“双碳”目标下城市空间数字化技术应用研究进展

doi:10.3981/j.issn.1000-7857.2022.06.005

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Abstract: The digital technology in the urban field provides a dynamic data information platform for the accurate acquisition and analysis of the spatial carbon source and sink data. The platform can improve the efficiency of all-factor control of the territorial spatial planning and promote the carbon emission reduction in the urban domain. By integrating the advantages of the multi-discipline, the multi-scale and the multi-probability, the digital technology can make full use of various models and sensors to update and run the data of each system anytime. The trajectory data mining in the digital technology of the urban spatial carbon emission is the development direction that needs attention, while the spatio-temporal trajectory mining of the urban carbon emission should focus on the system update, the spatial surface domain feature mining and the big data application. Based on the digital analysis method, it is found that the ecological vulnerability identification and the security pattern construction are the aspects that need to be focused on to establish the ecologically resilient urban space. Using the remote sensing technology to measure the carbon sinks of the same category will have errors due to different geographical features, and this difficulty deserves attention. The accurate measurement of the urban spatial carbon sinks in the future needs to be realized by combining macro and microscopic features and combining multi-platform and multi-temporal dynamic monitoring.

Key words： dual carbon goals urban space territorial spatial planning

Received: 15 December 2021

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	ZHU Li
	MA Junrong

Cite this article:

ZHU Li,MA Junrong. Research progress of urban digitization technology under “dual carbon” goals[J]. Science & Technology Review, 2022, 40(6): 38-45.

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http://www.kjdb.org/EN/10.3981/j.issn.1000-7857.2022.06.005 OR http://www.kjdb.org/EN/Y2022/V40/I6/38

[1] 本刊编辑部. 为力争二氧化碳排放于2030年前达到峰值, 努力争取2060年前实现碳中和而奋斗[J]. 中国能源, 2020, 42(10):1.
[2] Huovila A, Siikavirta H, Antuña R C, et al. Carbon-neutral cities:Critical review of theory and practice[J]. Journal of Cleaner Production, 2022, 341:130912.
[3] Wiedmann T, Chen G, Owen A, et al. Three-scope carbon emission inventories of global cities[J]. Journal of Industrial Ecology, 2021, 25:735-750.
[4] 翟云, 蒋敏娟, 王伟玲. 中国数字化转型的理论阐释与运行机制[J]. 电子政务, 2021, 18(6):67-84.
[5] 陈明星, 先乐, 王朋岭, 等. 气候变化与多维度可持续城市化[J]. 地理学报, 2021, 76(8):1895-1909.
[6] Mika J, Forgo P, Lakatos L, et al. Impact of 1.5 K global warming on urban air pollution and heat island with outlook on human health effects[J]. Current Opinion in Environmental Sustainability, 2018, 30:151-159.
[7] Djalante R. Key assessments from the IPCC special report on global warming of 1.5℃ and the implications for the Sendai framework for disaster risk reduction[J]. Progress in Disaster Science, 2019, 1:100001.
[8] Handayani K, Anugrah P, Goembira F, et al. Moving beyond the NDCS:Asean pathways to a net-zero emissions power sector in 2050[J]. Applied Energy, 2022, 311:118580.
[9] Sahimaa O, Mattinen M K, Koskela S, et al. Towards zero climate emissions, zero waste, and one planet living:Testing the applicability of three indicators in finnish cities[J]. Sustainable Production and Consumption, 2017, 10:121-132.
[10] Holtz G, Xia-Bauer C, Roelfes M, et al. Competences of local and regional urban governance actors to support low-carbon transitions:Development of a framework and its application to a case-study[J]. Journal of Cleaner Production, 2018, 177:846-856.
[11] Nagorny-Koring N C, Nochta T. Managing urban transitions in theory and practice-the case of the pioneer cities and transition cities projects[J]. Journal of Cleaner Production, 2018, 175:60-69.
[12] Moore M L, Milkoreit M. Imagination and transformations to sustainable and just futures[J]. Elementa:Science of the Anthropocene, 2020, 8(1):70-81.
[13] Shahani F, Pineda-Pinto M, Frantzeskaki N. Transformative low-carbon urban innovations:Operationalizing transformative capacity for urban planning[J]. Ambio, 2021, 1653:4.
[14] 何立峰. 完整准确全面贯彻新发展理念, 扎实做好碳达峰碳中和工作[N]. 人民日报, 2021-10-25(04).
[15] Axon S. The socio-cultural dimensions of communitybased sustainability:Implications for transformational change[J]. Journal of Cleaner Production, 2020, 266:121933.
[16] 鄢金明, 王建军. 双碳目标下的广州国土空间规划编制思考[C]//2020/2021中国城市规划年会暨2021中国城市规划学术季. 成都:中国城市规划学会, 2021:525-532
[17] 熊健, 卢柯, 姜紫莹, 等."碳达峰、碳中和" 目标下国土空间规划编制研究与思考[J]. 城市规划学刊, 2021(4):74-80.
[18] Lee S, Lee B. The influence of urban form on ghg emissions in the U. S. Household sector[J]. Energy Policy, 2014, 68:534-549.
[19] Makido Y, Dhakal S, Yamagata Y. Relationship between urban form and CO 2 emissions:evidence from fifty japanese cities[J]. Urban Climate, 2012, 2:55-67.
[20] Wang M, Madden M, Liu X. Exploring the relationship between urban forms and CO 2 emissions in 104 Chinese cities[J]. Journal of Urban Planning and Development, 2017, 143(4):04017014.
[21] 黄贤金, 张秀英, 卢学鹤, 等. 面向碳中和的中国低碳国土开发利用[J]. 自然资源学报, 2021, 36(12):2995-3006.
[22] Glaeser E L, Kahn M E. The greenness of cities:Carbon dioxide emissions and urban development[J]. Journal of Urban Economics, 2010, 67(3):404-418.
[23] 张赫, 胡佳慧, 王睿, 等. 面向国土空间规划的县域规划建设低碳评价方法研究——以河北省武安市为例[J]. 西部人居环境学刊, 2021, 36(6):23-30.
[24] Zheng B, Chevallier F, Ciais P, et al. Observing carbon dioxide emissions over China's cities and industrial areas with the orbiting carbon observatory-2[J]. Atmospheric Chemistry and Physics, 2020, 20(14):8501-8510.
[25] 庄少勤, 赵星烁, 李晨源. 国土空间规划的维度和温度[J]. 城市规划, 2020, 44(1):9-13.
[26] 喻文承, 李晓烨, 高娜, 等. 北京国土空间规划"一张图" 建设实践[J]. 规划师, 2020, 36(2):59-64.
[27] Shusterman A A, Teige V E, Turner A J, et al. The berkeley atmospheric CO₂ observation network:Initial evaluation[J]. Atmospheric Chemistry and Physics, 2016, 16(21):13449-13463.
[28] Han P, Zeng N, Oda T, et al. A city-level comparison of fossil-fuel and industry processes-induced CO₂ emissions over the Beijing-Tianjin-Hebei region from eight emission inventories[J]. Carbon Balance and Management, 2020, 15(1):25.
[29] Wang Y, Ciais P, Broquet G, et al. A global map of emission clumps for future monitoring of fossil fuel CO₂ emissions from space[J]. Earth System Science Data, 2019, 11(2):687-703.
[30] 赖玉珮, 李芬, 彭锐, 等. 基于城市能源系统的碳排放风险评估研究——以湖北荆门市为例[J]. 城市发展研究, 2020, 27(5):27-31.
[31] 杜金霜, 付晶莹, 郝蒙蒙. 基于生态网络效用的昭通市"三生空间" 碳代谢分析[J]. 自然资源学报, 2021, 36(5):1208-1223.
[32] 杨滔, 杨保军, 刘畅, 等. 数字孪生城市平台原型的初步设想[J]. 北京规划建设, 2021, 4:95-99.
[33] Söderberg R, Wärmefjord K, Carlson J S, et al. Toward a digital twin for real-time geometry assurance in individualized production[J]. CIRP Annals, 2017, 66(1):137-140.
[34] Wang Q, Li S, Pisarenko Z. Modeling carbon emission trajectory of China, us and india[J]. Journal of Cleaner Production, 2020, 258:120723.
[35] Dimitriou K, Remoundaki E, Mantas E, et al. Spatial distribution of source areas of PM_2.5 by concentration weighted trajectory (CWT) model applied in PM_2.5 concentration and composition data[J]. Atmospheric Environment, 2015, 116:138-145.
[36] Zhang J, Chen F, Wang Z, et al. Spatiotemporal patterns of carbon emissions and taxi travel using gps data in Beijing[J]. Energies, 2018, 11(3):500.
[37] Galford G L, Melillo J M, Kicklighter D W, et al. Historical carbon emissions and uptake from the agricultural frontier of the brazilian amazon[J]. Ecological Applications, 2011, 21(3):750-763.
[38] Sun Z, Hao P, Ban X, et al. Trajectory-based vehicle energy/emissions estimation for signalized arterials using mobile sensing data[J]. Transportation Research Part D:Transport and Environment, 2015, 34:27-40.
[39] Wang S, Li Z, Tan J, et al. A method for estimating carbon dioxide emissions based on low frequency GPS trajectories[C]//2017 Chinese Automation Congress (CAC). New York:IEEE, 2017:1960-1964.
[40] Saxe S, Miller E, Guthrie P. The net greenhouse gas impact of the sheppard subway line[J]. Transportation Research Part D:Transport and Environment, 2017, 51:261-275.
[41] Sun M, Xue C, Cheng Y, et al. Analyzing spatiotemporal daily travel source carbon emissions based on taxi trajectory data[J]. IEEE Access, 2021, 9:107012-107023.
[42] Kaya Y. Impact of carbon dioxide emission on GNP growth:Interprelation of proposed scenarios[R]. Paris:Presentation to the Energy and Industry Subgroup, Response Strategies Working Group, IPCC, 1989.
[43] 姜洋, 何永, 毛其智, 等. 基于空间规划视角的城市温室气体清单研究[J]. 城市规划, 2013, 37(4):50-56+67.
[44] 金乐琴, 吴慧颖. 中国碳排放的区域异质性及减排对策[J]. 经济与管理, 2013, 27(11):83-87.
[45] Huo T, Xu L, Feng W, et al. Dynamic scenario simulations of carbon emission peak in China's city-scale urban residential building sector through 2050[J]. Energy Policy, 2021, 159:112612.
[46] 吴萌, 任立, 陈银蓉. 城市土地利用碳排放系统动力学仿真研究——以武汉市为例[J]. 中国土地科学, 2017, 31(2):29-39.
[47] 胡玥昕, 江洪, 王颖, 等. 基于系统动力学的工业产业经济与碳排放综合分析——以无锡装备制造业为例[J]. 生态经济, 2014, 30(8):18-25.
[48] Löwe H, Seufert G, Raes F. Comparison of methods used within member states for estimating CO₂ emissions and sinks according to UNFCCC and EU monitoring mechanism:Forest and other wooded land[J]. BASE, 2011, 4:2000.
[49] 刘魏魏, 王效科, 逯非, 等. 全球森林生态系统碳储量、固碳能力估算及其区域特征[J]. 应用生态学报, 2015, 26(9):2881-2890.
[50] 郭泽呈, 魏伟, 庞素菲, 等. 基于SPCA和遥感指数的干旱内陆河流域生态脆弱性时空演变及动因分析——以石羊河流域为例[J]. 生态学报, 2019, 39(7):2558-2572.
[51] 王士远, 张学霞, 朱彤, 等. 长白山自然保护区生态环境质量的遥感评价[J]. 地理科学进展, 2016, 35(10):1269-1278.
[52] 袁媛, 白中科, 师学义, 等. 基于生态安全格局的国土空间生态保护修复优先区确定——以河北省遵化市为例[J]. 生态学杂志, 2022, doi:10.13292/j. 1000-4890.202203.031.
[53] 张莉, 郭志华, 李志勇. 红树林湿地碳储量及碳汇研究进展[J]. 应用生态学报, 2013, 24(4):1153-1159.
[54] 张颖. 基于高分辨率遥感和极化雷达数据的大兴安岭地区森林地上生物量估测[D]. 北京:北京林业大学, 2016.