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In the debate between proponents of green growth and degrowth, the core issue is whether decoupling carbon emissions and resource use from GDP growth is possible, and if so, possible at a rate fast enough to achieve policy goals such as global warming of maximum 1.5 °C or 2 °C. In this paper, the claims by degrowth scholars on the limits of decoupling growth and carbon emissions are critically examined by assessing the economic and climate consequences of their claims. It is claimed that their pessimistic view on decoupling is not based on robust arguments but rather mystifications of what decoupling is. Following the assumptions by leading degrowth scholars – that decoupling (decrease of the emission intensity of GDP) are unlikely to be larger than 4% and that levels of GDP need to converge in a degrowing world – indicates that the 1.5 °C target is ruled out altogether and that in order to reach the 2 °C target, the economies of the global north would have to be reduced with over 90% and for middle income countries with around 70%. This appears as very unlikely to happen. Yet, there might be alternatives, which are discussed by sketching a realist and dynamic theory of decoupling. • The pessimist assumptions on decoupling by some degrowth scholars presuppose an economic collapse of unseen magnitude • Alternatively, stringent climate goals such as 1.5 °C, are not attainable • These pessimist assumptions are, however, not based on robust arguments, and ways to demystify decoupling is discussed [ABSTRACT FROM AUTHOR]

The economic analysis of climate change requires estimates at both aggregate and local level. Although there are numerous studies that estimate the global impact of climate change, country-level studies are still rare, particularly as far as Italy is concerned. By exploiting a panel of 110 provinces observed between 1980 and 2014, this paper investigates the impact of variation of weather variables on GDP per capita and agricultural productivity in Italy. To address issues of model uncertainty, the analysis explores to what extent these economic outcomes are affected by weather variables, linearly or non-linearly, as well as how their growth rate and levels are affected as a result. Main findings show that there is considerable model uncertainty. The most robust econometric results showing statistically significant effects of temperature for both the GDP and agricultural reaction function are from a levels specification, where temperature is included in a non-linear form while weather variables and economic outcomes enter in first differences. Projections of the impact of climate change by the end of the century show slight average effects for GDP per-capita, but important losses in agriculture, due to a persistent increase in average temperatures under both the RCPs 4.5 and 8.5. • We study the economic impact of weather variation on GDP and agriculture in Italy • We exploit a rich panel dataset across 110 territorial units over 1980–2014 period • To address problems of model uncertainty, we compare several different specifications • The "best" performing model is a non-linear specification estimated in first differences • End of the century predictions, suggest nil effects on GDP, but important loss in agriculture. [ABSTRACT FROM AUTHOR]