# Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Study Area

## 4. Methods

#### 4.1. US-SBM Model

#### 4.2. Global Spatial Autocorrelation Analysis

_{i}and CEE

_{j}represent the values of space i and j, respectively; w

_{ij}represents the spatial weight; $\overline{\mathrm{CEE}}$ and CEE is the mean value and S

^{2}is the sample variance. The value of Moran’s I is within the interval [–1, 1]. If the value is close to 1, it is a spatially positive correlation; otherwise, it is a spatially negative correlation. When the value is close to 0, it means that CEE is randomly distributed and has no spatial autocorrelation. In addition, both Z and p values are used for the statistical analysis. Moreover, the heat analysis tool in ArcGIS 10.2 software is used to analyze the Getis-Ord Gi* index, and the natural breakpoint method is used to explore the hot and cold spots of CEE in Chinese cities [47].

#### 4.3. Spatial Markov Chains

_{ijt}represents the probability that the CEE type of a city changes from year t to year t + 1. The probability matrix relation of adjacent years can be expressed as:

_{a}is the spatial lag value of the region a; Y

_{b}represents the attribute value of the region; and W

_{ab}represents the spatial weight matrix, that is, the spatial relationship between region a and region b.

#### 4.4. Evaluating the Urbanization Level

_{+ij}represents the positive indicator; y

_{−ij}represents the negative indicator; x

_{ij}represents the value of indicator j in city i; and x

_{ijmax}and x

_{ijmin}indicate the maximum and minimum value of the indicator j, respectively. Then, the entropy weight calculation is used to determine the importance of each indicator:

_{i}refers to the development index of comprehensive urbanization (CU

_{i}), population urbanization (PU

_{i}), economic urbanization (EU

_{i}), spatial urbanization (SPU

_{i}), and social urbanization (SOU

_{i}).

#### 4.5. Geographically and Temporally Weighted Regression (GTWR)

_{i}is the CEE of city i; x

_{ij}represents the value of city i for the k-th explanatory variable of CEE, CU, PU, EU, SPU, and SOU are used as core explanatory variables. $lon{g}_{i}$ and $la{t}_{i}$ are the longitude and latitude coordinates of city i, and t

_{i}is the year t. ${\beta}_{0}\left(lon{g}_{i},la{t}_{i},{t}_{i}\right)$ is the intercept term, ${\beta}_{j}\left(lon{g}_{i},la{t}_{i},{t}_{i}\right)$ is the estimated coefficient of explanatory variable; ${\epsilon}_{i}\stackrel{iid}{~}N\left(0,{\sigma}^{2}\right)$ is a random error term. The estimated value of each regression coefficient of city i is:

^{T}is the transpose of the matrix; Y is the matrix of observed values.

_{i}-square spatial weight function, and d

^{s}is the space-time distance between observation point i and k. In Formula (6), Bandwidth B will affect the establishment of the space-time weight matrix. Considering the density of the data observation point distribution, adaptive bandwidth is adopted in this study, and the established criterion is AICc criterion [55].

## 5. Results and Discussion

#### 5.1. CEE in Chinese 283 Cities

#### 5.2. Spatial Static Agglomeration Characteristics of CEE

#### 5.3. Spatial Dynamic Agglomeration Characteristics of CEE

#### 5.4. Analysis on the Impact of Multidimensional Urbanization on CEE in China

#### 5.4.1. Results Tests

^{2}and adjusted R

^{2}reflect the fitting degree of the model, and the sum of squares of residual errors (RSS) reflects the size of the model accuracy. AICc information can be used as another important criterion to evaluate the fit quality of the model: the smaller the value is, the higher the model accuracy is [56]. Table 6 shows that the fitting degree of the GTWR model in two time periods is 0.7398 and 0.6078, respectively, which is greatly improved compared with OLS and GWR. AICc values are reduced to 2769.9600 and 4016.9100, respectively, indicating significant differences between the models. RSS decreased from 1023.7115 and 1372.6223 of OLS to 441.7600 and 776.8870 of GTWR, indicating that the accuracy of the GTWR model is relatively high. We took the mean values of the regression coefficients of the two stages respectively, and it can be seen from the quanta table of the regression coefficients (Table 7) that the parameter estimates of the respective variables differ greatly, with positive and negative values, and the intensity changes obviously, indicating that the influence intensity of CEE in Chinese cities is obviously non-stationary in both time and space.

#### 5.4.2. Spatial and Temporal Distribution of Urbanization’s Impact on CEE

- Spatio-temporal heterogeneity of CU’s influence on CEE.

- 2.
- Spatio-temporal heterogeneity of PU’s influence on CEE.

- 3.
- Spatio-temporal heterogeneity of EU’s influence on CEE.

- 4.
- Spatio-temporal heterogeneity of SPU’s influence on CEE.

- 5.
- Spatio-temporal heterogeneity of SOU’s influence on CEE.

## 6. Conclusions and Policy Implications

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Spatial distribution pattern of carbon emission efficiency at the city level in China, in (

**a**) 2005, (

**b**) 2010, and (

**c**) 2017.

**Figure 4.**Getis-Ord Gi* maps for carbon emission efficiency at the city level in China, in (

**a**) 2005, (

**b**) 2010, and (

**c**) 2017.

**Figure 5.**Spatial and temporal distribution of the influence of multidimensional urbanization on CEE from 2005 to 2017.

Variables | Indicators | Description |
---|---|---|

Input variables | Capital invested | Gross investment in fixed assets |

Labor force | Number of employees at the end of the year | |

Energy consumption | Urban Standard coal consumption | |

Desirable output | GDP | Gross regional domestic product |

Undesirable output | Carbon emissions | Amount of carbon emissions |

System | Subsystem | Specific Indicators | Unit | W1 | W2 |
---|---|---|---|---|---|

Comprehensive Urbanization (CU) | Population Urbanization (PU) | Proportion of urban population | % | 0.287 | 0.045 |

Population density | 10,000 people/km^{2} | 0.590 | 0.096 | ||

Proportion of persons employed by secondary and tertiary industries | % | 0.123 | 0.032 | ||

Economic Urbanization (EU) | Per capita GDP in urban areas | Yuan | 0.426 | 0.049 | |

Ratio of urban and rural per capita disposable income (-) | % | 0.235 | 0.084 | ||

Proportion of the added value of the second industry to GDP (-) | % | 0.225 | 0.056 | ||

proportion of the added value of the tertiary industry to GDP | % | 0.114 | 0.024 | ||

Spatial Urbanization (SPU) | Proportion of urban area to total area | % | 0.130 | 0.032 | |

Urban built-up area per capita | km^{2}/10,000 people | 0.319 | 0.129 | ||

Road area per capital | km^{2}/10,000 people | 0.246 | 0.094 | ||

Urban unit area Net Primary Productivity | gC/(m^{2}·a) | 0.304 | 0.078 | ||

Social Urbanization (SOU) | Per capita total retail sales of consumer goods in urban areas | Yuan/person | 0.304 | 0.043 | |

Number of hospital beds per 10,000 persons | One/10,000 people | 0.246 | 0.078 | ||

Number of college students per 10,000 persons | One/10,000 people | 0.319 | 0.114 | ||

Number of buses per 10,000 persons | One/10,000 people | 0.130 | 0.047 |

Year | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Moran’s I | 0.0046 | 0.0618 | 0.0498 | 0.0610 | 0.0422 | 0.0570 | 0.0277 | 0.0615 | 0.0268 | 0.0596 | 0.0524 | 0.1107 | 0.0998 |

Z-value | 0.4454 | 3.3510 | 2.7266 | 3.1469 | 2.1777 | 2.9714 | 1.5660 | 3.2965 | 1.4433 | 3.2353 | 2.7697 | 5.7000 | 5.1590 |

p-value | 0.287 | 0.002 | 0.005 | 0.009 | 0.026 | 0.007 | 0.069 | 0.005 | 0.076 | 0.004 | 0.009 | 0.001 | 0.002 |

t\t + 1 | n | 1 | 2 | 3 | 4 | |
---|---|---|---|---|---|---|

2005–2010 | 1 | 386 | 0.3601 | 0.2694 | 0.2124 | 0.1580 |

2 | 336 | 0.1042 | 0.6101 | 0.2083 | 0.0774 | |

3 | 345 | 0.0058 | 0.2377 | 0.5623 | 0.1942 | |

4 | 348 | 0.0000 | 0.0316 | 0.2155 | 0.7529 | |

2011–2017 | 1 | 395 | 0.7722 | 0.2152 | 0.0127 | 0.0000 |

2 | 428 | 0.2009 | 0.4883 | 0.2757 | 0.0350 | |

3 | 435 | 0.0460 | 0.2092 | 0.4920 | 0.2529 | |

4 | 440 | 0.0182 | 0.0705 | 0.2136 | 0.6977 |

**Table 5.**Spatial Markov probability transition matrix of the CEE type in Chinese cities from 2005 to 2017.

Neighbor Type | t\t + 1 | 2005–2010 | 2011–2017 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

n | 1 | 2 | 3 | 4 | n | 1 | 2 | 3 | 4 | ||

1 | 1 | 274 | 0.2336 | 0.2701 | 0.2883 | 0.2080 | 177 | 0.8192 | 0.1695 | 0.0113 | 0.0000 |

2 | 73 | 0.0548 | 0.4795 | 0.2055 | 0.2603 | 98 | 0.2653 | 0.5000 | 0.1837 | 0.0510 | |

3 | 23 | 0.0435 | 0.0435 | 0.6522 | 0.2609 | 71 | 0.0423 | 0.2535 | 0.5493 | 0.1549 | |

4 | 19 | 0.0000 | 0.1579 | 0.2105 | 0.6316 | 37 | 0.0000 | 0.0270 | 0.2432 | 0.7297 | |

2 | 1 | 52 | 0.6346 | 0.2692 | 0.0385 | 0.0577 | 91 | 0.7363 | 0.2527 | 0.0110 | 0.0000 |

2 | 112 | 0.1161 | 0.6607 | 0.2054 | 0.0179 | 123 | 0.1545 | 0.5122 | 0.2927 | 0.0407 | |

3 | 104 | 0.0096 | 0.3173 | 0.4712 | 0.2019 | 102 | 0.0490 | 0.1863 | 0.5098 | 0.2549 | |

4 | 66 | 0.0000 | 0.0606 | 0.1970 | 0.7424 | 94 | 0.0106 | 0.0638 | 0.1809 | 0.7447 | |

3 | 1 | 26 | 0.6154 | 0.3077 | 0.0385 | 0.0385 | 61 | 0.7213 | 0.2623 | 0.0164 | 0.0000 |

2 | 85 | 0.1294 | 0.6118 | 0.2353 | 0.0235 | 99 | 0.1919 | 0.4747 | 0.3030 | 0.0303 | |

3 | 118 | 0.0000 | 0.2288 | 0.6017 | 0.1695 | 140 | 0.0429 | 0.1500 | 0.5071 | 0.3000 | |

4 | 120 | 0.0000 | 0.0167 | 0.2500 | 0.7333 | 144 | 0.0139 | 0.0625 | 0.2569 | 0.6667 | |

4 | 1 | 34 | 0.7647 | 0.2353 | 0.0000 | 0.0000 | 66 | 0.7424 | 0.2424 | 0.0152 | 0.0000 |

2 | 66 | 0.1061 | 0.6667 | 0.1818 | 0.0455 | 108 | 0.2037 | 0.4630 | 0.3148 | 0.0185 | |

3 | 100 | 0.0000 | 0.2100 | 0.5900 | 0.2000 | 122 | 0.0492 | 0.2705 | 0.4262 | 0.2541 | |

4 | 143 | 0.0000 | 0.0140 | 0.1958 | 0.7902 | 165 | 0.0303 | 0.0909 | 0.1879 | 0.6909 |

Model | 2005–2010 | 2011–2017 | ||||||
---|---|---|---|---|---|---|---|---|

R^{2} | Adjusted R^{2} | RSS | AICc | R^{2} | Adjusted R^{2} | RSS | AICc | |

OLS | 0.3967 | 0.3950 | 1023.7115 | 3971.4995 | 0.3068 | 0.3050 | 1372.6223 | 4907.0476 |

GWR | 0.6690 | 0.6680 | 562.0600 | 3035.4700 | 0.4279 | 0.4264 | 1133.4200 | 4622.0500 |

GTWR | 0.7398 | 0.7391 | 441.7600 | 2769.9600 | 0.6078 | 0.6068 | 776.8870 | 4016.9100 |

Quantile | 2005–2010 | 2011–2017 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

Minimum | Lower Quartile | Median | Upper Quartile | Maximum | Minimum | Lower Quartile | Median | Upper Quartile | Maximum | |

Intercept | −1.269 | −0.905 | −0.666 | −0.497 | −0.354 | −0.959 | −0.814 | −0.685 | −0.616 | −0.577 |

CU | −8.981 | −7.049 | −5.729 | −4.689 | −3.209 | −7.231 | −6.675 | −4.602 | −3.232 | −2.056 |

PU | −0.825 | 0.142 | 1.104 | 1.600 | 5.997 | 0.892 | 1.145 | 1.704 | 2.269 | 2.857 |

EU | 2.067 | 5.124 | 5.970 | 7.659 | 9.619 | 2.792 | 3.835 | 4.713 | 5.947 | 7.176 |

SPU | −8.813 | −5.061 | 2.773 | 5.461 | 7.739 | −0.370 | −0.056 | 1.019 | 2.451 | 3.066 |

SOU | −1.332 | 1.037 | 3.561 | 4.496 | 8.596 | 0.620 | 1.084 | 1.375 | 1.864 | 2.777 |

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## Share and Cite

**MDPI and ACS Style**

Zhou, Z.; Cao, L.; Zhao, K.; Li, D.; Ding, C.
Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China. *Int. J. Environ. Res. Public Health* **2021**, *18*, 12712.
https://doi.org/10.3390/ijerph182312712

**AMA Style**

Zhou Z, Cao L, Zhao K, Li D, Ding C.
Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China. *International Journal of Environmental Research and Public Health*. 2021; 18(23):12712.
https://doi.org/10.3390/ijerph182312712

**Chicago/Turabian Style**

Zhou, Zhanhang, Linjian Cao, Kuokuo Zhao, Dongliang Li, and Ci Ding.
2021. "Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China" *International Journal of Environmental Research and Public Health* 18, no. 23: 12712.
https://doi.org/10.3390/ijerph182312712