# Extreme Climate Events and Energy Market Vulnerability: A Systematic Global Review
## 4 Impacts and Adaptation Strategies to Natural Events: A Review of Global Studies
### Europe
In the end, the authors conclude that the methodology has a good performance; however, they point out the importance of incorporating updated engineering standards and avoiding high-risk areas in planning decisions. The impact of natural disasters on energy policy has also been emphasized, specifically using the example of the Gudrun storm in Sweden in 2005, one of the most destructive storms in the region, causing substantial electricity outages and economic losses estimated at up to 3 billion euros. Among the authors' most important recommendations is to integrate climate risk analysis into the planning and management of electrical systems; in addition, the researchers point out the need to develop adaptive infrastructure to ensure sufficient continuity during extreme weather events [25]. The influence of different weather events on electricity prices has also been examined using the Italian market as a case study. The researchers developed a model that links energy demand and electricity prices with variables such as GDP, temperature, and other stochastic factors, as well as incorporating IPCC climate projections under different scenarios. The most important conclusions of the study show that electricity prices in Italy are very likely to fluctuate due to seasonal and climatic variations throughout the century. In particular, a decrease in energy demand can be observed during the winter season, while an increase is expected in summer and spring, which results in fluctuations in electricity prices [26]. Expanding on the previous findings, a study explores the delayed effects of geomagnetic storms on Czech power grids. The study focuses on the vulnerability of power lines and transformers to geomagnetically induced currents (GIC). For this, the analysis quantified the anomaly rates in the network infrastructure after periods of increased geomagnetic activity [27]. Once the measurements were made, the authors concluded that the anomaly rates on power lines increase significantly within one day after a geomagnetic storm, ranging between 5 and 10% in disturbances; however, something different happensin transformers; the effects are delayed, and the anomalies reach their peak between 2 and 3 days after the storm. With this, it was possible to say that prolonged storms increase the vulnerability of the systems, which later translates into high prices for consumers. Further exploration proposes an automatic vector regression (VAR) model. What this algorithm aims to do is to find how climate-related factors, such as temperature, solar radiation, total precipitation, and relative humidity, affect electricity prices in the Portuguese electricity system [28]. They achieved this by including meteorological data to improve the ability to capture the interaction between weather conditions and electricity prices [29]. The researchers reached different conclusions from the study; for example, extreme temperatures increase the demand for energy for heating or cooling, while variations in solar radiation affect the generation of renewable energy from solar and wind sources, which translates into fluctuating final prices. Electricity prices showed a 70% autoregressive behavior, and temperature explained up to 98% of its short-term variance. Regarding wholesale electricity markets, it was observed that atmospheric pressure is the most influential climatic factor in price variations; this may show that the Portuguese system is vulnerable to extreme changes in extreme weather conditions. This dynamic is similar elsewhere in Italy, specifically in the Puglia region (southern Italy), where the authors study the effect of climate change (in its various manifestations) on wastewater treatment plants, focusing in particular on understanding how increases in temperature and changes in precipitation patterns influence the operational capacity and economic stability of wastewater treatment plants [30]. The researchers made different projections and analyses of the different consumption patterns of these plants.