The objective of the present study is to monitor and predict the changes in land surface temperature (LST) in the North of Jordan during the Period 2000 to 2016. Due to political instability in the nearby countries Syria and Iraq, Jordan has witnessed increased influxes of refugees, starting from the year 2003, which has been led to the urban expansion in the area that reflected on the climatic conditions and affected the LST values. Satellite images were used for providing LST, the acquired images represented two seasons of each year, namely summer and winter. Simulation and prediction of LST values for the next 10 years were carried out using nonlinear autoregressive exogenous (NARX) artificial neural network (ANN) model. The inputs to the model consist of meteorological data collected from eight stations in the study area, population, and land use and land cover (LULC). In fact, LULC was expressed in terms of normalized difference building index (NDBI) and normalized difference vegetation index (NDVI) that were obtained from satellite images. The model showed a high correlation between these parameters and the values of simulated LST, where the correlation coefficient for the training set, validation set, testing set and for the entire data ranged from 0.91 to 0.92. Based on the predicted LST values, LST maps for the next 10 years were developed and compared with the present actual LST maps for the year 2016. The comparison has shown an average increase of 1.1 ^°C in the average LST values, which is considered a significant increase compared with previous studies.

Assessing and monitoring of the landfill temperature is of great importance, so as to assess environmental impacts of the landfill and to prevent fires that may lead to failure of the landfill operations. In this study, satellite images were used in mapping the landfill surface temperature (LFST) of Al Akeeder landfill site in Northern Jordan. Artificial Neural Network (ANN) model was developed to simulate and predict the LFST. Fifty-four Landsat satellite images on different dates covering the period 2000–2016 were collected and utilized after being subjected to correction of the thermal band. Multi-temporal thematic maps were developed for the landfill site and the LFST trends, patterns and magnitude were evaluated. Correlating LFST with the amount of the landfilled solid waste has resulted in a good correlation coefficient (r = 0.884), which implies that LFST can serve as an indicator of the amount of solid waste buried in the landfill. Solid waste amount, methane emitted as well as meteorological parameters, such as temperature, humidity, wind speed and evaporation, were used to simulate and predict the value of landfill LFST using ANN. Validation of the ANN has resulted in a good correlation between the predicted and calculated values of the LFST.

Renewable energy sources are environmentally friendly and sustainable resources. However, there is no unique renewable energy resource that suits all countries. As such, nations must select the right option ‒ or combination of options ‒ that aligns with their local economic, technical, and environmental circumstances. Such a selection process is usually performed using a decision-making tool based on multi-criteria analysis. This study aims to find the most effective renewable energy option for Jordan by soliciting experts’ opinions under several criteria and sub-criteria. The collected responses of experts from the energy field were analyzed using the analytical hierarchy process (AHP). The AHP model used in the study consisted of four criteria, eleven sub-criteria, and four renewable energy alternatives. The results indicate that the technical criterion had the highest weight of 53.6% as compared to the environmental criterion which came second with a weight of 29.0% followed by geographical and socioeconomic criteria which have the lowest weights of 11.3% and 6.0%, respectively. The results reveal that under the technical criterion a high rank has given to maturity of the technology followed by availability of know-how with a weight of 0.875 and 0.125, respectively. The sequence of the preferable options based on the study results was: wind energy with 51.9%, followed by the solar energy option with 31.3%, and finally biomass and hydropower with 10.5% and 7.1%, respectively. Sensitivity analysis was performed and showed that the renewable energy options are not sensitive to the technical or environmental criteria, while they were slightly sensitive to the geographical and socioeconomic criteria.

Municipal solid waste services in Jordan are being provided by municipalities, where 90% of the generated solid waste finds its way to the landfills and dump sites. All Jordanian municipalities are suffering from low cost recovery where it reaches 50% in its best case. Realizing these facts, recently, the Jordanian government has embarked on an ambitious package of regulations and policies to promote the adoption of circular economy options. The National Solid Waste Management Strategy (NSWMS) of Jordan has called for rationalization, gradual increase of the service fees and adoption of circular economy options through recycling and composting. To assess the impacts of the proposed policies by the NSWMS on the financial sustainability, a system dynamic modeling (SDM) was carried out for the two largest municipalities in the country, namely Greater Amman Municipality (GAM) and Greater Irbid Municipality (GIM). The share of solid waste management cost from the total municipal budget in GAM and GIM is 13.3% and 24%, respectively. On the other hand, the cost per ton of solid waste management in GAM is USD 85 and in GIM it is USD 50. Medium-term and long-term scenarios were considered in the SDM analysis. By considering the year 2016 as a baseline year, the simulation revealed that during the midterm period (2022–2026), the cost recovery will be increased from 54% to 82.5% in GAM, while in GIM it will be raised from 45% to 70%. Moreover, during the long-term period (2027–2034) as a result of adopting circular economy options by recycling and composting, both GAM and GIM will achieve full cost recovery of the solid waste services, in 2028 and 2029, respectively. Furthermore, diverting the solid waste from the landfill will lead to climate change mitigation, where about 652,275 tons of CO_2eq. and greenhouse gases will be avoided. It is recommended that municipalities in Jordan should consider a more efficient solid waste collection mechanism rather than through electricity bills. Further research is needed to assess the impact of other circular economy options such as waste to energy and extended producer responsibility (EPR).