Industry Level Research Articles

Comparative analysis of meso-level strategies: region, industry, corporation

Objective: to identify the features of strategies of various entities at mesoeconomical level: territorial systems (regions), industries and large corporations (ecosystems) based on a comparative analysis of their structure, key elements and attributes. Methods: a structural approach to strategic documents at the macro-, meso- and microlevels, comparative analysis and synthesis, review of theoretical research and practice of strategy formation and implementation. Results: the article defines the features of strategy formation at the regional, industry and corporate levels. The author states that the key difference between regional and sectoral strategies is a long planning horizon, while corporate strategies are more often subject to adjustment. The article shows that the strategies of mesoeconomical level entities have a number ofsystemic problems related to their formation and implementation. It was found that due to the variability and turbulence of environmental conditions, the entities at meso-level are forced to constantly adjust their strategies, while corporate strategies, unlike regional and sectoral ones, are not limited by the top-level strategy. The author reveals the shortcomings of interaction and coordination (institutional vacuum) of strategies of macro-, meso- and micro-level entities in terms of coherence and coordination of actions in long-term planning. Scientific novelty: it consists in applying a structural approach to the analysis of strategies of meso-level entities, comparing them and identifying common features and differences in approaches to their formation and implementation. Practical significance: it consists in the developing applied methods for the formation and implementation of strategies and improving the effectiveness of strategic planning at the mesoeconomical level.

Forecasting the Gender Wage Gap in Argentina from a Productivity-Based Approach

This paper analyzes the association between the gender wage gap (GWG) and productivity at the industry level in Argentina from 2002 to 2022, drawing on gender studies and literature on structural change. Using dynamic OLS regression with random effects across the 1st to 4th quantiles, we examine the relationship between the GWG, female labor participation, and industry productivity. Results indicate that increased female participation is positively associated with reductions in the wage gap in the least productive industries. Additionally, female education rates contribute positively to reducing the GWG. The findings also reveal that productivity gains in the most productive sectors help narrow the gender wage gap, suggesting that structural shifts toward higher productivity levels may play a role in closing this gap.

Dynamic Redeposition Over Bidirectional Amorphous NiFe-Oxides toward Surface Self-Healing for the Alkaline Oxygen Evolution Reaction.

The NiFe-oxy/hydroxides (NiFeOxHy) have emerged as promising candidates for alkaline oxygen evolution reaction (OER) but suffer from irreversible metal dissolution to pose a great challenge to long-term stability. Here, a self-supported electrode of NiFeOxHy/FeNiOx/SS-A (substrate-etched stainless steel, SS-A; interlayer-amorphous Fe3Ni1Ox oxide; catalytic layer-amorphous Ni3Fe1OxHy) is fabricated and presents rare self-healing property of surface cracks, as well excellent activity and stability. It is found that crack repair is driven by the redeposition of dissolved Fe and Ni ions from the amorphous interlayer involved in the OER process because no similar behavior is observed in Fe-free and crystalline interlayer-supported NiFeOxHy. Moreover, the repair performance is dependent on current density and electrolysis time, with 71% of surface cracks being repaired after 72h operated on an industrial level of 500mA cm-2. It needs to be emphasized that the irreversible dissolution of Fe and Ni from the catalytic layer of NiFeOxHy still occurs but is effectively suppressed. It is demonstrated that the construction of an amorphous FeNiOx oxide interlayer in self-supported electrodes plays an important role in improving the stability and is expected to open up an opportunity for the design and develop highly efficient and durable alkaline OER catalytic electrodes.

Silver‐Based Supportless Membrane Electrode Assemblies for Electrochemical CO2 Reduction

ABSTRACTMost commonly, electrochemical CO2 reduction is performed in a three‐compartment setup employing gas diffusion electrodes (GDEs) to decrease mass transport limitations of the gaseous reactant CO2 to the reaction interface. However recently, there has been a rising number of investigations on suitable membrane electrode assemblies (MEAs) to overcome ohmic potential losses caused by the electrolyte gaps in the systems. While the significant majority of MEAs exhibited in literature is based on catalyst‐coated gas diffusion layers, this work presents an approach that does not require a likewise support. On the basis of a catalyst suspension similar to mixtures already employed for GDE production on industrial level, a method to directly transfer the resulting catalyst layers to the membrane is developed. The Faradaic efficiency of carbon monoxide, i.e. target product formation of GDEs manufactured according to a similar procedure, can be matched or even exceeded for individual modifications of the exchange MEAs. Simultaneously, the cell potentials can be remarkably decreased in this setup. By gradual adaptation of the fabrication procedure, the influence of important manufacturing parameters is unraveled, also discussing the effect of hydrogen permeation through the membrane.

Radiological Assessment of Exposure to Background Ionizing Radiation in Ogbomoso South Local Government, Oyo State, Nigeria

The radiological evaluation of rural areas in developing countries is frequently overlooked due to the assumption of minimal radiation risk and low levels of industrialization. This research aims to fill this gap by assessing the background radiation level in Ogbomoso South Local Government, Oyo State, Nigeria, and estimating the radiological risks. One thousand sampling locations were randomly selected across the ten administrative wards of the study area, and the radiation level was examined using a dosimeter. The recorded dose rates varied between 0.09 and 0.17 μSvh-1, with an overall mean of 0.13 μSvh-1. This average is 48.2% lower than the global mean of 0.27 µSvh-1. The estimated annual effective dose equivalent ranged from 0.15 to 0.30 mSvy-1, with an average of 0.23 mSvy-1, which is 23.1% below the acceptable limit of 1 mSvy-1. The study shows that the current levels of radiation exposure do not present a significant radiological threat. It recommends establishing a regular monitoring program to observe changes over time, with this data serving as a baseline for Ogbomoso South Local Government.

A Non-Linear Exploration of the Digital Economy’s Impact on Agricultural Carbon Emission Efficiency in China

As the global climate crisis intensifies, improving agricultural carbon emission efficiency has become crucial for achieving the sustainable development goals (SDGs). This study investigates the complex, non-linear relationship between China’s digital economy and agricultural carbon emission efficiency, utilizing panel data from Chinese provinces spanning 2012–2022. We employ a multi-method approach, including the Super-SBM model for efficiency measurement, two-way fixed effects models, quantile regression, and Generalized Additive Models (GAMs) for empirical analysis. Our findings reveal: (1) The digital economy significantly enhances agricultural carbon emission efficiency, but with distinct non-linear characteristics across different dimensions. (2) The impact varies among digital economy aspects: the digital economy foundation shows the most substantial influence, followed by the rural digital industry level, while rural digital infrastructure has a relatively minor effect. (3) A threshold effect is observed, with the digital economy’s impact more pronounced in regions with higher agricultural carbon emission efficiency. (4) GAM analysis unveils complex non-linear patterns: the rural digital industry’s impact initially decreases before increasing, the digital economy foundation shows an overall increasing trend with plateaus, and rural digital infrastructure exhibits a near-linear relationship. (5) Sensitivity analysis indicates that agricultural carbon emission efficiency is most responsive to changes in the digital economy foundation, followed by the rural digital industry level. These findings provide nuanced insights into the digital economy’s role in enhancing agricultural sustainability. We propose targeted policy recommendations, including accelerating rural digital infrastructure development, optimizing the rural digital industry structure, and implementing context-specific digital facility construction. These strategies aim to fully leverage the digital economy’s potential in improving agricultural carbon emission efficiency, contributing to China’s “dual carbon” goals and sustainable agricultural development.

Research on key technologies for construction of steel structures in ultra large span cold storage

Taking the 100,000-ton automatic cold storage construction project of Weizhou Investment Holding Co., Ltd. as an example, this thesis systematically analyzes the main difficulties in the construction of super-large-span steel plant in terms of material transportation and handling, large-size components lifting, joints handling and welding, foundation reinforcement, and puts forward corresponding solutions. It not only provides practical suggestions for solving the difficulties in the construction of super-large-span steel structure plant, but also has guiding significance for improving the construction quality and safety management level of the whole industry.

Spatiotemporal Urban Evolution Along the China–Laos Railway in Laos Determined Using Multiple Sources of Remote-Sensed Landscape Indicators and Interpretable Machine Learning

Constructing high-speed railways (HSRs) is critical for developing countries to stimulate economic growth and urbanization. This study focuses on the Lao section of the China–Laos Railway (CLR) and employs explicitly spatial remote sensing images to investigate the urban development surrounding HSR stations. Data-driven machine learning and causal inference approaches are integrated to quantify the spatial–temporal evolution and discover its driving factors. The results suggest that the CLR has had positive spatial spillover effects on the development of the surrounding urban space. These spillover effects have exhibited a distance attenuation pattern, reflecting obvious development in 2D rather than in 3D urban space. Meanwhile, the distance to stations and adjacent city centers as well as functional urban characteristics, such as land use patterns and industrialization level, have significantly influences the surrounding spatial development. Specifically, in industrial-dominated cities, the surrounding spatial changes have been most significant under the influence of the HSR. Change related to industrial and residential land use has shown significant land expansion patterns and increased utilization efficiency, reflecting that industrialization and urbanization have been the primary drivers of land demand surrounding the HSR. The findings offer valuable insights and references for developing nations to formulate and implement spatial management policies and initiatives related to HSR.

Potential of Biogas Production from Wastewater of Shredded Pork Processing with Cow Manure

Small and Medium Enterprises (SMEs) play a crucial role in driving the economy, especially in developing countries. Since they are essential employment resources and support local development, in SMEs relate to processed meat production, large amounts of wastewater containing blood, fat, and protein require high-efficiency treatment methods to prevent environmental pollution. If the wastewater is not treated appropriately, the impact will cause environmental pollution. Therefore, this research aims to study the potential of biogas production from wastewater of shredded pork processing (WSPP) to find the optimal substrate-to-inoculum ratio and evaluate its industrial application potential. The research also experimented following the biochemical methane potential method, where the total volume was 1000 mL, and the working volume was 400 mL. The experiment used the substrate-to-inoculum ratio (SIR) ratios, which were 1:2, 1:1, and 2:1 VS-based. The operating temperatures were 35 ± 2 °C in the mesophilic period. The study results showed that the most appropriate ratio for biogas production was 2:1 VS-based. During the 33 days of the experiment, it was found that the potential of the highest cumulative biogas and methane yield was 1640.19 ± 97.46 and 650.59 ± 48.04 N mL/g VS added, respectively, which contained a maximum methane content of 68.60%. Moreover, COD, TS, and VS removal efficiency were 57.57 ± 2.28, 57.30 ± 2.19, and 1.49 ± 0.16, respectively. The research results demonstrated that the wastewater from the WSPP was a high-potential substrate for biogas production and would continuously be developed at the industrial level.

Unlocking regional value chains: Fintech’s role in the Western Balkans' integration into global markets

Digitalization and automation are restructuring the geographical landscape of operations and labor, significantly altering operations in global value chains (GVCs). Digital technologies are expected to boost resilience, enhance transparency, and foster sustainability in GVCs. In this context, this paper looks at how the application of fintech solutions fosters the creation of regional value chains (RVCs), increasing attractiveness and generating new opportunities for enterprises in the Western Balkan region to participate in GVCs To fill the existing gap for granular analysis at the industry level, the paper examines if enterprises in the Western Balkans that apply of fintech solutions in financial management have better opportunities to create RVCs than the ones that resist fintech implementation. Using a benchmarking instrument on fintech solutions implemented on 370 enterprises, this paper tries to identify industries with the highest potential to create RVCs and higher prospects for integration in GVCs. Our findings indicate that fintech solutions and applications can be one of the main driving forces in transforming and upgrading RVCs in Western Balkan countries.

Study on the pattern and driving factors of water scarcity risk transfer networks in China from the perspective of transfer value—Based on complex network methods

The transfer value of risk, measured by the asymmetric impact of virtual water flows, is vital for regions addressing the global water crisis, achieving Sustainable Development Goal 6 (SDG6), and implementing sustainable water management. This study uses multi-regional input-output models, complex network analysis, and the Exponential Random Graph Model (ERGM) to construct and analyze China's water scarcity risk transfer network. Findings show frequent but weakly connected risk transfers between provinces, with significant spatial clustering. The network displays both random and hierarchical features, with clustered development being prominent. Reciprocal cooperation and extensive connections are crucial to avoiding the “prisoner's dilemma” in water conservation. High levels of “dual circulation” and water resource utilization enhance risk transfer value, while lower industrialization and digitization levels facilitate value reception. The efficiency of water resource use is key in shaping high-risk value pathways, underscoring the need for risk management at both source and sink ends. This study offers a scientific basis for improving water resource management, ensuring national water security, and redefining risk flow patterns.

Zinc Oxide-Functionalized Hydrophobic Cellulose Nanofiber Aerogel Tailored for Eliminating Conventional Ibuprofen Tablets from Water.

A hydrophobic cellulose aerogel having water contact angle of 149⁰ was fabricated by harnessing the effect of both zinc oxide nanoflakes and stearic acid, and then employed for the elimination of emerging pharma residue Ibuprofen from water. The kinetic experimental data obtained for adsorption process was well suited with pseudo-second order kinetics. The adsorption process was well governed by Langmuir monolayer adsorption and the modified aerogel exhibited a maximum adsorption capacity of 33.52 mg/g. The hydrophobic aerogel exhibited a removal efficiency of 70 % within 60 min of contact time. Moreover, the efficiency of the adsorption process was also analyzed by altering various parameters such as pH, adsorbate dosage and adsorbent concentration. The adsorption efficiency was maximum at pH 2 and at 60 minutes of contact time. The elimination of IBP from water by the hydrophobic cellulose aerogel was assisted by hydrogen bonding, and hydrophobic-hydrophobic interaction occurred between the adsorbent and adsorbate, which was confirmed by the IR spectroscopy and SEM-EDX of the aerogels before and after the IBP adsorption process. The demonstrated work mainly emphasises assessing the practical applicability of the modified aerogel in adsorbing conventional ibuprofen (IBP) tablets directly obtained from the market rather than solely targeting the pharmaceutical ingredient and thereby, this approach could be effectively implemented at the industrial level.

A green approach for delignification of corn husks and their application as an unpowered thermo-responsive sensor

The world is adopting biodegradable materials to replace existing petroleum-based plastics, owing to their toxicity and unfriendly environmental aspects. Biodegradable corn husk (CH) is considered a promising alternative due to its environmental friendliness and widespread availability as agricultural waste. In this work, a green process for the delignification of corn husk was developed and preparation of CH-based reversible thermo-responsive delignified corn husk (RTDCH) sensors was done which exhibit different colors corresponding to particular temperature ranges. These RTDCH sensors employed with reversible thermo-responsive nanoparticles (RTNPs) were characterized thoroughly using X-ray Diffraction (XRD), scanning electron microscopy (SEM), thermo-gravimetric analysis (TGA), and Fourier transform spectroscopy (FTIR), which ensures their successful synthesis. Upon deploying them for sensing temperatures from 20 to 80℃, different color displays appeared rapidly within 5seconds, which helps to identify the particular temperature range. The RTDCH exhibited excellent reusability for several cycles and maintained consistency in color displays at certain exposed temperatures. The RTDCH, being unpowered, fast responsive, accurate, biodegradable, economical, and scalable production feasibilities, has the necessary traits to become practical at the industrial level. Such materials have tremendous potential for the food industry, scientific laboratories, and monitoring the temperature of industrial processes. The strategy adopted in this work is crucial since it is paving the way for the development of advanced functional materials from natural wastes through green synthesis approaches.

Effect of powder and absorptivity assumptions in the high-fidelity modeling of Laser Powder Bed Fusion processes

Laser Powder Bed Fusion (LPBF) technology represents the most promising metal additive manufacturing process to be scaled up to an industrial level for producing full batches of parts within the automotive aerospace and health sectors, among others. Despite this higher technological readiness, imperfections such as warping, porosities or undesired non-equilibrium microstructures can arise unexpectedly due to the complex thermal histories inherent to LPBF. Being able to anticipate to such imperfections is thus of high importance. With this purpose, numerical modeling strategies can be employed to significantly reduce experimental work on the melt pool scale. This allows to quantify the odds and magnitude of these process unwanted effects. More precisely, Computational Fluid Dynamics (CFD) enables high-fidelity models on the laser-matter interaction during LPBF processing. In this work, a modeling approach is presented in which the driving forces for melt formation are included in a CFD workspace, where multiple reflections of the laser on the melt pool Liquid-Gas Interface (LGI) are considered by means of an absorptivity function of the LGI depth. This simplification and the use of adaptive mesh strategies allows the model to be computed within a reasonable time. Results and applicability of the model on the determination of the melting mode (conduction or keyhole) are displayed and compared with experimental measurements of the melt pool width and depth for 316L and Ti64 single tracks produced on a LPBF machine. Likewise, further aspects such as total absorbed power are assessed.

Modifications of biological membranes, fat globules and liposomes promoted by cavitation processes. Consequences and applications

Cavitation-based technologies, such as ultrasound (or acoustic cavitation, AC) and hydrodynamic cavitation (HC), are gaining interest among green processing technologies due to their cost effectiveness in operation, toxic solvent use reduction, and ability to obtain superior processed products, compared to conventional methods. Both AC and HC generate bubbles, but their effects may differ and it is difficult to make comparisons as both are based on different phenomena and are subject to different operational variables. AC is one of the most used techniques in extraction and homogenization processes at the laboratory level. However, upscaling to an industrial level is hard. On the other hand, HC is based on the passage of the liquid through a constriction (orifice plate, Venturi, throttling valve), which causes an increase in liquid velocity at the expense of local pressure, forcing the pressure around the contraction below the threshold pressure that induces the formation of cavities.Some applications of cavitation technologies, such as the production of liposomes or lipid nanoparticles (LNPs) allow the generation of delivery systems for biomedical applications.Many others (inactivation of pathogenic viruses, bacteria and algae for water purification, extraction procedures, third generation of biofuel production, green extractions) are based on the disruption of lipid membranes. There are also applications aimed at the modification of membranes (like the milk fat globule) for the development of innovative products. Process parameters, such as cavitation intensity, duration and temperature define the impact of the process on the physical, chemical, and biological characteristics of the membranes. Thus, the adequate implementation of cavitation processes requires understanding of interactions and synergistic mechanisms in complex systems and of their effects on membranes at the microscopic or molecular level. In the present work, the use of cavitation technologies for the generation of LNPs or nanostructured lipid carriers, and the effects of AC and HC treatments on several types of membrane systems (liposomes, solid lipid nanoparticles, milk fat globules, algae and bacterial membranes) are discussed, focusing on the structural and chemical modifications of lipidic structures under cavitation.

Electrooxidation of furfural on an electrodeposited NiMoP electrode to simultaneously produce hydrogen and 2-furancarboxylic acid at industrial current levels

The electrocatalytic oxidation of furfural (FA) for the production of valuable chemicals, such as 2-furancarboxylic acid (FAC), presents an attractive alternative to the sluggish oxygen evolution reaction during water electrolysis for hydrogen production. However, the activity and stability of the current electrocatalysts still require further enhancement. In this study, NiMoP is fabricated on Ni foam through pulsed electrodeposition to facilitate the electrooxidation of FA for FAC production in an alkaline environment. A low voltage of 1.43 V vs. reversible hydrogen electrode (RHE) is adequate to achieve a current density of 500 mA cm−2, with FAC selectivity and Faraday efficiency reaching 97.5 % and 95.2 %, respectively. The extended electrolysis of FA using a continuous-flow electrolytic cell under high current density (500 mA cm−2) demonstrates consistent stability at a cell voltage around 1.8 V over a 35-h duration.

Logistics Industry Agglomeration Affects New-Type Urbanization—An Empirical Test Based on Spatial Panel Models

The rapid development of the logistics industry and its cooperation with other production factors have an impact on the promotion of new-type urbanization (NTU), a more sustainable and inclusive model of urban growth. This article analyzes in-depth the impact mechanism of logistics industry agglomeration, a new way to promote innovation and improve industrial efficiency, on NTU from various dimensions, including population, economy, society, space, ecology, and urban-rural coordination. Using panel data from 31 provinces in China between 2002 and 2021, the level of NTU was calculated using the entropy weight method, and a spatial econometric model was employed to examine the impact of logistics industry agglomeration on NTU and its spatial spillover effects. The research findings are as follows. The study reveals a spatial correlation between logistics industry agglomeration and NTU across Chinese regions. Logistics industry agglomeration significantly enhances NTU levels and generates spatial spillover effects, facilitating urban development in surrounding areas. The influence of regional logistics industry agglomeration on NTU demonstrates notable spatial heterogeneity, with the central region exerting a greater impact than the eastern and western regions. It can help in the understanding of regional differences and help policy makers optimize resource allocation. The impact of logistics industry agglomeration on NTU is nonlinear, primarily characterized by a substantial improvement in NTU at the medium and low levels due to logistics industry agglomeration. It helps identify regional differences and supports personalized decision making. The level of human capital, the degree of government intervention, the degree of marketization, the level of technological industrialization, the ratio of elderly dependency, and the level of innovation have direct effects and spillover effects on NTU. This paper elaborates on the mechanism of logistics industry agglomeration on NTU in China and proposes targeted proposals for promoting the development of NTU, which is highly important for promoting urbanization in other regions of the world from the perspective of industry.

Research on the Impact of Industry Digitization on Export Technology Complexity: Evidence from China

The rapid development of industrial digitization provides new opportunities for the development of China's real industry and the upgrading of export technical complexity. Based on the balanced panel data of 30 provinces in China from 2011 to 2022, this paper calculates the level of industrial digitalization in each province, and uses the fixed-effect model to discuss the effect of industrial digitalization level on the improvement of export technology complexity. The empirical findings are as follows: (1) The level of industrial digitization in China's provinces is increasing year by year and is related to the level of economic development; (2) China's industrial digitization level is unbalanced in the landing zone; (3) Industrial digitalization can promote the improvement of export technical complexity.

Utilizing the value added intellectual capital (VAIC) to tourism-related industries in a tourism intensively dependent country

This research examines intangible assets or intellectual capital (IC) performance of tourism-related industries in an underexplored area which is a tourism intensively-dependent country. In this study, VAIC which is a monetary valuation method and also the most widely applied measurement method, is utilized as the performance measurement method for quantifying IC performance to monetary values. Moreover, to better understand performance, the standard efficiency levels are further applied for classifying the performance levels of tourism industries. The sample sizes of study are 20 companies operating in the tourism-related industries in the world top travel destination or Thailand, and the companies’ data are collected from 2012 to 2021. Therefore, finally, there are 187 firm-year observations. The utilization of VAIC could assess IC performance of tourism firms and industries, and the standard efficiency levels further support the uniform interpretation of IC efficiency levels. The obtained results show the strong performance of both human and structural capital of the focused tourism dependent country especially in the logistics industry that directly supports and connects to the tourism attractions. Moreover, the finding also highlights the significance of human capital which plays as a major contributor for overall IC performance in this tourism dependent economy. This study contributes the new exploration of IC in the high impact industries and also specifically in the top significant tourism country. Moreover, the application of VAIC also confirms a practical application for management. The limited number of studied countries is a limitation of study. However, these new obtained data and information could be further applied for making comparisons or in-depth or statistical analysis in the future works.

The impact of artificial intelligence on global energy vulnerability

Investigating the effect of artificial intelligence (AI) on energy vulnerability (EVI) is crucial to understanding how technological advances are changing the resilience and sustainability of energy systems. However, their quantitative relationship still lacks empirical evidence. This study first constructs the EVI of 54 global economies from the perspective of energy security, energy consumption, energy efficiency, and energy availability from 2000 to 2019. Then, a fixed-effect model is employed to investigate the relationship between AI and EVI. Results show that (1) AI can considerably reduce global EVI. The core findings remain reliable after several robustness checks. (2) Mechanism analysis implies that AI can reduce EVI by promoting financial development and technological progress. (3) Heterogeneity analysis implies that the impeding role of AI on EVI is more pronounced in countries with low incomes and industrialization levels. Furthermore, the hindering effect of AI on EVI is strengthened after Industry 4.0 and the financial crisis. Some policy implications are further proposed accordingly to reduce global EVI.