Implications of climate change on mitigation potential estimates for forest sector in India

Climate change is projected to impact forest ecosystems, including biodiversity and Net Primary Productivity (NPP). National level carbon forest sector mitigation potential estimates are available for India; however impacts of projected climate change are not included in the mitigation potential estimates. Change in NPP (in gC/m²/yr) is taken to represent the impacts of climate change. Long term impacts of climate change (2085) on the NPP of Indian forests are available; however no such regional estimates are available for short and medium terms. The present study based on GCM climatology scenarios projects the short, medium and long term impacts of climate change on forest ecosystems especially on NPP using BIOME4 vegetation model. We estimate that under A2 scenario by the year 2030 the NPP changes by (−5) to 40% across different agro-ecological zones (AEZ). By 2050 it increases by 15% to 59% and by 2070 it increases by 34 to 84%. However, under B2 scenario it increases only by 3 to 25%, 3.5 to 34% and (−2.5) to 38% respectively, in the same time periods. The cumulative mitigation potential is estimated to increase by up to 21% (by nearly 1 GtC) under A2 scenario between the years 2008 and 2108, whereas, under B2 the mitigation potential increases only by 14% (646 MtC). However, cumulative mitigation potential estimates obtained from IBIS—a dynamic global vegetation model suggest much smaller gains, where mitigation potential increases by only 6% and 5% during the period 2008 to 2108.     

Random parameter models for accident prediction on two-lane undivided highways in India

Introduction

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.

Method

The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.

Results

The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.

Conclusions

The paper is concluded with a discussion on modeling results and the limitations of the present study.     

On-road and simulated driving: concurrent and discriminant validation

Introduction

A converging pair of studies investigated the validity of a simulator for measuring driving performance/skill.

Study 1

A concurrent validity study compared novice driver performance during an on-road driving test with their performance on a comparable simulated driving test.

Results

Results showed a reasonable degree of concordance in terms of the distribution of driving errors on-road and errors on the simulator. Moreover, there was a significant relationship between the two when driver performance was rank ordered according to errors, further establishing the relative validity of the simulator. However, specific driving errors on the two tasks were not closely related suggesting that absolute validity could not be established and that overall performance is needed to establish the level of skill.

Study 2

A discriminant validity study compared driving performance on the simulator across three groups of drivers who differ in their level of experience - a group of true beginners who had no driving experience, a group of novice drivers who had completed driver education and had a learner's permit, and a group of fully licensed, experienced drivers.

Results

The findings showed significant differences among the groups in the expected direction -- the various measures of driving errors showed that beginners performed worse than novice drivers and that experienced drivers had the fewest errors. Collectively, the results of the concurrent and discriminant validity studies support the use of the simulator as a valid measure of driving performance for research purposes.

Impact on industry

These findings support the use of a driving simulator as a valid measure of driving performance for research purposes. Future research should continue to examine validity between on-road driving performance and performance on a driving simulator and the use of simulated driving tests in the evaluation of driver education/training programs.     

Evaluating repeated patient handling injuries following the implementation of a multi-factor ergonomic intervention program among health care workers

Objective

The objective of this study was to evaluate repeated patient handling injuries following a multi-factor ergonomic intervention program among health care workers.

Methods

This was a quasi-experimental study which had an intervention group and a non-randomized control group. Data were collected from six hospitals in Saskatchewan, Canada from September 1, 2001 to December 1, 2006.

Results

A total of 1,480 individuals who had a previous injury were eligible for the study. Medium and small size hospitals in the intervention group had significantly fewer repeated injuries than in the control group. Multivariate analysis showed that the intervention group had 38.1% lower odds of having repeated injury compared to the control group, after adjusting for hospital size.

Conclusions

The work-related repeated injury after a multi-factor intervention program was reduced. The synergistic relationships between components of multi-factor intervention and applicability of injury prevention programs to different settings need to be further explored.

Impact on Industry

Implementing a multi-factor program with the right equipment and training can lower the risk of injury among health care workers.     

Overview of motorcycling in the United States: a national telephone survey

Objectives

Motorcycle registrations have risen in recent years. Although motorcyclist crash fatalities in 2009 were 16% lower than in 2008, they were double the number of deaths in 1997. The present study examined current motorcyclists’ travel patterns and views of motorcycle helmets and other safety topics.

Methods

Motorcycle drivers were interviewed in a national telephone survey conducted in 2009. A weighted sample of 1,606 motorcyclists resulted from adjusting for the oversampling of those younger than 40 and those in the three states without a motorcycle helmet use law (Illinois, Iowa, New Hampshire). All analyses were based on the weighted sample, which was intended to result in a nationally representative sample of motorcyclists.

Results

About one-quarter of respondents said they did not always wear helmets. Of these respondents, 57% said a law requiring helmet use would persuade them to do so, and 27% said nothing would. Ninety-four percent of respondents in states with universal helmet laws said they always ride helmeted, compared with about half of respondents in other states. About half of all respondents favored these laws. About three-quarters said they believe helmets keep riders safer, including two-thirds of respondents who oppose universal laws and almost half of drivers who rarely/never wear helmets. Drivers ages 18–29 and drivers of sport/unclad sport, sport touring, and super sport motorcycles were more likely to always wear helmets, support universal helmet laws, and believe helmets keep riders safer. About half of respondents said antilock braking systems (ABS) enhance safety and that they would get ABS on their next motorcycle. Less than one-quarter thought an airbag would protect a motorcyclist in a crash, and even fewer would consider getting one on their next motorcycle. Forty-three percent of motorcyclists said they had crashed at least once; 62% of the most recent crashes involved no vehicles besides the motorcycle. Respondents reported riding their motorcycles about 5,400 miles, on average, during the past year. Drivers ages 18–29 reported riding fewer miles, on average, than older drivers and more often rode at night and to/from work or school. Drivers of touring and sport touring motorcycles traveled more miles and took more long trips.

Conclusions

Motorcyclists’ travel patterns and views vary widely, but there are distinct patterns by driver age and motorcycle type. Drivers who believe helmets keep riders safer are more likely to always wear them, but this belief appears insufficient to motivate some drivers to wear them. However, universal helmet laws appear effective in increasing helmet use. Many drivers are receptive to purchasing ABS on their next motorcycle.

Impact on industry

States should be encouraged to enact universal helmet laws, and motorcycle manufacturers should be encouraged to offer ABS.