1. Introduction

Climate change is currently one of the biggest challenges for the Dutch economy. Not only do the effects of climate change need to be mitigated – e.g. by limiting greenhouse gas emissions – but we also need to adapt to the effects of climate change. According to projections from the Royal Netherlands Meteorological Institute (KNMI), climate change in the Netherlands will be driven by four different factors (known as pressure factors):

• It will get warmer: both the average temperature and the temperature extremes are going to rise. Heatwaves will increase in frequency, intensity, and duration.
• It will get dryer: the frequency of precipitation will be reduced, which, coupled with an increase in the evaporation of water, will intensify dry spells. It will get wetter: the warmer atmosphere will
• lead to increasing levels of humidity, which will make the few instances of precipitation much more severe.    
• Sea levels will rise: sea water temperatures will increase; combined with the melting of the polar ice caps, this will cause sea levels to rise. This will lead to issues such as soil salinisation in coastal areas.

By using the National Climate Adaptation Strategy (NAS) adaptation tool¹⁾ we can create conceptual diagrams for different economic sectors, showing the (relevant) consequences of the factors mentioned above. According to the definitions of climate impact and climate risks provided by the IPCC²⁾,  the impact of those factors can be subdivided under the terms climate-related hazards, exposure, and vulnerability; the latter term combines sensitivity and adaptive capacity. It is important to monitor all these components in order to design effective mitigation and adaptive policies. Statistical institutes around the world have a part to play in that regard³⁾ . 

The research consisted of two parts. The first part, ‘The impact of anomalous weather conditions on economic sectors’, used a time series analysis to relate meteorological variables to changes in the value added of various sectors: construction, manufacturing, food & accommodation services, mining & quarrying, and energy. This method allowed us to determine the impact of anomalous weather – and that of climate change – on those sectors. It can also be used indirectly to determine the factors and climate variables with a significant economic impact. The results of this study are an update of earlier research, which will be explained below in more detail. Additionally, we will provide information on related ongoing research. 

The second part of the study, ‘Size of the economy in flood-prone areas’, used Geographic Information Systems (GIS) to combine flood risk maps with economic information from regional accounts from CBS. That information included the value added⁴⁾ , production, and employment opportunities. The study showed the scope of these variables in areas with varying levels of flood risk. 

Summary

Study 1: ‘Impact of anomalous weather conditions on economic sectors’ 

• We quantified the impact of anomalous weather, using both economic data from CBS and weather data from KNMI of the last three decades. 
• The influence of meteorological differences on the economy, such as the numbers of warm days and frost days, is crucial to ascertaining the influence of climate change on the economy; climate change will greatly affect the frequency of these extremes. 
• According to the preliminary results, the construction and manufacturing sectors are especially affected by the number of frost days, whereas the food and accommodation services sector is primarily affected by changes in the maximum temperature. 
• This study will be refined further and expanded in 2025. Among other changes, we will be adopting a greater selection of weather variables.

Study 2: ‘Size of the economy in flood-prone areas’ 

• The economy in risk areas can be measured by combining regional economic data from CBS with a map of climate risks. 
• For illustrative purposes, this study shows economic figures for several flood risk maps from the National Information System for Water and Floods (LIWO). The figures can be disaggregated by economic sector and by region⁵⁾. 
• One flood scenario concerns the flooding of areas unprotected by the dykes, which repeats once every ten years (‘high risk’ scenario). In that scenario, the flood-prone area encompasses roughly 1 percent of the GDP and Limburg and areas along the Netherlands’ major rivers are especially at risk. 
• Another flood scenario involves the breach of primary dams and dykes, which repeats once every 100,000 years (‘extremely low risk’). In this scenario, the flood-prone areas mainly consist of the low-lying areas of the Netherlands and make up around 53 percent of the GDP. 
• Additionally, the study shows visible differences between sectors. The energy sector had the largest share of businesses in flood-prone areas, compared to the smallest share for the manufacturing sector