Derivation of Economic and Social Indicators for a Spatial Decision Support System to Evaluate the Impacts of Urban Development on Water Bodies in New Zealand

There is mounting evidence that urban development in New Zealand has contributed to poor water quality and ecological degradation of coastal and fresh water receiving waters. As a consequence, local governments have identified the need for improved methods to guide decision making to achieve improved outcomes for those receiving waters. This paper reports progress on a research programme to develop a catchmentscale spatial decision-support system (SDSS) that will aid evaluation of the impacts of urban development on attributes such as water and sediment quality; ecosystem health; and economic, social and cultural values. The SDSS aims to express indicators of impacts on these values within a sustainability indexing system in order to allow local governments to consider them holistically over planning timeframes of several decades. The SDSS will use a combination of deterministic and probabilistic methods to, firstly, estimate changes to environmental stressors such as contaminant loads from different land use and stormwater management scenarios and, secondly, use these results and information from a range of other sources to generate indicator values. This paper describes the project’s approach to the derivation of indicators of economic and social well being associated with the effects of urban storm water run-off on freshwater and estuarine receiving waters.Environmental Economics and Policy,

"Modelling Sustainable International Tourism Demand to the Brazilian Amazon"

The Amazon rainforest is one of the world's greatest natural wonders and holds great importance and significance for the world's environmental balance. Around 60% of the Amazon rainforest is located in the Brazilian territory. The two biggest states of the Amazon region are Amazonas (the upper Amazon) and Para (the lower Amazon), which together account for around 73% of the Brazilian Legal Amazon, and are the only states that are serviced by international airports in Brazil's North region. The purpose of this paper is to model and forecast sustainable international tourism demand for the states of Amazonas, Para, and the aggregate of the two states. By sustainable tourism is meant a distinctive type of tourism that has relatively low environmental and cultural impacts. Economic progress brought about by illegal wood extraction and commercial agriculture has destroyed large areas of the Amazon rainforest. The sustainable tourism industry has the potential to contribute to the economic development of the Amazon region without destroying the rainforest. The paper presents unit root tests for monthly and annual data, estimates alternative time series models and conditional volatility models of the shocks to international tourist arrivals, and provides forecasts for 2006 and 2007.

Modelling Sustainable International Tourism Demand to the Brazilian Amazon

The Amazon rainforest is one of the world’s greatest natural wonders and holds great importance and significance for the world’s environmental balance. Around 60% of the Amazon rainforest is located in the Brazilian territory. The two biggest states of the Amazon region are Amazonas (the upper Amazon) and Pará (the lower Amazon), which together account for around 73% of the Brazilian Legal Amazon, and are the only states that are serviced by international airports in Brazil’s North region. The purpose of this paper is to model and forecast sustainable international tourism demand for the states of Amazonas, Pará, and the aggregate of the two states. By sustainable tourism is meant a distinctive type of tourism that has relatively low environmental and cultural impacts. Economic progress brought about by illegal wood extraction and commercial agriculture has destroyed large areas of the Amazon rainforest. The sustainable tourism industry has the potential to contribute to the economic development of the Amazon region without destroying the rainforest. The paper presents unit root tests for monthly and annual data, estimates alternative time series models and conditional volatility models of the shocks to international tourist arrivals, and provides forecasts for 2006 and 2007.Brazilian Amazon; International Tourism Demand; Time Series Modelling; Conditional Volatility Models; Forecasting.

Understanding Social–Ecological Systems using Loop Analysis

The sustainable management of social–ecological systems (SESs) requires that we understand the complex structure of relationships and feedbacks among ecosystem components and socioeconomic entities. Therefore, the construction and analysis of models integrating ecological and human actors is crucial for describing the functioning of SESs, and qualitative modeling represents an ideal tool since it allows studying dependencies among variables of diverse types. In particular, the qualitative technique of loop analysis yields predictions about how a system’s variables respond to stress factors. Different interaction types, scarce information about functional relationships among variables, and uncertainties in the values of the parameters are the rule rather than exceptions when studying SESs. Accordingly, loop analysis seems to be perfectly suitable to investigate them. Here, we introduce the key aspects of loop analysis, discuss its applications to SESs, and suggest it enables making the first steps toward the integration of the three dimensions of sustainability

Development of a Conceptual, Mathematical and Model of System Dynamics for Landfill Water Treatment

Leachate is a major problem in landfills due to the type and amount of pollutants. In Croatia, the usual way of handling leachate is recirculation back to the landfill body. However, this method poses a danger of their leakage into the environment, especially during periods of increased precipitation. Leachate is heavily polluted with organic matter, and its spillage into the environment can cause environmental incident. This paper presents a model for efficient treatment of landfill water contaminated with organic matter, based on the operating parameters of the actual water treatment system. The aim of this scientific research is to develop a model for landfill water treatment and to design a methodology suitable for significant patterns of organic matter pollution behaviour. The developed conceptual model is a computer-based model that uses randomly selected values from the theoretical probability distribution of the applied variables. The mathematical model is based on a system of differential equations solved by the Runge-Kutta method. To validate the model, a nonparametric test was applied, given that the distributions are asymmetric non-Gaussian distributions. The methodology proposed in this paper is based on simulation modelling as a useful method in environmental protection. The developed and validated model has proven that landfill water can be effectively and economically purified. Simulation modelling and environmental informatics can effectively contribute to solving environmental problems on the computer without unnecessary risk to the environment

Integrating economic values and catchment modelling

Integrated catchment policies are widely used to manage natural resources in Australian catchments. Decision support tools available to aid integrated catchment management are often limited in their integration of environmental processes with socio-economic systems. Fully integrated models are required to support assessments of the environmental and economic trade-offs of catchment management changes. A Bayesian Network (BN) model is demonstrated to provide a suitable approach to integrate environmental modelling with economic valuation. The model incorporates hydrological, ecological and economic models for the George catchment in Tasmania. Information about the non-market costs and benefits of environmental changes is elicited using Choice Experiments, allowing an assessment of the efficiency of alternative management scenarios.Integrated catchment modelling, Bayesian networks, Uncertainty, Environmental values, Non-market valuation, Choice Modelling.,

Applying Bayesian belief networks (BBNs) with stakeholders to explore and codesign options for water resource interventions

Bayesian Belief networks (BBNs) are a useful tool to account for uncertainty and can be used to incorporate stakeholder understandings of how a system works. In this study, BBNs were applied to elicit and discuss local stakeholders’ concerns in conflicts over water resource planning in two cases in southern Thailand. One concerned the construction of a dam proposed by a top-down project. The other concerned a bottom-up participatory process at the catchment scale to assess the need for water resources interventions and explore perceptions on alternative design options. In the top-down project, the responses of participants during the elaboration of the BBN showed that potentially affected stakeholders were particularly concerned about limited consultation and lack of shared benefits, which led them to oppose the dam project. In the bottom-up project, local stakeholders expected and agreed with the benefits of a dam, proposing to locate the dam upstream of community land. The BBN method did not facilitate dialogue in the top-down dam-building project because no alternative design options could be discussed and potentially affected stakeholders did not want to discuss compensation because of mistrust and differences in valuation of effects. In the bottom-up project, the BBN method did facilitate dialogue on alternative intervention options and their effects. The replicable BBN framework can support policy-makers to better understand water conflict situations in different stages of planning. Its application supports exploring a wider repertoire of options, enlarging the scope for more inclusive and sustainable solutions to water resource conflicts

A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport

We formalise and present an innovative general approach for developing complex system models from survey data by applying Bayesian Networks. The challenges and approaches to converting survey data into usable probability forms are explained and a general approach for integrating expert knowledge (judgements) into Bayesian complex system models is presented. The structural complexities of the Bayesian complex system modelling process, based on various decision contexts, are also explained along with a solution. A novel application of Bayesian complex system models as a management tool for decision making is demonstrated using a railway transport case study. Customer satisfaction, which is a Key Performance Indicator in public transport management, is modelled using data from customer surveys conducted by Queensland Rail, Australia

Integrated Hydro-Economic Modelling: Challenges and Experiences in an Australian Catchment

Integrated catchment policies are widely used to manage natural resources in Australian catchments. Integration of environmental processes with socio-economic systems is often difficult due to the limitations of decision support tools. To support assessments of the environmental and economic trade-offs of changes in catchment management, fully integrated models are needed. This research demonstrates a Bayesian Network (BN) approach to integrating environmental modelling with economic valuation. The model incorporates hydrological, ecological and economic models for the George catchment in Tasmania. Choice experiments were used to elicit information about the non-market costs and benefits of environmental changes. This allows the efficiency of alternative management scenarios to be assessed.Hydro-economic modelling, Integrated catchment modelling, Ecological modelling, Valuation, Bayesian networks, Water quality, Community/Rural/Urban Development, Environmental Economics and Policy, Land Economics/Use,

Las Redes Bayesianas como herramienta para la evaluación del riesgo de reincidencia: Un estudio sobre agresores sexuales

La reincidencia, especialmente en delitos sexuales, constituye una problemática que afecta a los ámbitos político, jurídico-penal y social, con gran repercusión en los medios. Por este motivo es necesario desarrollar metodologías de análisis para su predicción, para mejorar la eficiencia de la gestión del riesgo asociado. Las Redes Bayesianas han demostrado ser una herramienta muy útil en la predicción de sucesos que dependen de muchas variables en un entorno de incertidumbre. Este trabajo introduce su uso como metodología novedosa en la evaluación del riesgo de reincidencia, a partir de un estudio sobre delincuentes sexuales basado en la información recogida en un informe previo. Se observa la coherencia de los resultados obtenidos con los del informe en cuanto a la influencia individual de las variables explicativas en el riesgo de reincidencia. El interés principal de esta metodología radica en que permite explorar la influencia simultánea de diversas variables, por lo que se podrán hacer estimaciones precisas de riesgo individualizado y estudiar las interacciones entre los factores de riesgo.Recidivism in sexual offences is a problem that affects the political, criminal justice and social context, with great media coverage. In order to improve the efficiency in managing the risk of these offences some methodologies for prediction need to be developed. Bayesian Networks have proved to be a useful tool in predicting events that depend on many variables in an uncertain environment. This paper introduces their use as a novel methodology in assessing the risk of recidivism, from a study of sex offenders based on the information in a report. Consistency of the results obtained with the report regarding the individual influence of the explanatory variables on the risk of recurrence was observed. The main interest of this methodology is to explore the simultaneous influence of several variables, so it may make accurate estimates of individual risk and study the interactions between the risk factors