Decomposing drivers of transportation energy consumption and carbon dioxide emissions for the Philippines: the case of developing countries

Abstract

Global CO₂ emissions increased by 57.9% from 1990 to 2014, of which 21% is known to be from the transportation sector. In line with policy development, driving forces to energy consumption and emissions may be determined using decomposition analysis techniques. However, the detail of information required to perform such studies for the transportation sector in developing countries can be challenging. An attempt was made in this study to formulate a decomposition analysis framework considering data availability and limitation in developing countries. Furthermore, a suggestion of adjusting transport activity data using average oil price was proposed. An illustrative case study in the Philippines revealed that the most significant driver was transport activity, followed by energy intensity, and then population growth, which was both similar and contrary to all previous studies performed in developed and rapidly urbanizing countries, which pointed out to transport activity as the primary contributing force. For the Philippines, transport activity was an inhibiting force, whereas energy intensity was the primary contributing factor. The difference could be explained by the differences in mode shares and quality of life between countries. Looking at private vehicle ownership data, it is observed that growth rates are higher in the rural, than in the urban centers. Deriving from the findings, developing a comprehensive public transport plan is recommend for future growth areas, expansion and modernization of public transport services in the city, and strategic deployment of transport policies.

Keywords

transportation / LMDI / decomposition / developing country / emissions

References

[1] International Energy Agency. CO₂ Emissions from Fuel Combustion: Highlights, 2016 ed. Paris, France, 2016

[2] Kaya Y. Impact of carbon dioxide emission control on GNP growth: interpretation of proposed scenarios. In: Proceedings of the IPCC Energy and Industry Subgroup, Response Strategies Working Group. Paris, France, 1990

[3] Lakshmanan T R, Han X. Factors underlying transportation CO₂ emissions in the USA: a decomposition analysis. Transportation Research Part D, Transport and Environment, 1997, 2(1): 1–15

[4] Kwon T. Decomposition of factors determining the trend of CO₂ emissions from car travel in Great Britain (1970‒2000). Ecological Economics, 2005, 53(2): 261–275

[5] Papagiannaki K, Leontarakis G, Diakoulaki D. Decomposition analysis of CO₂ emissions from road transport in EU15. In: Proceedings of the 10th International Conference on Environmental Science and Technology. Kos Island, Greece, 2007

[6] Jian J. A factor decomposition analysis of transportation energy consumption and related policy implications. International Association of Traffic and Safety Sciences, 2015, 38: 142–148

[7] Guo B, Geng Y, Franke B, Hao H, Liu Y, Chiu A. Uncovering China’s transport CO₂ emission patterns at the regional level. Energy Policy, 2014, 74: 134–146

[8] Wang W W, Zhang M, Zhou M. Using LMDI method to analyze transport sector CO₂ emissions in China. Energy, 2011, 36(10): 5909–5915

[9] Wang Y, Hayashi Y, Kato H, Liu C. Decomposition analysis of CO₂ emissions increase from the passenger transport sector in Shanghai, China. International Journal of Urban Sciences, 2011, 15(2): 121–136

[10] Zhang M, Li H, Zhou M, Mu H. Decomposition analysis of energy consumption in Chinese transportation sector. Applied Energy, 2011, 88(6): 2279–2285

[11] Ding J, Jin F, Li Y, Wang J. Analysis of transportation carbon emissions and its potential for reduction in China. Chinese Journal of Population Resources and Environment, 2013, 11(1): 17–25

[12] Wu H, Xu W. Cargo transport energy consumption factors analysis: based on LMDI decomposition technique. In: Proceedings of 2014 International Conference on Environment Systems Science and Engineering. IERI Procedia, 2014, 9: 168–175

[13] Fan R, Lei Y. Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing. Transportation Research Part D, Transport and Environment, 2016, 42: 135–145

[14] Sumabat A K, Lopez N S, Yu K D, Hao H, Li R, Geng Y, Chiu A S F. Decomposition analysis of Philippine CO₂ emissions from fuel combustion and electricity generation. Applied Energy, 2016, 164: 795–804

[15] Mousavi B, Lopez N S A, Biona J B M, Chiu A S F, Blesl M. Driving forces of Iran’s CO₂ emissions from energy consumption: an LMDI decomposition approach. Applied Energy, 2017, 206: 804–814

[16] Asian Development Bank. Asian Development Outlook 2017 Update. 2017–12–28, https://www.adb.org/data/southeast-asia-economy

[17] Ang B W, Zhang F Q, Choi K. Factorizing changes in energy and environmental indicators through decomposition. Energy, 1998, 23(6): 489–495

[18] Ang B W. The LMDI approach to decomposition analysis: a practical guide. Energy Policy, 2005, 33(7): 867–871

[19] Ang B W, Liu N. Energy decomposition analysis: IEA model versus other methods. Energy Policy, 2007, 35(3): 1426–1432

[20] Ang B W. LMDI decomposition approach: a guide for implementation. Energy Policy, 2015, 86: 233–238

[21] Papagiannaki K, Diakoulaki D. Decomposition analysis of CO₂ emissions from passenger cars: the cases of Greece and Denmark. Energy Policy, 2009, 37(8): 3259–3267

[22] Bureau of Transportation Statistics. Transportation’s contribution to the economy. 2016, https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/Transportation_Economic_Trends_2016_Chapter_2.pdf

[23] Philippine Statistics Authority. 2015 Philippine Statistical Yearbook. 2015, https://psa.gov.ph/sites/default/files/2015%20PSY%20PDF_0.pdf

[24] Philippine Department of Energy. Power statistics. 2015–10–08, https://www.doe.gov.ph/doe_files/pdf/02_Energy_Statistics/power_statistics_2014.pdf

[25] International Energy Agency. 2017 IEA statistics report. 2017–12–28, https://www.iea.org/statistics/statisticssearch/report/?year=2014&country=PHILIPPINE&product=Balances

[26] Intergovernmental Panel on Climate Change (IPCC). 2006 guidelines for national greenhouse gas inventories: mobile combustion. 2006, https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_3_Ch3_Mobile_Combustion.pdf

[27] Intergovernmental Panel on Climate Change (IPCC). 2006 guidelines for national greenhouse gas inventories: stationary combustion. 2006, https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_2_Ch2_Stationary_Combustion.pdf

[28] Environmental Protection Agency (EPA). Emission factors for greenhouse gas inventories. 2014, https://www.epa.gov/sites/production/files/2018-03/documents/emission-factors_mar_2018_0.pdf

[29] DiPippo R. Geothermal Power Plants: Principles, Applications, Case Studies and Environmental Impact, 3rd ed. Oxford: Elsevier, 2012

[30] Association of the German Petroleum Industry (MWV). Average annual OPEC crude oil price from 1960 to 2018 (in U.S. Dollars per Barrel). 2018–02–27, www.statista.com/statistics/262858/change-in-opec-crude-oil-prices-since-1960/

[31] Démurger S. Infrastructure development and economic growth: an explanation for regional disparities in China? Journal of Comparative Economics, 2001, 29(1): 95–117

[32] Deakin E. Sustainable development and sustainable transportation: strategies for economic prosperity, environmental quality, and equity. 2001, https://escholarship.org/uc/item/0m1047xc

[33] Metro Manila Urban Transportation Integration Study (MMUTIS). Metro Manila urban transportation integration study: technical report No. 4. —Transportation demand characteristics based on person trip survey. Japan International Cooperation Agency (JICA), 1999

[34] ALMEC Corporation. Oriental Consultants Global Co., Ltd. The project for capacity development on transportation planning and database management in the Republic of the Philippines, MMUTIS update and capacity enhancement project (MUCEP): transportation demand characteristics based on MUCEP person trip survey. Japan International Cooperation Agency (JICA), 2015

[35] Philippine Statistics Authority. Gross regional domestic product. 2018–03–14, https://psa.gov.ph/regional-accounts/grdp/data-and-charts

[36] Land Transportation Office. Registered motor vehicles by classification and region. 2018–03–20, https://www.gov.ph/data/dataset/registered-motor-vehicles-classification-and-region

[37] Bloomberg. Philippine GDP growth of 6.9% beats all estimates. 2018–03–14, https://www.bloomberg.com/news/articles/2017-11-16/philippine-gdp-growth-exceeds-6-for-a-ninth-consecutive-quarter

[38] Department of Finance, Philippines. Tax reform for acceleration and inclusion. 2018–03–14, http://www.dof.gov.ph/taxreform/index.php/vehicle

Acknowledgments

This work was financially supported by the Commission on Higher Education of the Philippines, through the Faculty Development Grant.

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature