Geospatial Framework for the Use of Natural Resource Extraction in Public Private Partnerships

Resources for the maintenance and expansion of existing highway infrastructure are scarce. Public Private Partnerships (PPP) are feasible solutions to the concern of lagging investment. PPP are increasingly used for the procurement of services and goods, because of their flexibility and ability to channel private resources. This research addresses the possible implementation of a barter approach in Public Private Partnerships (PPP), which includes natural resources for trade model to offset costs.;Federal law permits the extraction of coal when it is a byproduct of the construction process, coal which under normal circumstances would not be economically feasible to extract. West Virginia law allows PPP to extract coal by surface mining when they develop road beads for new highways. There is no exchange of funds between the coal company and the West Virginia Department of Transportation; the benefits are derived entirely from the construction cost savings for roadbed construction.;This dissertation develops a geospatial method to quantify the availability of natural resources along predetermined roadway alignments. The methodology is divided in three phases: Macroscopic (Level I), Mesoscopic (Level II) and Microscopic (Level III), for the King Coal Highway. The process considers laws and industry best practices in the calculation. The research outcome suggests that there are segments of the road with enough, as well as segments of the road without enough coal.

Name thesis:degree_name

PhD

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil and Environmental Engineering

Year dc:date.available

2013

Author dc:creator

• Barrios, Juan de Dios

Contributors dc:contributor

• David Martinelli
• Andrew Nichols
• Lloyd J. French
• Leonel Medellin
• Avinash Unnikrishnan

• Civil engineering
• Geographic information science and geodesy

Identifier

https://researchrepository.wvu.edu/etd/479

OAI identifier oai:identifier

oai:researchrepository.wvu.edu:etd-1482