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Spatiotemporal Distribution and Prediction of Carbon Dioxide Emissions in China
DOI: 10.12677/AEP.2023.136162, PDF, HTML, XML, 下载: 164  浏览: 239  科研立项经费支持

Abstract: This article, by reviewing and organizing carbon emission-related data, and based on existing re-search, utilizes the SuperMap spatial interpolation method to create spatial distribution maps and mathematical statistical analysis of carbon dioxide emissions in the spring, summer, autumn, and winter of 31 provinces and cities in China in 2020. It explores the trend of carbon emissions over the past decade and the relationship between carbon emissions and seasonal changes in 2020, It has been found that there are significant regional differences in carbon dioxide emissions among various provinces and cities in China. The eastern coastal region ranks among the top in carbon dioxide emissions throughout the year. Additionally, the overall trend over time is gradually in-creasing from 2011 to 2019, with some provinces and cities experiencing a significant decrease in carbon dioxide emissions from 2019 to 2020. Finally, a grey prediction model was established based on this, and it was found that the overall carbon dioxide emissions in China have decreased over the past five years after 2020, but all slightly higher than the results of 2020.

1. 研究背景

2. 研究现状

3. 研究对象与方法

3.1. 研究对象

3.2. 数据来源

3.3. 研究方法

3.3.1. 文献资料法

3.3.2. 数理统计法

3.3.3. 空间插值法

3.3.4. 数据标准化

${x}_{i,j}^{*}=\frac{{x}_{i,j}-\stackrel{¯}{{x}_{j}}}{{s}_{y}},i\in \left[1,N\right],j\in \left[1,P\right]$

${y}_{j}^{*}=\frac{{y}_{j}-\stackrel{¯}{{y}_{j}}}{{s}_{y}},j\in \left[1,P\right]$

3.3.5. 预测

(1) 灰色预测模型GM(1,1)

${X}^{\left(0\right)}\left({t}_{k}\right)=\left({x}^{\left(0\right)}\left({t}_{1}\right),{x}^{\left(0\right)}\left({t}_{2}\right),\dots ,{x}^{\left(0\right)}\left({t}_{n}\right)\right)$ ，若间距Δ𝑡_𝑘 = 𝑡_𝑘 − 𝑡_(𝑘 − 1) ≠ c，k = 2,3, …, n，则称该序列为非等间距序列。 ${X}^{\left(t\right)}\left({t}_{k}\right)$${X}^{\left(0\right)}\left({t}_{k}\right)$ 的r阶累加序列，其中

${x}^{\left(r\right)}\left({t}_{k}\right)=\left\{\begin{array}{c}{x}^{\left(0\right)}{\left({t}_{1}\right)}^{\infty }k=1\\ {x}^{\left(0\right)}\left({t}_{1}\right)+{\sum }_{j=2}^{k}\frac{{x}^{\left(0\right)}\left({t}_{j}\right)×\Delta {t}_{j}}{{t}_{j}^{1-r}}k=2,3,\dots n\end{array}$

r的取值范围为0 < r ≤ 1 [8] 。

$MAPE=\frac{1}{n}\underset{k=1}{\overset{n}{\sum }}\frac{|{x}^{\left(0\right)}\left(k\right)-{\stackrel{\to }{x}}^{\left(0\right)}\left(k\right)|}{{x}^{\left(0\right)}\left( k \right)}$

Table 1. Prediction model evaluation criteria

(2) 线性回归预测模型

$y={\beta }_{0}+{\beta }_{1}{x}_{1}+{\beta }_{2}{x}_{2}+\dots +{\beta }_{k}{x}_{k}+\epsilon$

$y={\beta }_{0}+{\beta }_{1}{x}_{t1}+{\beta }_{2}{x}_{t2}+\dots +{\beta }_{k}{x}_{tk}+{\epsilon }_{t}$

4. 中国31省市碳排放结果与分析

4.1. 中国31省市碳排放时间分布

Figure 1. Carbon dioxide emissions from 31 provinces and cities

4.2. 中国31省市碳排放空间分布

Figure 2. Spatial distribution of carbon dioxide emissions in 31 provinces and cities

4.3. 中国31省市碳排放预测

Table 2. Prediction model for carbon dioxide emissions in 31 provinces and cities in China from 2011 to 2020

Table 3. Grade comparison inspection result table

Table 4. Model Fitting Results Table

Table 5. Predicted total carbon dioxide emissions from 31 provinces and cities in China from 2021 to 2025

5. 结论与讨论

5.1. 总体趋势

5.2. 区域差异

5.2.1. 空间分布方面

5.2.2. 时间分布方面

5.3. 产业结构调整

5.4. 节能减排政策

NOTES

*通讯作者。

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