Study on the synergistic effect of industrial carbon dioxide and atmospheric pollutant emission control in Yulin City

SHI Hanxiao; WEI Anlei; ZHU Yaqi; XU Xiaozhen; HU Hao; MA Junjie

doi:10.16152/j.cnki.xdxbzr.2024-03-008

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Study on the synergistic effect of industrial carbon dioxide and atmospheric pollutant emission control in Yulin City

更新时间：2024-06-17

- Study on the synergistic effect of industrial carbon dioxide and atmospheric pollutant emission control in Yulin City
- Journal of Northwest University (Natural Science Edition) Vol. 54, Issue 3, Pages: 424-433(2024)
- 作者机构：
  
  1.西北大学　城市与环境学院，陕西　西安　710127
  2.陕西省地表系统与环境承载力重点实验室，陕西　西安　710127
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2024-03-008
  CLC： X32
- Published：25 June 2024，
  
  Received：08 November 2023，
扫描看全文
SHI Hanxiao, WEI Anlei, ZHU Yaqi, et al. Study on the synergistic effect of industrial carbon dioxide and atmospheric pollutant emission control in Yulin City. [J]. Journal of Northwest University (Natural Science Edition) 54(3):424-433(2024)
DOI：

SHI Hanxiao, WEI Anlei, ZHU Yaqi, et al. Study on the synergistic effect of industrial carbon dioxide and atmospheric pollutant emission control in Yulin City. [J]. Journal of Northwest University (Natural Science Edition) 54(3):424-433(2024) DOI： 10.16152/j.cnki.xdxbzr.2024-03-008.

摘要

在“双碳”背景下，了解工业二氧化碳与大气污染物排放之间的协同效应对于实现减污降碳具有重要意义。该研究以榆林市2015—2021年二氧化碳与大气污染物排放量为例。首先，利用灰色相关分析对榆林市工业碳排放影响因素的相关度进行计算。其次，对Kaya恒等式和LMDI模型进行拓展，分析影响二氧化碳及主要大气污染物排放的驱动效应。最后，对榆林市重点行业火力发电行业和镁冶炼行业进行协同性分析并对榆林市减污降碳提出建议。结果表明，协同减排效应是驱动大气污染物排放减少的第一大驱动效应。二氧化碳的排放与氮氧化物的排放相关性最强，且LMDI模型分析表明氮氧化物与二氧化碳协同减排具有显著的协同效应。研究结果可为榆林市减污降碳协同增效以及高质量发展提供科学依据，为政府在制定一系列更为有效且切实可行的环境保护政策提供理论基础。

Abstract

Under the background of " double carbon" policy

it is of great significance to understand the synergistic effect of industrial carbon dioxide and air pollutant emission to realize pollution reduction and carbon reduction. This study takes the carbon dioxide and air pollutant emissions in Yulin City from 2015 to 2021 as an example. Firstly

the correlation degree of the influencing factors of industrial carbon emissions in Yulin City is calculated by using gray correlation analysis. Secondly

the Kaya constant equation and LMDI model are expanded to analyze the driving effects affecting carbon dioxide and major air pollutant emissions. Finally

a synergistic analysis of the thermal power generation industry and the magnesium smelting industry

which are the key industries in Yulin City

was conducted

and recommendations were made for the reduction of pollution and carbon emissions. The results show that the synergistic emission reduction effect is the first major driving effect driving the reduction of air pollutant emissions. Carbon dioxide emissions have the strongest correlation with nitrogen oxide emissions

and the LMDI model analysis shows that nitrogen oxide and carbon dioxide have significant synergistic effect. The results of this study can provide a scientific basis for the synergistic effect and high-quality prevention of pollution and carbon reduction in Yulin City and provide theoretical support for the government to formulate more practical and feasible strategies for pollution and carbon reduction.

关键词

减污降碳灰色相关分析对数平均权重迪氏指数法碳排放协同发展

Keywords

pollution and carbon reductiongray correlation analysisLMDIcarbon emissionssynergistic development

references

习近平. 在第七十五届联合国大会一般性辩论上的讲话[N].人民日报. 2020-09-23(03).

榆林市经济社会总体发展规划(2016—2030年)[EB/OL].(2016-03-25)[2023-10-19].https://grc.yl.gov.cn/ghwj/3391https://grc.yl.gov.cn/ghwj/3391.

榆林市“十四五”生态环境保护规划[EB/OL].(2021-09-18)[2023-10-19].https://www.shaanxi.gor.cn/zfxxgk/zcwjk/szf_14998/qtwj/202208/t20220808_2235760.htmlhttps://www.shaanxi.gor.cn/zfxxgk/zcwjk/szf_14998/qtwj/202208/t20220808_2235760.html.

王乐, 田东方. 基于灰色关联分析法的宜昌市空气质量影响因素分析[J]. 能源环境保护, 2019, 33(5): 60-64.

WANG L, TIAN D F. Analysis of factors affecting air quality in Yichang city based on grey correlation analysis[J]. Energy Environmental Protection, 2019, 33(5): 60-64.

卜兴兵, 方自力, 俸强, 等. 基于主成分分析的空气质量综合评价研究：以四川省21个城市为例[J]. 四川环境, 2023, 42(3): 51-56.

BU X B, FANG Z L, FENG Q, et al. Comprehensive evaluation of air quality based on principal component analysis: Take 21 cities in Sichuan Province as an example[J]. Sichuan Environment, 2023, 42(3): 51-56.

王暖霞, 尹素真, 李超. 济南市2015—2021年PM2.5、O3和NO2污染变化特征及相关性分析[J]. 环境保护, 2023, 41(7): 122-126.

WANG N X, YIN S Z, LI C. Pollution change characteristics and correlation analysis of PM2.5, O3, and NO2 in Jinan City from 2015 to 2021[J]. China Resources Comprehensive Utilization, 2023, 41(7): 122-126.

马伟波, 赵立君, 王楠, 等. 长三角城市群减污降碳驱动因素研究[J]. 生态与农村环境学报, 2022, 38(10): 1273-1281.

MAN W B, ZHAO L J, WANG N, et al. Study on driving factors of pollution and carbon reduction in the Yangtze River delta urban agglomerations[J]. Journal of Ecology and Rural Environment, 2022, 38(10): 1273-1281.

REN F R, TIAN Z, PAN J J, et, al. Cross-regional comparative study on energy efficiency evaluation in the Yangtze River Basin of China[J]. Environmental Science and Pollution Research International, 2020, 27(27): 34037-34051.

谢元博, 李巍. 基于能源消费情景模拟的北京市主要大气污染物和温室气体协同减排研究[J]. 环境科学, 2013, 34(5): 2057-2064.

XIE Y B, LI W. Synergistic emission reduction of chief air pollutants and greenhouse gases based on scenario simulations of energy consumptions in Beijing[J]. Environmental Science, 2013, 34(5): 2057-2064.

王敏, 冯相昭, 杜晓林, 等. 工业部门污染物治理协同控制温室气体效应评价：基于重庆市的实证分析[J]. 气候变化研究进展, 2021, 17(3): 296-304.

WANG M, FENG X Z, DU X L, et al. Evaluation of co-controlling GHGs from pollutant reduction facilities in the industrial sectors, empirical analysis based on data in Chongqing city[J]. Climate Change Research, 2021, 17(3): 296-304.

阿迪拉·阿力木江, 蒋平, 董虹佳, 等. 推广新能源汽车碳减排和大气污染控制的协同效益研究：以上海市为例[J]. 环境科学学报, 2020, 40(5): 1873-1883.

ALIMUJIANG A, JIANG P, DONG H J, et al. Synergy and co-benefits of reducing CO2 and air pollutant emissions by promoting new energyvehicles: A case of Shanghai[J]. Acta Scientiae Circumstantiae, 2020, 40(5): 1873-1883.

张朝龙, 杨丽亚, 董欣宜, 等. 合成氨行业CO2与大气污染物排放清单及减污降碳潜力研究：以河南省为例[J]. 环境科学研究, 2023, 36(11): 2126-2137.

ZHANG C L, YANG L Y, DONG X Y, et al. Research on CO2 and air pollutant emission inventory and potential for pollution reduction and carbon reduction in synthetic ammonia industry: Taking Henan Province as an example[J]. Research of Environmental Sciences, 2023, 36(11): 2126-2137.

钱凤魁, 王祥国, 顾汉龙, 等. 东北三省农业碳排放时空分异特征及其关键驱动因素[J]. 中国生态农业学报, 2024, 32(1): 30-40.

QIAN F K, WANG X G, GU H L, et al. Spatial-temporal differentiation characteristics and key driving factors of agricultural carbon emissions in the three northeastern provinces of China[J]. Chinese Journal of Eco-Agriculture, 2024, 32(1): 30-40.

FANG G C, TIAN L X, FU M, et al. The effect of energy construction adjustment on the dynamical evolution of energy-saving and emission-reduction system in China[J]. Applied Energy, 2017, 196: 180-189.

JIANG T Y, HUANG S J, YANG J. Structural carbon emissions from industry and energy systems in China: An input-output analysis[J]. Journal of Cleaner Production, 2019, 240: 118116.

YU Y, JIN Z X, LI J Z, et al. Low-carbon development path research on China’s power industry based on synergistic emission reduction between CO2 and air pollutants[J]. Journal of Cleaner Production, 2020, 275: 123097.

WEI X Y, TONG Q, MAGILL I, et al. Evaluation of potential co-benefits of air pollution control and climate mitigation policies for China’s electricity sector[J]. Energy Economics, 2020, 92: 104917.

JIANG P, KHISHGEE S, ALIMUJIANG A, et al. Cost-effective approaches for reducing carbon and air pollution emissions in the power industry in China[J]. Journal of Environmental Management, 2020, 264: 110452.

苏佳, 韩倩, 张新生. 西北地区重点城市工业大气污染排放时空演化格局及影响因素研究[J]. 环境科学研究, 2023, 36(12): 2322-2330.

SU J, HAN Q, ZHANG X S. Research on the spatiotemporal evolution pattern and influencing factors of industrial air pollution emissions in key cities in Northwest China[J]. Research of Environmental Sciences, 2023, 36(12): 2322-2330.

杨婧雯, 陈远翔, 何燕. 云南省二氧化碳与大气污染物控制协同效应分析[J]. 环境科学导刊, 2023, 42(5): 11-16,20.

YANG J W, CHEN Y X, HE Y. Analysis of synergistic effect of carbon dioxide and air pollutant control in Yunnan Province[J]. Environmental Science Survey Agency, 2023, 42(5): 11-16,20.

李薇, 蒙平珠, 李彩弟, 等. 基于LMDI模型的甘肃省种植业生产碳排放影响因素分析及减排途径[J]. 作物杂志, 2023(5): 264-271.

LI W, MENG P Z, LI C D, et al. Analysis of influencing factors of carbon emissions from planting production based on LMDI model and approaches of carbon mitigation in Gansu Province[J]. Crops, 2023(5): 264-271.

LIU Y, JIANG Y, LIU H, et al. Driving factors of carbon emissions in China’s municipalities: ALMDI approach[J]. Environmental Science and Pollution Research, 2022, 29: 21789-21802.

王长建, 汪菲, 张虹鸥. 新疆能源消费碳排放过程及其影响因素：基于扩展的Kaya恒等式[J]. 生态学报, 2016, 36(8): 2152-2163.

WANG C J, WANG F, ZHANG H O. The process of energy-related carbon emissions and influencing mechanism research in Xinjiang[J]. Acta Ecologica Sinica, 2016, 36(8): 2151-2163.

邓宣凯. 武汉市土地利用碳排放的影响因素研究：基于扩展的Kaya等式和LMDI分解方法[J]. 农业与技术, 2021, 41(20): 104-109.

DENG X K. Study on the influencing factors of land use carbon emission in Wuhan City: based on extended Kaya equation and LMDI decomposition methods[J]. Agriculture and Technology, 2021, 41(20): 104-109.

姜欢欢, 李媛媛, 李丽平, 等. 国际典型城市减污降碳协同增效的做法及对我国的建议[J]. 环境与可持续发展, 2022, 47(4): 66-70.

JIANG H H, LI Y Y, LI L P, et al. Practices in typical international cities and suggestions for China on synergizing the reduction of pollution and carbon emissions[J]. Environment and Sustainable Development, 2022, 47(4): 66-70.

榆林市统计局. 榆林统计年鉴2022[M].北京: 中国统计出版社, 2023: 9-207.

邓聚龙. 社会经济灰色系统的理论与方法[M].武汉: 华中科技大学出版社, 2002: 47-60.

KAYA Y. Impact of carbon dioxide emisslon on GNP growth: Interpretation of proposed scenarios[R]. Response Strategies Working Group, IPCC, 1989: 76-77.

DONG F, DAI Y J, ZHANG S N, et al. Can a carbon emission trading scheme generate the Porter effect? Evidence from pilot areas in China[J]. Science of the Total Environment, 2019, 653: 565-577.

李云燕, 赵晗. 北京市碳达峰碳中和路径和大气污染物协同减排研究[C]//中国环境科学学会2021年科学技术年会论文集(一). 天津: 中国环境科学学会, 2021: 41-50.

赵奥, 武春友. 中国CO2排放量变化的影响因素分解研究：基于改进的Kaya等式与LMDI分解法[J]. 软科学, 2010, 24(12): 55-59.

ZHAO A, WU C Y. Analysis of decomposition of influencing factors of variation in CO2 emission of China: Based on improved Kaya identity and LMDI Method[J]. Soft Science, 2010, 24(12): 55-59.

ANG B W. The LMDI approach to decomposition analysis: a practical guide[J]. Energy Policy, 2005, 33(7): 867-871.

ANG B W. LMDI decomposition approach: A guide for implementation[J]. Energy Policy, 2015, 86: 233-238.

孟宇, 魏伟, 胡超. 榆林市金属镁产业现状分析及发展建议[J]. 有色金属加工, 2021, 50(2): 14-16,70.

MEN Y, WEI W, HU C. Analysis on Current Situation and Development Suggestion of Magnesium Industry in Yulin[J]. Nonferrous Metals Processing, 2021, 50(2): 14-16,70.

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