Search Results (3,690)

Green hydrogen is a clean, versatile, and future-oriented fuel that can play a key role in the energy transition, decarbonization of the economy, and climate protection. It offers an alternative to fossil fuels and can be used in various applications, including power generation, industry, and transportation. However, due to its wide flammability range, small molecular size, and high diffusivity, special attention must be paid to ensuring safety during its use, particularly in leakage control. This paper provides a review and analysis of equipment leakage testing methods used for natural gas, with a view to applying these methods to the leakage testing of gas meters intended for hydrogen-containing gas mixtures and pure hydrogen. Tests of simulated leaks were carried out using two common methods: the bubble method and the pressure decay method, for three different gases: nitrogen (most commonly used for leak testing), helium, and hydrogen. The results obtained from the tests and analyses made it possible to verify and select optimum leak-testing methods for gas meters designed for measuring fuels containing hydrogen. Full article

To remain competitive in the decarbonization process of the economy worldwide, energy companies must preserve their market value to attract new investors and remain resilient throughout the transition to net zero. This article examines the market value of US energy companies during the period 2012–2024 in relation to their power generation mix. Panel regression analyses reveal that Tobin’s q and price-to-book ratios increase significantly for solar and wind power, while they experience moderate increases for natural gas power. In contrast, Tobin’s q and price-to-book ratios decline for nuclear and coal power. Furthermore, accounting-based profitability, measured by the return on assets (ROA), does not show significant variation with any type of power generation. The findings suggest that market investors prefer solar, wind, and natural gas power generation, thereby attributing greater value (that is, demanding lower risk compensation) to green companies compared to traditional ones. These insights provide guidance to executives, investors, and policy makers on how the power generation mix can influence strategic decisions in the energy sector. Full article

Under the global context of climate governance and sustainable development, low-carbon energy transition has become a strategic imperative. As a critical force in resource allocation, the financial system’s impact on energy transition has attracted extensive academic attention. This paper presents the first comprehensive literature review on energy transition research in the context of financial development. We develop a “Financial Functions-Energy Transition Dynamics” analytical framework to comprehensively examine the theoretical and empirical evidence regarding the relationship between financial development (covering both traditional finance and emerging finance) and energy transition. The understanding of financial development’s impact on energy transition has progressed from linear to nonlinear perspectives. Early research identified a simple linear promoting effect, whereas current studies reveal distinctly nonlinear and multidimensional effects, dynamically driven by three fundamental factors: economy, technology, and resources. Emerging finance has become a crucial driver of transition through technological innovation, risk diversification, and improved capital allocation efficiency. Notable disagreements persist in the existing literature on conceptual frameworks, measurement approaches, and empirical findings. By synthesizing cutting-edge empirical evidence, we identify three critical future research directions: (1) dynamic coupling mechanisms, (2) heterogeneity of financial instruments, and (3) stage-dependent evolutionary pathways. Our study provides a theoretical foundation for understanding the complex finance-energy transition relationship and informs policy-making and interdisciplinary research. Full article

This study investigates the role of Big Data Analytics Capabilities (BDACs) in ambidexterity explorative innovation (EXPLRI) and exploitative (EXPLOI) innovation for achieving a sustainable performance (SP) in the manufacturing sector of a resource-constrained developing economy. While a BDAC has been widely linked to innovation in developed economies, its effectiveness in developing contexts shaped by indigenous innovation practices like Jugaad remains underexplored. Anchored in the Resource-Based View (RBV) and Dynamic Capabilities (DC) theory, we propose a model where the BDAC enhances both EXPLRI and EXPLOI, which subsequently leads to an improved sustainable performance. We further examine the Jugaad capability as a cultural moderator. Using survey data from 418 manufacturing firms and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM), results confirm that BDA capabilities significantly boost both types of innovations, which positively impact sustainable performance dimensions. Notably, Jugaad positively moderates the relationship between EXPLOI and financial, innovation, and operational performance but negatively moderates the link between EXPLRI and innovation performance. These findings highlight the nuanced influence of culturally embedded innovation practices in BDAC-driven ecosystems. This study contributes by extending the RBV–DC framework to include cultural innovation capabilities and empirically validating the contingent role of Jugaad in enhancing or constraining innovation outcomes. This study also validated the Jugaad capability measurement instrument for the first time in the context of Pakistan. For practitioners, aligning data analytics strategies with local innovative cultures is vital for sustainable growth in emerging markets. Full article

Governments worldwide are mandating e-invoicing and real-time VAT reporting, yet many cash-intensive service SMEs continue to under-report VAT, eroding fiscal revenues. This study investigates whether financial technology (FinTech) adoption can reduce this under-reporting among tourism SMEs in Greece—an economy with high seasonal spending and a persistent shadow economy. This is the first micro-level empirical study to examine how FinTech tools affect VAT compliance in this sector, offering novel insights into how technology interacts with behavioral factors to influence fiscal behavior. Drawing on the Technology Acceptance Model, deterrence theory, and behavioral tax compliance frameworks, we surveyed 214 hotels, guesthouses, and tour operators across Greece’s main tourism regions. A structured questionnaire measured five constructs: FinTech adoption, VAT compliance behavior, tax morale, perceived audit probability, and financial performance. Using Partial Least Squares Structural Equation Modeling and bootstrapped moderation–mediation analysis, we find that FinTech adoption significantly improves declared VAT, with compliance fully mediating its impact on financial outcomes. The effect is especially strong among businesses led by owners with high tax morale or strong perceptions of audit risk. These findings suggest that FinTech tools function both as efficiency enablers and behavioral nudges. The results support targeted policy actions such as subsidies for e-invoicing, tax compliance training, and transparent audit communication. By integrating technological and psychological dimensions, the study contributes new evidence to the digital fiscal governance literature and offers a practical framework for narrowing the VAT gap in tourism-driven economies. Full article

This article investigates the macroeconomic and labor market conditions that shape the adoption of artificial intelligence (AI) technologies among large firms in Europe. Based on panel data econometrics and supervised machine learning techniques, we estimate how public health spending, access to credit, export activity, gross capital formation, inflation, openness to trade, and labor market structure influence the share of firms that adopt at least one AI technology. The research covers all 28 EU members between 2018 and 2023. We employ a set of robustness checks using a combination of fixed-effects, random-effects, and dynamic panel data specifications supported by Clustering and supervised learning techniques. We find that AI adoption is linked to higher GDP per capita, healthcare spending, inflation, and openness to trade but lower levels of credit, exports, and capital formation. Labor markets with higher proportions of salaried work, service occupations, and self-employment are linked to AI diffusion, while unemployment and vulnerable work are detractors. Cluster analysis identifies groups of EU members with similar adoption patterns that are usually underpinned by stronger economic and institutional fundamentals. The results collectively suggest that AI diffusion is shaped not only by technological preparedness and capabilities to invest but by inclusive macroeconomic conditions and equitable labor institutions. Targeted policy measures can accelerate the equitable adoption of AI technologies within the European industrial economy. Full article

In the digital transformation era, understanding the relationship between digital and real economies is vital for regional development. This study analyses the interaction between these two economies in Henan Province using panel data from 18 cities (2011–2023). It incorporates policy support intensity through fuzzy set theory, applies an integrated weighting method to measure development levels, and uses regression models to assess the digital economy’s impact on the real economy. The coupling coordination degree model, kernel density estimation, and Gini coefficient reveal the coordination status and spatial distribution, while the ecological Lotka–Volterra model identifies the symbiotic patterns. The key findings are as follows: (1) The digital economy does not directly determine the state of the real economy. For example, cities such as Zhoukou and Zhumadian have low digital economy levels but high real economy levels. However, the development of the digital economy promotes the real economy without signs of diminishing returns. (2) The two economies are generally coordinated but differ spatially, with greater coordination in the Central Plains urban agglomeration. (3) The digital and real economies exhibit both collaboration and competition, with reciprocal mutualism as the dominant mode of integration. These insights provide guidance for policymakers and offer a new perspective on the integration of both economies. Full article

This study examines the development of the digital economy and society in the Republic of Kazakhstan by combining international benchmarking with a context-specific national framework. It highlights the limitations of existing global indices such as DESI, NRI, and EGDI in capturing the structural and institutional dimensions of digital transformation in emerging economies. To address this gap, the study introduces a novel composite metric, the Index of Digital Economy Development (IDED), which integrates five sub-indices: infrastructure, usage, human capital, economic digitization, and transformation effectiveness. The methodology involves comparative index analysis, the construction of the IDED, and statistical validation through a public opinion survey and regression modeling. Key findings indicate that cybersecurity is a critical yet under-represented component of digital development, showing strong empirical correlations with DESI scores in benchmark countries. The results also highlight Kazakhstan’s strengths in digital public services and internet access, contrasted with weaknesses in business digitization and innovation. The proposed IDED offers a more comprehensive and policy-relevant tool for assessing digital progress in transitional economies. This study contributes to the literature by proposing a replicable index structure and providing empirical evidence for the inclusion of cybersecurity in national digital economy assessments. The aim of the study is to assess Kazakhstan’s digital economy development by addressing limitations in global measurement frameworks. Methodologically, it combines comparative index analysis, the construction of a national composite index (IDED), and statistical validation using a regional survey and regression analysis. The findings reveal both strengths and gaps in Kazakhstan’s digital landscape, particularly in cybersecurity and SME digitalization. The IDED introduces an innovative, context-sensitive framework that enhances the measurement of digital transformation in transitional economies. Full article

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article

Environmental sustainability and responsible resource utilization are critical global challenges. In this work, we present a sustainable and circular-economy-based approach for synthesizing CuFe₂O₄ nanoparticles by directly utilizing copper oxide minerals sourced from Chilean mining operations. Copper sulfate (CuSO₄) was extracted from these minerals through acid leaching and used as a precursor for nanoparticle synthesis via both chemical co-precipitation and microwave-assisted methods. The influence of different precipitating agents—NaOH, Na₂CO₃, and NaF—was systematically evaluated. XRD and FESEM analyses revealed that NaOH produced the most phase-pure and well-dispersed nanoparticles, while NaF resulted in secondary phase formation. The microwave-assisted method further improved particle uniformity and reduced agglomeration due to rapid and homogeneous heating. Electrochemical characterization was conducted to assess the suitability of the synthesized CuFe₂O₄ for supercapacitor applications. Cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) measurements confirmed pseudocapacitive behavior, with a specific capacitance of up to 1000 F/g at 2 A/g. These findings highlight the potential of CuFe₂O₄ as a low-cost, high-performance electrode material for energy storage. This study underscores the feasibility of converting primary mined minerals into functional nanomaterials while promoting sustainable mineral valorization. The approach can be extended to other critical metals and mineral residues, including tailings, supporting the broader goals of a circular economy and environmental remediation. Full article

Archaeological sites located on the edge of growing cities often struggle to reconcile heritage protection with rapid development. To understand this tension, we examined a 50.83 km² zone around the Western Han Imperial Mausoleums in the Qin-Han New District. Using Landsat images from 1992, 2002, 2012, and 2022, this study applied supervised classification, land-use transfer matrices, and dynamic-degree analysis to trace three decades of land-use change. From 1992 to 2022, built-up land expanded by 29.85 percentage points, largely replacing farmland, which shrank by 35.64 percentage points and became fragmented. Forest cover gained a modest 5.78 percentage points and migrated eastward toward the mausoleums. Overall, urban growth followed a “spread–integrate–connect” pattern along major roads. This study interprets these trends through five interrelated drivers, including policy, planning, economy, population, and heritage protection, and proposes an integrated management model. The model links archaeological pre-assessment with land-use compatibility zoning and active community participation. Together, these measures offer a practical roadmap for balancing conservation and sustainable land management at imperial burial complexes and similar urban fringe heritage sites. Full article

Agricultural new quality productive forces (ANQPFs) signify the progressive trajectory of modern agriculture. However, their development encounters significant challenges in many nations. The digital economy, characterized by its strong innovative capacity, offers continuous impetus for advancing agricultural new quality productive forces (ANQPFs). Based on panel data from 30 Chinese provinces (2014–2023), this study employs a two-way fixed-effects model, mediation and threshold effect analyses, and a spatial Durbin model to comprehensively assess the influence of the digital economy (DE) on agricultural new quality productive forces (ANQPFs). The findings reveal that (1) the digital economy (DE) significantly enhances the advancement of agricultural new quality productive forces (ANQPFs); (2) while its positive effect is pronounced in eastern, central, and western China, the impact is weaker in the northeastern region; (3) rural financial development (RFD) acts as a mediator in the relationship between digital economy (DE) growth and agricultural new quality productive forces (ANQPFs); (4) the digital economy (DE)’s contribution to agricultural new quality productive forces (ANQPFs) demonstrates non-linear trends; and (5) spatially, while the digital economy (DE) boosts the local agricultural new quality productive forces (ANQPFs), it exerts a negative spillover effect on neighboring areas. This research offers fresh empirical insights into the determinants of agricultural new quality productive forces (ANQPFs) and suggests policy measures to support agricultural modernization. Full article

This study proposes an integrated framework for sustainable tropical agriculture by combining biochemical waste valorization with spatial carbon footprint estimation in ‘Phulae’ pineapple production. Peel and eye residues from fresh-cut processing were enzymatically converted into rare sugar, achieving average conversion efficiencies of 35.28% for peel and 37.51% for eyes, with a benefit–cost ratio of 1.56 and an estimated unit cost of USD 0.17 per gram. A complementary zero-waste pathway produced functional gummy products using vinegar fermented from pineapple eye waste, with the preferred formulation scoring a mean of 4.32 out of 5 on a sensory scale with 158 untrained panelists. For spatial carbon modeling, the Bare Land Referenced Algorithm (BRAH) and Otsu thresholding were applied to multi-temporal Sentinel-2 and THEOS imagery to estimate plantation age, which strongly correlated with field-measured emissions (r = 0.996). This enabled scalable mapping of plot-level greenhouse gas emissions, yielding an average footprint of 0.2304 kg CO₂ eq. per kilogram of fresh pineapple at the plantation gate. Together, these innovations form a replicable model that aligns tropical fruit supply chains with circular economy goals and carbon-related trade standards. The framework supports waste traceability, resource efficiency, and climate accountability using accessible, data-driven tools suitable for smallholder contexts. By demonstrating practical value addition and spatially explicit carbon monitoring, this study shows how integrated circular and geospatial strategies can advance sustainability and market competitiveness for the ‘Phulae’ pineapple industry and similar perennial crop systems. Full article

The shift from traditional linear economic models to circular economy principles has gained momentum, particularly in the wastewater treatment sector. This transition relies on resource use efficiency, waste minimisation, and value creation. Nevertheless, a review of 83 articles, yielding 386 key performance indicators, reveals a significant lack of standardised methodologies for assessing circular economy performance in Wastewater Treatment Plants. This absence allows ambiguous outcomes and hinders comparative analyses across facilities and countries. There is a clear need for a universally applicable Key Performance Indicators-based framework that evaluates all environmental dimensions without forgetting the technical component while encompassing the various circularity strategies. Such a framework should not discard defined parameters’ costs and data availability, avoiding abstract parameters that may lead to data manipulation and require extensive additional costs. Standardising Key Performance Indicators with explicit units of measurement is essential to ensure data comparability. In addition, they should comply with the regulatory institutions’ evaluation requirements, at least while these methodologies have not yet evolved to incorporate broader circular and environmental considerations. Developing a standardised Key Performance Indicators-based framework is crucial for effectively evaluating circular economy performance in Wastewater Treatment Plants, ensuring comprehensive environmental assessment, data comparability, and alignment with regulatory standards. This approach would facilitate a more consistent and transparent evaluation of circular economy initiatives across diverse contexts. Full article

To evaluate animal performance and meat quality, stoned olive cake and linseed were used in an experimental test conducted on thirty-six young Podolian bulls, divided into four groups: the control group (CON), OC group (with olive cake containing a 30% as-fed basis of stoned olive cake), EL group (with linseed containing a 15% as-fed basis of extruded linseed), and OCEL group (with olive cake + linseed containing 20% stoned olive cake and 10% extruded linseed). The results show that olive cake supplementation did not influence performance in vita or the post-slaughter animal measurements (final body weight, DMI, FCR, ADG, carcass weight, dressing percentage, and pH) (p > 0.05); this was not true of the TBARS and color measurements, for which the meat samples showed excellent values (p < 0.001), especially in diets supplemented with olive cake. In conclusion, incorporating olive cake and linseed into the diet of fattening cattle may be a way to utilize a by-product of the olive industry and naturally increase the nutritional value of meat and meat-based products in Mediterranean regions. This would reduce environmental impacts and promote the valorization of this local feed source in alignment with the principles of the circular economy. Full article