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Content archived on 2024-05-27

DYNamic feedbacks of climate impacts on current Adaptation and Mitigation Investment Choice

Final Report Summary - DYNAMIC (DYNamic feedbacks of climate impacts on current Adaptation and Mitigation Investment Choice)

The DYNAMIC (DYNamic feedbacks of climate impacts on current Adaptation and Mitigation Investment Choice) project has developed and tested a new framework to estimate the impacts of climate change on various sectors of the economy at the global scale. The ultimate objective was develop new impact estimates amenable of being incorporated into the integrated assessment models that are used in climate policy analysis.
Overall the project has highlighted the complexities of better evaluating climate change impacts and adaptation at global scale, which is the relevant dimension for the integrated assessment models used for climate policy analysis. At the same time, two studies conducted during the project using integrated models have highlighted the need for such improved estimates. The literature on mitigation reviewed by the Working Group III of the IPCC in the 5th Assessment has not included climate change impacts and adaptation. Accounting for impacts and adaptation could affect models’ results in terms of their projected costs and perceived fairness, as well as in terms of decarbonization pathways. In this project I used the integrated assessment model of mitigation and adaptation (WITCH) to show that 1) risk perception could induce agents to carry out more mitigation, even in the absence of a global agreement, yet adaptation would remain an important response to cope with the damages not reduced by mitigation; 2) accounting for the total costs of climate change and including adaptation and damage considerations could achieve an effort distribution being perceived fair by a wider group of countries. These are just two examples that motivate the relevance, and show the potential impact, of the research on climate change impacts and adaptation that has been the core of this project.
The project has focused on the development of a methodology that articulates in a transparent and consistent way the steps required to connect biophysical impacts, general equilibrium losses, and mitigation and adaptation strategies at the global scale. Accomplishing this task has turned out to be very challenging because of the need of combing global scenarios for climate drivers, such temperature and precipitation, with regional and sectoral impact response functions and adaptation strategies. In terms of methods, the project has used econometric and statistical models to better understand how human systems respond to weather shocks and changes in climate while taking into account uncertainty in spatial incidence, geographic and temporal variation in systems’ characteristics, also driven by intervening rates of economic and technological development.
The key novelty of the methodology developed and tested in the project is the use of data with high spatial and time resolution. This project has exploited the increased availability of meteorological data from various sources combined with geographically-scaled attributes and socio-economic variable, which have become available also through remote sensing techniques, makes it now easier to conduct these types of empirical assessments. A key output of the project has been improved econometric models for the analysis of systems’ sensitivity, of data inventories, and of a flexible data-processing infrastructure that will make it possible to quantify the future impacts of climate change on human and natural systems for large numbers of climate and socio-economics scenarios.
The methodology has been tested for three sectors or impact categories: agriculture, energy demand, and energy supply. Below I briefly summarize these three case studies.
By combining spatial analysis of climate historical and future data with econometric analysis the project has estimated the response functions of cereal productivity (maize, wheat, rice, and sorghum) to precipitation and temperature variation at the global scale, differentiating the response between tropical and temperate regions, between irrigated and rainfed areas. Using gridded climate and biophysical data, it was possible to identify the climate patterns relevant to the specific sector considered and to formulate an assessment of cereal exposure and vulnerability by weighting gridded climate data and subsequently aggregate them to the scale at which the physical endpoint (crop yields) is observed (country level). In a second step, the project has used the estimated response functions to evaluate the future vulnerability of cereal production, under different future warming scenarios using five different global circulation models’ outputs.
Energy demand
The project has developed an econometric model to estimate the impact of changes in exposure to hot and cold, dry and humid days on the demand for electricity, natural gas, and fuel oil in four different sectors of the economy (residential, industry, commercial and public services, and agriculture) at the global scale, using gridded population data to identify the relevant areas of exposure. The estimated responses provide insights into the potential impacts of climate change on the final use of energy and into the adaptation responses along the intensive and extensive margin. The implications on future on energy demand have been illustrated by combining the behaviors inferred from the past with the future climatic shifts of the Representative Concentration Pathways (RCPs) as predicted by the CMCC Global Circulation Model (GCM) and with the future socioeconomic trends for population and income growth of the Shared Socioeconomic Pathway (SSPs). More research in the future will look at the future change in energy demand across a wider range of climate and socio-economic scenarios.
Energy supply
Using a similar econometric approach, the project has explored the vulnerability of global hydropower generation to the variability in seasonal averages as well as changes in extreme conditions of precipitation, runoff, and temperature. A statistical model has been developed to estimate the elasticity of hydroelectricity generation to the historical variation (1980-2010) in precipitation or runoff, while controlling for temperature changes and other potential confounding factors at the global scale. A geo-references database on dams has been used to identify the relevant areas of exposure. The project has illustrated how the estimated response function of hydropower to meteorological variations can be used to assess the future vulnerability of generation from hydro in 83 countries. It has combined the estimated elasticities with future changes in exposure to runoff, drought, high and low temperature around 2050 in two warming scenarios (RCP 4.5 and 8.5) simulated by the Global Circulation Models (GCMs) from CMCC. More research in the future will look at the future change in energy demand across a wider range of climate and socio-economic scenarios.
The main target group that will benefit from this research is the integrated assessment modelling community, as the ultimate goal of the project is to develop improved estimates of impacts and adaptation that can be used to inform climate policy analysis on mitigation and adaptation, and ultimately decision makers. The framework developed in this project also lays the ground for the development of vulnerability maps that could directly inform policy makers and practitioners. We plan to achieve these objectives by furthering the collaboration between FEEM and Boston University, also within the broader network of the Integrated Assessment Modelling Community (IAMC), and in collaboration with the PhD Program Science and Management of Climate Change at the Ca’ Foscari University of Venice. Two PhD students from the PhD Program have already been involved in follow-up activities of this project.