23STUDENT Spatial distribution of the effect of per capita road area on carbon emissions

Introduction

Cities are the key to reducing carbon dioxide (CO2) emissions. Municipal facilities are the foundation of urban modernization, and their energy conservation and emission reduction are of great significance to the sustainable development of cities and the realization of the "dual-carbon" goal. Using the geographically weighted regression model, carbon emission data in 2005, 2010, 2015, and 2020 were collected as dependent variables and different municipal facilities were regarded as independent variables. The spatial distribution map of the impact of the corresponding municipal facility level on carbon emissions was obtained with the help of ArcGIS tools. Then, the results are compared with horizontal factors and vertical time dimensions, and the types of different facilities that affect the results are divided. Then, the reasons for the results are comprehensively analyzed. It is found that not all levels of municipal facilities can affect carbon emissions, and there is apparent regularity in the spatial distribution of carbon emissions. The research has guiding significance for the emission reduction of municipal facilities and designers' design.

In 2020, at the general debate of the 75th session of the United Nations General Assembly, China pledged to the international community the goal of carbon peaking and carbon neutrality. The "dual carbon" goals were subsequently included in the "14th Five-Year Plan" series of plans. Cities are the main contributors to carbon emissions, and China has experienced unprecedented urbanization, with the proportion of urban population in the total population rising from 10.64% in 1949 to 64.72% in 2021 (Source: China Statistical Yearbook). Municipal facilities are the foundation and important part of urban modernization. With the continuous development of urbanization, residents' demand for municipal facilities will inevitably continue to rise. Therefore, energy conservation and emission reduction of municipal facilities is of great significance to the sustainable development of cities and the realization of the "dual carbon" goal (Zhang 2012).

Nowadays, the academic community has studied the carbon emission level of a single municipal facility in more depth. Cao Zi et al. (2016) studied the relationship between urbanization and carbon dioxide emissions in China from 1979 to 2013, and pointed out the geographical differences in their distribution. Shota Tamura et al. (2018) quantitatively assessed that high population density would reduce carbon emissions and infrastructure costs, and the impact of different scenarios varied greatly. In terms of carbon emission accounting for municipal facilities, Zhang(2012) established a carbon emission distribution model for municipal infrastructure operation systems, calculated the carbon emissions of municipal infrastructure operations from 2001 to 2010 using Suzhou as an example, and predicted carbon emissions from 2011 to 2015.

However, there are few studies on the spatial differentiation of the impact of municipal facilities on carbon emissions nationwide. China has a vast territory, with different economic levels, humanistic characteristics, and living habits in different places; At the same time, after China entered the 21st century, various fields have developed rapidly, and the level of municipal facilities will be different at different stages of social development. Therefore, it is particularly important to study the spatial distribution of carbon emissions impacts of municipal facilities at different times and in different regions.

Ordinary least square (OLS) are often used to determine quantitative relationships that depend on each other between two or more variables, and the model usually has two main functions: explanation and prediction. Geographically weighted regression (GWR) models can build local models for each observation, making them more detailed and more interpretive than the global OLS model. In general, GWR models can also reveal the spatial heterogeneity of the influence of independent variables on dependent variables and can provide more accurate local spatial analysis results. OLS and GWR models are widely used in research on carbon emissions: Xing Meng et al. (2023) analyzed the global drivers, local causes and indirect impacts of carbon emissions in 187 cities in China. Xiao Zhou et al. (2022) analyzed the impact of built environment factors on carbon emissions in road traffic.

Although studies have dealt with the influencing factors of carbon emissions and spatial heterogeneity, there are still gaps in the research on the comprehensive evaluation of municipal facilities. In addition, few studies still evaluate GWR results from the perspective of agglomeration. In this study, different levels of municipal facilities were studied as the subjects. First, the carbon emission and municipal facilities data from 2005, 2010, 2015 and 2020 were collected. Then, ArcGIS tools are used to verify whether the relationship between municipal facilities on carbon emissions is valid and spatially heterogeneous. Finally, GWR models are applied over the four-year period.

We find that the significant impact of China's municipal facilities on CO2 emissions is generally concentrated in the central and western regions and northeast regions, and the relationship between the two in the relatively rapidly developing southeast region is relatively weak. Of the 13 municipal facility projects studied, 12 showed a clustered distribution, indicating obvious spatial agglomeration of plots with the same numerical interval. At the same time, the impact of different facility projects on carbon emissions also shows different changing trends in different regions, and they can be generally divided into two categories: regular and irregular; the regular is manifested as a continuous upward trend, falling or stable coefficient value, and irregular is manifested as continuous fluctuation. Municipal facilities in a given region can have an enormous impact on carbon emissions in certain circumstances, such as the maximum R2 value per capita road area reaching 0.76 in 2015, although this figure was only 0.1 in 2005.

The agglomeration effect of neighboring regions and provinces may also be an essential factor affecting GWR results. At the same time, the differences and correlations between different provinces can also promote the precise formulation and implementation of energy conservation and emission reduction policies.