Abstract
This paper analyses empirically the optimal climate change mitigation policy of Malaysia with the business as usual scenario of ASEAN to compare their environmental and economic consequences over the period 2010–2110. A downscaling empirical dynamic model is constructed using a dual multidisciplinary framework combining economic, earth science, and ecological variables to analyse the long-run consequences. The model takes account of climatic variables, including carbon cycle, carbon emission, climatic damage, carbon control, carbon concentration, and temperature. The results indicate that without optimal climate policy and action, the cumulative cost of climate damage for Malaysia and ASEAN as a whole over the period 2010–2110 would be MYR40.1 trillion and MYR151.0 trillion, respectively. Under the optimal policy, the cumulative cost of climatic damage for Malaysia would fall to MYR5.3 trillion over the 100 years. Also, the additional economic output of Malaysia will rise from MYR2.1 billion in 2010 to MYR3.6 billion in 2050 and MYR5.5 billion in 2110 under the optimal climate change mitigation scenario. The additional economic output for ASEAN would fall from MYR8.1 billion in 2010 to MYR3.2 billion in 2050 before rising again slightly to MYR4.7 billion in 2110 in the business as usual ASEAN scenario.
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Notes
The issue is more obvious due to the haze crisis that has plagued the region since 1995, which originated through intentional human activities from Indonesia.
The model follows closely the global model advanced by Nordhaus (2008). However, some modifications have been made in the original RICE model developed to find the country-specific outcomes with a regional scope by the downscaling adoption.
The mathematical estimation is carried out using the Geometric Algebraic Modelling System (GAMS) program.
Technological change is considered as exogenous in this study, which is assumed to be conditioned by market forces.
We used the Malaysian current price used as of 2010 as baseline, and the exchange rate at US$1 = RYM3.45
Although there are several uncertainties in the projections as they are sensitive to timeline, consumer and producer preferences, national and world actions, relevance, and the availability of backstop technologies, the comparison is useful against the existing plans used by Malaysia against the alternative business as usual scenario for ASEAN.
The full details of backstop technology are classified by Nordhaus (2008).
This top-down approach indicates global conditions so that individual country conditions can be disentangled to tackle climatic initiatives.
Otherwise, the projections will be affected as the environment—being a global common—is permeable, and hence, emissions from the neighbours diffusing into Malaysia cannot be avoided.
The year 2010 rather than 2015 was used because the latest data available for Malaysia only comes that far.
The threshold refers to a point beyond which the institutional and socioeconomic system would be affected or fundamentally disrupted by climate damage.
The selection of threshold level and climate variables was undertaken using prevailing standards (MMD 2009).
A separate scenario using existing patterns of production to project climate damage inflicted over the period 2010–2105 is contained in Al-Amin and Leal Filho (2014).
The full details of backstop technologies can be found in Nordhaus (2008).
We used positive values of the discount rate, though during deflationary times (or depression), it will be negative.
For ASEAN, we also considered the social cost of carbon and related estimations to compare the outcomes with the Malaysian scenario.
This figure was calibrated with the utility function to calculate the market returns.
Owing to the different Malaysian conditions, we chose a discount rate lower than that used by Stern (2007) by 1% and set the upper limit of carbon price at MYR854.6 per ton by 2060.
Our task was not to demonstrate directly how sensitivity tests from the different scenarios cause uncertainties.
In contrast, using 0.021% as the discount rate, this study found the social cost of carbon and technology options to only partly mitigate climate change.
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Acknowledgments
This work was prepared under the High Impact Research (HIR) project of HIR-Ref No: HIR/ASH/034 (HIR AC No: J-15001-73879), University of Malaya. The authors would like to thank the University of Malaya for the financial support and Charon Mokhzani for sharing the 2010 Input-Output tables of Malaysia.
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Appendix 1
Appendix 1
A. Mathematical Statement of dual Dynamic non-linear Quantitative Model
B. Variable definitions and Units (Endogenous Variables are Marked with Asterisks)
- A(t):
-
Total Factor Productivity (Productivity Units)
- *c(t):
-
Capita Consumption of Goods and Services (RM per Person)
- *C(t):
-
Consumption of Goods and Services (RM)
- E Land(t):
-
Emissions of Carbon from Land use (Carbon per Period)
- *E Ind(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 Labour Inputs (Number)
- *M AT(t), M UP(t), M LO(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 of Abatement and Damages (RM)
- T :
-
Time (Decades from 2010 to 2020, 2021–2030,…)
- *T AT(t), T LO(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)
- *Λ(t):
-
Abatement Cost Function (Abatement Costs as Fraction of World Output)
- *μ(t):
-
Emission 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)
- δ k :
-
Rate of Depreciation of Capital (Per Period)
- R(t):
-
Social Time Preference Discount Factor (Per Time Period)
- T max :
-
Length of Estimate Period for Model
- η :
-
Temperature-Forcing Parameter (°C Watts per Meter Squared)
- ϕ :
-
Parameters of the Carbon Cycle (Flows per Period)
- σ :
-
Pure Rate of Social Time Preference (Per Year)
- θ 1 … 2 :
-
Parameters of the Abatement Cost Function
- ζ :
-
Parameters of Climate Equations (Flows per Period)
- r:
-
Country/Region
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Rasiah, R., Ahmed, A., Al-Amin, A.Q. et al. Climate change mitigation: comparative assessment of Malaysian and ASEAN scenarios. Environ Sci Pollut Res 24, 2632–2642 (2017). https://doi.org/10.1007/s11356-016-7985-2
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DOI: https://doi.org/10.1007/s11356-016-7985-2