Carbon emission and economic growth model of beijing based on symbolic regression

• Autorzy: Wen L. , Li Q. , Li Y. , Ma Z.
• Języki publikacji: EN

Abstrakty

EN

With the continuous improvement of the economy, more and more attention has been paid to environmental problems. Beijing is China’s economic, political, and cultural center, and its low-carbon development by external concerns. In this paper, the relationship between economic development and environmental pollution is analyzed by using the symbolic regression method, which is based on the data of per capita CO₂ emissions, total energy consumption, energy intensity, and per capita GDP in Beijing city during 1980-2015. The study found that the presence of the M-curve model between per capita CO₂ emissions and per capita GDP, total energy consumption, and per capita GDP are in line with the traditional model of the EKC curve, and that the L-curve model exists between the energy intensity and per capita GDP, respectively, with promising performance. Based on our analysis, we present policy suggestions for reducing carbon emissions and developing a low-carbon economy in Beijing.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2018
• Tom: 27
• Numer: 1
• Opis fizyczny: p.365-372,fig.,ref.

Twórcy

autor

Wen L.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei 071003, China

autor

Li Q.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei 071003, China

autor

Li Y.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei 071003, China

autor

Ma Z.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei 071003, China

Bibliografia

• 1. IEA. Progress with Implementing Energy Efficiency Policies in the G8. Internal Energy Agency Paper. 2010.
• 2. CHEN J., CHENG S., SONG M., Wu Y. A carbon emissions reduction index: Integrating the volume and allocation of regional emissions. Applied Energy. 2016.
• 3. WANG Z., YANG L. Delinking indicators on regional industry development and carbon emissions: Beijing- Tianjin-Hebei economic band case. Ecological Indicators. 48, 41-48, 2015.
• 4. WU R., ZHANG J., BAO Y., TONG S. Using a Geographically Weighted Regression Model to Explore the Influencing Factors of CO₂ Emissions from Energy Consumption in the Industrial Sector. Polish Journal of Environmental Studies. 25, (6), 2641, 2016.
• 5. CHEN J., CHENG S., SONG M., WANG J. Interregional differences of coal carbon dioxide emissions in China. Energy Policy. 96, 1, 2016.
• 6. YAN Q., ZHANG Q., ZOU X. Decomposition analysis of carbon dioxide emissions in China's regional thermal electricity generation, 2000-2020. Energy. 112, 788, 2016.
• 7. MARASENI T.N., QU J., YUE B., ZENG J., MAROULIS J. Dynamism of household carbon emissions (HCEs) from rural and urban regions of northern and southern China. Environmental Science & Pollution Research. 23 (20), 14, 2016.
• 8. AHMAD A., ZHAO Y., SHAHBAZ M., BANO S., ZHANG Z., WANG S., LIUA Y. Carbon emissions, energy consumption and economic growth: An aggregate and disaggregate analysis of the Indian economy. Energy Policy. 96, 131, 2016.
• 9. KUZNETS S. Economic Growth and Income Inequality. The American Economic Review. 45 (1), 1, 1955.
• 10. SHAFIK N., BANDYOPADHYAY S. Economic Growth and Environmental Quality: Time Series and Cross-Country Evidence. Policy Research Working Paper. 18 (5), 55, 1992.
• 11. PANAYOTOU T. Demystifying the Environmental Kuznets Curve : Turning a Black Box into a Policy Tool. Environment and Development Economics. 2 (4), 465, 1997.
• 12. GROSSMAN G.M., KRUEGER A.B. Economic Growth and the Environment. The Quarterly Journal of Economics. 57 (1), 85, 1995.
• 13. AL-MULALI U., TANG C.F., OZTURK I. Estimating the Environment Kuznets Curve hypothesis: Evidence from Latin America and the Caribbean countries. Renewable & Sustainable Energy Reviews. 50, 918, 2015.
• 14. ALPER A., ONUR G. Environmental Kuznets curve hypothesis for sub-elements of the carbon emissions in China. Natural Hazards. 82 (2), 1327, 2016.
• 15. HOLTZ-EAKIN D., SELDEN T.M. Stoking the fires? CO₂, emissions and economic growth. Journal of Public Economics. 110 (2), 85, 1995.
• 16. COLE M.A., RAYNER A.J., BATES J.M. The environmental Kuznets curve: an empirical analysis. Environment and Development Economics. 2, 401, 1997.
• 17. STERN D.I., COMMON M.S. Is There an Environmental Kuznets Curve for Sulfur?. Journal of Environmental Economics & Management. 41, 162, 1998.
• 18. ANSUATEGI A., ESCAPA M. Economic growth and greenhouse gas emissions. Ecological Economics. 40 (1), 23, 2002.
• 19. DEHNAVI J. Energy consumption, economic growth, and pollution in selected OPEC countries: testing the environmental Kuznets curve hypothesis. Journal of Academic Research in Economics. 4 (2), 149, 2012.
• 20. ALKHATHLAN K., JAVID M. Energy consumption, carbon emissions and economic growth in Saudi Arabia: An aggregate and disaggregate analysis. Energy Policy. 62 (7), 1525, 2013.
• 21. WANG .P, WU W., ZHU B., WEI Y. Examining the impact factors of energy-related CO₂ emissions using the STIRPAT model in Guangdong Province, China. Applied Energy. 106 (11), 65, 2013.
• 22. O'NEILL B.C., LIDDLE B., JIANG L., SMITH K.R., PACHAURI S., DALTON M., FUCHS R. Demographic change and carbon dioxide emissions. Lancet. 380 (9837), 157, 2012.
• 23. XU J., WANG J., WEI Q., WANG Y. Symbolic Regression Equations for Calculating Daily Reference Evapotranspiration with the Same Input to Hargreaves-Samani in Arid China. Water Resources Management. 30 (6), 2055, 2016.
• 24. SCHMIDT M., LIPSON H. Distilling free-form natural laws from experimental data. Science. 324 (5923), 81, 2009.
• 25. KOTANCHEK M.E., VLADISLAVLEVA E.Y., SMITS G.F. Symbolic Regression Via Genetic Programming as a Discovery Engine: Insights on Outliers and Prototype. 55, 2010.
• 26. YANG G., SUN T., WANG J., LI X. Modeling the nexus between carbon dioxide emissions and economic growth. Energy Policy. 86, 104, 2015.
• 27. SZELĄG B., GAWDZIK J. Application of Selected Methods of Artificial Intelligence to Activated Sludge Settleability Predictions. Polish Journal of Environmental Studies. 25 (4), 1709, 2016.
• 28. O'REILLY U. Genetic Programming II: Automatic Discovery of Reusable Programs. Artificial Life. 1 (4), 439, 2010.
• 29. BATISHCHEVA V., POTAPOV A. Genetic Programming on Program Traces as an Inference Engine for Probabilistic Languages. Lecture Notes in Computer Science. 9205, 14, 2015.
• 30. LINO A., ÁLVARO ROCHA., SIZO A. Virtual teaching and learning environments: Automatic evaluation with symbolic regression. 31 (4), 2061, 2016.
• 31. CLAVERIA O., MONTE E., TORRA S. Quantification of Survey Expectations by Means of Symbolic Regression via Genetic Programming to Estimate Economic Growth in Central and Eastern European Economies. Eastern European Economics. 54 (2), 171, 2016.
• 32. MONTAÑA J.L., ALONSO C.L., BORGES C.E., et al. Model-driven regularization approach to straight line program genetic programming. Expert Systems with Applications. 57, 76, 2016.
• 33. MURARI A., PELUSO E., GELFUSA M., TIRNAUCA C. Symbolic regression via genetic programming for data driven derivation of confinement scaling laws without any assumption on their mathematical form. Plasma Physics & Controlled Fusion. 57 (1), 2015.
• 34. WU C.H., CHOU H.J., SU W.H. Direct transformation of coordinates for GPS positioning using the techniques of genetic programming and symbolic regression. Engineering Applications of Artificial Intelligence. 21 (8), 1347, 2008.
• 35. SOTTO L., FRAN O., PICCOL O.D., MELO V.V.D. Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression. Neurocomputing. 180 (C), 79, 2016.
• 36. CALVETEABA H.I. A new approach for solving linear bilevel problems using genetic algorithms. European Journal of Operational Research. 188 (1), 14, 2008.
• 37. LINO A., ÁLVARO ROCHA., SIZO A. Virtual teaching and learning environments: Automatic evaluation with symbolic regression. 31 (4), 2061, 2016.
• 38. ZELINKA I., OPLATKOVA Z., NOLLE L. Analytic programming symbolic regression by means of arbitrary evolutionary algorithm. Simulation. 6 (9), 1473, 2005.
• 39. JIN Y., SENDHOFF B. Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies. IEEE Transactions on Systems Man & Cybernetics Part C. 38 (3), 397, 2008.
• 40. SMITS G.F., KOTANCHEK M. Pareto-Front Exploitation in Symbolic Regression. Genetic Programming Theory and Practice II. Springer US. 8, 283, 2005.
• 41. IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories: volume II: energy. Japan: Institute for Global Environmental Strategies. 2006

Identyfikatory

DOI

10.15244/pjoes/74155