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.

Catastrophic natural disasters cause devastating damage and leave a huge number of homeless people. Waiting for resettlement in a post-disaster environment brings human suffering, which is defined by waiting cost in this paper. Taking into account waiting cost and fairness consideration simultaneously, a mixed integer linear programming model is constructed for the multiperiod location-allocation process. Two fairness indicators are incorporated to guarantee both the whole-process equity and the periodic equity. The model is implemented in the General Algebraic Modeling System (GAMS) and solved by the CPLEX solver. An illustrative example is provided to explain the model characteristics. Furthermore, a case study of the Yushu earthquake is conducted to demonstrate the applicability of the model to practical problems.

Earthquake activity can generate huge energy in a short period of time, bringing enormous risks to people’s lives and property safety. This poses a great challenge to regional sustainable development. Meanwhile, due to the complex mechanism, seismic activity is difficult to accurately predict. Therefore, it is of great significance to explore how to reduce earthquake disaster losses from the perspective of human society. In this study, we use vulnerability to reflect the relative impact of earthquake disasters on different counties. The vulnerability caused by earthquakes is calculated with the data envelopment analysis (DEA) method. We use CCR and BCC models to further decompose vulnerability into pure technology vulnerability and scale vulnerability. This study analyzes 69 earthquake disasters that occurred in the Chinese mainland from 2013 to 2020 and explores the influencing factors of pure technology vulnerability from both quantitative and qualitative perspectives. Three main conclusions are drawn. First, four factors, including the added value of the secondary industry, gross domestic product (GDP) per capita, investment density of fixed assets and energy released by earthquakes, have a significant impact on the pure technical vulnerability of counties caused by earthquake disasters. Second, in the samples under consideration, the average vulnerability of the regions with an earthquake magnitude below 5.0 is higher than that of the regions with an earthquake magnitude between 5.0 and 6.0. There are deficiencies in organization, management and facilities in regions with a small earthquake risk. Third, through qualitative analysis, it is shown that the seismic function of buildings affects the vulnerability of counties facing earthquake disasters. The results of the research can provide decision makers with new insights into earthquake prevention and disaster reduction management.

1. Rapid and effective post-earthquake emergency response is essential for reducing casualties and mitigating economic losses; therefore, timely and accurate disaster information must be acquired t...

Based on the InVEST and RUSLE models, and using 5 km × 5 km and 10 km × 10 km grid cells as well as counties as analysis units, this study assessed grain production, water yield, soil conservation, and carbon sequestration services in the middle reaches of the Yellow River (MRYR) in 2000, 2010, and 2020, and analyzed their spatiotemporal changes and trade-off/synergy relationships from both static and dynamic perspectives. From 2000 to 2020, grain production, water yield and soil conservation in the MRYR increased overall with distinct spatial differentiation, while carbon storage remained basically stable with a slight decline. Significant static trade-offs/synergies existed among ecosystem service (ES) bundles with temporal evolution characteristics, and their interaction intensity presented clear scale dependence with typical threshold effects. Dynamically, positive synergies dominated all ES pairs during 2000-2010, whereas trade-offs/negative synergies expanded significantly during 2010-2020, especially at the county scale. This study further reveals the hierarchical driving mechanism of ES trade-off/synergy spatial pattern: fine-scale fragmentation is driven by micro-topographic and local management factors, meso-scale integration by regional ecological and agricultural configuration, and county-level differentiation by macro-policy and socio-economic drivers. Beyond the traditional correlation-based analysis framework, we propose a multi-scale coupling analysis framework of ES trade-off/synergy that integrates static correlation, dynamic difference and hierarchical driving mechanism, and clarify the emergent system behavior of ES interactions (non-linear change of interaction intensity with scale expansion). This framework breaks the single-scale and pure statistical analysis paradigm of existing studies, enriches the scale effect theory of ES, and provides a new theoretical perspective and methodological reference for the coordinated regulation of ES in ecologically fragile areas with intense human-environment conflicts worldwide.