Greenhouse Gas Pollution Based on Energy use and its Mitigation Potential in the City of Surakarta, Indonesia

https://doi.org/10.22146/ijg.48802

Prabang Setyono(1*), Widhi Himawan(2), Cynthia Permata Sari(3), Totok Gunawan(4), Sigit Heru Murti(5)

(1) Faculty of Mathematics and Natural Science, Depatment of Environmental Science Universitas Sebelas Maret, Indonesia
(2) Faculty of Mathematics and Natural Science, Depatment of Environmental Science Universitas Sebelas Maret, Indonesia
(3) Faculty of Mathematics and Natural Science, Depatment of Environmental Science Universitas Sebelas Maret, Indonesia
(4) Faculty of Geography, Universitas Gadjah Mada, Indonesia
(5) Faculty of Geography, Universitas Gadjah Mada, Indonesia
(*) Corresponding Author

Abstract


Considered as a trigger of climate change, greenhouse gas (GHG) is a global environmental issue. The City of Surakarta in Indonesia consists mainly of urban areas with high intensities of anthropogenic fossil energy consumption and, potentially, GHG emission. It is topographically a basin area and most likely prompts a Thermal Inversion, creating a risk of accumulation and entrapment of air pollutants or GHGs at low altitudes. Vegetation has been reported to mitigate the rate of increase in emissions because it acts as a natural carbon sink. This study aimed to mitigate the GHG emissions from energy consumption in Surakarta and formulate recommendations for control. It commenced with calculating the emission factors based on the IPCC formula and determining the key categories using the Level Assessment approach. It also involved computing the vegetation density according to the NDVI values of the interpretation of Sentinel 2A imagery. The estimation results showed that in 2018, the emission loads from the energy consumption in Surakarta reached 1,217,385.05 (tons of CO2e). The key categories of these emissions were electricity consumption, transportation on highways, and the domestic sector, with transportation on highways being the top priority. These loads have exceeded the local carrying capacity because they create an imbalance between emission and natural GHG sequestration by vegetations.


Keywords


energy; greenhouse gas (GHG); vegetation; Surakarta

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References

Alyuz, U and Alp, K. (2014). Emission Inventory of Primary Air Pollutants in 2010 from Industrial Processes in Turkey, Science of The Total Environment 488-489: 369-381. Retrieved November 15, 2016, from www.elsevier.com/locate/scitotenv.

Angel, S. and S. Sheppard. (2005). The Dynamics of Global Urban Expansion

Black, S.C.C. (2006). Estimation of Grass Photosynthesis Rates in Mixed Grass Prairie Using Field and Remote Sensing Approaches. University of Saskatchewan Saskatoon

Barrera, F and Henriques, C. (2017). Vegetation Cover Change in Growing Urban Agglomeration in Chile. Ecological Indicators 8: 265-273

Gioli, B., Gualtieri, G., Busillo, C., Calastrini, F., Zaldei, A., and P. Toscano. (2015). Improving high- resolution emission inventories with local proxies and urban eddy covariance flux measurement. Atmospheric Environment 115: 246-256.

Gulia, S., Nagendra, S.M.S., Khare, M., and I. Khanna. (2014). Urban Air Quality Management: A Review. Atmospheric Pollution Research 6 (2015): 286-304

Henderson, R.M., Reinert, S.A., Dekhtyar, P., and A. Migdal. (2018). Climate Change in 2018: Implications for Business. Boston: Harvard Business School Publishing

IPCC. (2006). Guidelines for National Greenhouse Gas Inventories. Hayama, Japan: Institute for The Global Environmental Strategies

Keraf, S. (2000). EtikaLingkungan. Jakarta: PenerbitKompasGramedia

Kurniadi, K.G., Bayupati, I.P.A., dan I.D.N.N. Putra. (2016). Aplikasi Perhitungan Gross Primary Production dari Data Penginderaan Jauh. Lontar Komputer 7 (1) : 31-39

L.S.S. (2006). Estimasi Emisi CO2 dari Kebakaran Hutan

NASA Goddard Institute of Space (GISS). (2016). Global Temperature. Retrieved on January 5, 2019, from climate.nasa.gov/vital-signs/global-temperature.

National Geographic. (2018). Climate Change-5 Way It Will Affect You: How to live with it-Crop Changes. Retrieved on January 5, 2019, from https://www.nationalgeographic.com/climate-change/how-to-live-with-it/crops.html.

Sari C.P., Wiryanto., and Setyono, P. (2018). AplikasiPenginderaanJauhUntukMengkajiTutupanVegetasiKawasan Urban Kota Surakarta 2017 Menggunakan Citra Satelit Sentinel 2A. JurnalPengelolaanSumberDayaAlam Dan Lingkungan9 (1): 152-158

Saturi, S and I. Nugraha. (2015). Indonesia TargetkanPenurunanEmisikarbon 29% pada 2030.

MongabaySitusBeritaLingkungan on September 2, 2015.

Shahbazi, H., Taghvaee, S., Hosseini, V., and H. Afhsin. (2016). A GIS-based emission inventory development for Tehran. Urban Climate 17 (2016): 216-229.

Shuai, C., Liyin, S., Jiao, L., Wu, Y., and Y. Tan. (2017). Identifying key impact factors on carbon emission: Evidences on panel and time series data of 125 countries from 1999-2011. Applied Energy 187 (2017): 310-325.

Sunarto, Wiryanto, and W. Himawan. (2016). The estimation of emission from the gateways to Surakarta City, Indonesian using the software of Mobilev 3.0 as the basis for an action plan of emission control. Nusantara Bioscience 8 (2).

Winata, D.K. (2018). PenurunanEmisiKarbonTerbaruDihitung. Media Indonesia on September 7, 2018.

Wright, L. (2005). Car-Free Development. Sustainable Transport: A Sourcebook for Policy-makers in Developing Cities Module 3E. Bonn: GTZ Gmbh



DOI: https://doi.org/10.22146/ijg.48802

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Copyright (c) 2020 Prabang Setyono, Widhi Himawan, Cynthia Permata Sari, Totok Gunawan, Sigit Heru Murti

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Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

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