Effects Of Policy Measures Research Articles

Mitigating Climate Change in Urban Aotearoa: towards transformative policies

Carbon emissions, the dominant greenhouse gas emissions in urban Aotearoa New Zealand, must fall. This article considers how New Zealand can implement transformative urban mitigation policies. First, it is time to move beyond a fixation with the emissions trading system and vehicle electrification, and apply a comprehensive set of known, effective policy measures. Second, policies must consider effects on wellbeing. Third, systemic solutions are needed to redress systemic problems of urban sprawl and car dependence, and offer a credible prospect of transformative urban mitigation.

Alcohol policy measures are an ignored catalyst for achievement of the sustainable development goals.

By adopting Agenda 2030, governments agreed to review and report on their approach and action for achievement of sustainable development goals annually through the High-Level Political Forum (HLPF) on Sustainable Development. Health and well-being are at the heart of the United Nations Agenda 2030. Given the social and economic harm that can be done by alcohol, reducing the consumption of alcohol is a pre-requisite to achieve the sustainable development goals. We explored how selected European countries have considered alcohol-related harm as an obstacle to achievement of SDGs and the extent to which they view alcohol policy as a solution to the achievement of sustainable development by analysing their voluntary national reviews (VNRs) submitted to the HLPF between years 2016 and 2020. We developed our own framework with 260 questions reflecting three dimensions of alcohol-harm considerations: indication, action, and evaluation. We analysed 36 VNRs of 32 European countries by first assessing them against the 260 questions to find out how they report on alcohol harm and whether they, in their action, refer to evidence-based, cost-effective alcohol policy solutions. Afterwards we used content analysis to assess the extent to which the countries addressed alcohol related harm, whether they refer to alcohol harm within SDG 3 (good health and well-being) or look beyond the health goal and consider alcohol harm having impact on goals other than the Goal 3. Nine countries (28.1%) did not mention alcohol in their report. Only eight countries (25%) mentioned one or more of the alcohol policy best buys among the actions they are taking to reduce alcohol related harm and only three (9.3%) explicitly elaborated on their impact on goals other than goal 3. Only five countries referred to the agreed indicator 3.5.2 measuring alcohol per capita consumption in the adult population. Many of the remaining countries used a range of terminology rather than alcohol per capita consumption, including "excessive use of alcohol", "heavy use", "too much alcohol ", "harmful alcohol consumption", "use among young people". Alcohol use is, for example, associated with violence (SDG 5 and 16), it contributes to inequalities (SDG 5 and 10), it hinders economic growth (SDG 8), disrupts sustainable consumption (SDG 12) and it adversely impacts environment (SDG 13 and 14). The findings of this study show that these effects are not considered in the design of measures to achieve these goals. Moreover, inaccurate language related to alcohol harm indicates a gap in understanding of extend of alcohol burden and the consequences for sustainable development. So does the choice of ineffective measures to reduce alcohol consumption. Education programs and awareness raising campaigns focusing on individual lifestyle are neither in line with WHO Global Strategy to reduce the harm caused by alcohol that all selected countries adopted in 2010, nor do they reflect the seriousness of the problems related to alcohol use. Effective alcohol policy measures, so called three best buys, are missing from the transformative action that the Agenda 2030 calls for and governments committed to.

Intra-Urban Distribution of Child Hawking in Southeast Nigeria

This article investigated the intra-urban distribution of child hawking in Enugu Municipality, Nigeria. The study first enumerated child hawkers across three residential areas (core, transition and sub-urban) simultaneously on different weekdays and at different locations (activity-nuclei) in Enugu municipality. A questionnaire was then employed to obtain information from 95 incidentally selected child hawkers, comprising 49, 21 and 25, respectively. Information sourced was their socio-economic characteristics and factors influencing their taking to the street to hawk. The data were analysed using percentages, cross tabulation and standard scores. Results showed that 58.9% were males, 53.7% were out-of-school and 47.4% realized ₦501.00–₦1000.00 ($1.4USD–$2.78USD) per day. Factors influencing child hawking, measured through an index tagged ‘Child Hawkers’ Factor Index’ (CHFI) on a 5-point Likert-type scale, showed that poverty was the most prevalent factor in the core, transition and sub-urban areas of the municipality, respectively, with (CHFI = 3.67), (CHFI = 3.64) and (CHFI = 3.37). The study further showed that there was a relationship between child hawking incidence and land use activities. It observed that the core residential area, junction, Motor Park and market land uses were generators of child hawkers. The study suggested effective urban planning and policy measures in addressing the menace of child hawking.

Urban growth modelling and social vulnerability assessment for a hazardous Kathmandu Valley

In our rapidly urbanizing world, many hazard-prone regions face significant challenges regarding risk-informed urban development. This study addresses this issue by investigating evolving spatial interactions between natural hazards, ever-increasing urban areas, and social vulnerability in Kathmandu Valley, Nepal. The methodology considers: (1) the characterization of flood hazard and liquefaction susceptibility using pre-existing global models; (2) the simulation of future urban built-up areas using the cellular-automata SLEUTH model; and (3) the assessment of social vulnerability, using a composite index tailored for the case-study area. Results show that built-up areas in Kathmandu Valley will increase to 352 km2 by 2050, effectively doubling the equivalent 2018 figure. The most socially vulnerable villages will account for 29% of built-up areas in 2050, 11% more than current levels. Built-up areas in the 100-year and 1000-year return period floodplains will respectively increase from 38 km2 and 49 km2 today to 83 km2 and 108 km2 in 2050. Additionally, built-up areas in liquefaction-susceptible zones will expand by 13 km2 to 47 km2. This study illustrates how, where, and to which extent risks from natural hazards can evolve in socially vulnerable regions. Ultimately, it emphasizes an urgent need to implement effective policy measures for reducing tomorrow's natural-hazard risks.

Using Bayesian Belief Networks to Investigate Farmer Behavior and Policy Interventions for Improved Nitrogen Management

Increasing farmers’ adoption of sustainable nitrogen management practices is crucial for improving water quality. Yet, research to date provides ambiguous results about the most important farmer-level drivers of adoption, leaving high levels of uncertainty as to how to design policy interventions that are effective in motivating adoption. Among others, farmers’ engagement in outreach or educational events is considered a promising leverage point for policy measures. This paper applies a Bayesian belief network (BBN) approach to explore the importance of drivers thought to influence adoption, run policy experiments to test the efficacy of different engagement-related interventions on increasing adoption rates, and evaluate heterogeneity of the effect of the interventions across different practices and different types of farms. The underlying data comes from a survey carried out in 2018 among farmers in the Central Valley in California. The analyses identify farm characteristics and income consistently as the most important drivers of adoption across management practices. The effect of policy measures strongly differs according to the nitrogen management practice. Innovative farmers respond better to engagement-related policy measures than more traditional farmers. Farmers with small farms show more potential for increasing engagement through policy measures than farmers with larger farms. Bayesian belief networks, in contrast to linear analysis methods, always account for the complex structure of the farm system with interdependencies among the drivers and allow for explicit predictions in new situations and various kinds of heterogeneity analyses. A methodological development is made by introducing a new validation measure for BBNs used for prediction.

Evaluation of modelled LOTOS-EUROS with observational based PM10 source attribution

Due to its serious health impact particulate matter is one of the air pollutants subject to abatement policies. Information on the main sources responsible for high concentrations of pollutants is therefore crucial to enable effective policy measures. In this study we compared two different methods for attribution of particulate matter concentrations to different sources: A tagging approach within the regional chemistry transport model LOTOS-EUROS and an observational method using speciated particulate matter observations and Positive Matrix Factorisation (PMF). The methods have been applied for winter 2016/2017 over Eastern Germany where in wintertime high woodburning emissions, cold temperatures and regular easterly winds can lead to a build-up of pollutant concentrations. The comparison allows the validation of the modelled source attribution for a selection of source categories. The contributions for biomass and total combustion compare well between both methods providing trust in the determined contributions, applied emissions including their timing. The total contribution from combustion is estimated between 3.3-7.7 μg/m3 (PMF) and 3.3-7.2 μg/m3 (LOTOS-EUROS) for the 9 stations incorporated in the study. The temporal Pearson correlation coefficient ranges between 0.3-0.64 for total combustion and 0.34 and 0.7 for biomass combustion. The mean absolute contributions for traffic at background stations also compare well with most values between 1.5-2.0 μg/m3 for PMF and 1–1.6 μg/m3 for LOTOS-EUROS. A lack of correlation for this contribution however suggests that the model has difficulty in representing the source category traffic in a time consistent manner and developments are needed to improve the temporal distribution of the traffic emissions within the model. The modelled particulate matter concentrations displayed a 20–40% underestimation of the observed concentrations with an increasing bias during high pollution events. The underestimation showed a high correlation with the observed contribution from combustion and secondary particulate matter including ammonium sulfate and organic carbon suggesting that at least a part of the missing mass in LOTOS-EUROS is related to transformation of volatile combustion emissions, likely from solid fuels, to secondary particle mass and missing enhanced formation of sulfate. Implementation of these missing processes would help to improve the source attribution of particulate matter with the LOTOS-EUROS model.

Integrated assessment of regional approaches for biodiversity offsetting in urban-rural areas – A future based case study from Germany using arable land as an example

Human interventions, i.e. settlement and construction activities, in the agricultural landscape including farmland but also natural and semi-natural habitats are a major driver of biodiversity loss. Consequently, their impacts on nature and landscape have to be compensated by no net loss policies in many countries around the world. However, their practical implementation often poses challenges with regard to the optimal spatial coordination and assessment of measures, especially in the case of eco-accounts or other habitat banking approaches.Against this backdrop, different approaches to offset biodiversity loss at regional level are analysed with due consideration of indicators of economy, ecology, landscape aesthetics and food production. We used an interdisciplinary modelling approach based on estimates for offsetting demand until 2030. In the integrated land use model, we associated a biophysical crop growth model with an economic optimisation model. The Stuttgart Region – an area with stiff competition amongst anthropogenic land use patterns in Germany – served as the study area. Our main focus was on arable land that has a high potential for nature conservation enhancement. In this context, farmers are deemed to be a major stakeholder group.We observed differing economic and ecological outcomes for the offsetting scenarios we considered. In urban areas with high population density and low biodiversity (e.g. Stuttgart city), compensation close to the site of intervention (on-site) may be more expensive than off-site compensation. However, further added value can be generated by on-site compensation in terms of visual landscape quality enhancement and habitat connectivity, provided that the measures lend themselves to establishing connectivity. Consequently, spatially unrestricted markets for eco credits may exacerbate ecological polarisation between urban and rural areas. Therefore, we concluded that offset site selection should not be driven solely by economics, as this may not optimise overall welfare from a societal perspective, resulting in the need for legal constraints.Our results show the trade-offs between the political goals of spatial planning approaches and compensation strategies. They can, therefore, thus provide valuable information that enables political decision-makers to more clearly weigh up the effects of policy measures in this area.

Capabilities of Computer Algorithm Utilizing Artificial Neural Networks and its Implications to Economy: A Public Policy Analysis

The massive abundance of studies pertinent to Artificial Neural Networks (ANN) has produced exciting and dulcet effects on different industries and academic disciplines. Albeit the findings of the studies relating to ANNs invite potential enterprising opportunities, it is an incontestable fact that these enterprising opportunities, like fruits of the ANNs, valiantly interpose an economic threat to the working manpower. Employment retrenchment is portending as companies opt to enjoy the benefit yielded from the application and use of ANN mechanisms. ANNs will overshadow and replace the working manpower. This study is a meta-analysis that profoundly discourses on the implications of economic issues embedded in the application and use of ANNs. The findings hereof are critical and material considerations in the craft of effective public policy measures that necessarily balance the economic impact of the ANNs to working manpower. Thus this study aims to answer two primary inquiries; (1) what are the economic implications of the application and use of ANNs? and (2) what public policy measures balance the economic downsides of the application and use of ANNs?

Sustainable Development Goals and Problems of Measuring Poverty and Extreme Poverty

The article presents the results of the study on improving the methodology for measuring poverty and extreme poverty in accordance with the fundamental UN document – the UN Sustainable Development Goals (SDGs). The author argues his position that when assessing the achievement of targets for the eradication of extreme poverty within the framework of achieving Goal 1 (SDG 1) at the national level, one should not be guided by the average threshold value of extreme poverty for all countries. It is necessary to focus on the gross national income (GNI) per capita, an indicator developed by the World Bank (according to the Atlas method). For the first time, a comparative analysis of the interpretations of SDG 1 by Russian and European researchers, as well as experts of international organizations is carried out, and threshold values of global poverty for groups of countries with different levels of GNI per capita are analyzed. It allows us to identify the main problem of monitoring the eradication of extreme poverty in Russia: the low information content of data on socio-demographic groups experiencing extreme poverty, which, in turn, hinders the formation of effective public policy measures to reduce poverty and eradicate extreme poverty. The paper shows that ignoring the indicator of extreme poverty, calculated depending on the size of GNI per capita using the Atlas method, can lead to erroneous conclusions in the Voluntary National Review of the Russian Federation on achieving the UN Sustainable Development Goals. The author proposes to select indicators of global extreme poverty in accordance with the level of socio-economic development of Russia and to assess the eradication of extreme poverty, taking them into account on a par with the national poverty line. In this context, the author draws attention to inaccuracies in domestic publications related to the interpretation of the concept of «extreme poverty», which can lead to distortion of estimates.

디지털 전환의 미래사회 위험이슈 및 정책적 대응 방향: 인공지능을 중심으로

Digital transformation refers to the economic and social effects of digitisation and digitalisation. Although digital transformation acts as a useful tool for economic/social development and enhancing the convenience of life, it can have negative effects (misuse of personal information, ethical problems, deepening social gaps, etc.). The government is actively establishing policies to promote digital transformation to secure competitiveness and technological hegemony, however, understanding of digital transformation-related risk issues and implementing policies to prevent them are relatively slow. Thus, this study systematically identifies risk issues of the future society that can be caused by digital transformation based on quantitative analysis of media articles big data through the Embedded Topic Modeling method. Specifically, first, detailed issues of negative effects of digital transformation in major countries were identified. Then detailed issues of negative effects of artificial intelligence in major countries and Korea were identified. Further, by synthesizing the results, future direction of the government's digital transformation policies for responding the negative effects was proposed. The policy implications are as follows. First, since the negative effects of digital transformation does not only affect technological fields but also affect the overall society, such as national security, social issues, and fairness issues. Therefore, the government should not only promote the positive functions of digital transformation, but also prepare policies to counter the negative functions of digital transformation. Second, the detailed issues of future social risks of digital transformation appear differently depending on contexts, so the government should establish a policy to respond to the negative effects of digital transformation in consideration of the national and social context. Third, the government should set a major direction for responding negative effects of digital transformation to minimize confusion among stakeholders, and prepare effective policy measures.

Improving Global TBI Tracking and Prevention: An Environmental Science Approach

Traumatic brain injury (TBI) has grown to pandemic proportions, placing a significant burden on global public health, socio-economic condition, and human capital resources. This paper examines the global spatial distribution of TBI using currently available data from research studies to advance a comprehensive review of TBI that integrates geospatial and environmental perspectives. It reveals significant geographic differences and socioeconomic gaps in global TBI incidence tracking, prevalence, and mortality rates. It proposes an environmental science approach to improving public awareness, tracking and prevention of TBI through the integration of environmental data using GIS. The use of GIS for accurate location-based mapping and integrated analysis of environmental data subsequently helps reveal risk factors for targeted research, education outreach, and more effective public health policy and preventative measures.

Mainstreaming passive houses: more gradual reconfiguration than transition

ABSTRACT Buildings are major contributors to greenhouse gas emissions and energy consumption. A transition to low-carbon housing requires the introduction of very energy-efficient buildings on a global scale and effective policy measures to support such a transformation. In this article, we study one such radical solution for energy-efficient buildings – the passive house – through a national case study in Sweden. We identify three societal domains where passive houses increasingly become embedded in the building sector: Firstly, the framing of passive houses in the public debate shifted from being presented as a radical alternative for a future low-carbon housing sector to being perceived as a specific low-energy building market segment. Secondly, passive houses have become part of a broader regional institutional and political context rather than a niche. Finally, passive houses have become a driving force for stricter building regulations but in a way that rather led to the assimilation of selected passive house features into existing sectoral structures. We conclude that the dynamics of change we find is rather a ‘mainstreaming’ process of gradual adaptation of construction sector structures and passive houses than a radical transformation of the built environment or the diffusion of new building technology.

Reflections on nursing and COVID-19 in light of health education.

To reflect on the nursing and pandemic of COVID-19 considering health education, health promotion, and the Ottawa Charter action areas. A theoretical-reflexive study on health education and health promotion concepts and the areas of action presented in the Ottawa Charter. Educational actions are present in the contexts of epidemics and pandemics, as well as in the work of nurses, who need to be increasingly based on dialogue and individual and collective empowerment to enable users to adopt healthy and preventive behaviors - in this case, concerning COVID19. However, this professional needs effective and efficient public policy actions and measures based on scientific assumptions of health promotion. Final considerations: The actions of health education need to be increasingly valued because knowledge can be considered the first "vaccine" to combat any pandemic.

Transformation of Identity: Macro and Micro Levels of Research

This review of articles follows the Snyder methodology (2019) and is based on a study that was the collection, analysis and comparison of relevant publications on the topic of identity over the past fve years by quantitative and qualitative methods in the Web of Science and Scopus repositories.The scientometric analysis representing the research macrolevel made by means of the VOSviewer_1.6.16_exe CitNetExplorer_1.0.0_exe programs made it possible to distinguish in a vast array of publications the most relevant and cited articles, verified by the scientific community, focused the attention of scientists on semantic “nodes,” that is, values that guide social practices, and also allowed questions to be answered on background practices, organizing knowledge within the framework of discursive analysis M. Fuko. Micro-level, — analytical reading of texts, — made it possible to analyze the main trends in the development of identity studies and summarize the findings. The research undertaken shows that the concepts of political identity and youth identity are not limited to the traditional framework of ethnicity and race, but include a wide range of social and personal conditions, the study of which has great theoretical and practical significance. The study of the identity of emigrants, students, women, former military and many other social groups makes it possible to adopt more effective public policy measures and reduce the distance between managers and managers.Dedicated semantic clusters can be investigated in the new social conditions of Russia, and future finds of domestic researchers in this area will become a resource and contribution to the development of science and society. The absolute predominance of Anglo-Saxon studies in this topic, coupled with the obviously growing attention of researchers to unique and, sometimes, autonomous social groups, as well as identities in a state of transit, opens up great opportunities for Russian researchers to disseminate Russian empirical material and include examples from domestic social and political practices of transformation in the wider context of international sociology and political science.

COVID-19 Pandemic: Health Challenges and a Critical Study of the Structure of the Nigeria Economy

Sub-Saharan Africa has been characterized as having a high prevalence of malaria and HIV. The present COVID-19 outbreak compounds and complicates Nigeria’s health and socio-economic problems that require stringent public health measures, improved health facilities and achievable economic policies to tackle. The impact of COVID-19 on national economies depends on the level of preparedness and responsiveness to shocks. This perspective dissected the structure of the Nigerian economy and the effect of the pandemic on Nigeria’s economy, having given mild health implications. The study adopted an exploratory research design and thus, relied on the past socio-economic situation in Nigeria to explain how Nigeria was affected by the pandemic. This perspective concludes that the socio-economic foundation in Nigeria is weak. These weak socio-economic settings have made the economy vulnerable to external and internal shocks. This study recommends that the Nigerian government provide adequate and internationally standard training facilities for her health workers and ensure that they are paid well and as when due. To build a resilient economy that can survive external and internal shocks, there is a need to diversify the economy’s economic base fully. Alongside achieving economic diversification, there is the need to improve the existing agricultural-industrial link. The Central Bank should adopt effective monetary policy measures to facilitate credit to private sectors to promote investment in key sectors in the economy. There is a need to increase stimulus package for all industries and households to sustain and improve employment and consumption levels. There is a need to improve efficiency in government spending. The government should sincerely invest in infrastructures to reduce the cost burden on the private sector. Furthermore, there is a crucial need to increase government investment in the health and education sector to prepare the nation for any other health emergency and build its human capital.

Map Design for Public Health Emergencies: a Novel Conceptual Framework for Thematic Content Selection

Public health emergencies always lead to serious consequences which affect a lot on human health and socioeconomic progress. It is essential that governments and regional health commissions guide the public toward self-protection and better arranged social production during epidemic outbreaks and spreads. According to the need of risk communication and information disclosure, existing studies for COVID-19 maps and visualization applications are conducive to predicting the future trend of the pandemic, mitigating the harmful effect on public wellbeing by leading to effective intervention and policy measures. However, unsettled tasks remain on comprehensive organization of risk information, effective expression of data for public requirement, and systematic theoretical framework as a standard of map design for public health emergencies. To close the research gaps, this paper proposes a conceptual framework with a three-dimensional spatiotemporal-logic structure as a theoretical foundation for map thematic content selection, which is also a good basis for determining the effective visualization approaches of map design. It enhances the validity and legibility of the map expression by leading maps’ thematic content couple with features and processes of an epidemic. Then, using the COVID-19 outbreak in Shenzhen, China, as an example, this paper illustrates how to apply the conceptual framework for selecting the thematic content of COVID-19 maps, and explains the specific ways to transform epidemic data into objects for cartographic representation with proper principles and modes. To our knowledge, this paper is the very first study to bring the thematic content of maps for public health emergencies to the fore, and it is thus believed to shed fresh lights into thematic map design.

THE COST EFFICIENCY OF CAMBODIAN COMMERCIAL BANKS: A STOCHASTIC FRONTIER ANALYSIS

The Cambodian banking sector has rapidly expanded in recent decades, although there are concerns about the performance of Cambodian banks and the country’s banking sector. A paucity of empirical evidence to clarify the real issues in the banking sector also makes it difficult to formulate effective policy measures to address any potential problems. This study provides empirical evidence by estimating the cost function and efficiencies of 34 commercial banks over the period from 2012 to 2015. We find that the average cost efficiency scores range from 0.60 when measuring bank outputs as loan and deposit amounts, and 0.77 when measuring bank outputs as interest and non-interest income, suggesting that if they are operated more efficiently, they could cut costs by 40% in fund mobilization and 23% in profit making while keeping the same output level. We also find that the Cambodian banks have experienced an improvement in efficiency scores over the period for both aspects of banking activities. Furthermore, we find that expanding a branch network into rural areas is inefficient for bank management, and holding excessive liquidity is associated with higher efficiency, but diversification in bank business operations is negatively associated with cost efficiency of Cambodian commercial banks.

Investigating the effects of food production on sustainable development: The case of the upper middle‐income countries

AbstractDeveloping effective policy measures for sustainable development is possible by determining the health and environmental impact of durable goods production as well as non‐durable goods production. The findings obtained from researching the effects of each sector where nondurable goods are produced on the environment and health can be evaluated in policy making processes. The food industry mainly produces non‐durable goods and it is constantly growing. It is almost impossible to reduce production in this sector below a certain threshold. Therefore, the environmental and health effects of the food industry should not be neglected. The aim of this study is to investigate the impact of food production on the sustainable development in 23 upper middle‐income countries (Argentina, Armenia, Azerbaijan, Belarus, Brazil, Bulgaria, China, Colombia, Costa Rica, Ecuador, Georgia, Guatemala, Indonesia, Iran, Jamaica, Kazakhstan, Malaysia, Mexico, Peru, Russia, South Africa, Thailand, and Turkey) for the period 1995–2018. CO2 emissions and life expectancy at birth have been selected as indicators of sustainable development. The preferred method is the Panel ARDL. The long‐run analysis results show that while food production increases carbon emissions, there is a positive and significant relationship between food production and life expectancy at birth and these findings are consistent with empirical findings in the literature. The statistically significant relationship between food production and carbon emissions, and life expectancy at birth indicates that food production is an important determinant of sustainable development.

A Stackelberg–Nash–Cournot Equilibrium in a Pollution Reduction Scheme

This paper presents a Stochastic Stackelberg–Nash–Cournot Equilibrium model with continuous market demand distribution to examine the effectiveness of ambient charges as an effective policy measure for reducing nonpoint source pollution in a hybrid scheme. To do so, we consider the supply side of an energy market with hybrid technology that competes in an oligopoly market setting. Within such a setting, each power plant or firm uses a mix of fossil fuels (F) and renewable energy sources (R) to generate power at any given time. The demand for electricity is not realized at the time when the firm (leader) makes the decision. The competition between the two energy sources available to leader is assumed to be of Nash–Cournot equilibria, implying that they use one energy source to generate electricity, whilst holding the other energy source as a constant when the followers reactions are known. Based on the assumption that the demand function is affine and power plants cost functions are quadratic, we obtain the Stackelberg–Nash–Cournot equilibrium. Hence, our analysis provides an interesting insight into the effectiveness of using ambient charges, within the context of a Stochastic Stackelberg–Nash–Cournot competition, as an environmental economic policy measure when included within a robust hybrid scheme. From an economical point of view, this allows pollutants to develop specific control technologies by undertaking research and development (R&D) measures or production processes to maintain emissions standards in a hybrid scheme. From a policy implementations point of view, the environmental authority can use the pollution abatement technology ratio to set ambient charges and industry specific pollutant quantitative limits subject to technological variations.

Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle–bicycle crashes

Introduction: In recent years, Australia is seeing an increase in the total number of cyclists. However, the rise of serious injuries and fatalities to cyclists has been a major concern. Understanding the factors affecting the fatalities and injuries of bicyclists in crashes with motor vehicles is important to develop effective policy measures aimed at improving the safety of bicyclists. This study aims to identify the factors affecting motor vehicle-bicycle (MVB) crashes in Victoria, Australia and introducing effective countermeasures for the identified risk factors. Method: A data set of 14,759 MVB crash records from Victoria, Australia between 2006 and 2019 was analyzed using the binary logit model and latent class clustering. Results: It was observed that the factors that increase the risk of fatalities and serious injuries of bicyclists (FSI) in all clusters are: elderly bicyclist, not using a helmet, and darkness condition. Likewise, in areas with no traffic control, clear weather, and dry surface condition (cluster 1), high speed limits increase the risk of FSI, but the occurrence of MVB crashes in cross intersection and T-intersection has been significantly associated with a reduction in the risk of FSI. In areas with traffic control and unfavorable weather conditions (cluster 2), wet road surface increases the risk of FSI, but the areas with give way sign and pedestrian crossing signs reduce the risk of FSI. Practical Applications: Recommendations to reduce the risk of fatalities or serious injury to bicyclists are: improvement of road lighting and more exposure of bicyclists using reflective clothing and reflectors, separation of the bicycle and vehicle path in mid blocks especially in high-speed areas, using a more stable bicycle for the older people, monitoring helmet use, improving autonomous emergency braking, and using bicyclist detection technology for vehicles.