Developing a Routing Priority Map for Geospatial Modeling of CO2 Pipeline Deployment in the People's Republic of China

Carbon capture and sequestration (CCS) technologies are currently being researched as a potential component of a global portfolio of technologies to help reduce anthropogenic emissions of carbon dioxide (CO2) to the atmosphere. In China, currently a leading emitter of CO2 and a potentially critical player in future carbon emissions reduction strategies, it is important to evaluate the economic feasibility of CCS to understand its potential for large-scale deployment. This paper describes the development of a high resolution geospatial model to assist in efforts to estimate the construction costs of pipelines for transport of CO2 from sources to storage sites. The model assigns relative weights to geographic features throughout mainland China to form a relative prioritization map that may be used to model pipeline routing along paths that are likely to represent the lowest cost paths. The final routing priority map (RPM) differentiates between areas according to their relative cost for routing from sources to sinks. The RPM represents the weighted combination of all overlapping geographic and cultural features included in the model. By using the RPM in conjunction with a routing protocol, grid cells with low priority values (i.e., those for which construction and/or societal costs would be higher) would be avoided in favor of cells with higher priority values, all else equal. This mode of estimating least-cost pipeline routing could represent a significant enhancement to existing methodologies used to estimate CO2 transport costs for CCS in China. INTRODUCTION With growing public and political consciousness of climate change, there is a growing international dialogue about addressing global emissions of carbon dioxide (CO2), with China playing a key role as the world’s largest developing economy and one of its largest emitters of CO2. With its vast coal reserves accounting for approximately 70 percent of the nation’s current energy consumption [1], it is unlikely that China will significantly reduce its dependence on this cheap, indigenous energy source in coming years. Given the projected growth rates for the nation’s energy use and infrastructure, CCS may be a key technology for reducing carbon emissions in the coming decades. Dahowski, et al., [2] estimated the potential CO2 storage capacity within China to be in excess of 3000 billion metric tons of CO2 (GtCO2), which is enough to store all of China's coal emissions for hundreds of years at current emissions rates. In that study, sources were matched with sinks, and costs calculated using a straight-line routing algorithm to estimate CO2 pipeline transport costs. However, the data exist to allow for the modeling of more realistic pipeline routing in estimating pipeline costs for source-sink pairs. This present study is designed to facilitate modifications to the Dahowski methodology by creating a pipeline routing priority map (RPM) that can serve as the basis for a modifiable cost surface to drive pipeline route modeling in China, and for future iterations of the China CCS cost curve work. This was accomplished within a geographic information system (GIS) using almost exclusively free datasets primarily derived from official government sources. The purpose of this routing priority map is to model pipeline placement under real geographic constraints from point sources such as coal fired power plants, cement, and ethanol plants to sinks such as deep saline aquifers, depleted oil fields, and deep unmineable coal beds. These non-linear routing paths will provide a higher-resolution method for estimating the costs for routing hundreds of potential pipelines all throughout mainland China. This is a first of its kind attempt to create a national scale pipeline prioritization map for CCS in China, and therefore relies on correlating assumptions from various smaller scale individual projects as proxies for this model. This paper describes the methodology used to create the RPM, including key features and assumptions applied to each dataset. MATERIALS AND METHODS Pipeline routing and associated costs are commonly evaluated using a relative weighting system in which multiple data layers are assigned relative weights and combined to form a cost map [3]. The RPM differs from a cost map in that the weighting assumptions used reflect relative routing preferences as opposed to actual cost 1 . Each data layer falls under a data category, which has an assigned multiplication factor (category weight) reflecting its relative importance, compared to other data categories (see Table 1). Within each category, each feature is represented by one or more data layers and is assigned a relative weight in accordance with its estimated cost or routing preference within the category. Each data layer therefore has a relative weight for each feature it contains, and a multiplication factor from its category to yield its total weight. The total weights from each data layer are then summed to form the final RPM. Table 1: Categorical and relative weighting hierarchy used for the present study. Category Relative Weight Category Weight Category Relative Weight Category Weight Landslide Probability (% per anum) 10 Slope (Degrees) 5