DOI: 10.3303/CET24114092 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Paper Received: 29 April 2024; Revised: 24 July 2024; Accepted: 21 November 2024 
Please cite this article as: Aviso K.B., Migo-Sumagang M.V., Ramos C.A., Tan R.R., 2024, Economic Ripple Effects of Large-Scale Basalt 
Enhanced Weathering in the Philippines, Chemical Engineering Transactions, 114, 547-552  DOI:10.3303/CET24114092 
  

 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 114, 2024 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.cetjournal.it 

Guest Editors: Petar S. Varbanov, Min Zeng, Yee Van Fan, Xuechao Wang 

Copyright © 2024, AIDIC Servizi S.r.l. 

ISBN 979-12-81206-12-0; ISSN 2283-9216 

Economic Ripple Effects of Large-Scale Basalt Enhanced 

Weathering in the Philippines 

Kathleen B. Avisoa*, Maria Victoria Migo-Sumagangb, Czarina Alessandra Ramosa, 

Raymond R. Tana 

aDepartment of Chemical Engineering, De La Salle University, Philippines 
bDepartment of Chemical Engineering, University of the Philippines, Los Baños, Philippines 

 kathleen.aviso@dlsu.edu.ph 

Carbon dioxide removal using negative emissions technologies will be needed to achieve net zero 

greenhouse gas emissions. Enhanced weathering is a negative emissions technology that relies on the 

acceleration of natural geochemical reactions between alkaline rocks or minerals with carbon dioxide and 

water. It results in the permanent sequestration of carbon as bicarbonate ions in runoff water. Under some 

conditions, this technique can also give secondary co-benefits, such as improving the suitability of acidic soil 

for agriculture. However, large-scale enhanced weathering will require ramping up the activity in the mining, 

transportation, and electricity generation sectors. In this work, the potential economic effects of large-scale 

basalt-enhanced weathering in the Philippines are estimated using input-output analysis. The results show 

that offsetting 20 % of the country's carbon dioxide emissions requires substantial direct increases in 

quarrying, land transport, and power generation, coupled with smaller changes in other sectors via indirect 

ripple effects. 

1. Introduction 

It is now apparent that large-scale CO2 removal (CDR) will be needed to achieve net zero greenhouse gas 

(GHG) emissions by mid-century and limit temperature rise by 2100 to well below 2 C (Intergovernmental 

Panel on Climate Change, 2022). CDR can be achieved using negative emissions technologies (NETs) that 

rely on different pathways for sequestering carbon that is fixed from atmospheric CO2. The technology 

spectrum ranges from engineered NETs such as direct air capture (DAC) and bioenergy with carbon capture 

and storage (BECCS) to nature-based CDR such as afforestation and soil carbon sequestration (Smith et al., 

2019). Most NETs rely on photosynthesis as the initial carbon fixation step, with the notable exceptions of 

DAC and enhanced weathering (EW), which relies on the artificial acceleration of naturally occurring 

geochemical reactions. 

CDR via the engineered enhancement of the reaction of CO2 and water with alkaline minerals was first 

proposed by Seifritz (1990). Ex situ EW involves applying powdered rocks or minerals on land at a rate 

calibrated based on average local rainfall and temperature data (Strefler et al., 2018). Rocks which contain 

silicate minerals react with atmospheric CO2 in the presence of water, resulting in soluble bicarbonate ions; for 

example, calcium silicate weathers following the reactions indicated in Eq(1). The geochemical reactions lead 

to partial dissolution of the powder; carbon in the bicarbonate ions in the runoff water is eventually carried into 

the oceans for permanent sequestration. In addition to naturally occurring materials, different kinds of alkaline 

industrial waste can also be used (Renforth, 2019). Although EW can be done purely for CDR on marginal 

land, its use in conjunction with conventional (Beerling et al., 2020) or urban (Haque et al., 2021) agriculture 

can give significant co-benefits due to improvement of soil quality. EW can also be done in remote terrestrial 

ecosystems by spreading rock dust from aircraft (Goll et al., 2021) or in coastal environments (Meysman and 

Montserrat, 2017).  

CaSiO3 + 2CO2 +  H2O → Ca2+ + 2HCO3
− +  SiO2  (1) 

547



There are major challenges in ramping up NETs such as EW from laboratory or demonstration scale to meet 

the projected CDR demand in the order of multiple Gt/y (Nemet et al., 2018). Large-scale deployment of NETs 

can result in substantial environmental impacts or co-benefits, which cannot be ignored (Smith et al., 2019). 

Models will be needed to understand how the introduction of large-scale EW-CDR will affect the background 

technosphere (Tan and Aviso, 2021). Life-cycle assessment (LCA) and related methods can be used to gauge 

the direct and indirect effects of EW-CDR. Because of the high energy demand of grinding down rocks and 

minerals into powder form, Eufrasio et al. (2022) found that the local electricity mix is critical in determining the 

effectiveness of EW-CDR. Researchers have also examined the trade-offs involved in varying particle sizes 

for both terrestrial (Rinder and von Hagke, 2021) and coastal (Foteinis et al., 2023) EW-CDR systems. 

Lefebvre et al. (2019) used LCA to study the viability of basalt-based EW-CDR in Brazil. Jerden et al. (2024) 

developed a hybrid geochemical and LCA model for EW-CDR systems. Oppon et al. (2023) used a multi-

region input-output (MRIO) model to gauge the economic and environmental effects of increased basalt 

production for EW-CDR; however, this incremental output was imposed exogenously without the creation of a 

separate EW-CDR sector in the economic system. No work to date has reported an approach that includes 

the insertion of a new CDR sector into an input-output model.    

To address this research gap, an environmentally-extended input-output (EEIO) model is developed here 

based on the general formulation of Leontief (1970). This approach involves adding an extra row for CO2 

emissions and an extra column for CDR generation. The case of basalt-based EW-CDR in the Philippines is 

considered. The model allows the estimation of economic “ripple effects” (i.e., changes in the output levels of 

economic sectors) resulting from CDR deployment. The rest of this paper is organized as follows. Section 2 

gives the formal problem statement. Section 3 describes the EEIO model formulation. Section 4 applies the 

model to the case of the Philippines. Finally, Section 5 gives the conclusions and discusses future research 

directions.  

2. Problem statement 

The formal problem is as follows. Given: 

• Given the state of technology in an economy which quantifies the input and output flows of resources and 

wastes defined by an environmentally–extended input-output (EEIO) system, 

• Given the resource requirements and waste streams of a new economic sector meant for carbon dioxide 

removal (CDR); 

• Given the baseline economic and environmental performance of an economy; 

• Given the target CDR removal; 

The objective is to determine the economic effects of the large–scale implementation of CDR. 

A generic economic system with environmental extension is illustrated in Figure 1.  

 

Figure 1: Input-output system with environmental extension 

3. Input-output model formulation 

The input-output model was developed by Leontief (1936) to represent the network structure of an economy 

using a system of linear equations. Detailed tutorials on the basic model, as well as extensions and variants, 

can be found in the book of Miller and Blair (2009). The basic input-output model is given in Eq(2). Where y⃗  is 

548



the final demand vector, x⃗  is the total output vector, A⃗⃗  is the technology coefficient matrix, and I  is an identity 

matrix. 

y⃗ = (I − A⃗⃗ )x⃗   (2) 

This model can be solved by direct matrix inversion as shown in Eq(3): 

x⃗ = (I − A⃗⃗ )
−1

y⃗   (3) 

The EEIO model was then proposed by Leontief (1970) to consider the effect of pollutants and the introduction 

of a new sector for environmental clean-up (Eq(4)), where �⃗� 𝑇 is the CO2 emissions satellite vector, 𝑐  is the 

CDR cost vector, s is the CDR scale factor, z is the baseline CO2 emission of the system, and 𝛿 is the CO2 

reduction. 

(
(I − A⃗⃗ ) c 

b⃗ T −1
)(x⃗ 

s
) = (

y⃗ 
z − δ

)  (4) 

Note that the EEIO model requires an additional row for direct sectoral CO2 emissions, and an additional 

column for a new sector to produce CDR. The “final demand” of the additional row represents the residual 

CO2 emissions that society is willing to tolerate after the application of CDR (Leontief, 1970). The extended 

model can still be solved by matrix inversion (Eq(5)): 

(
(I − A⃗⃗ ) c 

b⃗ T −1
)

−1

(
y⃗ 

z − δ
) = (x⃗ 

s
)  (5) 

The required calculations can be implemented in different computing environments such as Microsoft Excel. 

4. Philippine case study 

This section describes the development of an EEIO model for assessing the effects of basalt-based EW in the 

Philippines. Data from 2018 is used since this is the most recent available. Due to space constraints, the 

official input-output data (Philippine Statistics Authority, 2024) is compressed into an 8-sector model. Sectoral 

CO2 emissions data from fuel combustion (Philippine Department of Energy, 2024) are used together with the 

input-output data to estimate the CO2 intensities. Techno-economic data for the EW-CDR sector are based on 

Rinder and von Hagke (2021) combined with assumptions as stated below. The resulting model is used to 

determine changes in sector total outputs at fixed GDP for 5–20 % reduction in CO2 emissions.  

Table 1 shows the dimensionless technical coefficients of the input-output model. Each column gives the 

average monetary value of purchased inputs needed to produce one unit of output of a sector, measured in 

monetary terms. In practice, these coefficients represent the performance of the average production 

technology used by each sector. Table 2 gives the monetary total output and final demand levels of the 

economic sectors. Total outputs are larger than the respective final demands because they include 

intermediate goods that are used as production inputs in other sectors. Note that physical quantities implied by 

these monetary values can easily be found if the average prices are known (Leontief, 1936). 

Table 3 gives the direct CO2 emissions intensities of the economic sectors in Mt CO2/billion PHP 

(or t CO2/thousand PHP, where EUR1 = PHP60). The values are derived from the absolute emissions 

inventories (Philippine Department of Energy, 2024) and the corresponding total outputs in Table 2. The actual 

emissions from the sectors are directly proportional to the level of output, assuming that the average 

technology mix represented by these factors remains constant.  

Table 4 gives the techno-economic coefficients of the new EW-CDR sector, which involves mining, 

comminution, transport, and application of silicate rocks; this constitutes c  in Eq(4). These coefficients are the 

values of major purchased inputs in billion PHP/Mt CO2 (or thousand PHP/t CO2). The physical factors 

assumed here are based on Rinder and von Hagke (2021). The coefficient for Mining (S2) is the cost of 

purchase of basalt, assuming a price of PHP555/t and a CDR factor of 0.3 t CO2/t. The coefficient for 

Manufacturing (S3) is the cost of purchase of supplies for direct EW-CDR operations; the cost of diesel fuel for 

the tractors used to apply basalt powder is taken as a proxy for all non-capital inputs, assuming an average 

price of PHP47/L. The coefficient for Electricity (S4) is based on a power demand of 180 kWh/t to grind the 

basalt to the required particle size (10 m), assuming an average price of PHP8.99/kWh. Finally, the 

coefficient for Transportation (S7) represents the cost of the service to move basalt by truck an average 

distance of 150 km from the grinding plants to the application sites. For all of these inputs, it is assumed that 

the marginal mix of technologies needed to meet the incremental intermediate demands of the EW-CDR 

549



sector is the same as the background technology mix. This assumption of fixed “recipes” is consistent with the 

properties of Leontief production functions (Leontief, 1936). 

Table 1: Matrix of dimensionless technical coefficients (adapted from Philippine Statistics Authority, 2024) 

Sector S1 S2 S3 S4 S5 S6 S7 S8 

Agriculture (S1)  0.288   0.004   0.122   0.002   0.001   0.003   0.000   0.002  

Mining (S2)  0.000   0.098   0.035   0.028   0.010   0.000   0.000   0.000  

Manufacturing (S3)  0.162   0.134   0.323   0.189   0.394   0.034   0.243   0.067  

Electricity (S4)  0.007   0.017   0.023   0.043   0.014   0.015   0.017   0.016  

Construction (S5)  0.000   0.001   0.000   0.000   0.000   0.001   0.001   0.002  

Trade (S6)  0.011   0.022   0.099   0.041   0.059   0.135   0.029   0.050  

Transportation (S7)  0.005   0.014   0.018   0.003   0.006   0.025   0.158   0.010  

Other services (S8)  0.018   0.044   0.060   0.073   0.059   0.093   0.151   0.250  

Table 2: Baseline sectoral total outputs and final demands (adapted from Philippine Statistics Authority, 2024) 

Sector Total output 

(billion PHP/y) 

Final demand 

(billion PHP/y) 

Agriculture (S1)  3,461   1,090  

Mining (S2)  245   −223 

Manufacturing (S3)  10,939   4,094  

Electricity (S4)  897   260  

Construction (S5)  3,008   2,969  

Trade (S6)  4,668   2,066  

Transportation (S7)  1,737   990  

Other services (S8)  11,584   7,019  

Table 3: Direct sectoral CO2 intensities (adapted from Philippine Department of Energy 2024) 

Sector Direct CO2 intensity 

(Mt CO2/billion PHP) 

Agriculture (S1)  0.0005  

Mining (S2)  0.0010  

Manufacturing (S3)  0.0010 

Electricity (S4)  0.0720  

Construction (S5)  0.0010  

Trade (S6)  0.0005 

Transportation (S7)  0.0119  

Other services (S8)  0.0005 

Table 4: Techno-economic coefficients of EW-CDR sector (adapted from Rinder and von Hagke, 2021)  

Sector Input (billion PHP/Mt CDR) 

Mining (S2) 1.85  

Manufacturing (S3)  0.23 

Electricity (S4)  5.4 

Transportation (S7)  2.8  

The model is used to estimate the effect of reducing the baseline emissions of 109.3 Mt CO2/y by 5–20 %. 

Achieving these decarbonization scenarios requires the generation of 10.4–41.7 Mt/y of CDR to reduce 

emissions to 87.5–103.9 Mt CO2/y. This scale of EW-CDR requires the application of 34.7–138.9 Mt/y of 

basalt on a total land area of 0.23–2.78 Mha, based on an application rate of 50–150 t/ha/y (Strefler et al., 

2018). Due to interdependencies among economic sectors, the introduction of EW-CDR increases the 

demand for other goods that are directly or indirectly linked to this new sector. Figure 1 shows the percentage 

increase in outputs of the economic sectors as a result of introducing EW-CDR. Note that the direct CO2 

emissions of these sectors also increase by the same proportions. The largest relative increases occur in 

Mining (S2), Electricity (S4) and Transportation (S7). Figure 2 shows the absolute values of the direct CO2 

emissions of the sectors under the same scenarios. The largest increase occurs in emissions from Electricity 

(S4) due to the energy requirement for grinding basalt. 

550



     

 

Figure 1: Percentage increase in sector total outputs for 5–20 % decarbonization scenarios 

 

Figure 2: Direct sectoral emissions for 5–20 % decarbonization scenarios 

5. Conclusions 

In this work, an EEIO model was developed to estimate the amount of basalt-based EW-CDR needed to 

reduce the CO2 emissions of the Philippine economy by 0–20 % from baseline. By considering 

interdependencies within the economic system, including the intermediate inputs drawn by the EW-CDR 

sector from the rest of the economy, the model can estimate the ripple effects of the large-scale use of this 

decarbonization strategy. Under the assumptions consistent with Leontief production functions, an estimated 

41.7 Mt/y of EW-CDR is needed to reduce the total CO2 emissions by 20 %, from 109.3 to 87.5 Mt/y. In future 

work, the modelling approach used here can be readily applied to other geographic contexts. It can also be 

extended to other NETs, such as DAC, provided the necessary techno-economic data is available. 

Mathematical programming variants can also be developed for optimizing the mix of NETs when multiple 

options are simultaneously considered.   

Nomenclature 

A⃗⃗  – Technical coefficient matrix 

b⃗  – CO2 emissions satellite vector 

c  – CDR cost vector 

I  – Identity matrix 

y⃗  – final demand vector 

z – baseline CO2 emissions  

δ – target CO2 reduction 

 

Decision variables 

s – CDR scale factor 

x⃗  – total output vector 

 

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