Pham Dinh Long, Nguyen Huynh Mai Tram and Pham Thi Bich Ngoc
The transition from fossil fuel-based energy systems to renewable energy sources, commonly referred to as the energy transition, is essential for combating climate change…
Abstract
Purpose
The transition from fossil fuel-based energy systems to renewable energy sources, commonly referred to as the energy transition, is essential for combating climate change. However, comprehensive studies that thoroughly examine the financial mechanisms involved in this process are lacking. Despite the availability of various financial tools, there is a notable absence of extensive research that synthesizes and categorizes these mechanisms into broad groups.
Design/methodology/approach
A systematic literature review is used to explore a comprehensive framework for financial mechanisms related to the energy transition and their application across six stages of the process.
Findings
The framework of financial mechanisms for energy transition encompasses these six factors: public financing mechanisms, private financing mechanisms, market-based mechanisms, innovative financing mechanisms, risk mitigation instruments and institutional support and capacity building.
Originality/value
This is the first study that thoroughly reviewed the financial mechanisms involved in the energy transition process.
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Dandub Palzor Negi and E.P. Abdul Azeez
This paper critically examines the state of tribal health in India by analyzing the accessibility and availability of traditional medicine and modern healthcare.
Abstract
Purpose
This paper critically examines the state of tribal health in India by analyzing the accessibility and availability of traditional medicine and modern healthcare.
Design/methodology/approach
This essay is the product of an extensive review of the literature and authors' personal experience in working with the tribal communities.
Findings
The traditional medicinal practices once very prevalent among the tribal communities are diminishing due to various socio-economic, environmental and political factors. Modern healthcare in India's tribal region is characterized by a lack of availability, accessibility and affordability. As a result of the diminishing traditional practices and inaccessible modern healthcare provisions, tribal communities depend on quacks and magico-religious practices.
Originality/value
This essay advocates for urgent policy interventions to integrate traditional medicine and modern healthcare practices to address critical tribal health issues. Preservation of traditional medicinal knowledge-base and improving research in the field have the potential to address the health of tribal communities and of others. The accessibility and availability of modern healthcare facilities in tribal regions should be improved to ensure better health outcomes.
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Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in…
Abstract
Purpose
Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.
Design/methodology/approach
As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.
Findings
The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.
Originality/value
This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.
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Guilherme F. Frederico, Vikas Kumar, Jose Arturo Garza-Reyes, Roberto A. Martins and Anil Kumar
Mangey Ram, Ioannis S. Triantafyllou, Liudong Xing and Ajit Kumar Verma