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Determination with Gene Expression Programming of the Relationship Between Socio-Economic Variables and Greenhouse Gas Emissions in Turkey

Year 2022, Volume: 24 Issue: 42, 81 - 96, 27.06.2022

Derya Şencan Erkan Dikmen

Abstract

One of the most important indicators of economic development is environmental quality. One of the most important sources of environmental pollution and climate change is greenhouse gas emissions. In this work, a new approach based on Gene Expression Programming (GEP) was used to forecast greenhouse gas (GHG) emissions depending on energy consumption, economic development (GDP), and population. The reliability of the GEP model was determined using several statistical indicators. In the relationship between energy consumption-GDP- population and GHG emissions, R2, MAPE, and RMSE values were found as 0.99337, 0.06987, and 7.1355, respectively. Sensitivity analysis seen that energy consumption have the highest effect on greenhouse gas emissions. The results obtained, it is showing that Gene Expression Programming can be successfully used to model greenhouse gas emissions.

Keywords

Gene expression programming greenhouse gas emissions energy consumption economic development population

References

  • Acheampong, A.O., and Boateng, E.B. (2019). Modelling Carbon Emission Intensity: Application Of Artificial Neural Network. Journal of Cleaner Production, 225, 833-856. http://dx.doi.org/10.1016/j.jclepro.2019.03.352 (2019).
  • Ahmadi, M,H., Jashnani, H., Chau, K.W., Kumar, R., and Rosen, M.A. (2019). Carbon Dioxide Emissions Prediction Of Five Middle Eastern Countries Using Artificial Neural Networks. Energy Sources. Part A: Recovery. Utilization. and Environmental Effects, 1-13. http://dx.doi.org/10.1080/15567036.2019.1679914
  • Amarante, J.C.A., Besarria, C.d.N., Souza, H.G.d., and dos Anjos Junior, O.R. (2021). The Relationship Between Economic Growth, Renewable and Nonrenewable Energy Use And CO2 Emissions: Empirical Evidences For Brazil. Greenhouse Gas Sci Technol., 11, 411–431. http://dx.doi.org/10.1002/ghg.2054
  • Antanasijević, D., Pocajt, V., Ristić, M., and Perić-Grujić, A. (2015). Modeling Of Energy Consumption and Related GHG (Greenhouse Gas) Intensity and Emissions In Europe Using General Regression Neural Networks. Energy, 84, 816-824. http://dx.doi.org/10.1016/j.energy.2015.03.060
  • Antanasijević, D.Z., Ristić M.Đ., Perić-Grujić, A.A., and Pocajt, V.V. (2014). Forecasting GHG Emissions Using an Optimized Artificial Neural Network Model Based on Correlation and Principal Component Analysis. International Journal of Greenhouse Gas Control, 20, 244-253. http://dx.doi.org/10.1016/j.ijggc.2013.11.011
  • Ashrafi, K., Shafiepour, M., Ghasemi, L., and Araabi, B. (2012). Prediction Of Climate Change Induced Temperature Rise in Regional Scale Using Neural Network. International Journal of Environmental Research, 6(3), 677-688. https://ijer.ut.ac.ir/article_538_84bdd019d072d1cd9ea97d4dfe4ab49d.pdf
  • Behrang, M.A., Assareh, E., Assari, M.R., and Ghanbarzadeh, A. (2011). Using Bees Algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission. Energy Sources. Part A: Recovery. Utilization. and Environmental Effects, 33(19), 1747-1759. http://dx.doi.org/10.1080/15567036.2010.493920.
  • Dong, K., Hochman, G., Zhang, Y., Sun, R., Li, H., and Liao, H. (2018). CO2 Emissions, Economic and Population Growth, And Renewable Energy: Empirical Evidence Across Regions. Energy Economics, 75, 180-192. http://dx.doi.org/10.1016/j.eneco.2018.08.017
  • Du, Q., Zhou, J., Pan, T., Sun, Q., ve Wu, M., (2019). Relationship Of Carbon Emissions and Economic Growth In China's Construction Industry. Journal of Cleaner Production, 220, 99-109. http://dx.doi.org/10.1016/j.jclepro.2019.02.123
  • Ferreira, C., (2001). Gene Expression Programming: A New Adaptive Algorithm For Solving Problems. Complex Systems, 13 (2), 87–129. https://arxiv.org/abs/cs/0102027
  • GeneXproTools, APS v2 (Limited version), Automatic Problem Solver Software. http://www.gepsoft.com/
  • Liu, X., and Bae, J., (2018). Urbanization And Industrialization Impact Of CO2 Emissions In China. Journal of Cleaner Production, 172, 178-186. http://dx.doi.org/10.1016/j.jclepro.2017.10.156
  • Liu, Y., and Hao, Y., (2018). The Dynamic Links Between CO2 Emissions, Energy Consumption and Economic Development in The Countries Along “the Belt and Road”. Science of the total Environment, 645, 674-683. http://dx.doi.org/10.1016/j.scitotenv.2018.07.062
  • Mardani, A., Liao, H., Nilashi, M., Alrasheedi, M., and Cavallaro, F., (2020). A Multi-Stage Method to Predict Carbon Dioxide Emissions Using Dimensionality Reduction, Clustering, And Machine Learning Techniques. Journal of Cleaner Production, 275, 122942. http://dx.doi.org/10.1016/j.jclepro.2020.122942
  • Marjanović, V., Milovančević, M., and Mladenović, I., (2016). Prediction of GDP Growth Rate Based on Carbon Dioxide (CO2) Emissions. Journal of CO2 Utilization, 16, 212-217. http://dx.doi.org/10.1016/j.jcou.2016.07.009
  • Mishra. S., (2004). Sensitivity Analysis with Correlated Inputs—An Environmental Risk Assessment Example. In Proceedings of the 2004 Crystal Ball User Conference.
  • Ohlan, R., (2015). The Impact of Population Density, Energy Consumption, Economic Growth and Trade Openness on CO2 Emissions in India. Natural Hazards. 79 (2), 1409-1428. https://link.springer.com/article/10.1007/s11069-015-1898-0
  • Ozbek, A., Unsal, M., and Dikec, A., (2013). Estimating Uniaxial Compressive Strength of Rocks Using Genetic Expression Programming. Journal of Rock Mechanics and Geotechnical Engineering, 5 (4), 325-329. http://dx.doi.org/10.1016/j.jrmge.2013.05.006
  • Ozturk, I., and Acaravci, A., (2010). CO2 Emissions, Energy Consumption and Economic Growth in Turkey. Renewable and Sustainable Energy Reviews. 14 (9), 3220-3225. http://dx.doi.org/10.1016/j.rser.2010.07.005
  • Quesada-Rubio, J.M., Villar-Rubio, E., Mondéjar-Jiménez J., and Molina-Moreno, V., (2011). Carbon Dioxide Emissions Vs. Allocation Rights: Spanish Case Analysis. International Journal of Environmental Research, 5 (2), 469–474. https://ijer.ut.ac.ir/article_331_ 794442aa9e35fc45c02ad2ebf959df6e.pdf
  • Radojević, D., Pocajt, V., Popović, I., Perić-Grujić, A., and Ristić, M., (2013). Forecasting Of Greenhouse Gas Emissions in Serbia Using Artificial Neural Networks. Energy Sources. Part A: Recovery. Utilization. and Environmental Effects, 35 (8), 733-740. http://dx.doi.org/10.1080/15567036.2010.514597
  • Salahuddin, M., Alam, K., Ozturk, I., and Sohag, K., (2018). The Effects of Electricity Consumption. Economic Growth, Financial Development and Foreign Direct Investment on CO2 Emissions In Kuwait. Renewable and Sustainable Energy Reviews, 81, 2002-2010. http://dx.doi.org/10.1016/j.rser.2017.06.009
  • Shahbaz, M., Hye, Q.M.A., Tiwari, A.K., and Leitão, N.C., (2013). Economic Growth, Energy Consumption, Financial Development, International Trade and CO2 Emissions in Indonesia. Renewable and Sustainable Energy Reviews, 25, 109-121. http://dx.doi.org/10.1016/j.rser.2013.04.009
  • Shi, A., (2003). The Impact of Population Pressure on Global Carbon Dioxide Emissions. 1975–1996: Evidence from Pooled Cross-Country Data. Ecological economics, 44 (1), 29-42. http://dx.doi.org/10.1016/S0921-8009(02)00223-9
  • Sözen, A., Gülseven, Z., and Arcaklioğlu, E., (2007). Forecasting Based on Sectoral Energy Consumption of GHGs in Turkey and Mitigation Policies. Energy Policy, 35 (12), 6491-6505. http://dx.doi.org/10.1016/j.enpol.2007.08.024
  • Sözen, A., Gülseven, Z., and Arcaklioğlu, E., (2009). Estimation of GHG Emissions in Turkey Using Energy and Economic Indicators. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 31 (13), 1141-1159. http://dx.doi.org/10.1080/15567030802089086
  • Stern, D.I., (2004). The Rise and Fall of The Environmental Kuznets Curve. World development, 32 (8), 1419-1439. http://dx.doi.org/10.1016/j.worlddev.2004.03.004
  • Tamazian, A., Chousa, P.J., and Vadlamannati, K.C., (2009). Does Higher Economic and Financial Development Lead to Environmental Degradation: Evidence from BRIC Countries. Energy policy, 37(1), 246-253. http://dx.doi.org/10.1016/j.enpol.2008.08.025
  • Teodorescu, L., and Sherwood, D., (2008). High Energy Physics Event Selection with Gene Expression Programming. Computer Physics Communications, 178 (6), 409-419. http://dx.doi.org/10.1016/j.cpc.2007.10.003
  • Turkish Statistical Institute, 1984. ‘‘Statistical indicators 1998–2019’’. https://www.tuik.gov.tr/ (10.03.2021).
  • Wu, Y., Tam, V.W., Shuai, C., Shen, L., Zhang, Y. and Liao, S., (2019). Decoupling China's Economic Growth from Carbon Emissions: Empirical Studies From 30 Chinese Provinces (2001–2015). Science of the Total Environment, 656, 576-588. http://dx.doi.org/10.1016/j.scitotenv.2018.11.384
  • Zhang, X.P., and Cheng, X.M., (2009). Energy Consumption, Carbon Emissions, And Economic Growth in China. Ecological Economics, 68 (10), 2706-2712. http://dx.doi.org/10.1016/j.ecolecon.2009.05.011
  • Zhu, Q., and Peng, X., (2012). The Impacts of Population Change On Carbon Emissions In China During 1978–2008. Environmental Impact Assessment Review, 36, 1-8. http://dx.doi.org/10.1016/j.eiar.2012.03.003

Türkiye'de Sosyo-Ekonomik Değişkenler ve Sera Gazı Emisyonları Arasındaki İlişkinin Gen İfade Programı ile Belirlenmesi

Year 2022, Volume: 24 Issue: 42, 81 - 96, 27.06.2022

Derya Şencan Erkan Dikmen

Abstract

Ekonomik kalkınmanın en önemli göstergelerinden biri çevre kalitesidir. Çevre kirliliği ve iklim değişikliğinin en önemli kaynaklarından biri sera gazı emisyonlarıdır. Bu çalışmada, enerji tüketimi, ekonomik kalkınma (GSYİH) ve nüfusa bağlı olarak sera gazı (GHG) emisyonlarını tahmin etmek için Gen İfade Programlamasına (GEP) dayalı yeni bir yaklaşım kullanılmıştır. GEP modelinin güvenilirliği, çeşitli istatistiksel göstergeler kullanılarak belirlenmiştir. Enerji tüketimi-GSYİH-nüfus ve GHG emisyonları arasındaki ilişkide R2, MAPE ve RMSE değerleri sırasıyla 0.99337, 0.06987 ve 7.1355 olarak bulunmuştur. Duyarlılık analizi sonucunda enerji tüketiminin sera gazı emisyonları üzerinde en yüksek etkiye sahip olduğu görülmüştür. Elde edilen sonuçlar, Gen İfade Programlamanın sera gazı emisyonlarını modellemek için başarılı bir şekilde kullanılabileceğini göstermektedir.

Keywords

Gen ifade programı sera gazı emisyonları enerji tüketimi ekonomik kalkınma nüfus

References

  • Acheampong, A.O., and Boateng, E.B. (2019). Modelling Carbon Emission Intensity: Application Of Artificial Neural Network. Journal of Cleaner Production, 225, 833-856. http://dx.doi.org/10.1016/j.jclepro.2019.03.352 (2019).
  • Ahmadi, M,H., Jashnani, H., Chau, K.W., Kumar, R., and Rosen, M.A. (2019). Carbon Dioxide Emissions Prediction Of Five Middle Eastern Countries Using Artificial Neural Networks. Energy Sources. Part A: Recovery. Utilization. and Environmental Effects, 1-13. http://dx.doi.org/10.1080/15567036.2019.1679914
  • Amarante, J.C.A., Besarria, C.d.N., Souza, H.G.d., and dos Anjos Junior, O.R. (2021). The Relationship Between Economic Growth, Renewable and Nonrenewable Energy Use And CO2 Emissions: Empirical Evidences For Brazil. Greenhouse Gas Sci Technol., 11, 411–431. http://dx.doi.org/10.1002/ghg.2054
  • Antanasijević, D., Pocajt, V., Ristić, M., and Perić-Grujić, A. (2015). Modeling Of Energy Consumption and Related GHG (Greenhouse Gas) Intensity and Emissions In Europe Using General Regression Neural Networks. Energy, 84, 816-824. http://dx.doi.org/10.1016/j.energy.2015.03.060
  • Antanasijević, D.Z., Ristić M.Đ., Perić-Grujić, A.A., and Pocajt, V.V. (2014). Forecasting GHG Emissions Using an Optimized Artificial Neural Network Model Based on Correlation and Principal Component Analysis. International Journal of Greenhouse Gas Control, 20, 244-253. http://dx.doi.org/10.1016/j.ijggc.2013.11.011
  • Ashrafi, K., Shafiepour, M., Ghasemi, L., and Araabi, B. (2012). Prediction Of Climate Change Induced Temperature Rise in Regional Scale Using Neural Network. International Journal of Environmental Research, 6(3), 677-688. https://ijer.ut.ac.ir/article_538_84bdd019d072d1cd9ea97d4dfe4ab49d.pdf
  • Behrang, M.A., Assareh, E., Assari, M.R., and Ghanbarzadeh, A. (2011). Using Bees Algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission. Energy Sources. Part A: Recovery. Utilization. and Environmental Effects, 33(19), 1747-1759. http://dx.doi.org/10.1080/15567036.2010.493920.
  • Dong, K., Hochman, G., Zhang, Y., Sun, R., Li, H., and Liao, H. (2018). CO2 Emissions, Economic and Population Growth, And Renewable Energy: Empirical Evidence Across Regions. Energy Economics, 75, 180-192. http://dx.doi.org/10.1016/j.eneco.2018.08.017
  • Du, Q., Zhou, J., Pan, T., Sun, Q., ve Wu, M., (2019). Relationship Of Carbon Emissions and Economic Growth In China's Construction Industry. Journal of Cleaner Production, 220, 99-109. http://dx.doi.org/10.1016/j.jclepro.2019.02.123
  • Ferreira, C., (2001). Gene Expression Programming: A New Adaptive Algorithm For Solving Problems. Complex Systems, 13 (2), 87–129. https://arxiv.org/abs/cs/0102027
  • GeneXproTools, APS v2 (Limited version), Automatic Problem Solver Software. http://www.gepsoft.com/
  • Liu, X., and Bae, J., (2018). Urbanization And Industrialization Impact Of CO2 Emissions In China. Journal of Cleaner Production, 172, 178-186. http://dx.doi.org/10.1016/j.jclepro.2017.10.156
  • Liu, Y., and Hao, Y., (2018). The Dynamic Links Between CO2 Emissions, Energy Consumption and Economic Development in The Countries Along “the Belt and Road”. Science of the total Environment, 645, 674-683. http://dx.doi.org/10.1016/j.scitotenv.2018.07.062
  • Mardani, A., Liao, H., Nilashi, M., Alrasheedi, M., and Cavallaro, F., (2020). A Multi-Stage Method to Predict Carbon Dioxide Emissions Using Dimensionality Reduction, Clustering, And Machine Learning Techniques. Journal of Cleaner Production, 275, 122942. http://dx.doi.org/10.1016/j.jclepro.2020.122942
  • Marjanović, V., Milovančević, M., and Mladenović, I., (2016). Prediction of GDP Growth Rate Based on Carbon Dioxide (CO2) Emissions. Journal of CO2 Utilization, 16, 212-217. http://dx.doi.org/10.1016/j.jcou.2016.07.009
  • Mishra. S., (2004). Sensitivity Analysis with Correlated Inputs—An Environmental Risk Assessment Example. In Proceedings of the 2004 Crystal Ball User Conference.
  • Ohlan, R., (2015). The Impact of Population Density, Energy Consumption, Economic Growth and Trade Openness on CO2 Emissions in India. Natural Hazards. 79 (2), 1409-1428. https://link.springer.com/article/10.1007/s11069-015-1898-0
  • Ozbek, A., Unsal, M., and Dikec, A., (2013). Estimating Uniaxial Compressive Strength of Rocks Using Genetic Expression Programming. Journal of Rock Mechanics and Geotechnical Engineering, 5 (4), 325-329. http://dx.doi.org/10.1016/j.jrmge.2013.05.006
  • Ozturk, I., and Acaravci, A., (2010). CO2 Emissions, Energy Consumption and Economic Growth in Turkey. Renewable and Sustainable Energy Reviews. 14 (9), 3220-3225. http://dx.doi.org/10.1016/j.rser.2010.07.005
  • Quesada-Rubio, J.M., Villar-Rubio, E., Mondéjar-Jiménez J., and Molina-Moreno, V., (2011). Carbon Dioxide Emissions Vs. Allocation Rights: Spanish Case Analysis. International Journal of Environmental Research, 5 (2), 469–474. https://ijer.ut.ac.ir/article_331_ 794442aa9e35fc45c02ad2ebf959df6e.pdf
  • Radojević, D., Pocajt, V., Popović, I., Perić-Grujić, A., and Ristić, M., (2013). Forecasting Of Greenhouse Gas Emissions in Serbia Using Artificial Neural Networks. Energy Sources. Part A: Recovery. Utilization. and Environmental Effects, 35 (8), 733-740. http://dx.doi.org/10.1080/15567036.2010.514597
  • Salahuddin, M., Alam, K., Ozturk, I., and Sohag, K., (2018). The Effects of Electricity Consumption. Economic Growth, Financial Development and Foreign Direct Investment on CO2 Emissions In Kuwait. Renewable and Sustainable Energy Reviews, 81, 2002-2010. http://dx.doi.org/10.1016/j.rser.2017.06.009
  • Shahbaz, M., Hye, Q.M.A., Tiwari, A.K., and Leitão, N.C., (2013). Economic Growth, Energy Consumption, Financial Development, International Trade and CO2 Emissions in Indonesia. Renewable and Sustainable Energy Reviews, 25, 109-121. http://dx.doi.org/10.1016/j.rser.2013.04.009
  • Shi, A., (2003). The Impact of Population Pressure on Global Carbon Dioxide Emissions. 1975–1996: Evidence from Pooled Cross-Country Data. Ecological economics, 44 (1), 29-42. http://dx.doi.org/10.1016/S0921-8009(02)00223-9
  • Sözen, A., Gülseven, Z., and Arcaklioğlu, E., (2007). Forecasting Based on Sectoral Energy Consumption of GHGs in Turkey and Mitigation Policies. Energy Policy, 35 (12), 6491-6505. http://dx.doi.org/10.1016/j.enpol.2007.08.024
  • Sözen, A., Gülseven, Z., and Arcaklioğlu, E., (2009). Estimation of GHG Emissions in Turkey Using Energy and Economic Indicators. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 31 (13), 1141-1159. http://dx.doi.org/10.1080/15567030802089086
  • Stern, D.I., (2004). The Rise and Fall of The Environmental Kuznets Curve. World development, 32 (8), 1419-1439. http://dx.doi.org/10.1016/j.worlddev.2004.03.004
  • Tamazian, A., Chousa, P.J., and Vadlamannati, K.C., (2009). Does Higher Economic and Financial Development Lead to Environmental Degradation: Evidence from BRIC Countries. Energy policy, 37(1), 246-253. http://dx.doi.org/10.1016/j.enpol.2008.08.025
  • Teodorescu, L., and Sherwood, D., (2008). High Energy Physics Event Selection with Gene Expression Programming. Computer Physics Communications, 178 (6), 409-419. http://dx.doi.org/10.1016/j.cpc.2007.10.003
  • Turkish Statistical Institute, 1984. ‘‘Statistical indicators 1998–2019’’. https://www.tuik.gov.tr/ (10.03.2021).
  • Wu, Y., Tam, V.W., Shuai, C., Shen, L., Zhang, Y. and Liao, S., (2019). Decoupling China's Economic Growth from Carbon Emissions: Empirical Studies From 30 Chinese Provinces (2001–2015). Science of the Total Environment, 656, 576-588. http://dx.doi.org/10.1016/j.scitotenv.2018.11.384
  • Zhang, X.P., and Cheng, X.M., (2009). Energy Consumption, Carbon Emissions, And Economic Growth in China. Ecological Economics, 68 (10), 2706-2712. http://dx.doi.org/10.1016/j.ecolecon.2009.05.011
  • Zhu, Q., and Peng, X., (2012). The Impacts of Population Change On Carbon Emissions In China During 1978–2008. Environmental Impact Assessment Review, 36, 1-8. http://dx.doi.org/10.1016/j.eiar.2012.03.003
There are 33 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Derya Şencan 0000-0001-6723-6198

Erkan Dikmen 0000-0002-6804-8612

Early Pub Date June 21, 2022
Publication Date June 27, 2022
Published in Issue Year 2022 Volume: 24 Issue: 42

Cite

APA Şencan, D., & Dikmen, E. (2022). Determination with Gene Expression Programming of the Relationship Between Socio-Economic Variables and Greenhouse Gas Emissions in Turkey. Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi, 24(42), 81-96.

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