Low-carbon Logistics Research Articles

Evaluation of Logistics Development Under the Visual Field of Low-Carbon Environmental Protection Based on Hierarchical Methods

The development of the modern logistics industry in China is relatively backward, and the logistics industry has always shown characteristics of high emissions, high energy consumption, and high pollution. The low-carbon transformation and development of the logistics industry has become a prominent issue. At present, research on low-carbon logistics mainly focuses on its definition and characteristics. The research on its influencing factors is mostly qualitative analysis, and there is relatively little research on performance evaluation. This article establishes a performance evaluation index system from the perspective of low-carbon environmental protection, including information technology level, warehousing, transportation, packaging, etc., and constructs a comprehensive evaluation model that combines an analytic hierarchy process with data envelopment analysis for low-carbon logistics performance evaluation. By reviewing the collected data and taking five logistics enterprises in China as examples, the feasibility of low-carbon logistics performance evaluation indicators and models was verified.

Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.

Sustainable Logistics: Synergizing Passive Design and PV–Battery Systems for Carbon Footprint Reduction

As more companies strive for net-zero emissions, mitigating indirect greenhouse gas emissions embedded in value chains—especially in logistics activities—has become a critical priority. In the European logistics sector, sustainability and energy efficiency are receiving growing attention, given the sector’s intersectional role in both transportation and construction. This transition toward low-carbon logistics design not only reduces carbon emissions but also yields financial benefits, including operational cost savings and new market opportunities. This study examines the impact of passive design strategies and low-carbon technologies in a Swedish logistics center, assessed using the low-carbon design criteria from the BREEAM International standard, version 6. The findings show that passive energy-efficient measures, such as the installation of 47 skylights for natural daylighting, reduced light power density in accordance with AHSHARE 90.1-2019 and the integration of free night flushing, contribute to a 23% reduction in total energy consumption. In addition, the integration of 600 PV panels and 480 batteries with a capacity of 268 ampere-hours and 13.5 kWh storage, operating at 50 volts, delivers a further 56% reduction in carbon emissions. By optimizing the interaction between passive design and active low-carbon technologies, this research presents a comprehensive feasibility analysis that promotes sustainable logistics practices while ensuring a future-proof, low-carbon operational model.

Decoupling relationship between logistics growth and carbon emissions and driving factors in Chongqing: A novel decomposition framework

A comprehensive understanding of the logistics decoupling status and driving factors is of immense theoretical and practical importance for rationally formulating low-carbon logistics policies and accelerating the realization of high-quality development. Present study introduces the logarithmic mean Divisia index method (LMDI) decomposition technique into Tapio index model, extends traditional decoupling model, and establishes a new analytical framework for the decoupling relationship between logistics growth and carbon emissions. Using long-term data from Chongqing (1997–2021), this study investigated the Chongqing logistics decoupling relationship and driving factors. The study found the following: (1) Chongqing logistics carbon emissions show phased changes and face greater pressure to reduce emissions. (2) The decoupling status of logistics growth and carbon emissions is predominantly expansive and weak decoupling, with an overall evolutionary trend of “expansive decoupling–weak decoupling–strong decoupling. The average decoupling index in 2013–2021 was 0.5523, indicating a decreasing trend; however, there was still a large gap in realizing a strong decoupling goal. (3) The energy consumption intensity effect facilitates logistics carbon decoupling, the economic scale effect has a strong inhibiting effect, and the industrial structure effect and transportation intensity effect are staged. Finally, targeted policy recommendations are proposed to expedite logistics carbon emissions decoupling in Chongqing.

Spatial Influence of Digital Economy on Carbon Emission Efficiency of the Logistics Industry across 30 Provinces in China

The logistics industry (LI) is a key pillar of the global economy, and its carbon emission efficiency (CEE) is crucial for achieving carbon neutrality. The rapid development of the digital economy (DE) has had a profound impact on the LI, but the spatial impact on its CEE is currently unclear and requires further research. Firstly, based on the collection of relevant data, we use the entropy weight method and linear weighted sum method to measure the level of development of the DE. Secondly, the SBM model is used to measure the CEE level of the LI. Using Moran’s I index model and OLS and GWR models, we analyze the impact and spatial distribution characteristics of the DE on the CEE of the LI and propose development strategies. The article uses statistical data from 30 provinces in China from 2013 to 2022 as an example to demonstrate the implementation process of the method. The results show that the DE has a positive impact on the CEE of the LI, and there are spatial differences. Based on this, this article proposes policy recommendations for the development of green and low-carbon logistics and digital logistics that are tailored to local conditions, providing theoretical and methodological support for low-carbon research in the LI, and providing reference for other countries and regions to explore the path of green and low-carbon transformation.

Sustainable development through urban agglomeration green and low-carbon logistics: efficiency insights from China’s urban agglomeration

Sustainable development through urban agglomeration green and low-carbon logistics: efficiency insights from China’s urban agglomeration

A Review of Research in the Field of Low Carbon Logistics based on the Cite-Space Tool

In order to gain a deeper understanding of the research dynamics and future trends in the field of low carbon logistics at home and abroad, Cite-Space visual analysis tool is used to construct a knowledge map to reveal the key information on the current research status, hot topic research, and development frontiers in the field of low carbon logistics.Through co-author mapping, keyword co-occurrence mapping and keyword clustering mapping, low-carbon logistics can be analyzed in multiple dimensions, and the literature can be classified into macro, meso and micro to sort out and summarize the policies, regionalization and technologies in the field of low-carbon logistics, so as to provide a basis for future research.

Carbon Emission Management under China-Africa Supply Chain Co-operation in Logistics

This study examines carbon emission management strategies in China-Africa supply chain cooperation, with a particular focus on logistics under the Belt and Road Initiative (BRI). Given the increasing carbon emissions from logistics in both regions, effective management is crucial for sustainable development. The study highlights the need to integrate low-carbon technologies, renewable energy deployment and green logistics practices. It also discusses the economic, political and technical challenges of implementing these measures. Recommendations include the improvement of policies and regulations, the enhancement of technology transfer and the promotion of stakeholder partnerships. Successful case studies, such as the Mombasa-Nairobi Standard Gauge Railway, demonstrate the potential of sustainable infrastructure projects. The study concludes that strategic alignment, policy strengthening and collaborative efforts are essential to achieve low-carbon logistics and support long-term China-Africa cooperation.

Low Carbon Logistics Research Review

The logistics industry is one of the main industries of "carbon emission", and low-carbon logistics is also related to the realization of the goal of "carbon peak" and "carbon neutrality". This paper uses the literature research method, takes the concept of low-carbon logistics as the starting point, and combs the relevant research progress. Firstly, this paper introduces the concept of low-carbon logistics from the perspective of low-carbon and circular economy, and introduces the realization method of low-carbon logistics. Secondly, it reviews the progress of literature research from the aspects of low-carbon transport, low-carbon packaging, low-carbon storage, and low-carbon policy. It is concluded that the main direction of future research will be to shift from traditional local low-carbon research to systematic low-carbon research, use modern information technology and intelligent tools to help low-carbon logistics, and pay attention to the local problems brought by China's logistics practice.

Spatial impact of the digital economy on low-carbon logistics efficiency in RCEP countries

A thorough study of the spatial impact of the digital economy on low-carbon logistics efficiency would be greatly significant for Regional Comprehensive Economic Partnership (RCEP) countries to improve low-carbon logistics efficiency and achieve sustainable cooperation. This study constructs a theoretical framework from the perspective of spatial effects on the impact of the digital economy on low-carbon logistics efficiency in RCEP countries. The entropy method was used to measure the level of digital economic development. The super-efficiency SBM model was used to measure low-carbon logistics efficiency. Spatial feature analysis was conducted using kernel density estimation and Moran's index, followed by empirical analysis using spatial econometric models to examine the spatial impact of the digital economy on low-carbon logistics efficiency in RCEP countries. The results indicate that in RCEP countries, both low-carbon logistics efficiency and the level of digital economic development exhibit significant spatial positive correlation. Furthermore, the digital economy can promote low-carbon logistics efficiency in economically neighboring countries through spatial spillover effects. The improvement of domestic low-carbon logistics efficiency can also promote low-carbon logistics efficiency in neighboring countries. This conclusion was supported by endogeneity tests and a convergence analysis. Additionally, the mechanism analysis revealed that improving the level of green energy can enhance the spatial spillover effects of the digital economy and promote low-carbon logistics efficiency. Finally, countermeasures and suggestions was proposed to improve the low-carbon logistics efficiency of RCEP countries through the digital economy.

Construction of urban low-carbon development and sustainable evaluation system based on the internet of things

Low-carbon (LC) cities are the cities that people long for today. LC environmental protection plays a very important role in people's health. The construction of a city in LC is a great cause that contributes to the present and benefits the future. In this study, we propose a development and sustainability evaluation system for building low-carbon cities based on the Internet of Things (IoT). The study is novel in that it considers key areas such as urban planning, environmental issues and solutions, and how the Internet of Things can optimize low-carbon logistics and smart grids, with the aim of promoting the formation of low-carbon city models. This comprehensive approach not only presents problems and solutions in low-carbon urban planning but also focuses on how the Internet of Things can be used as a key technology to promote low-carbon urban development. The urban development of LC was constructed with a sustainable evaluation system, so that people could experience the life of LC. Through the investigation of the degree of atmospheric pollution of LC cities using the Internet of Things, this article found that the highest degree of atmospheric pollution was 30. The highest degree of atmospheric pollution in cities in LC without IoT was 53. The severity of water pollution in cities in LC using IoT technology ranged from 10 to 25, while those without IoT ranged from 30 to 60. The degree of soil pollution in LC cities using IoT technology was concentrated in 10–30, while those without using IoT were concentrated in 30–50. Through these experimental data, it could be seen that IoT technology could reduce environmental pollution, thus achieving the effect of LC cities. This shows that the use of IoT technology in LC cities was highly feasible.

Study on the influence mechanism of low-carbon logistics on energy consumption structure in the Yangtze River Delta region

Low-carbon logistics refers to the use of low energy consumption, low pollution, low emission technology and methods in the logistics process, in order to reduce the environmental impact and improve the transportation efficiency. Under the background of the new period, the low-carbon strategy has become a hot topic in the modern logistics industry. This paper will discuss the factors and countermeasures affecting the development of low-carbon logistics, in order to provide some reference and inspiration for the realization of low-carbon logistics.

Efficiency measurement and inefficiency analysis of low-carbon logistics in the Beijing–Tianjin–Hebei region, China

Under the dual-carbon goals, enhancing the green development level of logistics industry and realizing its low-carbon transformation are important issues that need to be solved urgently. Amidst the continuous escalation in the total energy consumption of the national logistics industry, the Beijing-Tianjin-Hebei (BTH) region has exhibited a favorable descending trajectory in this respect. It is necessary to investigate the underlying reasons. Based on the panel data from 2012 to 2021, the DEA and Malmquist index are employed to analyze the low-carbon logistics efficiency of the BTH region from both static and dynamic perspectives. Furthermore, the inefficiency analysis is conducted to identify the deficiencies of low-carbon logistics industry in this region. Results show that (1) from the static perspective, the development of low-carbon logistics industry in the BTH region is relatively unbalanced. Compared to Tianjin and Hebei, Beijing's low-carbon logistics efficiency is significantly lower, becoming the focal area for attention; (2) from the dynamic perspective, technological progress is the main reason for the fluctuation of total factor productivity in the BTH region and a constraining factor for further improvements; (3) from the results of inefficiency analysis, the forthcoming emphasis on low-carbon logistics in Beijing should be on optimizing the number of logistics practitioners, transportation efficiency, and energy efficiency. Economic output and energy efficiency are relatively vulnerable aspects in Tianjin and Hebei, respectively, warranting due consideration. The research results of this paper have important practical implications for better developing low-carbon logistics in the BTH region and leveraging its leading role nationwide.

Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA

In order to tackle the issue of carbon emissions in logistics and distribution, a vehicle routing model was proposed with the aim of minimizing the overall cost, which includes the vehicle’s fixed cost, transportation costs, and carbon emission costs. An enhanced genetic algorithm, based on a modified Nearest Neighbor Construction (NNC) method, was developed to solve this model. A comparative analysis was conducted using the Solomon dataset to study the impact of carbon emissions on vehicle routing optimization, comparing scenarios with and without considering carbon emission costs. The research findings revealed that the Improved NNC (INNC) method exhibited faster convergence compared with the random generation and random insertion methods. Despite a slight increase of 0.5127% in transportation cost when factoring in carbon emission costs, there was a decrease of 4.6914% in carbon emission costs and 0.3578% in total cost. These results offer theoretical insights and empirical evidence to inform the development of models for the logistics industry in the context of a low-carbon economy.

Optimized Design of Multi-Level Low-Carbon Logistics Distribution Scheme Based on Two Stages

Optimized Design of Multi-Level Low-Carbon Logistics Distribution Scheme Based on Two Stages

A Snowmelt Optimization Algorithm Applied to Green Low Carbon Logistics Pathways Optimization Problems

INTRODUCTION: Efficient and accurate optimization of green and low-carbon logistics paths, as one of the key technologies of green and low-carbon logistics, can not only promote the high-quality development of the economy, but also reduce the negative impacts of logistics on the environment and increase the cost of logistics delivery.
 OBJECTIVES: To address the problems of slow convergence and easy to fall into local optimization in the current performance prediction research on talent team building.
 METHODS: This paper proposes a snowmelt heuristic optimization algorithm to solve the green low-carbon logistics path optimization problem. Firstly, the objective function of green low-carbon logistics path optimization is designed by analyzing the optimization cost and conditional constraints of the green low-carbon logistics path optimization problem; then, a method based on intelligent optimization algorithm is proposed by designing the position-order array coding and fitness function, combined with the snow-melting optimization algorithm; finally, the validity and superiority of the proposed method are verified by simulation experiments.
 RESULTS: The results show that the proposed method not only improves the convergence speed but also increases the optimization fitness value.
 Conclusion: The problem of slow convergence and easy to fall into local optimum in the solution of green low-carbon logistics path optimization problem is solved.

Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty.

As China's "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0-1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency.

Simulation Research on Logistics Distribution Path Based on Colony Intelligence Optimisation

Abstract In light of the growing emphasis on “logistics integration,” this paper contends that the logistics network system requires comprehensive integration and optimization. We propose the use of the Bacterial Foraging Optimization (BFO) algorithm to enhance the Vehicle Routing Problem (VRP). The penalty function method is employed to direct the BFO algorithm in identifying the optimal vehicle path encoding and to improve the convergence operations for logistics distribution. This study analyzes the fundamental characteristic elements of the VRP, introduces a basic model for the VRP, and develops a logistics distribution path optimization model that minimizes the total cost by thoroughly considering the expenses associated with cold chain low-carbon logistics distribution. A simulation environment is established, and algorithm parameters are set to determine the optimal solutions for both the BFO and the Improved BFO (IBFO) algorithms in the test function. An example is selected for practical application, wherein the design of the distribution center and points is carried out, and the colony intelligence optimization algorithm is applied to simulate the logistics and distribution paths. Combined with the specific case of cold chain distribution of food, the optimization of the cold chain logistics distribution path is solved. In the case study, when the carbon penalty coefficient is ε = 15, the optimal logistics and distribution site selection program is Guangxi and Guizhou, and the total cost of the supply chain is 16,363,365.92 yuan. At this time, the supply chain cost is optimal. Compared with the carbon penalty coefficient ε = 20, the economic cost increased by 70152.24 yuan, but the carbon emission decreased by 1890.9 kg. The optimization of logistics and distribution processes encourages enterprises to develop strategies that promote low carbon and environmental protection and achieve better economic and ecological outcomes.

Coupling Coordination and Spatiotemporal Evolution of Low-Carbon Logistics, Industrial Agglomeration, and Regional Economy in the Yangtze River Economic Belt

The logistics industry plays a crucial role in the global economy, but also poses significant challenges to the economy, society, and environment due to increasing carbon emissions. Therefore, coordinated development between the logistics industry and regional economy has become a strategic choice for achieving sustainable development. Taking the Yangtze River Economic Belt as an example, this study constructs an evaluation index system of “low-carbon logistics–industrial agglomeration–regional economy” to explore the coupling coordination relationship and spatiotemporal distribution characteristics of the three systems from 2006 to 2020. Furthermore, it analyzes the spatial correlation features and evolutionary trends of the coordinated development among the three systems. The results indicate that during the study period, the coupling coordination degree among the three systems in the Yangtze River Economic Belt showed a fluctuating upward trend but with a relatively low level of coordination. There were significant regional differences, presenting a stepped distribution pattern of “high in the east and low in the west.” The coordinated development among the three systems exhibited a significant positive spatial correlation, with “H–H” and “L–L” agglomerations being dominant. The spatial distribution of coupling coordination degree remained relatively stable, with the overall center of gravity located in the southeast of Hubei Province. The spatial evolution pattern showed a distinct “northeast–southwest” direction. Finally, suggestions for the coordinated and sustainable development of the three systems are put forward.

Application of Improving ABC in Cold Chain Low Carbon Logistics Path Planning

The market has set higher efficiency and environmental requirements for cold chain logistics, and path planning plays an important role. This study proposes a low-carbon cold chain logistics path planning model based on an improved artificial bee colony algorithm (this paragraph refers to ”fusion algorithm”). The study first establishes the fusion algorithm. Then, in response to the shortcomings of this algorithm, the artificial fish swarm algorithm and genetic algorithm are used to improve it. The final results express that the shortest distance for solving Eil51 using this algorithm is 421.38, the longest distance is 448.58, and the average distance is 439.34; The shortest distance for solving Ulysses22 is 72.46, the longest distance is 73.63, and the average distance is 72.84. The average convergence times for Eil51 and Ulysses22 are 133.57 and 7.86, and the optimal performance ratios for relative error are 0.0076 and 0.0051. The robust performance ratios are 0.0362 and 0.0117. The optimal total cost solution and the average value for solving the relevant distribution problem are 47,894.6 yuan and 48,562.7 yuan, respectively. In summary, the model proposed in the study has good application effects in cold chain low-carbon logistics path planning, and has a certain promoting effect on the development of cold chain logistics.