Sustainability indicators for environmental management and decision making

The general objective of this research is to explore theories and methodologies of sustainability indicators, environmental management and decision making disciplines with the operational purpose of producing scientific, robust and relevant information for supporting system understanding and decision making in real case studies. Several tools have been applied in order to increase the understanding of socio-ecological systems as well as providing relevant information on the choice between alternatives. These tools have always been applied having in mind the complexity of the issues and the uncertainty tied to the partial knowledge of the systems under study. Two case studies with specific application to performances measurement (environmental performances in the case of the K8 approach and sustainable development performances in the case of the EU Sustainable Development Strategy) and a case study about the selection of sustainable development indicators amongst Municipalities in Scotland, are discussed in the first part of the work. In the second part of the work, the common denominator among subjects consists in the application of spatial indices and indicators to address operational problems in land use management within the territory of the Ravenna province (Italy). The main conclusion of the thesis is that a ‘perfect’ methodological approach which always produces the best results in assessing sustainability performances does not exist. Rather, there is a pool of correct approaches answering different evaluation questions, to be used when methodologies fit the purpose of the analysis. For this reason, methodological limits and conceptual assumptions as well as consistency and transparency of the assessment, become the key factors for assessing the quality of the analysis.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Experiments on Pro-Sociality and Environmental Issues

The thesis comprises three essays that use experimental methods, one about other-regarding motivations in economic behavior and the others on pro-social behavior in two environmental economics problems. The first chapter studies how the expectations of the others and the concern to maintain a balance between effort exerted and rewards obtained interact in shaping the behavior in a modified dictator game. We find that dictators condition their choices on recipients' expectations only when there is a high probability that the the recipient will not be compensated for her effort. Otherwise, dictators tend to balance the efforts and rewards of the recipients, irrespective of the recipients' expectations. In the second chapter, I investigate the problem of local opposition to large public projects (e.g. landfills, incinerators, etc.). In particular, the experiment shows how the uncertainty about the project's quality makes the community living in the host site skeptical about the project. I also test whether side-transfers and costly information disclosure can help to increase the efficiency. Both tools succesfully make the host more willing to accept the project, but they lead to the realization of different types of projects. The last chapter is an experiment on climate negotiations. To avoid the global warming, countries are called to cooperate in the abatement of their emissions. We study whether the dynamic aspect of the climate change makes cooperation across countries behaviorally more difficult. We also consider inequality across countries as a possible factor that hinders international cooperation.