Search Results (2,317)

The endogenous money creation paradigm posits that banks generate money through lending, with deposits serving as a byproduct. This study investigates the mechanism driving the “credit–deposit paradox” during Poland’s high-interest-rate environment, introducing innovative methodological approaches to quantify systemic monetary impairment. Using comprehensive monthly data from 2006 to 2024, we employ a mixed-methods framework featuring: (1) Bayesian vector autoregression with Minnesota priors to test dynamic interdependencies; (2) a novel money shortage indicator (MSI) that operationalizes credit–deposit decoupling through three theoretically grounded components; (3) Markov regime-switching analysis to identify persistent monetary stress regimes. Key findings reveal a structural decoupling between deposit growth and credit creation, with robust evidence that exogenous money inflows accumulate as idle deposits rather than stimulating lending. The economy experienced significant periods of money shortage conditions, with the most severe impairment occurring during recent high-stress periods. The analysis confirms the dominance of cost-push inflation from energy and food prices, while monetary factors played a limited role. High interest rates amplified credit demand suppression, creating conditions consistent with endogenous money creation disruption. Methodologically, this study enables three key advances: (1) systematic measurement of monetary transmission breakdowns; (2) empirical identification of structural factors disrupting credit–deposit dynamics; (3) temporal characterization of monetary stress persistence patterns. These contributions advance the endogenous money framework by demonstrating its vulnerability to behavioral, policy-induced, and exogenous disruptions during high-stress periods. Practically, the MSI offers policymakers a real-time diagnostic tool for identifying monetary transmission breakdowns, while the regime analysis informs targeted countercyclical measures. Specific policy recommendations include developing sector-specific liquidity facilities, coordinating fiscal transfers with monetary policy to prevent deposit–loan decoupling, and prioritizing supply-side interventions during cost-push inflation episodes. By integrating post-Keynesian theory with empirical evidence from Poland, this study contributes to understanding money creation mechanisms in highly stressed economic environments. Full article

The rapid urbanization of recent decades has intensified the need for sustainable and adaptive city models that balance economic growth, environmental protection, and social well-being. This study addresses the challenge of assessing the performance of European smart cities by proposing a composite index of urban sustainability based on citizens’ perceptions. Using data from the Quality of Life in European Cities Survey (2023), the research applies a multi-criteria analytical framework grounded in the Benefit-of-the-Doubt (Data Envelopment Analysis) approach, which allows each city to determine optimal indicator weights and eliminates pre-assigned biases. The analysis integrates six dimensions of smart city performance—mobility, living, environment, economy, governance, and people—to evaluate cities’ adaptability to the needs of their residents. Results reveal that cities such as Aalborg (Denmark), Luxembourg (Luxembourg), Cluj-Napoca (Romania), and Zurich (Switzerland) exhibit the highest performance, demonstrating balanced progress across sustainability-oriented domains. The findings suggest that integrating citizens’ evaluations with data-driven weighting provides a more comprehensive and context-sensitive understanding of urban sustainability. The study concludes that the proposed composite index provides a robust methodological framework for benchmarking European smart cities, supporting policymakers in designing targeted strategies for enhancing livability, inclusiveness, and sustainable urban growth. Full article

Public open spaces play a vital role in supporting social connection and leisure among residents, enhancing quality of life while contributing to both economic growth and environmental health. The rapid global urbanization underscores the critical link between urban environments and human health, which demands focusing on sustainable, health-conscious urban planning. Accordingly, Public and green spaces are vital in this context, as recognized by global agendas like the Sustainable Development Goals (SDG) 11.7. This research aims to objectively evaluate the availability of public open spaces (POS) in Alexandria, Egypt. This study will utilize Geographic Information System (GIS) to formulate a methodology that incorporates spatial data analysis for quantifying public open spaces and assessing the proportion of the population with convenient access to these areas, evaluating their coverage, service area isochrones, spatial distribution, and proximity to residential areas. The study will benchmark its findings against global standards to expose critical spatial inequalities within cities of the Global South. The primary aim is to present evidence-based recommendations for sustainable urban public space design, tackling availability and accessibility issues to improve the well-being of Alexandria’s expanding urban population. This research offers a scientific foundation to inform policy and decision-making focused on creating more equitable, healthier, and resilient urban environments. Full article

As low-carbon supply chains increasingly integrate green transition strategies with digital transformation, coordinating high-cost green technology investments with data-driven marketing (DDM) becomes a complex managerial task. While these dual investments are essential for market growth, the inherent tension between economic efficiency and fairness concerns often triggers strategic friction phenomenon whose impact under cap-and-trade regulations remains insufficiently explored. This paper investigates the strategic implications of fairness concerns in a low-carbon supply chain in which a manufacturer invests in carbon emission reduction and a retailer engages in data-driven marketing (DDM), under a cap-and-trade regulation. We formulate four Stackelberg game models—Neutral Benchmark (NF), Retailer Fairness (RF), Manufacturer Fairness (MF), and Bilateral Fairness (BF)—to analyze the interplay between behavioral equity and economic efficiency. The main analytical results indicate that (1) fairness concerns universally function as an “efficiency tax” on the supply chain system, where the rational benchmark consistently yields the highest system efficiency. In contrast, bilateral fairness concerns lead to the worst performance due to double friction effects. (2) Counter-intuitively, the retailer can “weaponize” fairness concerns to extract surplus from the leader. Specifically, in environments with high carbon emission reduction costs, a fairness-concerned retailer compels the manufacturer to grant significant wholesale price concessions, thereby achieving higher profits than in a purely rational setting. (3) The manufacturer’s fairness creates a “Benevolence Trap” for the follower; to balance equity, a fair manufacturer tends to underinvest in green technologies, which severely contracts market demand and, unlike the retailer fairness scenario, fails to yield economic benefits for the retailer. (4) A critical “regime-switching” dynamic exists regarding the carbon trading price. While the retailer benefits from fairness strategies in nascent carbon markets, a pivot to rationality becomes optimal as carbon prices surge and efficiency dividends dominate. These findings offer novel managerial insights for supply chain members to navigate behavioral complexities and for policymakers to align incentive mechanisms. Full article

The Agricultural Park Integrated Energy System (APIES) is a key platform for integrating distributed renewable energy (DRE) with agricultural production. However, its economic operation and the stability of crop growth environments are severely challenged by bidirectional uncertainties from external meteorology. These include the inherent variability of wind-solar generation and critical agricultural loads, such as supplementary lighting and temperature control, a challenge that existing models with static environmental parameters fail to address. To solve this, a bi-level optimization scheduling model for APIES considering meteorological uncertainty is proposed. The upper layer minimizes operation costs by quantifying uncertainties via triangular fuzzy chance constraints, with core constraints on DRE output, energy storage charging-discharging, and load shifting, solved by YALMIP-Gurobi linear programming. The lower layer maximizes crop growth environment satisfaction using a dynamic weight adaptive mechanism and NSGA-II multi-objective algorithm. The two layers iterate alternately for coordination. Using a small agricultural park in Xinjiang, China, as a case study, the results indicate that the proposed two-layer optimal scheduling model reduces costs by 10.8% compared to the traditional single-layer optimization model, and improves environmental satisfaction by 4.3% compared to the fixed-weight two-layer optimization model. Full article

In parallel with achieving its 2040 Vision toward establishing smart cities, this study aims to pinpoint promising locations for future urban development in Oman, which reflect the unique physical attributes of the country, its renewable energy resources, and socio-economic conditions. To meet this goal at the national scale, the research relied on the following key factors: topography, diurnal temperature range, relative humidity, dust concentrations, wind speed, solar radiation, and access to electricity. These inputs were derived from remote sensing sources. A multi-layer spatial analysis was carried out within a Geographical Information System (GIS) environment to identify high-priority locations for future and sustainable urban growth. All parameters were assigned equal weights, particularly when applying a standard approach to produce a baseline suitability model at the national scale and to avoid subjective bias in the overall suitability assessment. Results showed that 2.1% of Oman’s land shows strong potential for sustainable urban development. Specifically, three locations stand out with the highest occurring along the southern section of the Arabian Sea between Al Jazir and Ad-Duqum. The other two locations occur at Salalah in the south and Sohar in the north. The promising locations occur proximate to major harbors and can benefit from existing infrastructure, including airports, highways, educational and medical services. Suggested locations also align well with earlier relevant studies. This study demonstrates the capabilities of integrating remotely sensed data with geospatial analysis in urban planning and development. Results are expected to help policymakers and planners to prioritize national-scale urban development. Full article

This case study is an overview of architectural design solutions implemented in the construction of farming facilities and the technological processes necessary to support a sustainable farm that runs with nearly zero waste in a closed-loop system that functions with full energy independence. The research will thoroughly investigate the specific location and configuration of the farm units in the target area—providing an extensive description of all necessary building typologies and infrastructures. The text will provide a summary of the agricultural solutions implemented at the farm, which is located in the region of Vojvodina in the Republic of Serbia. This region consists mainly of fertile agricultural land and could be a template for further designs and innovations in sustainable farming. This case study concerns the design of a resilient and self-reliant farm complex that consists of multiple animal species (broilers, pigs, and cattle), including a biogas station. The study is meant to show that adjustments made in architectural design, variations in building typology, and smart urban planning can contribute significantly to the improvement of sustainability in agricultural practices. This case study demonstrates that investments in sustainable solutions not only benefit the environment but can also deliver significant economic returns for investors—thereby further stimulating growth and development in the field of sustainable agriculture. Full article

The growth of aquaculture poses significant challenges for disease management, impacting economic sustainability and global food security. Traditional diagnostics are slow and require expertise, while current deep learning models, including CNNs and Transformers, face a trade-off between capturing global symptom context and maintaining computational efficiency. This paper introduces FishMambaNet, a novel framework that integrates selective state space models (SSMs) with convolutional networks for accurate and efficient fish disease diagnosis. FishMambaNet features two core components: the Fish Disease Detection State Space block (FSBlock), which models long-range symptom dependencies via SSMs while preserving local details with gated convolutions, and the Multi-Scale Convolutional Attention (MSCA) mechanism, which enriches multi-scale feature representation with low computational cost. Experiments demonstrate state-of-the-art performance, with FishMambaNet achieving a mean Average Precision at 50% Intersection over Union (mAP@50) of 86.7% using only 4.3 M parameters and 10.7 GFLOPs, significantly surpassing models like YOLOv8-m and RT-DETR. This work establishes a new paradigm for lightweight, powerful disease detection in aquaculture, offering a practical solution for real-time deployment in resource-constrained environments. Full article

Soil Health (SH) is a key concept in discussions on sustainable land use, with implications that extend beyond agriculture. To address the need for integrated assessments, this study developed a Sustainable Agricultural Development Index (SADI) by combining the Soil Health Index (SHI) with socioeconomic and management indicators. The analysis was conducted across Germany using 3300 soil analysis sites and environmental covariates, including climate, topography, vegetation indices, and bare soil reflectance. From this foundation, SADI was designed to evaluate agricultural sustainability across German states based on three dimensions: Management (Bare Soil Frequency), Environment (SHI Maps), and Economy (Profit per Hectare). Results revealed that SHI correlated significantly with land surface temperature (R = −0.47), bare soil frequency (R = −0.40), and vegetation indices (R = 0.43). Soil organic carbon also played a key role in explaining degradation patterns. While economically stronger states tended to achieve higher SH scores, environmentally sound and well-managed regions also performed well despite lower economic returns. These findings emphasize that sustainable agriculture depends on balancing economic growth, environmental integrity, and management efficiency. The SADI provides a comprehensive framework for policymakers and land managers to evaluate and guide sustainable agricultural development. Full article

Controlled environment agriculture technologies are traditionally applied to higher plants to enhance growth and cultivation periods, but such a concept has seldom been applied to seaweed aquaculture. A new dimension has been opened, wherein preliminary investigations in Ulva ohnoi revealed that continuous exposure (24 h) of light modulates chlorophyll-a fluorescence, carbohydrate content, and biochemical composition affecting the daily growth rate. DGR (daily growth rate) increased 2.6 times under continuous illumination for 24 h compared to the 12 h L/D photoperiod. Mg and carbohydrate contents were raised by 1.1 and 1.2 times, respectively, under continuous illumination. DGR formed a strong positive correlation with carbohydrate, protein, carotenoid, chlorophyll-a fluorescence, C, H, and Mg levels. A short cultivation cycle (15 days) was proposed to enable a consistent, continuous high growth and to avoid the induction of reproduction. The feedstock demand for bio-products, aquaculture feed, biomaterials, functional food, and food additives is registering unprecedented feedstock demand for Ulva. However, further detailed studies are desired to understand the seasonality and economic viability of scaling up this technique for commercial implementation. Full article

China has shifted from a stage of rapid growth to a stage of high-quality development. This highlights the critical need for policy frameworks that synergistically align technological innovation with sustainable development. To address the research gap in systematically assessing the collaboration between New Energy Industry (NEI) policies and New Quality Productive Forces (NQPF), this study proposes a three-dimensional “Goal-Instrument-Value” framework. Methodologically, we employ a combination of deep learning models (including LDA topic modeling, LSTM networks, and the Soft EDA algorithm) and policy quantification methods, analyzing 135 NQPF policies and nearly 800 NEI policies. The findings reveal a significant and strengthening synergy between the two policy domains. Notably, a misalignment exists in the goal dimension, where the weight of science and technology in NEI policies remains modest at 20%, indicating substantial potential for enhancement. In the instrument dimension, there is a predominant reliance on economically driven instruments, along with a notable underutilization of environmental instruments. Nevertheless, the overall synergy in policy value, as measured by a specialized New-Force Dictionary and the BM25 model, exhibits a consistent upward trend. Based on these findings, we recommend strengthening investment in NEI technology R&D, increasing the deployment of environment-oriented policy instruments, and establishing a cross-departmental policy synergy mechanism. These measures are crucial to fully harness the synergistic potential of NEI and NQPF policies for accelerating China’s green industrial transformation and achieving its sustainable development goals. Full article

This study investigates the impact of Internet of Things (IoT) demand on Digital Economic Growth (DEG) in Saudi Arabia between 2015 and 2023, employing both linear regression and a panel Autoregressive Distributed Lag (ARDL) model. The results show a long-term, significant, and positive association between IoT adoption and DEG, supported by the Technology Organization Environment (TOE) framework, highlighting the relevance of technology readiness and organizational capacity. Moreover, Internet penetration is a significant driver of digital transformation, aligned with the Diffusion of Innovations (DOI) theory, which emphasizes the role of connectivity in facilitating the adoption of digital devices. IoT will have little or no impact in the short term, but in the long run, the benefits are clear. Furthermore, despite the long- and short-term benefits of 5G deployment indicated by the results, a divergence between 5G deployment and electricity consumption is signaled by the significance of the error-correction term, which may be attributed to infrastructure and deployment prerequisites. Additionally, as an extension of the Resource-Based View (RBV) paradigm, the ultimate drivers of DEG through innovation and strategic resources highlight the importance of Research and Development (R&D) investment and Foreign Direct Investment (FDI) in inducing its growth. In contrast, inflation has an adverse impact on DEG, confirming macroeconomic instability as an obstacle to digital advancement, which relates to the environmental pillar of TOE. Policymakers can maximize Saudi Arabia’s digital economic growth on a sustainable, stronger path by investing in IoT infrastructure, increasing internet access and adoption, enhancing R&D and institutional support, and addressing challenges related to macroeconomic stability and 5G deployment. This study adds to the extant research by empirically evaluating the short- and long-term effects of IoT adoption on Saudi Arabia’s digital economic development, thereby providing insights into the roles of innovation, infrastructure, and institutional support in driving digital transformation. Full article

Family businesses play a central role in the ongoing growth and development of the Vietnamese economy. Economic, social, and demographic changes are undermining the transition of family business to succeeding generations. This study examines the challenges of intergenerational succession in Vietnamese family businesses through the lens of social capital theory. The article examines how the next generation of family business leaders in Vietnam is addressing social capital deficiencies that hinder effective business transition. The study employed a constructionist ontology and an interpretivist epistemology, utilising semi-structured interviews with family business owners and managers. The research draws from participants’ perceptions of social, political, and competitive contexts and the subsequent behaviour that is predicated by those contexts. Findings: Economic transformation, driven by disruptions to the business environment through central planning, coupled with demographic shifts and changes in educational attainment, has impacted family structures, complicating intergenerational business transfers. This is compounded by social transformation weakening familial relationships and connections critical to family cooperation and business continuity. The preservation and renewal of social capital are critical issues for succession planning in Vietnamese family businesses. This research addresses gaps in understanding the interplay between the generational divide, social capital, and family business succession in Vietnam. Full article

The United Nations Sustainable Development Goals (SDGs) set the criteria for sustainable economic development. These goals encompass four dimensions, including social, human, economic, and environment, of which the last two goals (i.e., economic and environment) were contemplated in this study. A case study for Corner Brook, a middle-sized city, located in the western region of the province of Newfoundland and Labrador, Canada, revealed that the current urban water use pricing mechanism is not matched with the SDGs, which reflects impediments to the city’s achievements to become a sustainable economic development community. Residents are billed a fixed rate for water use rather than a tiered or usage-based rate. This is not a resilient policy, as it fails to conserve water resources, ultimately leading to wasting freshwater produce, inhibiting economic growth, creating social exclusion, and degrading natural resources. We recommend changing the current flat-rate based water billing mechanism to either increasing block tariffs or two-part tariffs, adjusted by seasonal rates; issuing governmental policies, such as rebates, subsidies, and lower property taxes to entice residents’ willingness-to-install water meters on their premises; encouraging provisions such as using rain barrels to help cut down water consumption; and raising public knowledge through social media on how high per capita water use is in the region, including how much it costs to install water meters. These recommendations will also help provincial and municipal policymakers pursue the SDGs. Full article

In the era of the ‘Internet of Everything’ and amid growing demands for high-quality economic development, public data has emerged as a new core factor of production, establishing itself as a pivotal force behind regional economic growth. However, existing research rarely clarifies the multi-dimensional impact and influence mechanism of public data openness on regional development, and there are still deficiencies in the research on transforming the advantages of data elements into sustainable economic driving forces. This study, in conjunction with the interpretation of data elements, employed a fixed-effects model to empirically investigate the impact and path of public data opening on the high-quality development of regional economies, using panel data from 31 provincial regions in China from 2017 to 2024. Empirical findings provide clear evidence that public data openness acts as a significant catalyst for high-quality economic development, thereby solidifying its role as an indispensable engine for sustainable growth in the digital era. Analysis of the underlying mechanisms reveals two primary channels: business environment optimization and improved factor allocation efficiency, with the latter proving to be the more significant driver. Furthermore, heterogeneity analysis reveals that the positive effects are most pronounced in fostering economic structural optimization, advancing the low-carbon environment and expanding shared public welfare, while their influence on innovation dynamism remains comparatively modest. The research results support the government in increasing the openness of public data, establishing and improving a data opening mechanism oriented towards the business environment, and deepening the integration and application of data to enhance the efficiency of factor allocation. Full article