Prediction and Analysis of CO2 Emission in Chongqing for the Protection of Environment and Public Health
Abstract
:1. Introduction
2. Data Analysis and Mathematical Method for Prediction
2.1. Data Analysis
2.1.1. Energy Intensity and CO2 Intensity
2.1.2. Energy Consumption in Chongqing
2.1.3. Calculation and Analysis of CO2 Emission
2.2. Description of Input and Output
2.3. Support Vector Regression Method for Time Series Prediction
3. Simulation and Prediction
3.1. Determination of the Inputs and Outputs
3.2. Selection of Parameters Used in SVR
3.3. Evaluation of Model Accuracy
3.4. Prediction Result with Analysis
4. Analysis
5. Conclusions
- The price leverage can be applied to control the total amount of coal consumption by government. Meanwhile, the supplement of non-fossil fuels should be adequate, and the consumption of high-quality energy need to be encouraged, such as renewable energy, natural gas and electricity. Due to the geographical feature, the storage of natural gas in Chongqing is abundant. In the contrary, the supply systems of natural gas in Chongqing is under a lagging development relatively. Therefore, the fundamental constructions for these renewable energies are necessary.
- By enhancing the management of the energy consumption of high-energy consumption industry, the CO2 emissions need to be limited during industrial production process. Especially for the top five carbon-intensive and high-consumption of coal enterprise, which are thermal power, iron steel, non-ferrous metals, chemical industry and construction materials. The efficiency and cleaning-level of energy consumption should be improved to control the consumption of coal.
- The applications of high-quality and clean coal need to be promoted by government. By the cost reduction of clean coal, the promotion of application for clean coal is a primary strategy to achieve the goal of low CO2 emissions. Meanwhile, through eliminating production capacity with low-efficiency, the problem of excess production capacity will be receded, which will lead to the relief of energy pressure.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Year | Energy Intensity | Coal (%) | Coke (%) | Gasoline (%) | Diesel Oil (%) | Kerosene, Crude Oil, Fuel Oil (%) | Natural Gas (%) |
---|---|---|---|---|---|---|---|
1997 | 1.67 | 77.26 | 4.63 | 1.91 | 2.20 | 0.45 | 13.54 |
1998 | 1.70 | 74.75 | 6.78 | 3.22 | 3.08 | 0.45 | 11.72 |
1999 | 1.76 | 74.66 | 4.98 | 3.17 | 3.05 | 0.50 | 13.63 |
2000 | 1.63 | 71.93 | 6.01 | 3.31 | 3.06 | 0.55 | 15.14 |
2001 | 1.37 | 72.39 | 6.87 | 3.50 | 3.47 | 0.68 | 13.09 |
2002 | 1.32 | 74.11 | 6.26 | 3.27 | 3.41 | 0.60 | 12.35 |
2003 | 0.88 | 70.64 | 6.82 | 3.62 | 3.98 | 0.65 | 14.29 |
2004 | 1.13 | 68.05 | 6.10 | 3.69 | 8.10 | 0.83 | 13.24 |
2005 | 1.05 | 65.53 | 10.37 | 3.14 | 7.22 | 0.75 | 12.99 |
2006 | 1.01 | 67.35 | 8.07 | 3.20 | 6.86 | 1.07 | 13.45 |
2007 | 0.93 | 67.35 | 7.26 | 2.94 | 7.89 | 1.18 | 13.38 |
2008 | 0.92 | 70.78 | 6.25 | 2.67 | 7.06 | 1.06 | 12.18 |
2009 | 0.87 | 72.39 | 5.69 | 2.34 | 6.99 | 1.07 | 11.53 |
2010 | 0.80 | 72.20 | 4.60 | 2.39 | 7.79 | 1.17 | 11.86 |
2011 | 0.72 | 71.24 | 5.09 | 2.96 | 8.04 | 1.26 | 11.40 |
2012 | 0.63 | 67.35 | 6.84 | 2.97 | 8.34 | 1.31 | 13.19 |
2013 | 0.49 | 65.61 | 3.22 | 3.77 | 10.53 | 1.65 | 15.22 |
2014 | 0.47 | 64.43 | 4.61 | 3.95 | 9.18 | 1.66 | 16.17 |
2015 | 0.44 | 62.74 | 3.81 | 4.27 | 10.40 | 1.71 | 17.07 |
Energy Type | g | C | MAPE (%) |
---|---|---|---|
Gasoline | 1505.70 | 4553.73 | 7.16 |
Diesel oil | 3723.09 | 955.36 | 5.70 |
Natural gas | 5594.36 | 1102.49 | 3.76 |
Crude oil, kerosene, fuel oil | 2543.05 | 119.60 | 3.99 |
Time Series Type | p | D |
---|---|---|
Gasoline | 0.86 | 0.39 |
Diesel oil | 0.86 | 0.48 |
Natural gas | 1 | 0.22 |
Crude oil, kerosene, fuel oil | 1 | 0.23 |
Grade | p | D |
---|---|---|
Excellent | ≥0.95 | ≤0.35 |
Qualified | 0.80 ≤ p < 0.95 | 0.35 < C ≤ 0.50 |
Barely qualified | 0.70 ≤ p < 0.80 | 0.50 < C ≤ 0.65 |
Unqualified | <0.70 | >0.65 |
Year | Gasoline (Mt) | Diesel Oil (Mt) | Natural Gas (billion m3) | Kerosene, Crude Oil, Fuel Oil (Mt) |
---|---|---|---|---|
2016 | 2.18 | 5.37 | 98.33 | 0.87 |
2017 | 2.36 | 5.64 | 108.48 | 0.91 |
2018 | 2.51 | 6.08 | 117.25 | 0.95 |
2019 | 2.63 | 6.48 | 125.23 | 0.97 |
2020 | 2.721 | 6.82 | 131.72 | 0.99 |
Year | Gasoline (Mt) | Diesel Oil (Mt) | Natural Gas (billion m3) | Kerosene, Crude Oil, Fuel Oil (Mt) | Sum (Mt) |
---|---|---|---|---|---|
2020 | 7.95 | 21.13 | 22.21 | 2.98 | 54.27 |
Year | Energy Intensity | Coal and Coke Consumption (Mt) | Coal and Coke CO2 Emissions (Mt) |
---|---|---|---|
2015 | 0.44 | 60.47 | 122.64 |
2020 | 0.44 | 107.92 | 205.08 |
Year | CO2 Emission Intensity | Total CO2 Emissions (Mt) | The CO2 Emissions from Coal and Coke (Mt) |
---|---|---|---|
2015 | 1.025 | 161.05 | 122.64 |
2020 | 0.823 | 205.76 | 151.48 |
The Average Annual Decline in Energy Intensity (%) | Energy Intensity in 2020 | Energy Consumption in 2020 (Mtce 1) | Coal Consumption in 2020 (Mt) | The CO2 Emissions from Coal and Coke (Mt) |
---|---|---|---|---|
1% | 0.42 | 104.61 | 100.37 | 190.74 |
2% | 0.40 | 99.43 | 93.12 | 176.96 |
3% | 0.38 | 94.46 | 86.17 | 163.74 |
4% | 0.36 | 89.69 | 79.49 | 151.05 |
5% | 0.32 | 85.12 | 73.08 | 138.88 |
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Yang, S.; Wang, Y.; Ao, W.; Bai, Y.; Li, C. Prediction and Analysis of CO2 Emission in Chongqing for the Protection of Environment and Public Health. Int. J. Environ. Res. Public Health 2018, 15, 530. https://doi.org/10.3390/ijerph15030530
Yang S, Wang Y, Ao W, Bai Y, Li C. Prediction and Analysis of CO2 Emission in Chongqing for the Protection of Environment and Public Health. International Journal of Environmental Research and Public Health. 2018; 15(3):530. https://doi.org/10.3390/ijerph15030530
Chicago/Turabian StyleYang, Shuai, Yu Wang, Wengang Ao, Yun Bai, and Chuan Li. 2018. "Prediction and Analysis of CO2 Emission in Chongqing for the Protection of Environment and Public Health" International Journal of Environmental Research and Public Health 15, no. 3: 530. https://doi.org/10.3390/ijerph15030530