Current Output Levels Research Articles

Impedance spectroscopy analysis of thermoelectric modules under actual energy harvesting operating conditions and a small temperature difference

Thermoelectric (TE) devices can convert heat into electricity and have gained great interest as energy harvesters in many applications, such as self-powered sensors, solar TE generators and industrial waste heat recovery. Not many techniques are usually available for their characterization under operating conditions. Consequently, only current-voltage (power output) curves are typically reported to account for their performance, which provide limited information. Impedance spectroscopy, which has been mainly applied to the study of TE modules suspended in air or vacuum, has the advantage to be used under operating conditions. Here, we exploit this advantage and provide an in-depth analysis of the impedance response of standard TE devices when they operate under a small temperature difference. First, we derive the physical models (equivalent circuits) for the two most common operating conditions: (i) constant temperature difference and (ii) constant heat power input. Then, we use the obtained equivalent circuit to successfully fit experimental impedance measurements in both operating modes, which show significant differences. From the fittings, it is possible to obtain the thermal contact resistance between the TE device and the heat exchangers (heat source and heat sink) among other key parameters. In addition, we analyze the variations found in the impedance spectra for different current output levels. Moreover, we show how different electrical resistances, extracted from the impedance results, directly relate to the slope of the I-V curve (power output), and can provide very valuable information about the different processes that determine its value. All these results establish impedance spectroscopy as a very powerful tool to characterize TE devices under operating conditions and as a method that can provide highly valuable insights into their performance.

Irrigated Onion Production Efficiency in Humbo District, Southern Ethiopia

The study sought to establish the level of technical, allocative, and economic efficiencies in irrigated onion production in southern Ethiopia, as well as to identify the sources of inefficiencies. The cross-sectional data were obtained from a total of 165 random onion farmers selected from the Humbo area. The parametric stochastic frontier model was used to estimate technical, allocative, and economic efficiency levels, whilst the two-limit Tobit model was utilized to identify determinants of inefficiencies. The data acquired suited the Cobb-Douglas (CD) production function, with land, labor, fertilizer, and chemicals as input factors that had a positive and significant influence on irrigated onion production. The sampled onion farmers’ technical, allocative, and economic efficiencies were determined to be 77, 88, and 68%, respectively. The model results indicated that the age of the farmers, years of education, and total farm financial gain were identified as important sources of irrigated onion production inefficiencies in the research area. The mean technical efficiency levels indicate that irrigated onion producer farmers in the research area may raise their output by 23% with present inputs and technology. Alternatively, farmers will reduce their average production price by 12% while maintaining the current level of output. It is based on this that there is room to improve the efficiency of irrigated onion production. Therefore, the government’s policies and actions should focus on timely and quality agricultural input supply for onion farmers and backing them through availing of formal and informal educational services.

How Efficient Is the Cohesion Policy in Supporting Small and Mid-Sized Enterprises in the Transition to a Low-Carbon Economy?

Funds from the European Union that are devoted to fostering a low-carbon economy are aimed at assisting Member States and regions in implementing the required investments in energy efficiency, renewable energy, and smart distribution electricity grids, and for research and innovation in these areas. In this context, we assessed the implementation of these funds in small and medium-sized enterprises across different beneficiary countries and regions of the European Union. Therefore, this study uses a non-radial slack-based data envelopment analysis model coupled with cluster analysis that covers multiple aspects of evaluation, including two inputs and two outputs, to assess 102 programs from 22 countries. Overall, we were able to ascertain that there are 25 efficient operational programs that remain robustly efficient, whereas 51 remain robustly inefficient for data perturbations of 5 and 10%. Under the current output level, there was almost no input surplus. Therefore, to promote a low-carbon economy, operational program managers should concentrate on solving the problems behind the poor results achieved, both in terms of greenhouse gas emissions reduction and the pace of the programs’ implementation.

Comparing Cars With Apples? Identifying the Appropriate Benchmark Countries for Relative Ecological Pollution Rankings and International Learning

One of the most difficult tasks that economies face is how to generate economic growth without causing environmental damage. Research in economic complexity has provided new methods to reveal structural constraints and opportunities for green economic diversification and sophistication, as well as the effects of economic complexity on environmental pollution indicators. However, no research so far has compared the ecological efficiency of countries with similar productive structures and levels of economic complexity, and used this information to identify the best learning partners. This matters, because there are substantial differences in the environmental damage caused by the same product in different countries, and green diversification needs to be complemented by substantial efficiency improvements of existing products. In this article, we use data on 774 different types of exports, CO2 emissions, and the ecological footprint of 99 countries to create first a relative ecological pollution ranking (REPR). Then, we use methods from network science to reveal a benchmark network of the best learning partners based on country pairs with a large extent of export similarity, yet significant differences in pollution values. This is important because it helps to reveal adequate benchmark countries for efficiency improvements and sustainable production, considering that countries may specialize in substantially different types of economic activities. Finally, the article i) illustrates large efficiency improvements within current global output levels, ii) helps to identify countries that can best learn from each other, and iii) improves the information base in international negotiations for the sake of a cleaner global production system.

Eco-efficiency and Eco-productivity assessments of the states in the United States: A two-stage Non-parametric analysis

This study implements radial and non-radial Data Envelopment Analysis (DEA) models to assess eco-efficiency and eco-productivity of the 50 states in the United States in 2018. The models are based on three inputs (capital stock, employment, and energy consumption), a single desirable output (real gross domestic product) and a single undesirable output variable (CO2 emissions). The radial DEA models reveal that at least 32 states are operated efficiently. Five states perform at the most optimal scale size, whereas 17 states have considerable potential to boost their productive efficiencies by enlarging available resources, and 28 states are overinvested in their input variables given their current output levels. The non-radial DEA models show that, overall, the states’ capital efficiency is very high, whereas energy and emission efficiencies are very low. The states’ eco-productivity is relatively higher than the eco-efficiency levels. In the second stage of the analysis, non-parametric statistical tests and Tobit regressions are conducted for further investigation. According to the non-parametric statistical test, high capital stock, labor force, and energy usage do not affect the states’ productive efficiency. However, states with low carbon dioxide emissions have significantly higher eco-efficiency and eco-productivity levels. The Tobit regression results illustrate that nuclear power and renewable energy consumption significantly affect the states’ relative efficiencies.

Monitoring results of galvanic anodes in steel reinforced concrete over 20 years

Sacrificial anodes have been used in reinforced concrete structures for up to 20 years. This paper reviews the performance of the oldest set of anodes. It demonstrates, from results of current output and steel depolarisation levels, that the repair system is still functioning adequately overall but with signs of deterioration beyond 15 years. These prototype anodes designed for a 10-year life continue to deliver a reduced but significant current complying with currently accepted criteria. The current output of the anodes was shown to experience two events which caused a reduction in performance at 6 and 14 years likely to be related to a reduction in capillary porosity from the build-up of zinc corrosion products and the drop of the pH below that of saturated lithium hydroxide respectively. Nonetheless, the overall reduction in current was found to be adequately described by an anode aging model where the current delivered is halved over constant time intervals, enabling determination of an ‘aging-factor’. For these prototype anodes the ‘aging-factor’ was estimated to be around 7 years with a potential of extending this period if measures are taken to delay or eliminate the two current reduction events. Use of this anode aging model will enable better design of galvanic corrosion control systems and will allow the approximate current output of the system to be predicted at any particular time in its design life.

Application of centralised DEA in an automobile parts retail network in Taiwan

PurposeThe purpose of this study is to address three managerial issues in a retail network under a centralised regime, including resource allocation, expansion and downsizing. In particular, this study investigates how to optimise resource allocation across retailers and how to decide the optimal size of a retail network for an empirical case, where 27 retailers belong to an automobile parts supplier in Taiwan.Design/methodology/approachThe centralised data envelopment analysis (DEA) approach is used to optimise resource allocation among retailers and to seek the possibility of either an expansion and/or downsizing for a retail network.FindingsThe case company knows the best way to (re)allocate its available resources and (re)arrange the outcome goals for its own retailers. The 27 retailers are assigned specific targets for input and output adjustments to become Pareto or technically efficient units. Besides, an experiment is designed to measure how changing the number of retailers affects the group efficiency of the retail network. To keep the current aggregated output level of the retail network, the centralised supplier needs at least 24 retailers. However, the retail network with 37 retailers can achieve maximal group efficiency.Originality/valueCentralised resource allocation is an inherent feature of most supplier-retailer relationships, where the supplier is usually a central planner with the power to allocate available resources among its own retailers. To the best of our knowledge, this is the first study to assess retail performance under a centralised regime.

Growth Performance and Resource Use Efficiency in Cauliflower Production: A Micro level Study

The study gives insights on production performance of one of Bihar’s most important vegetable, the cauliflower. Compound annual growth in area, production and productivity of cauliflower were analyzed to investigate the expansion or shrinkage of the cauliflower in Samastipur district of Bihar as well as for the entire state. In this investigation, resource use efficiency of the respondent farmers were also calculated to know whether the farmers are operating technically, allocatively or cost effectively or not in production of cauliflower in the study area. Significant positive growth in area, production and productivity of cauliflower in Bihar was observed during the period of investigation. The growth in area, production and productivity of cauliflower exhibited almost similar trend in case of Samastipur district. The analysis of resource use efficiency of cauliflower production revealed that the mean level of technical, allocative and cost efficiencies of all the sample respondents were estimated at 0.67, 0.61 and 0.41 which implied that the cauliflower growers could decrease the current input by 33.00%, reduce the cost of production of cauliflower by 39.00% for which they would have to be more aware of relative input prices prevailing in the market, while deciding upon quantum of various inputs, and reduce the current cost of the produce by 59.00% to achieve a potential minimum cost of production relative to the efficient farmers given current level of output. In this way the efficiency scores suggest that there is a considerable scope for decreasing inputs and thereby reducing the cost of production in cauliflower cultivation in the study area. The study pointed out that the farmers in the study area were not properly aware about the package of practices of cultivation of cauliflower and under or over utilizing the resources of productivity causing gap in output of the crop. Proper farmer's field trials and awareness campaigns on improved practices and correct method of use of inputs need to be imparted which will ultimately benefit the producers.

Microbial Community Predicts Functional Stability of Microbial Fuel Cells.

Stability as evaluated by functional resistance and resilience is critical to the effective operation of environmental biotechnologies. To date, limited tools have been developed that allow operators of these technologies to predict functional responses to environmental and operational disturbances. In the present study, 17 Microbial Fuel Cells (MFCs) were exposed to a low pH perturbation. MFC power dropped 52.7 ± 35.8% during the low pH disturbance. Following the disturbance, 3 MFCs did not recover while 14 took 60.7 ± 58.3 h to recover to previous current output levels. Machine learning models based on genomic data inputs were developed and evaluated on their ability to predict resistance and resilience. Resistance and resilience levels corresponding to risk of deactivation could be classified with 70.47 ± 15.88% and 65.33 ± 19.71% accuracy, respectively. Models predicting resistance and resilience coefficient values projected postperturbation current drops within 6.7-15.8% and recovery times within 5.8-8.7% of observed values. Results suggest that abundances of specific genera are better predictors of resistance while overall microbial community structure more accurately predicts resilience. This approach can be used to assess operational risk and is a first step toward the further understanding and improvement of overall stability of environmental biotechnologies.

The collapse of Brazilian Social Security: Macroeconomic impacts of the increase of the minimum age of PEC nº 287/2016 reform

This paper presents a simulation of the economic impacts of the increase of the minimum age contained in the Proposal for Constitutional Amendment (PEC) nº 287/2016. For that, an overlapping generations (OLG) model with 57 generations was built, including the transition rule. The results suggest that not increasing the minimum age for retirement is a very bad choice for society. The fiscal situation becomes unsustainable, and the expansion of social security expenditures, in combination with the reduction of the labor supply, leads the country to a scenario of a sharp fall in consumption and output per capita. The simulation with the new minimum retirement age of PEC nº 287/2016 indicates that, although it is not the definitive solution to the Brazilian pension issue. The results of the model indicate that raising the minimum age avoids a very bad scenario, but does not seem to be even able to maintain the current level of output per capita. As a policy suggestion, although PEC nº 287/2016 has not even been voted, the recommendation is that it represents a minimum level for the next pension reform.

Assessing energy efficiency of Indian paper industry and influencing factors: A slack-based firm-level analysis

This study estimates efficiency in energy utilization at firm level and feasible energy saving target of the pulp and paper industry in India. For this analysis, data is extracted from prowess database for a panel of 67 major pulp and paper producing firms over the period of 2003–04 to 2013–14. We employed radial and non-radial variants of data envelopment analysis to assess the feasible energy saving target with the current level of output. Particularly, we employed slack-based method with variable returns to scale as a flexible and pragmatic approach to performance analysis. Results reveal no significant improvement in the energy efficiency level of the firms over the study period and show vast energy saving potential. Results of slack-based measure of energy efficiency show the feasible energy saving potential of 40%. Further, size of the firm, age and financial performance are important factors that influence energy efficiency. Therefore, this study recommends special attention to the paper industry while implementing energy efficiency programme. We also reviewed four major policies for industrial energy efficiency improvement initiated by Bureau of Energy Efficiency, India.

Multi-valued measures in DEA in the presence of undesirable outputs

Data envelopment analysis (DEA) evaluates the relative efficiency of a set of comparable decision making units (DMUs) with multiple performance measures (inputs and outputs). Classical DEA models rely on the assumption that each DMU can improve its performance by increasing its current output level and decreasing its current input levels. However, undesirable outputs (like wastes and pollutants) may often be produced together with desirable outputs in final products which have to be minimized. On the other hands, in some real-world situations, we may encounter some specific performance measures with more than one value which are measured by various standards. In this study, we referee such measures as multi-valued measures which only one of their values should be selected. For instance, unemployment rate is a multi-valued measure in economic applications since there are several definitions or standards to measure it. As a result, selecting a suitable value for a multi-valued measure is a challenging issue and is crucial for successful application of DEA. The aim of this study is to accommodate multi-valued measures in the presence of undesirable outputs. In doing so, we formulate two individual and summative selecting directional distance models and develop a pair of multiplier- and envelopment-based selecting approaches. Finally, we elaborate applicability of the proposed method using a real data on 183 NUTS 2 regions in 23 selected EU-28 countries.

Social Interactions and Modern Economic Growth

This paper offers a theoretical framework to understand the coevolution of social interactions and long-term economic growth. It begins by considering that most traditional societies did not have educational markets. Thus, access to the required knowledge for transiting to a modern economy had to be transmitted through social interactions, in particular, through the interaction between heterogeneous groups of people–i.e. distant interactions. Once immersed in a modern economy, the productive system should have increased the demand for knowledge, promoting more distant interactions. Simultaneously, the emergence of distant interactions should have affected the connectivity of society, reducing its heterogeneity, making cheaper posterior interactions but reducing their profitability. Moreover, social interactions competed and benefited from other nonmarket activities, child rearing specifically. The model arrives at four basic predictions. First, modern economic growth brings a more cohesive society. Second, modern economic growth brings long-term reductions in fertility with potential short-term increases. Third, initial barriers to social interactions could explain the timing of modern economic growth arrival. Fourth, the timing of modern economic growth arrival could explain current output levels. I exploit different data sources to offer evidence in support of these predictions.

Mission Impossible? Maintaining regional grain production level and recovering local groundwater table by cropping system adaptation across the North China Plain

Insufficient precipitation and continuous over-exploitation of groundwater for agricultural irrigation led to rapid drop of groundwater table in a large part of the North China Plain (NCP), the bread basket of China. It has become widely acknowledged that current practice of winter wheat-summer maize sequential cropping system (WM-S) in the NCP will have to come to an end as soon as possible. Great research efforts have been made at the local level via both field experiments and model simulations to construct groundwater neutral cropping systems but virtually all such constructs show a substantial penalty on total output per unit of land per year. In this research, we propose a strategy to meet the double challenge of maintaining regional grain production level and recovering local groundwater table: 1) Widely adopt winter fallow and early-sowing summer maize monocropping (E-M) in water scarce part of the region to enable groundwater recovery; 2) replace WM-S by wheat-maize relay intercropping system (WM-R) in the water richer part of the NCP to increase grain production so as to compensate yield losses in the water scarce part of the region. Our simulations using DSSAT 4.6 at the site level show that both yield and water productivity of E-M are 33.7% and 41.8% higher than those of existing summer maize, with less than 20% of increase in water requirement. In comparison with spring maize, E-M requires 62.4% less irrigation water, with a yield penalty of only 4.52%. At the regional scale, the simulations targeting at maximizing groundwater saving in water scarce area subject to maintaining the current level of regional total output indicate that about 20.45% of the wheat planting area can be put on fallow in winter, most of which is located in the driest regions of the NCP. This can result in a large amount of groundwater saving at 5.62×109m3 and a substitution of wheat by maize at 24.3% of the total wheat output. These findings provide new rooms for the relevant policy makers and stakeholders to address the urgent groundwater recovering issues in the northern NCP without compromising the level of food grain production of the region.

Enhancing Productivity and Resource Conservation by Eliminating Inefficiency of Thai Rice Farmers: A Zero Inefficiency Stochastic Frontier Approach

The study first identified fully efficient farmers and then estimated technical efficiency of inefficient farmers, identifying their determinants by applying a Zero Inefficiency Stochastic Frontier Model (ZISFM) on a sample of 300 rice farmers from central-northern Thailand. Next, the study developed scenarios of potential production increase and resource conservation if technical inefficiency was eliminated. Results revealed that 13% of the sampled farmers were fully efficient, thereby justifying the use of our approach. The estimated mean technical efficiency was 91%, implying that rice production can be increased by 9%, by reallocating resources. Land and labor were the major productivity drivers. Education significantly improved technical efficiency. Farmers who transplanted seedlings were relatively technically efficient as compared to those who practised manual and/or mechanical direct seeding methods. Elimination of technical inefficiency could increase output by 8.64% per ha, or generate 5.7–6.4 million tons of additional rice output for Thailand each year. Similarly, elimination of technical inefficiency would potentially conserve 19.44% person-days of labor, 11.95% land area, 11.46% material inputs and 8.67% mechanical power services for every ton of rice produced. This translates into conservation of 2.9–3.0 million person-days of labor, 3.7–4.5 thousand km2 of land, 10.0–14.5 billion baht of material input and 7.6–12.8 billion baht of mechanical power costs to produce current level of rice output in Thailand each year. Policy implications include investment into educating farmers, and improving technical knowledge of seeding technology, to boost rice production and conserve scarce resources in Thailand.

Variation in Music Player Listening Level as a Function of Campus Location.

There has been significant discussion in the literature regarding music player use by adolescents and young adults, including whether device use is driving an increase in hearing loss in these populations. While many studies report relatively safe preferred listening levels, some studies with college student participants have reported listening habits that may put individuals at risk for noise-induced hearing loss (NIHL) if those listening habits continue over the long term. The goal of the current investigation was to extend listening level data collection sites from urban city settings studied by others to a more rural campus setting. This was a prospective study. Participants were 138 students on the University of Florida campus (94 males, 44 females), 18 years or older (mean = 21 years; range: 18-33 years). In this investigation, the current output level (listening level) was measured from personal listening devices used by students as they passed by a recruiting table located in one of three areas of the University of Florida campus. One location was in an open-air campus square; the other two locations were outside the campus recreation building ("gym") and outside the undergraduate library, with participants recruited as they exited the gym or library buildings. After providing written informed consent, participants completed a survey that included questions about demographics and typical listening habits (hours per day, days per week). The output level on their device was then measured using a "Jolene" mannequin. Average listening levels for participants at the three locations were as follows: gym: 85.9 ± 1.4 dBA; campus square: 83.3 ± 2.0 dBA; library: 76.9 ± 1.3 dBA. After adjusting to free-field equivalent level, average listening levels were gym: 79.7 ± 1.4 dBA; campus square: 76.9 ± 2.1 dBA; library: 70.4 ± 1.4 dBA. There were no statistically significant differences between male and female listeners, and there were no reliable differences as a function of race. After accounting for daily and weekly use patterns, 5% were deemed at risk based on the criteria put forward by the Occupational Safety and Health Administration, and 9% were deemed at risk based on the guidance provided by the National Institute for Occupational Safety and Health. Some 5-10% of the participants were deemed at risk; this finding is consistent with other studies using similar methods. It is possible that the same listeners would have selected different listening levels in other noise backgrounds, however. This unknown variable makes it difficult to estimate risk with a single listening level measurement.

Impact of attending training course variable on technical efficiency of oyster mushroom production in Quang Tri province

This study based on cross sectional data of 94 oyster mushroom farms in Quang Tri province to measure their technical efficiencies at farm level and identify the impact of attending training course variable on it by using two stage Bootstrapped Data Envelopment Analysis. The empirical results confirm that attending training course is an important factor impact on technical efficiency of oyster mushroom farm in study area. The farms used to attend a relevant training course were more efficient than farms did not. Moreover, it also shows that gender of the farmer, gender of the farmer, source of irrigation water, duration of oyster mushroom production and number of oyster mushroom crops cultivated on this current farm also had significantly relationship with technical efficiency. Comparing to the best practice farms in this sample, the oyster mushroom farms in study area should use fewer inputs to produce the current level of output to be efficient. Especially, farmers should attend the relevant training course and local authority should organize more training course to improve current level of efficiency of those farms. Moreover, the impacts of gender of the farmer, source of irrigation water, duration of production and number of crops should also be included in the content of those up-coming training courses.

MinION™ nanopore sequencing of environmental metagenomes: a synthetic approach.

Background: Environmental metagenomic analysis is typically accomplished by assigning taxonomy and/or function from whole genome sequencing or 16S amplicon sequences. Both of these approaches are limited, however, by read length, among other technical and biological factors. A nanopore-based sequencing platform, MinION™, produces reads that are ≥1 × 104 bp in length, potentially providing for more precise assignment, thereby alleviating some of the limitations inherent in determining metagenome composition from short reads. We tested the ability of sequence data produced by MinION (R7.3 flow cells) to correctly assign taxonomy in single bacterial species runs and in three types of low-complexity synthetic communities: a mixture of DNA using equal mass from four species, a community with one relatively rare (1%) and three abundant (33% each) components, and a mixture of genomic DNA from 20 bacterial strains of staggered representation. Taxonomic composition of the low-complexity communities was assessed by analyzing the MinION sequence data with three different bioinformatic approaches: Kraken, MG-RAST, and One Codex. Results: Long read sequences generated from libraries prepared from single strains using the version 5 kit and chemistry, run on the original MinION device, yielded as few as 224 to as many as 3497 bidirectional high-quality (2D) reads with an average overall study length of 6000 bp. For the single-strain analyses, assignment of reads to the correct genus by different methods ranged from 53.1% to 99.5%, assignment to the correct species ranged from 23.9% to 99.5%, and the majority of misassigned reads were to closely related organisms. A synthetic metagenome sequenced with the same setup yielded 714 high quality 2D reads of approximately 5500 bp that were up to 98% correctly assigned to the species level. Synthetic metagenome MinION libraries generated using version 6 kit and chemistry yielded from 899 to 3497 2D reads with lengths averaging 5700 bp with up to 98% assignment accuracy at the species level. The observed community proportions for “equal” and “rare” synthetic libraries were close to the known proportions, deviating from 0.1% to 10% across all tests. For a 20-species mock community with staggered contributions, a sequencing run detected all but 3 species (each included at <0.05% of DNA in the total mixture), 91% of reads were assigned to the correct species, 93% of reads were assigned to the correct genus, and >99% of reads were assigned to the correct family. Conclusions: At the current level of output and sequence quality (just under 4 × 103 2D reads for a synthetic metagenome), MinION sequencing followed by Kraken or One Codex analysis has the potential to provide rapid and accurate metagenomic analysis where the consortium is comprised of a limited number of taxa. Important considerations noted in this study included: high sensitivity of the MinION platform to the quality of input DNA, high variability of sequencing results across libraries and flow cells, and relatively small numbers of 2D reads per analysis limit. Together, these limited detection of very rare components of the microbial consortia, and would likely limit the utility of MinION for the sequencing of high-complexity metagenomic communities where thousands of taxa are expected. Furthermore, the limitations of the currently available data analysis tools suggest there is considerable room for improvement in the analytical approaches for the characterization of microbial communities using long reads. Nevertheless, the fact that the accurate taxonomic assignment of high-quality reads generated by MinION is approaching 99.5% and, in most cases, the inferred community structure mirrors the known proportions of a synthetic mixture warrants further exploration of practical application to environmental metagenomics as the platform continues to develop and improve. With further improvement in sequence throughput and error rate reduction, this platform shows great promise for precise real-time analysis of the composition and structure of more complex microbial communities.

Allocation of emission permits using DEA-game-theoretic model

This paper discusses schemes for allocation of emission permits (AEP) among a group of manufacturing companies, aiming at controlling the total emissions of the group while maintaining efficient production. Game theory and data envelopment analysis have been integrated in different ways for constructing two variants of AEP models. The first situation is where all members in the group are cooperative and a reasonable permit allocation scheme maximizes the overall payoff of the group. The second situation is where group members are non-cooperative and each member makes every effort to selfishly maximize its own payoff. The decision maker allocates permits to firms according to their non-cooperative game equilibrium scores. Proposed models are applied to study a group of paper mills to analyze their payoffs. The results show that the methods proposed in this work can provide reasonable allocation results for all firms. In addition, although our allocation methods adopt the principle of maximizing the payoff of the firm, the efficiency of each firm from current output and input levels is still a factor that determines the permit allocation.

An optimisation model for spatially allocating commodity production and forecasting its impact on regional productivity

The prototype Spatial Optimizer 1.0 program for allocation of commodity production is demonstrated to show its potential as a decision support aid for policy making. The case study involves a set of agricultural commodities in south eastern Australia and the program uses estimates of each grid cell’s soil suitability for each of the eight crops, both in the year 2000 and in the year 2050, by which time soil characteristics are expected to have been affected by environmental change. We first predict how much total regional production will result from a judicious re-location of commodity types, both under conditions of complete flexibility and when constrained by more realistic, upper and lower limits on production, and we compare such predictions with current production levels. We also estimate potential total regional revenue, both in the short term when current prices are assumed to remain static and in the long term when prices are assumed to change according to how much of each commodity is produced compared to its current output level, and we compare these results with current agricultural revenue. Our long-term estimates are based on year 2000 soil-suitability values and then on year 2050 soil-suitability values in order to gauge the probable impacts of environmental change. Finally, we run the 2050 simulation twice more, with one of the recommended, dominant crops removed in each instance. This generates maps of some localised concentrations of other commodities that will become necessary in the future if maximum revenue is to be retained after one of the more lucrative crops is discontinued.