# Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method

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^{2}

^{3}

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## Abstract

**:**

## 1. Introduction

_{2}change in manufacturing in Indonesia using the logarithmic mean Divisia index (LMDI) and the results of the study showed that industrial economic activity and industrial energy intensity have the greatest impact in Indonesia. In another study, Zhang et al. [8] investigated the factors influencing carbon emissions in 29 Chinese provinces from 1995 to 2012 based on LMDI, and the results obtained showed that the decrease in the proportion of energy consumption in the secondary and tertiary sectors was an important reason for the decrease in carbon intensity. When using decomposition methods for analysis, it is assumed that the factors influencing carbon emissions are independent of each other; however, a large number of studies have shown that the factors influencing carbon emissions are not completely independent [9]. Unlike general statistical methods, quadratic assignment procedure (QAP) is based on matrix permutation, which does not require the assumption of complete independence of variables, and is more robust; therefore, QAP is used in this study to analyze the factors influencing carbon emissions in the Yellow River Basin.

_{2}, N

_{2}O, CH

_{4}, and fluorinated gases in Iran, which exhibited high accuracy.

## 2. Methodology

#### 2.1. Carbon Emissions Accounting

_{i}is the consumption of fossil fuel i. NCV

_{i}and CC

_{i}are the net caloric value and carbon content of fuel i. The CEADS database is based on an extensive survey and provides a carbon emission factor more in line with the national conditions of China [20]. In order to accurately measure carbon emissions in the Yellow River Basin, the carbon emission factors in this paper are taken from CEADS [21]. O is the oxidation efficiency and it is assumed to be 1.

#### 2.2. Quadratic Assignment Procedure

#### 2.3. Long Short-Term Memory Network

_{t}, the previous moment state h

_{t}

_{−1}of the hidden layer, and the previous moment state c

_{t}

_{−1}of the memory unit. Firstly, the useless information is filtered out through the forgetting gate:

_{t}, W

_{f}, and b

_{f}are the calculated results, weight matrix, and bias term of the forgetting gate, respectively. $\sigma $ denotes the sigmoid activation function.

_{t}, W

_{i}, and b

_{i}are the calculated results, weight matrix, and bias term of the input gate, respectively. $\stackrel{\sim}{C}$ is the intermediate cell state, and W

_{c}and b

_{c}are the corresponding weight matrices and bias terms, respectively. tanh is the activation function and $\circ $ denotes the dot product.

_{t}, W

_{o}, and b

_{o}are the computed results, weight matrix, and bias term of the output gate, respectively.

#### 2.4. Sparrow Search Algorithm

_{i}= [x

_{i}

_{1}, x

_{i}

_{2}, …,x

_{id}] and the fitness of the i sparrow is F

_{i}= f([x

_{i}

_{1}, x

_{i}

_{2}, …, x

_{id}]). The fitness of all n sparrows can be expressed as:

_{max}denotes the maximum number of iterations, α is a random number within 0–1, R

_{2}∈ [0, 1] and ST ∈ [0.5, 1] denote the warning value and safety value, respectively, with the position update strategy determined according to the relationship between them, Q is a random number obeying the standard normal distribution, and L is a 1 × d-dimensional all-1 matrix. In the alert state, the discoverers signal the population to move to the safe area. In the safe state, the discoverers expand the search area. The remaining sparrows are joiners and receive food through the discoverers, whose position is updated as described by the following equation.

_{worst}is the worst position and x

_{p}is the optimal position of the discoverers. A

^{+}= A

^{T}(AA

^{T})

^{−1}, A is a 1 × d-dimensional matrix with elements of 1 or −1. When the i joiner has a low fitness value, it will go to other locations to forage. In the rest of the cases, the joiners forage around the optimal position. There are also 10–20% of vigilantes in the population, whose positions are updated as described in the following equation.

_{i}is the current adaptation of sparrows, f

_{g}is the global best adaptation, f

_{w}is the global worst adaptation, and ε is a very small number to ensure that the denominator is not 0. f

_{i}> f

_{g}signifies that the sparrow is at the edge and has a high probability of being attacked. f

_{i}= f

_{g}, indicates that the sparrow in the middle of the population is aware of the danger and needs to move closer to other sparrows to reduce the risk of being predated.

#### 2.5. SSA-LSTM

^{′}is the normalized data and Y is the original data. Y

_{min}and Y

_{max}are the minimum and maximum values of the original data, respectively.

## 3. Results and Discussion

#### 3.1. Spatial and Temporal Evolution Characteristics of Carbon Emissions in the Yellow River Basin

_{2}. Shanxi province has been the industrial base of China, and heavy industry is the pillar industry of Shanxi; therefore, Shanxi province has been at the forefront of carbon emissions with a large increase. Inner Mongolia is rich in coal resources, resulting in a local economy heavily dependent on energy and a single industrial structure. The province with the lowest carbon emissions is Qinghai, whose carbon emissions in 2019 were less than one-tenth of Shandong’s carbon emissions. At the level of the entire basin, carbon emissions are highest in the middle reaches of the basin, followed by the lower reaches, and the smallest in the upper reaches.

#### 3.2. QAP Analysis Results

#### 3.3. SSA-LSTM Forecasting Results

_{p}is the predicted value of carbon emission and E

_{a}is the actual value of carbon emission.

#### 3.4. Discussion

## 4. Conclusions and Policy Recommendations

#### 4.1. Conclusions

#### 4.2. Policy Implications

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclature and Abbreviations

LMDI | logarithmic mean divisia index | PSO | particle swarm optimization |

QAP | quadratic assignment procedure | BPNN | back propagation neural network |

GA | genetic algorithm | MAE | mean absolute error |

LSTM | long short-term memory | RMSE | root mean squared error |

RNN | recurrent neural network | MAPE | mean absolute percentage error |

SSA | sparrow search algorithm |

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**Figure 6.**Differences between provinces in carbon emission influencing factors. (GS, HN, IM, NX, QH, SAX, SD, and SX are the abbreviations of Gansu, Henan, Inner Mongolia, Ningxia, Qinghai, Shaanxi, Shandong and Shanxi, respectively).

2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | |
---|---|---|---|---|---|---|---|---|---|---|

P | 0.314 * | 0.497 ** | 0.463 ** | 0.179 | 0.238 | 0.395 * | 0.376 * | 0.546 *** | 0.500 ** | 0.360 |

E | 0.013 | −0.089 | −0.144 | −0.118 | 0.017 | 0.109 | 0.085 | 0.083 | 0.088 | 0.127 |

G | 0.370 ** | 0.355 ** | 0.339 ** | 0.328 ** | 0.420 *** | 0.425 *** | 0.409 *** | 0.347 ** | 0.388 ** | 0.215 |

U | −0.009 | 0.133 | 0.150 | 0.091 | 0.053 | −0.003 | 0.015 | 0.032 | 0.011 | 0.116 |

I | −0.314 * | −0.182 | −0.193 | −0.491 * | −0.408 ** | −0.286 * | −0.291 * | −0.194 * | −0.212 | −0.447 ** |

2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
---|---|---|---|---|---|---|---|---|---|---|

P | 0.628 *** | 0.687 *** | 0.565 *** | 0.473 ** | 0.541 *** | 0.427 ** | 0.422 ** | 0.495 *** | 0.395 ** | 0.302 ** |

E | 0.071 | 0.143 | 0.261 * | 0.007 | −0.040 | −0.177 | −0.262 * | −0.187 | −0.065 | −0.076 |

G | 0.041 | 0.117 | 0.139 | 0.270 ** | 0.166 | 0.292 ** | 0.363 ** | 0.239 ** | 0.288 ** | 0.379 ** |

U | 0.219 * | 0.224 * | 0.209 * | 0.221 | 0.205 | 0.291 ** | 0.309 ** | 0.295 * | 0.382 ** | 0.444 ** |

I | −0.243 * | −0.125 | −0.438 * | −0.108 | −0.124 | 0.004 | 0.180 | 0.144 | 0.273 ** | 0.248 ** |

Parameter | Range |
---|---|

Learning rate | (1 × 10^{−3}, 1 × 10^{−2}) |

Number of iterations | (50, 200) |

Number of neurons | (1, 200) |

SSA-LSTM | PSO-LSTM | LSTM | BPNN | |
---|---|---|---|---|

MAE | 30.90 | 49.29 | 57.01 | 126.77 |

RMSE | 36.67 | 59.04 | 65.11 | 112.64 |

MAPE | 0.0099 | 0.0155 | 0.0178 | 0.0370 |

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**MDPI and ACS Style**

Zhao, J.; Kou, L.; Wang, H.; He, X.; Xiong, Z.; Liu, C.; Cui, H.
Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method. *Sustainability* **2022**, *14*, 6153.
https://doi.org/10.3390/su14106153

**AMA Style**

Zhao J, Kou L, Wang H, He X, Xiong Z, Liu C, Cui H.
Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method. *Sustainability*. 2022; 14(10):6153.
https://doi.org/10.3390/su14106153

**Chicago/Turabian Style**

Zhao, Jinjie, Lei Kou, Haitao Wang, Xiaoyu He, Zhihui Xiong, Chaoqiang Liu, and Hao Cui.
2022. "Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method" *Sustainability* 14, no. 10: 6153.
https://doi.org/10.3390/su14106153