Key Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China.
College of New Energy and Environment, Jilin University, Changchun 130021, China.
Int J Environ Res Public Health. 2022 Jun 26;19(13):7829. doi: 10.3390/ijerph19137829.
Peaking industrial carbon dioxide (CO2) emissions is critical for China to achieve its CO2 peaking target by 2030 since industrial sector is a major contributor to CO2 emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO2 emissions, which also have huge CO2 emissions reduction potential. It is significant to accurately forecast CO2 emission peak of industrial sector in heavy industrial regions from multi-industry and multi-energy type perspectives. This study incorporates 41 industries and 16 types of energy into the Long-Range Energy Alternatives Planning System (LEAP) model to predict the CO2 emission peak of the industrial sector in Jilin Province, a typical heavy industrial region. Four scenarios including business-as-usual scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS) and low-carbon scenario (LCS) are set for simulating the future CO2 emission trends during 2018−2050. The method of variable control is utilized to explore the degree and the direction of influencing factors of CO2 emission in four scenarios. The results indicate that the peak value of CO2 emission in the four scenarios are 165.65 million tons (Mt), 156.80 Mt, 128.16 Mt, and 114.17 Mt in 2040, 2040, 2030 and 2020, respectively. Taking ELS as an example, the larger energy-intensive industries such as ferrous metal smelting will peak CO2 emission in 2025, and low energy industries such as automobile manufacturing will continue to develop rapidly. The influence degree of the four factors is as follows: industrial added value (1.27) > industrial structure (1.19) > energy intensity of each industry (1.12) > energy consumption types of each industry (1.02). Among the four factors, industrial value added is a positive factor for CO2 emission, and the rest are inhibitory ones. The study provides a reference for developing industrial CO2 emission reduction policies from multi-industry and multi-energy type perspectives in heavy industrial regions of developing countries.
实现 2030 年二氧化碳排放峰值目标对中国至关重要，因为工业部门是二氧化碳排放的主要贡献者。重工业地区消耗大量的化石燃料，排放大量的二氧化碳，也具有巨大的二氧化碳减排潜力。从多产业和多能源类型的角度准确预测重工业地区工业部门的二氧化碳排放峰值具有重要意义。本研究将 41 个行业和 16 种能源纳入长期能源替代规划系统（LEAP）模型，以预测典型重工业地区吉林省工业部门的二氧化碳排放峰值。设定了四种情景，包括基准情景（BAU）、节能情景（ESS）、节能和低碳情景（ELS）和低碳情景（LCS），以模拟 2018-2050 年期间的未来二氧化碳排放趋势。利用变量控制方法，探讨了四种情景下影响二氧化碳排放的因素的程度和方向。结果表明，四种情景下二氧化碳排放峰值分别为 2040 年、2040 年、2030 年和 2020 年的 1.6565 百万吨（Mt）、1.568 Mt、1.2816 Mt 和 1.1417 Mt。以 ELS 为例，黑色金属冶炼等能源密集型较大的产业将在 2025 年达到二氧化碳排放峰值，而汽车制造等低能耗产业将继续快速发展。四个因素的影响程度如下：工业增加值（1.27）>产业结构（1.19）>各产业能源强度（1.12）>各产业能源消费类型（1.02）。在这四个因素中，工业增加值是二氧化碳排放的正因素，其余三个都是抑制因素。该研究为发展中国家重工业地区从多产业和多能源类型的角度制定工业二氧化碳减排政策提供了参考。
