<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">NS</journal-id><journal-title-group><journal-title>Natural Science</journal-title></journal-title-group><issn pub-type="epub">2150-4091</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/ns.2013.52A043</article-id><article-id pub-id-type="publisher-id">NS-28377</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biomedical&amp;Life Sciences</subject><subject> Chemistry&amp;Materials Science</subject><subject> Earth&amp;Environmental Sciences</subject><subject> Medicine&amp;Healthcare</subject><subject> Physics&amp;Mathematics</subject></subj-group></article-categories><title-group><article-title>
 
 
  A cost-optimal scenario of CO2 sequestration in a carbon-constrained world through to 2050
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>akayuki</surname><given-names>Takeshita</given-names></name><xref ref-type="aff" rid="aff1"><sub>1</sub></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>Transdisciplinary Initiative for Global Sustainability, The University of Tokyo, Tokyo, Japan</addr-line></aff><author-notes><corresp id="cor1">* E-mail:<email>takeshita@ir3s.u-tokyo.ac.jp</email></corresp></author-notes><pub-date pub-type="epub"><day>27</day><month>02</month><year>2013</year></pub-date><volume>05</volume><issue>02</issue><fpage>313</fpage><lpage>319</lpage><history><date date-type="received"><day>13</day>	<month>January</month>	<year>2013</year></date><date date-type="rev-recd"><day>10</day>	<month>February</month>	<year>2013</year>	</date><date date-type="accepted"><day>24</day>	<month>February</month>	<year>2013</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
   In this paper, a regionally disaggregated global energy system model with a detailed treatment of the whole chain of CO<sub>2</sub> capture and storage (CCS) is used to derive the cost-optimal global pattern of CO<sub>2</sub> sequestration in regional detail over the period 2010-2050 under the target of halving global energy-related CO<sub>2</sub> emissions in 2050 compared to the 2005 level. The major conclusions are the following. First, enhanced coalbed methane recovery will become a key early opportunity for CO<sub>2</sub> sequestration, so coalrich regions such as the US, China, and India will play a leading role in global CO<sub>2</sub> sequestration. Enhanced oil recovery will also have a participation in global CO<sub>2</sub> sequestration from the initial stage of CCS deployment, which may be applied mainly in China, southeastern Asia, and West Africa in 2030 and mainly in the Middle East in 2050. Second, CO<sub>2</sub> sequestration will be carried out in an increasing number of world regions over time. In particular, CCS will be deployed extensively in today’s developing countries. Third, an increasing amount of the captured CO<sub>2</sub> will be stored in aquifers in many parts of the world due to their abundant and widespread availability and their low cost. It is shown that the share of aquifers in global CO<sub>2</sub> sequestration reaches 82.0% in 2050. 
 
</p></abstract><kwd-group><kwd>CO&lt;sub&gt;2&lt;/sub&gt; Sequestration; Simulation;  Optimization; Global Energy System Model</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. INTRODUCTION</title><p>Avoiding dangerous climate change is an increasingly formidable challenge. CO<sub>2</sub> capture and storage (CCS) is now recognized as an important option for mitigating climate change. Research, development, and demonstration for CCS are ongoing not only in developed countries, but also in at least 19 developing countries. Although it has been indicated that CCS has advantages over other CO<sub>2</sub> mitigation options in terms of CO<sub>2</sub> emissions reduction potential and cost-effectiveness (e.g., [1,2]), there are still major hurdles to widespread deployment of CCS. First of all, it must be proven that CO<sub>2</sub> can be permanently and safely stored underground. Second, public acceptance of storing CO<sub>2</sub> underground must be gained.</p><p>Under these circumstances, identifying in advance the future likely CO<sub>2</sub> storage sites is considered to be highly useful in overcoming the above two hurdles. This is because much time and effort can be spent on better understanding the geological properties of potential CO<sub>2</sub> storage sites, on giving potential host communities for CO<sub>2</sub> storage sites a detailed explanation on the necessity, scope, and safety of the CO<sub>2</sub> storage project, and on building a reliable relationship with them. Also, taking into account that an important feature of CCS is that it is capital intensive, this would help all stakeholders (including governments, utilities, and CCS industries) make rational decisions on CCS infrastructure design.</p><p>Thus, the purpose of this paper is to derive the costoptimal global pattern of CO<sub>2</sub> sequestration in regional detail over the period to 2050 under a stringent CO<sub>2</sub> emissions reduction constraint (i.e., a halving of global energy-related CO<sub>2</sub> emissions in 2050 compared to the 2005 level). The cost-optimal global pattern of interregional CO<sub>2</sub> transportation to CO<sub>2</sub> storage sites is also drawn and analyzed under this constraint. These analyses are done by using the global energy system model REDGEM70 (an acronym for a REgionally Disaggregated Global Energy Model with 70 regions) [3,4], which treats the whole chain of CCS in detail.</p></sec><sec id="s2"><title>2. METHODOLOGY</title><sec id="s2_1"><title>2.1. Overview of the REDGEM70 Model</title><p>REDGEM70 is a technology-rich, bottom-up global energy systems optimization model formulated as an intertemporal linear programming problem. <xref ref-type="fig" rid="fig1">Figure 1</xref> schematically illustrates the structure of the model. With a 5% discount rate, the model is designed to determine the cost-optimal energy strategy (e.g., the cost-optimal</p><p>choice of technology options) from 2010 to 2050 at 10-year intervals for each of 70 world regions so that total discounted energy system costs are minimized under constraints on the satisfaction of exogenously given energy end-use demands, the availability of primary energy resources, material and energy balances, the maximum growth rates of new technologies, etc. In the model, price-induced energy demand reductions and energy efficiency improvements, fuel switching to less carbonintensive fuels, and CCS in geologic formations are the three options for CO<sub>2</sub> emissions reduction.</p><p>Furthermore, in the current version of the model used in this study, there is also a constraint that global energyrelated CO<sub>2</sub> emissions in 2050 are to be halved compared to the 2005 level. This constraint is the same as that given in the International Energy Agency’s BLUE Map scenario [1,5]. The model has a full flexibility in where CO<sub>2</sub> emissions reduction is achieved to meet this constraint.</p><p><xref ref-type="fig" rid="fig2">Figure 2</xref> shows how the 70 world regions are defined in REDGEM70. These 70 regions are categorized into “energy production and consumption regions” and “energy production regions”. The whole world was first divided into the 48 energy production and consumption regions to which future energy end-use demands are allocated. The 22 energy production regions, which are defined as geographical points, were then distinguished from the energy production and consumption regions to represent the geographical characteristics of the areas endowed with large amounts of fossil energy resources. While the 48 energy production and consumption regions cover the global final energy consumption, all the energy-related activities except final energy consumption are conducted in each of the two region types in the model. Such a detailed regional disaggregation enables the explicit consideration of regional characteristics in terms of energy resource supply, energy demands, CO<sub>2</sub> storage capacity, geography, and climate.</p><p>Future trajectories for energy end-use demands were estimated as a function of those for socio-economic driving forces such as population and income in the intermediate B2 scenario developed by [<xref ref-type="bibr" rid="scirp.28377-ref6">6</xref>]. Allocation of the energy end-use demand estimates to the 48 energy production and consumption regions was done by using countryand state-level statistics/estimates (and projections if available) on population, income, geography, energy use by type, and transport activity by mode, and by taking into account the underlying storyline of the B2 scenario that regional diversity might be somewhat preserved throughout the 21st century.</p><p>Assumptions on the availability and extraction cost of fossil energy resources are taken from [<xref ref-type="bibr" rid="scirp.28377-ref7">7</xref>]. For biomass resources, REDGEM70 considers not only terrestrial biomass (such as energy crops and modern fuelwood), but also waste biomass. The availability of these biomass resources and excess cropland that can be used for energy purposes without conflicting with other biomass uses such as food production was estimated for each region and each time point. They were estimated assuming that biomass is produced in a sustainable way so that biomass-derived energy carriers can be regarded as carbon neutral. Data for these biomass resources (e.g., resource availability, yields per hectare of land, and supply costs) are provided in [<xref ref-type="bibr" rid="scirp.28377-ref8">8</xref>]. These resource availability estimates were then allocated to the 70 model regions by using country-, state-, and site-level statistics/estimates.</p></sec><sec id="s2_2"><title>2.2. CCS Sector</title><p>In REDGEM70 as shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>, the CO<sub>2</sub> generated from power plants (excluding those used for on-site CHP production and biomass-fired steam-cycle power generation), synthetic fuels production plants (excluding those used for converting stranded gas and decentralized small-scale hydrogen production), ethanol production plants, oil/FT refinery plants, and industrial processes can be captured for subsequent sequestration in geologic formations. It is assumed that the captured CO<sub>2</sub> is transported intraregionally (and interregionally if needed) to a storage site and then stored in geologic formations. Data for CO<sub>2</sub> capture technologies are provided in [8,9]. The costs of all types of CO<sub>2</sub> capture technologies, which represent the bulk of the overall CCS costs, are assumed to be reduced by 15.6% from 2010 to 2050 [<xref ref-type="bibr" rid="scirp.28377-ref10">10</xref>].</p><p>The cost of intraregionally transporting the captured CO<sub>2</sub> from fossil-fueled plants to a storage site was estimated to be 24.1 US $<sub>2007</sub> per tonne of carbon, assuming that it is transported intraregionally through 250 km of pipeline [8,10]. On the other hand, due to the dispersed nature of biomass feedstocks, the intraregional transportation of the captured CO<sub>2</sub> from biomass-fueled plants to a storage site is assumed to suffer from diseconomies of small scale. Based on [<xref ref-type="bibr" rid="scirp.28377-ref8">8</xref>], the cost of intraregionally transporting the captured CO<sub>2</sub> from biomass-fueled plants to a storage site was estimated to be in the range of 59.0 - 95.4 US $<sub>2007</sub> per tonne of carbon depending on secondary energy carriers produced from biomass.</p><p>REDGEM70 treats the interregional transportation of CO<sub>2</sub> between representative cities/sites in the 70 model regions and is able to determine its cost-optimal evolution path. <xref ref-type="fig" rid="fig3">Figure 3</xref> shows the interregional CO<sub>2</sub> transportation costs as a function of transportation distance for each mode. In advance of model simulations, possible interregional transportation routes were given between representative cities/sites in the 70 model regions and their distances were calculated using geographic information system (GIS) land cover data, GIS land use data, and land elevation data. Thus, the cost of transporting a tonne of carbon between every couple of representative cities/sites in the 70 model regions is an input to the model.</p><p>The geologic CO<sub>2</sub> sequestration options included are enhanced oil recovery (EOR), enhanced coalbed methane recovery (ECBMR), depleted gas-field disposal, and aquifer disposal. <xref ref-type="table" rid="table1">Table 1</xref> shows the input data for CO<sub>2</sub> sequestration options. In addition to CO<sub>2</sub> sequestration costs listed in <xref ref-type="table" rid="table1">Table 1</xref>, a monitoring cost of 0.864 US $<sub>2007</sub> per tonne of carbon stored is assumed to be required [<xref ref-type="bibr" rid="scirp.28377-ref13">13</xref>].</p><p><xref ref-type="fig" rid="fig4">Figure 4</xref> shows the regional distribution of CO<sub>2</sub> storage capacity. 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