三明学院

Rising carbon emissions from tourism's rapid growth have placed its environmental sustainability under mounting scrutiny, underscoring the urgency of improving tourism carbon productivity (TCP). Meanwhile, the digital economy (DE) has increasingly emerged as a transformative driver of sustainable tourism. This study constructs a model to measure TCP and, using Chinese city-level data with econometric methods, analyzes the impact of DE on TCP. Results show that DE significantly enhances TCP, with stronger effects in eastern regions and excellent tourism cities. Mechanism analysis indicates that DE improves TCP mainly by stimulating green technological innovation and alleviating resource misallocation. Spatial econometric analysis further identifies positive spillover effects of DE on neighboring cities' TCP. These findings reveal the impact and spatial spillover pathways of DE on TCP, extend the theoretical framework of how DE affects tourism's low-carbon development, and provide valuable reference for collaborative low-carbon governance in the tourism industry in the digitalization context.

As global climate change intensifies, the urban heat island (UHI) has become an increasingly significant challenge, impacting both urban environments and public health. We used the SSP-RCP model to analyze the spatiotemporal dynamics of heat and cold island patches in Fuzhou under projected climate change, integrating spatial analysis and network evolution theory. The results show that: (1) Cold island patches are generally decreasing in size, while heat island patches are expanding. The most significant increase in heat island area occurs under the SSP585 scenario; (2) The spatial distribution of heat and cold sources has shifted, with cold sources moving from southwest to northeast and heat sources from south to north; (3) Climate change scenarios exert a substantial influence on the urban thermal environment (UTE). In low-intensity scenarios, the reduction of cold islands and the expansion of heat islands may exacerbate the heat island effect. Conversely, in high-intensity scenarios, the intensification of the heat network may further aggravate the UTE; (4) Synergistic development of cities within metropolitan areas, strengthening the connectivity of cold networks, and enhancing the resilience of heat networks are crucial strategies for mitigating the negative impacts of UHI and improving cities’ capacity to cope with climate change. This study reveals the spatial and temporal evolution of heat and cold networks in the Fuzhou metropolitan area, providing a theoretical foundation for UHI mitigation under varying development scenarios and for the optimization and adaptation of UTE in response to future climate change.

In constrained multi-objective optimization (CMOP), effectively exploiting infeasible solutions is essential for global exploration and for accurately approximating the constrained Pareto front (CPF). Nevertheless, when feasible regions are sparse or highly fragmented, many existing methods still suffer from slow feasibility attainment, a high proportion of ineffective evaluations, and inadequate front coverage, leading to premature clustering. Following the principle of 'broad exploration first, robust convergence later', DPNCMO (novelty-augmented population-differentiated cooperative multi-objective optimization) is developed as a cooperative dual-population framework that explicitly decouples exploration from exploitation. The main population is initialized via Latin hypercube sampling and evolved using a genetic algorithm equipped with a feasibility-aware adaptive constraint-relaxation mechanism, which progressively tightens the admissible violation level in response to the evolving feasibility state, thereby steering the search from informative infeasible regions toward accurate CPF refinement. In parallel, an assistant population is randomly initialized and evolved using a DE (differential evolution)-based operator with a novelty-crowding synergistic diversity-maintenance mechanism. By constructing a behaviour space that integrates objective and constraint information, the mechanism emphasizes novelty-driven selection when feasibility is scarce to enhance coverage and suppress clustering, and then gradually shifts toward crowding-driven exploitation once feasibility becomes sufficient to stabilize convergence and control computational overhead. Collectively, the population-differentiated cooperation, feedback-driven constraint relaxation, and stage-wise novelty-guided selection reduce ineffective evaluations, accelerate feasibility climbing, and improve CPF coverage and robustness on fragmented feasible landscapes. Extensive experiments on 43 CMOP benchmark instances and 12 real-world engineering problems demonstrate that DPNCMO achieves superior or at least comparable performance to representative state-of-the-art optimizers across convergence, distribution, and feasibility, with consistent improvements across multiple metrics.

In this work, we further investigate the surface wave attenuation performance of elastic metasurfaces composed of in-filled pipes in a layered half-space, focusing on the dispersion relations and transmission properties. Particularly, both Rayleigh waves and Love waves are considered. The introduction of soil layers will reduce the width of attenuation zones. Additionally, transmission simulations reveal complex propagation patterns for elastic metasurfaces in a layered half-space, including wave reflection, wave resonance, and higher-order wave modes, which will hinder the penetration of converted shear waves into the half-space. In contrast, in reference cases, only surface-shear wave mode conversion is observed. Moreover, the attenuation performance of elastic metasurfaces is also diminished in layered soils in the frequency domain, and a nonuniform displacement distribution behind the elastic metasurface is also found. Last but not least, the feasibility of elastic metasurfaces to train-induced ground-borne vibration mitigation is numerically verified in the time domain. Although the performance of elastic metasurfaces in layered soils is inferior to that in homogeneous soils, they are better than traditional trenches within the main frequency range. Snapshots from the transient simulation clearly show the evolution of wave fields, reinforcing the observed key findings. Due to excellent surface-wave-attenuation performance and ease of realization, these novel elastic metasurfaces hold great potential in ambient vibration mitigation.

In constrained multi-objective optimization problems, the discontinuity of the target space and the fragmentation of the feasible solution space caused by complex constraints make the optimization algorithm face irreconcilable conflicts between convergence, diversity, and feasibility. To this end, this paper proposes a dual population co-evolution algorithm based on a dynamic Manhattan-Harmony hybrid distance. The algorithm constructs a main and auxiliary population with complementary structures: the main population focuses on deep search in the feasible domain, the auxiliary population conducts global exploration in the infeasible area, and introduces an evolutionary stage perception mechanism for differentiated environmental selection. In particular, the proposed dynamic Manhattan-Harmony hybrid distance can effectively characterize the convergence and diversity characteristics of individuals and guide the auxiliary population to adopt adaptive selection strategies at different stages. In addition, the algorithm draws on the theory of biological potential energy diffusion and designs a dynamic resource allocation mechanism that combines three types of potential energy: goal orientation, constraint recovery, and structural diversity, to achieve adaptive scheduling of offspring resources. Furthermore, the constructed bidirectional knowledge transfer channel realizes information sharing and co-evolution between the main and auxiliary populations. Experimental results on 33 standard test functions and 12 real-world problems show that HDCMO outperforms many existing representative constrained multi-objective evolutionary algorithms in terms of convergence, feasibility, and distribution balance, and has significant performance advantages and adaptability.