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
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.
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.
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.
This study has analyzed and compared between the results from the scenarios based on several indicators of Malaysia.
However, Nordhaus (2017) also found a substantial increase in the estimated SCC over time using IPCC scenario.
References
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
Barrett S (1994) Self-enforcing international environmental agreements. Oxf Econ Pap 46:878–894. https://doi.org/10.1093/oep/46.Supplement_1.878
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
Bovenberg AL, Goulder LH (2002) Environmental taxation and regulation. In Handbook of public economics 3:1471–1545. Elsevier
CarbonBrief (2017). The social cost of carbon. 14 February 2017, The CarbonBrief. Available at https://www.carbonbrief.org/qa-social-cost-carbon
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
Cline, WR. (1992). The economics of global warming. Washington, DC: Institute for International Economics, USA
DOS (2010). Input-output table of Malaysia 2005: Ministry of Finance, Department of Statistics, Malaysia
DOS (2013a) Malaysia economic statistics (MES)-time series. Malaysia: Department of Statistics, Malaysia
DOS (2013b). Economic report, various issues. Ministry of Finance:Malaysia Department of Statistics, Malaysia
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
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
Fankhauser, S. (1995). Valuing climate change? The economics of the greenhouse. London: EarthScan, https://doi.org/10.1074/jbc.270.47.28457
Gao P, Chen W (2002) Carbon tax and carbon emission. Journal-Tsinghua University 42(10):1335–1338
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
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
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
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
IPCC (2007). Climate change 2007: the physical science basis. Fourth assessment report of the IPCC. United Kingdom: Cambridge University Press
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
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
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
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
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
Karl TR, Trenberth KE (2003) Modern global climate change. science 302(5651):1719–1723. https://doi.org/10.1126/science.1090228
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
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
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
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
Metcalf, G. (2017). Implementing a carbon tax. Resources for the Future, May, pp.17–18, https://doi.org/10.1038/s41598-017-15375-9
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
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
Nordhaus WD (1991) To slow or not to slow: the economics of the greenhouse effect. Econ J 101(444):920–937
Nordhaus WD (2006) Geography and macroeconomics: new data and new findings. Proc Natl Acad Sci 103(10):3510–3517
Nordhaus, WD. (2008). A question of balance: economic modeling of global warming. New Haven, CT: Yale University Press
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
Nordhaus WD (2017) Revisiting the social cost of carbon. PNAS 114(7):1518–1523. https://doi.org/10.1073/pnas.1609244114
Nordhaus, WD and Boyer JG. (2000). Warming the world: economic models of global warming, the MIT press, Cambridge, Massachusetts - London, England
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
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
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
Pigou A (1932) The economics of welfare. Routledge
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
Popp D (2002) Induced innovation and energy prices. Am Econ Rev 92(1):160–180. https://doi.org/10.1257/000282802760015658
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
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
Rasiah R, Al-Amin AQ, Chowdhurry AH, Ahmed F, Zhang C (2018) Climate change mitigation projections for ASEAN. J Asia Pac Econ:1–18
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
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
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
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
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
Stern, NH. (2007). The economics of climate change: the Stern review. Cambridge University press, UK
Tol RSJ (1995) The damage costs of climate change toward more comprehensive calculations. Environ Resour Econ 5(4):353–374
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
Tol RSJ (2008) The social cost of carbon: trends, outliers, and catastrophes. Economics 2(25):1–22
Tol, RSJ. (2011). The social cost of carbon. Working paper no. 377, Economic and Social Research Institute, Dublin, Ireland
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
Unit EP (2010). Tenth Malaysia Plan 2011–2015. Putrajaya: Prime Minister Department
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
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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APPENDIX
APPENDIX
Mathematical statement of the study model
Equations in the model
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)
*Λ(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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DOI: https://doi.org/10.1007/s11356-018-3947-1