Source Jouranl of CSCD
Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Environmental Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
Volume 43 Issue 4
Apr.  2025
Turn off MathJax
Article Contents
LI X,GAO B,LIU X Y,et al.A method for city-level high-resolution CO2 emission inventories: a case study of Yingkou[J].Environmental Engineering,2025,43(4):204-212. doi: 10.13205/j.hjgc.202504020
Citation: LI X,GAO B,LIU X Y,et al.A method for city-level high-resolution CO2 emission inventories: a case study of Yingkou[J].Environmental Engineering,2025,43(4):204-212. doi: 10.13205/j.hjgc.202504020

A method for city-level high-resolution CO2 emission inventories: a case study of Yingkou

doi: 10.13205/j.hjgc.202504020
  • Received Date: 2024-02-21
  • Accepted Date: 2024-05-06
  • Rev Recd Date: 2024-03-27
  • Publish Date: 2025-04-01
  • A carbon emission inventory with high-spatial-resolution covering various fields is an important foundation for cities to implement precise carbon reduction policies. A method for city-level high-resolution CO2 emission inventories was proposed. Based on the provincial energy balance table, the economic and industrial indexes, population, roads, land use, and points of interest (POI) data were applied to establish a 1 km × 1 km CO2 emission inventory for Yingkou in northestern China. The results showed that the total CO2 emissions in Yingkou in 2020 reached 17.9327 million tons, consisting of 18.0084 million tons from energy consumption and -0.0722 million tons from land use. In terms of energy consumption emissions, the main sources were concentrated in manufacturing and coal consumption. CO2 emissions from manufacturing accounted for 69.94%, and CO2 emissions from coal consumption accounted for 53.56%. The spatial agglomeration effect of CO2 emissions was obvious. Among the 5550 grids, CO2 emissions from 18 extremely high-emission grids (≥ 50000 tons) accounted for 68% of the total city, while CO2 emissions from 37 high-emission grids (≥ 20000 tons) accounted for 90%. As for the space distribution of key industries, the emissions from the steel making and power thermal industries were relatively concentrated, whereas the grid emissions from the non-ferrous and non-metallic mineral product industries showed gradient differences.High-emission grids from other industries were mainly distributed in areas with rapid economic development and dense population. It is necessary to conduct localized research on emission factors, improve the basic data statistical system and CO2 accounting for key enterprises to enhance the accuracy of city-level CO2 emission inventories.
  • loading
  • [1]
    SHAN Y,GUAN D,ZHENG H,et al. Data descriptor:China CO2 emission accounts[J]. Scientific Data,2017:1-14.
    [2]
    WANG Y,ZHAO T. Impacts of urbanization-related factors on CO2 emissions:evidence from China’s three regions with varied urbanization levels[J]. Atmospheric Pollution Research,2018,9(1):15-26.
    [3]
    SHAN Y,GUAN D,LIU J,et al. Methodology and applications of city level CO2 emission accounts in China[J]. Journal of Cleaner Production,Elsevier Ltd,2017,161:1215-1225.
    [4]
    LIANG S,CHANG W C,LI Y M,et al. High spatial resolution environmental dataset and its application[J]. Environmental Engineering,2022,40(6):1-11. 梁赛,常玮岑,李雨萌,等. 高空间分辨率环境数据库及其应用[J]. 环境工程,2022,40(6):1-11.
    [5]
    XIE P C,WANG W J,LIAO C P,et al. Guangzhou CO2 emissions inventory research based on the energy activity[J]. Ecological Economy,2018,34(3):18-22. 谢鹏程,王文军,廖翠萍,等. 基于能源活动的广州市二氧化碳排放清单研究[J]. 生态经济,2018,34(03):18-22.
    [6]
    ZHAO J Y,ZHU Y E,MA J C,et al. Research on carbon emission inventory based on energy activities and the driving factors in Shanxi[J]. Journal of Taiyuan University of Technology,2022,53(6):989-996 赵江燕,朱宇恩,马建超,等. 山西省能源消费碳排放清单和影响因素研究[J]. 太原理工大学学报,2022,53(6):989-996.
    [7]
    SHAN Y,HUANG Q,GUAN D,et al. China CO2 emission accounts 2016-2017[J]. Scientific Data,2020,7(1):54.
    [8]
    ZHAO R N,DONG L,BAI L,et al. Inventory analysis on carbon emissions of photo voltaic industry[J]. China Environmental Science,2020,40(6):434-440. 赵若楠,董莉,白璐,等. 光伏行业生命周期碳排放清单分析[J]. 中国环境科学,2020,40(6):434-440.
    [9]
    NIE H W,DENG J,SHI Z Z. Research on carbon emission inventory of Guizhou passenger transport[J]. Highway,2019,64(2):252-255. 聂华伟,邓捷,史忠震. 贵州客运交通碳排放清单研究[J]. 公路,2019,64(2):252-255.
    [10]
    CHEN G J,CHANG K L,CHEN H Q,et al. Decomposition analysis of CO2 emission factor in beijing-tianjin-hebei electric power industry based on production-side and consumption-side[J]. Science and Technology Management Research,2019.
    [11]
    SUN Z Q,WANG G J,CHEN Y N. Analysis on the uncertainty of carbon emission accounting based on energy balance sheet[J]. Ecological Economy,2015(07):35-40. 孙振清,汪国军,陈亚男. 基于能源平衡表的碳排放清单核算不确定性分析[J]. 生态经济,2015(7):35-40.
    [12]
    CHEN H L,GAO Z H,WANG Z B. Analysis on influencing factors of industrial carbon emission and carbon transfer pattern at provincial level[J]. Acta Ecologica Sinica,2023,43(14):5816-5828. 陈慧灵,高子恒,王振波. 省级尺度工业碳排放影响因素及碳转移格局[J]. 生态学报,2023,43(14):5816-5828.
    [13]
    JING Y,ZUO L L,PENG W F. Spatial-temporal change analysis of carbon emission from land use in Mianyang:the case study of Mianyang[J]. Environmental Science& Technology,2021,44(6):172-185. 景勇,左玲丽,彭文甫. 四川盆地西北部土地利用碳排放时空变化分析:以绵阳市为例[J]. 环境科学与技术,2021,44(6):172-185.
    [14]
    WANG J J,WEI J J. Temporal variation characteristics and a scenario analysis of carbon emissions in the operation of buildings in Beijing[J]. Journal of Beijing University of Technology,2022(3):220-229. 王京京,卫佳佳. 时间序列下北京市建筑运行碳排放变化特征与情景模拟[J]. 北京工业大学学报,2022(3):220-229.
    [15]
    HUANG G H,LIU C J,TU H L. Calculation of carbon emission inventory and analysis of carbon emission reduction potential in Hubei province[J]. Statistics& Decision,2019,35(12):102-106. 黄国华,刘传江,涂海丽. 湖北省碳排放清单测算及碳减排潜力分析[J]. 统计与决策,2019,35(12):102-106.
    [16]
    FAN Z H,XING W W,BU Y Q,et al. Calculation and peak analysis of carbon emissions from agricultural planting in Jiangsu province[J]. Journal of Soil and Water Conservation,2023,37(5):78-85. 范振浩,邢巍巍,卜元卿,等. 江苏省种植业碳排放的测算及达峰分析[J]. 水土保持学报,2023,37(5):78-85.
    [17]
    HUANG L L,WANG Y,ZHANG C,et al. A spatial-temporal decomposition analysis of CO2 emissions in Fujian Southeast Triangle Region[J]. China Environmental Science,2020,40(5):2312-2320. 黄琳琳,王远,张晨,等. 闽三角地区碳排放时空差异及影响因素研究[J]. 中国环境科学,2020,40(5):2312-2320.
    [18]
    JING Q N,LUO W,BAI H T,et al. A method for city-level energy-related CO2 emission estimation[J]. Acta Scientiae Circumstantiae,2018,38(12):4879-4886. 景侨楠,罗雯,白宏涛,等. 城市能源碳排放估算方法探究[J]. 环境科学学报,2018,38(12):4879-4886.
    [19]
    CAI B F,WANG J N,YANG S Y,et al. China city CO2 emission dataset:based on the China high resolution emission gridded data[J]. China Population,Resources and Environment,2017,27(2):1-4. 蔡博峰,王金南,杨姝影,等. 中国城市CO2排放数据集研究:基于中国高空间分辨率网格数据[J]. 中国人口·资源与环境,2017,27(2):1-4.
    [20]
    CAI B,LIANG S,ZHOU J,et al. China high resolution emission database(CHRED)with point emission sources,gridded emission data,and supplementary socioeconomic data[J]. Resources,Conservation and Recycling,Elsevier,2018,129:232-239.
    [21]
    LIU P Z,ZHANG LI Y,DONG H Z. Analysis on spatiotemporal evolution pattern and influencing factors of carbon emission intensity of“2+26" cities in Beijing-Tianjin-Hebei and surrounding areas[J]. Environmental Pollution& Control,2022,44(6):772-776. 刘鹏振,张力元,董会忠. 京津冀及周边"2+26"城市碳排放强度时空演变规律及影响因素分析[J]. 环境污染与防治,2022,44(6):772-776.
    [22]
    LIU J,GE X,ZHAO Z Y. Research on spatio-temporal evolution of carbon arrangement in north China cities and its influencing factors[J]. Environmental Engineering,2023,41(10):204-212,222. 刘杰,葛潇,赵振宇. 华北城市群碳排时空演变格局及影响因素研究[J]. 环境工程,2023,41(10):204-212,222.
    [23]
    Liaoning Provincial Bureau of Statistics. Liaoning Statistical Yearbook 2020[R]. Beijing:China Statistics Press,2021. 辽宁省统计局. 2020年辽宁省统计年鉴[R]. 2021.
    [24]
    JING Q N,HOU H M,BAI H T,et al. A top-bottom estimation method for city-level energy-related CO2 emissions[J]. China Environmental Science,2019,39(1):420-427. 景侨楠,侯慧敏,白宏涛,等. 自上而下的城市能源消耗碳排放估算方法[J]. 中国环境科学,2019,39(1):420-427.
    [25]
    Liaoning Provincial Bureau of Statistics. Communique on the Fourth National Economic Census of Liaoning Province[R]. 2020. 辽宁省统计局. 辽宁省第四次全国经济普查公报[R]. 2020.
    [26]
    Yingkou Municipal Bureau of Statistics. Communique on the Fourth National Economic Census of Yingkou City[R]. 2020. 营口市统计局. 营口市第四次全国经济普查公报[R]. 2020.
    [27]
    Liaoning Provincial Bureau of Statistics. Liaoning Province National Economic and Social Development Statistical Communique 2020[R]. 2021. 辽宁省统计局. 2020年辽宁省国民经济和社会发展统计公报[R]. 2021.
    [28]
    Yingkou Municipal Bureau of Statistics. Yingkou City National Economic and Social Development Statistical Communique 2020[R]. 2021. 营口市统计局. 2020年营口市国民经济和社会发展统计公报[R]. 2021.
    [29]
    Civil Aviation Administration of China. Civil Aviation Airport Production Statistical Communique 2020[R]. 2021. 中国民用航空局. 2020年民航机场生产统计公报[R]. 2021.
    [30]
    IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventorie[R]. 2008.
    [31]
    ZHANG R S,PU L J,WEN J Q. Hypothesis and validation on the Kuznets curve of construction land expansion and carbon emission effect[J]. Journal of Natural Resources,2012,27(5):723-733. 张润森,濮励杰,文继群. 建设用地扩张与碳排放效应的库兹涅茨曲线假说及验证[J]. 自然资源学报,2012,27(5):723-733.
    [32]
    XIAO H Y,YUAN X Z,LI B. The effects of land use changes on carbon emission:take Chongging as an example[J]. Journal of Chongqing Normal University(Natural Science),2012,29(1):38-42. 肖红艳,袁兴中,李波. 土地利用变化碳排放效应研究:以重庆市为例[J]. 重庆师范大学学报. 自然科学版,2012,29(1):38-42.
    [33]
    SHI H X,MU X M,ZHANG Y L. Effects of different land use patterns on carbon emission in Guangyuan city of Sichuan province[J]. Bulletin of Soil and Water Conservation,2010,32(3):101-106. 石洪昕,穆兴民,张应龙. 四川省广元市不同土地利用类型的碳排放效应研究[J]. 水土保持通报,2010,32(3):101-106.
    [34]
    ZHENG J Y,WANG S S,HUANG Z J,et al. Technical methods and applications for establishing regional high resolution atmospheric emission source inventory[M]. Beijing:Science Press,2014:258-272. 郑君瑜,王水胜,黄志炯,等. 区域高分辨率大气排放源清单建立的技术方法与应用[M]. 北京:科学出版社,2014:258-272.
    [35]
    YU Q. The impact of industrial agglomeration on environmental efficiency of Chinese iron and steel industry[D]. Harbin:Harbin Engineering University,2018. 于倩. 产业集聚对中国钢铁行业环境效率的影响研究[D]. 哈尔滨:哈尔滨工程大学,2018.
    [36]
    ZONG H M,CHEN X,YI Z. Spatial pattern and distribution characteristics of wholesale industry in Chongging's urban area based on POl data[J]. Modern Urban Research,2020(5):24-31. 宗会明,陈欣,易峥. 基于POI数据的重庆市主城区批发业空间分布特征研究[J]. 现代城市研究,2020(5):24-31.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(5)

    Article Metrics

    Article views (146) PDF downloads(1) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return