Stochastic Analysis Approach Research Articles

Measuring the Competitiveness and Efficiency of Indonesian Non-Oil and Gas Exports to Non-Traditional Markets

In order to improve the performance of the trade balance, Indonesia is currently always trying to diversify the market through the expansion of export markets to non-traditional countries. For this reason, measuring competitiveness and export efficiency is crucial to be researched first. This study aims to measure the competitiveness and efficiency of Indonesia's Non-Oil and Gas Exports to Non-Traditional Markets by using the analysis methods RCA, EPD, X-Model and Stochastic Frontier Analysis with Gravity Model (SFGM). The results of the analysis show that based on the results of the competitiveness analysis (RCA) and export performance using EPD and X-Model, it shows that the competitiveness of Indonesia's non-oil and gas exports to non-traditional countries is still low or below global competitiveness. Meanwhile, the results of estimation using the Stochastic Frontier Analysis approach with the Gravity Model (SFGM) show that the variables of GDP of non-traditional countries, the number of population of non-traditional countries, Indonesia's GDP, competitiveness (RCA) and the area of non-traditional countries have a positive and significant effect on Indonesia's exports. Meanwhile, economic distance is estimated to have a negative and significant influence on Indonesia's non-oil and gas exports.

Disaster Induced Agricultural Productivity in Coastal Regions of Bangladesh: A Stochastic Frontier Analysis Approach

The production and technical efficiency of the rice sector in Bangladesh’s coastal areas are vulnerable to a wide range of natural disasters and climate change issues. These regions face recurring threats from climatic events such as salinity intrusion, inundation, rising sea levels, and cyclones, which can significantly disrupt agricultural activities. In this paper, we conduct a comparative analysis of the rice producers’ technical efficiency based on their exposure to disaster-induced impacts over the past five years. Using the Stochastic Frontier Analysis, we examine factors influencing household-level productive efficiency in three coastal districts—Patuakhali, Cox’s Bazar, and Khulna. Our findings reveal that rice-producing households affected by natural disasters between 2014 and 2018 exhibited, on average, an 8.29 percentage point higher productive efficiency compared to unaffected households. When controlling for other confounding variables, such as household characteristics and external conditions, the efficiency gain rises to 19.8%. This suggests that households exposed to adverse climatic events may have adapted their farming practices to become more resilient, thus improving their productive efficiency in the long term. Moreover, our results indicate that a larger household size enhances efficiency by 6.7%. Households where rice farming is the primary occupation also tend to be more efficient, likely because of greater specialization and focus on improving agricultural practices. Finally, the age of the producer is positively associated with efficiency, reflecting the accumulation of farming experience and knowledge over time.

Quantitative analysis of subway station seismic deformation under random earthquakes and uncertain soil properties using the equivalent linearization method

The seismic performance of underground structures is strongly influenced by the characteristics of both the surrounding soil and the earthquake. In contrast to traditional deterministic analysis methods, this study uses a stochastic analysis approach to investigate the effect of uncertainties in nonlinear soil characteristics, shear wave velocity, density, and earthquake randomness on the response of underground stations. The equivalent linearization method is employed to approximate the nonlinear behavior of the soil. The soil was modeled using a linear elastic constitutive model combined with Rayleigh damping in the finite element model. Inter-story displacements are used to determine structural damage. Probabilistic analysis methods are used to obtain their statistical characteristics, and the probability of failure is calculated. The results show that, according to single parameter analysis, random ground motion results in the greatest probability of exceeding the threshold (PET), while ground shear wave velocity significantly affects the coefficient of variation (COV), and the effect of density is the smallest. The study also found that when soil nonlinearity, shear wave velocity, and random ground motion are considered simultaneously, the range, mean, standard deviation, and COV of interstory displacement all increase significantly, but the PET slightly decreases. In summary, the analysis results indicate that random ground motion has the greatest impact on interstory displacement, followed by shear wave velocity, with nonlinear soil characteristics having a smaller effect, and density the least. Therefore, the impact of various uncertainties should be fully considered in the analysis of underground structures, especially random ground motion and shear wave velocity.

Economies of scope in the Japanese banking industry

Economies of scope are usually considered a key factor in explaining bank diversification benefits, as joint production can help spread costs across different operations. However, previous studies have yielded little evidence of scope economies in the banking sector, which fails to explain the empirical results of diversification benefits, especially in Japan. Given that most relevant studies were conducted prior to 2000 and newer methodological approaches have emerged to enhance estimation accuracy, we refocus to this topic and explore the relationship with bank diversification. In this paper, we estimate economies of scope while considering the growing off-balance sheet activities within the Japanese banking industry. Using a sample including 120 Japanese banks from 2000 to 2020, we estimate the cost frontier with a translog cost function through the stochastic frontier analysis (SFA) approach. In addition to the traditional measures, we incorporate a new measure of scope economies proposed by Chavas and Kim (2010), allowing us to decompose the scope economies into four different effects. Our findings suggest that nearly all Japanese banks exhibit economies of scope, which may explain the diversification benefits observed in Japan. Furthermore, we find a negative relationship between economies of scope and the level of bank diversification.

A frequency-domain hydroelastic approach for modeling and stochastic dynamic analysis of pontoon-type floating bridges

In recent years floating bridges have been extensively studied owing to their potential application for crossing wide and deep fjords. An efficient approach for analyzing stochastic hydroelastic responses of floating bridges is valuable for fast assessment of various load effects for preliminary design. This study develops a frequency-domain approach for hydroelastic analysis of pontoon-type floating bridges subjected to wave loads. The frequency-dependent motion equation of the multi-pontoons is established based on the potential flow theory and linearized Morison’s equation for viscous damping. The structural dynamics of the elastic bridge girder are represented utilizing a beam model with excitations transferred from each pontoon. The proposed method is applied to an end-anchored curved floating bridge, with highlights on the verification against a validated time-domain simulation tool. Convincing agreements are found in terms of the eigenvalues, wave excitation loads, and response statistics. The method shows high fidelity with an accurate capture of the linear wave-frequency responses as well as the low-frequency resonances. A further discussion demonstrates that the wave viscous damping restrains low-frequency resonant responses, whereas the hydrodynamic interactions of pontoons induce more resonant peaks in the wave frequency.

Supervised Stochastic Approach for computational analysis of convectively heated magnetized nanofluid flow with bioconvection aspects

Our study delves into the dynamics of a convective Magneto-Hydrodynamic Bioconvective Nanofluid model (MHD-BCNFM) flowing over a convectively heated stretched sheet. To accomplish this, we utilize the distinctive capabilities of the Supervised Stochastic Approach for Computational Analysis (SSACA). By integrating similarity transformations, we convert the partial differential equations (PDEs) governing the system into coupled ordinary differential equations (ODEs). We generate the dataset for our approach using the Adam numerical technique specifically tailored for the (MHD-BCNFM). Achieving this involves systematic modulation of parameters such as λ, bioconvection Péclet number Pe, Bioconvection constant σ, Brownian motion parameter Nb, and thermophoresis parameter Nt. "Moreover, we utilize a reference dataset to compute numerical values of various physical quantities in the (MHD-BCNFM) employing SSACA-based Artificial Intelligence methods. The effectiveness of our devised SSACA approach is demonstrated by a negligible mean squared error, ranging from approximately 10−8 to 10−10. Histograms exhibit a maximum error range of 10−5, closely aligning with optimal correlation/regression measures. Outstanding performance metrics in terms of Mean Squared Error (MSE) are attained at levels such as l 9.93E−12, 1.07E−11, 6.28E−10, 1.43E−11, 2.09E−09, 3.65E−11, 2.97E−11, and 2.80E−13 against 320, 430, 209, 85, 356, 295, 66, and 136 epochs. A comparative study between the proposed and reference datasets underscores the authenticity and precision of SSACA, supported by error analyses ranging from E-05 to E-09 across all scenarios.

Environmental performance evaluation in the forest sector: An extended stochastic data envelopment analysis approach

This study addresses the global concern about undesirable outputs in the Forest Sector. We propose two innovative models, namely a directional weak disposable DEA model and an extended stochastic DEA model, to measure environmental efficiency. These models make a significant contribution to the field by specifically assessing the uncertain environmental efficiency of the forest sector. We validated our proposed models by conducting an empirical application using the United Nations Economic Commission for Europe (UNECE) forest sector dataset. The study examines important outputs such as above ground biomass stock, export unit prices of industrial roundwood, wood removals (desirable outputs), and CO2 emissions from wildfires (undesirable output). The results demonstrate that our novel stochastic weak disposability DEA model outperforms traditional approaches when the second scenario is applied. Specifically, the average technical efficiency (TE) score decreases to 0.92, and the number of efficient units reduces to 27, representing an approximate improvement of 55 %. Furthermore, the reduction rate of CO2 emissions is 4.09 % lower than the benchmark. Hence, our extended novel stochastic weak disposability DEA approach enhances the assessment of efficiency and inefficiency in decision-making units, contributing to the mitigation of risk and uncertainty. It also improves overall environmental performance in forest management.

Stochastic robustness analysis of wing-aileron flutter suppression systems

In this work a stochastic robustness analysis approach is used to compare the performances of two non-linear flutter suppression systems designed for stabilization of a wing-aileron lifting structure. A reduced order modelling approach based on an alternative aeroelastic beam finite element is presented and used for the flutter suppression systems design, which are driven by a simple adaptive controller and a sliding mode controller, respectively. A heuristic algorithm is used to determine the simple adaptive controller invariant parameters at the design point while a simple parametric study is performed to tune the sliding mode controller. The performances of each closed loop system are evaluated taking into account mass, stiffnesses, and aerodynamics uncertainties of the aeroelastic plant also considering the presence of discrete gust disturbances.

Assessing Production and Marketing Efficiency of Organic Horticultural Commodities: A Stochastic Frontier Analysis

Inefficiency is a problem in the production process, including in the organic farming sector. Over a long term period, this problem can disrupt the productivity of agricultural crops. This research aims to analyze the production and marketing efficiency of organic cabbage farming in the Kopeng agropolitan area, Indonesia. We utilized a Cobb–Douglas production efficiency analysis with the Stochastic Frontier Analysis (SFA) approach. The variables in this study include organic cabbage production, land area, seedlings, organic fertilizers, organic pesticides, and labor. We conducted in-depth interviews with 60 organic cabbage farmers in Kopeng, Indonesia, from January to August 2023. The research results showed that organic cabbage cultivation was economically inefficient in production, technical, and marketing. The use of organic fertilizers, the ability to diversify products on limited land, and the use of pesticides, have not been utilized optimally yet. The results of the marketing efficiency analysis showed that it was efficient. Organic plants were believed to have their market share and to have a higher selling value than non-organic ones. The implication was that the government needed to provide training in producing organic fertilizers and pesticides to reduce production costs so that organic farming could be technically and financially efficient. This research enriched the discussion regarding the need to analyze production and marketing efficiency to find strategies to increase organic cabbage productivity.

Technical efficiency in the Malaysian electric and electronic manufacturing industry: A stochastic frontier analysis approach

This study measured the level of technical efficiency (TE) that exerted a significant impact on the Malaysian electric and electronic manufacturing industry, which consists of 13 sub-industries, from 2010 to 2015. Unbalanced panel data from 1880 firms was obtained from the Department of Statistics Malaysia. We used stochastic frontier (SFA) analysis with the transcendental logarithmic approach to estimate the empirical analysis parametric. The maximum likelihood estimated regression of the SFA parameters revealed a high total average TE of 0.944%. Both the manufacturing sub-industries for communications equipment (MSIC 263) and irradiation, electromedical, and electrotherapeutic equipment (MSIC 266) demonstrated the highest average TE value and consistency (0.989%). The consumer electronics manufacturing sub-industry (MSIC 264) yielded a notably lower TE level (0.846%). Changes in TE for all industries were monitored via standard deviation analysis. The low standard deviation (SD) for MSIC 263, 271, and 275 indicated continuous progress and demands for these industries, while the higher SD for MSIC 264, 266, 272, and 273 pointed out major changes that happened within the period. The findings demonstrated that studies on the TE levels indicate that the E&E manufacturing industry has highly expended and continuous efforts to update technologies and human capital innovations. Therefore, the practical implication is to achieve Sustainable Development Goal 9 for sustainable industrialization and fostering innovation by 2030.

Pth Moment Exponential Stability of Impulsive Stochastic Differential Equations with Unbounded Delays

Stochastic impulsive differential equations with Markovian switching have been applied extensively in various areas including ecology systems, neural networks, and control systems, and stability analysis is one fundamental premise of their applications. For two categories of Markovian switched impulsive stochastic differential functional equations with unbounded delays, this paper investigates the pth moment exponential stability by adopting stochastic Lyapunov stability theory and stochastic analysis approach. Several criteria on pth moment exponential stability have been acquired. In the proposed model, the time-varying coefficients and the hybrid impulsive effects are considered simultaneously. It can be seen that the criteria derived in this paper are more concise and the conditions are easier to verify compared with those existing results based on Razumikhin theory. Finally, two examples are illustrated to show the effectiveness of the theoretical findings.

The Production Efficiency of Small Medium Enterprises in West Sumatera Province

The purpose of this study is to determine the effect of capital, labor, and resources on the technical efficiency production of SMEs in West Sumatra and to see whether the SMEs production in West Sumatra is technically efficient. The type of data used in this study is panel data for 19 regencies/cities in West Sumatra from 2015 to 2021 using the Stochastic Frontier Analysis (SFA) approach. This research examines production efficiency technically at SMEs in the West Sumatra region. The result of this study indicates that the production factors of capital and resources have a positive and significant effect on the production of SMEs in West Sumatra, while labor has a negative and insignificant effect on the production of SMEs in West Sumatra. The result of the technical efficiency calculation shows that the mean level of technical efficiency of the SMEs production in West Sumatra from 2015 to 2021 is 0.543 or 54.3 percent, which means that production in SMEs is technically inefficient. This research implies that the government can compile regulations and road maps that can increase the productivity of SMEs in the West Sumatra region, and for SMEs to be able to engage more broadly with sustainable programs for production efficiency such as attending training and business guidance, using the rural business credit program from the government, as well as establishing cooperation partners for raw materials with other SMEs.

How does industrial intelligence affect total-factor energy productivity? Evidence from China’s manufacturing industry

The flourishing industrial intelligence profoundly impacts manufacturing production, while empirical studies about the nexus between industrial intelligence and energy issues remain insufficient. This study evaluates total-factor energy productivity based on the stochastic frontier analysis (SFA) approach and then analyzes the energy productivity effects of industrial intelligence by employing the panel data of China’s manufacturing industry. We find that the total-factor energy productivity shows vast regional differences, and most regions have experienced a decline. Additionally, industrial intelligence significantly improves total-factor energy productivity, and the core results still hold after a set of robustness tests. Mechanism tests reveal the positive effects are mainly due to technological change and industrial structure upgrading. Heterogeneity analysis indicates that the impact of industrial intelligence is more pronounced in regions with higher economic development, more advanced information technology, and greater human capital. These findings provide insightful references for further intellectualization and carbon reduction in China’s manufacturing industry.

Impacts of internet access and use on grain productivity: evidence from Central China

The impacts of Internet use on farming productivity in China have been extensively examined. However, existing studies focus on non-grain crops and often do not distinguish between Internet access and Internet use. This study investigates the effects of both Internet access and Internet use for farming purposes on farmers’ technical efficiency (TE) using data collected from 855 grain-farmers in Central China. The TE is evaluated following a stochastic frontier analysis approach. Endogenous switching regression models are used to address the potential endogeneity issue associated with Internet access or use. Our empirical results show that the use of the Internet for obtaining farming-related information is crucial for improving farmers’ TE. Having access to the Internet itself does not necessarily improve TE. This is also true when considering the effects through the two channels of technology adoption and risk management. Furthermore, using the Internet for non-farming purposes could impede the improvement of TE. Heterogeneity analysis indicates that the Internet can help to reduce the discrepancy of TE among grain farmers.

Assessing Technical Efficiency in Malaysian Pineapple Farms: A Stochastic Frontier Analysis Approach

Pineapple cultivation proves to be a significant component of Malaysia's agricultural sector. This research was aimed at quantifying technical efficiency of Johor small-scale pineapple agriculturists. Stochastic Frontier Analysis (SFA), a statistical method used to estimate production or cost frontiers, was the preferred method for this purpose. This approach was also used to identify key elements, such as socio-economic characteristics, that influence technical implementation in pineapple farms. A total of 290 pineapple growers participated in the study, and data was collected between 2019 and 2021 during the growing season. The mean observed score of 0.681 is an indication of agriculturists non- maximum efficiency operation activity. However, the inefficiency model showed that aspects such as agricultural experience, extension visits, and participation in workshops significantly reduced inefficiency. The main problem in production points to suboptimal farming applications: agriculturists abstain from using all available agricultural inputs to achieve maximum production. The findings of the study emphasise the need for agriculturists’ proficiency refinement and upskilling in pineapple cultivation agrarian andragogy.
 Keywords: stochastic frontier analysis, technical efficiency

Integrated stochastic multicriteria acceptability analysis and data envelopment analysis with fixed-sum outputs: an application for evaluating participating nations in the Winter Olympics Games

Along with the popularity of Olympic Games, evaluating and ranking participating nations has become one of the hottest issues in recent years. Data envelopment analysis (DEA) is extensively used for participating nations’ performance evaluation, while the prior literature mainly addresses the maximum efficiency by ignoring the weights diversity, which might lead to unreasonable and biased results. By considering the fixed-sum medals constraint, this paper proposes an integrated approach of stochastic multicriteria acceptability analysis (SMAA) and DEA for evaluating the participating nations. The proposed approach addresses the weights diversity and multiple equilibrium efficient frontiers by investigating the overall feasible space, and two strategies are considered to guarantee the fairness concern for calculating the efficiencies. Besides, the proposed approach provides both rank acceptability and holistic acceptability index to obtain the complete ranking from the best to the worst and to get the relative ranking relationship in the overall feasible space. The proposed approach can provide more reasonable performance measurements since it explores different rank positions and improves the whole satisfaction of all participating nations. Finally, the proposed approach is applied to empirically analyze the participating nations in the Winter Olympic Games from 2010 to 2022 and some valuable analytical results are provided.

An Approach for Grading of Soil Corrosiveness: A Case Study in Vicinity of Ring Road-Kathmandu

Corrosion of the outer surface of underlying Zn-coated or carbon steel pipes in the soil becomes complex and intricate due the insufficient information about the electrochemical interactions between discrete pairs of all corrosive soil factors. To overcome such corrosive problems of the underlying metal pipes in the soil, an ongoing study has suggested a stochastic approach for a close analysis of the corrosive grading of each soil specimen, sampled from the vicinity of Ring Road (RR) of Kathmandu, Nepal, towards the pipes with modifying the previously utilized AWWA (American Water Works Association), ASTM and NACE methods. Four corrosive grades (CGs) of all the soil specimens were categorized based on their quantitatively calculated soil factors in the stochastic approach of the novel probabilistic modeling (NPM) method. Then, they grouped supplementary ten corrosive sub-grades (CSGs) taking the sum of the cumulative point (CuP) of every soil factor. An indeterminate examination of 10 soil specimens was accomplished to categorize their CSGs, which would be a more precise method to draw a corrosive soil mapping of the study areas. The outcomes of such analysis under the NPM method imparted that about 90% of the sampled soil specimens of the RR areas allied only to five specific CSGs belonging to two CGs, i.e., G-RAR and G-MID.

Management systems and productive efficiency along the certification life-cycle

Firms in the world today are pushed to demonstrate objectively that they are able to comply with increasingly stringent requirements to become part of sustainable supply chains. One of the most widely applied practice is the implementation of management systems’ certifications that contribute to increase the efficiency and competitive advantage of firms. Relying in the resource-based view of the firm, the aim of this paper is to analyze the impact of ISO 9001, ISO 14001 and OHSAS 18001 certifications on the productive efficiency of the firm. Specifically, the analysis considers three stages of the certification life-cycle: certification itself, withdrawal (decertification), and retrieval (recovery).Based on a longitudinal sample of 918 European and Asian firms from 2010 to 2019, and relying on a stochastic frontier analysis approach, this work presents empirical evidence on the effects of these certifications on firms’ productive efficiency along the certification life-cycle. Differences in countries and industries have been also found.The main findings suggest that ISO 9001 is the main driver of productive efficiency, mainly when combined with ISO 14001 as well as in the triple certification. Withdrawals and retrievals are not found to increase productive efficiency in most of the cases, probably because of the internalization of these practices. However, productive efficiency is hindered by the simultaneous withdrawal of ISO 9001+ISO 14001, whereas it is increased with the withdrawal of OHSAS 18001. Finally, the retrieval of ISO 9001 hinders productive efficiency.The main contributions of this paper are three: to analyze the impact of different combinations of certifications on productive efficiency; to study this relationship along the certification lifecycle, and to contribute to the resource-based view theory because these practices are unique resources which management allow firms to gain competitive advantage.

A meta‐analysis of aquaculture technical efficiency studies

AbstractAs the world population grows, the aquaculture sector faces challenges in the use of production systems to ensure the adequacy of the food supply. Technical efficiency analysis has been shown to be useful for improving resource management and production. This study provides a literature update and uses meta‐regression analysis to examine the variations in aquaculture mean technical efficiency (MTE) scores based on 101 studies published from 1998 to 2023. The findings show that studies with larger model dimensions, different output measures, an input‐oriented model specification and that analyse the exogenous variables of efficiency scores, freshwater aquaculture, and European data produce a narrower variance in MTE scores. In contrast, studies that use panel data and nonparametric data envelopment analysis (DEA) generate wider variance in MTE scores. As a result of these investigations, the findings suggest the importance of selecting appropriate variables and sample sizes for efficiency estimates. Robustness checks of efficiency scores from the DEA and stochastic frontier analysis (SFA) approaches are recommended for future studies. Different policies and regulations should be implemented in different countries and across different farming systems.

Chance-constrained optimization for active distribution networks with virtual power lines

Active distribution networks (ADNs) experience uncertainties because of the high integration of distributed renewable energy resources (RESs). Stochastic analysis approaches are attracting increasing attention owing to the uncertain nature of RESs and load demands. This study proposes a chance-constrained optimal operation framework for ADNs considering virtual power lines (VPLs) solved via the second- order cone programming (SOCP) optimal power flow (OPF) problem. VPLs comprise virtual lines connected to each network’s energy storage systems (ESSs). If one ESS is charged, another ESS is discharged by the same amount. We reformulate the constraints into tractable forms using a simplified Z-bus sensitivity matrix and first-order Taylor expansion to solve via SOCP OPF because the chance constraints on voltage magnitudes are generally non-convex and intractable. The efficacy of the proposed approach was verified through its application to two modified IEEE 33-bus systems, each featuring different consumer units, including commercial, residential, and industrial units. The effectiveness of the proposed methods was demonstrated by comparing three different types of constraints: conventional hard constraints, soft constraints with penalties, and chance constraints. The results showed that although the chance constraints case had a slightly higher voltage violation rate, the network power loss was reduced by 3.4% compared to the conventional hard constraints case. Furthermore, modified 85-bus and 65-bus systems are carried out to verify the algorithm’s scalability. The proposed approach achieves a trade-off between voltage regulation and power loss minimization, with a voltage violation rate of 0.88% and a power loss reduction to 1.02%.