This study investigates regional heterogeneity and spatial interdependence in digital skills mismatch across Indonesia by constructing a Digital Skills Supply–Demand Ratio (DSSDR) from the Indonesia Digital Society Index (IMDI). In line with SDG 10 (Reduced Inequalities) and SDG 4 (Quality Education), the study aims to provide policy-relevant evidence to support a more inclusive and balanced digital transformation. Using district-level data and spatial econometric models (OLS, SAR, and the SDM), the analysis evaluates both local determinants and cross-regional spillover effects. Model comparison identifies the Spatial Durbin Model as the best specification, revealing strong spatial dependence in digital skills imbalance. The results show that most local socioeconomic and digital readiness indicators do not have significant direct effects on DSSDR, while school internet coverage exhibits a consistently negative association, indicating that digital demand expands faster than local supply. In contrast, spatial spillovers are decisive: a higher share of ICT study programs in neighboring regions improves local DSSDR through knowledge and human-capital diffusion, whereas higher GRDP per capita in adjacent regions exacerbates local mismatch, consistent with a talent-attraction mechanism. These findings demonstrate that digital skills mismatch is a spatially interconnected phenomenon driven more by interregional dynamics than by local conditions alone, implying that policy responses should move beyond isolated district-level interventions toward coordinated regional strategies integrating education systems, labor markets, and digital ecosystem development. The study contributes a spatially explicit, supply–demand-based framework for diagnosing regional digital inequality and supporting more equitable and sustainable digital development in Indonesia.

Understanding the mechanisms through which wood industry agglomeration influences the optimization of the wood industrial structure and its spatial spillover effects holds substantial theoretical and practical significance for formulating regional industrial development policies and advancing the green transformation of industries. This paper systematically investigates the characteristics and spatial linkages of China's wood industry. Utilizing panel data from 30 provincial-level administrative regions in China spanning 2010 to 2020, a Spatial Durbin Model is employed to examine the impact of wood industry agglomeration on the optimization of industrial structure in both local and neighboring regions, while also testing for spatial dependence. The results reveal that the optimization of the wood industrial structure exhibits a significant positive spatial correlation across regions, indicating a high degree of spatial interdependence characterized by clusters of both high and low levels of industrial development. While industrial agglomeration significantly promotes the upgrading of the local wood industrial structure, it has a suppressive effect on the development of the wood industry in adjacent regions. This study provides empirical evidence for formulating localized wood industry policies. The findings offer valuable insights for optimizing industrial spatial distribution, fostering coordinated regional development, and supporting the achievement of carbon peaking and carbon neutrality goals.

This study investigates the spatial patterns and drivers of carbon emissions across China’s three major urban agglomerations—Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)—from 2011 to 2020. A sequential analytical framework was employed to examine emission inequality, spatial dependence, dynamic transitions, and multi-scale drivers. Specifically, the Gini and Theil indices were used to quantify and decompose regional disparities. Spatial clustering patterns and heterogeneity were then identified through global and local Moran’s I analysis. Following this, spatial Markov chains modeled state transitions and neighborhood spillover effects. Finally, the Spatial Durbin Model (SDM) was applied to distinguish between the direct and indirect effects of key socioeconomic drivers. The findings reveal that disparities in emissions are largely driven by factors within each region. In BTH, heavy industrial lock-in accounts for 47.1% of the within-group inequality. By contrast, the YRD and PRD show noticeable convergence, achieved through industrial synergy and technological restructuring, respectively. The mechanisms of spatial spillover also differ across regions. In the YRD, emissions exhibit strong clustering tied to geographic proximity, with Moran’s I consistently above 0.6. In BTH, policy linkages play a more central role in shaping emission patterns. Meanwhile, in the PRD, widespread technological diffusion weakens the conventional distance-decay effect. The influence of key drivers varies notably among the urban agglomerations. Economic growth has the strongest scale effect in the PRD, reflected by a coefficient of 0.556. Industrial transformation significantly lowers emissions in the YRD, with a coefficient of −0.115. Technology investment reduces emissions in BTH (−0.124) and the PRD (−0.076), but is associated with a slight rebound in the YRD (0.037). Overall, these results highlight the persistent path dependence and distinct spatial interdependencies of carbon emissions in each region. This underscores the need for tailored mitigation strategies that are coordinated across administrative boundaries.

ABSTRACT This paper investigates the presence and nature of public expenditure spillovers among municipalities in Mexico from 2000 to 2021. Using dynamic spatial panel data models with common factors, it estimates the spatial and temporal interdependence in municipal spending across four expenditure categories: total expenditure, use of services, gross capital formation, and subsidies and social benefits. Results show that municipal governments exhibit incremental spending behaviour and that public goods are largely complementary across space, with significant direct and indirect effects driven by employment rates, household income, federal transfers and population density. Notably, federal transfers have the strongest effects on gross capital formation and use of services expenditures.

Purpose This paper aims to investigate the inclusivity of economic growth and check whether spatial interdependencies matter in inclusive growth analysis across 39 African countries situated south of the Sahara. Design/methodology/approach These objectives were achieved using the inclusiveness matrix, social mobility curve and panel-based regression. This paper tests the existence of spatial effects by using the spatial Durbin approaches hinged on geographically and institutionally informed weight matrices. Findings Country-specific results reveal that 13 countries demonstrate both income and equity growth. In 20 countries, income growth is achieved at the expense of equity. Two countries record equity over income growth, while four experience declines in both. The social mobility curves support these findings, illustrating rising average incomes but an uneven income distribution. The panel-based regression indicates that economic growth, though modest, has been inclusive and that spatial effects are significant, with substantial cross-border spillovers influencing inclusive growth from neighbouring countries. Research limitations/implications The results point to the necessity of promoting inclusive growth through equitable income distribution and reiterate the importance of recognizing and managing spatial interdependencies across African economies. Strong spatial spillover observed in income distribution and growth suggests that efforts to improve equity nationally can portend positive regional effects. However, given resource constraints and varying institutional capacities, prioritized and targeted investment is essential. Countries should focus on high-impact and equity-enhancing sectors (e.g. basic education, rural health care and social safety nets) that benefit the bottom income cadre and exert strong multiplier effects on growth and equity. Given the presence of spatial effects, a coordinated regional approach is recommended in tandem with strengthened economic integration. While regional co-ordination is pivotal, policy actors should be wary of barriers of unequal bargaining power, weak regional institutions and donor dependency. Therefore, regional spillover policies can begin with blocs that already exhibit economic interlinkages and political alignment. Within these blocs, the equity benchmarks could be harmonized to track distributional outcomes. Cross-border infrastructure and labour mobility can be better aligned to spread growth benefits evenly. Donors and international financial institutions (IFIs) should support regional public goods that foster inclusive growth. Practical implications Given the presence of spatial effects, a coordinated regional approach is recommended in tandem with strengthened economic integration. While regional co-ordination is pivotal, policy actors should be wary of barriers of unequal bargaining power, weak regional institutions and donor dependency. Therefore, regional spillover policies can begin with blocs that already exhibit economic interlinkages and political alignment. Within these blocs, the equity benchmarks could be harmonized to track distributional outcomes. Cross-border infrastructure and labour mobility can be better aligned to spread growth benefits evenly. Donors and IFIs should support regional public goods that foster inclusive growth. Originality/value This study contributes to the literature by integrating spatial interdependence into inclusive growth analysis, a dimension often overlooked in economic development studies. Using the spatial Durbin approach, it provides novel empirical insights into cross-border spillovers on equity and income growth.

We have developed an AO-OCT-based method to characterize microscopic vasodilatory responses of peripapillary major retinal artery (ppRA) to the focal luminous flicker stimulation of peripheral retinal neurons (>15° temporal to the fovea) with various mean illuminance levels, frequencies, and spatial patterns in the living human eye. The results have demonstrated that our proposed method can effectively measure transient, microscopic vasodilatory responses of the ppRA to the focal flicker stimulation, which increased monotonically with mean illuminance levels (1, 5 and 11lx), and exhibited frequency band-pass-like behavior (2, 5, 10, 15, 20 and 30 Hz) consistent with expected increases in the activities of retinal ganglion cells (RGCs) and their associated axons. We also found that the proposed method can be used to measure the spatial interdependency of their neurovascular responses: superior arcuate ppRA response to the superior-temporal flicker was 58% greater than that to the inferior-temporal flicker, and the inferior arcuate ppRA response to the inferior-temporal flicker was 86% greater than that to the superior-temporal flicker. Collectively, these results indicate that, using the proposed method, we can now test the hypothesis that major retinal arteries respond in an orchestrated manner to increased firing activities of RGCs and their associated axons, by accounting for the spatial coordinates between the stimulated neurons and the ppRA arcs in the living human eye.

ABSTRACT Effective management of natural resources is paramount for achieving sustainable economic growth while mitigating environmental degradation in resource‐rich regions. This study investigates the impact and spatial spillover effects of natural resources on economic growth and CO 2 emissions for 54 African countries. The data from 2005 to 2022 were collected from the World Development Indicators (WDI). Recognizing the endogeneity issues, the study utilized two‐stage least square estimation approaches, including panel 2SLS, Spatial Autoregressive (SAR), and Spatial Durbin Model (SDM) estimations to obtain both spatial and non‐spatial estimates. The non‐spatial results indicate a strong positive impact of natural resources, CO 2 emissions, FDI, and governance on growth, while reliance on renewable energy sources may temporarily lower GDP due to initial transition costs. Moreover, GDP growth, FDI, and governance positively affect CO 2 emissions, whereas natural resources and renewable energy inversely influence CO 2 emissions. In addition, the spatial analysis findings reveal that GDP growth in one country positively influences GDP growth in neighboring countries and neighboring countries' natural resources also have significant spatial spillover effects on the GDP growth in the region. Moreover, CO 2 emissions exhibit spatial interdependence with neighboring nations and the natural resources of neighboring countries exert significant spatial spillover effects on CO 2 emissions. This study contributes to the literature by providing a comprehensive spatial and non‐spatial analysis of the natural resource‐economic growth‐environment nexus in Africa. It provides novel insights into the spatial spillover effects that have been overlooked in prior research. The findings recommend boosting regional cooperation in resource management and emission reduction to get benefits on positive GDP growth spillover effects and mitigate CO 2 emissions across neighboring African countries.

This study investigates how housing prices influence urban–rural income inequality (URG) in mainland China by explicitly incorporating spatial interdependence and nonlinear adjustment mechanisms, features often neglected in previous research. Using a balanced panel of 31 provinces from 2005 to 2023, we develop a dynamic spatial panel threshold model that jointly accounts for temporal persistence, spatial spillovers, and regime-dependent estimation. This framework enables a full decomposition of housing price effects into direct, indirect (spillover), and total impacts across distinct market regimes. The results reveal three major insights. First, URG in mainland China displays strong temporal persistence, suggesting that income disparities evolve gradually over time. Second, rising housing prices significantly widen the urban–rural income gap, both within provinces and through interprovincial transmission, underscoring the amplifying role of spatial spillovers. Third, threshold estimation identifies a critical housing price level of ln(HP) = 8.4843 (approximately 4838.21 RMB/m2), revealing that the inequality-enhancing effect of housing prices is stronger in low-price regions but diminishes as markets mature and affordability constraints intensify. These findings provide new empirical evidence that the housing market functions as a nonlinear and asymmetric driver of regional inequality in mainland China, with implications for housing policy and inclusive growth.

FVolatile fluctuations in large red chili prices pose a persistent challenge to Indonesia’s food security and regional economic stability, as price shocks directly affect household purchasing power, inflation, and agricultural income. Addressing this issue requires a forecasting framework that captures both spatial interdependence among producing and consuming regions and temporal price dynamics. This study develops an advanced forecasting model for large red chili prices in East Java covering Malang Regency, Banyuwangi Regency, and Surabaya City using the Generalized Space-Time Autoregressive–Seemingly Unrelated Regression (GSTAR-SUR) method. The model integrates the Generalized Least Squares (GLS) approach to enhance parameter estimation efficiency under correlated residuals and applies a partial t-test–based parameter elimination procedure to retain only statistically significant predictors. Compared to traditional univariate time-series approaches such as ARIMA, GSTAR-SUR more effectively captures cross-regional price linkages and residual dependencies, yielding higher forecasting accuracy. The best-performing specification, GSTAR-SUR(3,1)-I(1) with a uniform spatial weighting matrix, achieved RMSE = 1426.73, MAPE = 3.29%, and R² = 0.8482, representing a substantial improvement in precision over conventional GSTAR and ARIMA models. Fourteen-day forecasts reveal region-specific dynamics: a mild downward trend in Malang, an initial rise followed by decline in Banyuwangi, and relative stability in Surabaya. These results demonstrate that the GSTAR-SUR framework can effectively model complex spatio-temporal dependencies in commodity markets and serves as a practical decision-support tool for policymakers in stabilizing food prices, improving distribution strategies, and strengthening agricultural market resilience across East Java.

In the context of China’s “dual carbon” strategy, carbon productivity serves as a central in dicator for coordinating economic development with carbon emissions. While the digital economy reshapes spatial economic configurations and affects regional carbon productivity, its spatial interdependence and spillover effects remain insufficiently explored. Our study constructs composite indicators to measure both digital economy development and carbon productivity, examining 30 Chinese provinces from 2011 to 2022 using the super-efficiency SBM model and exploratory spatial data analysis. Spatial regression is applied to assess the spatial influences of the digital economy and the moderating role of industrial structure transforming. Results reveal that: (1) China’s carbon productivity has improved overall but with notable regional disparities; (2) a U-shaped linkage between digital development and carbon productivity is confirmed, with early-stage suppression and later environmental benefits; (3) industrial rationalization and upgrading significantly enhance this relationship, though structural frictions remain obstacles.

ABSTRACT This study investigates the relationship between environmental sustainability policies and tourism flows across Italian provinces using a spatial Durbin error model (SDEM) within a gravity framework. By incorporating both public and corporate environmental initiatives, the analysis highlights the direct and spatial spillover effects of sustainability measures on tourism demand. The findings indicate that corporate-led initiatives, such as ecocertifications and green investments, exert a stronger direct influence on tourism flows compared to public measures, underscoring the visibility and immediate impact of private sector actions. However, both types of initiatives generate significant positive spatial spillovers, suggesting that sustainability efforts extend beyond local boundaries. These results demonstrate the interconnected nature of regional tourism systems and emphasise the critical role of coordinated sustainability policies in fostering tourism growth while promoting environmental protection. By addressing the spatial interdependencies of tourism flows and sustainability practices, this research provides valuable insights for policymakers and stakeholders seeking to improve sustainable tourism development at regional and national levels.

This study develops a spatial transit accessibility assessment method within a multicriteria decision-making framework. The method adopts the analytic network process to account for interdependence between criteria and across neighborhoods, and is applied to a case study in the Taipei metropolitan area in Taiwan. The assessment incorporates nine criteria across three perspectives: access to transit (population coverage of rail, bus, and public bike systems), service quality (frequency of rail and bus services and the number of public bike docks), and access by transit (opportunities to reach jobs, schools, and neighborhoods within a sixty-minute travel time). Criteria data were obtained from governmental open data platforms, official statistics, and Google Maps route planning, with 2021 as the base year. Expert surveys were conducted to determine the relative importance and interdependence of criteria. Spatial regression models and sensitivity analyses are conducted to evaluate how effectively the proposed accessibility measure explains variations in neighborhood-level transit mode share and to assess the robustness of the results. Results reveal significant spatial clustering of transit accessibility, confirming the influence of interdependence across neighborhoods. Moreover, the proposed method yields assessment results with greater explanatory power than those derived from single-criterion or independence-assuming approaches. These findings underscore the method’s utility for local authorities in identifying transport-disadvantaged neighborhoods and developing targeted accessibility improvement plans.

Site-selective doping in structurally complex oxides remains challenging due to multiple inequivalent cation sites and distinct coordination environments. Here, we present a strategy that couples advanced structural characterization, DFT calculations, and molecular orbital analysis to achieve site-selective doping and rational band engineering in SrGa12O19, a magnetoplumbite-type oxide featuring five distinct Ga sites. High-resolution PXRD combined with DFT calculations reveal that In3+ preferentially occupies the Ga4 (octahedral) and Ga3 (tetrahedral) sites, modulating the conduction band minimum (CBM) via M3-O antibonding interactions. This site-selective doping reduces the band gap from 4.54 to 4.23 eV (SrGa10In2O19), enhancing photocatalytic H2 evolution rate from 10.5(3) to 34.8(5) μmol/h. The in-depth analysis of doping-induced structural evolution reveals how local stress propagates and is relieved through bond-length variations and atom displacements. For instance, In3+ at M4 causes octahedral expansion and c-axis elongation via Coulomb repulsion, while adjacent units undergo compensatory compression. In3+-doping at M3 similarly exerts mechanical stress on neighboring Ga1 polyhedra, revealing a mutual constraint mechanism that limits complete solid-solution formation. This spatial interdependence explains site preferences and highlights the role of structural voids in enabling selective incorporation. This study offers a generalizable strategy for designing semiconductors with tailored band structures through site-selective doping.

Abstract Shield tunnels in water‐rich strata are usually large in scale, long in length, and located in complex operating environments. The diagnostic results for tunnel structural uplift are easily affected by complex environmental factors. How to extract the key information related to the structural uplift of the tunnels from the massive monitoring data of distributed optical fiber sensors in this complex water‐rich environment and accurately diagnose the structural uplift of the tunnels remains a difficult problem that urgently needs to be solved. This paper proposes a diagnostic approach for structural uplift in water‐rich shield tunnels, which utilizes the spatiotemporal features of the densely distributed strain data to address this challenge. On the one hand, the spatial interdependence of the densely distributed strain data is analyzed. By combining k ‐means clustering with the artificial bee colony algorithm and referring to the distribution characteristics of the tunnel surrounding rock, a clustering algorithm for the dense strain measurements along the length direction of the tunnel is proposed. Then, a spatial interdependence model for the densely distributed measurements is established based on a one‐dimensional convolutional neural network. On the other hand, the spatial interdependence features of the strain data are analyzed in the time domain. The factors influencing the spatial interdependence residuals of the strain data within each category are analyzed by using the principal component analysis algorithm and a diagnosis index for the structural uplift of the tunnel is constructed on the basis of the aforementioned residuals, thereby achieving a diagnosis of the structural uplift of the water‐rich shield tunnel. Finally, the proposed method is validated using a synthetic numerical simulation and field monitoring data from an actual tunnel project.

Urban CO2 mitigation strategies typically aim at particular zones or sectors but do not account for spatial interdependencies among different components within the city. Understanding how land uses emit within and across districts can reveal systemic leverage points for climate-resilient urban planning. This study applies a bipartite spatial network approach using high-resolution Urban Atlas land-use data and a hierarchical spatial framework for emissions and sequestration estimation. The approach links urban land uses to their emissions profiles, offering a structural view of how different areas interconnect within urban carbon dynamics, moving beyond fragmented emission accounting. Using the Reggio Calabria Functional Urban Area in Italy as a case study, the analysis identifies influential areas and emission-intensive land uses. Subsequently, using centrality metrics highlights the spatial units with strong connections to emission-dense land uses, marking them as points of intervention. Results show that although 53% of districts act as net carbon sinks, their sequestration capacity is outweighed by the intensity of a smaller group of emitter districts. Among these, five central districts (IDs 94, 82, 107, 108, and 72) emit over 500 million kg CO2 per year, making them leverage points for systemic mitigation. The integration of bipartite spatial network and multiscale territorial analysis provides a replicable, data-driven framework for urban CO2 mitigation. Ultimately, the study demonstrates that mapping emissions through spatial interdependencies enables planners to target interventions where localized action yields the greatest network-wide climate impact.

Structural health monitoring using strain data faces a critical challenge: decoupling subtle structural degradation signatures from the dominant influence of operational loads. This paper introduces a novel methodology to address this by synergistically combining a custom health indicator (HI) with graph neural networks (GNNs). The proposed HI, derived from the cumulative absolute first derivative of strain over time, effectively isolates load-independent features indicative of damage progression. These features serve as input to our proposed GENConv with Edge Attributes (GENEA) model, a GNN that explicitly models the spatially distributed sensors as an interconnected network, leveraging spatial interdependencies and edge attribute information within the strain field to enhance damage assessment. This integrated approach enables accurate structural stiffness reduction estimation (diagnostics) and remaining useful life (RUL) prediction (prognostics). Applied to strain data from fatigue tests on representative aeronautical composite panels, the methodology is rigorously evaluated using Leave-One-Panel-Out cross-validation. The framework shows promising performance on unseen test data, although challenges in generalizing to out-of-distribution specimens were also identified, highlighting the importance of a diverse training set for real-world applicability. Experimental results confirm the framework’s superiority. The proposed GENEA model significantly outperforms both a fundamental multi-layer perceptron and a spatially aware convolutional neural network baseline, and successfully generalizes to an unseen panel with a different sensor count. This validates the benefits of using a tailored GNN framework to learn robust, geometrically invariant patterns from load-decoupled spatial strain data.

This study evaluates the determinants of public spending efficiency at the provincial level in Peru by integrating Data Envelopment Analysis (DEA) with two alternative econometric models: a bootstrapped random-effects Tobit model and a spatial Durbin model. Using panel data from 196 provinces over 7 years, DEA efficiency scores under constant returns to scale (CRS) and variable returns to scale (VRS) were estimated and analyzed to assess the impact of intergovernmental transfers, local tax revenues, and spatial interdependencies. The Tobit model accounts for the bounded nature of efficiency scores and shows that FONCOMUN transfers significantly improve both technical efficiency (VRS_TE) and scale efficiency, while canon transfers primarily enhance scale efficiency due to their infrastructure-oriented allocation but have limited effects on managerial performance. Local tax revenues positively influence technical efficiency, reflecting better expenditure management in fiscally autonomous municipalities, but they have no significant effect on scale efficiency. In contrast, the spatial Durbin model captures spatial dependencies and reveals significant negative spillover effects, indicating that higher efficiency in neighboring provinces can reduce local efficiency due to competition for resources and overlapping administrative functions. These findings contribute to the literature by providing robust empirical evidence on the interplay between fiscal transfers, managerial capacity, and spatial interdependencies, offering actionable insights for policymakers seeking to optimize local government performance and improve public service delivery.

The ESCRT machinery mediates membrane remodeling in fundamental cellular processes, including cytokinesis, endosomal sorting, nuclear envelope reformation, and membrane repair. Membrane constriction and scission are driven by the filament-forming ESCRT-III complex and the AAA-ATPase VPS4. Although ESCRT-III-driven membrane scission is generally established, the mechanisms governing the assembly and coordination of its 12 mammalian isoforms in cells remain poorly understood. Here, we examined the spatial organization and interdependence of ESCRT-III subunits during mammalian cytokinetic abscission by depleting CHMP2A, a core ESCRT-III component. Using live cell imaging, structured illumination microscopy (SIM) and correlative light-electron microscopy, we show that CHMP2A knockout cells display a significant delay-but not failure-in abscission, accompanied by distinct mislocalization phenotypes across ESCRT-III subunits. WhileIST1 and CHMP2B were minimally disrupted, CHMP4B, CHMP3, and CHMP1B display progressively severe organization defects at the abscission site. Dual-protein imaging reveals disrupted coordination between ESCRT-III subunits in individual CHMP2A-deficient cells, supporting an ordered assembly of ESCRT-III subunits in cytokinetic abscission. Together, our findings provide the first in vivo evidence for hierarchical assembly of ESCRT-III subunits during ESCRT-mediated membrane remodeling and identify CHMP2A as a key organizer of ESCRT-III architecture essential for timely membrane abscission.

<p><span>Tourism management in DI Yogyakarta is vital to ensure tourism benefits local communities. A key challenge lies in the uncertainty and spatial interdependence of tourist visits among neighboring regions. While the GSTAR model captures spatial relationships, its accuracy decreases with outliers, non-linearity, and assumption violations. To overcome these issues, this study integrates GSTAR with machine learning. Using 168 observations of tourist visits across DI Yogyakarta’s regencies/cities (January 2010–December 2023), GSTAR-GLS-XGBoost model achieved 22–34% lower RMSE than other models. Tourist numbers fluctuate greatly, with peaks in May, June, July, and December. Practically, these findings can help local governments and stakeholders optimize resource allocation, plan promotions, and prepare facilities during peak seasons for sustainable tourism management in DI Yogyakarta. </span></p>

In this study, we investigated the spatial and temporal dynamics of pork carcass prices across European Union Member States, focusing on the influence of wheat prices and population income levels between 2014 and 2023. Our analysis revealed that both input costs (reflected by wheat price fluctuations) and income-driven demand factors exert significant and spatially correlated effects on pork carcass prices. The results demonstrate the existence of spatial interdependencies among neighboring countries, indicating that price changes in one region may propagate through the broader European market. By integrating spatial econometric techniques within a panel data framework, this research provides empirical evidence of the interconnected nature of EU agricultural markets, advancing the existing literature by demonstrating how input markets and consumer income dynamics jointly shape price behavior within an integrated regional economy. Our findings contribute to a deeper understanding of price transmission mechanisms in the livestock sector and offer valuable insights for policymakers seeking to enhance market efficiency and resilience within the Common Agricultural Policy context.