# A Heterogeneity Study of Carbon Emissions Driving Factors in Beijing-Tianjin-Hebei Region, China, Based on PGTWR Model

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## Abstract

**:**

## 1. Introduction

- i.
- Which of the eight model estimation results of the PGWTR model reflects superior overall statistics properties?
- ii.
- Degree and characteristics of spatial and temporal heterogeneity of carbon emissions in the Beijing–Tianjin–Hebei region
- iii.
- How do governments at all levels in the region formulate carbon emission reduction policies?

## 2. Materials and Methods

#### 2.1. PGTWR Model

#### 2.2. Construction of the Empirical Model

_{1}, X

_{2}, X

_{3}, X

_{4}, X

_{5}, and X

_{6}are industrial structure, land area of urban construction, energy intensity, per capita GDP, population size, and foreign investment utilized, respectively. Definition and sources of relevant variables are shown in Table 1. Definition and sources of relevant variables.

#### 2.3. Methods of Carbon Accounting

_{2}is shown in Table 2. Among them, the average low calorific value and conversion coefficient of standard coal are mainly derived from The General Rules for Calculation of Comprehensive Energy Consumption (GB/2589-2008); the content of carbon of each fossil fuel per calorific value and carbon oxidation rate are derived from The Preparation Guide for Provincial Greenhouse Gas List (Office of NDRC: Climate Volume 1041, 2011). Considering that most parts of China use coal-generated power and a few areas are based on hydropower, natural gas power, and wind power, and that CO

_{2}generated by such clean energy as hydropower, wind power, and natural gas power can be omitted, the specific net calorific value consumed by electricity [45], specific net calorific value, carbon content per calorific value, and carbon oxidation rate consumed by electricity are therefore believed to be the same as those consumed by coal.

## 3. Results

#### 3.1. PGTWR Estimation of Results

#### 3.2. Temporal Heterogeneity Analysis of the Regression Coefficient of Influencing Factors of Carbon Emission

#### 3.2.1. Temporal Heterogeneity Analysis of the Influence of Industrial Structure on Carbon Emission

#### 3.2.2. Temporal Heterogeneity Analysis of the Impact of Urbanization Level on Carbon Emission

#### 3.2.3. Temporal Heterogeneity Analysis of the Impact of Energy Intensity on Carbon Emission

#### 3.2.4. Temporal Heterogeneity Analysis of the Impact of Level of Economic Development on Carbon Emission

#### 3.2.5. Temporal Heterogeneity Analysis of the Impact of Population Size on Carbon Emission

#### 3.2.6. Temporal Heterogeneity Analysis of the Impact of Opening-Up on Carbon Emission

#### 3.3. Spatial Heterogeneity Analysis of Regression Coefficient of Influencing Factors of Carbon Emission

## 4. Conclusions and Policy Recommendations

#### 4.1. Conclusions

#### 4.2. Policy Recommendations

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**(

**a**) Temporal heterogeneity of PGTWR regression coefficients of industrial structure; (

**b**) Temporal heterogeneity of PGTWR regression coefficients of urbanization level; (

**c**) Temporal heterogeneity of PGTWR regression coefficients of energy intensity; (

**d**) Temporal heterogeneity of PGTWR regression coefficients of level of economic development; (

**e**) Temporal heterogeneity of PGTWR regression coefficients of population size; (

**f**) Temporal heterogeneity of PGTWR regression coefficients of opening up.

**Figure 5.**(

**a**) Geographical location map of Beijing–Tianjin–Hebei region; (

**b**) Spatial heterogeneity of PGTWR regression coefficient of industrial structure; (

**c**) Spatial heterogeneity of PGTWR regression coefficients of urbanization level; (

**d**) Spatial heterogeneity of PGTWR regression coefficients of energy intensity; (

**e**) Spatial heterogeneity of PGTWR regression coefficients of level of economic development; (

**f**) Spatial heterogeneity of PGTWR regression coefficients of population size; (

**g**) Spatial heterogeneity of PGTWR.

Variable | Variable Meaning | Unit | Data Source |
---|---|---|---|

Carbon emissions | CO_{2} emissions from fossil fuels | 10,000 ton | See Section 2.3 |

Industrial structure | Proportion of output value of secondary industry | % | Local Statistical Yearbook |

Urbanization level | Land area of urban construction | Square kilometers | China City Statistical Yearbook |

Energy intensity | Energy consumption per unit of GDP | Tons of standard/100 million yuan | Local Statistical Yearbook |

Level of economic development | GDP | One hundred million yuan | Chinese Statistical yearbook |

Population size | Population of permanent residents | Ten thousand people | Local Statistical Yearbook |

Opening up | Amount of foreign investment actually utilized | Thousands of dollars | Local Statistical Yearbook |

Energy | Average Low Emission | Standard Coal Coefficient | Carbon Content Per Calorific Value | Carbon Oxidation Rate | CO_{2} Emission Coefficient |
---|---|---|---|---|---|

Coke | 28,435 KJ/kg | 0.7143 kgce/kg | 26.37 tons of carbon/TJ | 0.93% | 1.9003 kg-CO_{2}/kg |

Natural gas of oil field | 38.931 kg/m^{3} | 1.3300 kgce/m^{3} | 15.3 tons of carbon/TJ | 0.99% | 2.1622 kg-CO_{2}/m^{3} |

Raw coal | 20,908 KJ/kg | 0.7143 kgce/kg | 26.37 tons of carbon/TJ | 0.94% | 1.9003 kg-CO_{2}/kg |

Crude oil | 41,816 KJ/kg | 1.4286 kgce/kg | 20.1 tons of carbon/TJ | 0.98% | 3.0202 kg-CO_{2}/kg |

Fuel oil | 41,816 KJ/kg | 1.4286 kgce/kg | 21.1 tons of carbon/TJ | 0.98% | 3.1705 kg-CO_{2}/kg |

petroleum | 43,070 KJ/kg | 1.4714 kgce/kg | 18.9 tons of carbon /TJ | 0.98% | 2.9251 kg-CO_{2}/kg |

kerosene | 43,070 KJ/kg | 1.4714 kgce/kg | 19.5 tons of carbon/TJ | 0.98% | 3.0179 kg-CO_{2}/kg |

diesel | 42,652 KJ/kg | 1.4571 kgce/kg | 20.2 tons of carbon/TJ | 0.98% | 3.0959 kg-CO_{2}/kg |

Liquefied petroleum gas | 50,179 KJ/kg | 1.7143 kgce/kg | 17.2 tons of carbon/TJ | 0.98% | 3.1013 kg-CO_{2}/kg |

**Table 3.**Overall statistical properties of the example model under the two bandwidth dimensions and four kinds of effects.

AICc Criterion (Optimal Spatial Bandwidth = 13, Optimal Temporal Bandwidth = 6) | GCV\RSS Criterion (Optimal Spatial Bandwidth = 7, Optimal Temporal Bandwidth = 6) | |||||||
---|---|---|---|---|---|---|---|---|

Mixing Effect | Individual Fixed Effect | Period Fixed Effect | Individual–Period Fixed Effect | Mixing Effect | Individual Fixed Effect | Period Fixed Effect | Individual–Period Fixed Effect | |

significance ratio of the estimate of local coefficient | 0.8919 | 0.3397 | 0.9679 | 0.4423 | 0.6703 | 0.2885 | 0.6688 | 0.3376 |

Sample size | 78 | 78 | 78 | 78 | 78 | 78 | 78 | 78 |

Degree of freedom | 31 | 32 | 31 | 32 | 15 | 15 | 15 | 15 |

Estimate of Variance of stochastic disturbance | 13.835 | 0.538 | 1.928 | 16.832 | 28.157 | 1.087 | 2.956 | 32.830 |

Value of CV criterion | 428.9 | 17.2 | 59.8 | 538.6 | 422.4 | 16.3 | 44.3 | 492.4 |

Value of GCV criterion | 0.0851 | 0.0034 | 0.0119 | 0.1069 | 0.0838 | 0.0032 | 0.0088 | 0.0977 |

Value of AICc criterion | 448.1 | 192.2 | 291.7 | 461.2 | 501.7 | 245.2 | 323.5 | 511.3 |

Modified goodness of fit | 0.9996 | 0.9814 | 0.9952 | 0.9999 | 0.9998 | 0.9319 | 0.9880 | 0.9999 |

F statistical value | 48,378 | 1035 | 4521 | 3,822,745 | 80,040 | 264 | 1581 | 244,461 |

F probability of statistics | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Modified critical value of probability (α = 0.01, 0.05, 0.1) | 0.0169 0.0844 0.1688 | 0.0171 0.0853 0.1706 | 0.0202 0.1012 0.2024 | 0.0185 0.0924 0.1849 | 0.0149 0.0745 0.1489 | 0.0159 0.0796 0.1592 | 0.0178 0.0888 0.1775 | 0.0166 0.0829 0.1658 |

logarithmic likelihood values | −213.1 | −86.5 | −136.3 | −220.8 | −240.9 | −113.9 | −152.9 | −246.8 |

**Table 4.**PGTWR model’s descriptive statistics of regression coefficient of various explanatory variables.

Variable | Minimum | Maximum | Average | Upper Quartile | Lower Quartile | Quartile Range | Standard Deviation |
---|---|---|---|---|---|---|---|

X1 | 1.07 | 1.90 | 1.51 | 1.38 | 1.62 | 0.23 | 0.18 |

X2 | 0.76 | 1.12 | 0.90 | 0.84 | 0.94 | 0.10 | 0.09 |

X3 | 1.65 | 2.47 | 1.90 | 1.78 | 1.98 | 0.19 | 0.16 |

X4 | 0.35 | 0.62 | 0.50 | 0.43 | 0.55 | 0.12 | 0.07 |

X5 | −0.81 | −0.42 | −0.63 | −0.71 | −0.52 | 0.19 | 0.11 |

X6 | 0.08 | 0.15 | 0.11 | 0.09 | 0.13 | 0.04 | 0.02 |

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**MDPI and ACS Style**

Lou, T.; Ma, J.; Liu, Y.; Yu, L.; Guo, Z.; He, Y.
A Heterogeneity Study of Carbon Emissions Driving Factors in Beijing-Tianjin-Hebei Region, China, Based on PGTWR Model. *Int. J. Environ. Res. Public Health* **2022**, *19*, 6644.
https://doi.org/10.3390/ijerph19116644

**AMA Style**

Lou T, Ma J, Liu Y, Yu L, Guo Z, He Y.
A Heterogeneity Study of Carbon Emissions Driving Factors in Beijing-Tianjin-Hebei Region, China, Based on PGTWR Model. *International Journal of Environmental Research and Public Health*. 2022; 19(11):6644.
https://doi.org/10.3390/ijerph19116644

**Chicago/Turabian Style**

Lou, Ting, Jianhui Ma, Yu Liu, Lei Yu, Zhaopeng Guo, and Yan He.
2022. "A Heterogeneity Study of Carbon Emissions Driving Factors in Beijing-Tianjin-Hebei Region, China, Based on PGTWR Model" *International Journal of Environmental Research and Public Health* 19, no. 11: 6644.
https://doi.org/10.3390/ijerph19116644