Abstract
With economic development and the acceleration of urbanization, China’s energy demand has gradually increased and brought a lot of energy-related CO2 emissions. Energy-related CO2 emissions are affected by a variety of factors. Quantifying the correlation between energy-related CO2 and driving factors and constructing the driving factor system are conducive to predict the future energy-related CO2 emissions and analyze the impact of driving factors. In this paper, the improved grey relational analysis (IGRA) was proposed to screen the influencing factors of energy-related CO2 emissions considering the sample difference, and the factor analysis (FA) was used to reduce dimensionality of the influencing factors. Then, a carbon dioxide emission forecasting model based on the bacterial foraging optimization algorithm (BFO) and the least square support vector machine (LSSVM) was proposed. Empirical analysis results of Hebei show that the LSSVM optimized BFO significantly improves the accuracy of energy-related CO2 emissions forecasting, and IGRA-FA-BFOLSSVM model is significantly better than BP, PSOBP, SVM, and LSSVM models. The mean absolute percentage error (MAPE) of the proposed model is 0.374%. The forecasting results of the supplementary case show that the model has better generalization ability. In addition, education and technological progress have proven to be important drivers of energy-related CO2 emissions. Simultaneously, the research results can also offer more breakthrough points for policy makers to control carbon emissions.
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Abbreviations
- GDP:
-
Gross domestic product
- FAI:
-
Fixed asset investment
- LFR:
-
Local finance revenue
- TIETC:
-
Total import and export trade of customs
- SADR:
-
Savings deposit of residents
- PCDIP:
-
Per capita disposable income of the population
- PCCEP:
-
Per capita consumption expenditures of the population
- ENCOE:
-
Engle coefficient
- TOTP:
-
Total population
- RAEM:
-
Rate of employment
- AVWE:
-
Average wage of employees
- CPI:
-
Consumer price index
- NGIHE:
-
Number of general institutions of higher education
- NSHEI:
-
Number of students in higher education institutions
- EDUEX:
-
Education expenditure
- RDEX:
-
R&D expenditure
- NRDP:
-
Number of R & D personnel
- TECMT:
-
Technical market turnover
- NPAU:
-
Number of patent authorizations
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Sun, W., Zhang, J. Analysis influence factors and forecast energy-related CO2 emissions: evidence from Hebei. Environ Monit Assess 192, 665 (2020). https://doi.org/10.1007/s10661-020-08617-3
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DOI: https://doi.org/10.1007/s10661-020-08617-3