Close encounters of the worst kind: reforms needed to curb coral reef damage by recreational divers

Intentional and unintentional physical contact between scuba divers and the seabed is made by most divers and multiple times per dive, which often results in damage to corals and other marine life. Current efforts to reduce reef contacts (e.g., voluntary dive operator recognition programs and voluntary dive standards) can be effective, but lack sufficient incentive structures for long-term compliance. In their current capacity, these programs fail to reduce reef contacts to tolerable levels. Regulatory policies can facilitate pervasive and permanent shifts in human behavior, but have been underutilized to change unsustainable underwater norms. Most coral reefs open to recreational diving lie within territorial waters of individual countries, and many already have existing forms of protection with legislation that can be easily modified. Successful policy precedents in Marine Protected Areas (e.g., bans on underwater glove use) and elsewhere (e.g., anti-smoking laws in public spaces and legislation enforcing seat belt use) demonstrate the largely untapped potential of using effective governance to change destructive diving norms for good. To reduce intentional reef contacts, policy-makers can enact regulations in MPAs directly banning all contact between divers and the seabed. To reduce unintentional contacts, policy-makers can create policy safeguards that preempt such occurrences (e.g., requiring divers to keep a certain distance from the seabed). Crucially, such policies will need accompanying formal and informal enforcement measures that are equitable, effective, and efficient to motivate compliance and effect lasting behavior change. Having a robust, well-enforced, regulatory framework to tackle both types of reef contacts lends credence to the efforts of existing conservation programs, and is key to permanently changing divers’ underwater attitudes and fostering sustainable scuba diving behavior to the benefit of all.

A VR-Enhanced Rollover Car Simulator and Edutainment Application for Increasing Seat Belt Use Awareness

Most countries have active road safety policies that seek the objective of reducing deaths in traffic accidents. One of the main factors in this regard is the awareness of the safety measures, one of the most important being the correct usage of the seat belt, a device that is known to save thousands of lives every year. The presented work shows a VR-enhanced edutainment application designed to increase awareness on the use of seat belts. For this goal, a motorized rollover system was developed that, synchronized with a VR application (shown in a head-mounted display for each user inside a real car), rolls over this car with up to four passengers inside. This way, users feel the sensations of a real overturn and therefore they realize the consequences and the results of not wearing a seat belt. The system was tested for a month in the context of a road safety exhibition in Dammam, Saudi Arabia, one of the leading countries in car accidents per capita. More than 500 users tested and assessed the usefulness of the system. We measured, before and after the rollover experience, the perception of risk of not using the seat belt. Results show that awareness regarding the use of seat belts increases very significantly after using the presented edutainment tool.

Latent Class Model with Heterogeneous Decision Rule for Identification of Factors to the Choice of Drivers’ Seat Belt Use

The choice of not buckling a seat belt has resulted in a high number of deaths worldwide. Although extensive studies have been done to identify factors of seat belt use, most of those studies have ignored the presence of heterogeneity across vehicle occupants. Not accounting for heterogeneity might result in a bias in model outputs. One of the main approaches to capture random heterogeneity is the employment of the latent class (LC) model by means of a discrete distribution. In a standard LC model, the heterogeneity across observations is considered while assuming the homogeneous utility maximization for decision rules. However, that notion ignores the heterogeneity in the decision rule across individual drivers. In other words, while some drivers make a choice of buckling up with some characteristics, others might ignore those factors while making a choice. Those differences could be accommodated for by allowing class allocation to vary based on various socio-economic characteristics and by constraining some of those rules at zeroes across some of the classes. Thus, in this study, in addition to accounting for heterogeneity across individual drivers, we accounted for heterogeneity in the decision rule by varying the parameters for class allocation. Our results showed that the assignment of various observations to classes is a function of factors such as vehicle type, roadway classification, and vehicle license registration. Additionally, the results showed that a minor consideration of the heterogeneous decision rule resulted in a minor gain in model fits, as well as changes in significance and magnitude of the parameter estimates. All of this was despite the challenges of fully identifying exact attributes for class allocation due to the inclusion of high number of attributes. The findings of this study have important implications for the use of an LC model to account for not only the taste heterogeneity but also heterogeneity across the decision rule to enhance model fit and to expand our understanding about the unbiased point estimates of parameters.

Accounting for Attribute Non-Attendance and Common-Metric Aggregation in the Choice of Seat Belt Use, a Latent Class Model with Preference Heterogeneity

A choice to use a seat belt is largely dependent on the psychology of the vehicles’ occupants, and thus those decisions are expected to be characterized by preference heterogeneity. Despite the importance of seat belt use on the safety of the roadways, the majority of existing studies ignored the heterogeneity in the data and used a very standard statistical or descriptive method to identify the factors of using a seatbelt. Application of the right statistical method is of crucial importance to unlock the underlying factors of the choice being made by vehicles’ occupants. Thus, this study was conducted to identify the contributory factors to the front-seat passengers’ choice of seat belt usage, while accounting for the choice preference heterogeneity. The latent class model has been offered to replace the mixed logit model by replacing a continuous distribution with a discrete one. However, one of the shortcomings of the latent class model is that the homogeneity is assumed across a same class. A further extension is to relax the assumption of homogeneity by allowing some parameters to vary across the same group. The model could still be extended to overlay some attributes by considering attributes non-attendance (ANA), and aggregation of common-metric attributes (ACMA). Thus, this study was conducted to make a comparison across goodness of fit of the discussed models. Beside a comparison based on goodness of fit, the share of individuals in each class was used to see how it changes based on various model specifications. In summary, the results indicated that adding another layer to account for the heterogeneity within the same class of the latent class (LC) model, and accounting for ANA and ACMA would improve the model fit. It has been discussed in the content of the manuscript that accounting for ANA, ACMA and an extra layer of heterogeneity does not just improve the model goodness of fit, but largely impacts the share of class allocation of the models.