climate change impacts. This means that, although we are trying to build a standardized data set, the original information remains intrinsically heterogeneous. Consequently, our results have the same strengths and weaknesses as their primary references, which are difficult to judge, except for the fact that most of them are from published sources.

For the same reason, we provide central values (or best estimates) of climate change impacts in the various categories, but we refrain from tackling any analysis of uncertainty, or from evaluating the overall robustness of our findings. Actually, some of the original studies do not supply information like standard errors of the parameters, whereas for those in which such information is available (in some way), converting it to a different spatial and temporal scale would be a rather complicated process.

We understand that assessing uncertainty in climate change impacts is essential from both a scientific and a practical policy perspective, but we leave the issue for further future research. The full impact of climate change is a slowly unfolding event, and data continue to be gathered by experts in great efforts such as the Inter-governmental Panel on Climate Change (IPCC). New evidence will be available, and confidence on data and parameters will improve over time. Nonetheless, climate change impacts are and will remain differentiated among sectors and regions, which requires both a continuous interdisciplinary cooperation and the development of modeling platforms for the simultaneous appraisal of multiple impacts.

The paper is structured as follows. Sections from 2 to 7 are devoted to presenting the methodology and some estimates for the six impact typologies, whereas detailed numerical results are available in the Appendix at the end of the paper. Section 8 provides a synthesis of the findings by showing first-order approximations of the change in national GDPs triggered by the various effects, when the average temperature is assumed to increase by three Celsius degrees. The results are then discussed in a final concluding section.

# **2. Climate change impact #1: Sea Level Rise**

A large number of studies reviewed by the Fifth IPCC Assessment Report (IPCC, 2014) have shown that the increase in global temperature brings about an increase in the level of the sea. Sea level rise (hereafter SLR) affects the land stock through the erosion, inundation or salt intrusion along the coastline. This phenomenon is in turn generated by (i) the thermal expansion of water bodies and (ii) glaciers’ melting.

The share of land which may be lost (in terms of economic production factor) depends on several country-specific characteristics, like: (i) the composition of the shoreline (cliffs and rocky coasts are less subject to erosion than sandy coasts and wetlands); (ii) the total length of the country coast; (iii) the share of the coast which is suitable for productive purposes (i.e. in agriculture); (iv) the vertical land movement (VLM). The latter is a generic term for all processes affecting the elevation at a given location (tectonic movements, subsidence, ground water extraction), causing the land to move up or down. This is typically a slow process with values commonly between -10 mm/year (sinking) and +10 mm/year (rising). Local vertical land movement becomes relevant when looking at the local effects of sea level rise. The orders of magnitude are comparable, and VLM can thus either exacerbate or dampen the SLR.

The literature offers several studies dealing with the SLR, but they are mainly local and country-level studies or macro-level studies, where countries are aggregated into large macro-regions. Perhaps the most employed model is DIVA (Vafeidis et al., 2008), which is an integrated model of coastal systems that assesses biophysical and socio-economic consequences of SLR.

# _2.1 Methodology_

The latest IPCC Assessment Report (IPCC AR5) reports the global mean SLR (in meters) associated with the global mean surface temperature change (in °C), at the time intervals [2046-2065] and [20803