Output Distance Research Articles

Returns to scale, technical and efficiency changes in the Spanish hotel industry using technological heterogeneity models

This paper analyses returns to scale, productivity growth and its decomposition in the Spanish hotel industry (period 1997–2019). To do so, we consider that hotels can have technological heterogeneity and, therefore, parameters in their production function can differ between them. Also, we use a multiple input and output production function based on an output distance stochastic frontier approach with random parameters in a Bayesian framework. Results indicate that the percentage of hotels working under increasing returns to scale decreased during the period considered. Furthermore, productivity growth was low, indicating signs of stagnation. Efficiency change appears to be the main driving force behind productivity growth, although technical change had a positive effect on productivity after the start of the global financial crisis. Several practical implications are proposed based on these results.

Assessing the productivity effects of agricultural extension in a pluralistic system: an analysis of Brazilian family farming in 2017

ABSTRACT Purpose: This paper analyzes the impact of agricultural extension on the production of Brazilian family farmers, breaking down differences among extension providers. Design/Methodology/Approach: An Output Distance Function was estimated for Brazilian family farming using 5,463 municipality-level observations from the 2017 Brazilian Agricultural Census. The impact of extension on efficiency and technology was assessed. Findings: Rural extension services from public institutions, cooperatives, and private companies increased technical efficiency. Independent consultant services increased the adoption of more productive technologies and technical efficiency. However, public technical assistance increased productivity by 47.76%, technicians hired directly by farmers increased productivity by 31.25%, and technical assistance from cooperatives increased productivity by 31%. Practical implications: These findings could aid in the Brazilian Extension System’s goal of promoting productivity gains. The National Policy of Rural Extension has the ability to decentralize the role of the government and structure incentives to increase access to other providers based on these findings. Theoretical implications: This article expands the econometric estimation of Output Distance Functions to break down the impacts of extension on both efficiency and technology adoption. Originality/Value: Using readily available municipality-level data, this paper contributes to the understanding of the effectiveness of the different providers of rural extension for Brazilian family farmers and provides data regarding the impact on yield.

Production efficiency of internet-only banks and conventional banks: Evidence from China and Japan

In recent years, Internet-only banks have begun participating in many countries' financial markets; however, their impact on these markets requires clarification. This study focused on China and Japan and covered the period from 1995 to 2019. We utilized the stochastic frontier approach to estimate the output distance function with a view to comparing production efficiency before and after the entry of Internet-only banks into the financial markets of both countries. The results revealed that China's Internet-only banks are more efficient than conventional banks, while the opposite is true in Japan. Furthermore, although Japan's Internet-only banks outperformed China's, Japan's conventional banks were less efficient than were those in China; this finding suggests that Japan's Internet-only banks adopt more advanced production technologies. Finally, the results of stochastic meta-frontier regression supported the notion that national banks in both China and Japan outperformed private banks between 2015 and 2019.

An Improved Data Processing Algorithm for Spectrally Resolved Interferometry Using a Femtosecond Laser.

Spectrally resolved interferometry utilizing a femtosecond laser is widely employed for absolute distance measurement. However, deviations in the output time pulse of the conventional algorithm through inverse Fourier transform are inevitable. Herein, an improved data processing algorithm employing a time-shifting parameter is proposed to improve the accuracy of spectrally resolved interferometry. The principle of the proposed time-shifting algorithm is analyzed theoretically after clarifying the deviation source of the conventional algorithm. Simulation and experimental work were conducted to indicate the improvement in the accuracy of the output absolute distance. The results demonstrated that the proposed algorithm could reduce the deviation of output distances towards the reference values, reaching 0.58 μm by half compared to the conventional algorithm. Furthermore, the measurement uncertainty was evaluated using the Guide to the Expression of Uncertainty in Measurement (GUM), resulting in an expanded uncertainty of 0.71 μm with a 95% confidence.

Evaluation of marginal abatement cost and potential reduction in China's industrial carbon emissions: A quadratic directional output distance function approach

As a relevant indicator for quantifying the value of carbon emissions, the marginal abatement cost (MAC) of carbon emissions has received increasing attention from academics and policymakers. This study uses a quadratic directional output distance function to analyse the technical efficiency, potential emission reduction and shadow prices of China's industrial carbon emissions. The results show that the technical efficiency of China's industry gradually decreased during the sample period. From the perspective of regions with different carbon intensity and industrial output, regions with lower carbon intensity and higher industrial output gradually closed to the optimal frontier for the technology use level and had relatively high technical efficiency. Rising technical inefficiency and carbon emissions led to a potential emission reduction that also exhibits an upwards trend. In addition, the MAC of industrial carbon emissions exhibits a U-shaped trajectory, with 2011 as the turning point. Generally, in recent years, regions with lower carbon intensity and those with higher industrial output have a relatively high MAC for industrial carbon emissions. The empirical findings reveal that a carbon emission reduction policy should be formulated and targeted to each province, and the principle of ‘easy before difficult’ can be followed to promote emission reduction.

Enhanced Data-Processing Algorithms for Dispersive Interferometry Using a Femtosecond Laser.

Dispersive interferometry based on a femtosecond laser is extensively utilized for achieving absolute distance measurements with high accuracy. However, this method cannot measure arbitrary distances without encountering a dead zone, and deviations in its output results are inevitable due to inherent theory limitations. Therefore, two enhanced data-processing algorithms are proposed to improve the accuracy and reduce the dead zone of dispersive interferometry. The principles of the two proposed algorithms, namely the truncated-spectrum algorithm and the high-order-angle algorithm, are proposed after explaining the limitations of conventional methods. A series of simulations were conducted on these algorithms to show the improved accuracy of measurement results and the elimination of the dead zone. Furthermore, an experimental setup based on a dispersive interferometer was established for the application of these proposed algorithms to the experimental interference spectral signals. The results demonstrated that compared with the conventional algorithm, the proposed truncated-spectrum algorithm could reduce the output distance deviations derived from direct inverse Fourier transforming by eight times to reach as low as 1.3 μm. Moreover, the unmeasurable dead zone close to the zero position of the conventional algorithm, i.e., the minimum working distance of a dispersive interferometer, could be shortened to 22 μm with the implementation of the proposed high-order-angle algorithm.

Technical Efficiency and Returns to Scale in Vietnam’s Agriculture: Stochastic Output Distance Function Approach

Technical Efficiency and Returns to Scale in Vietnam’s Agriculture: Stochastic Output Distance Function Approach

Heterogeneity and time-varying efficiency in the Ecuadorian banking sector. An output distance stochastic frontier approach

This paper estimates the efficiency of Ecuadorian commercial banks from 2007 to 2017, considering multiple inputs and outputs, several factors explaining inefficiency, technological unobserved heterogeneity, and time-varying efficiency. To do this, we used an output distance function stochastic frontier model in a Bayesian framework and considered the profitability approach. In general, the results show evidence of unobserved cross-bank heterogeneity and time-varying inefficiencies, the latter of which presented scores which were high and stable over time. One factor that significantly explains inefficiency is foreign ownership, however internal factors such operating profitability and Central Bank policy reserve requirements did not. Finally, estimated returns to scale show bimodality indicating the existence of two bank groups associated with decreasing and constant returns to scale.

Laser-Scribed Pencil Lead Electrodes for Amperometric Quantification of Indapamide

Laser engraving is a convenient, fast, one-step, and environmentally friendly technique used to produce more conductive surfaces by local pyrolysis. The laser’s thermal treatment can also remove non-conductive materials from the electrode surfaces and improve electrochemical performance. The improvement was assessed by electrochemical tools such as cyclic voltammograms and electrochemical impedance spectroscopy using [Fe(CN)6]3−/4− and dopamine as redox probes. The electrochemical results observed showed that a treated surface showed an improvement in electron transfer and less resistance to charge transfer. To optimize the electrode performance, it was necessary to search for the most favorable graphite mines and optimize the parameters of the laser machine (laser power, scan rate, and output distance). The resultant material was adequately characterized by Raman spectroscopy and scanning electron microscopy (SEM), where an irregular surface composed of crystalline graphite particles was noticed. Furthermore, as a proof-of-concept, it was applied to detect indapamide (IND) in synthetic urine by flow injection analysis (FIA), a diuretic drug often used by athletes to alter urine composition to hide forbidden substance consumption in doping tests.

CO2 emissions abatement costs and drivers for Indian thermal power industry

Indian power industry contributes nearly half of its overall CO2 emissions. In this study, we answer three related questions. First, what is the abatement cost of CO2 emissions for the Indian thermal power industry under three different policy scenarios - business as usual, emissions reduction only, and simultaneous emissions reduction and energy efficiency enhancement. Second, what are the determinants of these abatement costs of CO2 emissions. Third, which abatement policy scenario is more beneficial. For this, we employ a plant level dataset covering 93% of installed capacity and use a parametric quadratic directional output distance function to estimate marginal abatement costs by applying deterministic linear programming methods. Our findings are: First, the abatement costs range between US$ 46.43–71.11 per ton CO2, depending on the policy scenario chosen. Second, plant age, location, size, ownership, and CO2 intensity are all significant drivers of abatement costs. Third, while greater emissions reductions are possible under the emissions reduction only scenario vis-à-vis simultaneous emissions reduction and energy efficiency enhancement scenario, both scenarios are compatible with meeting the Paris agreement goals. This paper helps decision makers to allocate CO2 abatement targets to plants in line with their abatement costs and potential.

Assessing performance of directional output distance function: Simulation-based Analysis

Assessing performance of directional output distance function: Simulation-based Analysis

Carbon abatement in the European chemical industry: assessing the feasibility of abatement technologies by estimating firm-level marginal abatement costs

As many other industries, the chemical industry has to strongly reduce its' carbon emissions, for which various abatement technologies exist. We study the economic feasibility of these abatement technologies by estimating the Marginal Abatement Cost (MAC) of CO2 emissions for 24 firms in the European chemical sector over the period 2015–2020. We estimate the firm-level MAC by using a quadratic directional output distance function (DDF) model. We find a median MAC of 429 €/t CO2, which is significantly above current carbon prices, indicating that most firms in this industry prefer to use carbon allowances instead of reducing their own emissions. We conclude that carbon abatement in the chemical industry is only likely when the carbon price is significantly higher or when financial support is provided for certain abatement technologies, such as renewable hydrogen and bio-based ammonia.

Estimating the CO2 marginal abatement cost and implications for climate policies in China’s industrial sector: A firm-level analysis

ABSTRACT This paper presents an in-depth analysis of the Marginal Abatement Cost Curve (MACC) for CO2 of China’s industrial sector. Leveraging comprehensive firm-level panel data spanning the period 2011–2015, we employ a parameterized directional output distance function to estimate the shadow price of CO2. By doing so, we derive the marginal abatement cost for individual firms across different years, which provides crucial insights into two fundamental aspects: first, the variation in shadow prices as indicators of the economic efficiency of existing climate policies; and second, the carbon price levels necessary to achieve CO2 mitigation targets in the future. Furthermore, we conduct scenario simulations to assess the potential industrial output loss resulting from forthcoming carbon policies, such as the European Union’s Carbon Border Adjustment Mechanism (CBAM). Our findings underscore the necessity for a considerably higher tax rate to stimulate pollution reduction in order to meet the desired emission targets.

Interplay between China’s grain self-sufficiency policy shifts and interregional, intertemporal productivity differences

In 2013, the Communist Party of China decided to partially relax its self-sufficiency targets for grain, historically first recognizing moderate imports as a policy option for ensuring food security in the country. This study empirically examines the interplay between the policy shifts and the interregional, intertemporal productivity differences in Chinese agriculture. It employs a meta-frontier stochastic output distance function approach. Our empirical result shows that input augmentation was the main contributor to the agricultural output growth during 1984–2000, whereas total factor productivity is the main driver of the growth during 2001–2020. This lends strong support to an argument in the literature that Chinese crop production has recently transformed from a resource-input-driven activity to one driven by science and technology. Our study also demonstrates that the western region, which far lagged behind others in the past in terms of agricultural technology, has made remarkable progress during 2001–2020, which confirms the cross-regional productivity convergence over time. It is likely that farmers in this region were better able to gain a higher economic return from crop diversification into horticulture, for which they might have been all the more motivated to improve their productivity.

Correcting for productivity growth misspecification: A local likelihood estimation in global banking

AbstractDecomposing firm productivity has been challenging for some time. We propose a flexible functional form for total factor productivity that treats misspecification and endogeneity. The model also treats heteroscedasticity. Our productivity measure nests both the input distance function and output distance function. We provide details of a novel Local Likelihood estimation, a non‐parametric technique, to estimate productivity which also has excellent finite‐sample properties. In an empirical application, we measure bank productivity at the global level. Results show that productivity is correctly identified, and the flexibility of our methodology allows to estimate the impact of equity and nonperforming loans on bank productivity. Technology has positively contributed to productivity. However, nonperforming loans, bank risk‐taking and raising capital have had the opposite effect.

Marginal abatement costs for GHG emissions in Canada: a shadow cost approach

This study approximates the marginal abatement costs (MACs) of reducing GHG emissions in Canada using the shadow cost approach. Utilizing industry level data, we are the first to offer Canadian estimates based on a Hyperbolic Output Distance Function (HODF) and the stochastic frontier estimation. Accounting for GHG emissions caused by energy consumption, we obtain an average shadow MAC of $130/t across 30 industries. In the GHG-intensive industries such as the electric utilities and non-conventional oil extraction, MACs are lower than the CO2 levy of $50/t imposed by the federal government. Since these low-MACs sectors account for about 98 per cent of total GHG emissions and 94 per cent of total energy use in industries studied, the envisaged $50/t carbon levy could notionally result in a significant GHG abatement in Canada.Graphical abstractFig. aEstimated Marginal Abatement Costs by Sector

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Since the coronavirus disease 19 (COVID-19) outbreak, the epidemiological analysis has raised a strong requirement for more effective and accurate contact tracing solution. However, the existing contact tracing solutions either lacked the evaluation of tracing proximity or the features used for the tracing proximity evaluation were susceptible to certain negative environmental factors (e.g., body shielding). In this article, we propose a novel distance estimation algorithm based on machine learning for contact tracing: DISTERNING, where we leverage machine learning algorithms including Learning Vector Quantization, Regression, and Deep Feed-forward (DFF) Neural Network, data processing methods, and digital filters to process the Bluetooth signal information collected by the mobile phone for contact distance estimation. A contact tracing scheme based on edge computing is also proposed for algorithm deployment due to the requirements of the computational power. Compared with the existing contact tracing solutions, our algorithm considers the factors that have significant negative influence on the Bluetooth signal for distance estimation in reality. The evaluation results show that when the collected Bluetooth signal is influenced by real-world negative environmental factors, employing our proposed algorithm DISTERNING can keep the accuracy of the estimated distance reliable. The output distance can be combined with some medical models to conduct infection risk assessments.

Assessing the costs of GHG emissions of multi-product agricultural systems in Vietnam

Besides a vital sector of the economy, agriculture is a primary source of greenhouse gas (GHG) emissions. The present paper investigates the impact of carbon tax policy on Vietnamese agriculture by focusing on multi-product systems such as rice, livestock, and aquaculture, traditionally called the Vuon (Garden)–Ao (Pond)–Chuong (livestock pen) system (VAC). In it, farmers use garden, pond, and pen by-products as fertilizer and feed. We use shadow prices and Morishima substitution elasticities as greenhouse gas emissions indicators, estimated with directional output distance function. Farmers in the Mekong Delta region are found to be technologically less efficient than in other regions of Vietnam, though the shadow prices of GHG emissions are lower there too. This indicates that farmers in the Mekong Delta, generally concentrating either on livestock or aquaculture, have greater potential for reducing GHG emissions by way of improvements in technical efficiency than do those in other regions. However, Morishima elasticity estimates show that policy impacts diminish more quickly in the Mekong than elswhere. We suggest the Vietnamese government encourage Mekong Delta farmers to employ technologically more efficient methods or shift to more balanced farming to reduce the shadow price of GHG emissions, encouraging more efficient emissions reduction.

Distance function approaches for efficiency analysis of organic production units: a critical review

Objectives: This review aims to make a comprehensive synthesis of distance function methods for the efficiency analysis of organic production units. Methodology and Results: The study reviewed and summarized the different approaches of distance functions applied in organic farming systems to assess performance. Original articles covering ten years, from 2010 to 2020 were identified through keyword searching using web search engines. After careful reading of the abstracts, 20 papers were finally retained. The selected papers were categorized based on study purpose and findings. Results showed a gap between the development of distance function methods and their usage in organic farming efficiency analysis. Four distance function methods were identified, and the most used in organic farming efficiency analysis were output distance function (40%) and directional output distance function (25%). Conclusion and application of findings: Results indicate that distance functions are powerful tools in modelling multi-input and multi-output production technology for assessing organic farming efficiency. Future research should focus on these methods to deepen their understanding and applications. Keywords: Distance function, efficiency analysis, literature review, organic farming

Adversarial Representation Learning for Hyperspectral Image Classification with Small-Sized Labeled Set

Hyperspectral image (HSI) classification is one of the main research contents of hyperspectral technology. Existing HSI classification algorithms that are based on deep learning use a large number of labeled samples to train models to ensure excellent classification effects, but when the labeled samples are insufficient, the deep learning model is prone to overfitting. In practice, there are a large number of unlabeled samples that have not been effectively utilized, so it is meaningful to study a semi-supervised method. In this paper, an adversarial representation learning that is based on a generative adversarial networks (ARL-GAN) method is proposed to solve the small samples problem in hyperspectral image classification by applying GAN to the representation learning domain in a semi-supervised manner. The proposed method has the following distinctive advantages. First, we build a hyperspectral image block generator whose input is the feature vector that is extracted from the encoder and use the encoder as a feature extractor to extract more discriminant information. Second, the distance of the class probability output by the discriminator is used to measure the error between the generated image block and the real image instead of the root mean square error (MSE), so that the encoder can extract more useful information for classification. Third, GAN and conditional entropy are used to improve the utilization of unlabeled data and solve the small sample problem in hyperspectral image classification. Experiments on three public datasets show that the method achieved better classification accuracy with a small number of labeled samples compared to other state-of-the-art methods.