The potential effects of autonomous vehicles (AVs) on greenhouse gas (GHG) emissions are uncertain, although numerous studies have been conducted to evaluate the impact. This paper aims to synthesize and review all the literature regarding the topic in a systematic manner to eliminate the bias and provide an overall insight, while incorporating some statistical analysis to provide an interval estimate of these studies. This paper addressed the effect of the positive and negative impacts reported in the literature in two categories of AVs: partial automation and full automation. The positive impacts represented in AVs' possibility to reduce GHG emission can be attributed to some factors, including eco-driving, eco traffic signal, platooning, and less hunting for parking. The increase in vehicle mile travel (VMT) due to (i) modal shift to AVs by captive passengers, including elderly and disabled people and (ii) easier travel compared to other modes will contribute to raising the GHG emissions. The result shows that eco-driving and platooning have the most significant contribution to reducing GHG emissions by 35%. On the other side, easier travel and faster travel significantly contribute to the increase of GHG emissions by 41.24%. Study findings reveal that the positive emission changes may not be realized at a lower AV penetration rate, where the maximum emission reduction might take place within 60-80% of AV penetration into the network.
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http://dx.doi.org/10.3390/ijerph18115567 | DOI Listing |
Sci Rep
January 2025
College of Ecology and Environment, Hainan University, Haikou, 570228, China.
Agroforestry systems are known to enhance soil health and climate resilience, but their impact on greenhouse gas (GHG) emissions in rubber-based agroforestry systems across diverse configurations is not fully understood. Here, six representative rubber-based agroforestry systems (encompassing rubber trees intercropped with arboreal, shrub, and herbaceous species) were selected based on a preliminary investigation, including Hevea brasiliensis intercropping with Alpinia oxyphylla (AOM), Alpinia katsumadai (AKH), Coffea arabica (CAA), Theobroma cacao (TCA), Cinnamomum cassia (CCA), and Pandanus amaryllifolius (PAR), and a rubber monoculture as control (RM). Soil physicochemical properties, enzyme activities, and GHG emission characteristics were determined at 0-20 cm soil depth.
View Article and Find Full Text PDFInt J Environ Res Public Health
December 2024
Department of Ophthalmology & Visual Sciences, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY 10461, USA.
(1) Background: Healthcare is a major contributor to global greenhouse gas (GHG) emissions, especially within the surgical suite. Ophthalmologists play a role, since they frequently perform high-volume procedures, such as cataract surgery. This review aims to summarize the current literature on surgical waste and GHG emissions in ophthalmology and proposes a framework to standardize future studies.
View Article and Find Full Text PDFEnviron Sci Technol
January 2025
College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, 300350 Tianjin, China.
Reclaimed asphalt pavement (RAP) is a widely used end-of-life (EoL) material in asphalt pavements to increase the material circularity. However, the performance loss due to using RAP in the asphalt binder layer often requires a thicker layer, leading to additional material usage, energy consumption, and transportation effort. In this study, we developed a parametric and probabilistic life cycle assessment (LCA) framework to robustly compare various pavement designs incorporating recycled materials.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
The co-gasification of biomass and plastic waste offers a promising solution for producing hydrogen-rich syngas, addressing the rising demand for cleaner energy. However, optimizing this complex process to maximize hydrogen yield remains challenging, particularly when balancing diverse feedstocks and improving process efficiency. While machine learning (ML) has shown significant potential in simulating and optimizing such processes, there is no clear consensus on the most effective regression models for co-gasification, especially with limited experimental data.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Renewable Energy Lab, College of Engineering, Prince Sultan University, Riyadh, 11586, Saudi Arabia. Electronic address:
Saudi Arabia is one of the largest greenhouse gas (GHG) emitters due to its heavy reliance on fossil fuels, has begun taking proactive steps to address climate change under Vision 2030. The initiative aims to reduce the country's GHG emissions. As part of this effort, the government is transitioning to renewable energy (RE) to decrease its dependency on oil and support sustainable environmental development.
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