Carbon Emission and Economic Growth Model of Beijing Based on Symbolic Regression
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- @Article{wen:2018:pjoes,
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author = "Lei Wen and Qiao Li and Yue Li and Zeyang Ma",
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title = "Carbon Emission and Economic Growth Model of Beijing
Based on Symbolic Regression",
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journal = "Polish Journal of Environmental Studies",
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year = "2018",
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volume = "27",
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number = "1",
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pages = "365--372",
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keywords = "genetic algorithms, genetic programming, carbon
emissions, symbolic regression, EKC curve, M-curve
model, L-curve model",
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ISSN = "1230-1485",
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URL = "https://www.pjoes.com/abstracts/2018/Vol27/No01/39.html",
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URL = "https://www.pjoes.com/pdf/27.1/Pol.J.Environ.Stud.Vol.27.No.1.365-372.pdf",
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DOI = "doi:10.15244/pjoes/74155",
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size = "8 pages",
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abstract = "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 centre, and its low-carbon development by
external concerns. In this paper, the relationship
between economic development and environmental
pollution is analysed by using the symbolic regression
method, which is based on the data of per capita Carbon
Dioxide 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.",
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notes = "Department of Economics and Management, North China
Electric Power University, Baoding, Hebei 071003,
China
39 https://www.pjoes.com/ Pol. J. Environ. Stud.
The articles published in Polish Journal of
Environmental Studies can be downloaded free of charge
only for personal scientific research.",
- }
Genetic Programming entries for
Lei Wen
Qiao Li
Yue Li
Zeyang Ma
Citations