23STUDENT Investigating the Spatial Heterogeneity of the Impact of Urbanization on Carbon Emissions in China

1. Introduction

In recent years, the world has faced a series of natural environmental problems stemming from the greenhouse effect, which include global warming, glacial retreat, and rising sea levels. The main driver of the greenhouse effect is the massive carbon dioxide emissions. As one of the world's largest CO2 emitters, China has committed to proactively addressing global climate challenges. During the 2015 Paris Climate Conference, the Chinese government pledged to reduce CO2 emissions by 60% to 65% by 2030, aiming to reach a peak around 2030. However, doubt persists regarding the feasibility of achieving these targets, especially during the midst of China’s rapid urbanization.

China is still in the rapid development stage of urbanization, and its strong industrial base has attracted a large rural population. The rapid industrialization has created a significant challenge in balancing urbanization with carbon reduction. A large number of studies have shown that there is a clear correlation between China's urbanization level and carbon emissions.

The impact of urbanization on carbon emissions is reflected in many aspects, mainly including Changes in industrial structure (Zhao & Qian,2022)、Transportation and building (Wang & Ma,2021)、Changes in consumption structure, and Population density (He & Zhang,2019) (Ma & Zheng,2021). However, few studies have thoroughly examined the urbanization impact covering all four aspects. Furthermore, there are significant differences in urbanization levels among provinces in China. Therefore, the focus of this study is to examine the influence of the urbanization development level of different regions on the factors of carbon emission, as well as the correlation, dependence, and spatial variability among different influencing factors. The analysis of this issue will help coordinate the new urban development model and the goals of achieving a low-carbon economy, adhering to the sustainability of urbanization development, and then promoting the higher quality and more efficient development of China's urbanization.

2. Materials and methods

2.1 Data Sources

The dependent variable considered in this research is the total carbon dioxide emissions from 2005,2010,2015 and 2020, following the IPCC (Intergovernmental Panel on Climate Change) guidelines.

Independent variables in this study encompass various aspects of the urban spatial structure in Chinese cities, including the green area rate within built-up areas, urban population density, per capita road area, per capita GDP, per capita vehicle ownership, built-up area road network density, and the GDP of industry, agriculture, and the service industry.

All data come from "The China Statistical Yearbook", "China Science and Technology Statistical Yearbook", "China Environmental Statistical Yearbook", the Wind database, the National Bureau of Statistics, and statistical yearbooks of various regions.

2.2 The geographically weighted regression model

There are two mainstream methods for analyzing the influencing factors of carbon emissions: the OLS (Ordinary Least Square) model and the GWR (Geographically Weighted Regression) model. This study used the urbanization indicators of 716 cities across the country to calculate the OLS numbers of the total carbon dioxide emissions of each city each city's total carbon dioxide emissions and related indicators, and the results showed that there was a significant linear relationship. Subsequently, this study conducted a GWR analysis between the statistical total CO2 emissions and related indicators.

3. Result

3.1 Industrial structure

From 2005 to 2020, the elasticity coefficient of industrial urbanization presents typical spatial characteristics of the eastern and western regions. The coefficient of industrial development level to carbon emissions is both positive and negative across the country, indicating significant regional differences. The coefficients of urbanization in the Northeast areas are negative, indicating that the industrial agglomeration effect in these regions could be more obvious. In contrast, the coefficients in other regions are significantly positive, suggesting that the increase in the level of urbanization has promoted carbon dioxide emissions. This may be because the degree of urbanization and industrialization in the other (especially the southern and central) regions is relatively high, and the development of industry and service industries is relatively concentrated, resulting in higher carbon emissions.

3.2 Transportation

When fitting the GWR model with road network density, a positive correlation between road network density and carbon dioxide emissions is observed in southern cities, as well as in the Beijing-Tianjin-Hebei region, and cities in the Shandong Peninsula. However, cities close to the southeastern coast and those in the western regions show a negative correlation.

3.3 Private car ownership

Private car ownership is a key driver of CO2 emissions. In the GWR model, the greatest impact of per capita private car ownership on carbon emissions from 2005 to 2020 is concentrated in central areas, while the impact weakens in peripheral areas (i.e., cities along the Yangtze River, the Yangtze River Delta, and the Beijing-Tianjin-Hebei region). In contrast, the impact is smaller in the southeast and southwest and in the northeast and northwest regions.

3.4 Population density

Population density is found to be another two-way driver of carbon emissions. The model shows that from 2005 to 2020, population density positively correlated with carbon dioxide emissions in the Northeast regions. The most significant and positive impact was observed in the eastern coastal cities, including Jiangsu, Zhejiang, Shanghai, and Fujian. However, as population density increased over the years, its impact on CO2 emissions diminished in the northernmost part of China, especially the Beijing-Tianjin-Hebei region and its surrounding areas and cities in southwestern China. This highlights the two-way role of population density in different regions, where an increase in population density may transition from a positive to a negative effect.

4. Conclusion

Comprehensive analysis shows that China's urbanization process shows significant differences in different regions, and its impact on carbon emissions is complexly affected by multiple factors. In order to achieve sustainable urban development and effectively respond to climate change, the interaction of multiple factors needs to be comprehensively considered. This requires policymakers, researchers, and society as a whole to work together to address the complex challenges posed by urbanization and carbon emissions.

Keywords

Urbanization, Carbon Emission, Spatial Autocorrelation, Spatial Heterogeneity