Truck Drivers Research Articles

Smart tachographs application in monitoring and controlling the Operation of Vehicles and Drivers in „ITS“ Environment

ITS systems enable information transparency, manageability and improved response of the traffic system, so they have the epithet of intelligent. The application of ITS solutions on tachographs, as devices that record all the activities of truck and bus drivers, is defined in the ISO 14813-1:2015 Standard in the context of remote data transmission from tachographs. In view of the growing problems regarding violations of regulations on working hours and mandatory vacations for mobile workers, frequent manipulations and misuse of tachographs, as well as regarding the state of traffic safety and increasing environmental pollution, the EU has set stricter conditions in the field of driving these commercial vehicles in its regulations, in the manner of implementing the ITS system of smart tachographs, which brings significant benefits, especially in terms of the work of control authorities as entities that influence better compliance with regulations and the enforcement of legal regulations.

Yasri Miftah Joenoes

Background: Mining accidents in truck transportation activities are the most common accidents in the mining industry in Southeast Sulawesi Province in the last 3 years and often cause fatalities. Previous studies have not identified all types of dominant causes of mining accidents in truck transportation activities, be it direct causes, basic causes, or lack of management control. The purpose of this study is to analyze the causes of mining accidents in truck transportation activities in the Southeast Sulawesi Region in 2021-2023. Method: The research used is qualitative phenomenology to understand and describe the dominant accident-causing factors from mining accident phenomena in transportation activities using trucks. Results: The dominant causes of mining accidents in truck transportation activities in Southeast Sulawesi Province in 2021-2023 were: driving above the speed limit; drivers not wearing safety belts and helmets; downhill road conditions with a grade above 12% and long; drivers lack knowledge/skills; lack of supervision; and inadequate SOP and HIRA. Conclusion: Corrective actions that can be taken to minimize the occurrence of mining accidents in truck transportation activities are: increasing supervision and socialization related to speed limits, as well as the use of safety belts and helmets; striving for engineering, maximizing supervision and completing road signs on downhill road segments with grades above 12% and long; implementing new worker training and annual refresher courses with output in the form of SIMPER which is valid for a maximum of 1 year; meeting the needs/ratio of operational supervisors, and ensuring that SOP and IBPR are adequate for truck transportation activities.

Techno-economic and environmental assessment of green hydrogen and ammonia production from solar and wind energy in the republic of Djibouti: A geospatial modeling approach

This study conducts a thorough economic and technical analysis to assess the viability of green hydrogen and green ammonia production using renewable energy sources in the Republic of Djibouti. We explore the economic competitiveness of utilizing wind and solar power for sustainable energy production through various measures including levelized cost of energy (LCOE), hydrogen (LCOH), and ammonia (LCOA). Our analysis incorporates sensitivity assessments and Monte Carlo simulations to predict financial feasibility and environmental impact, focusing on CO2 emission reductions. A cost mapping of electricity, green hydrogen, and green ammonia from solar and wind resources was created to find Djibouti's most cost-effective production sites. The feasibility of exporting green ammonia to neighboring countries with high fertilizer demand was also evaluated using rail and truck transport scenarios. Key findings demonstrate that Moulouhlé, endowed with robust wind and solar resources, presents a strategic site for deploying renewable energy technologies. Wind energy, with a lower LCOE of 0.066 $/kWh compared to solar's 0.093 $/kWh, emerges as a more cost-effective solution for both hydrogen and ammonia production. Notably, wind-powered hydrogen production costs are 2.25 $/kgH2, significantly lower than solar-powered costs at 4.17 $/kgH2. In terms of green ammonia, wind power achieves production costs of 399.76 $/tonNH3, outperforming solar power which stands at 537.11 $/tonNH3. Additionally, our study evaluates the logistics of exporting green ammonia, determining rail transport as a more economically viable option than trucking. This research not only underscores Djibouti's potential as a leader in green energy exportation but also aligns with global decarbonization efforts, showcasing significant CO2 savings—154.94 Mt annually from wind. This work emphasizes the importance of strategic resource utilization in Djibouti, offering insights critical for stakeholders in making informed decisions about investing in renewable energy infrastructures. The outcomes suggest a promising future for regional energy sustainability and economic resilience, fostering a transition towards low-carbon energy systems.

Keep on truckin’: how effective are health behaviour interventions on truck drivers’ health? A systematic review and meta-analysis

BackgroundTruck drivers are a vital workforce, but have higher rates of obesity and other chronic diseases than the general population. The occupation’s sedentary nature, limited physical activity opportunities and access to healthy food, and irregular sleeping patterns contribute to poor health. This systematic review and meta-analysis aimed to evaluate the effectiveness of interventions on health behaviours and cardiometabolic biomarkers of health in truck drivers.MethodsA systematic search was conducted in February 2024, and reported according to PRISMA 2020 guidelines. Experimental studies targeting physical activity, sedentary behaviour, sleep, diet, weight loss, drug/alcohol use, and/or smoking were eligible. Two reviewers independently screened and completed data extraction and risk of bias assessment. Data were combined at the study level. Pooled statistics were calculated using mean differences (MD) or standardised mean differences (SMD) for outcomes that were reported in ≥2 studies. Pre- and post-intervention means and standard deviations (SD) for the intervention and control groups were used to compute effect sizes.ResultsNineteen studies (n=2137 participants) were included. Meta-analyses found a small-to-moderate increase in fruit and vegetable consumption (SMD 0.32, p=0.03) with no other significant effects on other outcome variables.ConclusionsInterventions are moderately effective in increasing truck drivers’ fruit and vegetable consumption, but not other outcomes. There is a dearth of research in the driver population compared to other occupational groups. Future interventions should consider workplace and environmental factors to promote the health and wellbeing of truck drivers.Trial registrationThe study protocol was registered on PROSPERO (CRD42021283423).

A sustainable inventory model for a two-echelon cold chain with green technology investments and stock-dependent demand under carbon emissions tax regulation

Cold chains are specialised supply chains that make use of refrigerated storage facilities and distribution nodes. Products that have to be stored and distributed via cold chains are temperature-sensitive items such as perishable food or vaccines. If these products are not maintained at the correct temperatures, their safety and quality may be comprised with dire consequences for end-users. Operating refrigerated storage units consumes a lot of energy and the majority of transportation trucks used for distribution are powered by fossil fuels and therefore, cold chains are a major source of carbon emissions. With this in mind, this paper proposes a sustainable inventory model for a two-echelon cold chain with stock-dependent demand and green technology investments under a carbon emissions tax regulation. The carbon tax is imposed by policy makers to encourage the cold chain to reduce its emissions whereas, the green technology investment is a voluntary measure taken by the cold chain to reduce its carbon emissions. Based on the results, a carbon tax regulation is about 3% more effective than a green technology investment in terms of profit maximisation potential. Moreover, when it comes to investing in green technologies, there is an optimal investment amount above which increasing the investment results in decreased profits and increased emissions. Other avenues that the cold chain can explore in effort to improve profitability include increasing the potential market size, displaying higher volumes of stocks, using larger capacity transportation trucks and filling the trucks to capacity.

Long-Haul Truck Drivers' Perceptions of Truck Stops and Rest Areas: Focusing on Health and Wellness.

The work and life routine of long-haul truck drivers (LHTDs) involve the use of truck stops and rest areas to meet their basic human needs. These extensions of their workspaces on the road do not always offer adequate physical structures and services that drivers need for optimal health. This study aimed to evaluate long-haul truck drivers' perceptions of food services, safety, physical activity, rest, and personal hygiene offered at truck stops and rest areas, as well as the correlation between these perceptions and sociodemographic, health, and work conditions variables. A cross-sectional, quantitative, and descriptive study was conducted with long-haul truck drivers from the southern region of Brazil. For data collection, a sociodemographic questionnaire and a Likert scale on food, rest, personal hygiene, safety, and physical activity services offered at truck stops and rest areas along Brazilian roads from March to August 2023 were used. The data were analyzed with simple frequency descriptive statistics. The sample consisted of 175 long-haul truck drivers. Out of these, 70.29% declared that the services of the truck stops and rest areas were charged; more than half (53.59%) of the professionals evaluated the rest service as "good" or "excellent"; the food services were "good" or "excellent" for 42.24% of the drivers. The spaces for physical activities were the worst evaluated as "bad" or "terrible" by 41.61%, followed by bathroom services (28.42%) and safety (34.24%). Rest and feeding services had better evaluations, while the services of bathroom, safety, and physical activity presented worse evaluations. Variables such as nationality, weekly working days, and marital status presented positive significance and influenced drivers' perceptions of the services offered at truck stops and rest areas. Drivers who were Brazilian and worked more than five days a week negatively evaluated the services of rest (p = 0.018), safety [0.020], physical activity (0.003), and bathrooms (0.020). In addition, the physical activity services were better evaluated by single drivers than married drivers. These findings suggest that the work conditions and nationality may influence LHTDs' perceptions of services and structures of truck stops and rest areas. These findings may reflect a lack of investments and support efforts to improve basic services such as personal hygiene, a safe environment, and physical exercises, which are fundamental to the health of the workers and aimed at reducing vulnerability and a sedentary lifestyle and meeting the basic human needs of LHTDs.

Revealing the impacts of COVID-19 pandemic on intercity truck transport: New insights from big data analytics

Intercity truck transport emerged as a crucial lifeline for maintaining city operations during COVID-19 pandemic. Understanding pandemic-imposed impacts on intercity truck transport can inform policymakers in crafting more effective strategies for future crises and disruptions. However, to our best knowledge, previous research predominantly focused on freight movements under normal circumstances. Due to the data limitation, the pandemic-related studies commonly relied on freight survey and focused on specific industries, which cannot capture the full spectrum of factors influencing freight trip generation (FTG) during the pandemic. Here, a novel dataset capturing large-scale individual truck movements during the COVID-19 pandemic is provided. By leveraging the mobility dataset, pandemic-induced changes in truck transport demand structure are quantified using spatial statistical methods. Furthermore, an interpretable machine learning framework for intercity freight demand estimation is developed, revealing the complex interplay of factors that influence and shape the behavior shifts of intercity truck transport systems due to the pandemic outbreak. The findings suggest significant changes in various factors influencing intercity truck movements across local and broader regions, emphasizing city-specific challenges amidst pandemic. The developed FTG model could serve as a tool to predict freight demand between cities for future crises and to support policymaking in the practice of freight management.

Cardiovascular Disease Among Commercial Motor Vehicle Drivers: A Focus on Truck Drivers.

Cardiovascular Disease Among Commercial Motor Vehicle Drivers: A Focus on Truck Drivers.

Coordinated planning model for multi-regional ammonia industries leveraging hydrogen supply chain and power grid integration: A case study of Shandong

With the advancement of green hydrogen production technology, power-to-ammonia (P2A) has emerged as a pivotal approach for consuming large-scale renewable resources. However, the inherent intermittency of wind and solar resources leads to a spatial and temporal mismatch between supply and demand, rendering effective integration within a single region challenging and necessitating cross-regional collaborative planning. To address this, we propose a coordinated planning model for a multi-regional ammonia industrial system (MAIS) that leverages the integration of a hydrogen supply chain (HSC) and power grid (PG), termed HSC-PG-MAIS. During the modeling of the HSC, we developed a novel hydrogen truck transportation model that allows trucks to serve as both hydrogen storage and transportation resources. Furthermore, considering the cooperative relationships among ammonia industrial systems in different regions, we proposed a Nash bargaining method based on the magnitude of interactive value contributions to equitably allocate the cooperation costs and benefits of the MAIS. Finally, real data from Shandong Province in China is used to validate the effectiveness and significance of the proposed model and strategy. The results indicate that the proposed model can effectively reduce the overall operating costs of the system and enhance its flexibility, providing valuable insights into exploring the collaborative potential of large-scale renewable resources across regions.

Comprehensive survey of the truck transportation process: The impact of team driving on health and safety conditions

BackgroundGiven the limited research conducted on the role of a human co-driver in mitigating occupational health and safety risks, this study aims to investigate the impact of a co-driver on health and safety conditions in the truck transportation process. MethodsThe truck transportation process was divided into three main stages: truck loading, driving, and unloading. Using the job safety analysis (JSA) method, an analysis of the tasks within each stage was conducted, allowing for the identification of essential and safe tasks and conditions. A questionnaire, based on this information, was developed, validated, and used in this study. ResultsThe findings of this study demonstrate that a human co-driver positively impacted the drowsiness and alertness levels of truck drivers. Furthermore, improvements were observed in driving and parking performance, alongside a reduction in strenuous tasks and subsequent fatigue. The results conclusively indicate that the presence of a human co-driver significantly enhances health and safety conditions, particularly during the driving stage in comparison to the other stages of the truck transportation process.

Clinical Utility of Fourth Generation AlereTM HIV P24 Core Antigen Rapid Combo Test Panels for Improved Detection of Human Immunodeficiency Virus Infection among Pre-Screened HIV 1 and 2 Antibody Sero-Negative Long-Distant Truck Drivers in Calabar Municipality, Cross River State, Nigeria

Recent studies have shown that transmission of human immunodeficiency virus (HIV) infection from infected but screened HIV antibody sero-negative individuals have continued to be a public challenge. This unprecedented finding may be due to the long pre- seroconvertion / window period or other predisposing factors like resource poor settings especially where there are no availability of ideal HIV p24 antigen /antibody fourth generation panels or biomarkers during early phase of this infection. The current study investigated HIV p24 core antigen status of healthy Long Distant Truck Drivers (LDTDs) of commercial and public vehicles, who were previously screened and documented as HIV antibody- sero-negative males with ages above 20 years in Calabar Municipality, Cross River State, Nigeria. About five milliliters of blood samples were collected from precounseled and consented 400 apparently healthy married and unmarried male participants who were initially screened for HIV 1 and 2 antibodies using DetermineTM HIV-1 and 2 (Inverness Medical Japan company limited ), Stat-Pak HIV-1 and 2 (Chembio Diagnostic System International Inco-operation ,United State of America) and HIV Uni-gold rapid test kit (Trinity Biotech, United State of American ) respectively and repeated for HIV p24 antigens screening using Alere TM HIV-1 and 2 p24 Ag/ Ab Combo test kit-tool ( Inverness Medical Japan company limited, United State America).After data analysis using IBM-SPSS version 26, about 12 (3%), 10 (2.5%) and 9 (2.25%) of participants were reactive to HIV 1 and 2 Determine, Stat-Pak and Unigold test panels respectively, with statistical significant difference between the results according to marital status (p=0.7065) and mean age range in years .While some of the 15 (3.75%), 12 (3.0%) and 10 (2.5%) participants who reacted to HIV 1 and 2 antibodies panels including the 388, 390 and 391 participants respectively who initially tested non-reactive for the three HIV antibody kits become reactive to HIV p24 core antigens with no statistically significant difference between the results of the three HIV 1& 2 antibody test kits and HIV p24 antigen/antibody tests (P=0.901).In the current study, the use of fourth generation AlereTM HIV P24 Core Antigen rapid test kit tool had not only improved detection of HIV infection in the index population but had also demonstrated the discrepancies, limitations and short-coming associated with the routine antibody screening testing panels when done alone in this population at early stage of HIV infection and long HIV window period.

Case Study: Can Deaf Truck Drivers Be Trained in a Reasonably Safe Manner; An Equal Employment Opportunity Commission Suit

Case Study: Can Deaf Truck Drivers Be Trained in a Reasonably Safe Manner; An Equal Employment Opportunity Commission Suit

Advances in terminal management: simulation of vehicle traffic in container terminals

AbstractControlling and managing traffic flows on internal roads in container terminals are crucial in achieving expected productivity levels and reducing negative externalities caused by congestion inside and outside the terminal areas. This paper proposes a simulation approach which terminal operators can use as a decision-support tool to assess the effects of their management strategies and improve terminal performance, resilience, and sustainability. A microscopic traffic simulation approach models key operations of a typical container terminal affecting road traffic flows. In particular, to estimate quantitative indicators, an import truck process is reproduced, considering the overlapping of the external truck and internal trailer flows. To measure environmental impacts, the model is extended with an instantaneous emissions model linked directly to the step-by-step traffic data. The proposed method is tested on a sector of the PSA Genova Pra’, the main Italian container gateway terminal. Performance indicators related to the terminal’s efficiency and sustainability are estimated, to compare alternative scenarios considering possible operational configurations and disturbance events, such as the closure of a part of the yard. By focusing on the interactions between vehicle flows and terminal equipment operations, this approach offers a new perspective on terminal operations, oriented both towards container terminal operators and stakeholders, such as road hauliers.

Climate neutrality of the French energy system: overview and impacts of sustainable aviation fuel production

CO2 emission reduction of sectors such as aviation, maritime shipping, road haulage, and chemical production is challenging but necessary. Although these sectors will most likely continue to rely on carbonaceous energy carriers, they are expected to gradually shift away from fossil fuels. In order to do so, the prominent option is to utilize alternative carbon sources—like biomass and CO2 originating from carbon capture—for the production of non-fossil carbonaceous vectors (biofuels and e-fuels). However, the limited availability of biomass and the varying nature of other carbon sources necessitate a comprehensive evaluation of trade-offs between potential carbon uses and existing sources. Then, it is primordial to understand the origin of carbon used in sustainable aviation fuel (SAF) to understand the implications of defossilizing aviation for the energy system. Moreover, the production of SAF implies deep changes to the energy system that are quantified in this work. This study utilizes the linear programming cost optimization tool EnergyScope TD to analyze the holistic French energy system, encompassing transport, industry, electricity, and heat sectors while ensuring net greenhouse gas neutrality. A novel method to model and quantify carbon flows within the system is introduced, enabling a comprehensive assessment of greenhouse gas neutrality. This study highlights the significance of fulfilling clean energy requirements and implementing carbon dioxide removal measures as crucial steps toward achieving climate neutrality. Indeed, to reach climate neutrality, a production of 1,046 TWh of electricity by non-fossil sources is needed. Furthermore, the findings underscore the critical role of efficient carbon and energy valorization from biomass, providing evidence that producing fuels by combining biomass and hydrogen is optimal. The study also offers valuable insights into the future cost and impact of SAF production for air travel originating from France. That is, the European law ReFuelEU would increase the price of plane tickets by +33% and would require 126 TWh of hydrogen and 50 TWh of biomass to produce the necessary 91 TWh of jet fuel. Finally, the implications of the assumption behind the production of SAF are discussed.

Identity Disclosure and Anthropomorphism in Voice Chatbot Design: A Field Experiment

Fueled by the widespread adoption of algorithms and artificial intelligence, the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging the two design features identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to around 11% reduction in the response probability. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response probability, conversation length, and the probability of order acceptance intention by over 5.6%, 24.9%, and 10.1%, respectively. Moreover, even when the chatbot’s identity is disclosed along with humanizing features, the operational outcomes still improve. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation for the performance improvement—the enhanced trust between humans and algorithms—and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform. This paper has been This paper was accepted by Felipe Caro for the Special Issue on the Human-Algorithm Connection. Funding: This study is supported by the National Natural Science Foundation of China [Grants 72172169 and 91646125], Program for Innovation Research at the Central University of Finance and Economic, and Shanghai Pujiang Program. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03833 .

Joint optimization of truck-drone routing for last-mile deliveries in urban areas

This paper presents a joint routing optimization model to satisfy the on-demand customer requirements by minimising the transportation and time penalty costs of trucks and drones, considering the constraints on paths and delivery time of trucks and drones, the drone battery capacity limit, and the customer demands. The improved genetic algorithm is proposed to solve the optimization model followed by the computational experiments demonstrating the superiority of the improved genetic algorithm over the original genetic algorithm in computational efficiency, economy, and punctuality. Further, the comparative analysis demonstrates that the multi-drone joint delivery mode considering the customer demands performs better than the other delivery modes and that the delivery routes can be re-optimised promptly to satisfy the real-time customer demands. The sensitivity analysis on the drone design parameters provides theoretical insights into deploying the size and speed of drone platoons when promoting truck-drone joint delivery in applications.

Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network

In deep mine production operations, the challenging operating environment intensifies the workload and pressure on coal miners. Long-term exposure to high-intensity operating pressure can seriously impact the physical and mental health of miners, leading to unsafe behaviors and accidents. To identify the pressure of miners' operations, this paper examines various driving scenarios, such as the deep-well tunneling machine cutting the wall and opening the alley, the shoveling machine shoveling ore, and the pickup truck driver transporting. The paper randomly collects facial images of miners during each operation using an explosion-proof CCD camera to obtain the facial expression characteristic data of miners. The Ferface2013 facial expression dataset was used to establish the dataset. The depth separable convolutional neural network MiniXception was used for training and to output the classification results of the pressure degree of deep shaft miners. A MiniXception-based miners' operating pressure recognition model was established. The training time, precision, recall, F1 score, and classification accuracy confusion matrix were selected. The study evaluated the effectiveness of the recognition model by measuring its training time, precision, recall, F1 score, and classification accuracy confusion matrix. The results indicate that the model has a correct recognition rate of 88% for the pressure state, 91% for the pleasure state, and 74% for the normal state. The overall accuracy of the model is 0.843. Therefore, the MiniXception recognition model is suitable for recognizing the pressure of miners' operations in deep mines. This can meet practical needs and is useful for preventing major accidents in mines, managing on-site safety, and managing safety in non-hazardous areas. It has important theoretical and practical significance.

En-route recharging scheduling strategy for electric trucks serving inter-terminal container transportation

ABSTRACT Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size.

Analisis Kendala Pelayanan Jasa Bongkar Muat Peti Kemas Terhadap Kelancaran Operasional pada Perusahaan Ekspedisi Muatan Kapal Laut

Container services are services related to the use of containers or containers in the process of shipping goods. The container itself is a special container used to pack and transport goods efficiently, especially in trade. In the process of putting goods or stuffing goods that are packaged in the form of pallets, metal boxes and bulk. The process to enter a 20-foot or 40-foot container is determined by the number of buyer requests. The author chose qualitative descriptive research because this research is in accordance with the nature of the author's research which is descriptive and uses interview techniques, documentation, and others. In addition, this research is also in accordance with the practical research objectives. Mistakes in container stock management can have a devastating impact on the company, such as damage to goods, additional costs, and a tarnished reputation. To improve container loading and unloading services, companies need to improve communication, employee training, truck checks, forklift maintenance, and collaboration with service users. The process of shipping goods through the port involves various stages and parties such as consignees, suppliers, shippers, and field executives. The results of the study can be found that the process of container loading and unloading services in expedition companies experiences various obstacles, such as late truck drivers, delays in sending documents, shortage of containers, damage to container moving equipment at the depot, delays in the start of working hours, and too many containers that do not comply with the specifications by the exporter.

Carbon Footprint Quantification and Reduction Potential of Ecological Revetment in Water Net Region of China: Case Study in Yancheng, Jiangsu Province

With emphasis on constructing low-carbon cities, the renovation of the riverbank highlights energy conservation and carbon reduction. However, methods and standards for quantifying carbon emissions during ecological river channel construction are currently lacking. There is a scientific gap in research into carbon footprint assessment and reduction potential in ecological revetment technologies in water networks of China. This study attempts to clarify the carbon emission factors of different ecological revetment technologies and explore the carbon reduction potential during the construction stage of ecological rivers from the river revetment design, construction process and materials. The results show that in the carbon emission factors of six ecological revetment technologies, building materials have the largest adjusting potential for carbon reduction. The concrete material is responsible for 55.37–95.86% of carbon emissions in six ecological river technologies, with an average proportion of 69.96%. Accordingly, the concrete material emerges as the primary contributor to carbon emissions in ecological river engineering, followed by gasoline truck transportation and earthwork excavation. Moreover, the carbon emissions from ecological frame structures were the largest, followed by those of block structures, gabion structures, planted concrete and interlocking blocks and the wooden stake structure has the smallest carbon footprint. The choice of ecological revetment technologies is not only related to the realisation of regional water conservancy functions, but it also affects the carbon emissions of water conservancy projects. Engineers and decision-makers should pay great attention to the optimal design of the project, selection of low-carbon materials, energy saving and emission reduction in the construction process. This research not only provides guidance for design units in selecting appropriate river revetment technologies but also offers a theoretical foundation and data support for construction units to optimise their construction process management.