Developing Asia's Competitive Advantage in Green Products: Learning from the Japanese Experience

Right now, governments around the world are spending record amounts of money to kick-start their economies in response to the financial crisis. Fortunately, a great opportunity exists for this fiscal stimulus to be directed towards "green" economic growth, which can not only provide the new markets and jobs needed immediately for alleviating poverty, but also address the challenges of global warming. Working models already exist, proving that sustainable growth is possible. To achieve this will require social, technical and structural changes, as well as appropriate policies conducive to eco-innovation. For developing countries, there are lessons that can be learned from countries that have already gone through that process. The aim of this paper is to show what lessons can be learnt from the Japanese case. As the world's second largest economy, Japan is not only one of the most energy-efficient economies in the world; it also produces some of the world's leading green technologies. This paper focuses on current trends in the green product market and consumer behavior in Japan, which have been influenced by recent government policies, particularly the ¥15.4 trillion (more than US$100 billion) stimulus package. The aim of this paper is to provide some insight on, and present a repository of selected government policies promoting sustainable development. The scope of this paper will cover areas such as hybrid vehicles, renewable energy, energy efficient home appliances, and green certification schemes. It also provides a brief discussion on the environmental policies of the new Japanese government that came into power on 16 September 2009. The paper attempts to use the most recent data, from June to August 2009, however given the quickly-evolving global environment, these statistics may change drastically by the time this paper is presented.japanese government environmental policies; sustainable development; vehicle pollution policies

Trusted autonomous vehicles: an interactive exhibit

Recent surveys about autonomous vehicles show that the public is concerned about the safety consequences of system or equipment failures and the vehicles' reactions to unexpected situations. We believe that informing about the technology and quality, e.g., safety and reliability, of autonomous vehicles is paramount to improving public expectations, perception and acceptance. In this paper, we report on the design of an interactive exhibit to illustrate (1) basic technologies employed in autonomous vehicles, i.e., sensors and object classification; and (2) basic principles for ensuring their quality, i.e., employing software testing and simulations. We subsequently report on a public engagement event involving this exhibit at the Royal Society Summer Science Exhibition 2019 in the exhibit titled "Trusted Autonomous Vehicles". We describe the process of designing and developing the artefacts used in our exhibit, the theoretical background associated to them, the design of our stand, and the lessons learned. The activities and findings of this study can be used by other educators and researchers interested in promoting trust in autonomous vehicles among the general public

Longitudinal Study of Effects of Elevation Training on Cycling Performance: Cluster Analysis and Linear Mixed Effects Model

In cycling, for cyclists to be able to keep track of their evolution, performance can be measured using tests of maximal effort or, when those cannot be performed, parameters obtained on submaximal efforts, such as power output. Usually, training programs with specific elevation gains are prescribed in order to increase training load. With that in mind, the purpose of this study was, through data analysis techniques, namely cluster analysis and linear mixed-effects models, to compare different elevation gain profiles and determine whether they had a significant effect on performance evolution or not. To accomplish that goal, a database, available on the internet with name GoldenCheetah Open Data was utilized. It contained 2681 athletes, to whom inclusion and exclusion criteria were applied, resulting in a final 1308 cyclists included. Inclusion criteria were being 10 to 90 years old and having at least cycling 20 activities recorded. Exclusion criteria were all activities have missing values and all activities had outlier observations. For descriptive analysis, summarytools from R software was used. Cyclists were 42 years old, on average, and, from the 1308 athletes, 32 were women. In cluster analysis, cluster, imputeTS, factoextra and clValid libraries from R were used to apply four clustering algorithms in order to test and compare with stability measures. Through this analysis we concluded K-means algorithm was the one that performed better, which is why it was analyzed, and two clusters were obtained as a result: the cluster with high elevation gains and the cluster with low elevation gains. Significance tests showed a significant difference on age between clusters, but not on the proportion of female gender. As for training parameters, all were significantly different between clusters, including the ones related to power output. At last, with rstatix and nlme libraries from R, a linear mixed-effects model was applied using all response variables. However, after a moderate correlation was found among some variables, using corrplot R library, a new model with age, gender, number of activity, elevation gain, average speed and average heart rate was applied. The last linear mixed-effects model for average power revealed a significant negative influence of female gender and a significant positive influence of elevation gain, number of activity, average speed, average heart rate and the interaction between elevation gain and the number of the activity. The linear mixed-effects model for peak power on both 5 and 30 minute efforts evinced a significant negative influence of both age and female gender and a significant positive influence of elevation gain, number of activity, average speed, average heart rate and the interaction between elevation gain and the number of the activity. It was possible to conclude that both cluster analysis and repeated measures analysis were effective establishing a relationship between including vertical climbing on training programs and better performance improvement.No ciclismo, de modo que os ciclistas possam monitorizar a sua evolução, a performance é medida com testes de esforço máximo ou, quando a utilização destes não é adequada, parâmetros de esforços submáximos, como produção de potência. Normalmente são prescritos treinos com ganhos de elevação específicos, de forma a aumentar a carga de treino. Assim, o objetivo deste estudo foi, através de técnicas de análise de dados, nomeadamente análise de clusters e modelos lineares de efeitos mistos, comparar diferentes ganhos de elevação e determinar se o ganho de elevação tem efeito na evolução da performance. Para atingir este objetivo, utilizou-se uma base de dados, disponível na internet como GoldenCheetah Open Data, com 2681 atletas, que foram sujeitos a critérios de inclusão e exclusão, resultando num total de 1308 ciclistas. Os critérios de inclusão foram idades entre 10 e 90 anos e mínimo de 20 treinos de ciclismo registados. Os critérios de exclusão foram atletas com valores em falta em todas as atividades registadas ou todos os seus treinos terem observações outliers. Para a análise descritiva, utilizou-se a biblioteca summarytools do software R. Os ciclistas apresentaram média de 42 anos e 32 dos 1308 atletas eram mulheres. Na análise de clusters utilizaram-se as bibliotecas cluster, imputeTS, factoextra e clValid do R para aplicar quatro algoritmos de modo a testar e comparar com medidas de estabilidade. Nesta análise, concluímos que o algoritmo K-means obteve uma melhor performance, pelo que foi o que foi analisado, tendo sido obtidos dois clusters: cluster com grandes ganhos de elevação e cluster com pequenos ganhos de elevação. Testes de significância demonstraram diferenças significativas na idade entre clusters, embora não tenham sido detetadas diferenças na proporção de atletas do sexo feminino. Quanto aos parâmetros de treino, todos demonstraram ser significativamente diferentes entre os dois clusters, incluindo os relativos à produção de potência. Por último, com as bibliotecas rstatix e nlme do software R, aplicou-se um modelo linear de efeitos mistos utilizando todas as variáveis resposta. Contudo, após ter sido detetada uma correlação moderada entre algumas variáveis, com recurso à biblioteca corrplot do R, foi aplicado novo modelo com as variáveis idade, género, número do treino, ganho de elevação, velocidade média e frequência cardíaca média. O último modelo linear de efeitos mistos para a potência média revelou influência negativa significativa do género feminino e influência positiva significativa do ganho de elevação, número do treino, velocidade média, frequência cardíaca média e da interação entre ganho de elevação e número do treino. O modelo linear de efeitos mistos para os picos de potência a 5 e 30 minutos evidenciaram influência negativa significativa da idade e género feminino e influência positiva significativa do ganho de elevação, número do treino, velocidade média, frequência cardíaca média e da interação entre ganho de elevação e número do treino. Foi possível concluir que tanto a análise de clusters como a análise de medidas repetidas foram eficazes no estabelecimento de uma relação entre incluir ganho de elevação nos planos de treino e melhor evolução da performance

An Examination of the Strava Usage Rate-A Parameter to Estimate Average Annual Daily Bicycle Volumes on Rural Roadways

[EN] In Spain, a new challenge is emerging due to the increase of many recreational bicyclists on two-lane rural roads. These facilities have been mainly designed for motorized vehicles, so the coexistence of cyclists and drivers produces an impact, in terms of road safety and operation. In order to analyze the occurrence of crashes and enhance safety for bicycling, it is crucial to know the cycling volume. Standard procedures recommend using data from permanent stations and temporary short counts, but bicycle volumes are rarely monitored in rural roads. However, bicyclists tend to track their leisure and exercise activities with fitness apps that use GPS. In this context, this research aims at analyzing the daily and seasonal variability of the Strava Usage Rate (SUR), defined as the proportion of bicyclists using the Strava app along a certain segment on rural highways, to estimate the Annual Average Daily Bicycle (AADB) volume on rural roads. The findings of this study offer possible solutions to policy makers in terms of planning and design of the cycling network. Moreover, the use of crowdsourced data from the Strava app will potentially save costs to public agencies, since public data could replace costly counting campaigns.This research was funded by the Ministry of Science, Innovation, and Universities, grant number TRA2016-80897-R and the General Directorate of Education, Research, Culture and Sport of the Valencian Government, grant number GV/2017/038Camacho-Torregrosa, FJ.; Llopis-Castelló, D.; López-Maldonado, G.; García García, A. (2021). An Examination of the Strava Usage Rate-A Parameter to Estimate Average Annual Daily Bicycle Volumes on Rural Roadways. Safety. 7(1):1-20. https://doi.org/10.3390/safety7010008S1207

Transport sector decarbonisation - a social sciences and humanities annotated bibliography

The challenge: * By 2014, transport had overtaken power companies as the sector with the highest carbon emissions across the European Union (EU). * From 1990 to 2014, EU road transport emissions rose by 17% and aviation emissions by 82%. Road transport accounted for 70% of EU transport emissions in 2014. Aim: * European energy policy has so far mainly relied on research from Science, Technology, Engineering and Mathematics (STEM) disciplines. Energy-related Social Sciences and Humanities (energy-SSH) have been significantly underrepresented. This bibliography provides a broad overview of SSH perspectives on transport decarbonisation. It is not intended to be comprehensive, but rather aimed at presenting initial insights into the variety of questions posed, areas explored, and methods used by SSH scholars and demonstrating their relevance for EU energy policy. Coverage: * This bibliography presents publications from History, Human Geography, Sociology, Urban Planning, Political Science, Psychology, Anthropology, Theology, Economics, Philosophy and Ethics, Criminology, as well as intersectional disciplines such as Transport, Tourism, and Gender studies. * In order to better represent SSH debates, some transport publications which were of wider relevance to decarbonisation (but did not solely focus on it) were included. Key findings: * Much research concerns technological fixes and individual consumer choices. Consumer research tends to focus on attitudes towards technologies or policies, what determines transport mode preference, or what might prompt mode shift. There is less research on institutional and systemic issues, as well as the role of corporations. * Since the 1990s, the so-called ‘Mobilities turn’ has become dominant, associated with Miriam Sheller, John Urry, Tim Cresswell and Marc Augé. This paradigm emphasises the role of travel, globalisation and movement for our contemporary world. * A large volume of research was found on the car (including electric cars), cycling, commuting, and short distance urban travel. * Underrepresented topics include rural mobility, long distance travel, and shipping and freight. Walking has received far less decarbonisation focused enquiry than cycling. * Whilst not all EU research could be represented, intra-EU differences were noted: e.g. the greater importance of two wheelers in Latvia; how more children to walk to school in Eastern European countries; the renaissance of the tram in France; and the large proportion of urban Finns frequently driving to their rural second home. * Across the span of SSH, researchers frame the problem of transport decarbonisation differently (both from each other, and from more technical disciplines). These framings often point towards different solutions. For instance, they ask: what is the effect of technological, demographic and economic trends on transport emissions?; why do policymakers/scholars focus on certain transport solutions over others?; how do transport modes ‘compete’?; how does the meaning of transport change over time?; and why do we travel