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This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

The purpose of this paper is to explore the problem of land degradation as a category of environmental terrorism, which has been increasing along with the rise of open pit mining and oil palm plantation activities.

This study uses a doctrinal approach that aims to find the best solution to address the problem of land degradation and environmental terrorism activities in Indonesia through a comparative legal study conducted with the handling of similar cases in Malaysia, which has almost the same character as Indonesia.

This paper finds that both Indonesia and Malaysia have enacted laws and regulations to address environmental concerns. However, it turns out that relying only on law enforcement is not an optimal solution. Addressing the problem of land degradation would also require the implementation of sustainable development practices, public awareness and cooperative dialogue.

This paper provides a new approach to answer the inclusion of environmental degradation as environmental terrorism, which should also be considered a crime against humanity.

The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.

This research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.

Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.

This study implements in Indonesia region.

(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.

The agriculture industry has a considerable impact on Malaysia’s economy, as seen by its contribution of roughly 8.2% of gross domestic product in 2018 and its potential to absorb 11.09% of Malaysian labor in the same year. This study aims to simulate rising output in a system model of sustainable and profitable crude palm oil (CPO) supply chain management (SCM) and to formulate policy solutions to build sustainable and profitable SCM of Malaysian CPO.

This research included both primary and secondary data. This study used the dynamic system model to simulate palm oil land expansion, replanting policies and environmentally friendly growing techniques.

This study’s findings suggest that the dynamic system model of Malaysia’s CPO’s sustainable and profitable SCM is valid when its structure and performance are tested. The fifth scenario provides the best results, with the most significant net benefit value compared to the other scenarios.

The ideal policy alternative is replanting sustainable agricultural practices without burning technologies during new land clearing to achieve the best net advantages.

This study was conducted to analyse the socio-ecological problems faced by the Suhelwa Wildlife Sanctuary and understand its potential and challenges for developing ecotourism following Triple Bottom Line (TBL) principles. The study also benchmarked best ecotourism practices across the globe to create an ecotourism plan that would provide alternative livelihood and help in sustainable management of the area by reducing poverty, dependency on forests and biodiversity protection.

Suhelwa Wildlife Sanctuary was chosen because this area has several socio-ecological crises with limited livelihood options, and there is an urgent need for alternative livelihood opportunities in the form of ecotourism. The study followed an ethnographic approach through observation, participant observation, and semi-structured interviews. Content and thematic analysis was conducted through Atlas Ti9.0 software for data analysis. Subsequently, benchmarking best ecotourism practices through a literature review was done to develop an ecotourism action plan.

The First finding was related to the study area divided into three themes: problems, potential for ecotourism development, and challenges for ecotourism development. The second finding was related to benchmarking best practices and suggesting an action plan.

This work studied an area not sufficiently acknowledged by academicians and policymakers concerning ecotourism development. The work also benchmarks the best practices for ecotourism and proposes a sight-specific ecotourism action plan in accordance with TBL.

The purpose of this study is to explore the principal research query and whether strategic foresight deployed by entrepreneurial firms in energy transition aims at crafting future readiness.

With a focus on entrepreneurial firms working in the alternative energy segment in the Indian context, the intent is to examine the deployment of strategic foresight by incumbent firms and their entrepreneurial journey. The authors have adopted the approach of Eisenhardt for this research. The area of interest for the authors entailing strategic foresight by entrepreneurial firms in energy transition aligns with defining features reflecting the aim of Eisenhardt method for this exploratory research coupled with constructivism.

While the future scenarios in the energy sector have to be necessarily multiple, their alignment with different geographic, economic, demographic and political outlooks shall be defined by the pathways niched through the deployment of strategic foresight for arriving at those scenarios.

Strategic foresight deployed by entrepreneurial firms has the potential to create future readiness through self-reliant sustainable economic value chains for local populace, thus propagating holistic development in remote regions.

This paper attempts to knit together the domains of strategic foresight, entrepreneurial firms and energy transition through case research and present the future thinking deployed for navigation in uncharted pathways by capturing the foresight component of these incumbent firms chosen through careful case selection. The narrative has been strengthened by the varied interviews across participants and the observations made by the authors during the research work.

The purpose of this paper is to analyse the association between farm/farmer characteristics and unsubsidized farm insurance premium expenditure in Ireland. The distribution of farm insurance expenditures is wide, and it is important to understand the extent to which individual factors influence demand for different levels of insurance premium.

The quantile regression approach and farm accountancy data from the Teagasc National Farm Survey are used to model the association between farm/farmer characteristics and farm insurance demand in Ireland.

Asset values (livestock, buildings and machinery) are positively associated with total insurance expenditure. Both forestry area and crop area are significantly associated with farm insurance expenditure with a stronger influence on the middle and upper part of the distribution. The interaction between farm income and farmer age is positively associated with insurance expenditure pointing to the importance of farm income protection.

The research is mainly concerned with insuring against substantive risks, which are capable of threatening the asset base and continuation of the farm business. Future research can integrate questions in relation to farm safety and farmer health with research on the economic survival of the farm business.

Farmers in Ireland adopt unsubsidized farm insurance as a risk management tool. This situation is relevant to other EU member states including Belgium, Denmark, Germany and Sweden. The findings can be used to inform stakeholders and policymakers about the relative impact of different factors on insurance expenditure.

Previous research has typically focused on the linear relationship between farm/farmer characteristics and insurance demand without accounting for variability across the size distribution. This research is based on the quantile regression approach where the association between farm/farmer characteristics and farm insurance expenditure can be assessed at different points of the distribution.

This study aims to identify the regions with a high risk of natural disaster, estimate the proportion of households potentially participating in natural disaster insurance and analyze the relationship between disaster risk index and proportion of household potentially participating in natural disaster insurance.

Descriptive and quadrant analysis was applied on the 2019 Indonesia Disaster Risk Index (IRBI) scores and the 2019 National Socio-Economic Survey data.

The results showed there are only two categories of disaster risks in Indonesia based on IRBI categorization: “Medium” and “High.” Some districts in Aceh Province such as Simeuleu, Pidie Jaya and Banda Aceh City were observed to have a high proportion of households potentially participating in the natural disaster insurance while some in Jawa Tengah provinces have fairly low level even though they were categorized as high disaster-prone areas. Moreover, the quadrant analysis showed that 43 districts have high IRBI scores but low insurance participation rates with most discovered to be in Jawa Barat and Sumatera Selatan provinces.

Indonesia does not have a financial mitigation program up to the present time because almost all disaster resolutions are formulated based on emergency funds from the state budget even though it is important to use insurance schemes in all stages of disaster management. To the best of the authors’ knowledge, this study is the first attempt to identify households potentially participating in natural disaster insurance through the National Socio-Economic Survey in Indonesia.

There seems to be no controversy about Nigeria being an agricultural country with food sufficiency up till the late 1970s. However, in recent times the country is finding it very difficult to provide sufficient food for the teeming population which has resulted in the majority of the country’s citizens slipping into poverty. The ability of the country to provide sufficiently for the citizens was a result of a lack of reliable and effective developmental and transformational strategies in the agricultural sector of the country which is a major employer of labour in the rural community. To this end, this chapter mainly focuses on factors inhibiting the development of agricultural companies in Nigeria and how to overcome the developmental barriers in the agricultural sector in Nigeria. The findings from the review show that the bane of the agricultural sector in Nigeria is due to the lack of an agricultural regulatory framework and policy transmission mechanism and over-dependence on oil revenue amongst other things (Adams, 2016). It is therefore imperative for the country to embark on the development of a reliable agricultural framework and model that will aid food sufficiency in the country.