Search Results (596)

The logistics industry remains a critical bottleneck for decarbonization due to its extensive physical networks and high-carbon path dependencies. To address the spatial heterogeneity and transition constraints in the logistics industry of China, this study proposes a Performance–Topology–Mechanism (PTM) framework. Analyzing panel data from 30 Chinese provinces, we integrate a Super-SBM model with the Global Malmquist–Luenberger (GML) index, Dagum Gini decomposition, and a panel Tobit model to decode the spatiotemporal dynamics of green innovation performance (GIP) and its underlying spatial lock-in mechanisms. The results reveal a steady but spatially uneven increase in the national GIP (from 0.4526 to 0.5724), characterized by a leading East and a lagging West/Northeast. GML decomposition indicates this growth is predominantly driven by outward shifts in the technological frontier rather than efficiency improvements, highlighting the weak spatial conversion of green technologies into transport optimization. Topological tracing demonstrates that inter-regional disparities have become the dominant source of spatial inequality, with their contribution rising from 60% to 75%. Spatial autocorrelation further exposes a deepening core–periphery polarization and persistent low-performance spatial lock-in. Crucially, the mechanism analysis identifies a pronounced infrastructure-based carbon lock-in. While economic capacity and green patents stimulate GIP, road network density exerts a significant negative effect, reflecting a systemic path dependence on high-carbon road freight. The findings suggest that decarbonizing transport logistics requires shifting from singular technological investments toward multimodal transport restructuring, overcoming physical network dependencies, and promoting the regional diffusion of green innovations. These findings provide an empirical basis for differentiated green-logistics policies, coordinated low-carbon transport infrastructure planning, and cross-regional diffusion of green technologies. Full article

Provincial cold-chain investment decisions must reconcile food-loss prevention, digital visibility, logistics capability and the environmental burden of freight-intensive growth. This study develops a sustainability-oriented readiness–intensity framework for 31 provincial-level regions in mainland China. The baseline model uses 14 auditable public-data criteria and combines Criteria Importance Through Intercriteria Correlation (CRITIC) with the standard Combined Compromise Solution (CoCoSo) algorithm. Because the observations combine 2024 statistics, a 2023 digital-finance index and the cumulative 2020–2025 cold-chain-base list, the design is described as an asynchronous cross-sectional snapshot rather than a single-year panel. Municipal sewage and green-space variables are interpreted as regional enabling capacity, not direct cold-chain environmental performance; road freight turnover relative to gross domestic product is treated as a cost-type freight-intensity transition-pressure proxy. A separate diagnostic replaces the earlier inverse-size term with logistics residuals conditional on agri-food output. Shandong, Guangdong, Jiangsu, Henan and Zhejiang form the leading demonstration-readiness group. Equal-weight CoCoSo closely matches the CRITIC result (Spearman ρ = 0.996), while TOPSIS and VIKOR retain the broad ordering but expose local method sensitivity. Dropping either digital criterion, removing the three indirect green proxies, winsorizing the normalization range, varying the CoCoSo compromise parameter and substituting 2022 digital data do not alter the leading pattern. Under an assumed 5% indicator-error perturbation, Shandong and Guangdong remain within ranks 1–2, whereas the ordering of several adjacent provinces is less secure. The framework supports sequenced investment packages rather than a deterministic league table and distinguishes demonstration-ready, scale-led, intensity-led and coverage-building contexts. Full article

Background: Managing agrologistics supply chains under infrastructure scarcity requires integrative, spatially explicit decision-support tools. This study develops a macro-level digital twin of the multimodal agricultural supply chain in Kazakhstan’s Almaty region to optimize freight allocation and guide strategic investment planning. Methods: Our methodology integrates Earth observation data (ESA WorldCover 10 m) with a large-scale multimodal road–rail graph network (1.39 million nodes) to identify 135 crop production clusters. Using linear programming in MATLAB, we optimize the regional distribution of 322.2 thousand tons of seasonal maize, wheat, and soybeans while localizing new storage silos using Green Field Analysis. Results: The baseline simulation reveals a critical storage capacity deficit, yielding a Capacity Coverage Ratio of only 23.8%. However, implementing optimal multimodal rail-road routing mathematically reduces the Logistics Cost Index from 8,642,195 to 4,716,175 units, achieving overall cost savings of 45.4%. Conclusions: The proposed digital twin and its performance metrics provide a scientifically grounded, data-driven toolkit for public–private partnerships, ensuring robust infrastructure investment localization and facilitating the transition toward the Agriculture 4.0 paradigm. Full article

The spatial allocation of urban road infrastructure directly affects urban operational efficiency, social equity, and ecological environmental quality. Taking 36 major Chinese cities as the research subjects and drawing on data from municipal statistical yearbooks, this study employs a composite index method, Theil index decomposition, and correlation analysis to reveal spatial differentiation patterns, imbalances, and driving factors of urban road infrastructure levels, based on both aggregate and average indicators. The results indicate that the aggregate road infrastructure level exhibits a “high in the southeast, low in the northwest” pattern along the Hu Huanyong Line, while the average road infrastructure level reveals relatively lower performance in some first-tier cities. Imbalances exist in both aggregate and average dimensions, with Theil indices of 0.231 and 0.059, respectively; intra-regional disparities contribute more to total inequality than inter-regional disparities. Urban permanent population (ridge regression coefficient: 0.1991) and fiscal revenue (ridge regression coefficient: −0.1087) are the core driving factors among the four influencing factors of aggregate road infrastructure level spatial differentiation, whereas GDP (−0.0318) and built-up area (0.0703) exert only marginal effects. This suggests that current aggregate urban road infrastructure levels are shaped by the interplay of urbanization stage, economic development level, fiscal system, and spatial planning policies, all operating within the constraints imposed by the city’s natural geographical conditions, and have not yet adequately addressed residents’ demand for spatial equity. The study recommends establishing differentiated investment mechanisms, optimizing road network density in developed cities, and constructing a spatial matching early-warning system to promote people-oriented new urbanization and coordinated regional sustainable development. Full article

Industrial land allocation spatial morphology (ILASM) is crucial for economic development and the advancement of new-type industrialization. However, existing studies lack a comprehensive understanding of the evolutionary characteristics of the spatial morphology of industrial land allocation, let alone its driving factors and the spatial heterogeneity of their effects. Therefore, this paper classifies industrial land allocation spatial morphology into traditional industrial land allocation spatial morphology (TILASM) and high-tech industrial land allocation spatial morphology (HILASM), then evaluates and identifies their characteristics based on the precise geographic coordinates of each industrial land parcel between 2007 and 2024 in the Yangtze River Delta (YRD). Subsequently, the Random Forest Regression model and Multi-Scale Geographically Weighted Regression model are integrated to systematically investigate the driving factors and spatiotemporal patterns of ILASM, including both TILASM and HILASM. The results show that from 2007 to 2024, different types of ILASM exhibited distinct spatiotemporal evolutionary characteristics. Specifically, first, in terms of the evolution of spatial distribution direction, overall industrial land allocation exhibited a pronounced agglomeration pattern, extending from the western (slightly northern) part of the region to the eastern (slightly southern) part. Moreover, the evolutionary direction of traditional industrial land allocation was consistent with that of overall industrial land allocation. However, high-tech industrial land allocation exhibited an agglomeration trend extending from west (slightly south) to east (slightly north). Second, in terms of evolution of agglomeration pattern, the spatial distribution of TILASM evolved from three-core dispersed configuration to a multi-core linkage, before reverting to a multi-core dispersed state; by contrast, both ILASM and HILASM exhibited a spatial pattern that progressed from dispersion to contiguous agglomeration. Third, the spatial distribution characteristics of different types of ILASM were shaped by the combined influence of natural conditions, economic development, social environment, innovation environment and infrastructure. However, the dominant driving factors differed among them. Specifically, the number of foreign-invested enterprises exhibited a negative influence on ILASM, while having positive effects on both TILASM and HILASM. The effects of patent applications, population density and internet penetration rate on ILASM; foreign-invested level and slope proportion on TILASM; as well as road density, labor quality and foreign invested level on HILASM all exhibited U-shaped relationships. Moreover, the influences of opening-up level and per capital road area on ILASM and HILASM displayed relatively complex N-shaped relationships. Finally, the effects of these crucial drivers displayed significant spatial non-stationarity and certain gradient effects, manifesting in southern–northern, western–eastern and core–periphery spatial differentiation patterns. Overall, this study provides scientific evidence and practical references for optimizing the spatial allocation of industrial land. Full article

Digital diversification increasingly occurs not through acquisition or formal alliance but through staged, option-like commitments between large technology firms and frontier artificial intelligence (AI) developers. This paper uses the Amazon–OpenAI partnership (November 2025–March 2026) as an illustrative case to examine how such staged commitments can be understood through a real-options lens. The paper organizes the case around five synergy categories adapted from the M&A and ecosystem literature—market power, relational, network, non-market, and ecosystem synergies—and shows that Amazon’s two-tranche investment structure ($15 billion committed, with a further $35 billion conditional on technological and commercial milestones) resembles a compound sequential call option. The paper illustrates this logic with a compound option and binomial lattice valuation, using publicly disclosed deal terms and volatility proxies from comparable AI firms. The paper emphasizes that this valuation is indicative rather than precise: it depends on assumptions about OpenAI’s eventual IPO valuation, the durability of Amazon’s ownership share, and the appropriateness of peer-firm volatility as a proxy, all of which are uncertain at the time of writing. The paper’s contribution is conceptual: it introduces ‘staged ecosystem optionality’ as a mechanism of digital diversification and offers a road map for future research. Full article

The dominant discourse on tourism resilience has traditionally emphasized physical infrastructure, environmental management, and technological solutions as key mechanisms for adapting destinations to climate change and socio-economic disruptions. While these dimensions remain essential, growing evidence suggests that the long-term sustainability of tourism destinations, particularly in remote and vulnerable regions, depends equally on less visible but equally essential community capacities. This paper introduces the concept of human infrastructure as a complementary perspective for understanding resilience in tourism development. Human infrastructure encompasses social relationships, local leadership, collective efficacy, cultural identity, community participation, intergenerational knowledge transfer, and educational capacity, which enable communities to adapt, recover, and innovate under conditions of uncertainty. Drawing upon an integrative conceptual review of the literature on tourism studies, community psychology, rural development, resilience theory, and sustainable development, this conceptual paper proposes a human-centered framework explaining how communities themselves function as adaptive infrastructures. The article argues that resilient tourism destinations emerge not only from investments in roads, visitor centers, and digital technologies, but also from investments in social cohesion, local knowledge systems, community empowerment, and cultural continuity. The paper concludes by discussing policy implications for sustainable tourism planning and proposing future research directions for operationalizing and measuring human infrastructure in tourism contexts. Full article

To empirically verify whether China’s “Going-Out” strategy influences global carbon dioxide (CO₂) emissions, this study investigates the effect of China’s outward foreign direct investment (OFDI) on recipient countries’ CO₂ emissions, using a panel dataset of 68 countries over the period 2003–2021. In addition, we separately test the three major effects (i.e., scale, technical, and structure effects) of China’s OFDI on recipient countries’ CO₂ emissions. Considering the importance of the “Belt and Road Initiative” (B&RI) for China’s “Going-Out” strategy, the global panel is divided into two subpanels (i.e., B&RI and non- B&RI countries) for empirical analysis. The results show that the total effect of China’s OFDI on CO₂ emissions is positive for the global panel and B&RI countries but negative for the non-B&RI countries. Although China’s OFDI increases recipient countries’ CO₂ emissions by improving the economic scale (scale effect) and inhibiting the industrial upgrading (structure effect), the technical effect of China’s OFDI alleviates the greenhouse effect. Based on the above findings, several targeted policy suggestions for mitigating recipient countries’ CO₂ emissions and promoting growth in China’s OFDI are provided. Full article

This paper discusses the dynamic mechanism of stability and the possibility for breakthrough of the Hu Line based on the relationship between population, socioeconomic development and the environment. Against the background of sustainable development and climate change, we used historical census data from China and comparative analysis of three versions (IIASA, NUIST and Tsinghua) of the Shared Socioeconomic Pathways (SSPs) to analyze multidimensional demographic characteristics such as population size, age and education on both sides of the Hu Line (China’s population boundary). The study found that SSP scenarios and regional differences in the natural growth rate (defined by fertility and mortality) and urbanization rate jointly influenced the changes in the proportion on both sides of the Hu Line. In addition to the breakthrough of the population proportion on the west of the Hu Line, population quality deserves greater attention. The provinces on the east and west sides of the Hu Line can adopt different social and economic development paths to achieve sustained development of the population, economy, society, resources and environment. On the premise of sustained social and economic development, the total population will not rise or fall significantly, keeping the trend of aging under control, and enhancing the educational level. The central government should increase investment in basic education in the provinces west of the Hu Line and create more employment opportunities through the Belt and Road Initiative and the new urbanization strategy. In areas with abundant water resources on the west of the Hu Line, urbanization efforts should be rationally planned and advanced to gradually establish clusters of talent hubs. This will facilitate the effective conversion of human resources by retaining and attracting higher-education professionals to pursue employment and entrepreneurship opportunities. Full article

Dynamic freight transportation systems are critical for improving logistics performance in developing economies where multimodal integration remains limited. Despite Lao People’s Democratic Republic (Lao PDR)’s substantial investments to transition from a landlocked to a land-linked hub, quantitative assessments capturing heterogeneous cargo behavior across its multimodal networks are scarce. This study develops a cargo-specific, multi-source, multi-sink minimum-cost flow (MCF) framework to evaluate freight transport performance in Lao PDR. The framework integrates road, railway, inland waterway, and air transport into a unified directed network, capturing heterogeneous behaviors across six cargo categories. Testing under three demand scenarios (Q500, Q1000, and Q2000 tons/day) across both export and import systems, the results from 99 feasible solutions indicate that railway transport consistently dominates long-distance corridors within the assumptions of the proposed framework, reflecting its structural cost advantages. Conversely, road transport primarily serves first- and last-mile connectivity, while inland waterways and air transport serve as supplementary modes. Ultimately, these findings provide scenario-based insights to support policy decision-making for cross-border infrastructure investments, regional dry port integration, and synchronized rail-road connectivity across the Greater Mekong Subregion (GMS). Full article

Low-noise pavements (LNPs) are increasingly important under Green Public Procurement policies, yet public administrations still lack clear guidance on selecting pavement types based on noise-related externalities. Although traffic noise generates substantial societal costs—affecting health, education, and property values—these external burdens are often overlooked or excluded from traditional pavement appraisal and investment decisions, leading to systematically underestimated life cycle costs (LCC). This study develops an integrated framework to monetise traffic-noise impacts within an LCC perspective by combining health effects (Disability-Adjusted Life Years, DALYs), property-value capitalisation (willingness to pay, WTP), and noise-induced educational losses. The system limit is intentionally restricted to noise-related externalities during pavement operations, while agency, user, and vehicle operating costs are excluded. A comprehensive review of existing monetisation approaches is provided, and a new unified method is proposed. The framework is applied to a case study from the LIFE SNEAK project on Via La Marmora (Florence, Italy), comparing existing, acoustically non-optimised, and acoustically optimised surfaces. The results showed that the LIFE SNEAK pavement significantly alleviated the burden of noise on public health and education costs, which were 34% and 33% lower than in the baseline scenario, respectively, with a welfare surplus of +€2.38 million over the ten-year period. In particular, it was noted that the most important economic contribution of LNPs stems from the WTP approach. This study provides clear evidence that noise externalities play a considerable role in long-term pavement cost estimates, thereby supporting the systematic inclusion of these costs in LCC analyses. The proposed method puts forward a practical approach to support the selection of noise-sensitive, sustainable, and socially responsible road pavements. Full article

Container sea–rail intermodal transport is pivotal to the low-carbon transformation of global supply chains. However, traditional performance evaluation systems are prone to circular reasoning fallacies due to the nesting of input and output indicators and frequently suffer from spurious regression when analyzing high-dimensional macro time series under small-sample constraints. To address these endogeneity and attribution challenges, this study proposes a four-step progressive causal inference framework. Taking Tianjin Port—a pioneering hub of China’s “road-to-rail” freight restructuring policy—as the empirical subject, we use quarterly operational data covering a complete cycle from 2017Q1 to 2024Q4. First, we construct a strictly exogenous high-quality development index based on turnover efficiency, logistics cost reduction, and carbon emission mitigation, which completely isolates scale input factors. Second, from an initial pool of 35 operational and macroeconomic indicators, 17 candidate variables are rigorously pre-screened according to statistical consistency and logistics system theory. Third, an adaptive Double Machine Learning (DML) model integrated with leave-one-out cross-fitting is applied to disentangle complex collinearity among variables. The results show that DML effectively eliminates confounding noise, accurately identifies 15 true causal drivers, and excludes spurious correlations such as redundant macro-infrastructure investment. Furthermore, a causally weighted composite index reveals that the intermodal system exhibits strong resilience to global supply chain fluctuations and has undergone a four-stage evolution. Its development momentum has fundamentally shifted from extensive scale expansion to a refined mode driven by the synergy of efficiency and service quality. This study provides a robust methodological paradigm for port performance evaluation and targeted decision support for resource allocation. Full article

Climate adaptation in high-density cities requires equitable access to cooling and habitat benefits from blue–green infrastructure (BGI). However, the critical thresholds where human activity overwhelms these services remain unknown. Taking Shenzhen as a case study, we quantified habitat quality using the InVEST model and divided the urban landscape into low-, medium-, and high-quality zones. After screening significant drivers via multiple linear regression, we compared six machine learning algorithms and selected XGBoost (for low- and medium-quality zones) and LightGBM (for high-quality zones) with Optuna-tuned hyperparameters, then applied SHAP dependence analysis to identify nonlinear, threshold-driven responses. We identify a systemic thermal threshold of 23.7 °C. Above this temperature, BGI cooling efficiency decouples from habitat quality. We also quantify actionable intervention windows. Low-quality habitats—dominated by dense residential areas—collapse when building density exceeds 0.7 or road density exceeds 0.009. Medium-quality habitats offer a balance window (core area > 2844 m², building density < 0.361) that enables incidental nature contact during daily travel. High-quality refugia require a core area > 8600 m² with near-zero disturbance. These thresholds expose a stark green inequity: socioeconomically vulnerable groups in low-quality zones are systematically disconnected from cooling services. We translate these findings into a tiered spatial intervention framework—restoration, accessibility enhancement, and strict protection—to advance climate-resilient and socially equitable urban planning. Full article

In imbalanced binary classification, class overlap forces a familiar fork in the road: refine a preprocessing pipeline, or pivot to classifier selection. Which path dominates has remained empirically open. To address this question, a full factorial experiment was run across 58 KEEL datasets, 11 mitigation techniques, and 10 classifiers (yielding 6960 fitted models in total), with results read through a dual-baseline lens. Against the same classifier without mitigation, the techniques delivered a mean improvement of +4.13 points; gains concentrated on weak learners, with SMOTE and BorderlineSMOTE topping the rankings. Against an honest leave-one-dataset-out (LODO) baseline that selects the strongest standalone classifier from a held-out training pool, mean ΔF1 fell to −4.64 points, and 43.2% of mitigated configurations matched or beat the baseline. Among controllable factors, a Type II ANOVA placed classifier identity at η² = 0.157 against mitigation technique at η² = 0.010, so the choice of algorithm carried roughly sixteen times the explanatory weight of the choice of preprocessing. Dataset characteristics dominated the overall variance at η² = 0.620. The honest LODO selector returned LightGBM on every one of the 58 datasets, suggesting that under typical practitioner conditions, a strong-classifier default is the lower-effort, lower-variance path, with no expected performance cost relative to a pipeline-development investment. Full article

Automated Speed Enforcement (ASE) has emerged as a prominent speed enforcement practice, attracting policy attention as its adoption has increased. Although ASE has been widely studied on residential streets and urban corridors, empirical evidence regarding its effectiveness in school zones is limited, where vulnerable road users and time-specific exposure create distinct safety challenges. This study developed Crash Modification Factors (CMFs) for ASE in school zones that quantify the expected change in crash frequency associated with a safety treatment, using before-and-after studies with Empirical Bayes (EB) and comparison group methods. The before-and-after crash studies yielded CMFs below 1.0 in all scenarios considered in this study, indicating the safety benefits of ASE across multiple crash and school categories. The comparison group before-and-after study indicated that, following ASE implementation, total crashes decreased by 10 percent (CMF = 0.90) and 9 percent (CMF = 0.91), while speeding-induced crashes decreased by 35 percent (CMF = 0.65) and 54 percent (CMF = 0.46) for school zones on state-maintained and locally maintained roads, respectively. Thus, estimated CMFs provide quantitative inputs that agencies may consider, indicating that ASE is an effective speed management strategy for improving safety in school zones and justifying investment. Full article