# Asphal Road Pavements to Address Climate Change Challenges--An Overview
## 4 Solutions for Mitigating Climate Change Challenges
### Climate Change Prediction  
Using conventional asphalt pavement designs (stable climate conditions over the service life) can result in an incorrect pavement solution, since it does not consider the influence of future climate change challenges. To consider this, climate prediction models are crucial. The climate system is a complex system where five significant components interact: the atmosphere, the hydrosphere, the cryosphere, the land surface, and the biosphere [58], and a complete model should comprise all components. Not all predictions require a complete model; consequently, simplifications can be made. Several models have been developed [59]. They can be global (cover the whole Earth) or regional (cover a specific region). Climate change is not equal in all areas of the Earth; it impacts regions differently. Global models do not have enough resolution. Regional models and downscaling methodologies capture the finer-scale change resolution of a particular region. In the context of the Coordinated Regional Climate Downscaling Experiment (CORDEX), regional climate change predictions were improved [60].  
Future climate data (predictions) are usually made using projections under different Intergovernmental Panel on Climate Change (IPCC)'s Representative Concentration Pathway (RCP) scenarios. In the Fifth Assessment Report, the IPCC used four pathways: a stringent mitigation scenario (RCP2.6), two intermediate scenarios (RCP4.5 and RCP6.0), and one scenario with very high GHG emissions (RCP8.5) [11]. RCP4.5 is an optimistic scenario, while RCP8.5 is the worst scenario.  
Regarding temperature, one thing is the air temperature, and another is the pavement temperature. Pavement temperature depends, among other things, on air temperature, solar radiation and wind. [PERSON], et al. [61] present a review of models to predict pavement temperature. They can be numerical, analytical or empirical. The former two are based on heat conduction's partial differential equation, while the last is based on statistical analysis. To predict time-dependent pavement temperature using empirical models, neural network-based models have been developed. Currently, the models are developed in dry conditions. Rainfall challenges the model development, and the effect of precipitation is still unclear [59]. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [18] also present a comprehensive review of 38 models to predict asphalt pavement temperature and categorize them into numerical, analytical, and empirical. In turn, [PERSON], et al. [62] developed a model to predict pavement temperatures, using machine learning to optimise deicer applications in cold climates.  
Once climate change is predicted, several authors have been using the AASHTOWare PAvement Mechanistic-Empirical design tool [28] to calculate distresses over the lifetime and consequently to predict the impact of climate change challenges on pavement performance [63; 64]. It includes the Enhanced Integrated Climatic Model (EICM), which is a one-dimensional coupled heat and moisture flow model that predicts the changes in the behaviour of bound (asphal mixture) and unbound materials over service time due to climate conditions [65].