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

Abstract

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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Notes

  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.

References

  1. Agreement P (2015) United Nations framework convention on climate change. Paris, France. Available at https://unfccc.int/sites/default/files/english_paris_agreement.pdf. Accessed 16 Nov 2017

  2. Barrett S (1994) Self-enforcing international environmental agreements. Oxf Econ Pap 46:878–894. https://doi.org/10.1093/oep/46.Supplement_1.878

    Article  Google Scholar 

  3. Begum RA, Abdullah SMS, Sarkar MSK (2017) Time series patterns and relationship of energy consumption and CO 2 emissions in Malaysia. Asian J Water Environ Pollut 14(2):41–49. https://doi.org/10.3233/AJW-170014

    Article  Google Scholar 

  4. Bovenberg AL, Goulder LH (2002) Environmental taxation and regulation. In Handbook of public economics 3:1471–1545. Elsevier

  5. CarbonBrief (2017). The social cost of carbon. 14 February 2017, The CarbonBrief. Available at https://www.carbonbrief.org/qa-social-cost-carbon

  6. Carraro C, Siniscalco D (1993) Strategies for the international protection of the environment. J Public Econ 52:309–328. https://doi.org/10.1016/0047-2727(93)90037-T

    Article  Google Scholar 

  7. Cline, WR. (1992). The economics of global warming. Washington, DC: Institute for International Economics, USA

  8. DOS (2010). Input-output table of Malaysia 2005: Ministry of Finance, Department of Statistics, Malaysia

  9. DOS (2013a) Malaysia economic statistics (MES)-time series. Malaysia: Department of Statistics, Malaysia

  10. DOS (2013b). Economic report, various issues. Ministry of Finance:Malaysia Department of Statistics, Malaysia

  11. EPA (2014). Climate change indicators in the United states: global greenhouse gas emissions. Available at http://www.epa.gov/climatechange/indicators https://doi.org/10.1016/j.jccase.2014.02.005. Accessed 7 Oct 2015

  12. EPA (2017) The social cost of carbon: estimating the benefits of reducing greenhouse gas emissions. United States Environmental Protection Agency, USA. Available at https://19january2017snapshot.epa.gov/climatechange/social-cost-carbon_.html. Accessed 26 Apr 2018

  13. Fankhauser, S. (1995). Valuing climate change? The economics of the greenhouse. London: EarthScan, https://doi.org/10.1074/jbc.270.47.28457

  14. Gao P, Chen W (2002) Carbon tax and carbon emission. Journal-Tsinghua University 42(10):1335–1338

    Google Scholar 

  15. Gayer, T (2017). The social costs of carbon. Brookings, Tuesday, February 28, 2017. Available at EPA https://www.brookings.edu/testimonies/the-social-costs-of-carbon/, https://doi.org/10.1053/j.sult.2017.08.004

  16. Greenstone M, Kopitsy E, Wolvertony A (2013) Developing a social cost of carbon for US regulatory analysis: a methodology and interpretation. Rev Environ Econ Policy 7(1):23–46. https://doi.org/10.1093/reep/res015

    Article  Google Scholar 

  17. IAWG U (2010). Technical support document: social cost of carbon for regulatory impact analysis under executive order 12866. Interagency working group on social cost of carbon, United States Government, Washington, DC. Available at https://www.epa.gov/sites/production/files/2016-12/documents/scc_tsd_2010.pdf

  18. IPCC (2001). IPCC TAR WG1climate change 2001: the scientific basis, contribution of working group I to the third assessment report of the intergovernmental panel on climate change. London: Cambridge University

  19. IPCC (2007). Climate change 2007: the physical science basis. Fourth assessment report of the IPCC. United Kingdom: Cambridge University Press

  20. IPCC (2014a). Impacts, adaptation, and vulnerability. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York

  21. IPCC (2014b). Climate Change 2014: Mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York

  22. Jia J, Gong Z, Chen C, Jian H, Xie D (2018) Urban carbon dioxide equivalent (CO2e) accounting based on the GPC framework: a case of the underdeveloped city of Nanchang, China. Int J Clim Change Strategies Manage 10(5):812–832. https://doi.org/10.1108/IJCCSM-03-2017-0074

    Article  Google Scholar 

  23. Johnson LT, Hope C (2012) The social cost of carbon in US regulatory impact analyses: an introduction and critique. J Environ Stud Sci 2(3):205–221. https://doi.org/10.1007/s13412-012-0087-7

    Article  Google Scholar 

  24. Kaplow L (2012) Optimal control of externalities in the presence of income taxation. Int Econ Rev 53(2):487–509. https://doi.org/10.1111/j.1468-2354.2012.00689.x

    Article  Google Scholar 

  25. Karl TR, Trenberth KE (2003) Modern global climate change. science 302(5651):1719–1723. https://doi.org/10.1126/science.1090228

    CAS  Article  Google Scholar 

  26. Liski, M and Murto P. (2006). Backstop technology adoption. Helsinki Center of Economic Research, Finland. Available at http://hse-econ.fi/liski/papers/backstop.pdf

  27. Maddison DJ (2003) The amenity value of the climate: the household production function approach. Resour Energy Econ 25(2):155–175. https://doi.org/10.1016/S0928-7655(02)00024-6

    Article  Google Scholar 

  28. Markandya A, Paglialunga E, Costantini V, Sforna G (2017) Global and regional economic damages from climate change. Environ Sci. https://doi.org/10.1093/acrefore/9780199389414.013.409

  29. McMichael AJ, Woodruff RE, Hales S (2006) Climate change and human health: present and future risks. Lancet 367(9513):859–869. https://doi.org/10.1016/S0140-6736(06)68079-3

    Article  Google Scholar 

  30. Metcalf, G. (2017). Implementing a carbon tax. Resources for the Future, May, pp.17–18, https://doi.org/10.1038/s41598-017-15375-9

  31. MMD (2009). Climate change scenarios for Malaysia Scientific Report 2001–2099: Numerical Weather Prediction Development Section Technical Development Division, Malaysian Meteorological Department Ministry of Science, Technology and Innovation Kuala Lumpur, https://doi.org/10.2478/s11658-009-0005-5

  32. NAHRIM (2006) Final report: study of the impact of climate change on the hydrologic regime and water resources of peninsular Malaysia. Malaysia. https://doi.org/10.2478/s11658-006-0051-1

    Google Scholar 

  33. Nordhaus WD (1991) To slow or not to slow: the economics of the greenhouse effect. Econ J 101(444):920–937

    Article  Google Scholar 

  34. Nordhaus WD (2006) Geography and macroeconomics: new data and new findings. Proc Natl Acad Sci 103(10):3510–3517

    CAS  Article  Google Scholar 

  35. Nordhaus, WD. (2008). A question of balance: economic modeling of global warming. New Haven, CT: Yale University Press

  36. Nordhaus WD (2014) Estimates of the social cost of carbon: concepts and results from the DICE-2013R model and alternative approaches. J Assoc Environ Resour Econ 1(1/2):273–312

    Google Scholar 

  37. Nordhaus WD (2017) Revisiting the social cost of carbon. PNAS 114(7):1518–1523. https://doi.org/10.1073/pnas.1609244114

    CAS  Article  Google Scholar 

  38. Nordhaus, WD and Boyer JG. (2000). Warming the world: economic models of global warming, the MIT press, Cambridge, Massachusetts - London, England

  39. Nordhaus WD, Yang Z (1996) RICE: a regional dynamic general Equilib rium model of optimal climate-change policy. Am Econ Rev 86(4):741–765

    Google Scholar 

  40. NRC (National Research Council) (2016). Assessment of approaches to updating the social cost of carbon: phase 1 Report on a Near-Term Update (Natl Acad Press, Washington, DC), https://doi.org/10.1007/s41109-016-0018-8

  41. Olivier, JGJ, Janssens-Maenhout G, Muntean M and Peters JAHW. (2014). Trends in global CO2 emissions: 2014 report, PBL Netherlands Environmental Assessment Agency, and European Commission’s Joint Research Centre, The Hague, PBL publication number: 1490, ISBN: 978-94-91506-87-1

  42. Pigou A (1932) The economics of welfare. Routledge

  43. Pizer W, Adler M, Aldy J, Anthoff D, Cropper M, Gillingham K et al (2014) Using and improving the social cost of carbon. Science 346(6214):1189–1190. https://doi.org/10.1126/science.1259774

    CAS  Article  Google Scholar 

  44. Popp D (2002) Induced innovation and energy prices. Am Econ Rev 92(1):160–180. https://doi.org/10.1257/000282802760015658

    Article  Google Scholar 

  45. Rasiah R, Ahmed A, Al-Amin AQ, Chenayah S (2017) Climate change mitigation: comparative assessment of Malaysian and ASEAN scenarios. Environ Sci Pollut Res 24(3):2632–2642. https://doi.org/10.1007/s11356-016-7985-2

    CAS  Article  Google Scholar 

  46. Rasiah R, Al-Amin AQ, Ahmed A, Leal Filho W, Calvo E (2016) Climate mitigation roadmap: assessing low carbon scenarios for Malaysia. J Clean Prod 133:272–283. https://doi.org/10.1016/j.jclepro.2016.05.145

    Article  Google Scholar 

  47. Rasiah R, Al-Amin AQ, Chowdhurry AH, Ahmed F, Zhang C (2018) Climate change mitigation projections for ASEAN. J Asia Pac Econ:1–18

  48. Rehdanz K, Maddison DJ (2005) Climate and happiness. Ecological Eco nomics 52(1):111–125. https://doi.org/10.1016/j.ecolecon.2004.06.015

    Article  Google Scholar 

  49. Ricke, K, Drouet, L, Caldeira, K and Tavoni, M. (2018). Country-level social cost of carbon. Nature Climate Change, p.1, https://doi.org/10.1016/bs.aambs.2018.03.001

  50. Sarkar MSK, Begum RA, Sadeka S, Pereira JJ (2018) Current trends and policies of energy consumption and CO2 emission from the largest Asian economies. International J. of Global Warming 14(4):417–439. https://doi.org/10.1504/IJGW.2018.091530

  51. Soderholm P, Sundqvist T (2007) Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies. Renew Energy 32(15):2559–2578. https://doi.org/10.1016/j.renene.2006.12.007

    Article  Google Scholar 

  52. Spackman, M. (2015) Social time discounting: Institutional and analytical perspectives (No. 192). GRI Working Paper. Grantham Research Institute on Climate Change and the Environment, UK, https://doi.org/10.1016/j.jval.2015.09.2769

  53. Stern, NH. (2007). The economics of climate change: the Stern review. Cambridge University press, UK

  54. Tol RSJ (1995) The damage costs of climate change toward more comprehensive calculations. Environ Resour Econ 5(4):353–374

    Article  Google Scholar 

  55. Tol RSJ (2002) Estimates of the damage costs of climate change - part 1: benchmark estimates. Environ Resour Econ 21(1):47–73. https://doi.org/10.1023/A:1014500930521

    Article  Google Scholar 

  56. Tol RSJ (2008) The social cost of carbon: trends, outliers, and catastrophes. Economics 2(25):1–22

    Google Scholar 

  57. Tol, RSJ. (2011). The social cost of carbon. Working paper no. 377, Economic and Social Research Institute, Dublin, Ireland

  58. UNFCCC (2015). United Nations Framework Convention on Climate Change. INDCs as submitted by parties. Available on (accessed 02.05.2018). http://www4.unfccc.int/submissions/INDC/Submission%20Pages/submissions.aspx, https://doi.org/10.12865/CHSJ.41.03.14

  59. Unit EP (2010). Tenth Malaysia Plan 2011–2015. Putrajaya: Prime Minister Department

  60. Yu CF, van Sark WGJHM, Alsema EA (2011) Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects RID C-5009-2009. Renew Sustain Energy Rev 15(1):324–337. https://doi.org/10.1016/j.rser.2010.09.001

    Article  Google Scholar 

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Acknowledgements

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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APPENDIX

APPENDIX

Mathematical statement of the study model

Equations in the model

$$ W=\sum \limits_{t=1}^{T\max }u\left[c(t),l(t)\right]R(t) $$
(1)
$$ R(t)={\left(1+\rho \right)}^{-t} $$
(2)
$$ U\left[c(t),L(t)\right]=l(t)\Big[c{(t)}^{1-\alpha }/\left(1-\alpha \right) $$
(3)
$$ Q(t)=\varOmega (t)\left[1-\varLambda (t)\right]A(t)K{(t)}^{\gamma }L{(t)}^{1-\gamma } $$
(4)
$$ \varOmega (t)=1/\left[1+{\varPi}_1{T}_{AT}(t)+{\varPi}_2{T}_{AT}{(t)}^2\right] $$
(5)
$$ \varLambda (t)=\pi (t){\theta}_1(t)\mu {(t)}^{\theta_2} $$
(6)
$$ Q(t)=C(t)+I(t) $$
(7)
$$ C(t)=C(t)/L(t) $$
(8)
$$ K(t)=I(t)+\left(1-{\delta}_k\right)K\left(t-1\right) $$
(9)
$$ {E}_{Ind}(t)=\sigma (t)\left[1-\mu (t)\right]K{(t)}^{\lambda }L{(t)}^{1-\lambda } $$
(10)
$$ CCum\le \sum \limits_{t=0}^{T\max }{E}_{Ind(t)} $$
(11)
$$ E(t)={E}_{Ind}(t)+{E}_{Land}(t) $$
(12)
$$ {M}_{AT}(t)=E(t)+{\phi}_7{M}_{AT}\left(t-1\right)+{\phi}_{11}{M}_{UP}\left(t-1\right) $$
(13)
$$ {M}_{UP}(t)={\phi}_{11}{M}_{AT}\left(t-1\right)+{\phi}_{11}{M}_{UP}\left(t-1\right)+{\phi}_{11}{M}_{LO}\left(t-1\right) $$
(14)
$$ {M}_{LO}(t)={\phi}_{12}{M}_{UP}\left(t-1\right)+{\phi}_{12}{M}_{LO}\left(t-1\right) $$
(15)
$$ F(t)=\eta \left\{{\log}_2\right[{M}_{AT}/{M}_{AT}\left(1900\right]\Big\}+{F}_{EX}(t) $$
(16)
$$ {T}_{AT}={T}_{AT}\left(t-1\right)+{\zeta}_1\left\{F(t)-{\zeta}_2{T}_{AT}\left(t-1\right)-{\zeta}_3{T}_{AT}\left(t-1\right){T}_{LO}\left(t-1\right)\right\} $$
(17)
$$ {T}_{LO}(t)={T}_{LO}\left(t-1\right)+{\zeta}_4\left\{{T}_{AT}\left(t-1\right)-{T}_{LO}\left(t-1\right)\right\} $$
(18)
$$ \prod (t)=\varphi {(t)}^{1-{\theta}_2} $$
(19)

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). https://doi.org/10.1007/s11356-018-3947-1

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Keywords

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