Measurement of market power for the environmentally regulated Korean iron and steel manufacturing industry

For industries in which where market prices of certain inputs are not available, measuring the degree of market power by using the markup over the marginal market cost may be inappropriate. With regard to the Korean iron and steel manufacturing industry, which is subject to environmental regulations, the calculation of the price of abatement capital is hindered by a lack of relevant data. To increase the reliability of market power markups, this paper estimates the restricted cost function in which abatement capital is assumed to be quasi-fixed at an optimal level and the supply relation. The degree of market power for the industry, measured as the ratio of the estimated market power markup to the supply price, was estimated to be 0.54 on average between 1982 and 2001. The results indicate that ignoring environmental regulations can overstate the degree of market power by approximately 12%.     

Demonstrating Floodplain Uncertainty Using Flood Probability Maps1

Abstract: The U.S. Federal Emergency Management Agency (FEMA) flood maps depict the 100‐year recurrence interval floodplain boundary as a single line. However, because of natural variability and model uncertainty, no floodplain extents can be accurately defined by a single line. This article presents a new approach to floodplain mapping that takes advantage of accepted methodologies in hydrologic and hydraulic analysis while including the effects of uncertainty. In this approach, the extents of computed floodplain boundaries are defined as a continuous map of flood probabilities, rather than as a single line. Engineers and planners can use these flood probability maps for viewing the uncertainty of a floodplain boundary at any recurrence interval. Such a flood probability map is a useful tool for visualizing the uncertainty of a floodplain boundary and represents greater honesty in engineering technologies that are used for flood mapping. While institutional barriers may prevent adoption of such definitions for use in graduated flood insurance rates (as most other insurance industries use to account for relative risks), the methods open the door technically to such a reality.     

Forecasting the number of soil samples required to reduce remediation cost uncertainty

Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of volume of polluted soil and overall cost of remediation. This technique provides a flexible tool for decision makers to define the amount of investigation worth conducting from an environmental and financial perspective. The technique is based on nonlinear geostatistics (conditional simulations) to estimate the volume of soil that requires remediation and excavation and on a function allowing estimation of the total cost of remediation (including investigations). The geostatistical estimation accounts for support effect, information effect, and sampling errors. The cost calculation includes mainly investigation, excavation, remediation, and transportation. The application of the technique on a former smelting work site (lead pollution) demonstrates how the tool can be used. In this example, the forecasted volumetric uncertainty decreases rapidly for a relatively small number of samples (20-50) and then reaches a plateau (after 100 samples). The uncertainty related to the total remediation cost decreases while the expected total cost increases. Based on these forecasts, we show how a risk-prone decision maker would probably decide to take 50 additional samples while a risk-averse decision maker would take 100 samples.     

Assessing and communicating data quality in policy-relevant research

The quality of scientific information in policy-relevant fields of research is difficult to assess, and quality control in these important areas is correspondingly difficult to maintain. Frequently there are insufficient high-quality measurements for the presentation of the statistical uncertainty in the numerical estimates that are crucial to policy decisions. We propose and develop a grading system for numerical estimates that can deal with the full range of data quality—from statistically valid estimates to informed guesses. By analyzing the underlying quality of numerical estimates, summarized as spread and grade, we are able to provide simple rules whereby input data can be coded for quality, and these codings carried through arithmetical calculations for assessing the quality of model results. For this we use the NUSAP (numeral, unit, spread, assessment, pedigree) notational system. It allows the more quantitative and the more qualitative aspects of data uncertainty to be managed separately. By way of example, we apply the system to an ecosystem valuation study that used several different models and data of widely varying quality to arrive at a single estimate of the economic value of wetlands. The NUSAP approach illustrates the major sources of uncertainty in this study and can guide new research aimed at the improvement of the quality of outputs and the efficiency of the procedures.     

An uncertainty based multi-criteria ranking system for open pit mining cut-off grade strategy selection

Cut-off grade strategy (COGS) is a concept that directly influences the financial, technical, economical, and environmental issues in relation to the exploitation of a mineral resource. Despite the simple definition of cut-off grade, the COGS problem is one of the complex and complicated problems in the mine planning process. From the optimization point of view, the COGS with an objective of maximizing the present value of future cash flows is a non-linear and a non-convex problem that even in its deterministic form can be solved using approximate optimization methods. This optimization problem will also be more complex and complicated under uncertainty conditions. This paper proposes an uncertainty based multi-criteria ranking system to investigate the problem of COGS selection considering metal price and geological uncertainties. The proposed system aims at selection of the best COGS among technically feasible alternative COGSs under uncertainty circumstances. Our developed system is based on integrating metal price and geological uncertainties as well as operating flexibility to close the mine early. We incorporate this operating flexibility into the proposed system using a Monte Carlo based real options (RO) valuation model. For this purpose, in addition to the expected value, other risk criteria are considered to rank the alternatives. These risk criteria include abilities of strategies in producing extra profits, minimizing losses, and achieving the predefined goals of the production. In this study, the technically possible COGSs are generated using the Lane comprehensive algorithm. To demonstrate the effectiveness of the proposed system, we utilize data of an Iranian gold mine. Results show that the proposed system outperforms conventional methods in the sense that it shows significantly lower average mis-ranking than the other methods and also selects a strategy with a higher value. The sensitivity analysis of the proposed system relative to the gold price shows that the system is highly dependent on the parameters of the stochastic process used to model the evolution of the metal price. Therefore, special consideration should be given in estimating stochastic process parameters.     

Input–output analysis in an oil-rich economy: The case of Azerbaijan

This paper aims to provide empirical research to identify the linkages between final demand–total output, final demand–total supply, value-added ratios and prices, and also to analyze total factor productivity growth using input–output framework for 25 sectors. Studying the input–output tables for 2001 and 2006, the research estimates impact and response multipliers of non-oil sectors, as well as non-oil trading sectors. The results are important from the view of development of non-oil trading sectors and diversification of the economy in order to avoid the “resource curse”.     

Ongoing ascent to the helium production plateau—Insights from system dynamics

Recent, pre-downturn, disturbance in the global helium market can be traced to the tight supply–demand position, which characterizes today’s changing helium supply structure. A detailed System Dynamics model provides fresh insight into the helium question and suggests a production path that is closely associated with future natural gas production. Venting of un-extracted helium to the atmosphere remains a central issue. The model indicates that improving resource exploitation strategies might extend a production plateau that emerges in the 2030s. Substitution will result in more helium being vented. To mitigate this, the industry needs to ensure security of supply (particularly after the Bureau of Land Management reserve is sold).     

Quantifying sources of climate uncertainty to inform risk analysis for climate change decision-making

Quantitative estimates of future climate change and its various impacts are often based on complex climate models which incorporate a number of physical processes. As these models continue to become more sophisticated, it is commonly assumed that the latest generation of climate models will provide us with better estimates of climate change. Here, we quantify the uncertainty in future climate change projections using two multi-model ensembles of climate model simulations and divide it into different components: internal, scenario and model. The contributions of these sources of uncertainty changes as a function of variable, temporal and spatial scale and especially lead time in the future. In the new models, uncertainty intervals for each of the components have increased. For temperature, importance of scenario uncertainty is the largest over low latitudes and increases nonlinearly after the mid-century. It has a small importance for precipitation simulations on all time scales, which hampers estimating the effect which any mitigation efforts might have. In line with current state-of-the-art adaptation approaches, we argue that despite these uncertainties climate models can provide useful information to support adaptation decision-making. Moreover, adaptation decisions should not be postponed in the hope that future improved scientific understanding will result in more accurate predictions of future climate change. Such simulations might not become available. On the contrary, while planning adaptation initiatives, a rational framework for decision-making under uncertainty should be employed. We suggest that there is an urgent need for continued development and use of improved risk analysis methods for climate change adaptation.     

Approximating pollution abatement costs via alternative specifications of a multi-output production technology: a case of the US electric utility industry

Using a panel of observations from the US electric utility industry, we analyze the output set frontiers that are produced by modeling the technology with different functional forms. Our specifications rely on a series of mapping regimes, based on the paths in which the outputs are scaled toward the frontier, and their outcomes are assessed against the general axiomatic framework of the production model. We demonstrate that different parameterization methodologies produce different empirical analogues of the output set and, as a consequence, can generate rather different estimates of the shadow prices of socially undesirable outputs, or 'bads,' as well. We benchmark our results by assessing the shadow price estimates, often interpreted as the opportunity cost of pollution reduction, against the market prices of pollution permits. The outcomes of our analysis suggest that the estimate of the output set boundary varies with the choice of a parameterization technique, which provides a possible explanation for the sizeable differences between the shadow prices of bads reported in the literature. We also demonstrate that among a variety of existing parametric methodologies no single technique is superior to all others.     

Resource exploitation and cross-region growth trajectories: nonparametric estimates for Chile

As a sector of primary concern for national development strategies, mining keeps stimulating an intensive debate in Chile, regarding its role for long-term growth. Partly drawn on theoretical contributions to growth and mineral resource accounting, this analysis assesses patterns of economic growth across Chilean regions. The theoretical and methodological rationale for focusing on weak sustainability, by testing convergence across regions in a distribution dynamics perspective, is first discussed. This is followed by a brief review of policy issues and previous empirical findings of concern to Chile's mining and regional growth. Panel data over the period 1960-2001 are analysed, with growth measured in terms of both income per capita as such, and sustainable measures of this variable. Kernel density and quantile regression estimates indicate persistent bimodal (if not possibly trimodal) distribution of nationally standardised regional incomes per capita, whereby conditions for cross-region convergence are matched only within the inner range of this distribution.     

Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.     

Natural resources dynamics: Exhaustible and renewable resources,and the rate of technical substitution

In this paper we study both exhaustible and renewable resources in an endogenous growth model. In particular, we consider the hypotheses in which the rate of technical substitution (RTS) between those two inputs is or is not equal to one. Moreover, we depart from a basic theoretical framework to account for the negative externality constituted by waste accumulation. Finally, a comparative analysis is made between Pigouvian tax and waste recycling, as an environmental policy to correct market failure represented by refuse accumulation.     

Ecological Impact Assessment in Data-Poor Systems: A Case Study on Metapopulation Persistence

Legislation on the protection of biodiversity (e.g., European Union Habitat and Bird Directives) increasingly requires ecological impact assessment of human activities. However, knowledge and understanding of relevant ecological processes and species responses to different types of impact are often incomplete. In this paper we demonstrate with a case study how impact assessment can be carried out for situations where data are scarce but some expert knowledge is available. The case study involves two amphibian species, the great crested newt (Triturus cristatus) and the natterjack toad (Bufo calamita) in the nature reserve the Meinweg in the Netherlands, for which plans are being developed to reopen an old railway track called the Iron Rhine. We assess the effects of this railway track and its proposed alternatives (scenarios) on the metapopulation extinction time and the occupancy times of the patches for both species using a discrete-time stochastic metapopulation model. We quantify the model parameters using expert knowledge and extrapolated data. Because of our uncertainty about these parameter values, we perform a Monte Carlo uncertainty analysis. This yields an estimate of the probability distribution of the model predictions and insight into the contribution of each distinguished source of uncertainty to this probability distribution. We show that with a simple metapopulation model and an extensive uncertainty analysis it is possible to detect the least harmful scenario. The ranking of the different scenarios is consistent. Thus, uncertainty analysis can enhance the role of ecological impact assessment in decision making by making explicit to what extent incomplete knowledge affects predictions.     

不确定条件下矿产资源的最优开采

通过一个连续时间的随机动态规划模型,探讨了市场需求、资源存量的不确定性以及勘探活动对矿产资源价格和开采速度的影响,并给出了随机条件下Hotelling法则的表达形式.模型结果显示:不确定性对资源价格变化速度的影响主要取决于开采成本,如果相对于资源存量来说开采成本是不变的或者开采的规模成本是不变的,则不确定性对资源价格的变化速度没有影响.反之,如果开采的规模成本是递增的,则不确定性会加速资源价格的变化.此外,不确定性的存在将加快资源的开采速度.至于勘探活动,它一方面降低了地质条件的不确定性,另一方面增加了资源的存量,所以勘探活动降低了资源价格和开采速度的变化率,减少了不确定性对资源开采的影响.     

Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC

Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin‐wide water budgets. Each METRIC evapotranspiration (ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first‐order ET estimates that are similar to time intensive manual efforts with less time investment.     

Uncertainty Models for Estimates of Physical Characteristics of River Segments Over Large Areas

Spatially comprehensive estimates of the physical characteristics of river segments over large areas are required in many large‐scale analyses of river systems and for the management of multiple basins. Remote sensing and modeling are often used to estimate river characteristics over large areas, but the uncertainties associated with these estimates and their dependence on the physical characteristics of the segments and their catchments are seldom quantified. Using test data with varying degrees of independence, we derived analytical models of the uncertainty associated with estimates of upstream catchment area (CA), segment slope, and mean annual discharge for all river segments of a digital representation of the hydrographic network of France. Although there were strong relationships between our test data and estimates at the scale of France, there were also large relative local uncertainties, which varied with the physical characteristics of the segments and their catchments. Discharge and CA were relatively uncertain where discharge was low and catchments were small. Discharge uncertainty also increased in catchments with large rainfall events and low minimum temperature. The uncertainty of segment slope was strongly related to segment length. Our uncertainty models were consistent across large regions of France, suggesting some degree of generality. Their analytical formulation should facilitate their use in large‐scale ecological studies and simulation models.     

Corporate Social Responsibility and development in Africa: Redefining the roles and responsibilities of public and private actors in the mining sector

This paper revisits the role of Corporate Social Responsibility (CSR), suggesting the usefulness of adopting a holistic and historical perspective. It underlines the importance of taking into account the evolving regulatory frameworks within which mining activities take place in order to consider the changing roles played by the various actors involved, whether multilateral, national or local, public or private. In this broad context it then becomes possible to revisit issues of legitimacy, responsibility, and accountability, which CSR strategies aim to address. The article draws attention to the shortcomings of strategies, whether bilateral or multilateral, public or private, which, in an attempt to respond to problems of risk and legitimacy faced by mining companies, have put forward measures in the name of CSR that do not address the origins that give rise to such problems and, in so doing, tend to mask the very nature of the difficulties at hand. The analysis leads to quite different conclusions as compared to those that result from the adoption of a shorter term and investment-led perspective.     

A heuristic approach to stochastic cutoff grade optimization for open pit mining complexes with multiple processing streams

Cutoff grade specifies the available supply of metallic ore from an open pit mine to the multiple processing streams of an open pit mining complex. An optimal cutoff grade strategy maximizes the net present value (NPV) of an open pit mining operation subject to the mining, processing, and marketing/refining capacity constraints. Even though, the quantities of material flowing from the mine to the market are influenced by the expected variation in the available metal content or inherent uncertainty in the supply of ore, the majority of cutoff grade optimization models not only disregard this aspect and may lead to unrealistic cash flows, but also they are limited in application to an open pit mining operation with single processing facility. The model proposed herein determines the optimal cutoff grade policy based on a stochastic framework that accounts for uncertainty in supply of ore to the multiple ore processing streams. An application on a large-scale open pit mining operation develops a unique cutoff grade policy along with a portfolio of mining, processing, and marketing/refining rates. Owing to the geological uncertainty, the approach addresses risk by showing a difference of 14% between the minimum and maximum production rates, cash flows and NPV.     

The use of guide to the expression of uncertainty in measurement for uncertainty management in National Greenhouse Gas Inventories

Research basis for annual greenhouse gases (GHG) emissions assessment is national and branch statistics data. Quality and confidence of greenhouse gases inventory through assessment methodologies, preparation procedures and processing of data is confirm. Request at National Greenhouse Gases Inventory which contain assessment and analyses uncertainty elements on Intergovernmental Panel on Climate Change-GPG 2000 and IPCC 2006 are determine. Main approaches for uncertainty assessment in environmental and metrological guides are considered. The algorithm of expressing uncertainty and scheme for estimating uncertainty according to GPG 2000 and IPCC 2006 are proposed. The use approaches GUM 1993 for uncertainty assessment for greenhouse gases inventory are proposed.     

Financial and economic factors affecting the lignite prices in Turkey: An analysis of Soma and Can lignites

Using a simultaneous equation econometric model based on yearly data between 1997 and 2006 for the Aegean Lignite Enterprise this study examines factors that affect the lignite price in Turkey. The Aegean Lignite Enterprise produces and sells the lignite of Soma and Can and their data reflect the general Turkish situation. The results suggest that (1) lignite prices sold to the industry increase (decrease) depending on the decrease (increase) in the industrial production and (2) total electricity production and electricity price are the most important factors that potential investors and related persons have to take into consideration for the pricing of lignite in the thermal power plant market.