Revisiting the social cost of carbon after INDC implementation in Malaysia: 2050


This article projects the social cost of carbon (SCC) and other related consequences of climate change by using Malaysia’s intended nationally determined contribution (INDC) and climate vision 2040 (CV2040) by 2050. It compares the projections derived from the Dynamic Integrated Model of the Climate and Economy (DICME) based on the respective INDC and CV2040 scenario. The results reveal that industrial emissions would incur a substantial increase every 5 years under the scenario CV2040, while Malaysia would experience lower industrial emissions in the coming years under the scenario INDC. Emission intensity in Malaysia will be 0.61 and 0.59 tons/capita in 2030 for scenario CV2040 and scenario INDC respectively. Malaysia would face climate damage of MYR456 billion and MYR 49 billion by 2050 under CV2040 and INDC scenario respectively. However, climate damage could be much lower if the INDC regime were adopted, as this scenario would decrease climatic impacts over time. The estimated SSC per ton of CO2 varies between MYR74 and MYR97 for scenario CV2040 and MYR44 and MYR62 for scenario INDC in 2030 and 2050 respectively. Considering different aspects, including industrial emissions, damage cost, and social cost of carbon, INDC is the best policy compared to CV2040. Thus, Malaysia could achieve its emissions reduction target by implementing INDC by 2050.

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  1. 1.

    Social cost of carbon, also known as the marginal damage cost of carbon dioxide, is defined as the net present value of the incremental damage due to a small increase in carbon dioxide emissions (Toll, 2011). It is called an estimate of monetary damages caused by 1-ton increase in GHG emissions in a given year.

  2. 2.

    At present, there are few established integrated assessment models (IAMs) that are available for estimation of the entire path of cause and effect and can therefore calculate an internally consistent SCC.

  3. 3.

    There is little doubt that learning models pose certain obstacles, and therefore, efforts were made to address them by drawing on Yu et al. (2011) and Soderholm and Sundqvist (2007) who advocate the use of multi-factor learning curves. This approach decomposes the drivers of technological change into potentially any number of components. For example, scale, learning, and scarcity in theory could all be separately modeled. Soderholm and Sundqvist (2007) also discussed the importance of choosing the appropriate proxy for learning, viz. installed capacity, demand, and total output.

  4. 4.

    This study has analyzed and compared between the results from the scenarios based on several indicators of Malaysia.

  5. 5.

    Following the studies of Spackman (2015); Rasiah et al. (2018)

  6. 6.

    The TFP was estimated using national data as recommended by Nordhaus (2008) and Stern (2007).

  7. 7.

    However, Nordhaus (2017) also found a substantial increase in the estimated SCC over time using IPCC scenario.


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This work is partially supported by project UNITEN: BOLD grants of 10289176/B/9/2017/18 at the Institute of Energy Policy and Research (IEPRe), Universiti Tenaga Nasional (UNITEN), Malaysia. The authors would like to thank UNITEN for their financial support.

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Correspondence to Md. Sujahangir Kabir Sarkar.

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Mathematical statement of the study model

Equations in the model

$$ W=\sum \limits_{t=1}^{T\max }u\left[c(t),l(t)\right]R(t) $$
$$ R(t)={\left(1+\rho \right)}^{-t} $$
$$ U\left[c(t),L(t)\right]=l(t)\Big[c{(t)}^{1-\alpha }/\left(1-\alpha \right) $$
$$ Q(t)=\varOmega (t)\left[1-\varLambda (t)\right]A(t)K{(t)}^{\gamma }L{(t)}^{1-\gamma } $$
$$ \varOmega (t)=1/\left[1+{\varPi}_1{T}_{AT}(t)+{\varPi}_2{T}_{AT}{(t)}^2\right] $$
$$ \varLambda (t)=\pi (t){\theta}_1(t)\mu {(t)}^{\theta_2} $$
$$ Q(t)=C(t)+I(t) $$
$$ C(t)=C(t)/L(t) $$
$$ K(t)=I(t)+\left(1-{\delta}_k\right)K\left(t-1\right) $$
$$ {E}_{Ind}(t)=\sigma (t)\left[1-\mu (t)\right]K{(t)}^{\lambda }L{(t)}^{1-\lambda } $$
$$ CCum\le \sum \limits_{t=0}^{T\max }{E}_{Ind(t)} $$
$$ E(t)={E}_{Ind}(t)+{E}_{Land}(t) $$
$$ {M}_{AT}(t)=E(t)+{\phi}_7{M}_{AT}\left(t-1\right)+{\phi}_{11}{M}_{UP}\left(t-1\right) $$
$$ {M}_{UP}(t)={\phi}_{11}{M}_{AT}\left(t-1\right)+{\phi}_{11}{M}_{UP}\left(t-1\right)+{\phi}_{11}{M}_{LO}\left(t-1\right) $$
$$ {M}_{LO}(t)={\phi}_{12}{M}_{UP}\left(t-1\right)+{\phi}_{12}{M}_{LO}\left(t-1\right) $$
$$ F(t)=\eta \left\{{\log}_2\right[{M}_{AT}/{M}_{AT}\left(1900\right]\Big\}+{F}_{EX}(t) $$
$$ {T}_{AT}={T}_{AT}\left(t-1\right)+{\zeta}_1\left\{F(t)-{\zeta}_2{T}_{AT}\left(t-1\right)-{\zeta}_3{T}_{AT}\left(t-1\right){T}_{LO}\left(t-1\right)\right\} $$
$$ {T}_{LO}(t)={T}_{LO}\left(t-1\right)+{\zeta}_4\left\{{T}_{AT}\left(t-1\right)-{T}_{LO}\left(t-1\right)\right\} $$
$$ \prod (t)=\varphi {(t)}^{1-{\theta}_2} $$

Variable definitions and units (endogenous variables marked as asterisks)

A(t) = Total factor productivity (TFP) in units)

*c(t) = Capita consumption of goods and services (RM per person)

*C(t) = Consumption of goods and services (RM)

ELand(t) = Emissions of carbon from land use (carbon per period)

*EInd(t) = Industrial carbon emissions (carbon per period)

*E(t) = Total carbon emissions (carbon per period)

*F(t), FEX(t) = Total and exogenous radiative forcing

*I(t) = Investment (RM)

*K(t) = Capital stock (RM)

L(t) = Population and labor inputs (number)

*MAT(t), MUP(t), MLO(t) = Mass of carbon in reservoir for atmosphere, upper oceans, and lower oceans (carbon, beginning of period)

*Q(t) = Net output of goods and services, net abatement and damages (RM)

T = Time (decades from 2010 to 2020, 2021–2030,. ..)

*TAT(t), TLO(t) = Global mean surface temperature and temperature of lower oceans (°C increase from 1900)

*U[c(t), L(t)] = Instantaneous utility function (utility per period)

*W = Objective function in present value of utility (utility units)

(t)= Abatement-cost function (abatement costs as fraction of world output)

*μ(t)= Emissions-control rate (fraction of uncontrolled emissions)

*Ω(t)= Damage function (climate damages as fraction of world output)

*φ(t)= Participation rate (fraction of emissions included in policy)

*∏(t)= Participation cost markup (abatement cost with incomplete participation as fraction of abatement cost with complete participation)

*σ(t)= Ratio of uncontrolled industrial emissions to output

CCum = Maximum consumption of fossil fuels (tons of carbon)

γ= Elasticity of output with respect to capita (pure number)

δk= Rate of depreciation of capital (per period)

R(t) = Social time preference discount factor (per time period)

Tmax = Length of estimate period for model

η= Temperature-forcing parameter (°C per watts per meter squared)

ϕ= Parameters of the carbon cycle (flows per period)

σ= Pure rate of social time preference (per year)

θ1....2= Parameters of the abatement-cost function

ζ= Parameters of climate equations (flows per period)

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Sarkar, M.S.K., Al-Amin, A.Q. & Filho, W.L. Revisiting the social cost of carbon after INDC implementation in Malaysia: 2050. Environ Sci Pollut Res 26, 6000–6013 (2019).

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  • Social cost of carbon
  • Carbon emission
  • INDC
  • Climate vision
  • Scenario
  • Malaysia