中国火电行业多模型碳达峰情景预测

张金良; 贾凡

doi:10.12204/j.issn.1000-7229.2022.05.003

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电力建设 ›› 2022, Vol. 43 ›› Issue (5) : 18-28. DOI: 10.12204/j.issn.1000-7229.2022.05.003

支撑碳达峰、碳中和的能源电力技术、经济和政策·栏目主持赵俊华副教授、邱靖博士、文福拴教授·

中国火电行业多模型碳达峰情景预测

张金良 ,
贾凡

作者信息 +

Multi-model Carbon Peak Scenario Prediction for Thermal Power Industry in China

Jinliang ZHANG ,
Fan JIA

Author information +

文章历史 +

摘要

随着2030年碳达峰目标的提出,能源行业中火电行业的碳达峰情况备受瞩目。文章首先根据Kaya恒等式的扩展,分析得到影响碳排放的主要因素:人口、经济、产业结构、能源消费强度以及消费结构;其次,以2000—2018年数据为基础分别建立线性回归、径向基函数(radial basis function, RBF)神经网络、差分自回归移动平均(autoregressive integrated moving average, ARIMA)以及BP神经网络模型,对比得到最优的预测模型;最后,基于最优模型在基准发展、产业优化、技术突破、低碳发展这4种不同发展情景下对2021—2050年碳排放量进行预测,然后在此基础上对碳达峰情况进行分析。结果表明:低碳发展情景的碳达峰时间最早且峰值最低,是中国火电行业实现碳排放达峰的首选发展模式,为推动火电行业尽快实现较低的碳排放峰值提供借鉴。

Abstract

With the goal of carbon peaking in 2030, the carbon peak of thermal power industry in the energy industry has attracted much attention. In this paper, firstly, according to the extension of Kaya’s constant equation, the main factors affecting carbon emissions are analyzed and obtained: population, economy, industrial structure, energy consumption intensity and energy consumption structure. Secondly, linear regression, RBF neural network, ARIMA and BP neural network models are established according to data from 2000 to 2018, to get the optimal prediction models by comparison. Finally, on the basis of the optimal model, carbon emissions from 2021 to 2050 are predicted under four different development scenarios: baseline development, industrial optimization, technological breakthrough, and low-carbon development, and then carbon peak situation is analyzed on this basis. The results show that it has the earliest peak time and lowest peak value in the low-carbon development scenario, which is the preferred mode to achieve peak carbon emissions in China’s thermal power industry, and provides a reference for promoting the thermal power industry to achieve lower peak carbon emissions as soon as possible.

导出引用

张金良, 贾凡. 中国火电行业多模型碳达峰情景预测[J]. 电力建设. 2022, 43(5): 18-28 https://doi.org/10.12204/j.issn.1000-7229.2022.05.003

Jinliang ZHANG, Fan JIA. Multi-model Carbon Peak Scenario Prediction for Thermal Power Industry in China[J]. Electric Power Construction. 2022, 43(5): 18-28 https://doi.org/10.12204/j.issn.1000-7229.2022.05.003

中图分类号： TM61;X22

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摘要

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The transportation industry is one of the key industries necessary for the development of the national economy and the everyday life of residents, and it is also one of the main sources of carbon emissions. High energy consumption and high pollution have always been problems in the transportation industry. The effective control of carbon emissions in the transportation industry is greatly important for achieving China’s carbon emission peak target. This study took the three provinces in Northeast China as the research object and conducted a detailed examination on the carbon emissions of five different modes of transportation: road, railway, air, waterway, and pipeline transportation. First, we used the generalized divisia index method (GDIM) to examine the factors affecting the carbon emissions of the five transportation modes from 2005 to 2016 and Monte Carlo simulation to calculate the carbon emissions of the five major transportation modes in 2017-2030. The annual average rate of change was used for dynamic scenario analysis. The results show that the scale of investment is the primary factor affecting the carbon emissions of railway, road, air, and pipeline transportation. The transportation scale is the primary factor affecting the carbon emissions of waterway transportation. During the same period, the influencing factors are different. In different time periods, the same factors also affect growth or reduction of carbon emissions differently. Except under the baseline scenario, the carbon emissions of the five modes of transportation in 2017-2030 will gradually decline; the carbon emissions of the five types of transportation are expected to decline the most under the technological breakthrough scenario. The development of transportation equipment using clean energy, performance improvement, and vigorous promotion should be the main development path for energy conservation and emission reduction in the future transportation industry.

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