Universities play an essential role in spreading climate change awareness. However, slight information on climate change and environmental issues had been integrated into the curricula. Moreover, minimal research had been carried out to understand university role in spreading awareness, and students level of awareness and daily behaviour towards climate change, especially in developing countries. This paper aims to investigate the aforementioned issues. An experimental study was carried out on 448 undergraduate students enrolled at An-Najah National University Palestine. The study aimed to examine students’ knowledge and daily behaviour towards climate change, and the important role the university and students’ societies play in terms of spreading and enhancing awareness. The results revealed that female and male students had a non satisfactory interest level in environmental topics and activities, and gender equality did not seem to be an issue. Moreover, female students tend to have a significantly lower level of awareness on climate change compared to male students. On the other hand, being an engineering students or a member in students’ societies had a positive impact on students’ level of awareness and especially females. The results revealed that female students who are enrolled in the engineering faculty or members of students’ societies had a significantly higher level of awareness compared to female students who are enrolled in other faculties or not members of students’ societies. In general, students had a low level of awareness regardless of gender or faculty and universities should offer undergraduate students and especially female students’ opportunities to learn more about climate change by integrating climate change topics into higher education. Moreover, universities should support extracurricular activities held by student societies, and some of these activities should be directed towards environmental and climate change issues. This study entails the activities of the Mediterranean Gender Equality Community of Practice co-created by the Mediterranean Network of Engineering Schools (RMEI), where An-Najah University is an active member, with the support of the EU TARGET project entitled ’Taking a reflexive approach to gender equality at Institutional transformation’.

During the last two decades, the development of sustainable transportation systems has been highlighted as a key element in solving environmental problems related to climate change and impacts on greenhouse gases. Globally, the transportation sector has become one of the main contributors to these environmental problems. Thus, the environmental impact assessment of this sector and the implications of new vehicle technologies have begun to be considered as first steps for any long-term future strategies in this sector. In Palestine, the lack of environmental data related to the transportation sector and the absence of studies that address the new vehicle technologies (such as hybrid vehicles) and their future implications make it difficult to set up any future strategies or plans. In this study, the current and the future environmental impacts of the transportation sector have been assessed, and the future implications of hybrid vehicles have been determined. The gross domestic product (GDP), population, and the number of vehicles for the period 1994–2018 have been used to develop an auto regressive integrated moving average (ARIMA) prediction model for the future number of vehicles. Then, the total traveled kilometers and the total consumed fuels (by diesel and gasoline vehicles) have been predicted. After that, the current and future (2020 and 2030) greenhouse gas (GHG) emissions, including CO₂, N₂O, and CH₄, have been estimated. Finally, the future implications of hybrid vehicles, based on two scenarios (10% and 20% hybrid vehicles) have been estimated. The results have showed that the estimated CO₂, N₂O, and CH₄ emissions from the transportation sector in 2020 are 4,842,164.5, 213.8, and 445.8 tons, which are very high, and even much higher than the total national emissions of 2014 (the only officially available data). Moreover, in 2030, replacing 20% of internal combustion engine vehicles (ICEVs) by hybrid vehicles would lead to 4.66% and 13.31% reductions in CO₂ and N₂O, respectively, as compared to 100% ICEVs, while the CH₄ emissions will increase. However, the overall CO₂-equivalent will decrease by 5%; therefore, a more sustainable transport system will be achieved.

During the last two decades, problems related to high-energy consumption and greenhouse gas (GHG) emissions by the transportation sector have arisen. Therefore, several alternatives have been investigated, in order to reduce the dependence on the conventional fuels that are used by land transportation modes. One of these promising alternatives is the electric vehicles (EVs), which seem to be the future replacement for conventional vehicles. Thus, this study aims to quantify the energy and the environmental implications of EVs in Palestine in 2030, considering that the current percentage of EVs is almost 0%. In this study, the needed parameters related to the number of vehicles and energy costs by source in Palestine were collected. Then, several prediction models have been developed in order to determine the energy and environmental implications of EVs in 2030, considering that 10% of conventional vehicles could be replaced by EV during the next 10 years (2020–2030). The results have showed that this could save USD 464.31 million in operating energy costs during 2030 (one year of saving). More specifically, this number is almost equal to 3.18% of the gross domestic product (GDP) of Palestine in 2018. Furthermore, significant amounts of GHG emissions could be reduced. The reduction (during 2030) in NH₄, N₂O, and CO₂ emissions could be around 10.51%, 10%, and 6.86%, respectively.

Road crashes are problems facing the transportation sector. Crash data in many countries are available only for the past 10 to 20 years, which makes it difficult to determine whether the data are sufficient to establish reasonable and accurate prediction rates. In this study, the effect of sample size (number of years used to develop a prediction model) on the crash prediction accuracy using Autoregressive integrated moving average (ARIMA) method was investigated using crash data for years 1971–2015. Based on the availability of annual crash records, road crash data for four selected countries (Denmark, Turkey, Germany, and Israel) were used to develop the crash prediction models based on different sample sizes (45, 35, 25, and 15 years). Then, crash data for 2016 and 2017 were used to verify the accuracy of the developed models. Furthermore, crash data for Palestine were used to test the validity of the results. The used data included fatality, injury, and property damage crashes. The results showed similar trends in the models’ prediction accuracy for all four countries when predicting road crashes for year 2016. Decreasing the sample sizes led to less prediction accuracy up to a sample size of 25; then, the accuracy increased for the 15‐year sample size. Whereas there was no specific trend in the prediction accuracy for year 2017, a higher range of prediction error was also obtained. It is concluded that the prediction accuracy would vary based on the varying socioeconomic, traffic safety programs and development conditions of the country over the study years. For countries with steady and stable conditions, modeling using larger sample sizes would yield higher accuracy models with higher prediction capabilities. As for countries with less steady and stable conditions, modeling using smaller sample sizes (15 years, for example) would lead to high accuracy models with good prediction capabilities. Therefore, it is recommended that the socioeconomic and traffic safety program status of the country is considered before selecting the practical minimum sample size that would give an acceptable prediction accuracy, therefore saving efforts and time spent in collecting data (more is not always better). Moreover, based on the data analysis results, long‐term ARIMA prediction models should be used with caution.