中国と炭素排出ピーク国における炭素排出影響要因の比較分析

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Useful for comparative analysis methods and empirical findings on carbon emission drivers across countries.

🏢実務担当者:May inform corporate strategy in China regarding emission reduction targets and energy transition.

🏛政策担当者:Offers insights for setting emission reduction policies and understanding structural differences between China and peak countries.

中国2030年前实现碳达峰的任务艰巨而紧迫。对已实现碳达峰国家的碳排放影响因素进行比较研究，对于中国的碳减排具有重要意义。本研究基于1990—2023年的数据，选取中国和13个碳达峰国家，采用Mann-Kendall趋势检验法判定各国碳排放达峰状态，构建扩展的STIRPAT模型分析中国及达峰国家碳排放的影响因素，比较研究中国现状与达峰国家峰值年份特征值的差异，最后提出针对性的碳减排建议。结果表明：①对于达峰国家，能源消费总量是碳排放的核心驱动因素，而人均GDP、第二产业占比、能源结构和能源强度对碳排放具有正向驱动作用。人口规模、城镇化率、研发投入和森林覆盖率则具有抑制作用。②中国与大多数达峰国家的影响因素存在显著差异。除能源强度与中国碳排放呈显著负相关外，其余影响因素均呈正相关。人口规模、城镇化率、能源强度、研发投入和森林覆盖率与大多数达峰国家的作用方向相反。③与达峰国家峰值年份相比，中国2023年的现状显示，城镇化率（64.75%）尚未赶上达峰国家（67.97%—96.85%），人均GDP（12614美元）远低于达峰国家（19901—38032美元），第二产业占比（38.28%）普遍高于达峰国家（24.11%—34.05%），化石能源消费占比（22.93%）远高于大多数达峰国家（1.97%—7.77%）。尽管能源强度（18300吨/亿元）、研发投入（2.43%）和森林覆盖率（24.02%）已接近部分达峰国家的水平，但与领先国家相比仍存在明显差距。

AI 翻訳（deepseek-v4-flash）。 正確を期す場合は下の原文を参照してください。

China's task of achieving carbon peaking before 2030 is arduous and urgent. Comparative studies of the carbon emission influencing factors of countries that have reached carbon peaking are of great significance for China's carbon reduction. This study selects China and 13 carbon peaking countries based on data from 1990 to 2023， uses the Mann-Kendall trend test method to determine the carbon emission peaking status of each country， constructs an extended STIRPAT model to analyze the influencing factors of carbon emissions in China and the peaking countries， compares and studies the differences in characteristic values between China's current situation and the peak years of the peaking countries， and finally puts forward targeted carbon reduction suggestions. The results show that： ① For peaking countries， total energy consumption was the core driving factor of carbon emissions， while per capita GDP， the proportion of secondary industry， energy structure， and energy intensity had a positive driving effect on carbon emissions. Population size， urbanization rate， R&D investment， and forest coverage rate， on the other hand， had an inhibitory effect. ② There were significant differences in the influencing factors between China and most peaking countries. Except for energy intensity， which was significantly negatively correlated with China's carbon emissions， all other influencing factors were positively correlated. Population size， urbanization rate， energy intensity， R&D investment， and forest coverage rate had opposite effects compared to most peaking countries. ③ Compared with the peak years of peaking countries， China's current situation in 2023 shows that the urbanization rate （64.75%） has not caught up with that of peaking countries （67.97%-96.85%）， per capita GDP （12 614 US dollars） is far lower than that of peaking countries （19 901-38 032 US dollars）， the proportion of secondary industry （38.28%） is generally higher than that of peaking countries （24.11%-34.05%）， and the proportion of fossil energy consumption （22.93%） is much higher than that of most peaking countries （1.97%-7.77%）. Although energy intensity （18 300 tons/100 million yuan）， R&D investment （2.43%）， and forest coverage rate （24.02%） are close to the levels of some peaking countries， there are still significant gaps compared to leading countries.

• openalex https://pubmed.ncbi.nlm.nih.gov/42473354first seen 2026-07-22 05:11:39