Search Results (13)

To enhance the operational efficiency of container multimodal transportation and mitigate carbon emissions during freight transit, this study investigates carbon emission-conscious multimodal transportation route optimization models and solution methodologies. Addressing the path optimization challenges under uncertain conditions, triangular fuzzy numbers are employed to characterize transportation time uncertainty, while a scenario-based robust regret model is formulated to address freight price volatility. Concurrently, the temporal value attributes of cargo are incorporated by transforming transportation duration into temporal costs within the model framework. Through the implementation of four distinct low-carbon policies, carbon emissions are either converted into cost metrics or established as constraint parameters, thereby constructing an optimization model with total cost minimization as the objective function. For model resolution, fuzzy chance-constrained programming is adopted for defuzzification processing. Subsequently, a multi-strategy improved whale optimization algorithm (WOA) is developed to solve the formulated model. Numerical case studies are conducted to validate the proposed methodology through comparative analysis with conventional WOA implementations, demonstrating the algorithm’s enhanced computational efficiency. The experimental results confirm the model’s capability to adapt multimodal transportation schedules for cargo with varying temporal value attributes and effectively reduce CO₂ emissions under different carbon reduction policies. This research establishes a comprehensive decision-making framework that provides logistics enterprises with a valuable reference for optimizing low-carbon multimodal transportation operations. Full article

The ocean is one of the world’s largest anthropogenic carbon dioxide (CO₂) sinks, but closing the carbon budget is logistically difficult and expensive, and uncertainties in carbon fluxes and reservoirs remain. One specific challenge is that measuring the CO₂ flux at the air–sea interface usually requires costly sensors or analyzers (USD > 30,000), which can limit observational capacity. Our group has developed and validated a low-cost ΔpCO₂ system, able to measure both pCO₂^water and pCO₂^air, for USD ~1400 to combat this limitation. The device is equipped with Internet of Things (IoT) capabilities and built around a USD ~100 pCO₂ K30 sensor at its core. Our Sensor for the Exchange of Atmospheric CO₂ with Water (SEACOW) may be placed in an observational network with traditional pCO₂ sensors or ∆pCO₂ sensors to extend the spatial coverage and resolution of monitoring systems. After calibration, the SEACOW reports atmospheric pCO₂ measurements that are within 2–3% of the measurements made with a calibrated LI-COR LI-850. We also demonstrate the SEACOW’s ability to capture diel pCO₂ cycling in seagrass, provide recommendations for SEACOW field deployments, and provide additional technical specifications for the SEACOW and for the K30 itself (e.g., air- and water-side 99.3% response time; 5.7 and 29.6 min, respectively). Full article

As the core carrier of the low-carbon transportation transformation, the sustainable optimization of the supply chain of new energy vehicles is crucial to reduce carbon emissions throughout the life cycle and improve resource utilization efficiency. However, the current problems, such as resource waste, duplicate production, and low logistics efficiency caused by insufficient supply chain information coordination, have become bottlenecks restricting the green development of the industry. A large number of studies have shown that information collaboration plays a key role in reducing risks and costs, improving quality and innovation capabilities, adaptability, performance, and supply chain competitiveness in the new energy vehicle supply chain. Although the advantages of supply chain information collaboration are widely known, supply chain information collaboration has not been widely adopted in actual operation, and there are almost no studies on the lack of adoption or the restriction of the development of supply chain information collaboration. Based on the research methods of the modified Delphi technique and analytic hierarchy process (AHP), this paper finds that the lack of information quality, information security, and information collaboration motivation are important factors restricting the collaborative development of information in the new energy vehicle supply chain. Furthermore, an optimization model of the new energy vehicle supply chain information co-evolution game combined with traceability and blockchain technology is proposed, and it is found that the evolutionary game model that solves the stability of information quality and information security has a significant effect on the information collaborative optimization of the new energy vehicle supply chain. This study proposes an information co-evolution game model combined with blockchain traceability technology, which can improve the level of information collaboration in the supply chain of new energy vehicles, significantly reduce the “bullwhip effect” and redundant inventory in the supply chain, reduce energy waste and carbon emissions caused by information asymmetry, and improve the overall energy efficiency of the supply chain, so as to provide theoretical support for the sustainable and green supply chain transformation of the new energy vehicle industry. Full article

Cold chain logistics, with its high carbon emissions and energy consumption, contradicts the current advocacy for a “low-carbon economy”. Additionally, in the real delivery process, customers often generate dynamic demand, which has the characteristic of being sudden. Therefore, to help cold chain distribution companies achieve energy-saving and emission-reduction goals while also being able to respond quickly to customer needs, this article starts from a low-carbon perspective and constructs a two-stage vehicle distribution route optimization model that minimizes transportation costs and refrigeration costs, alongside carbon emissions costs. This research serves to minimize the above-mentioned costs while also ensuring a quick response to customer demands and achieving the goals of energy conservation and emission reduction. During the static stage, in order to determine the vehicle distribution scheme, an enhanced genetic algorithm is adopted. During the dynamic optimization stage, a strategy of updating key time points is employed to address the dynamic demand from customers. By comparing the dynamic optimization strategy with the strategy of dispatching additional vehicles, it is demonstrated that the presented model is capable of achieving an overall cost reduction of approximately 17.13%. Notably, carbon emission costs can be reduced by around 17.11%. This demonstrates that the dynamic optimization strategy effectively reduces the usage of distribution vehicles and lowers distribution costs. Full article

Cold chain logistics is an industry that generates high levels of carbon emissions. In the context of a low-carbon economy, it is crucial to recognize the low-carbon competitiveness of regional cold chain logistics and to implement effective measures to guide the development and improvement of their low-carbon competitiveness. This is essential for transitioning the economic development model and promoting low-carbon economic growth. This article proposes a low-carbon competitiveness evaluation model known as the Triangular Fuzzy–Gray Correlation Evaluation Model. This model is based on the Triangular Fuzzy Theory and Gray System Theory. According to the calculated logistics low-carbon competitiveness index, a scatter plot is used to rank and classify the evaluation objects. This method utilizes triangular fuzzy numbers as evaluation levels and further expands upon them by introducing the concept of gray correlation in group decision making. By constructing relative closeness based on curve similarity, the improved method possesses a strong ability to capture information and objectivity compared to traditional models. The selected critical indicators cover four significant aspects: low-carbon environment, low-carbon flow service capability, energy consumption in cold chain logistics, and low-carbon energy transition. Empirical research is being conducted using relevant data from Henan in 2022. The measured results are divided into four levels of competition. Using the diamond model, this study analyzes the development of low-carbon cold chain logistics at different levels in each city and provides corresponding recommendations. Full article

The fast-paced growth of the logistics industry has contributed significantly to China’s high-quality economic development. However, the growth of the logistics industry is frequently accompanied by high levels of pollution, carbon emissions, and energy consumption. How to increase low-carbon logistics capacity has emerged as a research hotspot under the dual carbon goals. This study used entropy weight TOPSIS to evaluate the low-carbon logistics capacity and dynamic fuzzy-set qualitative comparative analysis (fsQCA) to shed light on the antecedent conditions that influenced low-carbon logistics capability by using panel data from 30 Chinese provinces between 2008 and 2021. Based on the empirical results, several conclusions are drawn: (1) The comprehensive score show that while province low-carbon logistics capacity varies, most of them exhibit a general growing tendency in most provinces, where Beijing, Shanghai, Tianjin, Guangdong and Zhejiang rank among the top five. (2) Three configurations, digital empower capital intensive type, digital empower labor intensive type, and green ecology plus technology innovation type that lead to better low-carbon logistics capacity. Related policy recommendations are proposed, including strengthening the synergistic development of the digital economy and the logistics industry, promoting research and development of green and innovative technologies, and reinforcing the constraints of the dual-carbon target. Full article

The urbanization of a region is affected by the implementation of various policies, and to explore the specifics of the environmental regulation at today’s new level of urbanization, the increased logistics capacity of a region and the consequent carbon emissions must be the focus of our attention. For the values considered by the study, the six central provinces of China have obvious location advantages and urban–rural differences, so a static panel regression effect model was constructed based on the inter-provincial panel data of the six central provinces of China from 2005–2019, and the entropy weight method was applied to quantify the low-carbon logistics capacity and new urbanization level in the region. The model explores the relationship between environmental regulation, regional low-carbon logistics capabilities, and the level of new urbanization. The results of the study show that the levels of new urbanization in all six provinces are increasing rapidly, year on year. Environmental regulation has a positive impact on regional low-carbon logistics capabilities and the level of new urbanization, and environmental regulation promotes the improvement of the level of new urbanization through a significant positive impact on regional low-carbon logistics capabilities, and there is an intermediary conduction effect. This paper provides valuable reference suggestions for low carbon development and new urbanization in six central provinces through empirical research. Full article

Many countries are immersed in several strategies to reduce the carbon dioxide (CO₂) emissions of internal combustion engines. One option is the substitution of these engines by electric and/or hydrogen engines. However, apart from the strategic and logistical difficulties associated with this change, the application of electric or hydrogen engines in heavy transport, e.g., trucks, shipping, and aircrafts, also presents technological difficulties in the short-medium term. In addition, the replacement of the current car fleet will take decades. This is why the use of biofuels is presented as the only viable alternative to diminishing CO₂ emissions in the very near future. Nowadays, it is assumed that vegetable oils will be the main raw material for replacing fossil fuels in diesel engines. In this context, it has also been assumed that the reduction in the viscosity of straight vegetable oils (SVO) must be performed through a transesterification reaction with methanol in order to obtain the mixture of fatty acid methyl esters (FAMEs) that constitute biodiesel. Nevertheless, the complexity in the industrial production of this biofuel, mainly due to the costs of eliminating the glycerol produced, has caused a significant delay in the energy transition. For this reason, several advanced biofuels that avoid the glycerol production and exhibit similar properties to fossil diesel have been developed. In this way, “green diesels” have emerged as products of different processes, such as the cracking or pyrolysis of vegetable oil, as well as catalytic (hydro)cracking. In addition, some biodiesel-like biofuels, such as Gliperol (DMC-Biod) or Ecodiesel, as well as straight vegetable oils, in blends with plant-based sources with low viscosity have been described as renewable biofuels capable of performing in combustion ignition engines. After evaluating the research carried out in the last decades, it can be concluded that green diesel and biodiesel-like biofuels could constitute the main alternative to addressing the energy transition, although green diesel will be the principal option in aviation fuel. Full article

The interface between land and sea is a key environment for biogeochemical carbon cycling, yet these dynamic environments are traditionally under sampled. Logistical limitations have historically precluded a comprehensive understanding of coastal zone processes, including ocean acidification. Using sensors on autonomous platforms is a promising approach to enhance data collection in these environments. Here, we evaluate the use of an autonomous surface vehicle (ASV), the C-Worker 4 (CW4), equipped with pH and pCO₂ sensors and with the capacity to mount additional sensors for up to 10 other parameters, for the collection of high-resolution data in shallow coastal environments. We deployed the CW4 on two occasions in Belizean coastal waters for 2.5 and 4 days, demonstrating its capability for high-resolution spatial mapping of surface coastal biogeochemistry. This enabled the characterisation of small-scale variability and the identification of sources of low pH/high pCO₂ waters as well as identifying potential controls on coastal pH. We demonstrated the capabilities of the CW4 in both pre-planned “autonomous” mission mode and remote “manually” operated mode. After documenting platform behaviour, we provide recommendations for further usage, such as the ideal mode of operation for better quality pH data, e.g., using constant speed. The CW4 has a high power supply capacity, which permits the deployment of multiple sensors sampling concurrently, a shallow draught, and is highly controllable and manoeuvrable. This makes it a highly suitable tool for observing and characterising the carbonate system alongside identifying potential drivers and controls in shallow coastal regions. Full article

In order to solve the optimization problem of wet waste collection and transportation in Chinese cities, this paper constructs a chance-constrained low-carbon vehicle routing problem (CCLCVRP) model in waste management system and applies certain algorithms to solve the model. Considering the environmental protection point of view, the CCLCVRP model combines carbon emission costs with traditional waste management costs under the scenario of application of smart bins. Taking into the uncertainty of the waste generation rate, chance-constrained programming is applied to transform the uncertain model to a certain one. The initial optimal solution of this model is obtained by a proposed hybrid algorithm, that is, particle swarm optimization (PSO); and then the further optimized solution is obtained by simulated annealing (SA) algorithm, due to its global optimization capability. The effectiveness of PSOSA algorithm is verified by the classic database in a capacitated vehicle routing problem (CVRP). What’s more, a case of waste collection and transportation is applied in the model for acquiring reliable conclusions, and the application of the model is tested by setting different waste fill levels (WFLs) and credibility levels. The results show that total costs rise with the increase of credibility level reflecting dispatcher’s risk preference; the WFL value range between 0.65 and 0.75 can obtain the optimal solution under different credibility levels. Finally, according to these results, some constructive proposals are propounded for the government and the logistics organization dealing with waste collection and transportation. Full article

Developed societies with advanced economic performance are undoubtedly coupled with the availability of electrical energy. Whilst industrialized nations already started to decrease associated carbon emissions in many business sectors, e.g., by substituting combustion engines with battery-powered vehicles, less developed countries still lack broad coverage of reliable electricity supply, particularly in rural regions. Progressive electrification leads to a need for storage capacity and thus to increasing availability of advanced battery systems. To achieve a high degree of sustainability, re-used batteries from the electromobility sector are appropriate, as they do not consume further primary resources and still have sufficient residual capacity for stationary electrical storage applications. In this article, a blueprint for the electrification of a remote region by utilizing second-life lithium ion traction batteries for an integrated energy system in a stand-alone grid is presented and the implementation by the example case of a Tanzanian island in Lake Victoria is demonstrated. First, economic potentials and expected trends in the disposability of second-life lithium ion batteries and their foreseeable costs are outlined. Subsequently, key decision variables are identified to evaluate logistic aspects and the feasibility of the implementation of an off-grid electrical system in remote areas for economically and geographically unfavorable environments. The practical realization is pictured in detail with a focus on technical performance and safety specificities associated with second-life applications. Therefore, a new type of battery management system is introduced, which meets the special requirements of climate compatibility, low maintenance, enhanced cell balancing capability and cell configuration flexibility, and combined with a fiber-optical sensor system, provides reliable status monitoring of the battery. By carrying out on-site measurements, the overall system efficiency is evaluated along with a sustainability analysis. Finally, the socioeconomic and humanitarian impact for the people on the island is debated. Full article

In order to solve the optimization problem of emergency logistics system, this paper provides an environmental protection point of view and combines with the overall optimization idea of emergency logistics system, where a fuzzy low-carbon open location-routing problem (FLCOLRP) model in emergency logistics is constructed with the multi-objective function, which includes the minimum delivery time, total costs and carbon emissions. Taking into account the uncertainty of the needs of the disaster area, this article illustrates a triangular fuzzy function to gain fuzzy requirements. This model is tackled by a hybrid two-stage algorithm: Particle swarm optimization is adopted to obtain the initial optimal solution, which is further optimized by tabu search, due to its global optimization capability. The effectiveness of the proposed algorithm is verified by the classic database in LRP. What’s more, an example of a post-earthquake rescue is used in the model for acquiring reliable conclusions, and the application of the model is tested by setting different target weight values. According to these results, some constructive proposals are propounded for the government to manage emergency logistics and for the public to aware and measure environmental emergency after disasters. Full article

To improve carbon efficiency for an urban logistics service system composed of a third-party logistics service provider (3PL) and an e-business enterprise, a low-carbon operation game between them was studied. Considering low carbon technology investment cost and sales expansion effect of low carbon level, profit functions for both players were constituted. Based on their different bargaining capabilities, in total, five types of game scenarios were designed. Through analytical solution, Nash Equilibria under different scenarios were obtained. By analyzing these equilibria, four major propositions were given, in which some key variables and the system performance indexes were compared. Results show that the best system yields could only be achieved under the fully cooperative situation. Limited cooperation only for carbon emission reduction does not benefit the system performance improvement. E-business enterprise-leading game’s performance overtook 3PL-leading ones. Full article