# Routing India towards Net Zero: Optimal Planning of the CCS Infrastructure

Adrien Nicolle, Stéphanie Monjon, Olivier Massol

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Adrien Nicolle, Stéphanie Monjon, Olivier Massol. Routing India towards Net Zero: Optimal Planning of the CCS Infrastructure. 2026. hal-05488962

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Routing India towards Net Zero:
Optimal Planning of the CCS Infrastructure

Adrien Nicolle*¹,¹,², Stéphanie Monjon²,³, and Olivier Massol¹,²,⁴

¹Laboratoire Génie Industriel, CentraleSupélec, Université Paris Saclay, 3 Rue Joliot Curie,
Gif-sur-Yvette, 91190, France

²Climate Economics Chair, 28 Pl. de la Bourse, Paris, 75002, France

³Université Paris-Dauphine, Université PSL, LEDa, CNRS, IRD, CGEMP, 75016 Paris, France

⁴Department of Economics, City, University of London, Northampton Square, London, EC1V
0HB, UK

February 2, 2026

Abstract

Given its size, growing population, and emerging economy, India's energy choices are critically important for mitigating global climate change. The country's increasing energy consumption, heavily reliant on coal, raises concerns about its ability to achieve its net zero target by 2070. Carbon Capture and Storage (CCS) technologies offer a potential solution to reduce emissions from coal-fired power plants. However, the size, cost, and feasibility of CCS deployment in India remain largely unexplored. This paper addresses this gap by introducing a cost-minimizing Mixed Integer Linear Program (MILP) model for CCS deployment in India. The model incorporates detailed carbon capture investment, pipeline routing, and onshore and offshore storage investments. To examine the implications of CCS adoption, we analyze four scenarios that reflect alternative policy mandates, storage restrictions, and capture targets. Our results reveal several important findings. First, India's optimal CO₂ pipeline network is characterized by local infrastructures concentrated around storage basins rather than forming a nationally integrated network. Second, a significant portion of emissions is transported

<footnote>*Corresponding author: adrien.nicolle@chaireeconomieduclimat.org</footnote>
along an eastern transportation 'backbone' in all modeled scenarios.

**Keywords:** CCS, Mixed Integer Linear Program, India, Coal, Decarbonization.
# Glossary

## Acronyms

<table><tr><td>CCS</td><td>Carbon Capture and Storage</td></tr><tr><td>EOR</td><td>Enhanced Oil Recovery</td></tr><tr><td>GHG</td><td>Greenhouse Gas Emissions</td></tr><tr><td>MILP</td><td>Mixed Integer Linear Program</td></tr></table>

## Sets

<table><tr><td>B</td><td>Geological basins for $b \in \{1,...,7\}$</td></tr><tr><td>D</td><td>Pipeline sizes $d \in \{1,...,5\}$</td></tr><tr><td>K</td><td>Capture technologies $k \in \{0,...,4\}$</td></tr><tr><td>P</td><td>Time periods $p \in \{0,...,4\}$</td></tr><tr><td>G</td><td>Grid regions $g \in \{1,...,180\}$</td></tr></table>

## Parameters

<table><tr><td>α</td><td>Minimum carbon quota to be captured [%]</td></tr><tr><td>η</td><td>Capture efficiency</td></tr><tr><td>d<sub>b</sub></td><td>Average injection well depth in basin b [km]</td></tr><tr><td>disc<sub>p</sub></td><td>Discounting factor of period p</td></tr><tr><td>intra<sup>dist</sup></td><td>Length of diagonal of grid region g [km]</td></tr><tr><td>LD<sub>g,g'</sub></td><td>Matrix of distances between regions g and g' [km]</td></tr><tr><td>C<sup>dri</sup></td><td>Drilling costs [M$/km]</td></tr><tr><td>C<sup>well</sup></td><td>Fixed cost per injection well [M$]</td></tr><tr><td>C<sup>sd</sup></td><td>Investment cost for site development well [M$]</td></tr><tr><td>C<sup>sf</sup></td><td>Investment cost for surface facilities per injection well [M$]</td></tr><tr><td>Kbas<sub>b</sub></td><td>Maximal sequestration potential in basin b [MtCO<sub>2</sub>]</td></tr><tr><td>Kwell</td><td>Maximum injection well capacity [MtCO<sub>2</sub>/period]</td></tr><tr><td>Pmax<sub>g,p</sub></td><td>Maximum carbon capture in region g at time period p [MtCO<sub>2</sub>]</td></tr><tr><td>Q<sub>d</sub></td><td>Pipeline capacity of diameter d [MtCO<sub>2</sub>/period]</td></tr><tr><td>r</td><td>Discount rate</td></tr><tr><td>UCC<sub>k</sub><sup>inv</sup></td><td>Unitary investment cost of capture technology k [M$/MtCO<sub>2</sub>/period]</td></tr><tr><td>UCC<sub>k</sub><sup>var</sup></td><td>Unitary variable capture cost of capture technology k [M$/MtCO<sub>2</sub>]</td></tr><tr><td>USC<sub>b</sub><sup>well</sup></td><td>Investment cost of a single injection well in basin b [M$]</td></tr><tr><td>UTC<sup>flow</sup></td><td>Unitary transport cost due to volumes [M$/MtCO<sub>2</sub>/km]</td></tr></table>
UTC<sub>d</sub><sup>inv</sup> Unitary transport investment cost through pipeline of size d [M$/MtCO<sub>2</sub>/period · km)]

UTC<sub>intra</sub><sup>inv</sup> Unitary Intra-connection cost [M$/MtCO<sub>2</sub>/km]

UTC<sub>row</sub><sup>inv</sup> Unitary transport cost due to right-of-way [M$/km]

Continuous variables

Ptot<sub>k,g,p</sub> Volume of CO<sub>2</sub> processed through carbon pature technology k in region g at time period p [MtCO<sub>2</sub>]</

Ctot<sub>k,g,p</sub> Volume of CO<sub>2</sub> captured in region g at time period p with capture technology k [MtCO<sub>2</sub>]</

Kcap<sub>k,g,p</sub> Capacity of capture technology k installed during period p in region g [MtCO<sub>2</sub>/period]

Qtrans<sub>g,g',p</sub> Volume of CO<sub>2</sub> flowrate transported from g to g' during period p [MtCO<sub>2</sub>]</

Stot<sub>g,p</sub> Volume of CO<sub>2</sub> stored in region g during period p [MtCO<sub>2</sub>]</

Stot<sub>b,p</sub> Volume of CO<sub>2</sub> stored in basin b during period p [MtCO<sub>2</sub>]</

TCC Total discounted capture cost [M$]

TCC<sub>k,g,p</sub><sup>inv</sup> Total discounted investment cost for capture technology k in region g at period p [M$]

TCC<sub>g,p</sub><sup>var</sup> Total discounted variable cost for capture in region g at period p [M$]

TSC Total discounted storage cost [M$]

TSC<sub>b,p</sub><sup>inv</sup> Total discounted storage investment cost during period p in basin b [M$]

TSC<sub>b,p</sub><sup>om</sup> Total discounted storage operation and maintenance cost during period p in basin b [M$]

TTC Total discounted transport cost [M$]

TTC<sub>p</sub><sup>inv</sup> Effect of investment on total transport cost during p [M$]

TTC<sub>p</sub><sup>flow</sup> Effect of transported flow on total transport cost during p [M$]

TTC<sub>p</sub><sup>row</sup> Effect of right-of-way licensing on total transport cost during p [M$]

TTC<sub>p</sub><sup>int</sup> Effect of intra-connection cost on total transport cost during p [M$]

Integer variables

ROW<sub>g,g',p</sub> Right-of-way license between regions g and g' delivered in period p (binary variable)
$Npip_{g,g'd,p}$ Pipeline built during period $p$ of size $d$ between regions $g$ and $g'$ (binary variable)

$Nwells_{b,p}$ Number of new injection wells built in basin $b$ during period $p$
# 1 Introduction

India is experiencing a remarkable demographic and economic boom. By the end of the century, the United Nations (UN) projects its population to reach approximately 1.5 billion, more than double that of the second most populous country, China [1]. According to the International Energy Agency (IEA), India might become the world's third-largest economy by 2028 [2] and experience the most significant increase in energy consumption by 2040, with a growth rate three times larger than the global average [3].

This expansion raises significant concerns about the sustainability of India's future energy mix and, given the projected scale of the Indian economy, poses a broader challenge to the global carbon budget and efforts to limit global warming to the 2°C threshold [4, 5]. Currently, India is the fourth largest emitter of CO₂ after the US, China and the EU [2]. Coal – the abundant domestic energy resource and most carbon-intensive hydrocarbon – serves as the cornerstone of the country's energy system, fueling half of the domestic primary energy demand [3] and mitigating national energy security risks [6]. Despite the country's rapid expansion of solar power [7], the literature emphasizes that India's reliance on coal will persist in the coming decades. For example, Ray [8] projects that coal will supply nearly half of the country's increasing energy demand. Similarly, Roy and Schaffartzik [9] argue that, despite the energy transition rhetoric, India is not shifting away from coal production but rather intensifying its use. The Central Electricity Authority also confirms this worsening carbon footprint as it projects the installation of more than 50 GW of additional coal-fired power generation capacity by 2050 [10]. This fleet of new coal-fired power stations will contribute to increasing Indian CO₂ emissions by almost 50% by 2040 [3]. This trend jeopardizes the country's commitment to Net Zero by 2070 [11].

Against this background, one possible way forward is to deploy carbon capture capabilities at Indian coal-fired power plants. Indeed, Carbon Capture and Storage technologies (CCS) can attenuate the projected boom in CO₂ emissions by capturing and storing these emissions. However, CCS is reputed to be costly, and no Indian coal power plant currently captures its emissions [12]. So far, public support for CCS has been limited in India, with only a handful of demonstration projects and research programs being funded [13]. However, the recent report by NITI Aayog and Dastur [14] – a government-affiliated think tank – indicates that the Indian central government is increasingly showing interest in CCS at the national level. This comprehensive report also outlines key policy measures to enable CCS development in India. A blind spot of that deployment strategy is the lack of consideration for carbon transportation as the report overlooks the specific infrastructure problems faced in installing a capital-intensive costly CO₂ transportation system. However,
such infrastructures are critically needed to deploy CCS [15, 16].

The purpose of this paper is to provide a comprehensive examination of the eco-
nomics of CCS deployment in India. It maps the cost-minimizing pipeline network needed
to transport CO₂ between the carbon capture equipment installed at coal power plants and
the storage sites where the captured emission will be stored.

Compared to other regions (e.g., the United States, Europe, or China), the lit-
erature on CCS infrastructure deployment in India is scarce and more recent. The early
study by Garg et al. [17] examines the problem of CO₂ pipeline network deployment in the
country. However, the analysis suffers from a significant limitation: the lack of credible
data on the location and capacity of possible storage sites. Because of this data availability
issue, the authors rely on theoretical storage locations that minimize the total cost of CCS
infrastructure. Since the publication of Garg et al. [17], an important milestone has been
the engineering-based work of Vishal et al. [18], which clarified the country's endowment in
potential CO₂ storage sites and their geographic distribution. Other recent studies include
Zhang et al. [19] or Vishal et al. [20], which estimate the cost of CCS deployment in India,
including estimations of transport costs. However, these models provide little insight into the
pipeline network needed to transport the CO₂ emissions or into the location of the storage
injection wells. In contrast, the present work combines an infrastructure optimization model
with the storage data in Vishal et al. [18], thus offering a complementary perspective to the
burgeoning literature on CCS in India.

From a methodological standpoint, this paper specifies, calibrates, and solves an
intertemporal infrastructure optimization problem. Our model aims to determine the least-
cost design of a complete CCS infrastructure installed during a given planning horizon to
capture and store a predefined volume of CO₂ emissions. The objective is to minimize the
total discounted cost of installing and operating the CCS infrastructure. This optimization
problem includes several technical and physical constraints affecting CCS deployment. Tech-
nically, the model is an instance of a Mixed Integer Linear Program (MILP) that is similar
to the models developed for studying CCS development in Europe, China, and the US [21,
22, 23, 24].

However, our study differs from these earlier contributions in several aspects. First,
we organize our analysis around a reference scenario that aims to process 50% of the emissions
from all the coal-based power plants over 2023–2048. We then introduce three scenarios to
illustrate the consequences of political decisions on CCS deployment: one prohibits onshore
storage, another imposes carbon capture on all power plants entering operation, and the last
increases the capture target significantly. Comparing these scenarios’ outcomes allows us to
comprehensively understand India’s potential CCS development. Second, from a modeling
perspective, we map each Category I basin detailed in Vishal et al. [18], allowing the model to store CO₂ in multiple parts of each basin, not only in their center, thereby providing a more accurate representation of the Indian CCS network. Accordingly, this study is aimed at any policymaker, energy expert, investor, or scholar interested in CCS deployment in India.

This paper’s main contribution is the first representation of a cost-minimizing routing of India’s CO₂ pipelines. The first key finding is that the pipeline network is divided into local infrastructures near each storage basin rather than forming a single nationally integrated network, as in its natural gas counterpart. A second key finding is that a large portion of emissions is transported along an eastern transportation backbone, which remains robust across our four scenarios.

The paper is organized as follows: Section 2 presents the modeling framework. Section 3 details the data used to parameterize our model, and Section 4 the counterfactual scenarios used to investigate the possible future deployment of CCS in India. Section 5 contains our results, and Section 6 discusses them. The last section offers a summary and some concluding remarks.

# 2 The model

This section presents an overview of our modeling framework and details the objective function and constraints defining our optimization problem.

## 2.1 Overview

This paper aims to determine the least-cost design of a Carbon Capture and Storage (CCS) infrastructure for capturing and storing CO₂ emissions from coal-fired power plants in India. To this end, we formulate a mixed integer linear programming (MILP) problem for investment planning and system operations. By nature, its solution provides the decisions a central planning agency should consider to deploy CCS effectively. The model is dynamic and incorporates a spatial dimension. For the temporal aspect, we consider a discrete-time representation of a 25-year time horizon, divided into five-year periods *p* from Period 0 (2023–2027) to Period 4 (2044–2048). For the spatial aspect, we discretize the Indian territory into a grid of square cells *g* with *g* ∈ {1, 2, ..., 180}, as shown in Figure 1.
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Indian Grid representation (black cells) and storage basins (light-blue)</figcaption></figure>

Spatially, the grid representation is, by design, consistent with the following assumptions. First, the quantity of CO₂ captured in a given cell is assumed to be available at its center before being transported to another cell or stored. Second, if a cell has storage capabilities, we assume the storage site is located at the cell's center. Third, we assume that inter-regional transport between two adjacent cells is based on the distance between their centers. These assumptions are similar to those in d'Amore and Bezzo [21].

For each new parameter or new variable, we introduce its unit in square brackets. For instance, the unitary capture investment cost for technology *k* is in million dollars per million tons of CO₂ per period and will be introduced as: $UCC_k^{inv}$ [M$/MtCO₂/period].

The model's objective function minimizes the total discounted cost $TC$ [M$] of building and operating the CCS infrastructure over the planning horizon (evaluated in constant 2024 US dollars). The model covers the CCS value chain's capture, transportation, and storage stages. The objective function stated in Equation (1) can thus be decomposed into the three components:
$$min(TC) = TCC + TTC + TSC \quad (1)$$

where $TCC$ [M$], $TTC$ [M$]$ and $TSC$ [M$]$ respectively denote the total discounted costs of the equipment and operations used for carbon capture, $CO_2$ transportation and $CO_2$ storage.

In the following subsections, we detail each of these three CCS stages to specify their respective costs and constraints.

## 2.2 Carbon capture constraints

First, we clarify the components of the capture cost. A key assumption is that new capture technologies emerge due to learning effects: in each period, investment and operating costs are assumed to decrease, leading to a new technology $k$. We detail this assumption in section 3.

Equation (2) gives the total discounted investment cost incurred to install the carbon capture unit of technology $k$ in region $g$ during period $p$, denoted $TCC_{k,g,p}^{inv}$ [M$]. This cost is determined by the installed capacity $Kcap_{k,g,p}$ [MtCO$_2$/period], which is a decision variable, and the unit investment cost $UCC_k^{inv}$ [M$/MtCO$_2$/period], which is a parameter of the model. We let $disc_p$ denote the discounting factor for the given period.<fnref n="1" /> Similarly, the total discounted cost to operate the carbon capture unit depends on the variable cost $UCC_k^{var}$ [M$/MtCO$_2$] and $Ctot_{k,g,p}$ [MtCO$_2$] the volumes being captured (see equation (3)). The total discounted cost of capture $TCC$ [M$] discussed above is the sum of investment and operating costs for all technologies, periods, and regions (equation (4)).

$$TCC_{k,g,p}^{inv} = UCC_k^{inv} \cdot disc_p \cdot Kcap_{k,g,p} \quad \forall k, \forall g, \forall p \quad (2)$$

$$TCC_{k,g,p}^{var} = UCC_k^{var} \cdot disc_p \cdot Ctot_{k,g,p} \quad \forall k, \forall g, \forall p \quad (3)$$

$$TCC = \sum_{p} \sum_{g} \sum_{k} (TCC_{k,g,p}^{inv} + TCC_{k,g,p}^{var}) \quad (4)$$

The decisions pertaining to carbon capture depend on the four following constraints:

<footnote n="1">¹Because we use five-year periods, this discount factor is $\beta^{5p}(1 - \beta^5)/(5(1 - \beta))$ with $\beta = 1/(1 + r)$ and $r$ the discount rate. In this paper, we set $r = 5\%$ as in Oei and Mendelevitch [25].</footnote>
$$ \sum_{k} Ptot_{k,g,p} \leq Pmax_{g,p} \qquad \forall g, \forall p \qquad (5) $$

$$ Ctot_{k,g,p} = \eta \cdot Ptot_{k,g,p} \qquad \forall k, \forall g, \forall p \qquad (6) $$

$$ Ptot_{k,g,p} \leq \sum_{p' < p} Kcap_{k,g,p'} \qquad \forall k, \forall g, \forall p \qquad (7) $$

$$ Kcap_{k,g,p} \leq \sum_{p} Pmax_{g,p} \cdot \nu_{k,p} \qquad \forall k, \forall g, \forall p \qquad (8) $$

In constraint (5), $Ptot_{k,g,p}$ [MtCO$_2$] denotes the volume of CO$_2$ that can be processed by carbon capture technology $k$ in region $g$ during period $p$. Thus, this constraint states that $\sum_k Ptot_{k,g,p}$ the total amount of CO$_2$ that can be processed (across all carbon capture technologies) is capped by the amount of CO$_2$ emitted during this period in this region $Pmax_{g,p}$ [MtCO$_2$]. Equation (6) indicates that the amount of CO$_2$ captured by carbon capture technology $k$, $Ctot_{k,g,p}$ [MtCO$_2$] is set by the capture unit's efficiency rate $\eta$ times the total quantity of emissions processed by the capture unit during that period. Constraint (7) embodies the capture investment constraint, which implies that CO$_2$ can be captured by technology $k$ only if the capture capacity of this technology $Kcap_{k,g,p}$ has been expanded during preceding periods. Equation (8) states that the capture capacity of technology $k$ that can be installed during period $p$ is capped by the emissions of that region $g$. The parameter $\nu_{k,p}$ indicates whether the capture technology $k$ is available at period $p$ (i.e., $\nu_{k,p} = 1$ if $k \le p$ and $\nu_{k,p} = 0$ otherwise).

Lastly, we introduce the intertemporal constraint (9) affecting the cumulative quantity of emissions that CCS must treat. That volume must be larger than an exogenously defined fraction $\alpha$ of the cumulative emissions $\sum_p \sum_g Pmax_{g,p}$. This constraint allows us to define the ambitions of the policy in terms of CO$_2$ capture. This intertemporal constraint is similar to the one used in Kuby, Bielicki, and Middleton [23] and d'Amore and Bezzo [21].

$$ \sum_{k} \sum_{g} \sum_{p} Ptot_{k,g,p} \geq \alpha \cdot \sum_{g} \sum_{p} Pmax_{g,p} \qquad (9) $$

## 2.3 Transportation constraints

CO$_2$ transportation is constrained through three generic equations (10)–(12). The associated total discounted cost is determined by equations (13)–(16):
$$ \sum_k Ctot_{k,g,p} + \sum_{g'} Qtrans_{g',g,p} = Stot_{g,p} + \sum_{g'} Qtrans_{g,g',p} \quad \forall g, \forall p \quad (10) $$

$$ \sum_{d} Npip_{g,g',d,p} \le \sum_{p' \le p} ROW_{g,g',p'} \quad \forall g, \forall g', \forall p \quad (11) $$

$$ Qtrans_{g,g',p} \le \sum_{p' < p} \sum_{d} Q_{d} \cdot Npip_{g,g',d,p'} \quad \forall g, \forall g', \forall p \quad (12) $$

$$ TTC_{g,g',p}^{inv} = disc_p \cdot LD_{g,g'} \sum_{d} UTC_{d}^{inv} \cdot Q_{d} \cdot Npip_{g,g',d,p} \quad \forall g, \forall g', \forall p \quad (13) $$

$$ TTC_{g,g',p}^{flow} = disc_p \cdot LD_{g,g'} \cdot Qtrans_{g,g',d,p} \cdot UTC^{flow} \quad \forall g, \forall g', \forall p \quad (14) $$

$$ TTC_{g,g',p}^{row} = disc_p \cdot LD_{g,g'} \cdot Pop_{g,g'} \cdot UTC^{row} \cdot ROW_{g,g',p} \quad \forall g, \forall g', \forall p \quad (15) $$

$$ TTC_{g,p}^{intra} = disc_p \cdot UTC^{intra} \cdot \sum_{k} Ctot_{k,g,p} \cdot \frac{intra_{g}^{dist}}{2} \quad \forall g, \forall p \quad (16) $$

For each region g and each period p, the mass balance rule applies according to Equation (10). This rule states that the sum of emissions entering a region g – either through capture in region g or through transport from adjacent regions g' – must be equal to the sum of emissions leaving the region – either through storage if region g has storage capacities or through transport to adjacent regions g'. We define adjacent regions as regions that share a similar flank or touch on a grid cell corner (see Figure 1). If two regions are not adjacent, we impose $Qtrans_{g,g',p} = 0$.

We assume through equation (11) that a pipeline can be built during the same period as a right of way (ROW) is granted – i.e., no delays are associated with obtaining a ROW. Equation (12) states that the volume $Qtrans_{g,g',p}$ that can be transported is capped by the number $\sum_{d} Npip_{g,g',d,p}$ and capacities $Q_{d}$ [MtCO$_{2}$/period] of the pipelines installed in previous periods. Building a pipeline $Npip_{g,g',d,p}$ (binary variable) of diameter d between two grid regions g and g' during period p is itself limited by the granting of a $ROW_{g,g',p}$.

The discounted costs associated with pipeline investment $TTC_{g,g',p}^{inv}$ [M$], the volume of fluid transported $TTC_{g,g',p}^{flow}$ [M$] and obtaining a ROW between two grid regions $TTC_{g,g',p}^{row}$ [M$], are given by Equations (13), (14), (15) respectively. These equations thus relate to inter-regional transport. Conversely, the discounted intra-connection cost $TTC_{g,p}^{intra}$ [M$] defined in equation (16) is a proxy for estimating the cost of gathering emissions inside a grid region.

Equation (13) develops the discounted cost of investing into an inter-regional pipeline $TTC_{g,g',p}^{inv}$. It depends on the distance between the two regions $LD_{g,g'}$ [km], the capacities
$Q_d$ of the pipelines built, and a unitary investment cost depending on the diameter of the pipeline $UTC_d^{inv}$ [M$/\text{(MtCO}_2/\text{period} \cdot \text{km})]$. Because the model can pick between various pipeline sizes (represented through the set of pipeline diameters $d$), the equation sums over all sizes of pipelines. The binary variable $Npip_{g,g',d,p}$ ensures that only built pipelines are accounted for in the cost. Equation (14) shows that the discounted variable cost between two regions $TTC_{g,g',p}^{flow}$ solely depends on the distance, the volumes transported, and a unique flow cost $UTC^{flow}$ [M$/\text{MtCO}_2/\text{km}]$. Equation (15) provides the discounted cost of obtaining a ROW, $TTC_{g,g',p}^{row}$ [M$], between two regions $g$ and $g'$. It depends on the distance between the centers of the two regions, a population density multiplier between each region $Pop_{g,g'}$ (a value between 1 and 5, detailed in the Appendix A), and a unitary ROW cost $UTC^{row}$ [M$/\text{km}]$. Similar to the discounted variable cost, we define in equation (16) the discounted intra-connection cost as the product of a unitary intra-connection cost $UTC^{intra}$ [M$/\text{MtCO}_2/\text{km}] the volumes transported (in this case, the captured CO$_2$ in the region) and an intra-connection distance $intra^{dist}$. Thus, we approximate the gathering of CO$_2$ distances inside a cell by considering each cell's diagonal length $intra_g^{dist}$ [km].

Total transportation cost $TTC$ [M$] is the sum of all aforementioned costs over each region and during each period, as stated by Equation (17) below.

$$TTC = \sum_{p} \left( \sum_{g} \sum_{g'} \left( TTC_{g,g',p}^{inv} + TTC_{g,g',p}^{flow} + TTC_{g,g',p}^{row} \right) + \sum_{g} TTC_{g,p}^{intra} \right) \quad (17)$$

## 2.4 Storage constraints

We now describe the four generic constraints (18)–(21) affecting storage operations and then describe the equations (22)–(23) that determine the associated storage cost.
$$ \sum_{p} Stot_{b,p} \leq Kbas_{b} \qquad \forall b \quad (18) $$

$$ Stot_{g,p} \leq \sum_{p' < p} Nwells_{g,p'} \cdot Kwell \qquad \forall g, \forall p \quad (19) $$

$$ Nwells_{b,p} = \sum_{g \text{ in } b} Nwells_{g,p} \qquad \forall b, \forall p \quad (20) $$

$$ Stot_{b,p} = \sum_{g \text{ in } b} Stot_{g,p} \qquad \forall b, \forall p \quad (21) $$

$$ TSC_{b,p}^{inv} = disc_{p} \cdot USC_{b}^{well} \cdot Nwells_{b,p} \qquad \forall b, \forall p \quad (22) $$

$$ TSC = \sum_{p} \sum_{b} TSC_{b,p}^{inv} + TSC_{b,p}^{om} \qquad (23) $$

Similar to the capture problem, storage in a basin *b* is limited by its physical capacity $Kbas_b$ [GtCO₂] (equation (18)) and its anterior investments in injection wells' capacity, which is the product of the injection capacity $Kwell$ [MtCO₂/period] and the number of injection wells $Nwells_{b,p}$ installed in the basin (equation (19)). Equations (20) and (21) indicate that the number of injection wells installed (respectively the quantities stored) in a basin are the sum of the number of injection wells (respectively the quantities stored) in each region that constitute the basin. Equation (22) calculates the total storage investment cost $TSC_{b,p}^{inv}$ [M$] by multiplying the unitary storage cost of investing into an injection well $USC_{b}^{well}$ [M$] with the number of injection wells in that basin. Equation (23) gives the total cost of storage, with total operation and maintenance cost $TSC_{b,p}^{om}$ chosen as 10% of total investment cost.

## 3 Data

This section details the data used for the parametrization of the model. The two first subsections present the coal-fired power plants and storage sites. The third subsection focuses on the different components of the objective function $TC$, including capture, transportation, and storage costs.
## 3.1 The coal power plants

The data on the coal-fired power plants come from the Global Energy Monitor (GEM) database.<fnref n="2" /> The database provides the location, capacity, annual emissions, remaining lifetime, and current status (e.g., operating, announced, permitted) of each coal-fired power plant unit of 30 MW or larger [26]. According to this database, 840 coal-fired power plant units are currently operating in India. This fleet emitted 1,090 million tons of CO₂ in 2023. This figure is in line with IEA [3].

<figure><img src="image_3.png" /><figcaption>**Fig. 2** Map of the location and status of coal-fired power plants in India.</figcaption></figure>

Figure 2 shows the spatial distribution of these coal-fired power plants. We observe a marked prevalence of coal-fired generation in the most populous state, Uttar Pradesh (north India), in the central industrialized states Maharashtra and Madhya Pradesh, in the southern industrialized state Tamil Nadu, and in the coal-rich north-eastern states of Chhattisgarh and Odisha.

<footnote n="2">²A comparison between data on coal-fired power plants from the GEM database and those from the Central Electricity Authority [10] is presented in Appendix B. The datasets are consistent, particularly regarding the future development of coal-fired power capacities.</footnote>
Table 1 summarizes the data provided by the GEM database concerning operating plants, new plant openings, and upcoming closures: 32 GW are currently 'under construction', and 20 GW are in the 'pre-permit' and 'permitted' stages, with no specified start date of operation. To maintain consistency with the National Electricity Plan projections, we assume that the 32 GW 'under construction' will become operational in 2028 (i.e., the start of Period 1 of the model), while the 20 GW in the 'pre-permit' and 'permitted' stages will come online in 2033 (i.e., the start of Period 2). Finally, we assume that units with an 'announced' status will become operational in 2038 (start of Period 3).

<table><thead><tr><th></th><th colspan="5">Period</th></tr><tr><th></th><th>2023-2027</th><th>2028-2032</th><th>2033-2037</th><th>2038-2043</th><th>2044-2048</th></tr></thead><tbody><tr><td>Capacity additions [MW]</td><td>0</td><td>32,000</td><td>20,013</td><td>8,490</td><td>0</td></tr><tr><td>Retired capacity [MW]</td><td>0</td><td>17,576</td><td>13,171</td><td>18,481</td><td>1,052</td></tr><tr><td>Operating capacity [MW]<sup>a</sup></td><td>234,256</td><td>248,680</td><td>255,522</td><td>245,531</td><td>244,479</td></tr><tr><td>Total CO<sub>2</sub> emissions [MtCO<sub>2</sub>]<sup>b</sup></td><td>5,372</td><td>5,525</td><td>5,563</td><td>5,287</td><td>5,218</td></tr></tbody></table>

<caption>**Table 1** Projected operating capacity of coal-fired power plants and associated CO₂ emissions trajectory</caption>

Given the assumed capacity additions and closures, CO₂ emissions vary little over time (less than 10% between the periods of highest and lowest emissions). Indeed, new thermal power plants are gradually replacing those that have been closed.

## 3.2 The storage sites

Following the comprehensive study by Vishal et al. [18], we include only geological formations that are identified as having "very high" or "high" potential for carbon storage. These basins correspond to Category I basins as determined by the Indian Directorate General of Hydrocarbons [27]. Seven basins with this potential have been identified: Krishna–Godavari, Mumbai, the Assam Shelf, Rajasthan, Cauvery, the Assam–Arakan Fold Belt and Cambay. The location of these basins is shown in Figure 3.<fnref n="3" /> For ease of pre-

<footnote>a Operating capacity is the cumulated capacity of all coal power plants in operation during at least one year of the considered period. Source: authors' analysis based on the GEM database.</footnote>

<footnote>b For details regarding the calculations of a coal fired power plant's annual emissions, its capacity factor, and its lifetime, see Global Energy Monitor [26].</footnote>

<footnote n="3">³We mapped each basin using QGIS by layering the Indian Sedimentary Basins' map done by the DGH [28] on a map of India provided by the Natural Earth website.</footnote>
sentation, we have renamed some of these basins (i.e. the Assam-Arakan fold Belt became 'Arakan' in our figure). We note that the basins are located all over India – in the west, east, and south-east. As a result, these basins provide local storage opportunities for most coal power plants. However, note that some basins have only onshore capacities, while others have both onshore and offshore capacities. As an exception, the Mumbai basin shows exclusively offshore storage capacities.

<figure><img src="image_4.png" /><figcaption>**Fig. 3** Indian basins.

*Numbering of basins follows Vishal et al. [18].*</figcaption></figure>

These saline aquifer basins have a total storage capacity of 108.6 GtCO₂ emissions, with individual capacities ranging from 9.3 GtCO₂ to 32.3 GtCO₂ (see Table 5). A total enhanced oil recovery (EOR) capacity of 3.4 Gt is also available in these basins [18]. Although EOR sites represent economically relevant storage sites thanks to their existing infrastructure and in-depth geological knowledge, we exclude them from our study for two reasons. Firstly, the economic analysis of EOR technology significantly contrasts with that of standard CCS [29]. Secondly, our goal is to consider the development of transportation infrastructure to sites capable of storing carbon for several decades, requiring a CO₂ storage capacity
significantly greater than 3.4 Gt.

## 3.3 Cost parameters

### 3.3.1 Carbon capture

Two CO₂ capture technologies are generally considered to capture emissions from coal-fired power plants: post-combustion capture and oxy-fuel capture. The post-combustion capture is available as generally considered a more mature, cost-effective, and less uncertain technology [30, 21, 31, 32, 12]. In order to simplify the model, we assume only this technology. According to d'Amore and Bezzo [21], we set the capture efficiency at $\eta = 0.87$.

Similar to Oei, Herold, and Mendelevitch [24], we divide the capture cost into two components: an investment cost and a variable operating cost. The evaluation of investment cost is based on the methodology in Tzimas [33] and escalated figures drawn from Rubin, Davison, and Herzog [34].<fnref n="4" /> Tzimas [33] compares the cost of a coal-fired power plant initially equipped with a capture unit to the same plant without a capture unit. The extra-cost $UCC_{p=0}^{inv}$ obtained corresponds to the investment capture cost in Period 0.

In Period 0, the variable cost, $UCC_{p=0}^{var}$, is defined so that the total cost per ton of CO₂ is equal to the cost determined by Singh, Rao, and Chandel [35] of 67$/tCO₂ in the case of an Indian coal power plant. Thus, our initial variable cost is $UCC_{p=0}^{var} = 56$/tCO₂. Note that this variable cost is greater than those used in previous studies for CCS deployment in Europe [21, 24].

As analyzed in IEA [36], Comello and Reichelstein [37], and Banal-Estañol, Eckhause, and Massol [38], reductions in capture technology costs are expected due to learning effects. The reduction relates to both investment and operating costs [39, 40]. From this perspective, IEA [36] developed a learning curve depicting the cost evolution of carbon capture at coal power plants in the Sustainable Development Scenario (see Appendix C). This study assumes that the Indian CCS industry benefits from positive learning spillovers and uses the exogenously determined reduction cost trends proposed in IEA [36].

Table 2 presents the assumed investment and variable cost decreases. From a modeling perspective, each 5-year period defines a newly available capture technology k.

<footnote n="4">⁴Using Rubin, Davison, and Herzog [34] data, we find an investment cost of around 285$ per annual ton of CO₂, almost double the value of 150$ per annual ton of CO₂ used in the study by Oei, Herold, and Mendelevitch [24]. This difference is due to a higher incremental cost of capture in the Rubin, Davison, and Herzog [34] study than in the Tzimas [33] study on which the work by Oei, Herold, and Mendelevitch [24] is based. We then escalate this cost with the Chemical Engineering Plant Cost Index (CEPCI).</footnote>
For instance, the technology $k = 2$ with a variable cost $UCC_{k=2}^{var} = 43.5$/tCO$_2$ is available from 2033 onwards. Every capture unit installed during Period 2 benefits from this reduced operation cost for the remaining periods.

<table><caption>**Table 2** Posited evolution of carbon capture's investment cost $UCC^{inv}$ and variable cost $UCC^{var}$</caption><thead><tr><th></th><th colspan="5">Period</th></tr><tr><th></th><th>2023-2027</th><th>2028-2032</th><th>2033-2037</th><th>2038-2043</th><th>2044-2048</th></tr></thead><tbody><tr><td>UCC<sup>inv</sup><br>[M$/MtCO<sub>2</sub>/period]</td><td>81</td><td>69</td><td>61</td><td>58</td><td>54</td></tr><tr><td>UCC<sup>var</sup><br>[M$/MtCO<sub>2</sub>]</td><td>56</td><td>48.5</td><td>43.5</td><td>41</td><td>38</td></tr></tbody></table>

### 3.3.2 Pipeline transportation

Carbon dioxide is generally transported in two states: as a 'dense' liquid or in a supercritical phase [41]. Large-scale transportation methods for CO$_2$ include pipelines (both onshore and offshore) and ships (for long offshore distances). In the context of the Indian CCS problem, shipping is deemed irrelevant, given that no long-distance offshore transport is needed.

From the literature, several cost components must be considered. The International Energy Agency Greenhouse Gas R&D Programme provides information on the investment, operation, and maintenance costs associated with CO$_2$ pipeline transportation in North America [42] and Europe [43]. The report by IEAGHG and ZEP [44] updated these cost values. According to these studies, costs mainly depend on pipeline capacity, length, and quantity transported. Other studies also advocate for the importance of right-of-way costs [24].

In our model, all of these aspects are included with four different kinds of costs: a pipeline investment cost, a volume cost, a ROW cost, and an intra-connection cost. We first consider pipeline investment costs by discretizing the possible transport capacities (see Table 3). This investment cost exhibits economies of scale, meaning that the larger the diameter, the lower the investment cost. We associate the capacities with a unit investment cost by extrapolating the values of the study by d'Amore and Bezzo [21]. However, as we are working over 5-year periods, we adapt the annual capacity to a 5-year capacity.
<table><caption>**Table 3** Unitary transportation investment cost ($UTC_d^{inv}$) for a CO₂ onshore pipeline (based on d'Amore and Bezzo [21])</caption><thead><tr><th>Pipeline size index d</th><th>Q<sub>d</sub><br>[MtCO<sub>2</sub>/period]</th><th>UTC<sup>inv</sup><br>[M$/ (MtCO<sub>2</sub>/period · km)]</th></tr></thead><tbody><tr><td>1</td><td>150</td><td>0.0094009</td></tr><tr><td>2</td><td>215</td><td>0.003895</td></tr><tr><td>3</td><td>355</td><td>0.002846</td></tr><tr><td>4</td><td>540</td><td>0.002192</td></tr><tr><td>5</td><td>775</td><td>0.001752</td></tr></tbody></table>

Second, we introduce a unitary cost of transportation $UTC^{flow}$ depending on the quantity transported (i.e., not the pipeline capacity).

Third, we consider a unitary ROW cost, $UTC^{row}$. Focusing on natural gas, Heddle, Herzog, and Klett [45] use right-of-way costs to represent possible damages: they are between 4% and 9% of the total capital cost in the case of natural gas. We introduce a similar parameter that depends on the population density of a cell and reflects the greater difficulty in deploying large-scale infrastructure in these areas. We infer the ROW cost by taking 10% of the high total capital cost calculated by the study of Smith et al. [46], which updated values of the USDOE [47] report.

Lastly, we consider the intra-connection cost, which reflects the cost of aggregating CO₂ emissions of all coal power plants of a region. We assume these emissions are aggregated to the center of the region. This cost is calculated as the mean of Table 3 costs [21]. Table 4 lists the values for each cost parameter.

<table><caption>**Table 4** Transportation costs parameters.</caption><thead><tr><th>Unit Transportation Cost</th><th>Value</th><th>Source</th></tr></thead><tbody><tr><td>UTC<sup>flow</sup> [M$/MtCO<sub>2</sub>/km]</td><td>0.01</td><td>Oei, Herold, and Mendelevitch [24]</td></tr><tr><td>UTC<sup>row</sup> [M$/km]</td><td>0.06</td><td>adapted from Smith et al. [46]</td></tr><tr><td>UTC<sup>intra</sup> [M$/MtCO<sub>2</sub>/km]</td><td>0.0031</td><td>adapted from d'Amore and Bezzo [21]</td></tr></tbody></table>
### 3.3.3 Storage

Table 5 lists the basins considered in the model, giving their capacity, average depth, and storage investment costs. We adopt a storage cost that depends on the number of injection wells installed and a variable cost linked to the operation and maintenance of these wells. This variable cost is set at 10% of the investment cost of each basin [24]. The methodology for calculating the investment cost per injection well is given in Appendix D.

<table><thead><tr><th>Basin<br>b</th><th>Capacity<sup>a</sup><br>Kbas<sub>b</sub> [GtCO<sub>2</sub>]</th><th>Average depth<sup>a</sup><br>d<sub>b</sub> [km]</th><th>Investment Cost<sup>b</sup><br>USC<sub>b</sub><sup>well</sup> [M$]</th></tr></thead><tbody><tr><td>Krishna-Godavari</td><td>13.39</td><td>3</td><td>98.34</td></tr><tr><td>Mumbai Offshore</td><td>9.26</td><td>3</td><td>98.34</td></tr><tr><td>Assam Shelf</td><td>14.16</td><td>4</td><td>124.34</td></tr><tr><td>Rajasthan</td><td>7.34</td><td>3</td><td>98.34</td></tr><tr><td>Cauvery</td><td>16.08</td><td>3.25</td><td>104.84</td></tr><tr><td>Assam-Arakan</td><td>32.3</td><td>5.5</td><td>163.34</td></tr><tr><td>Cambay</td><td>16.13</td><td>4.5</td><td>137.34</td></tr></tbody></table>

<caption>**Table 5** Characteristics of geological formations for carbon storage and cost parameters.</caption>

## 4 Scenarios

In this section, we present the four scenarios under scrutiny.

### 4.1 The Reference scenario

In the Reference scenario, we assume that Indian public authorities set a target to process 50% of the emissions generated by coal-fired power plants from 2023 to 2048. This target aligns with the Indian central government's objective of achieving carbon neutrality by 2070. Technically, this is represented by setting $\alpha = 0.5$ in Equation (9).

As shown in Table 1, almost 27 GtCO₂ is assumed to be emitted during the period 2023–2048. With an efficiency capture rate of 87%, this leads to the objective of storing around 11.7 GtCO₂. For storage of the captured emissions, we assume that all the geological

<footnote>a Source: Vishal et al. [18].</footnote>

<footnote>b Source: Carneiro et al. [48].</footnote>
formations listed in Table 5 are available.

## 4.2 The "Offshore" scenario

In the *Offshore* scenario, the carbon capture target remains the same as in the Reference scenario, but onshore CO₂ storage is assumed to be prohibited. This assumption is inspired by social acceptability issues that have hindered onshore CO₂ storage in Europe [25, 49]. India may not face similar opposition, but its population distribution and density could lead to difficulties implementing onshore storage sites.

As a result, four basins cannot be used to store CO₂ emissions: Assam-Shelf, Rajasthan, Assam-Arakan Fold Belt, and Cambay, representing about 64% of the country's storage capacity. Consequently, there is no storage potential in northern India. The length of the transport network is expected to be higher than in the Reference scenario, leading to a new distribution of stored CO₂ volumes and certainly higher overall costs.

## 4.3 The "NewBuild" scenario

In the *NewBuild* scenario, we explore the effects of a policy mandate requiring each newly operational coal power plant to capture its emissions. While the overall carbon capture target remains the same as in the Reference scenario, this obligation targeting new coal power plants could lead to a different network configuration and cost structure. In this scenario, CO₂ storage is allowed in both onshore and offshore basins.

From a modeling perspective, we extend the model in Section 2 by adding the following constraint that sets a minimum level of captured emissions for each region and period, equal to the volumes emitted by new power plants:

$$ \sum_k Ptot_{k,g,p} \geq Pmax_{g,p}^{newbuild} \quad \forall g, \forall p \quad (24) $$

where $Pmax_{g,p}^{newbuild}$ is the emissions emitted by new coal power plants during the period $p$ in region $g$ defined by the grid (see Figure 1).
## 4.4 The "Capture+" scenario

The Capture+ scenario assumes a more ambitious CO₂ capture target, with public authorities increasing the process target by 30%, resulting in a total storage volume of 15.25 GtCO₂. All other assumptions remain unchanged from the Reference scenario.

# 5 Results

In this section, we first provide a general comparison of our main results. We then present the results for each scenario separately, comparing each with the Reference scenario. A sensitivity analysis of the most influential parameters is provided in the Appendix E.

## 5.1 General comparison of scenarios

In this subsection, we provide a summary of the key modelling results for each scenario, including: (i) the present value of all costs of installing and operating the CCS infrastructure over the period 2023-2048 (Table 6); (ii) the total volume of CO₂ stored (Table 7); (iii) the levelized cost of CO₂ stored (Table 8); the total length of the pipeline (Table 9); (iv) the filling levels of the geological basins (Table 10); and (v) the distribution of storage volumes in the basins (Table 11).

Table 6 shows that capture costs account for about 80% of total costs across all scenarios. This is consistent with the literature [50, 51, 24, 15, 21]. The share of storage costs is about 12% of total costs, a figure close to that found in Carneiro et al. [48].

<table><caption>**Table 6** Cost breakdown for each scenario</caption><thead><tr><th rowspan="2"></th><th colspan="4">Total discounted cost [M$]</th></tr><tr><th>Capture</th><th>Transportation</th><th>Storage</th><th>Total</th></tr></thead><tbody><tr><td>Reference</td><td>469,314</td><td>27,986</td><td>76,366</td><td>573,667</td></tr><tr><td>Offshore</td><td>463,793</td><td>39,020</td><td>75,018</td><td>577,832</td></tr><tr><td>New Build</td><td>480,553</td><td>30,271</td><td>78,678</td><td>589,503</td></tr><tr><td>Capture+</td><td>679,297</td><td>40,024</td><td>94,306</td><td>813,627</td></tr></tbody></table>

Table 7 shows the volumes of CO₂ captured and stored per period for each scenario. Due to the delay caused by investment in CCS capacity, no emissions are stored during period
0 (2023–2027). While the volumes stored remain homogeneous during the last two periods, we observe significant differences between the scenarios in the volumes stored in periods 2 and 3.

<table><caption>**Table 7** Volumes stored per period in MtCO₂ by scenario</caption><thead><tr><th rowspan="2"></th><th colspan="5">Period</th></tr><tr><th>2023–2027</th><th>2028–2032</th><th>2033–2037</th><th>2038–2043</th><th>2044–2048</th></tr></thead><tbody><tr><th>Reference</th><td>0.0</td><td>65.455</td><td>3052.42</td><td>4322.85</td><td>4288.73</td></tr><tr><th>Offshore</th><td>0.0</td><td>0.0</td><td>2897.35</td><td>4411.94</td><td>4420.18</td></tr><tr><th>NewBuild</th><td>0.0</td><td>558.54</td><td>2392.28</td><td>4365.03</td><td>4413.61</td></tr><tr><th>Capture+</th><td>0.0</td><td>1511.26</td><td>4624.81</td><td>4585.80</td><td>4526.43</td></tr></tbody></table>

Table 8 shows the levelized cost of CO₂ captured, transported and stored in each scenario. This levelized cost is defined as the ratio of the total discounted cost of building and operating the CCS infrastructure divided by the total discounted volume of CO₂ stored during the planning horizon. For the whole CCS chain, we observe only small variations in this cost metric across the scenarios, with values ranging from $112.3 to $114.8 per tCO₂. This finding is largely due to the modest variations in the levelized cost of carbon capture, which is the dominant cost component.

<table><caption>**Table 8** Levelized cost of CO₂ captured, transported and stored</caption><thead><tr><th rowspan="2"></th><th colspan="4">Levelized cost of CO₂ with d=5% [$/tCO₂]</th></tr><tr><th>Capture</th><th>Transportation</th><th>Storage</th><th>Total</th></tr></thead><tbody><tr><th>Reference</th><td>91.8</td><td>5.5</td><td>14.9</td><td>112.3</td></tr><tr><th>Offshore</th><td>91.6</td><td>7.7</td><td>14.8</td><td>114.2</td></tr><tr><th>NewBuild</th><td>93.2</td><td>5.9</td><td>15.3</td><td>114.4</td></tr><tr><th>Capture+</th><td>94.2</td><td>5.5</td><td>13.1</td><td>112.8</td></tr></tbody></table>

In terms of CO₂ transport, the total length of all pipelines varies significantly between scenarios (Table 9). For example, in the Offshore scenario, the pipeline network is 50% longer than in the Reference scenario.
<table><caption>**Table 9** Total pipeline length by scenario.</caption><thead><tr><th></th><th>Total length [km]</th></tr></thead><tbody><tr><td>Reference</td><td>11,903</td></tr><tr><td>Offshore</td><td>17,867</td></tr><tr><td>NewBuild</td><td>13,711</td></tr><tr><td>Capture+</td><td>15,839</td></tr></tbody></table>

Table 10 shows the cumulative filling levels of the different basins at the end of the planning horizon. It gives an indication of the possible long-term use for storage. We observe that Krishna-Godavari in the south-east and Rajasthan in the north-west, two of the cheapest but modestly sized basins, are the most filled basins in the scenarios (except in the Offshore scenario in which storage in Rajasthan is not available).

<table><caption>**Table 10** Filling of each basin in % by scenario</caption><thead><tr><th rowspan="2"></th><th colspan="4">Scenario</th></tr><tr><th>Reference</th><th>Offshore</th><th>NewBuild</th><th>Capture+</th></tr></thead><tbody><tr><td>Krishna-Godavari</td><td>46.6</td><td>51.8</td><td>44.1</td><td>60.9</td></tr><tr><td>Mumbai</td><td>11.1</td><td>35.8</td><td>9.0</td><td>16.5</td></tr><tr><td>Assam-Shelf</td><td>5.3</td><td>0.0</td><td>8.4</td><td>7.3</td></tr><tr><td>Rajasthan</td><td>28.30</td><td>0.0</td><td>26.0</td><td>33.3</td></tr><tr><td>Cauvery</td><td>9.5</td><td>9.2</td><td>9.9</td><td>12.4</td></tr><tr><td>Assam-Arakan</td><td>0.0</td><td>0.0</td><td>0.2</td><td>0.0</td></tr><tr><td>Cambay</td><td>0.70</td><td>0.0</td><td>1.5</td><td>0.6</td></tr></tbody></table>

Finally, Table 11 shows the distribution of volumes across geological basins in each scenario. Again, the Krishna-Godavari basin receives more than half of the stored volumes in all scenarios.
<table><caption>**Table 11** Stored volumes per basin in MtCO₂ by scenario</caption><thead><tr><th rowspan="2"></th><th colspan="4">Scenario</th></tr><tr><th>Reference</th><th>Offshore</th><th>NewBuild</th><th>Capture+</th></tr></thead><tbody><tr><td>Krishna-Godavari</td><td>6,239</td><td>6,937</td><td>5,908</td><td>8,160</td></tr><tr><td>Mumbai</td><td>1,025</td><td>3,318</td><td>834</td><td>1,529</td></tr><tr><td>Assam-Shelf</td><td>754</td><td>0</td><td>1,183</td><td>1,027</td></tr><tr><td>Rajasthan</td><td>2,079</td><td>0</td><td>1,910</td><td>2,441</td></tr><tr><td>Cauvery</td><td>1,523</td><td>1,473</td><td>1,597</td><td>1,996</td></tr><tr><td>Assam-Arakan</td><td>0</td><td>0</td><td>51</td><td>0</td></tr><tr><td>Cambay</td><td>106</td><td>0</td><td>243</td><td>92</td></tr></tbody></table>

Following this general overview of the results, the following subsections provide detailed analysis for each scenario.

## 5.2 The Reference scenario

In the Reference scenario, the present value of the total cost of processing half of the CO₂ emissions from India's coal-fired power plants between 2023 and 2047 is $574 billion: 81.8% of the expenditure is on capture, 4.9% on transport and 13.3% on storage (Table 6).

The map in Figure 4a illustrates the deployment trajectory of capture equipment at coal-fired power plants and injection wells at storage sites over time. Figure 4b shows the time evolution of the CO₂ network layout, showing the organic growth of transport infrastructure over time. These figures show that the timing of deployment differs between capture, pipeline and injection wells: investments in capture and storage wells are spread over the first four periods (period 0 to period 3), while transport investments are concentrated in periods 1 and 2.
<figure><img src="image_5.png" /><figcaption>**Fig. 4** Spatial and temporal evolution of the CCS infrastructure – Reference scenario.</figcaption></figure>

Although investments are spread over the first four periods, they dominate in periods 1 and 2. In period 0, as shown in Figure 5a, only coal-fired power plants close to the storage sites are equipped: this allows a small amount of carbon to be captured as early as possible and some injection wells to be installed, but there is no investment in the grid. Figure 5a shows the investment in carbon capture only. This figure shows that in the Reference scenario, the model invests in carbon capture at a few sites in the initial period (p = 0). For clarity, these sites are shown in black in Figure 5a. Finally, in period 3, investments are concentrated in the power plants furthest away from the storage sites.

The model delays transport investment in periods 1 and 2 as shown in Figure 4b.<fnref n="5" /> The transport network consists of pipeline clusters around the storage basins. The model first invests in pipelines close to the storage sites and then in more distant regions. An interesting feature is that the model then builds most of the transport network in period 1

<footnote n="5">⁵Some of the pipelines in the figure appear to discharge CO₂ before the storage area. This is due to our grid representation: when a grid region has storage capacity, then we allow the model to store CO₂ in its center. However, the center of the grid region may not coincide exactly with the actual storage location.</footnote>
(creating a backbone) and extends each of the routes to collect more distant – and therefore
more costly – emissions in period 2.

<figure><img src="image_6.png" /><figcaption>**Fig. 5** Capture capacity deployment (left) and pipeline utilization (right) – Reference scenario.</figcaption></figure>

Notes: ¹Investments in Period 0 are enclosed by black cells. ²The volumes transported in Periods 3 and 4 are almost identical, resulting in overlapping colors in the figure, thus covering up the volumes transported in Period 4.

Figure 5b takes a volumetric view and illustrates how tonnes of CO₂ are transported to storage sites, showing the concentration of capture in the eastern part of the country. In addition to this main route, there are several smaller routes throughout India.

The Table 7 shows the total volumes stored in different periods, reflecting the low investment in period 0, followed by a sharp increase in investment in periods 1 and 2 (recall that there is a one period lag between investment and operation).

All geological basins are used for CO₂ storage except the Assam-Arakan basin. This result was expected due to its higher unit storage costs (Table 5). However, the distribution
of the stored volumes between the different basins is quite contrasting, as shown in Table
11: 53% of the total volume is stored in Krishna-Godavari, while Cambay receives less than
1%.

In the reference scenario, the model adopts a 'wait-and-see' approach: it starts to
capture and store a small amount of CO₂ as early as possible, while delaying investment by
five to ten years to take advantage of discounted costs and learning effects.

5.3 The Offshore scenario

In the Offshore scenario, the present value of the cost of treating half of the CO₂
emissions from India's coal-fired power plants between 2023 and 2047 is $578 billion: this
represents a limited increase of +0.7% in the total cost compared to the Reference scenario.
As expected, the cost of the transport network increases significantly from $27,987 million to
$39,020 million due to the much longer total pipeline length of 17,867 km (Table 6): +50.1%
increase in total length compared to the Reference scenario.

Two key findings emerge from this scenario: (i) storing CO₂ exclusively offshore
increases transport costs by about 40%, but (ii) the total CCS infrastructure costs increase
by less than 1%. The first result is a consequence of building a more extensive network
as only basins in southern India are available: the network length increases by 50%. This
increase is offset by the slight decrease in capture costs compared to the Reference scenario.

Figures 6a and 6b show the timing and intensity of investments for installation of
capture equipment, construction of injection wells and pipelines.
<figure><img src="image_7.png" /><figcaption>**Fig. 6** Spatial and temporal evolution of the CCS infrastructure – Offshore scenario</figcaption></figure>

The ban on the use of onshore basins leads to a different investment dynamic than in the Reference scenario: no investments are made in period 0. The main investments are made in periods 1 and 2. Due to the lack of storage basins in the north of the country, the transport network is organised around two main pipeline structures, one in the west and one in the east. The development of the network is sequential, with pipelines first being built close to the basins, then followed by the extension of the two networks towards the north and centre of the country.

The decision to store only offshore has an impact on the volumes stored in each basin (see Table 11). In fact, only three basins can accommodate CO₂ in this scenario. The other four have no offshore storage. As a result, these basins show significant levels of filling compared to the situation where storage can be onshore (Table 10). In particular, the Mumbai offshore basin has risen from around 10% to more than a third full. From a long-term storage perspective, we also note that the Krishna-Godavari basin is more than half filled in both cases. This means that at a constant storage rate, the basin would reach
its maximum storage capacity in 50 years.

Figure 7 shows the transport routes of the CO₂ captured. The ban on onshore storage concentrates the volumes in the west and the east, resulting in two main routes.

<figure><img src="image_8.png" /><figcaption>Fig. 7 Transported volumes – Offshore scenario

*Note: The volumes transported in Periods 3 and 4 are almost identical, resulting in overlapping colors in the figure, thus covering up the volumes transported in Period 4.*</figcaption></figure>

## 5.4 The NewBuild scenario

In the NewBuild scenario, every new power plant is equipped with a capture unit. We expect this regulation to have an impact on the transport network and overall costs. Onshore storage is allowed.

The total discounted cost is $590 billion, while the total length of the pipeline is 13,711 km. This represents a +2.8% increase in total cost and +15.2% increase in total length compared to the Reference scenario.

Consequently, the cost increase of forcing the model to capture emissions from new operating coal-fired coal power plants is greater than to the cost increase of banning the
onshore storage sites, while the network length is more limited.

<figure><img src="image_9.png" /><figcaption>**Fig. 8** Spatial and temporal evolution of the CCS infrastructure – NewBuild scenario</figcaption></figure>

Figures 8a and 8b show the timing and intensity of investment for installing capture equipment, building injection wells and laying pipelines. The obligation to equip new coal-fired power plants requires investment in the three stages of CCS from period 0: plants built in this period may be located away from storage sites. As a result, the development of pipelines connecting these new power plants to storage sites is implemented from period 0, as shown in Figure 8b. This also requires the storage sites to be equipped. These early pipelines have little impact on the final transport network compared to the Reference scenario, but increase transport costs significantly. The obligation to equip new power plants then accelerates the development of CCS infrastructure, but limits the positive effects of discounting and learning. As a result, the amount of CO₂ stored from period 1 is much higher than in the two previous scenarios (Table 7).

Figure 9 shows that most of the volumes follow the same route as in the Reference scenario, notably a north-south pipeline corridor that transports much of the CO₂ to the
Krishna-Godavari basin. Interestingly, we observe that some transport routes are strengthened as coal-fired power plants enter operation, instead of oversizing pipelines during initial periods.

<figure><img src="image_10.png" /><figcaption>**Fig. 9** Transported volumes – NewBuild scenario

*Note: The volumes transported in Periods 3 and 4 are almost identical, resulting in overlapping colors in the figure, thus covering up the volumes transported in Period 4.*</figcaption></figure>

The requirement to capture emissions from new coal-fired power plants affects the distribution of volumes stored in different reservoirs (see Table 11). The most notable results are the use of all basins and the reduced replenishment of the Krishna-Godavari and Mumbai basins and the increased replenishment of the Assam basin.

As a result, the NewBuild policy leads to less filling of the two most used basins compared to the Reference and Offshore scenarios (Table 10).

## 5.5 The Capture+ scenario

The Capture+ scenario requires 30% more emissions to be processed and stored. The ambition of the climate policy is significantly increased. This leads to a fairly substantial
increase in total discounted costs: $574 billion, an increase of 42% compared to the Reference scenario. This brings the total length of the pipeline to 15,839 km.

This cost increase is mainly due to the need to equip a larger number of coal-fired power plants with a capture unit to process more emissions, which increases the cost of capture by about 45% compared to the Reference scenario. Table 6 shows that the share of capture costs increases to over 83% of total costs. Transport and storage costs increase to 43% and 23% respectively compared to the Reference scenario.

Figures 10a and 10b show the timing and intensity of capacity additions in terms of carbon capture and injection wells and pipeline infrastructure expansion.

<figure><img src="image_11.png" /><figcaption>**Fig. 10** Spatial and temporal evolution of the CCS infrastructure – Capture+ scenario</figcaption></figure>

Investments in capture units are made in all periods, especially from period 0 onwards (Figure 10). On the other hand, investments in transport pipelines and storage facilities are concentrated in periods 0 and 1 and only increase in period 3. The requirement to capture much more CO₂ is implemented in two main ways: first, by bringing forward the
date of capture on certain units, and second, by equipping more power plants, particularly
in the east of the country. Bringing the investment forward does not allow for the benefit of
reduced costs due to learning effects. As a result, the total cost of the policy skyrockets.

This early move results in 10% of the total being captured and stored in period 1
– which is 20 times more than in the Reference scenario – and 30% in period 2, an increase
of 51% compared to the Reference scenario, as shown in Table 7. The increase in period 3
is more modest at only 9%.

Figure 11 shows the volumes of CO₂ transported and stored in each period. As the
volumes transported in periods 2, 3 and 4 are approximately the same, the colours overlap.

<figure><img src="image_12.png" /><figcaption>Fig. 11 Transported volumes – Capture+ scenario.</figcaption></figure>

*Note: The volumes transported in Periods 2, 3 and 4 are almost identical, resulting in overlapping colors in the figure, thus covering up the volumes transported in Period 4.*

Once again, the regions north of the Krishna-Godavari basin are those with the
highest CO₂ capture. This leads to a high rate of filling in this basin: almost 61%, 14
percentage points more than in the Reference scenario. The Rajasthan basin is also filled
more. However, the increase compared to the Reference scenario is more limited than in the
Krishna-Godavari basin.
# 6 Discussion

In this section we discuss the results of our scenarios in more detail. First, we focus on the pipeline network and identify the similarities and differences between the scenarios. We then compare the storage results across the scenarios. Finally, we examine the financing and emerging regulatory issues associated with the development of India's envisaged CCS infrastructure.

## 6.1 Networks: a fragmented structure with an eastern backbone

We note the presence of a significant transport backbone on the eastern side of India. The bulk of emissions are transported along a north-south corridor to the Krishna-Godavari basin, highlighting a robust transport route that remains resilient regardless of our scenarios. From a central planner's perspective, this finding is critical, as their deployment strategy will remain broadly consistent regardless of their assumptions regarding the coal plants they intend to equip (i.e. NewBuild vs. Reference scenarios), the mitigation target, or the available storage sites (Offshore vs. Reference scenarios). Therefore, our paper recommends prioritising the implementation of storage tests in the Krishna-Godavari basin.

However, the Indian network is more than just an eastern pipeline. Unlike other national CCS strategies [52, 53], the Indian network consists of several smaller clusters in all scenarios. This result is due to the geographical specificity of India, which is characterised by the distribution of storage sites across India. This leads to proximity between coal-fired power plants and storage sites, especially in southern India. As short distances are more cost effective, the transport network is based on a fragmented structure, which contrasts with the transcontinental networks in Europe and the US [53, 22, 54, 55, 25, 21]. Interestingly, the capture and storage clusters identified in our study correspond to the source-sink clusters identified in Vishal et al. [20].

We now turn to a detailed examination of the network in each scenario. In the Offshore scenario, where onshore storage is banned, the transport network develops two main branches leading to the Krishna-Godavari and Mumbai basins. This network expands considerably as CO₂ captured from certain power plants can no longer be stored in nearby onshore basins. In particular, the large capacities in the northern and eastern regions of the country are located far from any storage basin. Paradoxically, the ban on onshore storage results in an extended network with a greater presence on the ground. In contrast, the requirement for new coal-fired power plants to be equipped with capture facilities results in
a network characterised by numerous branches throughout India. Although the total length of the network is reduced by 30% compared to the Offshore scenario, it effectively serves all basins in the country, including the Assam-Arakan region. This stringent requirement increases the cost of the policy, but facilitates the development of a more balanced network across the country. Finally, the transport network in the Capture+ scenario extends the framework established in the NewBuild scenario.

## 6.2 Storage considerations

The most used basins in all scenarios are the least costly: Krishna-Godavari, Mumbai and Rajasthan. In terms of the filling of the different storage sites, the results show that the ban on onshore storage leads to a high filling of the Mumbai and Krishna-Godavari basins, which could compromise a CCS policy with such a ban in the long term. The New-Build scenario allows the CO₂ captured to be distributed across all basins, even more than in the Reference scenario. In the Capture+ scenario, the increase in the target in terms of CO₂ captured results in the Krishna-Godavari basin being filled to over 60%. However, in the same region, a little further south, another basin with a greater capacity than Krishna-Godavari, the Cauvery basin, could eventually be used in the long term. Similarly, the Cambay basin, a high-capacity basin between Mumbai and Rajasthan, could be used in the long term. Reaching these basins would only mean extending existing pipelines over relatively short distances without jeopardising the existing transport infrastructure. On the other hand, all scenarios barely fill the north-eastern basins, which are poorly connected to capture facilities.

However, it is important to note that the present analysis focuses on basins for which a preliminary characterisation of the underground storage potential is available. However, India has yet to undertake a comprehensive study of its geological potential. To date, most estimates have been made using preliminary approaches [12, 56]. In addition, with the exception of a few preliminary studies such as Singh, Sharma, and Dunn [57] and Vishal et al. [18], there is limited knowledge of the nation's potential for geological sequestration in unconventional formations such as coal and shale reservoirs. Depending on the suitability of such formations for carbon storage, the country's storage potential could be significantly increased.
## 6.3 Financing the CCS infrastructure

In all scenarios, the total cost is in the hundreds of billions of dollars, fluctuating around $580 billion for the scenarios with a target of processing 50% of the emissions generated. Given the high total cost of CCS deployment, financing is a critical issue. Although these figures should be treated with caution and should not be regarded as cost estimates, they do provide an order of magnitude of the investment required.

In the Reference scenario, we observe that the model delays investment, adopting a kind of 'wait-and-see' approach. While this strategy minimises costs, it delays the necessary CO₂ reductions and could jeopardise climate targets. This approach is mainly due to learning effects and the discounting factor, which make last-minute investments more attractive. In the real world, however, the Indian power industry may prefer not to invest in carbon capture in the absence of tangible cost reductions. Conversely, if coal-fired power plants see large cost reductions, they may also prefer to delay investment and wait for costs to reach a minimum. In either case, India could be encouraged to delay investment, jeopardising the goal of reducing India's CO₂ emissions.

In response to this scenario, the NewBuild scenario, which requires all new coal-fired power plants to be equipped with capture units, represents a viable alternative to mitigate these delays. Indeed, many investments take place in period 0 of our model. In terms of costs, this scenario leads to a small percentage increase in total costs, but a significant amount in absolute terms of $17 billion.

We propose two approaches to partially finance this infrastructure. First, developed countries could finance the cost increase through the financing channels discussed at UN-FCCC's Conference of the Parties (COP). Indeed, developed countries have a vested interest in global emissions reductions, and international climate diplomacy repeatedly emphasises the need to phase out coal use or neutralise its associated emissions [5]. Moreover, developed countries could benefit from the learning effects generated by financing CCS in India. In this respect, Article 6.2 of the Paris Agreement, which allows for bilateral transfers of emission reductions, provides an avenue for such cooperation. These agreements should be based on fair burden-sharing principles, ensuring an equitable distribution of the relative burdens on different parties [58].

Second, the Indian central government, through the NITI Aayog report [14], has proposed subsidies to cover part of the cost of CCS, offering $45/tCO₂ by 2040, falling to $34/tCO₂ by 2050. These figures focus primarily on capture costs and do not outline a comprehensive subsidy framework for transport and storage infrastructure. We advocate ex-
tending this subsidy to network development, as suggested by Nicolle, Lowing, and Cebreros [59], as well as promoting technological progress through R&D support [60].

## 6.4 Emerging regulatory challenges

The issues raised above are linked to a fundamental question: which authority should be responsible for planning, monitoring and regulating CCS in India? Indeed, policy uncertainty severely undermines the incentives to invest in CCS at coal-fired power plants [61, 62]. Currently, energy legislation is primarily the responsibility of the federal government, which also coordinates implementation efforts at the state level [5]. However, this governance landscape has evolved significantly in recent decades. Historically, responsibility for power infrastructure development has fluctuated between the central government and state electricity boards. As Dubash and Rajan [63] discuss, the government, influenced by domestic and international political pressures, has gradually pursued privatisation and restructuring initiatives, initially through a pilot reform in Odisha, underpinned by successive policy shifts rather than a cohesive public interest rationale. The literature presents different perspectives on the regulatory structures needed to manage these infrastructures effectively. Some scholars argue for the establishment of an independent regulatory body completely insulated from political influence [64], while others assert the need for transparent and trustworthy regulation, without specifying whether it should reside within a new or existing institution [65]. Clarifying the roles of states, the government and external organisations is therefore essential for the successful deployment and governance of CCS in India.

This issue of governance and accountability is all the more crucial as our results show the need for multi-state cooperation. Indeed, the backbone identified in our study transports emissions from coal-fired power plants located in northern Indian regions such as Uttar Pradesh, Bihar and Jharkhand and crosses the states of Odisha and Chhattisgarh before storing emissions in the Andhra Pradesh region. Thus, the deployment is largely dependent on Andhra Pradesh's willingness to store CO₂ from other regions. Similarly, CO₂ transit states have veto power that could jeopardise the transport backbone. Nevertheless, cooperation between states makes it possible to build large infrastructures with significant economies of scale. Against this background, further research seems necessary to explore whether the benefits of cooperation can be distributed among the participating states in an incentive-compatible manner.

The next essential step in the deployment of CCS in India is the creation of a
tailored legal and regulatory framework. Given the natural monopoly status of the trans-
port operator, as demonstrated by Nicolle et al. [66], a specialised regulatory approach is
required. The study further argues that simple average cost pricing does not achieve alloca-
tive efficiency and may conflict with environmental objectives. While some countries have
introduced CCS-specific frameworks, these are often adapted from existing gas and electric-
ity regulations. Nicolle et al. [66] identifies three emerging models: (i) an explicit regulatory
approach, as in the UK; (ii) direct government involvement in infrastructure construction,
as in Norway; and (iii) a governance-dependent approach, as in the US and EU. India is
likely to adopt a model similar to the latter, but adaptation to its federal structure will be
essential and may require a unique regulatory regime. The involvement of external funding
sources, such as the World Bank and developed countries under the Paris Agreement, could
further complicate the regulatory landscape.

7 Conclusion

Decarbonation in India is a huge challenge. In particular, coal-fired power plants are responsible for half of the country's CO2 emissions [67]. Decarbonising this sector means reducing emissions from its fleet of coal-fired power plants, which emit nearly a gigatonne of CO2 a year. While India plans to install a large amount of renewable energy capacity in the coming years, coal-fired power plants will continue to meet most of the country's growing energy needs [12]. Capturing CO2 emissions from these plants is therefore essential to reducing the country's carbon footprint and bridging the gap between India's pledged climate commitments and its projected emissions trajectory.

This study is the first to map the pipeline network for capturing CO2 emissions
from coal-fired power plants in India. We study the deployment of CCS in India, a region
that has received comparatively less attention in the literature than in the US, China or
Europe. Our MILP model considers all stages of CCS: capture, transport and storage. By
setting a minimum amount of CO2 to be processed over 25 years, we find a global CCS
infrastructure that includes both capture units, the location and size of storage sites, and
the routes of the pipelines connecting them. The total cost in our reference scenario is $574
billion for storage of around 12 GtCO2, giving a cost of 49$/tCO2 and a total pipeline length
of 11,900 km.

We then present three scenarios based on specific policy choices to provide a com-
prehensive overview of their impact on the CCS infrastructure: one bans onshore storage,
one requires carbon capture for all new power plants entering operation, and the last signif-
icantly increases the capture target. Several key findings emerge from our study: firstly, the main pipeline routes are robust in all our scenarios; secondly, the main storage sites to be equipped are also robust in all our scenarios; thirdly, given the amount involved, there is a risk that the Indian authorities will adopt a 'wait-and-see' strategy, which could be limited by the involvement of international funding to start building the CCS infrastructure quickly.

Of course, our study has certain limitations. Future studies could contribute by refining the data and methodology. From a methodological perspective, this paper assumes that a benevolent social planner with perfect information builds the entire infrastructure. Models representing the agents’ problem in detail through mixed complementarity problems [68] or cooperative game theory [15] could address this shortcoming. Another perspective would be to directly consider the uncertainty associated with the transport and storage phases. For example, the study by d’Amore et al. [69] considers the potential risks associated with CO₂ leakage in the design of its infrastructure. Middleton and Yaw [70] take into account the uncertainty surrounding the storage capacity of a given injection well. From this information, they derive the least-cost infrastructure that is robust to their criteria. Combining their approach with ours would lead to an even more robust deployment strategy. Similarly, relying on regret-minimising methods (e.g. by adapting the analytical framework in Nicolle and Massol [71]) is another promising avenue of research.

From a data perspective, we assume here that the injection rate was the same for all wells regardless of basin and location, which is most likely not the case [48]. This requires specific geological assessments of India’s storage basins to determine the effective injection rate of each region. These results could then be easily incorporated into our framework.

Despite these limitations, the study opens the debate on the development of CCS in India, which will be an essential component of Indian climate policy, but also of an ambitious international climate policy.

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**Statements and Declarations**

**Competing interests** The authors have no relevant financial or non-financial interests to disclose.

**Author contributions** Conceptualization: all authors; Methodology: Adrien Nicolle; Formal analysis and investigation: Adrien Nicolle; Visualization: Adrien Nicolle; Software: Adrien Nicolle; Writing - original draft preparation: Adrien Nicolle; Writing - review and editing: all authors.

**Data availability** For data related to coal power plants, please refer to the Global Coal Plant Tracker (Global Energy Monitor, January 2023 release). Additional data is available upon request.

**Acknowledgements** We thank participants of the 15th Trans-Atlantic Infraday Conference and the FAEE Student Workshop for their useful remarks. We also thank members of the Laboratoire Génie Industriel and the Climate Economics Chair for their feedback. Adrien Nicolle is grateful for financial support provided by the Climate Economics Chair. Olivier Massol gratefully acknowledges funding from the Franco-German research project MInDSET Clean H2 jointly funded by the Agence Nationale de la Recherche (ANR grant number ANR-24-ENER-0003) on behalf of the French Ministry of Higher Education and Research (MESR), and the German Federal Ministry of Education and Research (BMBF).
# Appendices

## Appendix A Population density multiplier

To the best of our knowledge, the notion of right-of-way for CCS pipelines was first introduced in the study by Oei, Herold, and Mendelevitch [24]. In their study, the unitary right-of-way cost depends on both the length and the diameter of the pipeline. Since this cost should reflect the potential social opposition to pipeline construction, we prefer to consider that it depends on length and population density. Obviously, this cost depends on many other sociocultural factors, but we simplify in order to use comparable data. For further information on including social acceptance in a MILP for CCS deployment, we recommend the study by d'Amore, Lovisotto, and Bezzo [72], in which they differentiate countries depending on their sociocultural characteristics. Such an approach was not possible in our case, as we study a single country.

For each region in our model, we have determined its density, and grouped these densities into quintiles numbered 1 to 5 (1 being the least populated, 5 being the most populated), as shown in the Figure 12 below. For each inter-regional pipeline, $Pop_{g,g'}$ takes the average of the quintile values of the two regions it crosses (e.g., for a pipeline crossing a region belonging to quintile 2 and another to quintile 3, $Pop_{g,g'}$ takes the value 2.5 between these two regions).

<figure><img src="image_13.png" /><figcaption>**Fig. 12** Population density per region.</figcaption></figure>
# Appendix B GEM database and the Indian National Electricity Plan (NEP)

In May 2023, the Central Electricity Authority published its National Electricity Plan [10], which indicates to the Union Government the amount of investments in coal power needed between 2022 and 2032 to meet projected energy demand. This document divides the decade into two 5-year periods. During the first (i.e., from 2022 to 2027), the report states there is no need to build new coal-fired power stations besides those already planned. According to the report, 25.6 GW of coal power capacity will be constructed over 2022–2027. Over the second period (i.e., from 2028 to 2032), the report forecasts the need for the installation of an extra 25.5 GW. Over the entire 2022–2032 period, this represents an addition of 51.1 GW.

<figure><img src="image_14.png" /><figcaption>**Fig. 13** Schematic representation of capacity additions. In red: Indian National Electricity Plan projections. In blue: Assumptions in our model based on GEM database</figcaption></figure>

Alongside these projections, the GEM database indicates that 32 GW are under construction and 20 GW are in the pre-permit and permitted stages. For these plants, the database does not indicate the start date of operation. For consistency with the National
Electricity Plan projections, we assume that the 32 GW "under construction" will enter simultaneous operation in 2028 (at the beginning of Period 1 of the MILP model), while the 20 GW in "pre-permit" and "permitted" stages will enter simultaneous operation in 2033 (Period 2 of the MILP model). Installed capacity over the first period therefore slightly exceeds that of the National Electricity Plan, while capacity deployed over the second period is slightly lower (see Figure 13). We suppose that units whose status is "announced" enter into operation in the Period 3 of the MILP model. In the model, we aggregate coal-fired plant units by region and suppose they are located at the region's center. For each region and each year, the CO₂ emissions are equal to the sum of the emissions of each operating emitter during that year.

## Appendix C Carbon capture learning effects

For learning effects linked to capture, we use the figures in the following Table 12.

<table><caption>**Table 12** Learning effects for carbon capture's investment cost according to IEA [36].</caption><thead><tr><th></th><th colspan="5">Year</th></tr><tr><th></th><th>2025</th><th>2030</th><th>2035</th><th>2040</th><th>2045</th></tr></thead><tbody><tr><td>UCC<sup>inv</sup><br>[M$/MtCO<sub>2</sub>/period]</td><td>64</td><td>58</td><td>52</td><td>49</td><td>46</td></tr></tbody></table>

We consider that these costs correspond to the total capture cost for each period. In particular, the IEA uses a cost of 64 $/tCO₂ for capture in Period 0, which is consistent with our value of 67 $/tCO₂ from the study by Singh, Rao, and Chandel [35]. In our case, we divide this cost into an investment cost and an operating cost, in which the investment cost represents 16% of the costs – based on our calculations in section 3. We assume that this ratio remains constant over time, independent of learning effects. Thus, from the total cost of capturing a tonne of CO₂ provided by the IEA's work and this ratio, we deduce values for the investment cost and operating costs over time presented in Table 2.

## Appendix D Storage investment cost

The cost of storage in each basin depends on numerous parameters of the reservoir (e.g., porosity, depth, compressibility), the brine contained in the aquifer (e.g., salinity,
viscosity, density) and the stored CO₂ (e.g., viscosity, density). Most of these parameters are unknown at this stage for Indian basins. Consequently, we represent here the cost of storage in a simplified manner, considering the following investment cost adapted from Carneiro et al. [48].

$$USC_b^{well} = C^{dri} \cdot d_b + C^{well} + C^{sf} + C^{sd}$$

with $USC_b^{well}$ the investment cost for a single injection well [M$], $C^{dri}$ = 26 [M$/km] the drilling costs, $d_b$ the depth of the storage basin b [km], $C^{well}$ = 8.2 [M$] the fixed cost per well [M$] $C^{sf}$ = 6.12 [M$] the investment cost for surface facilities per injection well and $C^{sd}$ = 6.02 [M$] the investment cost for site development per well. The maximal injection rate is set equal to 0.85 MtCO₂/year which implies a maximal injection rate capacity per period $Kwell$ = 4.25 MtCO₂/period.<fnref n="6" /> Following this investment cost, the investment cost for each basin is computed in Table 5. The operation and maintenance cost are supposed equal to 10% of the investment cost.

# Appendix E Sensitivity analysis

Figure 14 below illustrates the sensitivity of the total cost of the infrastructure to unilateral variations in the following parameters: (i) the discount rate r, (ii) the carbon capture variable cost $UCC^{var}$, and (iii) the carbon reduction target $\alpha$.

The sensitivity analyses of the discount rate and the variable cost of capture reveal overall linear trends. Specifically, a 50% increase in the unitary variable capture cost results in a 20% increase in the total cost of CCS. While this might seem modest given that capture costs account for approximately 70% of the total CCS cost, it shows that the model adapts the CCS operations to minimize costs. These adjustments include reconfiguring the pipeline network and altering the timing of investments and operations to minimize costs.

<footnote n="6">⁶We adapt the formula of Carneiro et al. [48] by supposing that the number of injection wells per site W is equal to four (i.e., the average number of injection wells per site found in their study). The same rule applies for the average injection rate.</footnote>
<figure><img src="image_15.png" /><figcaption>Fig. 14 Sensitivity analysis for various parameters.</figcaption></figure>

The sensitivity curve for the capture target is piecewise linear, with different slopes across its segments. Between -50% and +20% variations, the rate of total CCS cost remains relatively stable, with a slope of approximately one. However, beyond +20%, the slope doubles. This behavior is due to the saturation of the inter-regional transportation network, which forces the model to invest in new routes that lack economies of scale. This result has significant implications for CCS deployment. An aggressive CCS-focused policy aiming to capture as many emissions as possible from coal power plants may become less effective beyond a 60% carbon capture target. At this point, the marginal cost of achieving each additional percentage of the carbon capture target doubles, suggesting that other decarbonization options might be more relevant.