Review Article
The Role of Blockchain Technology in Addressing Climate Change: A Review
Issue:
Volume 14, Issue 1, February 2025
Pages:
1-6
Received:
1 January 2025
Accepted:
17 January 2025
Published:
10 February 2025
Abstract: Climate Change is one of the biggest concerns of the 21st century due to its worldwide economic, social, and environmental causes and consequences which primarily impact poor countries. Climate models indicate that if present emissions trends continue, temperatures might rise by more than 2°C, which is alarming. As a result, over the next 40 years, yearly emissions per person must be gradually reduced from about seven tons to two tons. Blockchain technology, which provides a decentralized, transparent, and unchangeable system that can encourage sustainable practices, has become a game-changing instrument in the worldwide struggle to combat climate change. This study investigates how blockchain can be used to improve environmental programs' efficiency, accountability, and transparency in the fight against climate change. Better carbon tracking, renewable energy certificate verification, and assistance for sustainable supply chains are all made possible by blockchain's special features. Blockchain technology has a lot of promise to combat climate change and promote sustainable development, but its uptake needs to be balanced with factors like scalability and energy efficiency. It can offer long-term answers to climate issues by advancing low-energy consensus methods and enabling legislation, fostering a more transparent and sustainable global economy. This review offers important insights for the different stakeholders looking to use technology for environmental improvement by highlighting the benefits and difficulties of incorporating blockchain into climate action plans. In the end, this research emphasizes that although blockchain is not a magic bullet for climate change problems, it has great potential as a component of a larger set of solutions required to successfully lessen its effects.
Abstract: Climate Change is one of the biggest concerns of the 21st century due to its worldwide economic, social, and environmental causes and consequences which primarily impact poor countries. Climate models indicate that if present emissions trends continue, temperatures might rise by more than 2°C, which is alarming. As a result, over the next 40 years,...
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Research Article
Next-Generation K-Means Clustering: Mojo-Driven Performance for Big Data
Issue:
Volume 14, Issue 1, February 2025
Pages:
7-19
Received:
6 March 2025
Accepted:
18 March 2025
Published:
31 March 2025
DOI:
10.11648/j.ijiis.20251401.12
Downloads:
Views:
Abstract: K-means clustering, a fundamental unsupervised machine learning technique, is widely used in anomaly detection, image recognition, and customer segmentation. Traditional Python implementations, especially those using NumPy, face performance challenges with large, high-dimensional datasets due to Python’s interpreted nature and dynamic typing. This paper introduces an innovative approach using the Mojo programming language, designed for AI development, to significantly improve the performance of the k-means clustering. Mojo combines Python’s usability with the performance of system programming languages by offering features like vectorization, parallelization, and strong typing. We compare a NumPy-based Python implementation with an optimized Mojo implementation, detailing the translation process and optimization techniques, including Mojo’s support for Single Instruction, Multiple Data (SIMD) operations, explicit memory management, and efficient data structures. These features significantly accelerate distance calculations crucial to the k-means algorithm. Benchmarks on synthetic datasets with varying sample sizes, feature counts, and cluster numbers demonstrate that the Mojo implementation consistently outperforms both the standard Python implementation and the highly optimized sci-kit-learn k-means, achieving speedups of 6x to 250x. These results highlight Mojo’s potential as a powerful tool for high-performance data analysis, particularly for computationally demanding algorithms like k-means clustering, and contribute to high-performance computing in machine learning. This research sets the stage for further exploration of Mojo’s applicability to other algorithms and hardware-specific optimizations for modern computing architectures.
Abstract: K-means clustering, a fundamental unsupervised machine learning technique, is widely used in anomaly detection, image recognition, and customer segmentation. Traditional Python implementations, especially those using NumPy, face performance challenges with large, high-dimensional datasets due to Python’s interpreted nature and dynamic typing. This ...
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,
Chinwe Gilean Onukwugha
