| 中国碳排放及影响因素的市域尺度分析 |
| 摘要点击 3561 全文点击 1000 投稿时间：2022-05-30 修订日期：2022-08-08 |
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| 中文关键词 碳排放 贝叶斯信念网络(BBN) 多尺度地理加权回归(MGWR) 驱动因素 中国 |
| 英文关键词 carbon emissions Bayesian belief network(BBN) multiscale geographically weighted regression(MGWR) driving factors China |
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| 中文摘要 |
| 评估区域碳排放及其与社会经济状况的关系对于制定碳减排措施至关重要.以中国339个地级及以上城市（不含新疆部分城市和港澳台地区）为研究对象，探究了非化石能源占比、土地开发度、常住人口城镇化率、第二产业占比、人均GDP和人均建设用地面积对人均CO2排放量的影响.通过构建模拟人均CO2排放量的贝叶斯信念网络，识别各因素对人均CO2排放量的全局影响；采用多尺度地理加权回归模型，分析各因素对人均CO2排放量的局部影响.结果表明：①2020年，中国地级及以上城市人均CO2排放量呈现出由南向北递增，东部沿海向内陆递减的格局.②从全局来看，人均CO2排放量对各因素的敏感性从高到低依次为：人均建设用地面积>人均GDP>常住人口城镇化率>土地开发度>第二产业占比>非化石能源占比.③从局部来看，各因素与人均CO2排放量的空间关系方向与全局关系一致，关系强度上存在空间异质性.④清洁能源、脱碳技术、土地节约集约利用和绿色生活是实现双碳目标的有效途径. |
| 英文摘要 |
| Assessing regional carbon emissions and their relationship with socio-economic conditions is very important for developing strategies for carbon emission reduction. This study explored the impact of the proportion of non-fossil energy, the land development degree, the urbanization rate of permanent residents, the proportion of secondary industry, per capita GDP, and per capita construction land area on per capita CO2 emissions in 339 prefecture-level and above cities in China (excluding some cities in Xinjiang, Hong Kong, Macao, and Taiwan). A Bayesian belief network modeling carbon emissions was constructed to identify the global effects of various factors on per capita CO2 emissions, and multiscale geographically weighted regression was used to analyze their local effects. The results showed that first, per capita CO2 emissions of cities in China increased from the south to the north and decreased from the eastern coast to the inland region. Second, globally, the sensitivity of per capita CO2 emissions to various factors from high to low was in the order of per capita construction land area>per capita GDP>urbanization rate of permanent residents>land development degree>proportion of secondary industry>proportion of non-fossil energy. Third, locally, the direction of the spatial relationship between each factor and per capita CO2 emissions was consistent with the global relationship, and there was spatial heterogeneity in the strength of the relationship. Finally, clean energy, decarbonization technologies, saving and intensive use of land, and green living were effective ways to achieve the dual-carbon goal. |
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