Global Climate Change and Heat Wave Research from 2010 to 2019 : An Analytical Research Review

1. Introduction

1.2. Research method and scope

In order to analyze the trends in the latest climate change-related research, information from studies published from 2010 to 2019 was collected and analyzed, with a focus on the topics of climate change adaptation, urban climate, and adaptive capacity. Using the SciVal tool, we collected and analyzed information on keywords, citation counts, and countries of publication to confirm the overall trend of research in the field of climate change. The scope of this study was further specified using “vulnerability,” “disaster,” and “urban climate,” which were derived as a result of keyword analysis. Depending on the scope of the identified studies, the final purpose of this study is to analyze the trend of research on vulnerability to disasters in cities due to climate change, especially heat wave vulnerability, which is the main focus of this study. This study compares and analyzes relevant studies that have investigated heat wave, vulnerability, and major issues in heat wave vulnerability. In particular, as part of the comprehensive climate change research trends analysis, we collected studies from various regions, including the five major countries listed in Table 1. (US, UK, Australia, Germany, China), and analyzed the trends of methods and materials in heatwave vulnerability-related research fields. Google Academic was used to help select studies published after 2010. The following key words were used individually and in combination as inclusion criteria: heat wave, vulnerability, risk, climate change, heat index (indices), multiple regression, machine learning, ANN, and deep learning.

Table 1.

IPCC Framework[2,4,5,7]

After doing a keyword search for existing studies, the review process consisted of method and material analyses. When analyzing methods, we investigated the methods and approaches used in the selected studies to understand the study objectives. When analyzing materials, we also investigated the types of data used and variables that impacted vulnerability to heat waves.

2. Heat wave vulnerability concept

The IPCC is an intergovernmental consultative body on climate change whose mission is to assess the threat of climate change to human activities. The IPCC regularly publishes reports on climate change; it has published five comprehensive reports on climate change to date. The fifth and most recent report was issued in 2014; the sixth report will be published in 2022. One of the most remarkable aspects of the transition between the 4th report, issued in 2007, and the 5th report, in 2014, was that the climate change impact assessment framework was altered. It changed from a vulnerability-oriented framework[5][6] to a risk-oriented framework[7]. The vulnerability assessment framework was largely defined as a function of exposure, sensitivity, and adaptive capacity. The risk assessment framework, on the other hand, is defined as a function of hazard, exposure, and vulnerability. Table 1. shows the detailed contents of the vulnerability and risk assessment frameworks.

Although the most recent IPCC research framework is risk-based, many studies have been conducted from the point of view of climate change vulnerability. Thus, this study adopts the vulnerability assessment framework that was the content of the 4th report (2007) to analyze previous studies(see Fig. 1.).

Fig. 1.

Vulnerability assessment framework (IPCC, 2007)

3. Analysis of research trends in the field of climate change

In order to visualize trends in the latest climate change-related research, information from papers published between 2010 and 2019 was collected and analyzed with a focus on the areas of climate change adaptation, urban climate, and adaptive capacity. The SciVal tool and Scopus data were used for the analysis. For qualitative and quantitative analysis of research trends, information on keywords, citation counts, and countries of publication was collected.

SciVal uses the Elsevier Fingerprint Engine to identify important keywords by applying a variety of natural language processing techniques to analyze the data on publications, titles, abstracts and author keywords of documents in a subject or cluster of subjects. The method uses a text mining technique and extracts a standardized keyword list for each publication to derive important keywords based on inverse document frequency (IDF) analysis.

Fig. 2. shows the top 50 keywords related to climate change adaptation, urban climate, and adaptive capacity, which were identified based on a total of 6,112 publications. In Fig. 2., the size of the words represents their relevance and green represents an increasing trend year-over-year. Accordingly, when the contents of Fig. 2. are summarized, the overall trend of publication from 2010 – 2019 in climate change-related research fields increased, and the most influential keywords were vulnerability, climate change adaptation, and resilience.

Fig. 2.

Analysis of research trends in the field of climate change (2010 – 2019)

In order to investigate trends according to a more specific research scope, an additional analysis was performed on countries/regions with the largest academic output related to the keywords vulnerability, disaster, and urban climate. The findings showed that the greatest number of papers were contributed by the US, UK, Australia, Germany, and China, in decreasing order (see Table 2.). Analyzing the number of publications by year using the same keywords confirmed that the publications were concerned with vulnerability, disaster, and urban climate. In particular, the keywords vulnerability and disaster showed a tendency to increase gradually, and all three keywords showed a tendency to increase in 2019 compared to 2018 (see Fig. 3.).

Table 2.

Top five countries and regions by scholarly output

Fig. 3.

Analysis of climate change research trends by year (2010 – 2019)

This chapter analyzes trends in urban climate change research from 2010 to 2019. Given that the number of publications containing the keywords vulnerability and disaster gradually increased over the past 10 years, we can conclude that vulnerability to disasters caused by climate change is a major global issue and an area of intense interest. This confirms the significance of the analysis of the latest trends in research in the field of heatwave vulnerability, which is the final purpose of this study.

4. Trends in Heat Wave Vulnerability Studies

This paper compares and analyzes the papers identified through Google Academic using a combination of heatwave, vulnerability, and other related keywords to examine in detail the trends in research on heatwave vulnerability. As in Chapter 3, the analysis was conducted mainly on papers published from 2010 to the present. Papers from various regions including the top five countries (USA, UK, Australia, Germany, and China) with the highest contributions were reviewed as a result of the study in Chapter 3.

20 papers were identified through the search for relevant studies. The papers were analyzed to identify major issues and topics in methods and materials.

4.１. Methodological trends in heat wave vulnerability studies

The analysis of 20 papers mainly categorized the content by research methodology into four major groups. The four categories were vulnerable (hot) spot extraction, phenomenon review, analysis of heat-related indicators, and predictive model development. Table 3. summarizes the four categories. The category of vulnerable (hot) spot extraction includes studies that identify hot spots that are vulnerable to heat waves, or areas that are extremely hot. The category of phenomenon review includes studies that review the characteristics of heat wave phenomena. The category of heat wave index analysis includes studies that extract heat-related indicators and examine correlations between those indicators.

Table 3.

The four categories of heat wave-related studies

Recent studies in the category of prediction model development have incorporated statistical, multiple regression (MR), machine learning (ML), and deep learning techniques and information from various fields. Accordingly, this category includes studies that aim to develop a predictive model by grafting various techniques onto heat wave and related indicator datasets. Table 4. summarizes the contents of these studies.

Table 4.

Techniques and test sites of papers in the prediction model development category

According to Tables 3. and 4., the test bed or sites analyzed by the relevant studies, and their corresponding methodology and focus, were mainly located in developed countries. According to the year of publication (Table 3.), most of the studies in the categories of prediction model development were published in more recent years than those in the other three categories. Additionally, according to Table 4., recent studies on heat wave prediction model development adopted relatively diverse statistical, machine learning, and deep learning-based techniques. For example, the most recently published study [27] developed a predictive model that describes thermal phenomena by grafting one of the deep learning techniques, the long short-term memory network (LSTM) technique, onto the heat island phenomenon. Recent studies of heat waves and heat wave vulnerabilities frequently sought to combine various statistical techniques and machine learning techniques, and have expanded to deep learning techniques.

4.２. Material trends in heat wave vulnerability studies

The urban component indicators used in the 23 papers were quite varied. We extracted and classified the indicators using the sectors of the IPCC vulnerability analysis framework, i.e. exposure, sensitivity, and adaptive capacity (Fig. 1.). We also identified the trend of utilization of indicators in the field of heat wave studies(Table 5.). However, because two papers[11, 12] reviewed studies relevant to the phenomenon of heat waves, we excluded them from this classification process. Table 5. summarizes the analysis of relevant indicators in heat wave studies.

Table 5.

Analysis of indicators in heat wave studies

According to Table 5., studies of heat wave vulnerability most frequently use indicators related to climate exposure and sensitivity. Moreover, the exposure sector most frequently uses indicators related to external temperature, wind, and heat wave definitions. In the sensitivity sector, population (age)-related indicators are used most frequently, followed by health-related indicators. Finally, in the sector of adaptive capacity, the green areas and medical services indicators are used most frequently.

5. Discussion and Conclusion

In order to analyze the trends in the latest climate change-related research, information from studies published from 2010 –2019 was collected and analyzed, with a focus on the topics of climate change adaptation, urban climate, and adaptive capacity. The scope of this study was made more concrete by focusing on the keywords vulnerability, disaster, and urban climate among the keywords identified by the climate change research trend analysis. As a result, five countries that frequently publish climate change-related research were identified, and the scope of the research was specified as heatwave and vulnerability. Accordingly, 20 papers were collected from Google Academic through a combination of various keywords related to heat wave vulnerability research in major regions, including the five regions derived from the results of climate change trend analysis.

Through a review of 20 publications thus identified, this study demonstrated the most recent methodological and material trends in heat wave vulnerability. The trends in Tables 3. and 4., which examine the study sites of the 20 studies, showed that most of those studies focused on sites in developed countries. However, developing countries are more vulnerable to the negative effects of heat waves than developed countries. An insufficient amount of work has been done in developing countries. It might be more difficult to collect data in developing countries due to local conditions. Or, researchers in developed countries might not be interested in issues that are primarily relevant to developing countries.

According to the analysis of methodological trends, recent studies on predictive model development have used various machine learning and deep learning techniques. Predictive models are necessary to effectively respond to the phenomenon of heat waves. Accordingly, the accuracy of predictions should be increased, and high-performance models should continue to be developed. According to the findings in material trends, the indicators used in the chosen papers are overwhelmingly concerned with the sectors of exposure and sensitivity, and relatively less concerned with the sector of adaptive capacity. Accordingly, we suggest that it is necessary to conduct more studies on the development of indicators in the sector of adaptive capacity. Also, the indicators used in those studies vary by region. It would be helpful to establish a comprehensive list of indicators in order to develop a plan to respond to heat wave vulnerabilities. Continuously development of optimal indicators for local conditions is also important.

Climate change is accelerating worldwide. Accordingly, the damage caused by unusual heat wave phenomena is emerging. It would be helpful to prepare locally optimized measures for each region and to develop a regional heat wave prediction model based on those locally optimized measures. A list of input indicators that could be used in heat wave prediction models and their associated vulnerabilities will be necessary to consider regional characteristics. Since it is important to understand the true extent and effects of heat waves and the regional characteristics of the phenomenon, practical response strategies and adaptation techniques for heat waves should be prepared for each region.

Acknowledgments

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport of Korea (20NSPS-B149840-03 and 20UMRG-B158194-01).

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