Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.     

Output-inflation tradeoffs in 34 countries

This article examines the responsiveness of real output to the variability of inflation and aggregate demand. In the manner of Lucas (1973), estimates of the output-inflation tradeoff are computed for a large sample of countries. This measure is then correlated with the variances of the inflation rate and the growth rate in nominal income. Because differences in inflation variance (and hence the tradeoff) are viewed as the outcomes of differences in demand variance, correlations between these two variables are also reported. Cross-time and cross-country results provide a good measure of support for Lucas and the notion that attempts to exploit the tradeoff weaken it.     

Coupling GIS with Hydrologic and Hydraulic Flood Modelling

Geographic Information Systems (GIS) have been recognised as a powerful means to integrate and analyse data from various sources in the context of comprehensive floodplain management. As part of this comprehensive approach to floodplain management, it is very important to be able to predict the consequences of different scenarios in terms of flooded areas and associated risk. Hydrologic and hydraulic modelling plays a crucial role and there is much to gain in incorporating these modelling capabilities in GIS. This is still a rather complex task and research is being done on the full integration of these models. Interfacing between these models and GIS may be a very efficient way of overcoming the difficulties and getting very good results in terms of engineering practice. This paper presents results based on the use of Intergraph GIS coupled with Idrisi GIS. Using these two systems substantially increased the flexibility of using GIS as a tool for flood studies. A lumped (XSRAIN) and a distributed (OMEGA) hydrologic models were used to simulate flood hydrographs. The well known HEC-2 Hydraulic model was used to compute flooded areas. These models were applied in the Livramento catchment with very good results. The computation of flooded areas for different flood scenarios, and its representation in GIS, can be used in the assessment of affected property and associated damages. This is a very useful GIS-based approach to floodplain management.     

Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies

This paper draws attention for the fact that traditional Data Envelopment Analysis (DEA) models do not provide the closest possible targets (or peers) to inefficient units, and presents a procedure to obtain such targets.It focuses on non-oriented efficiency measures (which assume that production units are able to control, and thus change, inputs and outputs simultaneously) both measured in relation to a Free Disposal Hull (FDH) technology and in relation to a convex technology. The approaches developed for finding close targets are applied to a sample of Portuguese bank branches.     

Biomic river restoration: A new focus for river management

River management based solely on physical science has proven to be unsustainable and unsuccessful, evidenced by the fact that the problems this approach intended to solve (e.g., flood hazards, water scarcity, and channel instability) have not been solved and long‐term deterioration in river environments has reduced the capacity of rivers to continue meeting the needs of society. In response, there has been a paradigm shift in management over the past few decades, towards river restoration. But the ecological, morphological, and societal benefits of river restoration have, on the whole, been disappointing. We believe that this stems from the fact that restoration overrelies on the same physical analyses and approaches, with flowing water still regarded as the universally predominant driver of channel form and structural intervention seen as essential to influencing fluvial processes. We argue that if river restoration is to reverse long‐standing declines in river functions, it is necessary to recognize the influence of biology on river forms and processes and re‐envisage what it means to restore a river. This entails shifting the focus of river restoration from designing and constructing stable channels that mimic natural forms to reconnecting streams within balanced and healthy biomes, and so levering the power of biology to influence river processes. We define this new approach as biomic river restoration.     

The relationship between satisfaction and loyalty: A mediator analysis

This study proposes a multiple mediation model to analyze the relationship between satisfaction and loyalty. The study's contribution to the literature is to examine, empirically, the main antecedents and determinants of this endogenous variable in greater depth. Thus, the research fills a gap in the literature through its analysis of the mediating role of perceived switching costs and the perceived lack of attractiveness of alternative offerings. This study applies variance-based structural equation modeling via partial least squares to a sample of 785 customers from 74 insurance companies in the service sector. The results show that perceived switching costs – to a greater extent – and the perceived lack of attractiveness of alternative offerings – to a lesser extent – are significant mediators in the relationship between satisfaction and loyalty.     

HEGY Tests in the Presence of Moving Averages*

We analyze the asymptotic distributions associated with the seasonal unit root tests of Hylleberg et al. (1990) for quarterly data when the innovations follow a moving average process. Although both the t‐ and F‐type tests suffer from scale and shift effects compared with the presumed null distributions when a fixed order of autoregressive augmentation is applied, these effects disappear when the order of augmentation is sufficiently large. However, as found by Burridge and Taylor (2001) for the autoregressive case, individual t‐ratio tests at the semi‐annual frequency are not pivotal even with high orders of augmentation, although the corresponding joint F‐type statistic is pivotal. Monte Carlo simulations verify the importance of the order of augmentation for finite samples generated by seasonally integrated moving average processes.     

Estimate of Suspended Sediment Concentration from Monitored Data of Turbidity and Water Level Using Artificial Neural Networks

Artificial neural networks (ANNs) are promising alternatives for the estimation of suspended sediment concentration (SSC), but they are dependent on the availability data. This study investigates the use of an ANN model for forecasting SSC using turbidity and water level. It is used an original method, idealized to investigate the minimum complexity of the ANN that does not present, in relation to more complex networks, loss of efficiency when applied to other samples, and to perform its training avoiding the overfitting even when data availability is insufficient to use the cross-validation technique. The use of a validation procedure by resampling, the control of overfitting through a previously researched condition of training completion, as well as training repetitions to provide robustness are important aspects of the method. Turbidity and water level data, related to 59 SSC values, collected between June 2013 and October 2015, were used. The development of the proposed ANN was preceded by the training of an ANN, without the use of the new resources, which clearly showed the overfitting occurrence when resources were not used to avoid it, with Nash-Sutcliffe efficiency (NS) equals to 0.995 in the training and NS = 0.788 in the verification. The proposed method generated efficient models (NS = 0.953 for verification), with well distributed errors and with great capacity of generalization for future applications. The final obtained model enabled the SSC calculation, from water level and turbidity data, even when few samples were available for the training and verification procedures.