Climate change is a hotly debated topic. We have seen recently rallies in the streets of Europe, led by a young Swedish activist, and the issue recurrently emerges in the newspapers, especially after the occurrence of some unusual weather event. Scientific evidence on the phenomenon is mainly provided by the Intergovernmental Panel on Climate Change (IPCC), an intergovernmental body of the United Nations, which won the 2007 Nobel Peace Prize (jointly with Al Gore).
Climate change policies are typically classified into two broad fields: mitigation and adaptation. Mitigation is about reducing the human influence on climate, by lowering emissions of greenhouse gases (GHG). Examples of mitigation policies are: carbon taxes and permits, electric vehicles, solar energy.
Adaptation policies are about curbing the negative effects of climate change, which cannot be avoided altogether because the climate system is characterized by a huge inertia, such that global warming would continue even if we would stop GHG emissions completely. Example of adaptation policies are: changes in seeds and sowing times in agriculture, air conditioning, coastal land protection.
Both mitigation and adaptation policies have something in common: they are expensive to implement and their effects will be felt far into the future. Those effects can be measured against a reference “business-as-usual” scenario (BaU): what would happen if we do nothing.
Effects of climate change
Providing an economic assessment of the BaU baseline is a very complex endeavor. On one hand, effects of climate change are manifold: “There are so many and so different effects: crops hit by worsening drought, crops growing faster because of carbon dioxide fertilization, heat stress increasing, cold stress decreasing, sea levels rising, cooling energy demand going up, heating energy demand going down, infectious disease spreading, and species going extinct. It is hard to make sense of this.” (Tol, 2015).
On the other hand, it is necessary to consider that the economy is also a complex system, and the consequences of climate change – because of trade relationships – could be felt in sectors and regions quite distant from where they physically occur.
Macroeconomic models for the appraisal of climate policies and impacts, therefore, must have an adequate degree of sectoral detail, such as the one provided by Computable General Equilibrium (CGE) models. Furthermore, estimates of physical impacts should be associated to economic variables and parameters in the model, like productivity or endowments of (human, natural, capital) resources.
In Roson and Sartori (2016) we elaborated results from a series of meta-analyses aimed at estimating parameters for six specific damage functions, referring to: sea level rise, agricultural productivity, heat effects on labor productivity, human health, tourism flows, and households’ energy demand. Damage functions are one or more relationships between climate variables (typically average temperature, but sometimes also humidity or “heating days”) and economic variables (potential income, productivity, resource endowments, etc.).
Our estimates are intended to generate exogenous shocks for numerical simulations with CGE models, which could then be employed to model structural adjustment processes induced in the economic systems by the climate change.
Even without a model of this kind, however, we can provide first-order approximations of the impact on the real GDP, because most of the impacts affect variables which are components of the Gross Domestic Product, with the exception of the variation in energy demand. Because of that, an approximated impact on the GDP can be readily obtained by multiplying the variation of one GDP component by its share, and in particular:
- impacts of sea level rise on GDP can be gauged by multiplying the estimated changes in available land resources by the share of land rents income on total GDP;
- agricultural productivity variations can be evaluated by multiplying the changes by the share of agricultural value added on total GDP;
- the reduction in labor productivity due to heat stress has an effect on the GDP that can be estimated as the sum of variations in labor productivity in the three sectors (agriculture, manufacturing, services) multiplied by the shares of (sectoral) labor income on total GDP;
- human health effects can be obtained by multiplying the estimated changes by the share of labor income on total GDP;
- the net inflow of foreign currency due to tourism flows can be directly expressed as relative to a baseline GDP level.
Even if the sum of the different impacts on GDP is only limited to first-order effects and does not consider general equilibrium feedbacks, we believe that such an approximation of the composite GDP footprint could reveal important insights about the order of magnitude, relevance, and distribution of the various impacts.
As a reference point, we consider a change in the global average temperature of +3°C. For agricultural productivity, we consider regional variations, which could be larger or smaller than the global one, and for sea level rise we set the year 2100 as the one corresponding to the temperature increment.
We found that only a few countries (Mongolia, Canada, and central-northern European countries, including Russia) are expected to get moderate gains from a +3°C increase in temperature, and these gains are typically due to an increase in tourists’ arrivals (and diminished outgoing domestic tourists). Many countries (whose estimates are highlighted in red) are expected to suffer from dramatic reductions in GDP. The most negatively affected countries are Togo in Africa (-18.29%) and Cambodia in South-East Asia (-18.25%), where again Tourism is the most important factor.
In addition to tourism income, variations in agricultural and labor productivity are also very relevant in many countries. Sea level rise, on the other hand, never appears as the primary factor, because of its limited incidence on total land and the relative small share of land income on GDP.
We report our results for a limited set of countries in the table below, where we highlight the specific cases of Italy and Spain.
We found that impacts on agricultural productivity (lower yields) and especially on tourism revenue (because of competition from other touristic destinations, offering more attractive climatic conditions) can significantly affect the Spanish economy. To properly interpret our findings, please consider that they are only first-order approximations, that they are central values (thereby we do not account for extremes), and that they were built without considering adaptation behavior. Furthermore, they refer to an aggregate national economy, which does not exclude the possibility that some impacts (e.g., sea-level rise) could have dramatic local effects.
- Roson, R., Sartori, M. (2016), “Estimation of Climate Change Damage Functions for 140 Regions in the GTAP 9 Database”, Journal of Global Economic Analysis, Volume 1 (2016), No. 2, pp. 78-115.
- Tol, R.S.J. (2015), “Who Benefits and Who Loses from Climate Change?”, Handbook of Climate Change Mitigation and Adaptation, Springer Science Business Media, New York.