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In the agricultural sector, climate change (CC) affects multiple weather variables at different stages of crop cycles. CC may influence the mean level or affect the distribution of events (e.g., rainfall, temperature). This work evaluates the economic impact of CC-related changes in multiple climatic components, and the resulting uncertainty. For this purpose, a three-stage discrete stochastic programming model is used to represents farm sector of an irrigated area of Italy and to examine the influence of CC on rainfall and on maximum temperature. These variables affect the availability of water for agriculture and the water requirements of irrigated crops. The states of nature, and their change, are defined more broadly than in previous analyses; this allows examining the changes of more climatic variables and crops cultivation. The effect of CC is obtained by comparing the results of scenarios that represent the climatic conditions in the current situation and in the future. The results show that the agricultural sector would seek to lower costs by modifying patterns of land use, farming practices and increasing the use groundwater. The overall economic impact of these changes is small and due primarily to the reduced availability of water in the future. The temperature increase is, in fact, largely offset by the effects of the increase in CO2 levels, which boosts the yield of main crops of the irrigated zone. Therefore, availability and water management becomes a crucial factor to offset the increase of evapotranspiration and of water stress resulting from the increase of temperature. However, the costs of CC are very high for some types of farming, which suffer a large reduction in income.

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.

The transport sector needs to go through an extended process of decarbonisation to counter the threat of climate change. Unfortunately, the International Energy Agency forecasts an enormous growth in the number of cars and greenhouse gas emissions by 2050. Two issues can thus be identified: (1) the need for a new methodology that could evaluate the policy performances ex-ante and (2) the need for more effective policies. To help address these issues, we developed an Agent-Based Model called Mobility USA aimed at: (1) testing whether this could be an effective approach in analysing ex-ante policy implementation in the transport sector; and (2) evaluating the effects of alternative policy scenarios on commuting behaviours in the USA. Particularly, we tested the effects of two sets of policies, namely market-based and preference-change ones. The model results suggest that this type of agent-based approach will provide a useful tool for testing policy interventions and their effectiveness.

This paper provides empirical evidence on delinking and Environmental Kuznets Curve (EKC) for municipal waste production in Italy. First, methodological issues and literature on delinking and EKC for waste are critically re-examined. Secondly, we analyse two very disaggregated panel datasets on Italian Regions and Provinces (1996-2004 data for the 20 regions, 2000-2004 data for the 103 provinces) to estimate the extent to which delinking between waste production and economic drivers is taking place. The empirical analysis of different specifications shows mixed evidence in favour of an EKC relationship. Evidence supporting an EKC hypothesis significantly arises at a provincial level, which presents a very high data heterogeneity. Nevertheless, the turning point is at very high levels of added value per capita (around 23,000-26,000€), which characterise a very limited number of wealthy (Northern) Italian provinces. The analysis does not reveal a similar evidence for the regional dataset: only a relative delinking dynamic emerges at the provincial level, we also note a positive relationship between waste production and the share of separated waste collection, which can be explained by the sharp difference in income and waste-policy performance between Northern and Southern Italy. Population density is not significant. Finally, the test on some policy proxies, i.e. the diffusion of the new waste tariff regime at the local-level and the ability of utilities to recover waste service cost, leads to the conclusion that they are not (yet) impacting waste production. To lower the turning points and to avoid an increasing gap between geographical areas, innovative (market based) and more effective policy instruments should be implemented. In particular, the weight of waste policies should be rebalanced towards waste prevention targets and instruments, in line with the priorities stated by the EU and Member Countries. In fact, the indirect feedback effect of good post-production waste management policies/practices on reducing waste production at a source can be weak and slow. In general, the results confirm that more geographically-disaggregated data may offer more insights with respect to cross-country datasets, also from the policy perspective.

The stark observation of the co-existence of undernourishment, nutrient deficiencies and overweight and obesity, the triple burden of malnutrition, is inviting us to reconsider health and nutrition as the primary goal and final endpoint of food systems. Agriculture and the food industry have made remarkable advances in the past decades. However, their development has not entirely fulfilled health and nutritional needs, and moreover, they have generated substantial collateral losses in agricultural biodiversity. Simultaneously, several regions are experiencing unprecedented weather events caused by climate change and habitat depletion, in turn putting at risk global food and nutrition security. This coincidence of food crises with increasing environmental degradation suggests an urgent need for novel analyses and new paradigms. The sustainable diets concept proposes a research and policy agenda that strives towards a sustainable use of human and natural resources for food and nutrition security, highlighting the preeminent role of consumers in defining sustainable options and the importance of biodiversity in nutrition. Food systems act as complex social–ecological systems, involving multiple interactions between human and natural components. Nutritional patterns and environment structure are interconnected in a mutual dynamic of changes. The systemic nature of these interactions calls for multidimensional approaches and integrated assessment and simulation tools to guide change. This paper proposes a review and conceptual modelling framework that articulate the synergies and tradeoffs between dietary diversity, widely recognised as key for healthy diets, and agricultural biodiversity and associated ecosystem functions, crucial resilience factors to climate and global changes.

This paper illustrates the main results of an expert elicitation survey on advanced (second and third generation) biofuel technologies. The survey focuses on eliciting probabilistic information on the future costs of advanced biofuels and on the potential role of Research, Development and Demonstration (RD&D) efforts in reducing these costs and in supporting the deployment of biofuels in Organisation for Economic Co-operation and Development (OECD) and non-OECD countries. Fifteen leading experts from different EU member states provide insights on the future potential of advanced biofuel technologies both in terms of costs and diffusion. This information results in a number of policy recommendations with respect to public RD&D strategies and is an important contribution to the integrated assessment modelling community.

Résumé Les transports génèrent de nombreuses externalités, dont celles liées à la pollution atmosphérique. Cet article analyse deux d’entre elles, les gaz à effet de serre et la pollution locale, qui, en général, sont étudiées séparément dans les travaux sur les politiques optimales de transport. Ici, ces externalités sont analysées conjointement grâce à un modèle de prise de décision séquentielle. Celui-ci permet de tenir compte de l’irréversibilité des politiques menées ainsi que de la possibilité d’une diminution progressive des incertitudes grâce à l’arrivée d’informations supplémentaires. Nous trouvons que des mesures structurelles qui permettent de limiter le recours aux transports privés sont économiquement plus avantageuses que des dispositifs techniques qui réduisent les émissions de substances polluantes. L’utilité de cette analyse conjointe des externalités est illustrée à travers deux cas : les dispositifs fiscaux qui grèvent l’automobile et la politique du logement. Classification JEL : C6; D62; D81; R48.