The efficiency of the transport sector is under close examination due to multiple different reasons. Among them are the environmental aspects of emission reduction along with the need to maintain a tight time schedule. Heavy trucks have a significant negative impact on the environment and are sensitive to external factors. Planning green routes is a way to minimize the emissions from heavy trucks by reducing the fuel consumption without sacrificing travel time. This thesis will investigate suspected parameters relevance to the fuel consumption of heavy trucks and their effect on the fuel consumption on heavy trucks. To achieve this, two independent literature searches were conducted, the first to find the relevance and the second to understand the effect. Then a comparison was made with the NVDB to see if the suspected parameters were represented by the attributes in the database. The result of the first literature search varied and the speed and congestion parameter showed the strongest relevance to the fuel consumption of the heavy truck. The second literature search found past research that stated that the fuel consumption of heavy trucks were affected by the parameters, gradient, speed, road curvature, road roughness, congestion, road elements and weather. The result of the investigation of attributes in the NVDB is displayed with respect to green routing. The relevance measure in the first literature search was assumed to be higher if the number of relevant articles were high. The results of the second literature search were discussed with respect to green routing. This was followed by suggesting eventual improvements in the NVDB and improvements in the method used in this thesis. Furthermore, the parameters usage and implementation in GIS were discussed. It was concluded that all parameters found in the second literature search except weather were appropriate for green routing. Other parameters could also have an effect on the fuel consumption of heavy trucks but further research is necessary to investigate this.