Search Results (532)

Purpose: This study investigates the structural pathways driving the startup potential of Generation Z students in Romania’s post-transition economy. It examines the sequential relationship between Entrepreneurial Mindset (EM), Entrepreneurial Intentions (EIs), and Entrepreneurial Behavior (EB), focusing on the mediating role of intentions in bridging the gap between cognition and action. Design/methodology/approach: Using a quantitative explanatory design, data were collected through a two-stage hybrid framework from a sample of 215 Romanian business students associated with the Hackathon Innovation Labs (HILs), selected via a non-probability purposive sampling method. Hypotheses were tested using Structural Equation Modeling (SEM) with maximum likelihood estimation, supported by Confirmatory Factor Analysis (CFA) to ensure statistical rigor. Findings: Results confirm that EM is a robust predictor of EI, which significantly drives EB, explaining 45.2% of its variance. The findings highlight that intentions fully mediate the relationship, suggesting that a growth-oriented mindset turns into firm intentions to overcome institutional and cultural barriers. Originality: This research applies a rigorous SEM framework to a cohort of digital natives in an underrepresented Eastern European emerging market. It integrates mindset as a foundational cognitive precursor and provides empirical evidence of the sequential path to entrepreneurial behavior. Research limitations/implications: The study is limited by its sample size and geographic focus on Romanian business students. Future longitudinal research should explore external contingency factors, such as access to capital and ecosystem support, to validate model generalizability. Practical and social implications: Higher education should shift toward experiential programs like HILs, embedding credit-bearing hackathons and micro-credentials to foster an entrepreneurial mindset. Socially, sustaining this momentum with structured mentorship bridges the intention–action gap, transforming Gen Z’s digital potential into tangible economic value for the post-transition ecosystem. Full article

Using panel data on Chinese A-share listed firms from 2002 to 2024, this study examines the impact of the Air Pollution Prevention and Control Action Plan (the Air Ten policy) on firms’ cross-regional investment. We identify the policy effect using a multi-period difference-in-differences (DID) model with firm and year fixed effects. The findings remain robust across a series of tests, including the parallel trend test, propensity score matching combined with DID (PSM-DID), placebo tests, alternative variable specifications, additional control variables, and high-dimensional fixed effects. The results show that the Air Ten policy significantly promotes firms’ cross-regional investment. Mechanism analyses suggest three potential channels through which the policy operates: enhancing firms’ green innovation capability, improving human capital quality, and facilitating regional industrial upgrading. The resulting industrial upgrading creates a more favorable external environment for firms to expand their investment activities. Heterogeneity analyses show that the positive effect is more pronounced among firms with higher R&D intensity, weaker internal control, and those located in eastern China. We find no evidence that the policy-induced cross-regional investment is driven by pollution relocation. Instead, the results suggest that firms’ cross-regional expansion is more likely to reflect a capability-driven mechanism than regulatory arbitrage. This finding is broadly consistent with the pollution halo perspective and provides further support for the Porter hypothesis. Overall, this study offers new insights into how environmental regulation reshapes corporate capital allocation and geographic expansion strategies. 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

Current post-fire recovery strategies in Mediterranean Chile primarily emphasize discernible outcomes, such as residential reconstruction and expedited reforestation. In this context, neglecting soil degradation remains a critical geographical oversight. This perspective proposes the integration of a soil security framework as an “invisible reconstruction” to mitigate the unacknowledged deterioration of the five soil dimensions (capacity, condition, capital, connectivity, and codification) within fire-affected territories. Also, this study advocates for nature-based solutions as instruments for territorial management, asserting that soil should be classified as an essential infrastructure within national disaster risk policies during the recovery phase. A strategic roadmap and an organizational map are presented for public planners and researchers, facilitating a transition from immediate recovery to an integrated reconstruction paradigm centered on soil security. The proposed approach aims to reconcile the disparities between soil, vegetation, and anthropogenic reconstruction in fire-prone regions characterized by extractive economic models. Full article

Agricultural subsidy policies have increasingly shifted from production-oriented support toward incentives that reward environmental stewardship and the provision of ecosystem services. Despite their rapid expansion, evidence regarding the environmental effectiveness, economic efficiency, market implications, and food-security consequences of these eco-centric agricultural subsidies remains fragmented across policy frameworks and regions. This review synthesizes current evidence on eco-centric agricultural subsidies by comparatively evaluating their environmental, economic, and policy outcomes across developed and developing economies. The review was conducted using a structured literature search following PRISMA-informed review procedures, drawing upon peer-reviewed articles, systematic reviews, policy evaluations, and international institutional reports retrieved from major scientific databases and policy sources. The evidence indicates that eco-centric subsidies generally improve biodiversity conservation, soil health, carbon sequestration, water quality, and reductions in chemical inputs when payments are appropriately targeted and supported by effective monitoring and institutional capacity. Performance-based and results-oriented payment schemes frequently demonstrate greater environmental additionality and cost-effectiveness than conventional practice-based payments; however, their broader implementation remains constrained by monitoring costs, verification requirements, administrative complexity, and regional institutional capacity. Economic outcomes are more heterogeneous, with benefits depending on program design, agroecological conditions, market structures, and farm characteristics. While these subsidies can enhance environmental returns on public investment, challenges including land-value capitalization, unequal benefit distribution, transaction costs, market distortions, and potential short-term productivity trade-offs remain important policy concerns. Evidence regarding food-security impacts is similarly context-dependent and varies across production systems and geographical regions. Overall, the review demonstrates that no single subsidy instrument is universally effective. Instead, the greatest environmental and economic benefits are achieved through integrated policy portfolios combining targeted incentives, outcome-based payments, robust monitoring systems, digital technologies, carbon-market integration, and equitable program design. The review also identifies important evidence gaps concerning developing-country experiences, long-term cost-effectiveness, and standardized evaluation frameworks, providing priorities for future research and policy development. Full article

Natural resource management under climate change generates interdependent economic, social, and environmental impacts. However, the scientific evidence remains fragmented. This fragmentation limits the design of integrated policies capable of reducing vulnerability and preventing the degradation of natural capital. The objective of this study is to analyze recent scientific literature to assess how natural resource management in the context of climate change simultaneously influences economic stability, social resilience, and environmental sustainability. To this end, a systematic review of literature published in indexed journals on environmental economics, climate change, and natural resource management was conducted, selecting quantitative and mixed-methods studies that examine economic, social, or biophysical impacts associated with environmental degradation, extractive dependence, and adaptation and mitigation strategies. The review integrated research at macroeconomic, microeconomic, and ecological scales, organized using comparative matrices that allowed for the identification of common patterns in indicators of economic loss, emissions, natural capital depreciation, and effects on social welfare. Subsequently, a comparative analysis was conducted to detect relationships between management failures, social vulnerability, and long-term costs, as well as to identify conceptual, methodological, and geographical gaps in the literature. The results show that the degradation of natural resources under climate change produces simultaneous effects on macroeconomic stability, household income, and ecosystem resilience, increasing the costs of inaction when policies are designed sectorally. The evidence synthesized in this review indicates that dependence on extractive activities, limited productive diversification, and institutional weaknesses are frequently associated with greater economic and social vulnerability, particularly in communities dependent on natural resources. The reviewed studies also suggest that adaptation and mitigation strategies that incorporate participatory governance, social capital, and natural capital conservation may contribute to strengthening resilience. However, given the heterogeneity of methodologies, spatial scales, and indicators among the analyzed studies, these findings should be interpreted as evidence of consistent patterns rather than causal relationships. Therefore, integrated approaches that consider economic, social, and environmental dimensions represent a promising direction for sustainable natural resource management under climate change, although further empirical research is required to evaluate their effectiveness across different contexts. Full article

Tourism accessibility is an important indicator for assessing the coordinated development of transport and tourism. Using counties and districts in Gansu Province as the units of analysis, this study developed a three-dimensional evaluation framework comprising temporal accessibility, economic accessibility, and balanced accessibility, based on real-world travel data between county and district centres and China’s A-rated tourist attractions obtained from the Amap API. Spatial autocorrelation analysis, the Geographical Detector, the Spatial Durbin Model (SDM), and Multiscale Geographically Weighted Regression (MGWR) were employed to systematically investigate the spatial patterns and driving mechanisms of tourism accessibility in Gansu Province. The results indicate that: (1) tourism accessibility exhibits significant spatial clustering, with high-value areas primarily concentrated in the Hexi Corridor and low-value areas mainly distributed in the mountainous regions of central and southern Gansu; (2) distance to the provincial capital, elevation, and the number of adjacent counties constitute the core determinants of tourism accessibility, while interactions among factors generally exhibit bi-factor enhancement or nonlinear enhancement effects; and (3) tourism accessibility exhibits significant spatial spillover effects and spatial heterogeneity, with the effects of different driving factors varying considerably across space. The findings provide a theoretical basis for optimising tourism transport and promoting balanced regional tourism development in Gansu Province. Full article

Accurate short-horizon demand forecasting is essential for efficient bike-sharing rebalancing operations, yet most existing approaches validate on a single city and predict pickup demand only, leaving open questions about generalizability and the joint modeling of departure, arrival, and net supply flows. This paper proposes a CNN-LSTM architecture augmented with two components: a dynamic weather attention gate that conditions model output on forecast-horizon weather conditions and a community graph integration module that diffuses spatial context through a trip-volume-weighted adjacency matrix derived from Leiden community detection. A four-variant ablation study isolates the contribution of each component across two geographically and climatically distinct bike-sharing systems, BIXI Montreal and Capital Bikeshare Washington DC, using 15 min resolution trip data from two consecutive riding seasons. A single-seed evaluation initially suggested that community graph integration consistently reduces pickup prediction error in both cities and that the weather attention gate improves pickup prediction in Washington DC but not Montreal. However, a subsequent multi-seed check (three random seeds) found that these improvements do not hold up as consistently as the single-seed result suggested: The community graph variant outperforms the base CNN-LSTM in only 25–47% of seed-community combinations across the two cities and the weather attention variant in only 27–60%, indicating that small percentage improvements reported from a single training run in this class of model are frequently within the range of ordinary seed-to-seed variations rather than reliable architectural effects. We report this directly as a methodological finding in its own right: Ablation studies at the scale typically reported in this literature, including our own initial single-seed results, may substantially overstate the reliability of small reported improvements. For net supply change, the quantity most directly relevant to rebalancing decisions, all deep learning variants significantly outperform Random Forest in Montreal (paired Wilcoxon $p=0.0005$), a pattern that trends similarly but does not reach significance in Washington DC at the available sample size ($p\approx 0.06$–$0.07$); no significant difference is observed between the base CNN-LSTM and the full proposed model in either city, indicating that the added architectural components do not provide a demonstrable further benefit specifically for this derived, signed target. Taken together with the multi-seed instability observed for pickup prediction, these results caution against over-interpreting small single-seed ablation margins in this modeling setting more broadly and point to multi-seed evaluation as necessary practice for this class of architecture comparison. Full article

The maritime industry faces an unprecedented challenge to achieve the net-zero emissions goal by or around 2050. While numerous alternative fuels are undergoing intense evaluation, the industry remains trapped in a profound ‘technological deadlock’ driven by the irreconcilable trade-offs of current options, where fuel might offer excellent environmental profiles but fail on economic viability or safety acceptance. This study addresses this uncertainty by systematically reviewing 39 studies published from 2015 to 2025 that evaluated alternative fuels for the shipping industry using various criteria. Unlike traditional reviews, this study employs a horizontal synthesis across environmental, economic, technical, safety, and social criteria. By cross-examining these fragmented dimensions, this study maps the core friction points that prevent the scalable deployment of alternative fuels. The synthesis indicates that the regulatory framework should shift from static measures to dynamic, phased carbon pricing to address the immediate green premium of alternative fuels. Additionally, the analysis points to a looming global human capital bottleneck and a significant geographic risk of a two-tier global fleet. Most significantly, this study consolidates a decade of marine fuel evaluations into a practical, strategic fuel-deployment matrix that provides industry managers and global policymakers with a clear roadmap to overcome decision-making inertia and safely guide the maritime transition. Full article

Green total factor productivity (GTFP) is an important indicator for assessing urban green development under resource and environmental constraints. Using panel data from 110 prefecture-level cities in the Yangtze River Economic Belt from 2007 to 2023, this study examines changes in urban green total factor productivity. A super-efficiency slack-based measure model that includes undesirable outputs is adopted to measure GTFP, while the Malmquist–Luenberger index is used to decompose its dynamic changes. Spatial variation is then analyzed through trend surface analysis, center-of-gravity migration analysis, spatial pattern analysis, and the geographical detector model. The results indicate that GTFP in the Yangtze River Economic Belt improved on the whole, but its growth did not follow a smooth upward path. Among the decomposed effects, technological progress (TC) was the main source of improvement. Clear spatial differences were also observed. Cities in the middle and lower reaches generally had higher GTFP levels than those in the upper reaches, although this gap became less marked over time. The center of gravity of GTFP stayed mainly in the middle reaches and shifted gradually toward the northeast. The driving factors behind spatial differentiation were not constant. In the early stage, energy intensity and economic development level had stronger effects, whereas technological innovation, human capital, and industrial structure upgrading became more influential in the later stage. These findings provide empirical support for differentiated green development policies and coordinated regional governance in the Yangtze River Economic Belt. Full article

The groundwater resources are increasingly stressed in the Islamabad–Rawalpindi metropolitan area of Pakistan due to unplanned urbanization, growth of industries, and inadequate waste management. In this study, the aquifer vulnerability was evaluated in the productive alluvial zone of Islamabad Watershed using a Geographic Information System (GIS)-based DRASTIC model and critically comparing it with independent measured contamination of groundwater, which is a common weakness in many machine-learning-based DRASTIC studies considering the vulnerability index as the model input. The data from 21 boreholes supplied by the Capital Development Authority (CDA) were used to map seven hydrogeological parameters in ArcGIS Pro at a 30 m resolution. The DRASTIC Index values ranged from 69 to 188, with 12.9% of the mapped watershed (209.3 km²) being rated as Very High vulnerability, mainly in the shallow western urban alluvium where water tables are below 5 m. Single-parameter sensitivity analysis showed that the most influential factors of the index were impact of the vadose zone (S_i = 1.14) and depth to water table (S_i = 1.09). A Random Forest model was trained on independently measured nitrate instead of the DRASTIC Index, but had a poor predictive skill (cross-validated R² = 0.08), and the SHapley Additive exPlanations (SHAP) analysis suggested that increased vulnerability (shallow water table and high recharge) was correlated with lower nitrate concentrations. The inverse relationship between groundwater intrinsic vulnerability and measured nitrate was statistically significant when compared to 233 groundwater samples collected at the same locations during two different campaigns (2018 and 2024) (pooled Pearson r = −0.27, p < 0.001; Spearman ρ = −0.19, p = 0.007; Kruskal–Wallis H = 14.10, p = 0.003). Levels of nitrate in both Low and Moderate vulnerability zones (6.0 and 7.7 mg/L, respectively) were higher than in Very High zones (3.4 mg/L). The inverse direction was consistent across both campaigns and robustly significant in the 2024 dataset (ρ = −0.33, p < 0.001), which covered a wider contamination gradient; in the 2018 dataset, only the parametric test was significant. Nitrate showed no significant difference between land-use classes (H = 7.23, p = 0.065) and was found as a few individual high concentrations, suggesting that these were not diffuse loading issues or intrinsic susceptibility, but were likely influenced by point sources. These results show that intrinsic DRASTIC vulnerability is useful to identify areas vulnerable to potential future contamination, but does not explain the current distribution of contamination in this aquifer, which is influenced by point-source loading and residence-time effects. To provide effective groundwater protection, intrinsic vulnerability assessment must be complemented with specific monitoring of point sources. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

Understanding the pricing of climate risks in bond markets is relevant to financial stability. The real estate sector, characterized by geographically fixed and long-duration assets, exhibits high exposure to environmental shocks; yet, empirical matching between specific climate channels and real estate bond pricing remains sparse. This analysis examines the impact of climate risks on corporate bond credit spreads within the real estate sector by constructing three thematic indicators: transition risk (CTRI), chronic physical risk (ChroCPRI), and acute physical risk (AcuCPRI). Initial feature selection via machine learning suggests all three risk categories as predictive covariates for bond pricing. Subsequent regression estimations indicate that climate transition risk and acute physical risk expand credit spreads, whereas chronic physical risk compresses them—with these statistical patterns being more pronounced among state-owned enterprises (SOEs). Mechanism analyses yield threefold insights: first, transition risk elevates spreads by tightening financing constraints and restricting corporate asset growth, a channel concentrated in short-term tranches and low-liquidity firms; second, the counterintuitive spread-compressing effect of chronic risk is localized among firms with lower credit ratings and lower profitability, consistent with institutional climate support frameworks and strategic green adaptations; third, acute physical risk widens spreads by compressing operational cash flows and exacerbating financing friction, particularly for smaller enterprises. These channels align with the structural attributes of SOEs, which are characterized by larger asset scales, superior capital liquidity, and a higher propensity to secure state guarantees. Full article

Transport infrastructure is commonly viewed as a key driver of development, although its actual contribution remains debated and appears to be dependent on geographical and economic context. This study investigates the impact of transport infrastructure on regional economic growth in Romania, with a particular focus on spatial spillover effects. Using panel data for Romanian regions over the period 2000–2024, the analysis applies spatial econometric techniques to capture both direct and indirect effects of transport infrastructure and economic factors. A structured model selection procedure, based on Lagrange Multiplier tests and robust diagnostics, supports the use of the Spatial Autoregressive Model (SAR) as the preferred specification. The results reveal significant spatial dependence in regional economic performance, indicating that growth processes extend across regional boundaries. Nonetheless, the findings show that transport infrastructure does not exert a statistically significant direct effect on economic growth once spatial and structural factors are controlled. Instead, labor and private gross capital formation emerge as the primary drivers, generating both strong local impacts and substantial spillover effects. These results suggest that transport infrastructure acts mainly as an enabling factor rather than a standalone driver of growth, making the concept of “political mythification” of transport infrastructure effectiveness relevant in the Romanian context. Full article

This systematic review examines why empirical studies report conflicting effects of diversification on insurer performance and under what governance, risk-management, and institutional conditions diversification creates or destroys value. Following a PRISMA 2020-guided search of Scopus, Web of Science, and Google Scholar, and after screening 238 of the 415 identified records, the review synthesizes 56 empirical studies and develops a multi-level institutional–contingency framework that integrates institutional theory, the resource-based view, and agency theory. The review finds that diversification premiums, discounts, and non-linear effects coexist across the literature rather than forming a single dominant pattern, because—across product, geographic, human capital, and technological diversification—outcomes depend on institutional context, governance quality, ERM maturity, and firm capabilities rather than on diversification per se. Theoretically, the review moves the field beyond a premium-discount binary by explaining how institutional conditions, resource-based execution capacity, and agency problems interact across contexts. Practically, it indicates that insurers should evaluate diversification as a governance-sensitive risk-management decision requiring ERM maturity, internal controls, and incentive alignment, rather than as a standalone growth strategy. Full article