Intensity Of Natural Disasters Research Articles

POLÍTICAS PÚBLICAS HUMANITÁRIAS PORTUGUESAS: DA VISÃO DAS CONDICIONANTES À BREVE VISÃO DA PRÁTICA ASSOCIADA AOS COSTUMES

This article aims to an integrative literature review of humanitarian public policies in Portugal to understand how these policies are developed and implemented in the country. To this end, we will analyze the European Union and Portugal regulatory legislation, which encompasses the topic from different perspectives, to provide a sufficiently comprehensive view of the subject. Humanitarian public policies have been gaining prominence in the international scenario, especially given the frequency and intensity of natural disasters and humanitarian crises. In Portugal, these policies are influenced by such regulatory documents, from EU legislation to the Portuguese Constitution, encompassing relevant laws and culminating in the implementation of strategies, plans, and government programs with their humanitarian characteristics and approaches to resilience and humanitarian action determinants, permeating the practice of action related to the country's customs. The literature review conducted in this article seeks to contribute to the understanding of humanitarian public policies in Portugal and provide a general overview and support for academic debate on the subject.

Regional Climate Change and Drought Dynamics in Tunceli, Turkey: Insights from Drought Indices

Global warming and climate change are causing temperatures to rise, which is having a negative impact on water resources. Climate change is a worldwide problem that affects and will continue to affect the frequency and intensity of natural disasters in many regions of the world. Tunceli region in Turkey, which until 10 years ago was known as an environmentally friendly city with abundant water resources and frequent rainfall, is experiencing a decrease in precipitation during the snowy winter season. This situation has made the investigation of climate change impacts an important issue in the region. Therefore, effective climate change adaptation strategies need to be developed. To determine these strategies, in this study, we assessed long-term drought conditions using multiple drought indices such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Normal Precipitation Index (PNI), and Aridity Index (AI). The SPI and RDI analyses were performed in annual reference periods on a time scale of 3, 6, 9, and 12 months using temperature, precipitation, and evaporation data. Consequently, the SPI and RDI results were compared, and both indices show similar behavior in dry, wet, and normal seasons. Nevertheless, RDI shows less variation between different time scales, which is an advantage over SPI and is probably due to the inclusion of potential evapotranspiration in RDI. The variations in PNI between humid and dry sub-humid categorizations throughout the years, combined with the AI results, indicate that the Tunceli region predominantly experiences a climate ranging from dry sub-humid to semi-arid. This study could help decision-makers take effective measures to become more resilient to climate change in temperate climate regions and take important steps toward sustainable water resources management.

United States Federal Policies Contributing to Health and Health Care Inequities in Puerto Rico.

Puerto Rico, a territory of the United States since 1898, has recently experienced an increasing frequency and intensity of natural disasters and public health emergencies. In 2022, Hurricane Fiona became the latest storm to attract media attention and cast a light on Puerto Rico's deteriorating conditions, including infrastructural failings, health care provider shortages, and high levels of chronic illness. Although recent events have been uniquely devastating, decades of inequitable US federal policy practices have fueled the persistence of health inequities in the territory. Here we demonstrate how existing health and health care inequities in Puerto Rico have been exacerbated by compounding disasters but are rooted in the differential treatment of the territory under US federal policies. Specifically, we focus on the unequal US Federal Emergency Management Agency response to disasters in the territory, the lack of parity in federal Medicaid funding for Puerto Rico, and Puerto Rico's limited political power as a territory of the United States. We also provide empirically supported policy recommendations aimed at reducing health and health care inequities in the often-forgotten US territory of Puerto Rico. (Am J Public Health. 2024;114(S6):S478-S484. https://doi.org/10.2105/AJPH.2024.307585) [Formula: see text].

Optimization-driven artificial intelligence-enhanced municipal waste classification system for disaster waste management

This research addresses the critical challenge of disaster waste management, a growing concern exacerbated by the increasing frequency and intensity of natural disasters like flooding. Traditional waste systems often struggle with the volume and heterogeneity of disaster waste, highlighting the need for innovative solutions. In this study, we present a novel disaster waste classification model integrating advanced artificial intelligence (AI) and optimization techniques to streamline waste categorization in post-disaster environments. Our approach leverages a dual ensemble deep learning framework. The first ensemble combines various image-segmentation methods, while the second integrates outputs from diverse convolutional neural network architectures. A modified artificial multiple intelligence system serves as a decision fusion strategy, enhancing accuracy at both ensemble points. We rigorously evaluated our model using three datasets: the “TrashNet” dataset for benchmarking against existing methods, as well as two meticulously curated, real-world datasets collected from flood-affected areas in Thailand. The results demonstrate that our method outperforms existing algorithms like VGG19, YoloV5, and InceptionV3 in general solid waste classification, achieving an average improvement of 11.18%. Regarding disaster waste specifically, our model achieves 96.48% and 96.49% accuracy on the curated datasets, consistently outperforming ResNet-101, DenseNet-121, and InceptionV3 by an average of 3.47%. These findings demonstrate the potential of our AI-enhanced model to revolutionize disaster waste management practices. Thus, we advocate integrating such technologies into municipal waste management policies to enhance resilience and optimize disaster responses. Future research will explore scaling the model to diverse disaster types and incorporating real-time data for adaptable waste management strategies.

A Multi-Hazard Risk Assessment Model for a Road Network Based on Neural Networks and Fuzzy Comprehensive Evaluation

The frequency of extreme weather events has increased worldwide, leading to more intense natural disasters, which pose significant threats to human life and property safety. The main form of disaster occurrence is multi-hazard coupling and multi-hazard chaining. This paper constructs a road natural disaster risk assessment model using a fuzzy comprehensive evaluation method and neural network to quantitatively analyze road disasters with multiple hazards, and provides valuable insights for the predication of road natural disaster risk. Here, ten factors, including temperature, relative humidity, precipitation, elevation, slope, slope orientation, vegetation cover, geologic lithology, historical impact factors, and road density, were selected as input variables, and risk grade was selected as the output value (the evaluation results). The remaining hidden layers use the fully connected neural network. This model was first trained using historical data (from 2011 to 2021) obtained from road networks and natural disasters in Guangxi, China. Then, taking Lingchuan County as an example, the model was used to predict the risk of natural disasters on its roads, and, finally, the prediction accuracy of the model was determined by comparing the results with actual disaster situations. This study can provide theoretical support and technical operations for the development of subsequent early warning systems.

MONETARY POLICY UNDER NATURAL DISASTER SHOCKS

AbstractWith climate change increasing the frequency and intensity of natural disasters, what should central banks do in response to these catastrophic events? Looking at IMF reports for 34 disaster‐years, which occurred in 16 disaster‐prone countries from 1999 to 2017, reveals lack of any systematic approach adopted by monetary authorities in response to climate shocks. Using a small‐open‐economy New‐Keynesian model with disaster shocks, we show that consistent with textbook theory, inflation targeting remains the welfare‐optimal regime. Therefore, the best strategy for monetary authorities is to resist the impulse of accommodating in response to catastrophic natural disasters, and focus on price stability.

Ontology-based crisis simulation system for population sheltering management

Climate change has led to an increase in the frequency and intensity of natural disasters, necessitating the development of efficient crisis management strategies for population sheltering. However, existing research on this topic primarily focuses on the use of public resources such as ambulances and fire trucks, which may sometimes be insufficient due to high demand and impacted locations, worsening the shortage of resources. This research introduces an ontology-based crisis simulation system for population sheltering management that focuses on the integration and distribution of citizen–volunteer drivers/vehicles into the evacuation process. Recognizing the limitations of public resources in current crisis management models, our approach incorporates citizen resources to enhance overall evacuation capacity. We develop an ontology to standardize crisis management knowledge, frame vehicle distribution as a recommendation problem, and design a simulation module incorporating a constraint-based recommender system. The proposed scenario illustrates how the simulation system can recommend citizen resources during crisis situations by considering the constraints to be satisfied. With our system, we aim at helping stakeholders to be prepared for various disaster scenarios: optimizing resource allocation and reducing time to make decisions by decision-makers.

Standardized Precipitation Index (SPI)-Based Flood and Drought Hotspot Mapping in Niger, West Africa

Niger is a Sahelian territory in West Africa, with a surface area of 1,267,000 km². Most of it is desert, sparsely populated, or uninhabitable, with a low population density of around 18 inhabitants per km². The adverse effects of climate change and the frequency and intensity of natural disasters are increasing, while the country is evolving in a context characterised by a lack of and/or limited access to detailed, reliable, and up-to-date data, useful for better risk and disaster management. The methodological approach was based on the use of the agroecological map of Niger offering four main zones (Sudanian, agropastoral, pastoral and desert), the calculation and projection on this map of the Standardized Precipitation Index (SPI) of 3935 localities based on the rainfall proxy data series (1979-2035), and then the identification of zones impacted by extreme climatic events. The populations impacted in these identified areas are estimated by projecting onto these areas the updated population of more than 36,000 georeferenced localities in ReNaLoc. The result of this approach shows that it is possible to opportunely identify zones and collect key data for use in the disaster risk management process in the context of a country where data is difficult to acquire. Keyword: Floods, Droughts, SPI, Extreme event, Risk Management Parameter, Characterisation, Impact, Niger.

Development of a Simple Design Method for an Infiltration Facility using Curve Number

As climate change accelerates, the scale and intensity of natural disasters are increasing, and the frequency of localized heavy rains is increasing rapidly. Urban watersheds have a high ratio of impervious areas. So there is a high possibility of flooding in the event of torrential rain, and large-scale damage can occur because the population and material resources are highly concentrated. River improvements, sewerage maintenance, and rainwater storage installations are frequently used to reduce flood damage. More recently, low-impact development (LID) techniques have been proposed; however, LID facilities are installed on a small scale and in various locations. In addition, they require many parameters for a rainfall-runoff analysis; consequently, the design of these facilities require too much manpower and time. In this study, we analyze the rainfall-runoff reduction effect based on the size of a facility to present a simpler, more efficient design method. Next, the curve number (CN) corresponding to the capacity of each facility is estimated and the correlation between the facility’s area ratio and the CN is analyzed. As a result, a relational expression that can calculate the new CN with the same rainfall-runoff reduction effect as the installed LID facility was derived by using the CN before the installation of the LID facility and the facility area ratio. Thus, we present a simple design method using the recalculated CN without directly entering LID facilities into a storm water management model (SWMM).

Development and application of a social vulnerability index (SOVI) to landslide risk analysis in ribeira medium valley, Brazil

In the last decades, the frequency and intensity of natural disasters in Brazil has increased, resulting in substantial economic, environmental, and social damage. Knowing the specific vulnerabilities of populations exposed to risks reflects on their disaster preparedness, response, and recovery capacities. However, social vulnerability studies in Brazil are still incipient. This study developed a Social Vulnerability Index (SoVI) for the Ribeira Medium Valley to fill this gap. The region is highly susceptible to landslides, and is one of the most impoverished and underdeveloped regions of the state of São Paulo, with a predominantly rural population. The index was composed by selecting 30 variables from 373 census sectors. Principal Component Analysis (PCA) was used to extract seven principal components, accounting for 71.64% of the data variability. The obtained SoVI scores were mapped and subsequently applied in a risk analysis. The most vulnerable municipalities were identified, and each vulnerability component's spatial distribution was visualized. It was also noted that the areas most vulnerable to landslides are also the most socially vulnerable. The results of this study can steer the government towards actions aimed at reducing social vulnerability. They may also help in developing disaster risk mitigation strategies.

Risk Estimation of Typhoon Disaster Based on Three-Dimensional Information Diffusion Method

In the context of the increasing frequency and intensity of natural disasters, assessing the risk of typhoon disasters can provide significant assistance for risk control and emergency management of typhoon disasters. In this paper, based on the three-dimensional information diffusion method, the formal expected loss model is transformed into a computable typhoon risk assessment model. The fuzzy information in the small sample data is deeply mined, and the typhoon disaster risk assessment with the expected loss as the connotation is carried out, and the probability density distribution estimation of disaster-causing factors at different levels and the functional relationship identification between disaster-causing factors at different levels and direct economic loss rate are realized by using the information matrix. At the same time, combined with the frequency of typhoon occurrence, the annual risk of disasters is predicted to make up for the problem of insufficient marine environmental data and improve the calculation accuracy of risk assessment models. Taking Guangdong Province as an example, a typhoon risk assessment was conducted, estimating the probability distribution, direct economic loss rate distribution, and annual loss expectation of typhoon disasters under different wind speed scales and extreme wave heights. The results indicate that the risk estimation value of the three-dimensional information diffusion model is higher than that of the traditional model, which weakens the limitations of the low-dimensional information diffusion model and makes the evaluation results more reasonable and reliable.

Evaluation of indicators for desertification risk assessment of Oleshky sands desertification based on Landsat data time series

Earth's surface monitoring allows the assessment of the dynamics and mapping of desertification indicators and is currently one of the priority research regions in remote sensing. In addition to the fact that desertification is a serious global threat to economic, social and food security, the risks of desertification have also become relevant for southern Ukraine. In recent years, climate change has become more and more noticeable in Ukraine. As a result, the time frames of the seasons are blurred, the boundaries of natural zones are shifting, abnormal values of meteorological indicators are increasingly observed, and the intensity of natural disasters is increasing. Since southern Ukraine is most vulnerable to adverse climate and landscape changes, including desertification, the article considered long-term landscape changes on the right bank of the Dnieper River in the Kherson region, mainly represented by sandy massifs of the Oleshky region, which are called the Oleshky Sands. Arid landscapes, which include the vast majority of Oleshky, are especially vulnerable to degradation processes, so space monitoring this territory is an urgent task given the current global climate changes. Within the framework of the presented study, the long-term dynamics of several indicators of desertification within the sandy arenas of the Oleshkovsky sands were mapped. Based on a series of multispectral space images obtained by Landsat satellites for the period 1986-2020 was considered. Spectral ranges characterize the key biophysical aspects of arid landscapes: the degree of soil moisture, vegetation density and sand distribution. Regression analysis was used to illustrate the dynamics of each indicator, which showed a general increase in each indicator over the experimental period. Furthermore, we determined the relationship between the values of the rise in desertification indicators and landscape changes that occurred in the research region, the classification of several images for different years was also carried out, and spatial and quantitative changes in the distribution of land cover classes were characterized.

Afetlerde Bebek Beslenmesi ve Bakımı

On February 6th, 2023, two major earthquakes occurred in Kahramanmaraş, Turkey, a 7.7 magnitude earthquake centered in Pazarcık and a 7.6 magnitude earthquake centered in Elbistan, which was described as the “disaster of the century.” They are considered the worst earthquakes in Turkey’s history due to the high number of deaths and the effects spread to 11 provinces. According to the report issued by the Disaster and Emergency Management Presidency (AFAD) on March 1st, 2023, more than 45.000 people died, numerous houses and workplaces collapsed, and the intact buildings were severely damaged. Following health assessments of survivors, it was clear that there was an increased need for shelter, food, hygiene products, and heating, due to the winter. Feeding and hygiene are essential for many surviving infants, who need the care and protection of an adult. Breastfeeding is the safest recommended form of infant feeding during disasters. It is essential to maintain and support breastfeeding during disasters. Breastfeeding is the safest form of infant feeding, especially when the infant is living in crowded and unhygienic conditions with a high risk of infection, and in the case of diarrheal diseases. In addition, infant care should continue meticulously under the harsh conditions of disasters. In this regard, nurses have an important role in maintaining the breastfeeding of infants and young children in disasters, as they are closest to children and families in the field and health facilities. Considering the impact of climate change on the frequency and intensity of natural disasters, this review study will shed light on the appropriate methods to maintain the feeding and care of infants and young children after disasters. This study is also expected to raise awareness about the feeding and care of infants, who are a vulnerable group during and after disasters. This study aims to investigate the issue of infant feeding and care during disasters in light of the literature. Keywords: infant, infant care, natural disaster, breastfeeding

Глобальні еколого-економічні передумови змін клімату та їх прогнозна динаміка в середньо- та довгостроковій перспективах

The purpose of the article is to study global trends in climate change, which is one of the most pressing threats with a long-term negative impact on the population, environment and economy. The article analyzes the impact of global climate change on land resources, agriculture, forestry, water resources, emergencies, etc. It is defined that global climate change is one of the most acute problems of human development, ignoring which in the long run will lead to a leveling of economic growth. In addition, climate change can lead to various negative consequences, such as an increase in the frequency and intensity of natural disasters, worsening conditions for agricultural production, threatening changes in the regimes of water resources, high temperatures, air pollution, etc. The article examines the anthropogenic causes that have led to the global problem of climate change. The main causes and features of the global warming and climate change process are identified. The natural and anthropogenic factors that influence global warming and climate change are described. Examples of climate change in different regions of the world are given, and their consequences are characterized. The article presents a forecast of regional manifestations of global climate change, which is important for regional development and strategic planning. Forecasts help to develop adaptation strategies and measures to reduce the negative impacts and take advantage of opportunities that may arise as a result of changes. In addition, regional analysis makes it possible to determine the specifics of the impact of climate change on specific territories and take them into account in the development of strategies for balanced development, ensuring public safety and preserving natural resources. Approaches to determining the characteristics in the context of future climate change are proposed, as well as approaches to developing an adaptation policy in relation to possible consequences of climate change. The results of the study can be used by local government officials to assess the causes and consequences of global warming and extreme weather events, as well as to formulate adaptation policies.

기후변화 대응을 위한 디지털 물 관리 기술의 적용

As abnormal climates occur frequently around the world, the frequency and intensity of natural disasters are increased. In this study, trends of natural disasters are analyzed and identified the applied technology of smart water grid and digital water management, which are the latest water management systems to respond to climate change. Water-related damages such as typhoons and floods accounted for a large proportion of natural disasters around the world. In the case of Asia, it was found that over the past 50 years, typhoons caused the largest casualties of human life, and the biggest economic loss was caused by floods. In the case of Korea, the biggest economic loss over the past 10 years was disasters caused by floods and typhoons. In order to minimize the damage caused by these disasters, digital water resource technology is being applied worldwide. In this study, application cases of smart water management and digital technologies were investigated. In addition, the application direction of digital water management technology was proposed.

Conducting an adaptive evaluation framework of importance and performance for community-based earthquake disaster management

The frequency and intensity of natural disasters have been increasing in recent decades; especially earthquakes are one of the causes of major natural disasters. Improving community adaptive response to disasters based on community capacity has gradually become an effective means of coping with disaster risks, as well as improving residents' well-being and community participation in disaster planning and management. We integrate community resilience and disaster management to establish an evaluation framework for community-based earthquake disaster management (CEDM) based on community perspectives under importance performance analysis method, and identify the factors affecting community adaptive behavior. Features that affect the differentiation of community residents' adaptive behavior in CEDM program are classified into risk perception, learning earthquake knowledge, the ability on earthquake prevention, and creating a platform on CEDM. The results identify that the CEDM has to integrate the higher community education plan, the stronger flexible to disaster preparedness, the higher the residents' awareness of disaster prevention, the stronger the ability to adapt to disasters; the higher the ability of the government and non-profit organizations to cooperate with the community to deal with disasters, and the stronger the community's ability to manage disasters. These findings provide valuable insights into the construction of CEDM systems and related policymaking.

A Study on the Impact of Natural Disasters on Farmers’ Relative Poverty

In 2020, China announced the successful completion of its poverty alleviation mission, noting that the focus of China’s poverty alleviation mission has shifted from eliminating absolute poverty to alleviating relative poverty. Due to global warming and frequent natural disasters, natural disaster shocks have seriously affected farmers’ livelihoods and aggravated relative poverty. Based on 5,804 rural household samples from the China Family Panel Studies, the impact of natural disasters on farmers’ relative poverty was investigated using the logit model. In addition, the interaction terms between the impact and intensity of natural disasters, non-agricultural employment and productive investment were included in the model. The results show that: 1) Natural disaster shocks and natural disaster intensities had a significant positive impact on farmers’ relative poverty. 2) Migrating for work and stable employment effectively alleviated the positive impact of natural disaster shocks and natural disaster intensities on farmers’ relative poverty, respectively. 3) Productive investment weakened the positive impact of natural disaster shocks on farmers’ relative poverty. 4) Scale management effectively alleviated the positive impact of natural disaster shocks on farmers’ relative poverty, but the moderating effect of scale management was not significant in areas with high disaster intensities.

Increased prescriptions for irritable bowel syndrome after the 2018 Japan Floods: a longitudinal analysis based on the Japanese National Database of Health Insurance Claims and Specific Health Checkups

BackgroundThe frequency and intensity of natural disasters are increasing worldwide, which makes our understanding of disaster-related diseases more important than ever. Natural disasters cause mental stress and infectious diarrhea, but the causal relationship between disasters and a potential consequence of these conditions, irritable bowel syndrome (IBS), is unreported. The 2018 Japan Floods, which took place in July 2018 was one of the largest water disasters in Japan’s recorded history. We investigate the change of drug prescriptions for IBS between disaster-suffers and non-sufferers throughout the disaster period to examine the relationship.MethodsThis is a retrospective cohort study based on the Japanese National Database of Health Insurance Claims and Specific Health Checkups in flood-stricken areas between July 2017 and June 2019. We included subjects older than 15 years of age who had visited a medical institution or been hospitalized in the hardest-hit areas of the disaster. Ramosetron, polycarbophil calcium, and mepenzolate bromide (IBS drugs) approved solely for the treatment of IBS in Japan were analyzed. The monthly rate of prescriptions for IBS drugs was compared between municipality-certified disaster victims and non-victims using a controlled interrupted time series analysis. For those who were not prescribed IBS drugs before the disaster (non-users), the occurrence of an IBS drug prescription after the disaster was evaluated using a multivariable logistic regression analysis adjusted for gender and age.ResultsOf 5,287,888 people enrolled, 32,499 (0.61%) were certified victims. The prescription rate for IBS drugs among victims increased significantly by 128% immediately after the disaster, while it was stable among non-victims. The trend for the post-disaster prescription rate among victims moved upward significantly when compared to non-victims (0.01% per month; 95% confidence interval (CI) 0.004–0.015; P = 0.001). Among non-users, the occurrence of an IBS drug prescription for victims was 0.71% and was significantly higher than non-victims (0.35%, adjusted odds ratio 2.05; 95% CI 1.81–2.32).ConclusionsThe 2018 Japan Floods increased the rate of prescriptions for IBS drugs, suggesting that the disaster caused or worsened IBS among victims.

Damage Estimation of a Concrete Pier When Exposed to Extreme Flood and Debris Loading

The structural safety and serviceability during extreme weather, such as floods and storms, is critical. Due to global warming in the last decades, the increase in the intensity of natural disasters, i.e., flood loading and the durability of the road structures and infrastructures, is becoming critical. Bridges and structures lose their capacity because of ageing over time. On the other hand, the load intensity is another reason for the structural damage. Debris loading due to the flooding on bridges is one of the reasons for the increase in flood loading and eventually structural damage. Measuring the level of structural damage under extreme events is vital in determining the vulnerability and resilience of structures during a disaster. A damage index (DI) can be defined as a measurement tool for the levels of structural damage. Oftentimes, damage indices are developed to measure the deterioration of the system under earthquake loading. Little work has been published on damage indices (DIs) under flood loading, where a uniform pressure is applied to a structure. This paper presents a comprehensive review of DIs published in the literature and compares two approaches to assess the system’s damage utilising finite element methodologies. The structure model developed in the ABAQUS software package is used to predict the failure of a concrete component under applied lateral loading. The model is validated using published experimental work. The model is verified, and then it is used to compute the damage indicators using two primary techniques, including a deflection-based method and an energy loss-based approach. Using the two offered DIs, the change in damage levels is displayed underwater flow uniform loading. A comparison of the two methods is conducted. In this paper, differences between the two concepts are analysed and presented.

Climate change and its impact on biodiversity and human welfare

Climate change refers to the long-term changes in temperature and weather due to human activities. Increase in average global temperature and extreme and unpredictable weather are the most common manifestations of climate change. In recent years, it has acquired the importance of global emergency and affecting not only the wellbeing of humans but also the sustainability of other lifeforms. Enormous increase in the emission of greenhouse gases (CO2, methane and nitrous oxide) in recent decades largely due to burning of coal and fossil fuels, and deforestation are the main drivers of climate change. Marked increase in the frequency and intensity of natural disasters, rise in sea level, decrease in crop productivity and loss of biodiversity are the main consequences of climate change. Obvious mitigation measures include significant reduction in the emission of greenhouse gases and increase in the forest cover of the landmass. Conference of Parties (COP 21), held in Paris in 2015 adapted, as a legally binding treaty, to limit global warming to well below 2 °C, preferably to 1.5 °C by 2100, compared to pre-industrial levels. However, under the present emission scenario, the world is heading for a 3–4 °C warming by the end of the century. This was discussed further in COP 26 held in Glasgow in November 2021; many countries pledged to reach net zero carbon emission by 2050 and to end deforestation, essential requirements to keep 1.5 °C target. However, even with implementation of these pledges, the rise is expected to be around 2.4 °C. Additional measures are urgently needed to realize the goal of limiting temperature rise to 1.5 °C and to sustain biodiversity and human welfare.