Search

Wildlife-vehicle collisions (WVCs) pose a serious global issue. Factors influencing the occurrence of WVC along roads can be divided in general into two groups: spatially random and non-random. The latter group consists of local factors which act at specific places, whereas the former group consists of globally acting factors. We analyzed 27,142 WVC records (roe deer and wild boar), which took place between 2012 and 2016 on Czech roads. Statistically significant clusters of WVCs occurrence were identified using the clustering (KDE+) approach. Local factors were consequently measured for the 75 most important clusters as cases and the same number of single WVCs outside clusters as controls, and identified by the use of odds ratio, Bayesian inference and logistic regression. Subsequently, a simulation study randomly distributing WVC in clusters into case and control groups was performed to highlight the importance of the clustering approach. All statistically significant clusters with roe deer (wild boar) contained 34% (27%) of all records related to this species. The overall length of the respective clusters covered 0.982% (0.177%) of the analyzed road network. The results suggest that the most pronounced signal identifying the statistically significant local factors is achieved when WVCs were divided according to their occurrence in clusters and outside clusters. We conclude that application of a clustering approach should precede regression modeling in order to reliably identify the local factors influencing spatially non-random occurrence of WVCs along the transportation infrastructure.

Many approaches have been developed in order to mitigate wildlife-vehicle collisions (WVC), their causes and consequences. Reliable data on the amount and location of killed animals along roads are therefore necessary. The existing WVC databases are usually, however, far from complete. This data underreporting causes problems when identifying the riskiest places along a transportation infrastructure. WVC data underreporting can distort the results of WVC hotspots determination. In this work, we simulated WVC hotspots identification and stability under various rates of WVC data underreporting. Our aim was to investigate whether WVC hotspots can be found at the original locations even when data are strongly underreported. We applied the KDE + method for WVC hotspots identification. The KDE + method also allows for hotspots ranking according to cluster strength and collective risk. These two measures were then used for detection of diminishing hotspot signals with a rising level of underreporting. We found that WVC hotspots with a greater cluster strength suffered less from underreporting whereas hotspots will lower values of both cluster strength and collective risk were not detected when underreporting in the data increased. Hotspots with a cluster strength above 0.5 were almost always detected when data underreporting remained below 50%. More than 50% of these hotspots (with cluster strength above 0.5) were detectable even when underreporting rate was between 50 and 80%. We further studied the effects of both spatial and temporal underreporting. Whereas temporal change of underreporting was not a problem in hotspots detection, spatial underreporting introduced significant errors producing both false positive and false negative results (hotspots). We conclude that both researchers and practitioners should be aware of the phenomenon of underreporting and should also try to maintain the same sampling effort of spatial reporting.

The purpose of the present study is to explain the long-run and causal effects of innovation, financial development, and transportation infrastructure on CO2 emissions using the combined cointegration and wavelet coherence approaches over the period from 1971 to 2018, while using economic growth as a control variable in the model. The outcomes of the Bayer-Hanck cointegration test show that there is an important cointegration equation among CO2 emissions, innovation, financial development, transportation infrastructure, and real GDP. Moreover, the findings from a wavelet power spectrum reveal that there is a significant vulnerability in innovation, financial development, transportation infrastructure, and CO2 emissions at different time frames and frequencies. Furthermore, the outcomes of wavelet coherence approach reveal that (i) Innovation is observed as a significant predictor of CO2 emissions over the period from 2007 to 2013; (ii) In the long run, there are negative correlations between CO2 emissions and financial development; (iii) Over the periods from 2000 to 2015, and from 1985 to 1989, transportation significantly causes CO2 emissions. Our findings have substantial policy implications that suggest there is a need to strengthen innovation and transportation infrastructure to achieve environmental sustainability targets.

This paper examines the present state of environmental impact assessment (EIA) in Nigeria, using some examples. It then focuses on the ways and means of integrating EIA with development planning, including financial, legislative and institutional requirements. In conclusion, the paper stresses the significant role of EIA in future project cycles and decision making, provided that EIA studies begin early enough and with public input.