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West Java, one of the provinces in Indonesia, is trying to develop the coastal tourism sector by applying the eco-tourism concept. The observation results found that there were differences in the concept applied by the local government, namely toward mass tourism. Therefore, one of the strategies proposed is developing tourist areas using a combination of eco-tourism and mass tourism. The increase in the tourism sector in this area will not only help the economic growth of the local community but also contribute to the improvement of the economy of West Java. This study aims to estimate the risks that will occur if the developer uses the mix-use method. The research was conducted by interviewing and distributing questionnaires to the Tourism Office, National Disaster Relief Agency, Business Actors and Communities, and Tourists. The method used is descriptive with quantitative analysis using a risk matrix based on AS/NZS 4360-2004 (Australian-New Zealand Standard). Meanwhile, the qualitative analysis used the interview method and Focus Group Discussion (FGD). The findings in this study are that both quantitative and qualitative analyses can provide input through risk identification and priority. This research also provides theoretical and managerial implications.

Introduction: The insurance industry is vulnerable to attacks as it deals with the personal information of its consumers and puts the insurance company’s business at risk in the event of data breach or abuse. To ensure the security of customer data, insurance companies must comply with various data protection requirements, including requirements imposed by laws, regulations, and standards. Following such a wide range of conditions can be challenging for insurance providers. For a long time, risk management has controlled data protection to ensure compliance with data protection law and ensure that data are processed correctly and that people’s fundamental rights are protected effectively.

Purpose: This chapter explains the role and significance of risk management. An organised way to identify and assess risks, mitigate or avoid risks as much as possible, and then manage and accept the remaining risks, implemented in data protection as needed, explained by the supervisory authority, is implemented by the responsible organisation. This document highlights the growing consensus surrounding risk management as an essential tool for adequate data protection. Furthermore, it addresses vital considerations that affect the role of risk in data protection law and practice.

Need for study: There is an increasing consensus towards the role and significance of risk management in data protection in the insurance market. As a result, regulators and legislators are focussing on valuable and new attention on standardising and expanding data protection in risk management practices. This paper has attempted to identify critical issues and principles of risk management in data protection.

Methodology: Secondary data analysis was conducted in this study by reviewing literature related to data protection, risk management, and the insurance sector. Again, science direct was used as a source of information. For this study, the literature review approach was chosen since it allows us to trace the growth of the subject matter and identify the patterns that have formed through time.

Findings: The insurance industry comprises general insurance and life insurance. It is found that there are various studies conducted on the privacy violation and data breaches of individuals in the insurance industry. The study also identifies the factors causing privacy issues and recommends improving data privacy management in the insurance market.

Practical implications: The current study can be referred to by academicians, marketers, industry people, and policymakers. In addition, the study encourages companies and academicians to investigate further the process of data protection in the insurance industry.

Purpose – This chapter provides details of research that attempts to relate traffic operational conditions on uninterrupted flow facilities (e.g., freeways and expressways) with real-time crash likelihood. Unlike incident detection, the purpose of this line of work is to proactively assess crash likelihood and potentially reduce the likelihood through proactive traffic management techniques, including variable speed limit and ramp metering among others.

Methodology – The chapter distinguishes between the traditional aggregate crash frequency-based approach to safety evaluation and the approach needed for real-time crash risk estimation. Key references from the literature are summarised in terms of the reported effect of different traffic characteristics that can be derived in near real-time, including average speed, temporal variation in speed, volume and lane-occupancy, on crash occurrence.

Findings – Traffic and weather parameters are among the real-time crash-contributing factors. Among the most significant traffic parameters is speed particularly in the form of coefficient of variation of speed.

Research implications – In the traffic safety field, traditional data sources are infrastructure-based traffic detection systems. In the future, if automatic traffic detection systems could provide reliable data at the vehicle level, new variables such as headway could be introduced. Transferability of real-time crash prediction models is also of interest. Also, the potential effects of different management strategies to reduce real-time crash risk could be evaluated in a simulation environment.

Practical implications – This line of research has been at the forefront of bringing data mining and other machine-learning techniques into the traffic management arena. We expect these analysis techniques to play a more important role in real-time traffic management, not just for safety evaluation but also for congestion pricing and alternate routing.

Conceptualizing development in terms of risk management has become a prominent feature of mainstream development discourse. This has led to a convergence between the rubrics of financial inclusion and risk management whereby improved access for poor households to private sector credit, insurance and savings products is represented as a necessary step toward building “resilience.” This convergence, however, is notable for a shallow understanding of the production and distribution of risks. By naturalizing risk as an inevitable product of complex systems, the approach fails to interrogate how risk is produced and displaced unevenly between social groups. Ignoring the structural and relational dimensions of risk production leads to an overly technical approach to risk management that is willfully blind to the intersection of risk and social power. A case study of the promotion of index-based livestock insurance in Mongolia – held as a model for innovative risk management via financial inclusion – is used to indicate the tensions and contradictions of this projected synthesis of development and risk management.

The Philippines is one of the most vulnerable countries in the world as far as climate-related and other forms of disasters (e.g., earthquake and volcanic eruption) are concerned (World Bank and NDCC, 2004; UNISDR, 2008a, 2008b). In 2004 alone, 25 weather disturbances hit the country, of which four occurred successively in November and the first week of December. These triggered massive landslides and flooding in Southern and Central Luzon, leading to damages in lives and properties (Duque, 2005). With this the country is considered as one of the most disaster prone, ranking 12th among the 200 countries most at risk to natural hazards in the 2009 Mortality Risk Index of the United Nations International Strategy for Disaster Reduction (UNISDR, 2009).

Despite a hangover from the worldwide economic crisis, international trade rebounded nicely with a record-level growth in late 2010. A sharp rise in international trade has sparked the international traffic growth. A majority of this traffic growth originated from maritime logistics which could move cargoes in large volume and at cheaper freight costs. Due to its cost-efficiency and easy access, maritime logistics typically accounts for more than half of the worldwide freight volume. However, maritime logistics poses a greater supply chain risk, since ocean carriers used for maritime logistics are more vulnerable to unpredictable weather conditions, piracy attacks, terrorist hijacking, and cargo damages on the open sea than any other modes of transportation. Also, given the vast areas that maritime logistics covers, it is more difficult to protect maritime logistics activities from potential hazards and threats.

To better protect maritime logistics activities from potential security lapses, this chapter introduces and develops a variety of systematic security measures and tools that were successfully used by best-in-class companies and government entities across the world. Also, this chapter proposes a total maritime security management model as a way to formulate maritime risk mitigation strategies. To elaborate, this chapter sheds light on the roots of maritime security measures and tools, the ways that those measures and tools are best utilized, the roles of advanced information technology in maritime security from the global supply chain perspectives, the visualization and identification of potential maritime and its related supply chain risks, and policy guidelines that will help enhance maritime security.

Climate and environment-related financial risks could significantly and negatively impact the financial sector in future, particularly its financing to those sectors adversely impacted by the climate-related risks, low-carbon policies and the transition from traditional energy sources-based economy to a more sustainable system with alternative energy sources. The participatory countries of the Paris Agreement agreed to align finance flows with a low-emission, low-carbon and climate-resilient growth, in order to facilitate achieving the long-term climate goals. The financial sector, therefore, needs to play a proactive role in aligning financial flows. It is, therefore, of utmost importance to study low-carbon finance and climate-related financial risks. This chapter examine how climate change can affect the financial sector. It discusses the concept, nature, measurement of climate risks and climate-related financial risks and associated prospects and challenges in the assessment and the measurement of these risks. It also presents the green financing initiatives and role of central banks and supervisory authorities and their monetary and financial policies in enhancing green financing and redirecting finance to low-carbon activities. In the financial sector, the insurance industry is highly vulnerable to such risks. The banking sector is yet to witness the serious impact of these risks. With the slowing of global economic growth, appropriate policies are needed to encourage banks to provide increased green finance with an adequate profitability. Studies recommend that climate-related risk has a strong potential impact on banks’ loan default rate as well as on the financial stability, there is hence a need to incorporate climate-related criteria and the systemic risk arising out of climate change into banks’ decision-making process and risk modelling and management. There is a need for developing an appropriate methodology for assessing and reducing these risks. Moreover, observers also anticipate a need for cooperation between banking regulators and banks to develop and adopt best practices in the management of environmental risks. The environment-related risks will call forth a multi-country, or regional, research office to collect and compile the required data and undertake analysis to enhance the banking sectors’ understanding of, and capacity to address, potential systemic environmental risks. What is needed is to test the feasibility of incorporating forward-looking scenarios for assessing potential impacts of providing credit to environmentally unsustainable or sustainable activities on financial stability.

This paper describes the application of, enhancements to, and use of surface fire spread models in predicting and mitigating fire risk in the Wildland–Urban Interface (WUI). Research and fire management strategies undertaken in the East Bay Hill region (containing the 1991 Tunnel Fire) of the San Francisco Bay area over the past decade are reported. We ascertain that surface fire spread modeling has impacted policy and decision making, resulting in a regional strategic plan where large landowners and public agencies are able to implement fire mitigation practices. Although these practices involve extensive fuel management within a buffer zone between the wildland and residential properties, the residential property owners are still at risk, as no strategy within neighborhoods can be accurately mapped using the current scale of the data and models. WUI fires are eventually extinguished by fire fighters on the ground, up close, and at the backyard scale. We argue that large-scale (backyard scale) mapping and modeling of surface fire spread is necessary to engage the individual homeowner in a fuels management strategy. We describe our ongoing research and strategies, and suggest goals for future research and development in the area of large-scale WUI fire modeling and management.