Global warming caused by excessive carbon dioxide emissions is threatening the sustainable development of human society. Considering the upcoming carbon tax policy in China, this paper studies the planning of dual-level urban logistics distribution network in the context of carbon emissions. Based on reasonable assumptions, a bi-objective mixed-integer programming model considering logistics operating costs and carbon emission costs is constructed. Given the problem size, an improved genetic algorithm is designed. Based on a numerical example, the optimization results of the improved genetic algorithm and the GLPK optimization suite are compared to verify the effectiveness of the proposed model and algorithm. Under different carbon tax rates, by adjusting the logistics distribution network along, the logistics operators can achieve a maximum cost savings of 19.4%, while carbon emissions can be reduced by up to 47.8%. The major conclusions include: carbon tax will bring about huge cost burdens for urban logistics operators; the reconfiguration of urban logistics network is a powerful measure to reduce carbon emission with the least extra costs; with problem size rises quickly, the intelligent algorithm as proposed in this article can always find near optimal solutions with acceptable time costs.

This paper examines a complex supply chain consisting of a raw material manufacturer, a consumer goods fabricator, a retailer and an end user. A sequential decision game model is constructed under carbon tax regulation, considering multiparty green technology innovation and green product marketing efforts in the supply chain. Using a backward-induction analysis process, the Nash equilibria of the game are obtained. With the help of numerical simulation, the sensitivity analysis of the key parameters affecting system performance is carried out. We find the following results. (i) Carbon tax regulation brings about a new cost burden for supply chain members in different ways, with the largest impact being on the profit of the raw material manufacturer. (ii) The marginal emission reduction effect of carbon tax regulation shows a declining trend. The cost of greening efforts will exceed the carbon tax rate as it continues to rise. (iii) Customer demand is significantly affected by the footprint and energy efficiency of products and is much more relevant for energy efficiency than for footprint in the production phase.

With the growing severity of global climate change, forecasting and managing carbon dioxide (CO₂) emissions has become one of the critical tasks in addressing climate change. To improve the accuracy of CO₂ emission forecasting, an innovative framework based on variational mode decomposition (VMD), improved black-winged kite algorithm (IBKA), and BiLSTM networks is proposed. This framework aims to address the challenges associated with predicting non-stationary data and optimizing model hyperparameters. Initially, experiments were conducted on 29 benchmark functions using the IBKA algorithm, demonstrating its superior performance in highly nonlinear and complex environments. Subsequently, the BiLSTM model optimized by IBKA was employed to predict CO₂ emission trends across four major industries in China, confirming its enhanced prediction accuracy. Finally, a comparative analysis with other mainstream machine learning and deep learning models revealed that the BiLSTM model consistently achieved the best predictive performance across all industries. This research proposes an efficient and practical technical pathway for intelligent carbon emission prediction under the “dual-carbon” strategic goals, offering scientific support for policy formulation and the low-carbon transition.

The prediction of casualties in earthquakes is very important for improving the efficiency of emergency rescue measures and reducing the number of casualties. Given the time lag and poor accuracy of population density data published in statistical yearbooks, a Baidu heatmap is used in this study to accurately estimate the regional population density. Based on the standard support vector machine (SVM) prediction model, a piecewise loss function and a robust wavelet kernel function are proposed to effectively reduce the prediction error. Given a characteristic attribute set of factors related to earthquake casualties, the new prediction model is tested in 34 cases involving earthquake cases on the Chinese mainland since 2011. Compared with other prediction techniques, the proposed robust wavelet SVM can converge more quickly, and the prediction error is lower than that of the standard backpropagation neural network (BPNN) and standard SVM.

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.

To improve carbon efficiency for urban logistics service system composed of a third-party logistics service provider (3PLs) and an e-business enterprise, 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, totally 5 types of game scenarios were designed. Through analytical solution, Nash Equilibria under varied scenarios were obtained. By analyzing these equilibria, 4 major propositions were given, in which, some key variables and system performance index 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 would not benefit the system performance improvement; E-business enterprise-leading game’s performance overtook 3PLs-leading ones.