Primary Metal Industries Research Articles

Using spectral indices and terrain attribute datasets and their combination in the prediction of cadmium content in agricultural soil

The continuous demand placed on farmland to yield optimal harvest is dependent on the continuous application of agrochemicals and fertilizers to increase soil fertility and manage diseases. Successive application of fertilizers and use of agrochemicals coupled with metal and steel industries introduce potentially toxic elements into the soil. Active agricultural activities and industrial emissions that result in atmospheric cadmium (Cd) injection and active deposition on agricultural soil (particularly from the primary metal industry, steel and iron industrial production). The concentration of cadmium in the study area exceeds the local background value. As a result, excessive cadmium soil concentration will contribute to increased toxic and carcinogenic effects, with negative implications for both environmental and human health. Therefore, determining the spatial distribution of Cd is critical for environmentally friendly agricultural production and reducing Cd emission into soils. The goals of this study are to (i) determine the variability of Cd prediction in agricultural soil using spectral indices or terrain attributes coupled with modeling algorithms, and (ii) determine whether combining spectral indices and terrain attributes coupled with modeling algorithms can improve Cd prediction efficiency in agricultural soil. The study applied three modelling scenarios, comprised prediction using terrain attributes coupled with digital soil mapping (DSM) approaches (Scenario 1), prediction using spectral indices combined with DSMs (Scenario 2), and prediction using a combination of terrain attributes, spectral indices, and DSMs (Scenario 3). Gaussian process regression (GPR), partial least square regression (PLSR), extreme gradient boosting (EGB), multivariate adaptive regression splines (MARS), Bayesian regularized neural network (BRNN), regularized random forest (RRF), Bayesian generalized linear model (BGLM), and M5 tree models were the DSMs used in the study. The M5 tree model and terrain attributes {Scenario 1 R2 = 0.77, concordance correlation coefficient (CCC) = 0.73, root mean square error (RMSE) = 0.45, mean absolute error (MAE) = 0.37 and median absolute error (MdAE) = 0.35}, EGB and spectral indices {Scenario 2, R2 = 0.83, CCC = 0.76, RMSE = 0.54, MAE = 0.33 and MdAE = 0.23} and the M5 tree model, spectral indices and terrain attributes {Scenario 3, R2 = 0.84, CCC = 0.81, RMSE = 0.39, MAE = 0.31 and MdAE = 0.24} were the overall best combinations that predicted Cd in the agricultural soil. The overall evaluation of the approaches suggested that the combination of spectral indices, terrain attributes, and the M5 tree model in Scenario 3 was the optimal technique for predicting Cd in agricultural soil. Thus, a combination of environmental covariates with a high correlation with the response variable, combined with appropriate modeling techniques predicting potentially toxic elements in agricultural soil, will produce the best results.

Contribución de los subsectores manufactureros en la variación de las emisiones de CO2 por el uso de combustible en San Luis Potosí, México

El análisis de la variación de las emisiones de dióxido de carbono (CO2) proporciona información útil para las alternativas de reducción. En el presente estudio se realizó una descomposición de los factores que determinan la variación anual de las emisiones de CO2, identificando el efecto de cada uno de los subsectores manufactureros de San Luis Potosí, México, durante el periodo 2000-2012. Se utilizó el enfoque Índice Divisia de Media Logarítmica (LMDI, por sus siglas en inglés) para identificar la contribución desagregada de los subsectores manufactureros en la variación de las emisiones, de acuerdo con la ecuación de Kaya. La intensidad energética y el Producto Interno Bruto (PIB) manufacturero fueron los factores de mayor efecto en la variación del CO2, con contribuciones del 53.85% y 37.32% respectivamente. Los subsectores de la fabricación de productos a base de minerales no metálicos, la industria metálica básica y la alimentaria son los que inciden principalmente sobre la dinámica de los factores identificados.

EVALUATION OF ENTERPRISE SURVIVAL: CASE OF LATVIAN ENTERPRISES

Authors study the nature of insolvency both from the legal point of view and scientist position as well as updating tendencies of an insolvency of enterprises in recent years. The subject of the study has been selected company’s survival potential that is analyzed with financial ratio analysis using bankruptcy prediction models. Considering research results, authors identify models that are applicable to a particular industry. Authors put primary metal industry (NACE 24) for the study. The aim of the paper is to investigate the survival potential of enterprises by testing existing parametric models of insolvency forecasting and assessing their potential for use in the economic conditions of Latvia. During the investigation has been reviewed the concept of the financially healthy company and its relation with the main success development factors.

LMDI Decomposition Analysis of Energy Consumption in the Korean Manufacturing Sector

The energy consumption of Korea’s manufacturing sector has sharply increased over the past 20 years. This paper decomposes the factors influencing energy consumption in this sector using the logarithmic mean Divisia index (LMDI) method and analyzes the specific characteristics of energy consumption from 1991 to 2011. The analysis reveals that the activity effect played a major role in increasing energy consumption. While the structure and intensity effects contributed to the reduction in energy consumption, the structure effect was greater than the intensity effect. Over the periods, the effects moved in opposite directions; that is, the structure effect decreased when the intensity effect increased and vice versa. The energy consumption by each industry is decomposed into two factors, activity and intensity effects. The increase of energy consumption due to the activity effect is largest in the petroleum and chemical industry, followed by the primary metal and non-ferrous industry, and the fabricated metal industry. The decrease of energy consumption due to the intensity effect is largest in the fabricated metal industry, followed by the primary metal and non-ferrous industry, and the non-metallic industry. The energy consumption due to intensity effect in the petroleum and chemical industry has risen. To save energy consumption more efficiently for addressing climate change in this sector, industrial restructuring and industry-specific energy saving policies should be introduced.

Board compensation committees: CEO pay and market cap performance with implications for investors

Board of Directors’ compensation committees currently have no pay provisions requiring CEO or top executives’ compensation claw-backs for market capitalization destruction which could have huge impacts on such top executive pay. For example, CEO pay was correlated with market capitalization performance for 24 companies in the metal mining, primary metal, and coal mining industries. Simple correlation tests of 2013 total CEO pay with market capitalization destruction over the five-year period, January 2011 through December 2015, yielded a 74% weighted average strong correlation. The total annual pay for these 24 CEOs was $198 million or an estimated $1 billion over the five-year period from 2011-2015. During this same five-year period, the market capitalization for these 24 companies decreased 73% or $180 billion. During this same five-year time period, the S&P 500 Index increased 63%. Some corporate governance researchers (Kostyuk, 2014 and Hilb, 2008) have advocated: “Pay for Performance, not Presence” which could include such correlations with claw-back provisions as part of executive compensation packages from Board of Directors’ compensation committees.

Does Effective Inventory Management Improve Profitability?

The present research investigates the effect of inventory performance on profitability. The objective is to find empirical evidence for the theory that operational excellence in inventory management improves profitability in the long run. To this end, the authors examine industry level longitudinal data (14 manufacturing industries at the SIC two-digit level) over the 1958-1999 period by employing a series of hierarchical regression analyses. The statistical results confirm that a lower inventory level measured as the industry inventory-to-sales ratio has a positive effect on industry profitability measured as the profit-to-sales ratio. This evidence is found significant in 9 out of the 14 U.S. manufacturing industries. This study also reveals that not all the inventories, categorized by stage of fabrication, equally contribute to improving industry profitability. For instance, the profitability of the primary and fabricated metal industries has benefited from reductions in finished goods inventories, whereas that of the petroleum and coal products industry has been affected mainly by declines in work-in-process inventories.

The Rise of Shale Gas and Russia's Countermeasures

The recent expansion of shale gas development in the US has been greatly contributing to the drop in natural gas prices in the country, and to job growth and enhanced competitiveness in relevant manufacturing areas such as the petrochemical industry. Since the first half of 2009, the US natural gas spot price (Henry Hub), unlike the crude oil (WTI) spot price (Cushing Hub), has been showing a steady downward trend. Lowered gas prices have been serving as cause for cost cuts in gas production, the petrochemical industry, the primary metal industry and so forth, and this is helping improve price competitiveness in the US manufacturing sector. Spot price decoupling between crude oil and natural gas has led to a change in the energy mix, with natural gas enjoying higher consumption.

The Environmental Pollutant Cadmium Promotes Influenza Virus Replication in MDCK Cells by Altering Their Redox State.

Cadmium (Cd) is a toxic heavy metal that is considered an environmental contaminant. Several sources of human exposure to Cd, including employment in primary metal industries, production of certain batteries, foods, soil and cigarette smoke, are known. Its inhalation has been related to different respiratory diseases and toxic effects, among which alterations of the physiological redox state in individuals exposed to the metal have been described. Host-cell redox changes characteristic of oxidative stress facilitate the progression of viral infection through different mechanisms. In this paper, we have demonstrated that pre-treatment with CdCl2 of MDCK cells increased influenza virus replication in a dose-dependent manner. This phenomenon was related to increased viral protein expression (about 40% compared with untreated cells). The concentration of CdCl2, able to raise the virus titer, also induced oxidative stress. The addition of two antioxidants, a glutathione (GSH) derivative or the GSH precursor, N-acetyl-l-cysteine, to Cd pre-treated and infected cells restored the intracellular redox state and significantly inhibited viral replication. In conclusion, our data demonstrate that Cd-induced oxidative stress directly increases the ability of influenza virus to replicate in the host-cell, thus suggesting that exposure to heavy metals, such as this, could be a risk factor for individuals exposed to a greater extent to the contaminant, resulting in increased severity of virus-induced respiratory diseases.

The AS4059 Hydraulic System Cleanliness Classification System: Replacement of NAS1638

The NAS 1638 cleanliness classification system was originally developed in 1966 by the US Aircraft Industries of America to both simplify reporting of particle count data and to control the introduction of dirt during the assembly of aircraft fluid systems. The numbers of particles at stated sizes are represented by broad bands where the interval was generally a doubling of contamination. A number of systems have been introduced since this to suit differing requirements. NAS 1638 and AS4059 are used in other industrial sectors such as the Off-shore & Sub-Sea and the Primary Metal Industries. The changes to ISO contamination measurement standards controlled by ISO/TC131/SC6 in 1999 meant that a revision of most of these classification systems was necessary. The body responsible for NAS 1638 decided to withdraw it for new installations and replace it with an update of an existing standard, SAE AS 4059. This paper details the philosophy behind the contamination coding systems, the reasons for the changes to the ISO contamination standards and explains the workings of AS 4059, the replacement for NAS 1638. It goes on to detail the latest changes to this standard.

Releases Of Dioxins

For years, chemical manufacturers have been the main industrial source of highly toxic chlorinated dioxins and furans released in the U.S., according to the Environmental Protection Agency’s Toxics Release Inventory (TRI) data. Now, because of changes in how EPA analyzes these data, a different pattern is emerging: When both the quantity and toxicity of releases are considered, the primary metals industry emerges as the main source of dioxin toxicity. In TRI terms, “release” has a broad definition: It could mean disposing of dioxin-contaminated waste in a hazardous landfill; emitting it to air; or discharging it in a flow of wastewater to a river, lake, or stream. And the category of dioxin and dioxin-like compounds consists of the most toxic congener—2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)—as well as six other chlorinated dioxins and 10 chlorinated furans. “These related chemicals act via the same toxicity pathway and produce similar health effects: increasing the risk of cancer, ...

Toxics Release Inventory indicates big increases in releases

Nearly 4 billion pounds of tracked toxic chemicals were released into the environment throughout the United States during 2010, according to an analysis by the U.S. Environmental Protection Agency (EPA) of the Toxics Release Inventory (TRI), the agency announced on 5 January. This is a 16% increase above 2009. The agency said the increase is mainly due to changes in the metal‐mining sector, where differences in the chemical composition of ore being mined can result in significant changes in the amount of toxic chemicals. The chemical and primary metals industries were other sectors with increases in toxic releases in 2010, the latest year for which data collection is complete. EPA also noted that although releases in 2010 were higher than during the previous 2 years, they were lower than in 2007 and in prior years.

Carbonyl Sulfide Removal with Compost and Wood Chip Biofilters, and in the Presence of Hydrogen Sulfide

Carbonyl sulfide (COS) is an odor-causing compound and hazardous air pollutant emitted frequently from wastewater treatment facilities and chemical and primary metals industries. This study examined the effectiveness of biofiltration in removing COS. Specific objectives were to compare COS removal efficiency for various biofilter media; to determine whether hydrogen sulfide (H2S), which is frequently produced along with COS under anaerobic conditions, adversely impacts COS removal; and to determine the maximum elimination capacity of COS for use in biofilter design. Three laboratory-scale polyvinyl chlo-ride biofilter columns were filled with up to 28 in. of biofilter media (aged compost, fresh compost, wood chips, or a compost/wood chip mixture). Inlet COS ranged from 5 to 46 parts per million (ppm) (0.10–9.0 g/m3fihr). Compost and the compost/wood chip mixture produced higher COS removal efficiencies than wood chips alone. The compost and compost/wood chip mixture had a shorter stabilization times compared with wood chips alone. Fresh versus aged compost did not impact COS removal efficiency. The presence of H2S did not adversely impact COS removal for the concentration ratios tested. The maximum elimination capacity is at least 9 g/m3·hr for COS with compost media.

Residential proximity to industrial facilities and risk of non-Hodgkin lymphoma.

Industrial pollution has been suspected as a cause of non-Hodgkin lymphoma (NHL), based on associations with chemical exposures in occupational studies. We conducted a case-control study of NHL in four SEER regions of the United States, in which residential locations of 864 cases and 684 controls during the 10 years before recruitment were used to characterize proximity to industrial facilities reporting chemical releases to the Environmental Protection Agency's Toxics Release Inventory (TRI). For each of 15 types of industry (by 2-digit SIC code), we evaluated the risk of NHL associated with having lived within 2 miles of a facility, the distance to the nearest facility (miles categories of < or =0.5, >0.5-1.0, >1.0-2.0, >2 [referent]), and the duration of residence within 2miles (years categories of 10, 1-9, 0 [referent]), using logistic regression. Increased risk of NHL was observed in relation to lumber and wood products facilities (SIC 24) for the shortest distance of residential proximity (< or =0.5 mile: odds ratio [OR]=2.2, 95% confidence interval [CI]: 0.4-11.8) or the longest duration (10 years: OR=1.9, 95% CI: 0.8-4.8); the association with lumber facilities was more apparent for diffuse large B-cell lymphoma (lived within 2 miles: OR=1.7, 95% CI: 1.0-3.0) than for follicular lymphoma (OR=1.1, 95% CI: 0.5-2.2). We also observed elevated ORs for the chemical (SIC 28, 10 years: OR=1.5, 95% CI: 1.1-2.0), petroleum (SIC 29, 10 years: OR=1.9, 95% CI: 1.0-3.6), rubber/miscellaneous plastics products (SIC 30, < or =0.5mile: OR=2.7, 95% CI: 1.0-7.4), and primary metal (SIC 33, lived within 2miles: OR=1.3, 95% CI: 1.0-1.6) industries; however, patterns of risk were inconsistent between distance and duration metrics. This study does not provide strong evidence that living near manufacturing industries increases NHL risk. However, future studies designed to include greater numbers of persons living near specific types of industries, along with fate-transport modeling of chemical releases, would be informative.

Prevalence of Lifetime Asthma and Current Asthma Attacks in U.S. Working Adults: An Analysis of the 1997–2004 National Health Interview Survey Data

To estimate national prevalences of lifetime asthma and asthma attacks among workers by age, sex, race, occupation and industry, and estimate population attributable fraction to employment for asthma attacks in the United States. The 1997-2004 National Health Interview Survey data for currently working adults aged > or = 18 years were analyzed. Lifetime asthma prevalence was 9.2%; the social services religious and membership organizations industry and the health service occupation had the highest asthma prevalence. Asthma attack prevalence among workers with asthma was 35.4%; the primary metal industry and the health assessment and treating occupation had the highest attack prevalence. Approximately, 5.9% of cases reporting an asthma attack were attributed to employment when considering industries and 3.8% when considering occupations. Future studies and intervention strategies should address the higher prevalence of asthma in certain industries and occupations.

Globally sustainable manganese metal production and use

The “cradle to grave” concept of managing chemicals and wastes has been a descriptive analogy of proper environmental stewardship since the 1970s. The concept incorporates environmentally sustainable product choices—such as metal alloys utilized steel products which civilization is dependent upon. Manganese consumption is related to the increasing production of raw steel and upgrading ferroalloys. Nonferrous applications of manganese include production of dry-cell batteries, plant fertilizer components, animal feed and colorant for bricks. The manganese ore (high grade 35% manganese) production world wide is about 6 million ton/year and electrolytic manganese metal demand is about 0.7 million ton/year. The total manganese demand is consumed globally by industries including construction (23%), machinery (14%), and transportation (11%). Manganese is recycled within scrap of iron and steel, a small amount is recycled within aluminum used beverage cans. Recycling rate is 37% and efficiency is estimated as 53% [Roskill Metals and Minerals Reports, January 13, 2005. Manganese Report: rapid rise in output caused by Chinese crude steel production. Available from: http://www.roskill.com/reports/manganese.]. Environmentally sustainable management choices include identifying raw material chemistry, utilizing clean production processes, minimizing waste generation, recycling materials, controlling occupational exposures, and collecting representative environmental data. This paper will discuss two electrolytically produced manganese metals, the metal production differences, and environmental impacts cited to date. The two electrolytic manganese processes differ due to the addition of sulfur dioxide or selenium dioxide. Adverse environmental impacts due to use of selenium dioxide methodology include increased water consumption and order of magnitude greater solid waste generation per ton of metal processed. The use of high grade manganese ores in the electrolytic process also reduces the quantity of solid wastes generated during processing. Secondary aluminum facilities have reported hazardous waste generation management issues due to baghouse dusts from rotary furnaces processing selenium contaminated manganese alloys. Environmental impacts resulting from industry are represented by emission inventories of chemical releases to the air, water, and soil. The U.S. metals industry releases reported to EPA Toxic Release Inventory indicate the primary metals industry is the major source of metal air toxic emissions, exceeding electric utility air toxic emissions. The nonferrous metals industry is reported to be the Organization for Economic Co-operation and Development (OECD) most intensive airborne and land pollution source of bioaccumulative metals. However, total waste emissions from industries in the OECD countries have declined due to improving energy consumption. Emission registers and access are improving around the world. However, environmental databases for metal particulates have low confidence ratings since the majority of air toxic emissions are not reported, not monitored, or are estimated based on worst-case emission factors. Environmental assessments including biological monitoring are necessary to validate mandated particulate metal emission reductions and control technologies during metal processing.

Identification of occupational cancer risk in British Columbia: A population‐based case–control study of 2,998 lung cancers by histopathological subtype

Few studies have investigated occupational lung cancer risk in relation to specific histopathological subtypes. A case-control study was conducted to evaluate the relationship between lung cancer and occupation/industry of employment by histopathological subtype. A total of 2,998 male cases and 10,223 cancer controls, diagnosed between 1983 and 1990, were identified through the British Columbia Cancer Registry. Matched on age and year of diagnosis, conditional logistic regression analyses were performed for two different estimates of exposure with adjustment for potentially important confounding variables, including tobacco smoking, alcohol consumption, marital status, educational attainment, and questionnaire respondent. For all lung cancers, an excess risk was observed for workers in the primary metal (OR = 1.31, 95% CI, 1.01-1.71), mining (OR = 1.53, 95% CI, 1.20-1.96), machining (OR = 1.33, 95% CI, 1.09-1.63), transport (OR = 1.50, 95% CI, 1.08-2.07), utility (OR = 1.60, 95% CI, 1.22-2.09), and protective services (OR = 1.27, 95% CI, 1.05-1.55) industries. Associations with histopathological subtypes included an increased risk of squamous cell carcinoma in construction trades (OR = 1.25, 95% CI, 1.06-1.48), adenocarcinoma for professional workers in medicine and health (OR = 1.73, 95% CI, 1.18-2.53), small cell carcinoma in railway (OR = 1.62, 95% CI, 1.06-2.49), and truck transport industries (OR = 1.51, 95% CI, 1.00-2.28), and large cell carcinoma for employment in the primary metal industry (OR = 2.35, 95% CI, 1.11-4.96). Our results point to excess lung cancer risk for occupations involving exposure to metals, polyaromatic hydrocarbons and asbestos, as well as several new histopathologic-specific associations that merit further investigation.

Residential Proximity to Industrial Facilities and Risk of Non-Hodgkin Lymphoma

Industrial pollution has been suspected as a cause of non-Hodgkin lymphoma (NHL), based on associations with chemical exposures in occupational studies. We conducted a case-control study of NHL in four SEER regions of the United States, in which residential locations of 864 cases and 684 controls during the 10 years before recruitment were used to characterize proximity to industrial facilities reporting chemical releases to the Environmental Protection Agency's Toxics Release Inventory (TRI). For each of 15 types of industry (by 2-digit SIC code), we evaluated the risk of NHL associated with having lived within 2 miles of a facility, the distance to the nearest facility (miles categories of 0.5-1.0, >1.0-2.0, >2 [referent]), and the duration of residence within 2miles (years categories of 10, 1-9, 0 [referent]), using logistic regression. Increased risk of NHL was observed in relation to lumber and wood products facilities (SIC 24) for the shortest distance of residential proximity (< or =0.5 mile: odds ratio [OR]=2.2, 95% confidence interval [CI]: 0.4-11.8) or the longest duration (10 years: OR=1.9, 95% CI: 0.8-4.8); the association with lumber facilities was more apparent for diffuse large B-cell lymphoma (lived within 2 miles: OR=1.7, 95% CI: 1.0-3.0) than for follicular lymphoma (OR=1.1, 95% CI: 0.5-2.2). We also observed elevated ORs for the chemical (SIC 28, 10 years: OR=1.5, 95% CI: 1.1-2.0), petroleum (SIC 29, 10 years: OR=1.9, 95% CI: 1.0-3.6), rubber/miscellaneous plastics products (SIC 30, < or =0.5mile: OR=2.7, 95% CI: 1.0-7.4), and primary metal (SIC 33, lived within 2miles: OR=1.3, 95% CI: 1.0-1.6) industries; however, patterns of risk were inconsistent between distance and duration metrics. This study does not provide strong evidence that living near manufacturing industries increases NHL risk. However, future studies designed to include greater numbers of persons living near specific types of industries, along with fate-transport modeling of chemical releases, would be informative.

U.S. manufacturing aggregate energy intensity decomposition: The application of multivariate regression analysis

This paper introduces the use of a multivariate regression analysis to explain factors that impact aggregate energy intensity. This kind of study is useful to evaluate the past and predicts the future trends for energy-policy evaluation. Historical aggregate fuel and electricity intensities of the entire U.S. manufacturing sector (Standard Industrial Classification, SIC, codes of 20–39) over the 1977–1998 period are used to develop the proposed multivariate regression model. The proposed model allows identifying the structural effect of aggregate energy intensity changes without relying on detailed disaggregated energy data. Its results are validated by comparison with those from conventional decomposition techniques based on economic index numbers. For illustration, the historical aggregate fuel intensity of the U.S. primary metal industry (SIC 33) is used as an example of a situation for which economic index numbers fail to decompose the historical aggregate energy intensity since the disaggregated energy data are unavailable, while the multivariate regression analysis can still be applied. Empirical results show that a structural shift contributes to decreases of about 28, 41 and 19% of total declines of U.S. manufacturing aggregate fuel, U.S. manufacturing aggregate electricity, and U.S. primary metal industry aggregate fuel intensities, respectively, for the 1977–1998 period. The method based on multivariate regression models estimates the time series structural effects within deviation averages of 8.5 and 7.0% of the time series structural effect estimates based on the economic index numbers for the U.S. manufacturing aggregate fuel and electricity intensities, respectively, even though the multivariate regression model does not require disaggregated energy data. Copyright © 2007 John Wiley & Sons, Ltd.

Measuring Green Productivity Growth for China's Manufacturing Sectors: 1991–2000*

Over the past two decades, China has sustained rapid economic growth of 8–10 percent, part of which is attributed to the positive total factor productivity (TFP) growth. However, this extraordinary economic performance has been accompanied by severe environmental pollution and associated health damage. The conventional TFP method is biased in interpreting the progress of technology change because it does not consider non‐marketable residues, such as environmental pollution, and, hence, efficiency improvements in terms of pollution abatement technology and environmentally friendly management are ignored. This bias might direct our attention to less efficient use of environmental friendly abatement technologies or send wrong signals to policy‐makers. To address this issue, the present paper applies a modified welfare‐based green TFP approach, treating environmental damage as non‐desirable (negative) residual output. Therefore, environmental efficiency is taken into account to accurately interpret technological progress from a social welfare point of view. Based on a national time‐series input–output table, historical capital and labor input data for China and sectoral level air pollution emission data from 1991 to 2000, the empirical results suggest that with increasingly stringent environmental regulations, many pollution intensive sectors, such as electricity, primary metal and chemical industries, improved their environmental efficiency in the late 1990s. However, because of the weak environmental regulations in construction and transportation, and in sectors primarily composed of small private or township and village industrial enterprises, firms within these industries contributed to increasing environmental degradation.

Bayesian Estimation of Stochastic Frontier Models with Multivariate Skew t Error Terms

In this article, we apply the new multivariate skew t distribution introduced by Sahu et al. (2003) to stochastic frontier models, thereby allowing for dependence of the composed errors and heavy tails in their distribution. Bayesian analysis of the model is provided. The model is applied to a cross-section of data from the U.S. primary metal industry previously analyzed by Aigner et al. (1977) using exponential and half-normal models. Results indicate that the multivariate skew t model yields significantly higher estimated variance of the one-sided error compared to exponential and half-normal models.