direct grading by teachers.

Transitioning to sustainable energy: opportunities, challenges, and the potential of blockchain technology

The pressing issues of climate change and the limited availability of non-renewable energy resources have created a growing need for sustainable energy alternatives. This study provides a comprehensive overview of the pressing need for sustainable energy solutions and the complex relationship between energy and the economy. The challenges and opportunities presented by the transition to sustainable energy sources are explored, including the need for investment in renewable energy technologies, policy changes to incentivize sustainable energy use, and the potential for job creation in the sustainable energy sector. On the other hand, it is recognized that there are considerable hurdles that need to be addressed, including the substantial initial expenses associated with establishing renewable energy systems, as well as the political and societal barriers to enacting change. The economic benefits of transitioning to sustainable energy, such as improved energy security, reduced dependence on fossil fuels, and the potential for increased economic growth, are evaluated. The complex relationship between energy and the economy is thoroughly analyzed, presenting a valuable contribution to the academic literature on sustainable energy. Furthermore, an inquiry is being made into the potential contribution of blockchain technology in advancing a sustainable energy landscape. This includes its ability to augment the effectiveness and openness of energy markets, as well as its capacity to assist in the assimilation of renewable energy resources. Hence, this research underscores the importance of transitioning to sustainable energy sources for their environmental and economic merits. The findings presented offer valuable insights to inform policy decisions and guide future research endeavors in this field. By promoting the advancement of sustainable energy technologies, this study contributes to the development of a more sustainable global economy.

Fine-scale characterization of irrigated and rainfed croplands at national scale using multi-source data, random forest, and deep learning algorithms

Knowledge of the extent and distribution of irrigated and rainfed croplands is critical in providing the necessary baseline data for enhancing agricultural efficiency and making informed policy decisions. Accurately identifying and mapping irrigated and rainfed croplands can hasten the attainment of Sustainable Development Goals (SDGs) 1 and 2, aimed at reducing poverty and hunger, respectively. However, traditional methods employed to identify and map cropland areas are expensive and require substantial labour, particularly in extensive environments. As a result, this study presents a comprehensive and spatially explicit methodological framework for identifying and mapping national-scale irrigated and rainfed croplands in South Africa. This framework leverages low-cost earth observation technologies (Sentinel-2 MSI) and employs highly accurate classification algorithms, namely Deep Learning Neural Network (DNN) and Random Forest (RF). The proposed methodology strategically integrates data from multiple sources, including public repositories (e.g., cropland data, evapotranspiration), ongoing research (e.g., land cover maps), and field data, to enhance the accuracy and reliability of the results. The methodology begins by employing a robust random forest model to classify the study area into distinct land cover types. Leveraging the power of a deep learning neural network (DNN), the method accurately distinguishes between irrigated and rainfed croplands in South Africa. The random forest model achieved a notable classification accuracy of 0.77 when identifying the main land-use and land cover types. Meanwhile, the deep learning neural network (DNN) model achieved an accuracy of 0.71 in differentiating rainfed and irrigated croplands at a national scale. These results highlight the effectiveness of the proposed methodology in providing baseline information relevant to crop monitoring, yield forecasting, and understanding agricultural food supply systems. Furthermore, the proposed methodology has the potential to offer timely and accurate information on cropland areas and their extent which could assist in implementing targeted interventions for optimising agricultural productivity. With its potential to be upscaled to other sub-Saharan countries, this methodology enriches agricultural decision-making and plays a vital role in bolstering food security and advancing the attainment of SDGs.

Predicting unmet activities of daily living needs among the oldest old with disabilities in China: a machine learning approach.

The ageing population in China has led to a significant increase in the number of older persons with disabilities. These individuals face substantial challenges in accessing adequate activities of daily living (ADL) assistance. Unmet ADL needs among this population can result in severe health consequences and strain an already burdened care system. This study aims to identify the factors influencing unmet ADL needs of the oldest old (those aged 80 and above) with disabilities using six machine learning methods. Drawing from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2017-2018 data, we employed six machine learning methods to predict unmet ADL needs among the oldest old with disabilities. The predictive effects of various factors on unmet ADL needs were explored using Shapley Additive exPlanations (SHAP). The Random Forest model showed the highest prediction accuracy among the six machine learning methods tested. SHAP analysis based on the Random Forest model revealed that factors such as household registration, disability class, economic rank, self-rated health, caregiver willingness, perceived control, economic satisfaction, pension, educational attainment, financial support given to children, living arrangement, number of children, and primary caregiver played significant roles in the unmet ADL needs of the oldest old with disabilities. Our study highlights the importance of socioeconomic factors (e.g., household registration and economic rank), health status (e.g., disability class and self-rated health), and caregiving relationship factors (e.g., caregiver willingness and perceived control) in reducing unmet ADL needs among the oldest old with disabilities in China. Government interventions aimed at bridging the urban-rural divide, targeting groups with deteriorating health status, and enhancing caregiver skills are essential for ensuring the well-being of this vulnerable population. These findings can inform policy decisions and interventions to better address the unmet ADL needs among the oldest old with disabilities.

O.6.2-5 Data infrastructure Sport and Physical Activity: the creation of a thematic classification for indicators

Abstract Accurate facts and figures concerning physical activity are crucial to inform policy decisions. In the Netherlands, a vast amount of data related to this domain is currently available. However, accessing the required information is increasingly challenging. Therefore, the National Institute for Public Health and the Environment (RIVM), in collaboration with several key knowledge institutes, initiated a project to integrate existing data on sport and physical activity into a thematic data-infrastructure. The key knowledge institutes involved in this project are NOC*NSF, Mulier Institute, the Knowledge Center for Sport and Physical Activity, RIVM, the Dutch Knowledge Center for Injury Prevention, and Statistics Netherlands. These organizations have been responsible for collecting data on 20 key-indicators for sports and physical activity since 2015. Having experts in the domain of sports and physical activity (e.g. research, practice, policy) endorse the thematic data structure was crucial. In total, 99 experts from 27 organizations across the domains of research, practices, and policy were involved in the project. An extensive inventory of existing and new indicators was carried out by the team in collaboration with the experts. The process involved multiple online and live sessions, as well as two consultations with online forms for each theme, resulting in the identification of 12 themes and nearly 400 indicators. These indicators were categorized within each theme based on the relevance assigned by the experts. In addition, the position of the current set of 20 Key-Indicators was assessed within each theme. Results showed that the set of Key-Indicators was indeed largely still up-to-date. Nevertheless, there is reason to more closely examine and evaluate the current set in the near future. In the future RIVM will continue to coordinate the sports and physical activity data-infrastructure in the Netherlands by advocating and facilitating harmonized data collection at the national and local level.

Evaluation of potential power production and reduction in GHG emissions from bio-compressed natural gas derived from food waste in Africa

There is an increased food waste generation globally due to socio-cultural factors, inadequate food waste management, and economic factors. Developing countries have major issues with proper food waste management, contributing to several environmental problems. The ineffective food waste management in Africa has had detrimental consequences on the ecosystem and people's health. There is a need for an improved sustainable food waste management system on the continent. Biomethane from organic resources could be upgraded into bio-compressed natural gas (bio-CNG), which could be used as green fuel for the environment, climate, and human health. This study evaluates the environmental and electricity benefits of bio-CNG derived from food waste in African nations. The major findings show that the annual bio-CNG yield from food waste in Africa is 98.34 × 109 m3/y, and that could generate 4035.7 GWh/y of electricity. The annual electricity generation could contribute 3.4 % (on average) to Africa's estimated electricity needs (798,958.7 GWh). The findings also show that using energy generated from bio-CNG could substitute for 94.47 million L of diesel fuel annually, resulting in a 256.71 kt CO2eq/y reduction in global warming. This research has the potential to inform policy decisions on the ecological viability of bio-CNG to power projects in Africa and other developing nations.

The Development and Effects of Environmental Law on Green Governance

Objective: The legal framework governing ecological preservation and sustainability is best appreciated via research into the history and impact of environmental legislation on the "green" notion. It explains how laws and regulations affect the environment, shifting business models, government regulations, and individual attitudes and actions in a more sustainable direction. In a world where environmental legislation, sustainable practices, and ecological challenges are on the rise, it is crucial to have this knowledge. There are several obstacles in the way of the growth and consequences of environmental law on the "green" idea, including the need to strike a balance between economic interests and ecological preservation, the need to ensure worldwide harmonization of rules, and the need to adjust legal frameworks to handle developing environmental difficulties.
 
 Method: This paper proposes the Contrastive Exploration and Influence of Environmental Law (CE&IEL), which will examine the development and current relevance of environmental law, focusing on its transformative effects on sustainable practices within the "green" framework. The research has a number of potential uses, including informing policy decisions by illuminating the ways in which environmental laws affect "green" practices, assisting industries in aligning with sustainable regulations to increase corporate responsibility, and assisting educational institutions in integrating legal and ecological perspectives to better prepare students for careers in environmental governance.
 
 Result: Computerized models (CM) are used in the growth and impacts of environmental law on green to foresee how shifting environmental legislation would affect green business practices.
 
 Conclusion: This strategy helps policymakers, industries, and researchers understand the dynamic interplay between legal reforms and their impact on developing ecological consciousness through the use of simulated trials of alternative legal approaches, revealing previously unknown insights.

Renewable energy deployment in Europe: Do politics matter?

This study analyzes non-hydroelectric renewable capacity across 27 European countries from 2000 to 2020, using advanced econometric techniques like the Dumitrescu and Hurlin panel Granger non-causality method, Panel Autoregressive Distributed Lag and Panel Quantile Autoregressive Distributed Lag estimations. This investigation reveals complex relationships extending beyond immediate variables. These relationships, rooted in pairwise causalities and broader interactions, underpin observed phenomena. Causality tests show that achieving non-hydroelectric renewable capacity is a long-term endeavor, emphasizing persistent policy approaches for effective energy transition. Three key variables emerge as potent policy drivers: maintaining an environment conducive to economic freedom, fostering financial development, and driving non-hydroelectric renewable patents’ research and development. These variables play a pivotal role in capacity expansion. Additionally, the findings of this empirical investigation spotlight the role of political orientations. Leftist governments have lagged in prioritizing energy transition, prompting questions about neglecting environmental concerns and necessitating comprehensive policy reform. In essence, this study offers novel insights into non-hydroelectric renewable energy deployment. By unraveling complex relationships, emphasizing persistent policies, and identifying key variables, this investigation provides a nuanced perspective in line with sustainable energy transition urgency. As global ecological imperatives heighten, our work guides informed policy decisions for a greener future.

Understanding the biases to sepsis surveillance and quality assurance caused by inaccurate coding in administrative health data

PurposeTimely and accurate data on the epidemiology of sepsis are essential to inform policy decisions and research priorities. We aimed to investigate the validity of inpatient administrative health data (IAHD) for surveillance and quality assurance of sepsis care.MethodsWe conducted a retrospective validation study in a disproportional stratified random sample of 10,334 inpatient cases of age ≥ 15 years treated in 2015–2017 in ten German hospitals. The accuracy of coding of sepsis and risk factors for mortality in IAHD was assessed compared to reference standard diagnoses obtained by a chart review. Hospital-level risk-adjusted mortality of sepsis as calculated from IAHD information was compared to mortality calculated from chart review information.ResultsICD-coding of sepsis in IAHD showed high positive predictive value (76.9–85.7% depending on sepsis definition), but low sensitivity (26.8–38%), which led to an underestimation of sepsis incidence (1.4% vs. 3.3% for severe sepsis-1). Not naming sepsis in the chart was strongly associated with under-coding of sepsis. The frequency of correctly naming sepsis and ICD-coding of sepsis varied strongly between hospitals (range of sensitivity of naming: 29–71.7%, of ICD-diagnosis: 10.7–58.5%). Risk-adjusted mortality of sepsis per hospital calculated from coding in IAHD showed no substantial correlation to reference standard risk-adjusted mortality (r = 0.09).ConclusionDue to the under-coding of sepsis in IAHD, previous epidemiological studies underestimated the burden of sepsis in Germany. There is a large variability between hospitals in accuracy of diagnosing and coding of sepsis. Therefore, IAHD alone is not suited to assess quality of sepsis care.

Did implementation of no-fault auto-insurance in British Columbia, Canada, impact return to work following road trauma? Protocol for a before–after survival analysis

IntroductionRoad trauma (RT) is a major public health problem that often results in prolonged absenteeism from work. Limited evidence suggests that recovery after RT is associated with automobile insurance compensation...

OBESITY IN CHILDREN

Childhood obesity is a growing health concern that has reached epidemic proportions in many parts of the world. This research paper delves into the intricate issue of obesity in children, aiming to provide a comprehensive overview of its causes, consequences, and potential solutions. The paper draws from a wide range of studies and data sources to offer insights into the complex landscape of childhood obesity.The prevalence of childhood obesity has risen dramatically in recent decades, with profound implications for public health. This research paper examines the global prevalence of childhood obesity, emphasizing the alarming rates in various regions. It highlights the urgency of addressing this issue and calls for effective strategies to curb its escalation. Childhood obesity is a multifaceted problem influenced by a combination of genetic, environmental, and behavioral factors. This paper explores the contributing factors, including dietary patterns, physical inactivity, genetics, socio-economic disparities, and cultural influences. Understanding these factors is pivotal in designing targeted interventions.Childhood obesity has both immediate and long-term consequences for affected individuals and society as a whole. This research paper elucidates the health risks associated with childhood obesity, such as cardiovascular diseases, type 2 diabetes, and psychological distress. It also examines the long-term implications, including a higher likelihood of obesity persisting into adulthood and its associated comorbidities.The prevalence of childhood obesity is not evenly distributed, with certain demographic groups facing higher risks. This paper addresses health disparities related to childhood obesity, emphasizing the need for equity-focused interventions. Socio-economic, racial, and geographical disparities are analyzed to inform targeted approaches.To address childhood obesity effectively, a multi-pronged approach is essential. This research paper presents an array of intervention strategies, encompassing dietary modifications, physical activity promotion, school-based programs, policy initiatives, and family-centered approaches. It discusses evidence-based interventions and their potential impact on reducing childhood obesity rates. Effective policy measures are critical in the fight against childhood obesity. This paper evaluates the role of government policies, including regulations on food marketing, school nutrition programs, and urban planning, in shaping environments that support healthy lifestyles for children.Childhood obesity is a pressing public health issue with far-reaching consequences. This research paper underscores the urgency of addressing the root causes of childhood obesity and the importance of early prevention efforts. It calls for a concerted effort from healthcare professionals, educators, policymakers, and families to create a healthier environment for children.In conclusion, this research paper provides a comprehensive examination of childhood obesity, encompassing its prevalence, causes, consequences, health disparities, and potential interventions. By shedding light on the complexities of this issue, it aims to inform policy decisions and public health initiatives aimed at combating the rising rates of obesity in children.

Composition analysis (pick analysis) of waste generated from household: A pilot study in Ujjain city, India

Waste segregation is an essential function in improving waste management. Waste segregation not only facilitates recycling and reduces waste going to landfills, rather it can benefit our environment and human in various ways. A pick analysis of waste composition is used to characterize the household waste stream and thus can analyze the segregation rate among the residents. In addition, it can measure the actual waste sorting behaviour at the household/community level. The objective of the study was to assess feasibility of a large-scale waste composition study, identify methodological and operational challenges, and estimate the resources needed to conduct the main waste composition study in order to obtain and get indicative figures about waste generation, composition, and miss-sorted proportions. The study team went door-to-door to collect waste in colour coded bags. We also collected the socio-demographic data of the households. The collected waste was weighed and segregated to analyze the waste composition. The analysis was done among 45 households, and it was found that the per capita waste generation per day is 0.25 kg (0.24 kg from slum and 0.27 kg from non-slum). Challenges identified in conducting waste composition study were lack of standard waste fraction classifications, difficulty in recruitment of personnel to conduct study due to social taboo around waste, challenge in co-coordinating with Ujjain Municipal Corporation waste collection vehicle for collection of waste. 53 household activities were completed in 5 and half hours with INR 24685 (USD 300.5). Pick analysis could be adopted by the Ujjain Municipal Corporation after cost effective analysis to generate precise estimate of waste generation, resource recovery, efficient resource allocation and will help in future interventions and informed policy decision making to improve segregation.

Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches

Transport electrification, which entails replacing fossil fuel-powered engines with electric drivetrains through the use of electric vehicles (EVs), has been identified as a potential strategy for reducing emissions in the transportation sector. As the adoption of EVs increases, there is a growing need to understand their performance and characteristics, particularly the factors that influence energy consumption under actual driving conditions. This study sought to investigate the actual energy consumption of commercial battery electric vehicles (BEVs) in Thailand by conducting real-world driving tests under various route conditions, including urban and rural route modes. Data collection was performed through the use of onboard diagnostics and global positioning system devices. The result shows that the average energy consumption of the BEVs in this study was 148.03 Wh/km. Moreover, several machine learning (ML) techniques were utilized to analyze the collected dataset to predict energy consumption and identify the key factors influencing energy consumption. A comprehensive investigation of factor significance was carried out by employing a specific algorithm in conjunction with the SHapley Additive exPlanations (SHAP) approach. This investigation provided insights into the influence of battery current and vehicle speed on the energy consumption of BEVs, particularly in the context of urban route conditions. The results of this study provide valuable insights into the energy consumption of BEVs and the factors affecting it, which can aid in improving energy efficiency and informing policy decisions related to transport electrification.

Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States

BackgroundIn the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available.MethodsWe developed a mathematical model of TB epidemiology linked to a web-based user interface — Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool’s functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.–born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029.ResultsBetween 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.–born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective.ConclusionsTabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.

Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool.

The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than the mean SI. In the EpiEstim R package, we implement an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which Rt can then be estimated. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. Rt estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that Rt was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, Rt estimates from reconstructed data were more successful at recovering the true value of Rt than those obtained from reported daily data. These results show that this novel method allows Rt to be successfully recovered from aggregated data using a simple approach with very few data requirements. Additionally, by removing administrative noise when daily incidence data are reconstructed, the accuracy of Rt estimates can be improved.

The cost-effectiveness of small-quantity lipid-based nutrient supplements for prevention of child death and malnutrition and promotion of healthy development: modelling results for Uganda.

Recent meta-analyses demonstrate that small-quantity lipid-based nutrient supplements (SQ-LNS) for young children significantly reduce child mortality, stunting, wasting, anaemia and adverse developmental outcomes. Cost considerations should inform policy decisions. We developed a modelling framework to estimate the cost and cost-effectiveness of SQ-LNS and applied the framework in the context of rural Uganda. We adapted costs from a costing study of micronutrient powder (MNP) in Uganda, and based effectiveness estimates on recent meta-analyses and Uganda-specific estimates of baseline mortality and the prevalence of stunting, wasting, anaemia and developmental disability. Rural Uganda. Not applicable. Providing SQ-LNS daily to all children in rural Uganda (> 1 million) for 12 months (from 6 to 18 months of age) via the existing Village Health Team system would cost ∼$52 per child (2020 US dollars) or ∼$58·7 million annually. SQ-LNS could avert an average of > 242 000 disability-adjusted life years (DALYs) annually as a result of preventing 3689 deaths, > 160 000 cases of moderate or severe anaemia and ∼6000 cases of developmental disability. The estimated cost per DALY averted is $242. In this context, SQ-LNS may be more cost-effective than other options such as MNP or the provision of complementary food, although the total cost for a programme including all age-eligible children would be high. Strategies to reduce costs, such as targeting to the most vulnerable populations and the elimination of taxes on SQ-LNS, may enhance financial feasibility.

Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data

The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes imperative. We therefore predict the distribution of the major members of the Anopheles gambiae s.l. in Nigeria. Immature stages of Anopheles were collected from 72 study locations which span throughout the year 2020 resulted in the identification of over 60,000 Anopheline mosquitoes. Of these, 716 breeding sites were identified with the presence of one or more vector species from the An. gambiae complex and were subsequently used for modelling the potential geographical distribution of these important malaria vectors. Maximum Entropy (MaxEnt) distribution modeling was used to predict their potentially suitable vector habitats across Nigeria. A total of 23 environmental variables (19 bioclimatic and four topographic) were used in the model resulting in maps of the potential geographical distribution of three dominant vector species under current climatic conditions. Members of the An. gambiae complex dominated the collections (98%) with Anopheles stephensi, Anopheles coustani, Anopheles funestus, Anopheles moucheti, Anopheles nilli also present. An almost equal distribution of the two efficient vectors of malaria, An. gambiae and Anopheles coluzzii, were observed across the 12 states included in the survey. Anopheles gambiae and Anopheles coluzzii had almost equal, well distributed habitat suitability patterns with the latter having a slight range expansion. However, the central part of Nigeria (Abuja) and some highly elevated areas (Jos) in the savannah appear not suitable for the proliferation of these species. The most suitable habitat for Anopheles arabiensis was mainly in the South-west and North-east. The results of this study provide a baseline allowing decision makers to monitor the distribution of these species and establish a management plan for future national mosquito surveillance and control programs in Nigeria.

ENHANCING THE SKILLS OF SPECIAL EDUCATION TEACHERS: A SCOPING REVIEW OF PROFESSIONAL DEVELOPMENT APPROACHES

As the field of special education continually evolves, special education teachers must stay current with emerging trends and engage in ongoing professional development. The present review aims to synthesize existing research on the professional development of special education teachers. A search was conducted using the Web of Science and Scopus databases to locate relevant articles. In total, 63 articles satisfied the inclusion requirements for this review. We identified several important topics related to the professional development of special education teachers, including inclusive education, assistive technologies collaboration with other professionals, and instructional methods. The findings from this review have the potential to guide the design and implementation of tailored professional development programs and inform policy decisions related to the ongoing education of special education teachers.

A Decadal, Temporal and Seasonal Variation of Black Carbon Aerosol at High Altitude Region of Ooty, Tamil Nadu, India

Climate change is accelerating at an unprecedented pace due to escalating greenhouse gas emissions, emphasizing the urgency of understanding its drivers. Aerosols present in the atmosphere, play an significant role in the intricate climate system. In this study, we examine the temporal and seasonal trends of black carbon aerosol, a significant contributor to warming, in the high-altitude locale of Ooty, Tamil Nadu, India. Utilizing the Aethalometer instrument, this research presents a comprehensive analysis of black carbon concentrations over a decade (2013-2023). Observations reveal distinct patterns, with higher concentrations during certain months and seasons. Specifically, April consistently exhibited the highest concentrations, while the monsoon season recorded the lowest. The exceptional decline in 2020, attributed to COVID-19 lockdown measures, highlights the significant role of anthropogenic activities.
 These findings underscore the significance of proactive black carbon monitoring in regions like Ooty. The study contributes valuable insights into the complex interplay between black carbon aerosols and climate systems, especially in high-altitude environments. It underscores the need for continued research to comprehensively understand black carbon's implications on local and global climate dynamics. In conclusion, this research establishes a foundation for grasping the intricate dynamics of black carbon aerosols in Ooty and serves as a stepping stone for broader insights into their role in shaping climate patterns and effects. By addressing these critical knowledge gaps, we can better inform mitigation strategies and policy decisions aimed at tackling climate change.

The Role of Green Legacy in Promoting Sustainable Development and Combating Climate Change

The Impact of Green Legacy on Sustainable Development and Climate Change in Ethiopia is a topic that requires further research to address the existing research gap. While the Green Legacy initiative launched by the Ethiopian government aims to combat deforestation and soil degradation by promoting tree planting, there is limited scholarly literature specifically examining its impact on climate change mitigation and adaptation. A research gap exists in understanding the effectiveness of the Green Legacy in reducing greenhouse gas emissions, promoting carbon sequestration, and enhancing the resilience of local ecosystems to climate change impacts. Further research in this area is crucial to assess the initiative's contribution to climate change mitigation and adaptation efforts in Ethiopia, inform policy decisions, and guide the implementation of effective strategies to address the country's environmental challenges. The objective of this study was to investigate the impact of green legacy in promoting climate change. Mixed research methods were employed. To get the primary data needed for the study, questionnaires and interviews as data collection tools. The study adopted both descriptive and explanatory research designs. Through explanatory research design, the study investigated the effect of planting and growing trees on climate change. The study used a descriptive method to assess changes and opportunities in Ethiopian green legacy. Logit regression analysis was applied to examine the factors that affect business climate change. While working on the green legacy agenda, the changes such as water scarcity to grow trees, overexploited forests, and lands that are difficult to easily recover through green legacy. The research findings on this topic can contribute to the understanding of the effectiveness of large-scale reforestation initiatives in combating climate change. By evaluating the outcomes of the Green Legacy, researchers can identify successful strategies, challenges, and potential improvements that can be adapted to other regions facing similar environmental issues. Secondly, this research can provide insights into the potential contribution of tree planting initiatives to carbon sequestration, which is vital for global efforts to reduce greenhouse gas emissions and mitigate climate change impacts. Additionally, understanding the impact of the Green Legacy on local ecosystems can guide the development of sustainable land management practices, habitat restoration, and biodiversity conservation. Lastly, the findings of this research can inform evidence-based policy decisions, guiding the formulation and implementation of effective environmental and climate change policies at the national and international levels. Overall, the research implications highlight the importance of studying the impact of the Green Legacy in Ethiopia to address climate change challenges and advance sustainable development goals. Based on regression analysis, the results of the study revealed that planting and growing have a negative effect on climate change. The scholars suggested the federal government of Ethiopia continue the plantation of trees and growing of trees to maintain sustainable development through green and healthy countries after climate action.