In the following analysis, we apply the described modeling framework to the case of Austria. We focus on the impact fields with the highest federal budgetary importance and with potentially strong impacts for the Austrian economy (Bednar-Friedl et al. 2017): Agriculture, Forestry, and Catastrophe Management (including protection from natural hazards). Agriculture is heavily subsidized and thus public resources indirectly fund adaptation in this sector. Forestry is of high relevance since its expected contribution to macroeconomic damages is relatively high (− 0.8% GDP loss in 2050; Bachner et al. 2015). In addition, the government owns a large share of the protection forests in Austria. Catastrophe Management is closely connected to the public domain, since the Austrian disaster fund is fully financed out of tax revenue (Schinko et al. 2016). Our scenarios are in line with the RCP-SSP framework, which is standard in climate change research. Representative Concentration Pathways (RCPs) describe different global emission trajectories, while Shared Socioeconomic Pathways (SSPs) describe different narratives for socioeconomic development. By combining RCPs and SSPs, different states of the world emerge in which climate change impacts materialize (Moss et al. 2010; O’Neill et al. 2014). For the Baseline scenario, we choose a “middle-of-the-road” shared socioeconomic pathway (SSP2; O’Neill et al. 2014). For details on the Baseline calibration, see Appendix A.1.
Current and future public adaptation at the federal level in Austria
To identify current federal expenditures on public adaptation (step 1), we screened the federal budget in the base year (2016) for adaptation-relevant expenditure items and categorized them into gray, green, or soft measures (see Appendix A.2 for details). Soft measures comprise information measures such as early warning systems; gray measures are comprised of structural protection, for example, flood protection dams; and green measures are ecosystem-based measures such as natural flood retention areas or forest management. Additionally, we include a separate category for research and development (R&D).
The result of the screening is shown in the base year (2016) in Fig. 2, with the largest expenditure items being gray measures in the Catastrophe Management impact field (CATM, € 471 million p.a.) and green measures in the Agriculture impact field (AGRI, € 257 million p.a.). Expenditures on soft measures and R&D play only a minor role. In total, current annual adaptation-relevant expenditures sum up to € 908 million (15% of the screened budgetary subdivisions).
Starting from 2016, we co-developedFootnote 1 an indicative adaptation expenditure pathway until 2050 (step 2). This pathway combines expert judgment on the additional resources needed for single adaptation measures, on limits to further increase gray adaptation measures and international recommendations on the phasing of gray, green, and soft measures (Watkiss et al. 2015). For the Baseline expenditure path, the growth rate of the mid-term budget forecast for the Federal State of Austria is used (1.65% annually; assuming the same shares as in 2016) (BMF 2015). Figure 2 illustrates the pathway by type of adaptation measure and impact field in which adaptation is carried out and funded. On average, expenditures of this indicative Adaptation scenario rise by 3% p.a. over the period 2016–2050, which is above the 1.65% assumed in the Baseline. For details on this co-developed pathway, see Appendix A.2.
Macroeconomic model and scenario implementation
For the macroeconomic analysis (step 3), we use a single-country, comparative static CGE model of Austria (Bachner et al. 2015; Steininger et al. 2016; Bachner and Bednar-Friedl 2018). The model covers 40 economic sectors and one representative private household, which is endowed with labor and capital. The respective factor income is spent for consumption or is saved. In addition, there is a public household providing public services, financed by the following taxes: sales taxes on output, tax on capital gains, labor tax, value added tax, and export tax. All tax rates are fixed, thus determining flexible government income, which in turn gives the total amount of available public budget to be spent. The model includes classical unemployment and international trade is depicted via the Armington (1969) assumption, where domestic goods are imperfect substitutes for imports. The foreign balance is fixed at the share of the model’s benchmark year (2008). Regarding the development of the budget deficit, we assume a constant deficit-to-GDP ratio, which is an empirically well-supported assumption for the structural deficit and also in accordance with the criteria of the EU Stability and Growth Pact. For more details and the algebraic formulation, see Bachner (2017).
Implementation of the impact scenario
Climate change is implemented as average changes for the future 30-year climatic period 2036–2065 (i.e., 2050), relative to the average of the reference climatic period 1981–2010. Specifically, we use the SRES (Special Report on Emissions Scenarios) A1B emissions scenario (Nakićenović and Swart 2000), which corresponds to the RCP6.0 scenario with + 2.5 °C global mean temperature by the end of the century (Knutti and Sedláček 2013). For details, see Appendix A.1.
Climate change impacts are implemented in ten impact fields. For each of these fields, different types of impacts are quantified using a range of (bio)physical models. These impacts are implemented into the macroeconomic CGE model by (i) changes in production cost structures (e.g., a different production process in Agriculture), (ii) changes in productivity (e.g., yield changes in Agriculture and Forestry), (iii) changes in investments (e.g., reconstruction of infrastructure after flood events), and/or (iv) changes in public expenditures (e.g., more post-disaster relief payments in Catastrophe Management). Table 1 summarizes the impacts for the three impact fields under consideration (for all other, see Appendix A.1). As explained in Section 2, to keep expenditures on public service provision (i.e., government consumption) at the same level as in the Baseline scenario, we assume in the Impact scenario that transfers to private households are adjusted accordingly.
Implementation of the adaptation scenario
The Adaptation scenario builds on the Impact scenario but additionally incorporates direct costs and benefits (i.e., avoided impacts) of public adaptation measures in the three impact fields under consideration. Adaptation costs are divided into operating costs (e.g., labor costs, contracting to spatial planning bureaus, maintenance costs for public infrastructure) and investment costs. Changes in sectoral operating costs are modeled as shifts within the production cost structures while holding unit costs constant (but having a different composition of costs). Changes in government consumption patterns and levels (which are also a part of operating costs) are implemented as additional consumption, financed via cuts in transfers to the private household (and hence reduced private consumption). Accumulation effects of annual investment changes are accounted for, resulting in a changed capital stock in 2050 with associated changed annual capital costs (depreciation). Note that the deployed CGE model is not dynamic but comparative static. The development of the capital stock is therefore no explicit part of the model but accounted for when developing the adaptation pathway. Changes in investment are financed via changed savings and thus corresponding changes in private consumption. Table 3 shows how the indicative adaptation pathway (Fig. 2) translates into annual changes of sectoral costs as well as changes in government consumption (annualized for 2050, relative to the Impact scenario). For details on the calculations, see Appendix A.2 (Table 2).
The benefit of adaptation is avoided damage. The respective assumptions on the effectiveness of different measures (i.e., by how much damages can be reduced) are summarized in Table 3. Following the literature and expert estimates, we assume that agricultural crop yields can be increased by 10% and damages in Forestry can be reduced by 30–40%. For the Catastrophe Management impact field (flood protection), we use benefit-cost ratios to quantify effectiveness. For the Adaptation scenario, we use mean values for a central simulation run, but also provide results for a bandwidth (lower and upper bound) to address uncertainty. For details, see Appendix A.3.
Finally, note that also in the Adaptation scenario, we assume that expenditures of public service provision are maintained at the Baseline level by adjusting transfers to households. In addition, however, we allow for increased consumption for adaptation measures (as indicated in Fig. 1).