The SITES reserve selection system: A critical review

Numerous models have been put forth to help with the growing demand for the establishment of biodiversity reserves. One site selection model that has been used in several recent studies is SITES [S.J. Andelman, I. Ball, F.W. Davis and D.M. Stoms, SITES V 1.0: an analytical toolbox for designing ecoregional conservation portfolios, Unpublished manual prepared for the nature conservancy, 1999, 1–43. (available at )]. SITES includes two heuristic solvers: based on Greedy and Simulated Annealing. We discuss the formulation of the SITES model, present a new formulation for that problem, and solve a number of test problems optimally using off-the-shelf software. We compared our optimal results with the SITES Simulated Annealing heuristic and found that SITES frequently returns significantly suboptimal solutions. Our results add further support to the argument, started by Underhill [L.G. Underhill, Optimal and suboptimal reserve selection algorithms, Biol. Conserv. 70 (1994) 85–87], continuing through Rodrigues and Gaston [A.S.L. Rodrigues and K.J. Gaston, Optimization in reserve selection procedures – why not?, Biol. Conserv. 107 (2002) 123–129], for greater integration of optimal methods in the reserve design/selection literature.     

Waste management modeling by MARKAL model: A case study for Basilicata Region

Air pollution is one of the most pressing environmental problems which affects likewise urban, industrial and rural areas. Environmental planners, regulators and decision makers need reliable, scientifically based tools to find out strategies for controlling air pollution in a cost-effective way, taking into account the whole productive system. In this framework the basic elements of energy-environmental planning have to be extended to include also waste processing technologies amongst the usually considered pollution sources. Bottom-up optimizing models, based on linear programming techniques and customized for specific cases, represent a powerful tool in energy-environmental management. This paper focuses on the integrated modeling of material flows and energy system performed on a local scale case study (Basilicata Region, Southern Italy) using the linear programming model IEA-MARKAL. We have evaluated the feasibility of the model in representing the waste management system to estimate the environmental impact of the waste processing technologies in the context of the whole productive system. A sensitivity analysis has been carried out to emphasize the connections between tariffs, waste disposal technologies assessment and atmospheric emissions. This revised version was published online in July 2006 with corrections to the Cover Date.     

Counterpart Models in Facility Location Science and Reserve Selection Science

Five classes of zero–one programming models for discrete facility location problems are compared to counterpart models for the selection of conservation reserves. The basic problem of siting facilities to cover demand for services is analogous to the problem of selecting reserves to support species diversity. The classes of models include the set covering and maximal covering models, as well as models for backup and redundant coverage. Issues of reliability and uncertainty are addressed by chance constrained covering models and maximal expected covering models. Exact and heuristic solution approaches are discussed. Multi-objective and economic issues are considered.     

A multi-objective programming model for assessment the GHG emissions in MSW management

In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH₄ generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.     

A meta‐heuristic approach for supporting adaptive disassembly sequencing using a multi‐objective concept

One of the key features of environmentally conscious manufacturing has been the efforts to promote product recycling and remanufacturing. Efficient material re‐utilisation through product disassembly to retrieve the desired parts and/or subassemblies is one rational approach. This is because it can promote the conservation of both material and energy resources whilst concurrently reducing environmental impact. However, because manufactured products may be made from many components, disassembly load becomes a critical factor that may obstruct the recovery of materials. Accordingly, it is essential to develop a practical method for deriving a disassembly plan to decrease such load, and to endow a certain value to the product at the end of its life cycle. With this understanding, the authors have developed a practical procedure to produce an adaptive disassembly strategy. The authors have applied a meta‐heuristic method known as genetic programming (GP) as a search engine to derive the adaptive disassembly sequence together with a multi‐objective optimisation method termed MOON^2R. The authors have also proposed a hierarchical sequencing method to cope with large/complex products and added several ideas to increase the applicability associated with the interests in disassembly of hazardous and/or valuable parts, and alternative disassembly actions. Through numerical experiments, the authors examined the effectiveness of the proposed approach by showing its support for relevant planning and design decisions for product recycling and remanufacturing from various viewpoints.     

Optimizing the treatment and disposal of municipal solid wastes using mathematical programming—A case study in a Greek region

Goal of the work is to present a simplified methodology to optimize an integrated solid waste management system. The methodology performs two optimizations, namely: (i) minimization of the total cost of the MSW system and (ii) minimization of the equivalent carbon dioxide emissions (CO_2e) generated by the whole system. The methodology is modeled via non-linear mathematical equations, uses 32 decision variables and does not require complex LCA databases. The proposed model optimally allocates eight MSW components (paper, cardboard, plastics, metals, glass, food wastes, yard wastes and other wastes) to four MSW management technologies (incineration, composting, anaerobic digestion, and landfilling) after source separation of recyclables has taken place. The Region of East-Macedonia and Thrace in Greece was selected as a case study. Results showed that there is a trade off between cost and CO_2e emissions. Incineration and composting were favored as the principal treatment technologies, while landfilling was always the least desirable management technology under both objective functions. The recycling participation rate significantly affected all optimum scenarios.     

ICCLP: An Inexact Chance-Constrained Linear Programming Model for Land-Use Management of Lake Areas in Urban Fringes

Lake areas in urban fringes are under increasing urbanization pressure. Consequently, the conflict between rapid urban development and the maintenance of water bodies in such areas urgently needs to be addressed. An inexact chance-constrained linear programming (ICCLP) model for optimal land-use management of lake areas in urban fringes was developed. The ICCLP model was based on land-use suitability assessment and land evaluation. The maximum net economic benefit (NEB) was selected as the objective of land-use allocation. The total environmental capacity (TEC) of water systems and the public financial investment (PFI) at different probability levels were considered key constraints. Other constraints included in the model were land-use suitability, governmental requirements on the ratios of various land-use types, and technical constraints. A case study implementing the system was performed for the lake area of Hanyang at the urban fringe of Wuhan, central China, based on our previous study on land-use suitability assessment. The Hanyang lake area is under significant urbanization pressure. A 15-year optimal model for land-use allocation is proposed during 2006 to 2020 to better protect the water system and to gain the maximum benefits of development. Sixteen constraints were set for the optimal model. The model results indicated that NEB was between $1.48 × 10⁹ and $8.76 × 10⁹ or between $3.98 × 10⁹ and $16.7 × 10⁹, depending on the different urban-expansion patterns and land demands. The changes in total developed area and the land-use structure were analyzed under different probabilities (q _i ) of TEC. Changes in q _i resulted in different urban expansion patterns and demands on land, which were the direct result of the constraints imposed by TEC and PFI. The ICCLP model might help local authorities better understand and address complex land-use systems and develop optimal land-use management strategies that better balance urban expansion and grassland conservation.     

南水北调东线水资源配置中的期权契约研究

针对南水北调东线实际和传统水资源配置方法的局限性。借鉴金融工程的理念。引入水期权契约作为市场机制下水资源配置的一种方式．用以提高干旱年水供给保证率、规避水价波动风脸，从而提高东线水资源使用价值。分析期权契约理论用于南水北调水资源配置的可行性。并根据东线受水区流域水资源供求缺口的影响因素分析．得出水价的均值回复特性。在上述分析基础上引入多次执行期权契约。考虑应用动态规划方法对期权价值进行求解。并通过算例分析验证了该方法的可行性．最后根据南水北调东线实际构建了水期权契约在东线的应用流程。     

Solving the probabilistic reserve selection problem

The Reserve Selection Problem consists in selecting certain sites among a set of potential sites for biodiversity protection. In many models of the literature, the species present and able to survive in each site are supposed to be known. Here, for every potential site and for every species considered, only the probability that the species survives in the site is supposed to be known. The problem to select, under a budgetary constraint, a set of sites which maximizes the expected number of species is known in the literature under the name of probabilistic reserve selection problem. In this article, this problem is studied with species weighting to deal differently with common species and rare species. A spatial constraint is also considered preventing to obtain too fragmented reserve networks. As in Polasky et al. (2000), the problem is formulated by a nonlinear mathematical program in Boolean variables. Camm et al. (2002) developed a mixed-integer linear programming approximation that may be solved with standard integer programming software. The method gives tight approximate solutions but does not allow to tell how far these solutions are from the optimum. In this paper, a slightly different approach is proposed to approximate the problem. The interesting aspect of the approach, which also uses only standard mixed-integer programming software, is that it leads, not only to an approximate solution, but also to an upper limit on the true optimal value. In other words, the method gives an approximate solution with a guarantee on its accuracy. The linear reformulation is based on an upper approximation of the logarithmic function by a piecewise-linear function. The approach is very effective on artificial instances that include up to 400 sites and 300 species. Within an average CPU time of about 12 min, near-optimal solutions are obtained with an average relative error, in comparison to the optimum, of less than 0.2%.     

Inexact credibility constrained programming for environmental system management

This study proposed an inexact credibility constrained programming (ICCP) to deal with multi-formats of uncertainties in parameters and variables for an agricultural water planning system. The study system includes three subareas with different crop distributions. The redundant water in the wet season can be stored in the reservoir and utilized in the dry season. The ICCP method can reflect not only inexact uncertainties in the objective function, variables and parameters, but also fuzzy uncertainties in the right-hand side. Interval credibility levels which represent satisfaction degrees of the constraints can be analyzed. Scenario analysis is conducted to analyze possible events in wet and dry years. The resulting solutions can provide stable intervals for the objective function and decision variables with different levels of risk when violating the constraints.