An uncertain energy planning model under carbon taxes

In this study, an interval fuzzy mixed-integer energy planning model (IFMI-EPM) is developed under considering the carbon tax policy. The developed IFMIEPM incorporates techniques of interval-parameter programming, fuzzy planning and mixed-integer programming within a general energy planning model. The IFMIEPM can not only be used for quantitatively analyzing a variety of policy scenarios that are associated with different levels of carbon tax policy, but also tackle uncertainties expressed as discrete intervals and fuzzy sets in energy and environment systems. Considering low, medium and high carbon tax rates, the model is applied to an ideal energy and environment system. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired carbon tax policy.     

River water quality management model using genetic algorithm

Conventional mathematical programming methods, such as linear programming, non linear programming, dynamic programming and integer programming have been used to solve the cost optimization problem for regional wastewater treatment systems. In this study, a river water quality management model was developed through the integration of a genetic algorithm (GA). This model was applied to a river system contaminated by three determined discharge sources to achieve the water quality goals and wastewater treatment cost optimization in the river basin. The genetic algorithm solution, described the treatment plant efficiency, such that the cost of wastewater treatment for the entire river basin is minimized while the water quality constraints in each reach are satisfied. This study showed that genetic algorithm can be applied for river water quality modeling studies as an alternative to the present methods.     

An inexact-stochastic with recourse model for developing regional economic-ecological sustainability under uncertainty

Effective planning of resources management is important for facilitating socio-economic development and eco-environmental sustainability. Such a planning effort is complicated with a variety of uncertain, dynamic and nonlinear factors as well as their interactions. In this study, an inexact-stochastic quadratic programming with recourse (ISQP-R) method is developed for reflecting dynamics of system uncertainties based on a complete set of scenarios as well as tackling nonlinearities in the objective function to reflect the effects of marginal utility on system benefits and costs. Moreover, since penalties are exercised with recourse against any infeasibility, the ISQP-R can support the analysis of various policy scenarios that are associated with different levels of economic consequences when the promised targets are violated. The developed method is applied to a case study of planning resources management and developing regional ecological sustainability. The results have been generated and are helpful for decision makers in not only identifying desired resources-allocation strategies but also gaining insight into the tradeoff between economic objective and eco-environment violation risk.     

Environmental-economic models for total emission control of regional environmental pollution — input-output approach

This paper deals with a Leontief-type dynamic input-output analysis for total emission control of pollution. A dynamic input-output model can be described as a linear programming problem. In this paper an objective function to be minimized is chosen as the sum of pollutants emitted in a regional area throughout the planning period. It is assumed that consumption grows with a constant growth rate throughout the planning period. With this model, we can tell how to assign the elimination rate of pollutants to each production sector, under the constraint that the total emission standard is satisfied in each term of the planning period.     

Planning for alkali land reclamation under rainfall uncertainty

A chance-constrained linear programming model with rainfall as stochastic input has been developed to plan optimal land and water use in alkali soils under reclamation. The model is based on the water balance of a typical alkali catchment in the command area of the Western Jamuna Canal in Haryana (India). The zero-order decision rule has been used to obtain the deterministic equivalent of chance constraint and optimal solutions have been obtained for four levels of rainfall probability. Available water supplies at low rainfall probabilities were found to be inadequate to sustain the high cost reclamation technology. Exploitation of the full potential of agricultural land calls for the augmentation of available water supplies.     

A model for the analysis of seasonal aspects of water quality control

A multiseasonal mathematical programming model for the analysis of water quality control within a river basin is presented. Phenomena of interseasonal variation in flow intensity and the river's pollutant assimilative capacity are introduced into the model by defining several seasons and their characterizations by seasonal parameters. An application of the model to a typical case situation is reported. The results suggest that seasonal adjustments in the treatment levels of wastewater treatment plants involve a considerable saving potential in comparison with a situation in which treatment levels are rigidly determined for the entire year.     

Identification of optimal strategies for improving eco-resilience to floods in ecologically vulnerable regions of a wetland

In this study, a mixed integer fuzzy interval-stochastic programming model was developed for supporting the improvement of eco-resilience to floods in wetlands. This method allows uncertainties that are associated with eco-resilience improvement and can be presented as both probability distributions and interval values to be incorporated within a general modeling framework. Also, capacity-expansion plans of eco-resilience can be addressed through introducing binary variables. Moreover, penalties due to ecological damages which are associated with the violation of predefined targets can be effectively incorporated within the modeling and decision process. Thus, complexities associated with flood resistance and eco-resilience planning in wetlands can be systematically reflected, highly enhancing robustness of the modeling process. The developed method was then applied to a case of eco-resilience enhancement planning in three ecologically vulnerable regions of a wetland. Interval solutions under different river flow levels and different ecological damages were generated. They could be used for generating decision alternatives and thus help decision makers identify desired eco-resilience schemes to resist floods without causing too much damages. The application indicates that the model is helpful for supporting: (a) adjustment or justification of allocation patterns of ecological flood-resisting capacities, (b) formulation of local policies regarding eco-resilience enhancement options and policy interventions, and (c) analysis of interactions among multiple administrative targets within a wetland.     

Software for prioritizing conservation actions based on probabilistic information

Marxan is the most common decision-support tool used to inform the design of protected-area systems. The original version of Marxan does not consider risk and uncertainty associated with threatening processes affecting protected areas, including uncertainty about the location and condition of species’ populations and habitats now and in the future. We described and examined the functionality of a modified version of Marxan, Marxan with Probability. This software explicitly considers 4 types of uncertainty: probability that a feature exists in a particular place (estimated based on species distribution models or spatially explicit population models); probability that features in a site will be lost in the future due to a threatening process, such as climate change, natural catastrophes, and uncontrolled human interventions; probability that a feature will exist in the future due to natural successional processes, such as a fire or flood; and probability the feature exists but has been degraded by threatening processes, such as overfishing or pollution, and thus cannot contribute to conservation goals. We summarized the results of 5 studies that illustrate how each type of uncertainty can be used to inform protected area design. If there were uncertainty in species or habitat distribution, users could maximize the chance that these features were represented by including uncertainty using Marxan with Probability. Similarly, if threatening processes were considered, users minimized the chance that species or habitats were lost or degraded by using Marxan with Probability. Marxan with Probability opens up substantial new avenues for systematic conservation planning research and application by agencies.     

Global Warming, Human Population Pressure, and Viability of the World's Smallest Butterfly

Abstract:  The effects of climate change and habitat destruction and their interaction are likely to be the greatest challenge to animal and plant conservation in the twenty-first century. We used the world's smallest butterfly, the Sinai baton blue ( Pseudophilotes sinaicus ), as an exemplar of how global warming and human population pressures may act together to cause species extinctions. We mapped the entire global range of this butterfly and obtained extensive data on the intensity of livestock grazing. As with an increasing number of species, it is confined to a network of small habitat patches and is threatened both by indirect human-induced factors (global warming) and by the direct activities of humans (in this case, livestock grazing and collection of medicinal plants). In the absence of global warming, grazing, and plant collection, our model suggested that the butterfly will persist for at least 200 years. Above a threshold intensity of global warming, the chance of extinction accelerated rapidly, implying that there may be an annual average temperature, specific to each endangered species, above which extinction becomes very much more likely. By contrast, there was no such threshold of grazing pressure—the chance of extinction increased steadily with increasing grazing. The impact of grazing, however, decreased with higher levels of year-to-year variation in habitat quality. The effect of global warming did not depend on the future level of grazing, suggesting that the impacts of global warming and grazing are additive. If the areas of habitat patches individually fall below certain prescribed levels, the butterfly is likely to go extinct. Two patches were very important for persistence: if either were lost the species would probably go extinct. Our results have implications for the conservation management of all species whose habitats are at risk because of the direct activities of humans and in the longer term because of climate change.     

An integrated optimization and simulation approach for air pollution control under uncertainty in open-pit metal mine

Air Pollution Control model is developed for open-pit metal mines. Model will aid decision makers to select a cost-effective solution. Open-pit metal mines contribute toward air pollution and without effective control techniques manifests the risk of violation of environmental guidelines. This paper establishes a stochastic approach to conceptualize the air pollution control model to attain a sustainable solution. The model is formulated for decision makers to select the least costly treatment method using linear programming with a defined objective function and multi-constraints. Furthermore, an integrated fuzzy based risk assessment approach is applied to examine uncertainties and evaluate an ambient air quality systematically. The applicability of the optimized model is explored through an open-pit metal mine case study, in North America. This method also incorporates the meteorological data as input to accommodate the local conditions. The uncertainties in the inputs, and predicted concentration are accomplished by probabilistic analysis using Monte Carlo simulation method. The output results are obtained to select the cost-effective pollution control technologies for PM_2.5, PM₁₀, NO_x, SO₂ and greenhouse gases. The risk level is divided into three types (loose, medium and strict) using a triangular fuzzy membership approach based on different environmental guidelines. Fuzzy logic is then used to identify environmental risk through stochastic simulated cumulative distribution functions of pollutant concentration. Thus, an integrated modeling approach can be used as a decision tool for decision makers to select the cost-effective technology to control air pollution.     

An interval joint-probabilistic programming method for solid waste management: a case study for the city of Tianjin, China

Currently, environmental protection and resources conservation continue to be challenges faced by solid-waste managers in China. These challenges are being further compounded by rapid socioeconomic devel- opment and population growth associated with increased waste generation rates and decreased waste disposal capacities. In response to these challenges, an interval joint-probabilistic mixed-integer programming （IJMP） method is developed for supporting long-term planning of waste management activities in the city of Tianjin, which is one of the largest municipalities in the northern part of China. In the IJMP, joint probabilistic constraints are introduced into an interval-parameter mixed-integer programming framework, such that uncertainties presented in terms of interval values and random variables can be reflected. Moreover, a number of violation levels for the waste-management-capacity constraints are examined, which can facilitate in-depth analyses of tradeoffs among economic objective and system-failure risk. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment of the city＇s existing waste-management practices and the long- term planning of the city＇s waste-management facilities.     

Urban ecological security assessment and forecasting,based on a cellular automata model: A case study of Guangzhou,China

Forecasting changes in urban ecological security could be important for the maintenance or improvement of the urban ecological environment. However, there are few references in this field and no landmark research work has been reported, particularly quantitative research. A forecasting model for ecological security based on cellular automata (CA) was developed using preliminary spatial data from an ecological security assessment of Guangzhou conducted previously (1990–2005). The model was constrained using transformation rules based upon proposed planning for 2010–2020. A simulation accuracy of 72.09% was acquired. Using a one-bit assessment grid for 2005 as the starting state for the simulation, the model was used to forecast ecological security for 2020. This revealed that although the ecological security status would be improved relative to current trends, there would still be an overall decline in ecological security over the next 15 years. Even if new urban plans were implemented, landscape pattern analysis suggested a more scattered and homogenous distribution in the urban landscape of Guangzhou and significant variation in landscape characteristics among districts. This suggests that further measures must be adopted to reverse the current trends in Guangzhou's ecological security. The model highlights the need to make ecological protection an integral part of urban planning. This study demonstrates the potential of CA models for forecasting ecological security. Such models could make an important contribution to decision-making for regional governors and to the development of urban planning incorporating assessment and prediction of ecological security.     

Continuous dose-response modeling and risk analysis with the gamma and reciprocal gamma distributions

Kodell and West (1993) describe two methods for calculating pointwise upper confidence limits on the risk function with normally distributed responses and using a certain definition of adverse quantitative effect. But Banga et al. (2000) have shown that these normal theory methods break down when applied to skew data. We accordingly develop a risk analysis model and associated likelihood-based methodology when the response follows either a gamma or reciprocal gamma distribution. The model supposes that the shape (index) parameter k of the response distribution is held fixed while the logarithm of the scale parameter is a linear model in terms of the dose level. Existence and uniqueness of the maximum likelihood estimates is established. Asymptotic likelihood-based upper and lower confidence limits on the risk are solutions of the Lagrange equations associated with a constrained optimization problem. Starting values for an iterative solution are obtained by replacing the Lagrange equations by the lowest order terms in their asymptotic expansions. Three methods are then compared for calculating confidence limits on the risk: (i) the aforementioned starting values (LRAL method), (ii) full iterative solution of the Lagrange equations (LREL method), and (iii) bounds obtained using approximate normality of the maximum likelihood estimates with standard errors derived from the information matrix (MLE method). Simulation is used to assess coverage probabilities for the resulting upper confidence limits when the log of the scale parameter is quadratic in the dose level. Results indicate that coverage for the MLE method can be off by as much as 15% points and converges very slowly to nominal coverage levels as the sample size increases. Coverage for the LRAL and LREL methods, on the other hand, is close to nominal levels unless (a) the sample size is small, say N < 25, (b) the index parameter is small, say k 1, and (c) the direction of adversity is to the left for the gamma distribution or to the right for the reciprocal gamma distribution.     

Risk assessment of coastal flood in a site of special scientific interest

The Canary Islands have a long coastline with varying levels of exposure to severe sea conditions. Frequent states of alert and emergency in some parts of their coastline are commonly related to the occurrence of extreme wave conditions. Among the phenomena directly driven by the waves when reaching the shore are the wave run-up and overtopping. Both the study of the flood level, including its variability, and the associated risks are key tools in the planning and management of coastal zones. The aim of this research is to examine the probability of occurrence of run-up events capable of exceeding different topographical levels, for estimating the risk level associated with flooding of the different areas in which the Boca Barranco Beach can be divided, in terms of their nature and use. This beach is located on the island of Gran Canaria, Spain, and is part of the site of scientific interest of Jinámar. A large wave dataset is used as input to a high-resolution numerical model for propagating offshore wave conditions to shallow waters in the study area. Furthermore, the morphology of the study area is reproduced by combining different bathymetric databases. Finally, the probability of occurrence of different levels of run-up and the corresponding levels of consequences are assigned, to assess the flood risk in the different areas of the beach, which are presented in a risk map of flooding in the study area.     

A modeling system for drinking water sources and its application to Jiangdong Reservoir in Xiamen city

Drinking water sources are highly valued by authorities for safeguarding the life of a city. Models are widely applied as important and effective tools in the management of water sources. However, it is difficult to apply models in water source management because water managers are often not equipped with the professional knowledge and operational skills necessary for making use of the models. This paper introduces a drinking water source simulation and prediction system that consists of a watershed model, a hydrological model and a water quality model. This system provides methods and technical guidance for the conventional management of water sources and emergency water event response. In this study, the sub-models of the system were developed based on the data of the Jiangdong Reservoir in Xiamen, and the model validation was based on local monitoring data. The hydrological model and water quality model were integrated by computer programming, and the watershed model was indirectly integrated into the system through a network platform. Furthermore, three applications for Jiangdong Reservoir water protection utilizing the system were introduced in this paper, including a conventional simulation, an emergency simulation, and an emergency measures evaluation.     

沿河城市多点污染排放量优化控制

本文针对沿河城市污染排放的情况,分析了污染物排放对河流造成的污染,并根据Streeter—Phelps模型和城市河流对水质的要求,建立了沿河城市多点污染排放优化控制的线性规划模模,并编制了计算程序用来求解该数学模型.文中提供的算例及计算结果表明,本文提出的沿河城市多点污染排放量优化控制方法对城市排水系统的建设有较大的指导意义.     

Causes of couspecific nest parasitism in the northern masked weaver

Summary Conspecific nest parasitism is a common reproductive strategy in the northern masked weaver (Ploceus taeniopterus). Parasites appear to be females with nests of their own who lay an additional egg in another female's nest as a way to enhance their reproductive success. Brood size in the northern masked weaver is practically constrained to three: starvation in four-chick broods is very common. The constraint on brood size is probably imposed by the extreme hatching asynchrony characteristic of this species: the last egg in a four-egg clutch hatches 72 h after the first egg, and when a chick starves, it is almost always the youngest chick. Late-hatching chicks also grow more slowly than do early-hatching chicks. If a female were to lay a fourth egg in her own nest, there is little chance that it would succeed. However; if she places it in another female's nest before that host lays her third egg, then it may have a greater chance of success.     

Competing probabilistic models for catch-effort relationships in wildlife censuses

Two probabilistic models are presented for describing the chance that an animal is captured during a wildlife census, as a function of trapping effort. The models in turn are used to propose relationships between sampling intensity and catch-per-unit-effort (C.P.U.E.) that were field tested on small mammal populations. Capture data suggests a model of diminishing C.P.U.E. with increasing levels of trapping intensity. The catch-effort model is used to illustrate optimization procedures in the design of mark-recapture experiments for censusing wild populations.     

Bayesian Joint Estimation of Binary Outcome and Time-to-event Data: Effects of Leaf Quality on Pupal Survival and Time-to-Emergence in the Winter Moth

Plant–herbivore interactions are complex and affect herbivore fitness components and life history traits in many different ways. In this paper, we present results from an experiment studying the effects of leaf quality on pupal survival and duration of pupation (as measured by time-to-emergence) in the winter moth. Because only surviving pupae are at risk of emerging, analysis of time-to-emergence should exclude the dead pupae. However, due to right censoring, the survival status could not be determined for each individual. This failure to determine the group of moths at risk of emerging a priori motivated the development of a joint model of both survival probability and time-to-emergence. We formulate the model in a Bayesian framework and apply Monte Carlo Markov Chain (MCMC) to obtain posterior distributions. Time-to-emergence is modeled by a Cox Proportional Hazards (CPH) model where only the surviving pupae are at risk of emergence. Probability of pupal survival was modeled by a Generalized Linear Mixed Model (GLMM). The censored individuals were included in the analysis as a missing value in the GLMM. The GLMM then generated prior distributions of survival probabilties—and thus of the probability of being at risk of emergence—for these 19 individuals, conditional on the model parameters. The CPH model was formulated as a count process and the binary frailty was incorporated as a zero-inflated Poisson model. Zeros in this model represent the non-survivors. Leaf quality did not appear to influence time-to-emergence. Pupal survival was affected in a complex and unexpected way showing opposite effects in males and females. We also explored the robustness of our model against increased levels of censoring. While the degree of censoring was low in our study (< 1%), we artificially increased it to 67%. Although further study is required to study the generality of these results in a theoretical framework, our explorations suggest that the newly proposed technique may be widely applicable in a variety of situations where the identification of the at risk population cannot be done in a straightforward way. Received: January 2005 / Revised: June 2005