Industrial Size Research Articles

The impact of data factor-driven industry on the green total factor productivity: evidence from the China

Data factors have become an essential factor of production in today’s digital era, and provided renewed energy for China’s green and high-quality development. This study analyses the impact of data factor-driven industries on green total factor productivity (GTFP) and the underlying mechanisms using panel data of 277 Chinese cities from 2008 to 2020. The results show that: First, the urban data factor-driven industry can enhance GTFP. Second, the data factor-driven industry can increase the input–output ratio and enhance GTFP by promoting AI technological innovation and AI technological entrepreneurship. Third, the heterogeneity analysis show that in regions with high levels of digital technological innovation, the market size of data factor-driven industries is larger and the effect of data factor-driven industries in enhancing GTFP is more significant. And in regions with low levels of savings, the data factor-driven industry has limited capital availability and is more efficient in using capital, which has a stronger effect on GTFP enhancement. This study provides valuable empirical evidence on the relationship between data factor-driven industries and urban GTFP, and important policy implications for fully leveraging the green economic effects of data factors, and promoting green and high-quality urban development.

An explicit solution to harvesting behaviors in a predator–prey system

AbstractThis paper derives closed‐form solutions for a strategic, simultaneous harvesting in a predator–prey system. Using a parametric constraint, it establishes the existence and uniqueness of a linear feedback‐Nash equilibrium involving two specialized fleets and allows for continuous time results for a class of payoffs that have constant elasticity of the marginal utility. These results contribute to the scarce literature on analytically tractable predator–prey models with endogenous harvesting. A discussion based on industry size effects is provided to highlight the role played by biological versus strategic interactions in the multispecies context.Recommendations for Resource Managers This model presents a thorough examination of the economic inefficiencies inherent in the exploitation dynamics of two interdependent species, elucidating the complex interplay between ecological interactions and economic outcomes. The size of the fishing industries constitutes a significant variable that must be integrated into the formulation of pertinent policy recommendations. This constitutes an advancement towards a more time‐consistent approach to Ecosystem‐Based Fishery Management (EBFM).

The moderating role of corporate governance mechanism on the relationship between product market competition and earnings management: evidence from Bangladesh

ABSTRACT This article evaluates the moderating effects of corporate governance mechanisms on the relationship between earnings management and product market competition. Market concentration, as measured by the HHI has shown significantly negative association with accrual and real earnings management. In contrast, product market power, as measured by the industry size, demonstrates a significant positive association. The result further illustrates that the cost of industry entry significantly impacts accrual earnings management. The corporate governance mechanism (CGI) significantly decreases earnings manipulation. Moreover, the corporate governance mechanism (CGI) considerably moderates the association between market concentration, industry entry cost, and earnings management. Compared to high and low-competitive industries, the impact of market competition on earnings management in moderately competitive industries is more significant. After establishing the Bangladesh Competition Commission, product market competition has shown more impact on earnings management. This research is the initial attempt to examine the moderating effect of corporate governance mechanisms on the relationship between product market competition and earnings management from the perspective of Bangladesh.

Impact of state-level labour law reforms in India: an empirical analysis

The impact of Indian state labour law reforms on employment, output, new industrial units, investment and size of industrial units is examined from 2000–01 to 2017–18. States with inflexible labour laws had lower growth of large industrial units and regular employment. Contract worker hiring spiked in firms with 301–500 workers in both flexible and inflexible states. Capital growth was higher than growth of workers in all state categories. Rajasthan, the first state to carry out additional reforms in 2014, had good performance. Regressions carried out to control for factors that impact employment other than labour laws indicate that: (i) reforms had a positive impact on employment; (ii) good infrastructure, reduction in industrial disputes, low crime and better education resulted in significant employment generation. Difference-in-differences estimates show that post-reform, employment, fixed capital, and output were higher for flexible labour law states, when compared to inflexible labour law states.

A MILP-based approach to address the production and distribution planning of large industrial gas supply chains

This paper focuses on addressing the Production Routing Problem (PRP) of industrial gas supply chains (SC). The work introduces a Mixed-Integer Linear Programming (MILP) based approach to effectively tackle this challenging problem. The proposed framework aims to increase company profits by optimizing both production and distribution activities simultaneously. For the distribution part, a route generation algorithm is employed to initially generate a set of feasible routes, ensuring that only routes with practical significance are considered. Subsequently, the mathematical model is developed considering only the set of routes generated. The formulation includes decisions associated with plants production levels, plant selection to supply the different consumers, clients to be visited per trip and day, quantity to be delivered and routes to be used. As part of the problem, it is considered that trucks can make several trips per day, as well as trips of several days. To take into account the last feature, the approach accounts for the explicit effect of the lead time on production and distribution decisions. In this way, the work allows for different delivery times for customers included in the same route, according to their inventories and usage levels. The model considers fleet unavailability until they return to the plants after finishing a trip. Finally, two case studies, one motivating and the other industrial size, are presented and solved. The results obtained demonstrate the effectiveness of the proposed approach to solve the case studies in reasonable CPU times.

The cost of electricity interruption for manufacturing firms in Ethiopia: valuing outage by applying stated preference approach

ABSTRACT In sub-Saharan African countries, firms are suffering from frequent, long-lasting and random power outage, which makes planning and production activities difficult. Thus, understanding a firm’s cost associated with unreliable power supply is crucial for policymakers and potential investors in the energy sector. To this end, we estimated the economic cost of power interruption to manufacturing firms in selected cities of Oromia Regional State in Ethiopia using a mixed logit model. We collected data from a sample of 600 manufacturing firms. The result shows that a firm’s average cost of power interruption is equivalent to ETB 51,777 (~US$976) per month, which corresponds to a nine-fold of their current monthly electricity bill. In addition, the interruption costs amount to 2.22% of a firm’s gross monthly revenue. Moreover, we observed a considerable variation in our results, which be partly attributed to company size and type of industry where a company belongs to.

Compressor Surge Identification in Innovative Heat-Pump Systems Equipped by Vaned Diffuser Centrifugal Compressor

Abstract In the present energy scenario, heat-pumps play an important role to improve energy efficiency in reversible cooling systems. In case of industrial size plants, centrifugal compressors are preferred with respect to other solutions; nonetheless, they work in variable operation and therefore they must withstand off-design conditions. Carrier provided the University of Genoa with a small size chiller rig equipped with an innovative high speed centrifugal compressor driven by a variable speed motor to study unstable operation of refrigerant closed loop systems. To this aim, vibro-acoustic signature analysis of such closed loop rig is performed mainly focusing on compressor mechanical response. This experimental characterization is firstly conducted in system stable operation. Afterwards, surge transients are obtained by acting on plant feeding lines valves; meanwhile, experimental signals are acquired at relevant plant locations to investigate system response just before instability onset. Indeed, system dynamics is affected by interposed volumes, and therefore the effect of heat exchangers in system response may be relevant. Therefore, system dynamics is analysed both in sub-synchronous frequency range and in high frequency region to assess how its vibro-acoustic response varies when moving from stable conditions towards surge. By doing so, suitable surge precursors can be defined by relying only on vibro-acoustic signals both in low and high frequency ranges to perform early surge detection in such chiller systems. The proposed approach main advantage is to exploit non-intrusive probes, which allow to define diagnostic indicators without interacting directly with the working fluid.

Scale diseconomies and capacity in fund management: variation across equity markets

PurposeWe model the relation between excess returns, fund size and industry size for active equity funds.Design/methodology/approachWe study and contrast four markets – global equities, emerging markets, Australia core and Australia small caps – and use the results to investigate the extent to which funds deviate from estimated capacity.FindingsWe uncover a significantly negative relation between returns and both fund size and industry size across all markets. The estimated percentage of funds operating above versus below capacity varies both across markets and over time, as does the role played by fund size versus industry size. We find a greater prevalence of funds operating significantly below than above capacity, in contrast to findings for US equity mutual funds. Significant deviations from estimated capacity persist for a median of between two and six quarters.Originality/valueOur main contribution is to show that the dynamics governing deviations from capacity for active equity funds vary across markets.

Data resource profile: the Korean Workers' Compensation-National Health Insurance Service (KoWorC-NHIS) cohort.

The KoWorC-NHIS cohort was established to investigate the longitudinal health outcomes of Korean workers who have been compensated for occupational injuries or diseases. This cohort study, which utilized data spanning from 2004 to 2015, merged workers' compensation insurance claim data with the National Health Insurance Database (NHID), encompassing 858,793 participants. The data included sociodemographic factors such as age, sex, income, address, insurance type, and disability grade. It also covered the types of occupational accidents, International Classification of Diseases, 10th revision codes for diseases or accidents, work tenure, industry, occupation code, and company size. Additional details such as the occupational hire date, date of claim, date of recognition, and affected body parts were recorded. The cohort predominantly consisted of male workers (80%), with the majority experiencing their first occupational accident in their 40s (27.6%) or 50s (25.3%). Notably, 93.1% of the cases were classified as occupational injuries. By integrating this data with that from the NHID, updates on health utilization, employment status, and income changes were made annually. The follow-up period for this study is set to conclude in 2045.

Relationship between Urban Industrial Land Use Size and Sulfur Dioxide Emissions: Evidence from 294 Cities in China

Relationship between Urban Industrial Land Use Size and Sulfur Dioxide Emissions: Evidence from 294 Cities in China

Study on the Influence of Industrial Coke Oven Size on the Quality of Metallurgical Coke

The large-scale coke oven is a developing trend in coking technology, and the influence of the industrial coke oven size on the quality of metallurgical coke needs to be revealed. Under the same raw coals and blending ratios, metallurgical cokes were obtained from top-loading coke ovens with carbonization chamber heights of 6 and 7 m, respectively. The macroscopic quality and microstructure of the refined metallurgical cokes were comprehensively tested. The results showed that the conventional cold and thermal strength indexes of Coke-7m were better than those of Coke-6m. The comprehensive thermal strength at multiple temperatures further showed that the thermal strength advantage of Coke-7m was mainly manifested at low temperatures (1050 and 1100 °C) or a high temperature (1300 °C), where the solution-loss reaction mode of coke tended to be the “uniform reaction model” or “unreacted core model”. At medium temperatures, the solution-loss reaction mode tended to be the “gradient reaction model”, which had a greater destructive effect on the thermal strength of coke. At this time, the thermal strengths of the two cokes were close. Microstructure characterization revealed that industrial coke oven size mainly affected the carbon structure of coke but had little effect on pore structure. The advantage of Coke-7m regarding carbon structure is an important reason for its superior macroscopic quality compared to Coke-6m.

Energy structure optimization of shandong province by 2035 under double carbon goal

Abstract In this paper, based on the existing population size, economic development level, industrial structure, current status of energy production and consumption, and current status of electricity production and consumption in Shandong Province, the LEAP-Shandong model was constructed by using the Long-range Energy Alternatives Planning (LEAP) model as a research tool, which is used to forecast the population size, GDP, energy consumption, and electricity structure of different industries in Shandong Province up to 2035 under three scenarios: policy development scenario, energy efficiency improvement scenario, and enhanced low-carbon scenario. The result shows that the population of Shandong province is expected to peak at 10, 426, 000 by 2033, and then slowly decline; in the enhanced low-carbon scenario, with an appropriate slowdown in economic development, further optimization of industrial structure and enhancement of energy efficiency can effectively reduce the energy consumption of the terminal energy-using sector in Shandong province. Based on the results, the main recommendations for Shandong’s energy development are as follows: to build a low-carbon industrial system in Shandong in light of new development opportunities; to build a new energy development system in light of Shandong’s energy characteristics; and to promote the low-carbon transformation of Shandong’s energy in light of the new model of the energy industry.

PENGARUH KOMITE AUDIT, KEPEMILIKAN MANAJERIAL, UKURAN PERUSAHAAN, DAN LEVERAGE TERHADAP INTEGRITAS LAPORAN KEUANGAN DENGAN KULITAS AUDIT SEBAGAI VARIABLE MODERASI (STUDI EMPIRIS PADA PERUSAHAAN NON-CYCLICALS DAN CYCLICALS DI BEI PERIODE 2018-2022)

The purpose of this research is to test and share empirical facts regarding the influence of audit committees on theintegrity of financial statements, industrial aspects of management ownership, leverage and audit quality. The purposeof this research is to determine the influence of audit committees, management ownership, industry size and financialreporting integrity with audit quality as a moderating variable The expected results of this research can be summarizedas follows: audit committees have a significant positive influence on the integrity of financial reports; corporateresponsibility has a significant positive effect on the integrity of financial reports, industrial aspects do not have amaterial impact on the completeness of financial reports. Leverage has no significant effect on the integrity of financialreports; Audit quality does not moderate the influence of the audit committee on the integrity of financial reports; Auditquality moderates the influence of corporate responsibility on the integrity of financial reports, audit quality does notmoderate the influence of industry aspects of the completeness of financial reports, audit quality does not moderate theinfluence of leverage on the completeness of financial reports.

AHP기법을 이용한 건설기업별 안전수준평가에 관한 연구

Disaster in the construction industry is a major social and economic problem in Korea due to its unique risk, high incidence rate, various causes and complex factors. In order to reduce the risk of accidents in the construction industry, various preventive activities of each individual company are needed, and disaster evaluation should be conducted by various methods. In this study, a disaster score evaluation model could be proposed to evaluate and compare the safety level of each individual construction company. At this time, eight factors were selected: the size of the construction industry, the amount of construction work, the type of construction industry accidents, the age of construction industry injuries, the timing og accidents in the construction industry, the length of service for construction workers, the area of accidents, and the degree of accidents for construction workers, and industrial accident statistics for four years from 2029 to 2022 were used. In addition, in order to obtain the weights of eight factors, a survey of 39 safety experts and a model for calculating the accident score of each construction company using the AHP technique could be proposed. As a result of calculating the accident scores of two construction companies with similar accident rates using this model, it was found that the safety level of the two companies was different. This model was used to calculate the accident score of the two construction companies with similar accident rates, and it was found that there was a difference in the safety level of the two companies, because even in the same disaster, the disaster score of the companies with large-disasters is high. Therefore, by applying the model proposed in this study, it is believed that it will be of great help in accurately knowing the safety level of individual construction companies and developing safety activities suitable for the company.

Experimental Validation of Stationary and Transient Models of a Methanol Demonstration Plant

AbstractThe system integration of cross‐industrial networks in the Carbon2Chem® project relies on numerical simulations. Hence, this study validates the methanol synthesis loop models vs. the measurements from a demonstration plant, focusing on the thermodynamic, kinetic, and dynamic aspects. The plant can produce up to 500 L per week of methanol from real blast furnace gases of the thyssenkrupp steel plant in Duisburg. Despite its small size, it comprises a recycle gas loop and an original reactor tube of 6 m in length. After validation, the simulation models are used to analyze the dynamic operation limits of the plant, and they are transferred to the model of an industrial size to analyze the operation of cross‐industrial networks with volatile boundary conditions.

표준산업분류 기반의 공공데이터를 활용한 산업 시장규모 추정 방안에 관한 연구: 미래 모빌리티 산업을 중심으로

The advancement of information technology has increased the need for estimating the market size of new industries that did not previously exist. Companies require market size estimations to pursue new business ventures, but these emerging markets are not yet mature, making it difficult to define the boundaries of sub-industries and systematically establish industry-related data such as corporate sales and operating profits. Therefore, this study explores methods for estimating market size using public data. The economic indicator data, which are industrial statistics public data released by the National Statistical Office, include monthly, quarterly, and yearly industrial statistics. Specifically, these data encompass economic indicators such as the Mining and Manufacturing Production Index, the Mining and Manufacturing Shipment Index, the Domestic Supply Index, and the Value Added by Industry. This study analyzed whether market size can be estimated through standardized panel data analysis, focusing on the future mobility sector, and estimated the market size for 2021 and 2022 through statistical significance tests. The results of the study confirmed the existence of interrelationships among economic indicator data in the future mobility sector, enabling market size estimation. However, it was found that if the annual data for economic indicators are insufficient, the temporal correlations among indicators may not be reflected, leading to significant errors in long-term market size predictions.

An investigation of antecedents and consequences of green value internalisation among sampled UK enterprises

Despite the growing popularity of the concept of green value internalisation, research on how this concept is being accomplished at the enterprise level is still limited. The purpose of this study is to address this knowledge gap by drawing from previously known concepts of green value and value internalisation. It examines the antecedents and consequences of green value internalisation and evaluates how these environment-leaning approaches impact competitive advantage. This study uses the resource-based view and the stakeholder theory as theoretical lenses in linking green value internalisation to its antecedents and how these impact competitive advantage. A two-step approach involving a measurement model and a structural model was used to analyse survey data from 213 UK enterprises to validate the research hypotheses. Hypotheses testing shows that green value internalisation has a positive and significant impact on green criteria development. The results also show that external pressure positively and significantly affects green value internalisation. These findings extend prior knowledge by establishing the level of significance in the relationship among the antecedents and consequences in the research model. The research design for this study draws from a systematic literature review. The study offers rigorous empirical insights for implementing green value internalisation as a value-creating strategy. However, the antecedents and consequences examined in this study may not capture in detail all underlying constructs. Hence future studies should proffer valid and reliable instruments for these constructs. The findings provide managers from enterprises across a broad industry size range seeking to implement green value internalisation with resources for embedding an enterprise-level pro-environmental strategy.

Numerical simulation and experimental validation of microstructure evolution during the upsetting process of a large size martensitic stainless steel forging

The microstructure evolution, plastic deformation, and damage severity during the open die hot forging of a martensitic stainless steel were investigated using finite element (FE) simulation. A microstructure evolution model was developed and combined with a visco-elastoplastic model to predict the strain, the strain rate, and the temperature distribution, as well as the volume fraction and the size of dynamically recrystallized grains over the entire volume of an industrial size forging. The propensity to damage during hot forging was also evaluated using the Cockcroft & Latham model. The three models were implemented in the FE code and the results analyzed in terms of microstructure inhomogeneity and stress levels in different regions of the forging. A good agreement was obtained between the predicted and the experimental results, demonstrating that the simulation provided a realistic representation of the forging process at the industrial scale.

Investigating the Digital Transformation Level Across Industrial Dimensions

Digital transformation drives operational efficiency, customer interaction and innovation. Hence it is necessary for industries to adopt digitalization in order to remain in competition with the global world. Hence, the present study investigated the factors influencing digital transformation within the textile industry, focusing on the roles of industry type, size, and sustainability concerns within industry. Using multinomial logistic regression, the study analyzed data from a sample of 124 textile companies of Pakistan to assess the impact of these factors on different levels of digital transformation. The industry size consists of small, medium and large industries while type of enterprises comprises of spinning, weaving, dying and printing. The digital transformation is classified into four levels in the present study, referring to as traditional, limited, moderate and high. Our results demonstrated that industry type, size, and sustainability concerns significantly impact digital transformation within the textile sector and it was observed that moderate level of digital transformation was found in many industries followed by limited and traditional level where as high level of transformation was found to be lacking, which indicates a strong need for policy making and initiatives in this regard. This research contributes to our understanding of digital transformation dynamics in the textile sector and offers directions for future research in broader contexts.

Nearest‐neighbor, BERT‐based, scalable clone detection: A practical approach for large‐scale industrial code bases

AbstractHidden code clones negatively impact software maintenance, but manually detecting them in large codebases is impractical. Additionally, automated approaches find detection of syntactically‐divergent clones very challenging. While recent deep neural networks (for example BERT‐based artificial neural networks) seem more effective in detecting such clones, their pairwise comparison of every code pair in the target system(s) is inefficient and scales poorly on large codebases. We present SSCD, a BERT‐based clone detection approach that targets high recall of Type 3 and Type 4 clones at a very large scale (in line with our industrial partner's requirements). It computes a representative embedding for each code fragment and finds similar fragments using a nearest neighbor search. Thus, SSCD avoids the pairwise‐comparison bottleneck of other neural network approaches, while also using a parallel, GPU‐accelerated search to tackle scalability. This article describes the approach, proposing and evaluating several refinements to improve Type 3/4 clone detection at scale. It provides a substantial empirical evaluation of the technique, including a speed/efficacy comparison of the approach against SourcererCC and Oreo, the only other neural‐network approach currently capable of scaling to hundreds of millions of LOC. It also includes a large in‐situ evaluation on our industrial collaborator's code base that assesses the original technique, the impact of the proposed refinements and illustrates the impact of incremental, active learning on its efficacy. We find that SSCD is significantly faster and more accurate than SourcererCC and Oreo. SAGA, a GPU‐accelerated traditional clone detection approach, is a little better than SSCD for T1/T2 clones, but substantially worse for T3/T4 clones. Thus, SSCD is both scalable to industrial code sizes, and comparatively more accurate than existing approaches for difficult T3/T4 clone searching. In‐situ evaluation on company datasets shows that SSCD outperforms the baseline approach (CCFinderX) for T3/T4 clones. Whitespace removal and active learning further improve SSCD effectiveness.