This study attempts to analyze the impact of population, property, technology, energy factors, and spatial agglomeration in the logistics industry on carbon emissions. To achieve the goal of peak carbon and carbon neutrality, the relationship between influencing factors and carbon emissions was analyzed based on panel data from the logistics industry for 30 provinces in China from 2003 to 2017 using an improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and a spatial lag model (SLM). The results show that population, property, technology, and energy factors in the logistics industry all have different degrees of influence on carbon emissions, wherein population, energy, and property have a greater influence, which implies that carbon emission reduction policies can be carried out considering the relevant aspects. In addition, under the influence of spatial agglomeration, the degree of influence of freight mileage (FM), total fixed-asset investment (TFAI), and industry population (IPOP) on carbon emissions decreases, and the degree of influence of energy intensity (EI) and industry per capita GDP (IPCG) increases. This suggests that corresponding emission reduction policies should be formulated for large urban areas based on technological innovation, infrastructure, and talent training, while smaller urban areas can focus on developing new energy and industrial economies. These findings help to complement the existing literature and provide policymakers with some insights related to urban logistics development.

Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China

A systematic review of transportation carbon emissions based on CiteSpace

A eutectic hydrated salt (EHS) formed by disodium hydrogen phosphate dodecahydrate (DHPD) and sodium carbonate decahydrate (SCD) was used as the cold energy storage functional medium, and then the nucleating agent sodium pyrophosphate decahydrate (SPD), the phase change temperature regulators ammonium sulfate (N) and potassium sulfate (K) were added to prepare a sample DSSNK5, and by adding superabsorbent polymer (SAP), a phase change gel DSSNK5-SAP was finally obtained. The results show that the phase change temperature of DSSNK5-SAP is 7.71 °C, and its latent heat value is 122.1 J g−1, which remains unchanged after 50 cycles. Compared with pure DHPD, the supercooling degree of DSSNK5-SAP is reduced to 3 °C, and the thermal conductivity is 1.5 times that of DHPD. In addition, the crystal structure and chemical compatibility of DSSNK5-SAP were analyzed, and the regulation mechanism of auxiliaries and SAP on the phase change behavior of EHS was described. The cold energy storage/release and transient response performance of DSSNK5-SAP were tested, and the application experiment of fruit preservation was also carried out. As a result, DSSNK5-SAP can make the fruit have a good fresh-keeping effect, which proves its considerable application potential in cold chain transportation. It reduces the cost of cold chain transportation from the root and contributes to the stable development of the city and society. 

Optimization of super water-retention phase change gels for cold energy storage in cold chain transportation

Construction of digital twin system for cold chain logistics stereo warehouse

The research evaluates the vehicular routing problem for distributing refrigerated products. The mathematical model corresponds to the vehicle routing problem with hard time windows and a stochastic service time (VRPTW-ST) model applied in Santiago de Chile. For model optimization, we used tabu search, chaotic search and general algebraic modeling. The model’s objective function is to minimize the total distance traveled and the number of vehicles using stochastic waiting restrictions at the customers’ facilities. The experiments were implemented in ten scenarios by modifying the number of customers. Experiments were established with several customers that can be solved using the general algebraic modeling technique in order to validate the tabu search and the chaotic search methods. The study considered two algorithms modified with Monte Carlo (tabu search and chaotic search). Additionally, two modified algorithms, TSv2 and CSv2, were proposed to reduce execution time. These algorithms were modified by delaying the Monte Carlo procedure until the first set of sub-optimal routes were found. The results validate the metaheuristic chaotic search to solve the VRPTW-ST. The chaotic search method obtained a superior performance than the tabu search method when solving a real problem in a large city. Finally, the experiments demonstrated a direct relationship between the percentage of customers with stochastic waiting time and the model resolution time.

Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile

Location optimization of a competitive distribution center for urban cold chain logistics in terms of low-carbon emissions

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Location optimization of a competitive distribution center for urban cold chain logistics in terms of low-carbon emissions