Assessing heatwave resilience in municipalities around Lake Balaton: A comparative analysis 269Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282.DOI: 10.15201/hungeobull.73.3.4 Hungarian Geographical Bulletin 73 2024 (3) 269–282. Introduction Climate change is significantly impacting almost every settlement in the world by in- creasing the magnitude and frequency of extreme weather events (Wamsler, C. et al. 2013; IPCC 2018, 2021). Although the chal- lenges are global, local-level solutions and detailed analysis are required to effectively increase the resilience of various stakehold- ers (Aboagye, P.D. and Sharifi, A. 2023). The number of publications addressing as- pects of climate resilience at the local level has increased significantly in recent years (Woodruff, S. et al. 2021; Datola, G. et al. 2022), as the need for local governments to strengthen their capacities and develop their own climate policies increases (Reckien, D. et al. 2023). The dynamically changing exter- nal factors require quantitative and qualita- tive approaches to increase the co-benefits of mitigation and adaptation activities (Sharifi, A. 2021); also to avoid unintended long-term effects, so-called lock-ins (Ürge-Vorsatz, D.. et al. 2018; Buzási, A. and Csizovszky, A. 2023). In general, vulnerability and resil- ience-oriented topics have received increas- ing attention in academia, particularly with 1 Department of Environmental Economics and Sustainability, Budapest University of Technology and Economics. Műegyetem rkp. 3. H-1111 Budapest, Hungary. Corresponding author’s e-mail: buzasi.attila@gtk.bme.hu Assessing heatwave resilience in municipalities around Lake Balaton: A comparative analysis Tamás SÁGI1 and Attila BUZÁSI 1 Abstract Changing climate patterns represent a major challenge for Hungarian municipalities, particularly with regard to the increasing severity and frequency of heatwaves. As a result of the COVID-19 lockdowns, thousands of people moved to communities around Lake Balaton; therefore, cities and villages should place more emphasis on their long-term sustainability and climate resilience. This article addresses the literature gap in assessing the heatwave resilience of Hungarian settlements, focusing on the municipalities of the Lake Balaton Resort Area. Our main objective was to uncover spatial and temporal patterns in the 180 settlements involved in the analysis by using an indicator-based comparative method. The set of indicators included nine sensitivity and six adaptive capacity measures referring to the base years 2015 and 2022. Our results show heterogeneous spatial patterns across the analysed categories; however, several regional clusters can be identified: 1) in general, set- tlements from the northern part of the study area had above-average adaptive capacity, while the southern and south-western municipalities had significantly lower values, 2) only one micro-regional cluster can be defined in terms of sensitivity values in the northern part of the study area; 3) below average resilience values were found in the south-western and southern areas; 4) finally, neither sensitivity nor adaptive capacity nor overall resilience scores had changed significantly over time at the regional level. The applied methodology can easily be adopted in other Hungarian or even Central and Eastern European cities; consequently, new results can contribute to a better understanding of inter- and intra-regional patterns of heatwave resilience at the local level. Keywords: heatwave resilience, adaptive capacity, municipalities, Lake Balaton, Hungary Received May 2024, accepted August 2024. mailto:buzasi.attila@gtk.bme.hu Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282.270 regard to heatwave-related challenges (Tong, P. 2021; Kiarsi, M. et al. 2023). There are nu- merous works in the literature that demon- strate the relevance of heatwave-related resil- ience factors through cross-country compar- isons or individual assessments (Alonso, L. and Renard, F. 2020; Arshad, A. et al. 2020; Shi, Y. et al. 2021). The growing body of lit- erature contributes to a better understanding of the general characteristics of heatwave re- silience at the local scale. However, since ad- aptation problems strongly depend on local characteristics, there is undoubtedly a need to carry out comparative assessments at the regional level in order to deepen the existing knowledge about problems at the local level. According to regional climate models, Hungary is facing significant changes in climate patterns: an increasing number of days with heatwaves (Torma, C.Z. and Kis, A. 2022; Simon, C. et al. 2023) when the aver- age daily temperature exceeds 25 °C (Uzzoli, A. et al. 2018), and heavy rainfall (Jakab, G. et al. 2019; Schmeller, G. et al. 2022) are ex- pected. Apart from these external factors, nu- merous indicators dealing with social issues point to potential sustainability problems: a decreasing size of the total population, a negative migration balance, especially in small settlements (Ritter, K. 2018), or an aging population (Menyhárt, O. et al. 2018) all contribute to the increasing vulnerability of the Hungarian society (Uzzoli, A. et al. 2018). However, more papers can be found in the literature that address local climate adaptation issues in Hungarian settlements from different perspectives (Li, S. et al. 2017; Patkós, C. et al. 2019; Szalmáné Csete, M. and Buzási, A. 2020 Szalmáné Csete, M. and Buzási, A. 2020; Kiss, E. et al. 2022; Jäger, B.S. and Buzási, A. 2023), it can be noted that comparative analyses involving a high- er number of Hungarian municipalities are still almost completely missing, which rep- resents a relevant literature gap. The same argument was put forward (Ferenčuhová, S. 2020) by analysing the existing literature on post-socialist cities in light of the challenges related to climate change. It is explained that current studies focusing on the local level of Central and Eastern Europe are quite rare and underdeveloped. Consequently, further studies and critical assessments are needed to improve the capacity of local stakeholders to address the negative impacts of climate change. Local governance and related bodies play a crucial role in reducing vulnerabili- ty at the settlement level by making climate change adaptation more integral to every- day decision-making and planning practic- es (Óvári, Á. et al. 2023). However, limited capacity in climate governance has been re- vealed by Óvári, Á. et al. (2024), therefore, the analytical assessment of non-planning aspects of Hungarian settlements can con- tribute to a better understanding of the local status of resilience production practices. Therefore, our paper aims to analyse the heatwave resilience of a large number of Hungarian settlements by applying an indi- cator method. In this topic, a limited number of previously published papers can be found in the literature. This paper focuses on the municipalities surrounding Lake Balaton, the so-called Lake Balaton Resort Area (Balaton Kiemelt Üdülőkörzet in Hungarian, and its short form “BKÜ” in the following), which involves 180 settlements. Lake Balaton has been the focus of numerous works from different scientific areas, however, the local level is rarely studied as the lake itself is taken into account instead. Most commonly, water quality and related problems, challenges, and potential opportunities have been anal- ysed in terms of climate change (Kutics, K. and Kravinszkaja, G. 2020; Istvánovics, V. et al. 2022) or human activities as main driv- ers and their impacts on water balance (Rizk, R. et al. 2021; Kocsis, M. et al. 2024). In ad- dition to the natural science studies, several papers addressed more holistic sustainability issues: Pomucz, A.B. and Csete, M. (2015), and Lőrincz, K. et al. (2020) analysed various aspects of sustainable tourism considering the Balaton region; Marton, I. (2006) devel- oped a set of indicators to describe complex development patterns at the community 271Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282. level; Molnár, T. and Molnár-Barna, K. (2019) assessed settlements in Veszprém County from a development policy perspec- tive; finally, Obádovics, C. (2020) provided population development forecasts in the Balaton resort area and argued that the pro- portion of people over 65 years of age could reach 35 percent by 2062. This latter analysis sheds light on the importance of climate-re- lated assessments in the region, taking into account the continuously aging population. The present paper reflects the literature and scientific gap by analysing the resilience patterns of the municipalities of BKÜ. The scientific novelty of this paper is based on the study area since similar research focusing on these settlements cannot be found in the liter- ature, especially not with regard to heatwave resilience. Secondly, the applied set of indica- tors derives from publicly available databas- es; therefore, it can easily be adapted to other Hungarian settlements or regions. Finally, our analysis not only provides a snapshot of resilience values but uses data from 2015 and 2022 can also reveal spatiotemporal patterns that also contribute to identifying regional differences and hotspots. Literature review In the pages of the Hungarian Geographical Bulletin, there are several examples where scientists focused on climate adaptation is- sues at the local level. One of the first results was published by Gál, T. et al. (2016) and analysed urban heat island patterns in Sze- ged using different local climate zones. Ac- cording to their results, dense urban areas are significantly hotter, than surrounding zones, which is a thought-provoking result given the increasing development rate in the BKÜ settlements over the last 10 years. Since the study area is associated with Lake Balaton as an attractive tourist destination (Medarić, Z. et al. 2021), the tourism sector and its vulnerability are relevant factors to be analysed in previously published works. Csete, M. et al. (2013) found that the Lake Balaton region has medium vulnerability with above-average exposure, which is, how- ever, complemented by high adaptive capac- ity. A more recent article from the Hungar- ian Geographical Bulletin (Kovács, A. and Király, A. 2021) assessed the climate expo- sure of tourism in Hungary and argued that a significant decline in climatic conditions is observed in summer - precisely the time when most tourists arrive in the surround- ing settlements at Lake Balaton and possibly worsened the resilience of the communities. Since an intensive urbanization process can be seen in numerous settlements from the BKÜ, those studies that focused on micro- climatic peculiarities in the sense of differ- ent or changing land use patterns should be mentioned. Gál, T. et al. (2021) analysed various cities in the Carpathian Basin regard- ing their thermal comfort issues in light of changing climate patterns in the 21st century. Their results indicate that an increase in the number of tropical nights is associated with densely populated urban areas compared to rural areas. Since Szeged is one of the most studied Hungarian cities with regard to the challenges related to climate change, another article by Kolcsár, R.A. et al. (2022) analysed the urban green space provision of different population groups. Since urban green spaces play a crucial role in mitigating the sever- ity of heatwaves. In addition, it is assumed that the settlements around Lake Balaton will become more urban in the future. Fi- nally, intensive urbanization contributes to an exacerbation of the urban heat island ef- fect, which disproportionately affects the lo- cal population, as shown Szemerédi, E. and Remsei, S. (2024) in the case of Győr. At the LAU-1 level, two comprehensive assessments can be found in the literature (Uzzoli, A. et al. 2018, 2019) that focus on micro-regional differences in heatwave vul- nerability patterns in Hungary. The set of indicators used consists of exposure, sensi- tivity and adaptive capacity indicators based on public databases. Second, Farkas, J.Z. et al. (2017) provided a detailed analysis of climate vulnerability at the regional scale by Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282.272 focusing on the Southern Great Plain and including more than 250 settlements in their studies. In addition to the results of regional climate models, the authors also measured the ecological and socioeconomic aspects of climate vulnerability using the CIVAS model (Pálvölgyi, T. and Czira, T. 2011). One of the most recent settlement-scale studies ad- dressing climate vulnerability was developed by Lennert, J. et al. (2024), who adopted the CIVAS model to develop a multi-indicator method that assesses exposure, sensitivity and adaptive capacity at a local level taken into account. The results show an above- average risk to settlements around Lake Balaton. Therefore, it prepares the ground for the present study to highlight the spati- otemporal dynamics of these settlements in terms of their heatwave resilience. Methodology The study area is located in western Hungary, embedded between the central and southern Transdanubian NUTS 2 region (Figure 1). At its heart lies Lake Balaton, a natural wonder and the largest freshwater lake in Central Europe. Within this area, there are 45 coastal settlements, 7 of which are directly adjacent to the coast and 128 of which have no direct access to it. The BKÜ includes 180 towns in the three counties of Veszprém, Somogy and Zala. According to the Hungarian Central Statistical Office database, the total popula- tion of the BKÜ is about 270,000 people, oc- cupying an area of about 3,884 km2. Several important cities shape the region’s landscape: Balatonalmádi, Balatonfűzfő and Balaton- füred are rapidly developing urban centres on the north-eastern shore of Lake Balaton. Notable attractions on the western side in- clude Tapolca, known for its natural treasures such as sea caves, and Keszthely, the third largest city in Zala County, which serves as a centre for culture, trade and education in the region. In the southeast, Zamárdi and Siófok offer special attractions. Zamárdi hosts vari- ous festivals, while Siófok offers a wealth of permanent events that appeal to both tour- ists and locals. The towns in this defined area have a vibrant culture and benefit from excel- Fig. 1. The study area situated around the Lake Balaton 273Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282. lent natural conditions, making them popular destinations for residents and visitors alike. In the following paragraphs, the socio- economic background of the study area is introduced by emphasizing selected statis- tical data. Firstly, the social composition of Hungary is characterized by aging and this phenomenon can also be observed in the study area (Figure 2). In 2015, Kisberény had the lowest aging index, with 38 elderly peo- ple per 100 children and minors. This year, Szigliget had the highest number of people aged 65 and over with 416.4 per 100 chil- dren and minors – the highest in the region. However, by 2022, this number had declined as the number of young people and children increased from 61 to 75, while the number of older people also increased from 254 to 279. In 2022, Tikos had the lowest aging in- dex with 28 elderly people per 100 teenagers and children. Notably, Tikos reduced its ag- ing index from 2015 to 2022 as the number of young people and children increased from 17 to 42, while the number of older people de- creased by 3. Meanwhile, in Balatonszepezd, the aging index increased from 305 in 2015 to 559 in 2022, indicating a significant increase in the proportion of elderly people compared to children and adolescents. Overall, the ag- ing index in the study areas was calculated as 167 in 2015, rose to 197.40 in 2022. It is worth noting that the National Aging Index was 123.6 in 2015 and 142.5 in 2022, according to the Hungarian Central Statistical Office. Secondly, the number of taxpayer under HUF 300,000 annual consolidated tax base income band per year allows us to draw conclusions about the financial situation of the area, as the more people there are, the more people live in extreme poverty. Among the municipalities in the study area, Siófok had the highest population included in the indicator for both 2015 and 2022. However, it is encouraging to note that this number has decreased from 2,313 in 2015 to 2,151 in 2022. In 2015, the municipality of Óbudavár had no residents in the lowest income group, but by 2022 the number had increased to 10. In 2022, Kékkút had the fewest inhabitants living in extreme poverty, with only 4 resi- dents, down from 13 in 2015, indicating an improvement in its situation. In the entire study area, 24,170 people belonged to this poverty group in 2015; by 2022 the number fell to 22,731. At the national level, 681,765 people belonged to one of the most vulner- able social groups in 2015, with the number decreasing slightly to 674,536 by 2022. If we Fig. 2. Aging index in selected settlements from the study area. Source: Compiled by the authors. Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282.274 examine the study area in the national con- text, we see that 3.54 percent of the country’s population in 2015 in the income range un- der HUF 300,000 lived and this percentage fell to 3.37 percent by 2022. From a climate policy perspective, it is worth noting that several settlements are members of a Hungarian umbrella orga- nization, the so-called Alliance of Climate- Friendly Settlements: Balatonfőkajár, Küngös, Zalaszántó and Gyenesdiás. However, this does not mean that every municipality has a publicly available cli- mate strategy; nevertheless, Gyenesdiás and Tapolca have uploaded their thematic strat- egies to public websites. Both policies have focused on extreme heat events with vary- ing degrees of emphasis, although Tapolca pays much more attention to this issue. In addition, Balatonfüred, Tab and Zalacsány are signatories of the Covenant of Mayors of Europe with available Sustainable Energy and Climate Action Plans or climate strat- egies, which distinguish heat waves as one of the most serious threats to everyday tour- ism, alongside above-average vulnerability of lake shores. The applied assessment methodology is based on the 2014 IPCC framework. We se- lected social, environmental and economic data from the National Spatial Development and Planning Information System (TeIR), that formulate sensitivity and adaptive capacity indicators, which would be separated into sensitivity and adaptation indicator groups. The observed years were 2015 and 2022; the starting date refers to the last year when the list of BKÜ settlements was finalized (when Balatonkenese and Balatonakarattya were divided); moreover, 2022 has begun an end- point in this analysis due to the full availabil- ity of statistical data. Table 1. summarizes the applied set of indicators, representing their names, units of measure, and years. Sensitivity indicators reflect the intrinsic characteristics of a particular system in terms of climate change; therefore, sensitivity value indicates the negative or positive effects of cli- mate change affecting the system both directly or indirectly (Füssel, H.M. and Klein, R.J.T. 2006). The indicator “number of residents per general practitioners” shows the workload of doctors. When the indicator is high, doc- tors cannot efficiently care for locals suffer- ing from diseases caused by climate change. People aged 0-2 and 60+ years are sensitive to the negative effects of climate change, such as heat waves (Smith, C.J. 2019). According to several European and North American stud- ies, there is a positive association between heat waves and mortality, with older people and women at greatest risk (McMichael, A.J. et al. 2006). Nowadays, natural gas is a criti- cal resource in Hungary due to its quantity and price. We assumed that the price of the resource is high, and locals then have to pay a higher share of their income, which reduces the well-being of the population, leading to lower adaptive capacities. The next indicator was the total number of municipal green spaces in relation to the size of the settlement. Green spaces have numer- ous benefits from a sustainability and climate adaptation perspective: they provide natural shade, increase urban biodiversity, reduce the urban heat island effect (Szabó, B. 2015), protect against UV radiation, and thereby re- duce numerous health problems (Coutts, C. and Hahn, M. 2015). The smaller the green area, the greater the sensitivity of the settle- ment; however, it is worth noting that the indicator we applied in this paper only re- fers to green spaces owned by the municipal- ity. In addition, there may also be privately owned green spaces in the municipality, which can change the status of the indicator. The indicator of the ratio of taxpayers for an annual tax base under 300,000 HUF reflects the number of the poor population, which may be economically and socially sensitive. This population cannot move away or recov- er from climate damage due to lower income; in addition, the health of poorer people is at greater risk from extreme weather events (Sarkodie, S.A. et al. 2022). The number of heat wave days is expected to increase, which will also have a negative impact on people with cardiovascular diseases, which is why 275Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282. the health of city residents is also sensitive. Therefore, we used an indicator of per capita sales of drugs for circulatory diseases and per capita attendance and visits to general practitioners, as the high values may indi- cate poor health and reflect a high level of sensitivity. Adaptation is the ability to help systems, institutions, and people adapt to the negative impacts of climate change and also provide the means to respond to the consequences (Sharma, J. and Ravindranath, N.H. 2019). Since the study area faced above-average population growth during the first years of the COVID-19 pandemic, two statistical data (the number of newly built flats per 1000 flats; population density) refer to the related challenges from a social perspective. As we mentioned in the previous part of this study, green spaces, and forests can im- prove the adaptive capacity of the analysed municipalities. Reforestation of slopes pre- vents landslides and the growth of green spaces creates a comfortable environment against heat waves (Donatti, C.I. et al. 2020). In order to describe the adaptive capacity of the selected settlements, indicators for the total municipal green space per capita and the forest area per capita were selected; higher values of both indicators mean im- proved adaptive capacity. An indicator of net domestic income per capita is an economic index that can reflect the well-being of local residents. In this case, the higher income of locals indicates higher adaptive capacity. As previously mentioned, heat wave days are expected to increase, and these negative impacts will be stressful for locals; there- fore, we aimed to monitor this effect using the household electricity indicator. Our hy- pothesis is that a higher electricity supply leads to households using air conditioning, which helps reduce heat stress. On the other hand, we must highlight that air condition- ing has two negative effects, both of which increase the sensitivity of settlements. First, they increase the urban heat island effect and are associated with enormous energy con- sumption, which is a problem when the en- ergy does not come from renewable sources (Lundgren, K. and Kjellstrom, T. 2013). Resilience and mobilization can increase adaptability because, during heat stress, which can cause illness, locals can more eas- ily reach another city with a hospital. For this reason, we chose the indicator “passenger cars per 1000 per capita”, where a higher number of indicators means higher mobi- lization and, therefore, increased adaptive capacity. As already mentioned, the nega- Table 1. The applied set of indicators for 2015 and 2022 Indicator type Name of indicator Unit Sensitivity Number of residents per general practicioners Proportion of people aged 0–2 and 60–x in relation to the resident population Gas supplied for households (without conversion) Total municipally-owned green areas in relation to the size of the settlement Ratio of taxpayers for under HUF 300,000 annual tax base Per capita sales of medicines purchased for circulatory diseases General practitioner attendances and visits per capita Number of newly built flats / 1000 flats Population density person % m3 % % person person pcs ppl/km2 Adaptive capacity Total municipally-owned green areas per capita Net domestic income per capita Forest land per capita Electricity supplied to households Passenger cars per 1000 capita Number of pharmacies and branch pharmacies per 100 capita m2/capita HUF ha kWh pcs pcs Source: Compiled by the authors. Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282.276 tive effects of climate change can affect the health of local people, which can be a major social and economic problem. Therefore, a well-developed health infrastructure is very important and also ensures an above-aver- age ability to cope with the adverse effects of climate change. To highlight the situation in municipalities, the indicator “number of pharmacies and branch pharmacies per 100 capita” was used, with a lower value indicat- ing a lower level of adaptive capacity. In this study, we applied a weighted in- dicator-based approach consisting of socio- economic and environmental data from publicly available data sources by adopting the IPCC 2014 framework of climate change vulnerability assessments. From the selected statistical data, we constructed different indi- cators for 2015 and 2022 and then categorized them into adaptation and sensitive indica- tor groups. We first calculated the minimum and maximum values of the indicator for the observed years and then normalized the indi- cators for each city. To facilitate comparison, the indicators were sorted into a range from 0 to 1 using the following equation: (1) where x’ is a normalized value, x is the value of the official indicator, min(x) and max(x) are the minimum and maximum values of the indicator. If the value of indicators showed lower resilience (Number of residents for one doctor, Proportion of people aged 0–2 and 60-x in relation to the resident population, Gas supplied for households (without con- version), Ratio of taxpayers for under HUF 300,000 annual tax base, per capita sales of medicines purchased for circulatory diseases. General practitioner attendances and visits per capita), we used the following equation: (2) We scaled the indicators in the range 0–l so that the settlements could be compared. For adaptation, a value of 0 indicates low adaptive capacity, and a value of 1 indicates this feature higher. For sensitivity, the value 0 represents high sensitivity, and the value 1 represents low performance. Then, we sepa- rated the adaptation and sensitivity indica- tors for the observed years 2015 and 2022 by calculating the average of the adaptation and sensitivity indicators and then averaged the average values of the adaptation and sensi- tivity indicators to obtain a resilience score for 180 settlements. For the overall calculated resilience score, a value of 0 means low abili- ties and a value of 1 means the opposite, rep- resenting a resilient settlement. Results In this section, we present our results using QGIS to better visualize the regional patterns we can reveal and identify based on the cal- culated categories. It is important to note that municipalities are ranked on a scale of 0 to 1. Values close to 0 indicate low adaptive capac- ity and resilience with high sensitivity, while a value of 0.5 indicates moderate levels of adaptive capacity, sensitivity and resilience. The analysed settlements were divided into different colour groups; those with values around 0 were shown in red, while those around 0.5 were shown in blue. It is note- worthy that there were minimal differences between the calculated normalized values of the municipalities. Consequently, settlements were generally ranked at around 0.5, facilitat- ing comparisons between them. The sensitivity values from 2015 and 2022 show minimal regional clustering; however, several spatial trends can be identified. First, northern settlements, especially those in the first and second tiers from the lake shore, are generally more sensitive than those in the southern part. Sensitive clusters are notable on the western side of the Tihany Peninsula, particularly around the north- western basin of Lake Balaton. A common feature of the most sensitive settlements is the frequent absence of general practitioners; in the applied methodology, we assigned a value of “0” when the number of residents 277Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282. per GP and the number of GP attendances and visits were zero, indicating the highest level of sensitivity due to the lack of avail- able healthcare services. Furthermore, the proportion of municipal green spaces was, in many cases, below average, reflecting a lower ability to contain heatwaves in these settlements. Apart from these similarities, the most sensitive settlements have different weaknesses across all indicators, without any clear clusters emerging between them. With regard to the change in values over time, Figure 3 shows a relatively stable situation without any significant shifts in the relative positions of the municipalities. The spatial characteristics of the adaptive capacity values are fundamentally different from the sensitivity performances discussed previously (Figure 4). However, a limited num- ber of settlements along the northern border of the study area may be characterized by low sensitivity and relatively high adaptive capac- ity, making them among the most resilient in the BKÜ. In general, northern coastal com- munities have significantly greater adaptive capacity compared to their southern counter- parts, particularly those settlements further from the lake. A clearly defined collection of above-average values can be seen around the Káli Basin, west of the Tihany Peninsula. This area is characterized by high well-being fac- tors, which is consistent with the existing lit- erature: the higher the well-being, the greater the ability to deal with the negative impacts of climate change. Micro-regions with low adaptive capacity can now be identified in the southwest corner and the southern part of the study. Similar to the highly sensitive Fig. 3. Sensitivity averages for the settlements of the study area, 2015 and 2022. Source: Compiled by the authors. Fig. 4. Adaptive capacity averages for the settlements of the study area, 2015 and 2022. Source: Compiled by the authors. Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282.278 settlements, two indicators contribute to the low adaptive capacity of these communities: public green spaces and the lack of pharma- cies. Consequently, it is evident that poor healthcare and limited public green spaces are the main contributors of reduced resilience to extreme heat events in the region. Since resilience values are based on the averages of sensitivity and adaptive capac- ity categories, no large regional clusters were identified in either 2015 or 2022 (Figure 5). However, highly resilient clusters of settle- ments were observed in three areas: 1) along the northern lakeside, west of the Tihany Peninsula; 2) in the north-western corner of the study area; and 3) on the opposite side of the first cluster, in some municipalities further from the lake. Meanwhile, two intraregional clusters, consisting of relatively small villages in the south-western and southern regions, exhibited the lowest resilience. As it was seen regarding sensitivity and adaptive capacity categories, the overall resilience values and quintiles remained relatively the same with- out huge changes over the analysed 7 years. Discussion In addition to the regional resilience patterns we previously identified, the relationship be- tween population size and sensitivity, adap- tive capacity and resilience requires further investigation. Using normalized data, R² cor- relation coefficients were calculated for 2015 and 2022, as shown in Table 2. The analysis shows that there is no significant correlation between population size and the analysed re- silience dimensions. This suggests that local characteristics and related socioeconomic fac- tors play a more important role in determin- ing resilience at the local level. This finding is consistent with the nature of climate adap- tation, where the effectiveness of responses largely depends on local characteristics and tailored solutions to standardized climate- related challenges. Our results highlight the importance of locality, as evidenced by cases where settlements from the highest and low- est quintiles in terms of sensitivity, adaptive capacity, and resilience are situated next to each other, despite facing similar challenges. Our study has several limitations regard- ing the applied methodology. The first co- hort of limitations relates to the selected in- dicators due to their limited availability in our analysis. The set of indicators would be Fig. 5. Resilience averages for the settlements of the study area, 2015 and 2022. Source: Compiled by the authors. Table 2. Correlation coefficients between population size and the analysed categories Category Year R2 values Sensitivity 2015 2022 0.152 0.160 Adaptive capacity 2015 2022 0.112 0.148 Resilience 2015 2022 0.075 0.028 Source: Compiled by the authors. 279Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282. expanded to include measures highlighting the speed and effectiveness of emergency responses, the availability of higher-level health facilities, and heatwave-related mor- tality and morbidity incidence, to name just a few statistical indicators. Apart from this, it would be useful to study land use and land cover dynamics over a longer period of time to identify the changes in artificial and natu- ral areas that play a crucial role in identifying vulnerable settlements. The second limitation has its origins in the comparative nature of our analysis. Although the selected indica- tors are available for all settlements, a com- parative study always encounters significant limitations due to the very different sizes and capabilities of the settlements. In our study, the number of municipalities analysed is 180, meaning that settlements with inherently different socioeconomic, environmental and institutional backgrounds were involved. Consequently, the calculated resilience, sen- sitivity and adaptive capacity values should be interpreted with this limitation in mind and accept the fact that all colours and values are based on the study area and do not have universal meaning. In addition to the limitations mentioned here, some future research directions can also be identified: 1) detailed analysis of land use and land cover changes over time using remote sensing data for all settlements; 2) calculation of heatwave-related mortality to reveal regional patterns in the most vul- nerable communities. Conclusions The aim of this study was to analyse the heatwave resilience of BKÜ settlements by using a set of indicators, including aspects of sensitivity and adaptive capacity. For this purpose, we collected social, economic and environmental statistical indicators from the National Information System for Spatial Development and Planning; then, average values for each category were calculated us- ing the normalization method to compare and synthesize different units. Our dataset covered the years 2015 and 2022 to analyse changes in individual values and regional patterns over time. The number of assessed settlements allowed us to draw conclusions about the applicability of our methodology with a view to a possible future analysis of Hungarian settlements: it can be noted that all indicators are available at the local level; therefore, the calculations can be easily re- peated to assess heatwave aspects in the Car- pathian Basin. Our results also shed light on previously unknown regional patterns. Regarding the adaptive capacity values, the northern part of the BKÜ can be charac- terized by higher values, while the southern and west-south micro-regions had below- average values and, therefore, lower adap- tive capacity. However, intraregional clus- ters cannot be formulated in either 2015 or 2022. Furthermore, these values appeared to be quite stable over time. Finally, the overall resilience values showed the same spatial characteristics over time: north-eastern and north-western settlements were less vulnera- ble, while south-western communities can be characterized as less resilience to heatwave problems. Our study can pave the way for further analysis of the heatwave resilience of different Hungarian cities and villages based on the easy-to-apply methodology and coun- try-specific set of indicators. Although there is an emerging gap in the literature in this research area, this paper can contribute to filling this gap and drawing the attention of policy makers to climate adaptation aspects on a local level as a new input for planning processes. Acknowledgements: This research was funded by the Sustainable Development and Technologies National Programme of the Hungarian Academy of Sciences (FFT NP FTA). The authors declare that they have no known compet- ing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282.280 REFERENCES Aboagye, P.D. and Sharifi, A. 2023. Post-fifth as- sessment report urban climate planning: Lessons from 278 urban climate action plans released from 2015 to 2022. Urban Climate 49. 101550. https://doi. org/10.1016/j.uclim.2023.101550 Alonso, L. and Renard, F. 2020. A comparative study of the physiological and socio-economic vulner- abilities to heat waves of the population of the metropolis of Lyon (France) in a climate change context. International Journal of Environmental Research and Public Health 17. (3): 1004. https://doi. org/10.3390/ijerph17031004 Arshad, A., Ashraf, M., Sundari, R.S., Qamar, H., Wajid, M. and Hasan, M. 2020. Vulnerability assessment of urban expansion and modelling green spaces to build heat waves risk resiliency in Karachi. International Journal of Disaster Risk Reduction 46. (February): 101468. https://doi. org/10.1016/j.ijdrr.2019.101468 Buzási, A. and Csizovszky, A. 2023. Urban sustain- ability and resilience: What the literature tells us about “lock-ins”? Ambio 52. (3): 616–630. https:// doi.org/10.1007/s13280-022-01817-w Coutts, C. and Hahn, M. 2015. Green infrastructure, ecosystem services, and human health. International Journal of Environmental Research and Public Health 12. (8): 9768–9798. https://doi.org/10.3390/ijerph120809768 Csete, M., Pálvölgyi, T. and Szendrő, G. 2013. Assessment of climate change vulnerability of tour- ism in Hungary. Regional Environmental Change 13. (5): 1043–1057. https://doi.org/10.1007/s10113-013-0417-7 Datola, G., Bottero, M., De Angelis, E. and Romagnoli, F. 2022. Operationalising resilience: A methodological framework for assessing ur- ban resilience through System Dynamics Model. Ecological Modelling 465. (C): 109851. https://doi. org/10.1016/j.ecolmodel.2021.109851 Donatti, C.I., Harvey, C.A., Hole, D., Panfil, S.N. and Schurman, H. 2020. Indicators to measure the climate change adaptation outcomes of ecosystem- based adaptation. Climatic Change 158. (3–4): 413–433. https://doi.org/10.1007/s10584-019-02565-9 Farkas, J.Z., Hoyk, E. and Rakonczai, J. 2017. Geographical analysis of climate vulnerability at a regional scale: The case of the southern great plain in Hungary. Hungarian Geographical Bulletin 66. (2): 129–144. https://doi.org/10.15201/hungeobull.66.2.3 Ferenčuhová, S. 2020. Not so global climate change? Representations of post-socialist cities in the aca- demic writings on climate change and urban areas. Eurasian Geography and Economics 61. (6): 686–710. https://doi.org/10.1080/15387216.2020.1768134 Füssel, H.M. and Klein, R.J.T. 2006. Climate change vulnerability assessments: An evolution of con- ceptual thinking. Climatic Change 75. (3): 301–329. https://doi.org/10.1007/s10584-006-0329-3 Gál, T., Skarbit, N. and Unger, J. 2016. Urban heat island patterns and their dynamics based on an urban climate measurement network. Hungarian Geographical Bulletin 65. (2): 105–116. https://doi. org/10.15201/hungeobull.65.2.2 Gál, T., Skarbit, N., Molnár, G. and Unger, J. 2021. Projections of the urban and intra-urban scale thermal effects of climat change in the 21st cen- tury for cities in the Carpathian Basin. Hungarian Geographical Bulletin 70. (1): 19–33. https://doi. org/10.15201/hungeobull.70.1.2 IPCC 2018. Global warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change. Geneva, IPCC. Available at https:// www.ipcc.ch/sr15/ IPCC 2021. Climate Change 2021. The Physical Science Basis. The Working Group I contribution to the Sixth Assessment Report addresses the most up-to- date physical understanding of the climate system and climate change, bringig together the latest advances in climate science. Intergovernmental Panel on Climate Change. Geneva, IPCC. Available at https://www.ipcc.ch/report/ar6/wg1/ Istvánovics, V., Honti, M., Torma, P. and Kousal, J. 2022. Record-setting algal bloom in polymictic Lake Balaton (Hungary): A synergistic impact of climate change and (mis)management. Freshwater Biology 67. (6): 1091–1106. https://doi.org/10.1111/ fwb.13903 Jäger, B.S. and Buzási, A. 2023. Adaptation to climate change at district level in the case of Budapest, Hungary. Geographia Polonica 96. (2): 221–237. https://doi.org/10.7163/GPol.0252 Jakab, G., Bíró, T., Kovács, Z., Papp, Á., Sarawut, N., Szalai, Z., Madarász, B. and Szabó, S. 2019. Spatial analysis of changes and anomalies of in- tense rainfalls in Hungary. Hungarian Geographical Bulletin 68. (3): 241–253. https://doi.org/10.15201/ hungeobull.68.3.3 Kiarsi, M., Amiresmaili, M., Mahmoodi, M.R., Farahmandnia, H., Nakhaee, N., Zareiyan, A. and Aghababaeian, H. 2023. Heat waves and adapta- tion: A global systematic review. Journal of Thermal Biology 116. 103588. https://doi.org/10.1016/j.jther- bio.2023.103588 Kiss, E., Balla, D. and Kovács, A.D. 2022. Characteristics of climate concern – attitudes and personal ac- tions. A case study of Hungarian settlements. Sustainability (Switzerland) 14. (9): https://doi. org/10.3390/su14095138 Kocsis, M., Pásztor, L., Makó, A., Kassai, P., Csermák, K., Csermák, A., Aradvári-Tóth, E. and Szatmári, G. 2024. Geospatial data on the sedi- ments of Lake Balaton. Scientific Data 11. (1): 1–5. https://doi.org/10.1038/s41597-024-02936-7 https://doi.org/10.1016/j.uclim.2023.101550 https://doi.org/10.1016/j.uclim.2023.101550 https://doi.org/10.3390/ijerph17031004 https://doi.org/10.3390/ijerph17031004 https://doi.org/10.1016/j.ijdrr.2019.101468 https://doi.org/10.1016/j.ijdrr.2019.101468 https://doi.org/10.1007/s13280-022-01817-w https://doi.org/10.1007/s13280-022-01817-w https://doi.org/10.3390/ijerph120809768 https://doi.org/10.1007/s10113-013-0417-7 https://doi.org/10.1016/j.ecolmodel.2021.109851 https://doi.org/10.1016/j.ecolmodel.2021.109851 https://doi.org/10.1007/s10584-019-02565-9 https://doi.org/10.15201/hungeobull.66.2.3 https://doi.org/10.1080/15387216.2020.1768134 https://doi.org/10.1007/s10584-006-0329-3 https://doi.org/10.15201/hungeobull.65.2.2 https://doi.org/10.15201/hungeobull.65.2.2 https://doi.org/10.15201/hungeobull.70.1.2 https://doi.org/10.15201/hungeobull.70.1.2 https://www.ipcc.ch/sr15/ https://www.ipcc.ch/sr15/ https://www.ipcc.ch/report/ar6/wg1/ https://doi.org/10.1111/fwb.13903 https://doi.org/10.1111/fwb.13903 https://doi.org/10.7163/GPol.0252 https://doi.org/10.15201/hungeobull.68.3.3 https://doi.org/10.15201/hungeobull.68.3.3 https://doi.org/10.1016/j.jtherbio.2023.103588 https://doi.org/10.1016/j.jtherbio.2023.103588 https://doi.org/10.3390/su14095138 https://doi.org/10.3390/su14095138 https://doi.org/10.1038/s41597-024-02936-7 281Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282. Kolcsár, R.A., Csete, Á.K., Kovács-Győri, A. and Szilassi, P. 2022. Age-group-based evaluation of residents’ urban green space provision: Szeged, Hungary. A case study. Hungarian Geographical Bulletin 71. (3): 249–269. https://doi.org/10.15201/ hungeobull.71.3.3 Kovács, A. and Király, A. 2021. Assessment of climate change exposure of tourism in Hungary using observations and regional climate model data. Hungarian Geographical Bulletin 70. (3): 215–231. https://doi.org/10.15201/hungeobull.70.3.2 Kutics, K. and Kravinszkaja, G. 2020. Lake Balaton hydrology and climate change. Ecocycles 6. (1): 88–97. https://doi.org/10.19040/ecocycles.v6i1.165 Lennert, J., Koós, B. and Vasárus, G.L. 2024. A mag- yarországi klímasérülékenység területi különbségei (Spatial differences in the climate vulnerability in Hungary). Tér és Társadalom 38. (2): 103–129. https:// doi.org/10.17649/TET.38.2.3525 Li, S., Juhász-Horváth, L., Pedde, S., Pintér, L., Rounsevell, M.D.A. and Harrison, P.A. 2017. Integrated modelling of urban spatial develop- ment under uncertain climate futures: A case study in Hungary. Environmental Modelling and Software 96. 251–264. https://doi.org/10.1016/j. envsoft.2017.07.005 Lőrincz, K., Banász, Z. and Csapó, J. 2020. Customer involvement in sustainable tourism planning at Lake Balaton, Hungary-analysis of the con- sumer preferences of the active cycling tourists. Sustainability (Switzerland) 12. (12): 1–18. https:// doi.org/10.3390/su12125174 Lundgren, K. and Kjellstrom, T. 2013. Sustainability challenges from climate change and air condition- ing use in urban areas. Sustainability (Switzerland) 5. (7): 3116–3128. https://doi.org/10.3390/su5073116 Marton, I. 2006. Települések fejlettségének komplex statisztikai elemzése a Balaton régió példáján (Complex statistical analysis of the development of settlements on the example of the Lake Balaton region). Területi Statisztika 9. (46): 255–263. McMichael, A.J., Woodruff, R.E. and Hales, S. 2006. Climate change and human health: present and future risks. The Lancet 367. (9513): 859–869. https:// doi.org/10.1016/S0140-6736(06)68079-3 Medarić, Z., Sulyok, J., Kardos, S. and Gabruč, J. 2021. Lake Balaton as an accessible tourism desti- nation – the stakeholders` perspectives. Hungarian Geographical Bulletin 70. (3): 233–247. https://doi. org/10.15201/hungeobull.70.3.3 Menyhárt, O., Fekete, J.T. and Győrffy, B. 2018. Demographic shift disproportionately increases cancer burden in an aging nation: Current and expected incidence and mortality in Hungary up to 2030. Clinical Epidemiology 10. 1093–1108. https:// doi.org/10.2147/CLEP.S155063 Molnár, T. and Molnár-Barna, K. 2019. Social and economic development of settlements of Veszprém County. DETUROPE – The Central European Journal of Tourism and Regional Development 11. (2): 169–184. https://doi.org/10.32725/det.2019.021 Obádovics, C. 2020. A Balaton régió népességdina- mikája 2017–2062 között (Population dynamics in the Balaton region between 2017 and 2062). Földrajzi Közlemények 144. (1): 27–42. https://doi.org/10.32643/ fk.144.1.3. Óvári, Á., Kovács, A.D. and Farkas, J.Z. 2023. Assessment of local climate strategies in Hungarian cities. Urban Climate 49. 101465. https://doi. org/10.1016/j.uclim.2023.101465 Óvári, Á., Farkas, J.Z. and Kovács, A.D. 2024. A klímavédelem realitásai a hazai városokban (Climate planning and governance in Hungarian cities). Tér és Társadalom 38. (1): 110–128. https://doi. org/10.17649/TET.38.1.3512 Pálvölgyi, T. and Czira , T. 2011. Éghajlati sérülékenység a kistérségek szintjén (Climate change related vulnerability on the level of mi- cro-regions). In Sebezhetőség és adaptáció – a reziliencia esélyei. Eds.: Tamás, P. and Bulla, M., Budapest, MTA Szociológiai Kutatóintézet, 237–252. Patkós, C., Radics, Z., Tóth, J.B., Kovács, E., Csorba, P., Fazekas, I., Szabó, G. and Tóth, T. 2019. Climate and energy governance perspectives from a munic- ipal point of view in Hungary. Climate 7. (8): 1–18. https://doi.org/10.3390/cli7080097 Pomucz, A.B. and Csete, M. 2015. Sustainability assess- ment of Hungarian lakeside tourism development. Periodica Polytechnica Social and Management Sciences 23. (2): 121–132. https://doi.org/10.3311/PPso.7506 Reckien, D., Buzasi, A., Olazabal, M., Spyridaki, N., Eckersley, P., Simoes, S.G., Salvia, M., Pietrapertosa, F., Fokaides, P.A., Goonesekera, S.M., Tardieu, L., Balzan, M.V., de Boer, C., De Gregorio Hurtado, S., Feliu, E., Flamos, A., Foley, A., Geneletti, D., Grafakos, S., Heidrich, O., Ioannou, B., Krook-Riekkola, A., Matosović, M., Orru, H., Orru, K., Paspaldzhiev, I., Rižnar, K., Smigaj, M., Szalmáné Csete, M., Viguié, V. and Wejs, A. 2023. Quality of urban climate adaptation plans over time. npj Urban Sustainability 3. 1–14. https://doi.org/10.1038/s42949-023-00085-1 Ritter, K. 2018. Special features and problems of rural society in Hungary. Studia Mundi – Economica 5. (1): 98–112. https://doi.org/10.18531/Studia. Mundi.2018.05.01.98-112 Rizk, R., Alameraw, M., Rawash, M.A., Juzsakova, T., Domokos, E., Hedfi, A., Almalki, M., Boufahja, F., Gabriel, P., Shafik, H.M. and Rédey, Á. 2021. Does Lake Balaton affected by pollution? Assessment through surface water quality monitoring by us- ing different assessment methods. Saudi Journal of Biological Sciences 28. (9): 5250–5260. https://doi. org/10.1016/j.sjbs.2021.05.039 Sarkodie, S.A., Ahmed, M.Y. and Owusu, P.A. 2022. Global adaptation readiness and income mitigate https://doi.org/10.15201/hungeobull.71.3.3 https://doi.org/10.15201/hungeobull.71.3.3 https://doi.org/10.15201/hungeobull.70.3.2 https://doi.org/10.19040/ecocycles.v6i1.165 https://doi.org/10.17649/TET.38.2.3525 https://doi.org/10.17649/TET.38.2.3525 https://doi.org/10.1016/j.envsoft.2017.07.005 https://doi.org/10.1016/j.envsoft.2017.07.005 https://doi.org/10.3390/su12125174 https://doi.org/10.3390/su12125174 https://doi.org/10.3390/su5073116 https://doi.org/10.1016/S0140-6736(06)68079-3 https://doi.org/10.1016/S0140-6736(06)68079-3 https://doi.org/10.15201/hungeobull.70.3.3 https://doi.org/10.15201/hungeobull.70.3.3 https://doi.org/10.2147/CLEP.S155063 https://doi.org/10.2147/CLEP.S155063 https://doi.org/10.32725/det.2019.021 https://doi.org/10.32643/fk.144.1.3. https://doi.org/10.32643/fk.144.1.3. https://doi.org/10.1016/j.uclim.2023.101465 https://doi.org/10.1016/j.uclim.2023.101465 https://doi.org/10.17649/TET.38.1.3512 https://doi.org/10.17649/TET.38.1.3512 https://doi.org/10.3390/cli7080097 https://doi.org/10.3311/PPso.7506 https://doi.org/10.1038/s42949-023-00085-1 https://doi.org/10.18531/Studia.Mundi.2018.05.01.98-112 https://doi.org/10.18531/Studia.Mundi.2018.05.01.98-112 https://doi.org/10.1016/j.sjbs.2021.05.039 https://doi.org/10.1016/j.sjbs.2021.05.039 Sági, T. and Buzási, A. Hungarian Geographical Bulletin 73 (2024) (3) 269–282.282 sectoral climate change vulnerabilities. Humanities and Social Sciences Communications 9. (1): 1–17. https://doi.org/10.1057/s41599-022-01130-7 Schmeller, G., Nagy, G., Sarkadi, N., Cséplő, A., Pirkhoffer, E., Geresdi, I., Balogh, R., Ronczyk, L. and Czigány, S. 2022. Trends in extreme precip- itation events (SW Hungary) based on a high-den- sity monitoring network. Hungarian Geographical Bulletin 71. (3): 231–247. https://doi.org/10.15201/ hungeobull.71.3.2 Sharifi, A. 2021. Co-benefits and synergies between urban climate change mitigation and adaptation measures: A literature review. Science of The Total Environment 750. 141642. https://doi.org/10.1016/j. scitotenv.2020.141642 Sharma, J. and Ravindranath, N.H. 2019. Applying IPCC 2014 framework for hazard-specific vul- nerability assessment under climate change. Environmental Research Communications 1. (5): 051004. https://doi.org/10.1088/2515-7620/ab24ed Shi, Y., Ren, C., Luo, M., Ching, J., Li, X., Bilal, M., Fang, X. and Ren, Z. 2021. Utilizing world urban database and access portal tools (WUDAPT) and machine learning to facilitate spatial estimation of heatwave patterns. Urban Climate 36. (February): 100797. https://doi.org/10.1016/j.uclim.2021.100797 Simon, C., Kis, A. and Torma, C.Z. 2023. Temperature characteristics over the Carpathian Basin – project- ed changes of climate indices at regional and local scale based on bias-adjusted CORDEX simulations. International Journal of Climatology 43. (8). 3552–3569. https://doi.org/10.1002/joc.8045 Smith, C.J. 2019. Pediatric thermoregulation: Considerations in the face of global climate change. Nutrients 11. (9): 15–18. https://doi.org/10.3390/ nu11092010 Szabó, B. 2015. A városi zöldfelületek hatása a város klímájára (Impact of urban green areas on the ur- ban climate). BSc Thesis. Budapest, ELTE Eötvös Loránd Tudományegyetem, Meteorológiai Tanszék. Available at https://nimbus.elte.hu/tanszek/docs/ BSc/2015/SzaboBeata_2015.pdf Szalmáné Csete, M. and Buzási, A. 2020. Hungarian regions and cities towards an adaptive future – analysis of climate change strategies on different spatial levels. Időjárás 124. (2): 253–276. https://doi. org/10.28974/idojaras.2020.2.6 Szemerédi, E. and Remsei, S. 2024. Disproportionate exposure to urban heat island intensity – The case study of Győr, Hungary. Hungarian Geographical Bulletin 73. (1): 17–33. https://doi.org/10.15201/ hungeobull.73.1.2 Tong, P. 2021. Characteristics, dimensions and meth- ods of current assessment for urban resilience to climate-related disasters: A systematic review of the literature. International Journal of Disaster Risk Reduction 60. (September): 102276. https://doi. org/10.1016/j.ijdrr.2021.102276 Torma, C.Z. and Kis, A. 2022. Bias-adjustment of high-resolution temperature CORDEX data over the Carpathian region: Expected changes including the number of summer and frost days. International Journal of Climatology 42. (12): 6631–6646. https://doi. org/10.1002/joc.7654 Ürge-Vorsatz, D., Rosenzweig, C., Dawson, R.J., Sanchez Rodriguez, R., Bai, X., Salisu Barau, A., Seto, K.C. and Dhakal, S. 2018. Locking in positive climate responses in cities Adaptation-mitigation interdependencies. Nature Climate Change 8. 174–185. https://doi.org/10.1038/s41558-018-0100-6 Uzzoli, A., Szilágyi, D. and Bán, A. 2018. Climate vulnerability regarding heat waves – A case study in Hungary. DETUROPE – The Central European Journal of Tourism and Regional Development 10. (3): 53–69. https://doi.org/10.32725/det.2018.023 Uzzoli, A., Szilágyi, D. and Bán, A. 2019. Az égha j la tvá l tozás egészségkockázata i és népegészségügyi következményei – A hőhullámok- kal szembeni sérülékenység területi különbségei Magyarországon. (Health risks and public health consequences of climate change – Climate vul- nerability regarding heat waves and its regional differences in Hungary). Területi Statisztika 59. (4): 400–425. https://doi.org/10.15196/TS590403 Wamsler, C., Brink, E. and Rivera, C. 2013. Planning for climate change in urban areas: From theory to practice. Journal of Cleaner Production 50. 68–81. https://doi.org/10.1016/j.jclepro.2012.12.008 Woodruff, S., Bowman, A.O.M., Hannibal, B., Sansom, G. and Portney, K. 2021. Urban resil- ience: Analyzing the policies of U.S. cities. Cities 115. (May): 103239. https://doi.org/10.1016/j.cit- ies.2021.103239 https://doi.org/10.1057/s41599-022-01130-7 https://doi.org/10.15201/hungeobull.71.3.2 https://doi.org/10.15201/hungeobull.71.3.2 https://doi.org/10.1016/j.scitotenv.2020.141642 https://doi.org/10.1016/j.scitotenv.2020.141642 https://doi.org/10.1088/2515-7620/ab24ed https://doi.org/10.1016/j.uclim.2021.100797 https://doi.org/10.1002/joc.8045 https://doi.org/10.3390/nu11092010 https://doi.org/10.3390/nu11092010 https://nimbus.elte.hu/tanszek/docs/BSc/2015/SzaboBeata_2015.pdf https://nimbus.elte.hu/tanszek/docs/BSc/2015/SzaboBeata_2015.pdf https://doi.org/10.28974/idojaras.2020.2.6 https://doi.org/10.28974/idojaras.2020.2.6 https://doi.org/10.15201/hungeobull.73.1.2 https://doi.org/10.15201/hungeobull.73.1.2 https://doi.org/10.1016/j.ijdrr.2021.102276 https://doi.org/10.1016/j.ijdrr.2021.102276 https://doi.org/10.1002/joc.7654 https://doi.org/10.1002/joc.7654 https://doi.org/10.1038/s41558-018-0100-6 https://doi.org/10.32725/det.2018.023 https://doi.org/10.15196/TS590403 https://doi.org/10.1016/j.jclepro.2012.12.008 https://doi.org/10.1016/j.cities.2021.103239 https://doi.org/10.1016/j.cities.2021.103239