整合可持续发展目标和新一代信息技术体系,实现城市“热—碳—污”协同降减
1.浙江大学城乡规划理论与技术研究所,杭州 310058
2.浙江大学城乡规划设计研究院有限公司,杭州 310030
收稿日期: 2024-07-24
修回日期: 2024-08-12
网络出版日期: 2024-10-23
基金资助
国家自然科学基金面上项目(51578482)
Integrating sustainable development goals and next-generation information technology systems to achieve synergistic heat-carbon-pollution reductions in cities
1. Institute of Urban and Rural Planning Theories and Technologies, Zhejiang University, Hangzhou 310058, China
2. Zhejiang University Urban-Rural Planning & Design Institute Co., Ltd., Hangzhou 310030, China
Received date: 2024-07-24
Revised date: 2024-08-12
Online published: 2024-10-23
城市化与气候变化的双重压力正加剧城市热岛效应、碳排放和空气污染,对环境可持续性与城市宜居性构成严峻挑战。随着城市生态环境多目标协同治理需求日益增长,将热、碳、污纳入同一框架进行综合评估,已经成为未来城市规划与政策制定的关键方向。本文系统比较并分析了全球发展议程与城市热、碳、污降减目标之间的一致性,强调了新一代信息技术在智能优化与协同调度、数据融合与分析、实时监控与反馈、决策支持与模拟等方面具有显著潜力和优势。从城市空间形态的新视角出发,综合评述了多尺度、多维度开展“热—碳—污”多目标协同降减规划的具体内容、面临问题及未来挑战,为我国城市“热—碳—污”多目标协同治理和可持续发展提供了创新性的解决途径。
王伟武, 何杰, 厉华笑
.
整合可持续发展目标和新一代信息技术体系,实现城市“热—碳—污”协同降减[J]. 科技导报, 0
The dual pressures of urbanization and climate change are intensifying the urban heat island effect, carbon emissions, and air pollution, posing significant challenges to environmental sustainability and urban livability. As the demand for multi-objective coordinated management of urban ecological environments continues to increase, integrating heat, carbon, and pollution into a unified framework for comprehensive assessment has become a key direction for future urban planning and policy-making. This article systematically compares and analyzes the consistency between global development agendas and the goals of reducing urban heat, carbon, and pollution, highlighting the significant potential and advantages of new-generation information technologies in intelligent optimization and coordinated scheduling, data fusion and analysis, real-time monitoring and feedback, and decision support and simulation. From the new perspective of urban spatial form, it comprehensively reviews the specific content, challenges, and future issues in conducting multi-scale, multi-dimensional “heat-carbon-pollution” multi-objective coordinated reduction planning. It provides innovative solutions for multi-objective coordinated management and sustainable development of “heat-carbon-pollution” in Chinese cities.
[1] Tan X L, Zhou Z, Wang W L. Relationships between urban form and PM2.5 concentrations from the spatial pattern and process perspective[J]. Building and Environment, 2023, 234: 110147.
[2] Deng X W, Cao Q, Wang L C, et al. Characterizing urban densification and quantifying its effects on urban thermal environments and human thermal comfort[J]. Landscape and Urban Planning, 2023, 237: 104803.
[3] Blasi S, Ganzaroli A, De Noni I. Smartening sustainable development in cities: Strengthening the theoretical linkage between smart cities and SDGs[J]. Sustainable Cities and Society, 2022, 80: 103793.
[4] Wang R Y, Zhao J S, Lin Y L, et al. Study on the response and prediction of SDGs based on different climate change scenarios: The case of the urban agglomeration in central Yunnan[J]. Ecological Indicators, 2023, 156: 111076.
[5] Said O, Tolba A. Accurate performance prediction of IoT communication systems for smart cities: An efficient deep learning based solution[J]. Sustainable Cities and Society, 2021, 69: 102830.
[6] Feng Z K, Zheng L N, Ren B L, et al. Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration[J]. The Science of the Total Environment, 2024, 945: 174089.
[7] Bibri S E, Huang J, Krogstie J. Artificial intelligence of things for synergizing smarter eco-city brain, metabolism, and platform: Pioneering data-driven environmental governance[J]. Sustainable Cities and Society, 2024, 108: 105516.
[8] Ma J, Ding Y X, Cheng J C P, et al. A Lag-FLSTM deep learning network based on Bayesian Optimization for multi-sequential-variant PM2.5 prediction[J]. Sustainable Cities and Society, 2020, 60: 102237.
[9] Wang P, Zhang H, Qin Z D, et al. A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting[J]. Atmospheric Pollution Research, 2017, 8(5): 850-860.
[10] AlDousari A E, Al Kafy A, Saha M L, et al. Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait[J]. Sustainable Cities and Society, 2022, 86: 104107.
[11] Kow P Y, Hsia I W, Chang L C, et al. Real-time image-based air quality estimation by deep learning neural networks[J]. Journal of Environmental Management, 2022, 307: 114560.
[12] Feng R, Zheng H J, Gao H, et al. Recurrent Neural Network and random forest for analysis and accurate forecast of atmospheric pollutants: A case study in Hangzhou, China[J]. Journal of Cleaner Production, 2019, 231: 1005-1015.
[13] Ao X Y, Qian J, Lu Y W, et al. Mapping fine-scale anthropogenic heat flux in Shanghai by integrating multi-source geospatial big data using Cubist[J]. Sustainable Cities and Society, 2024, 101: 105125.
[14] Rezapour A, Tzeng W G. RL-PMAgg: Robust aggregation for PM2.5 using deep RL-based trust management system[J]. Internet of Things, 2021, 13: 100347.
[15] Jang S, Bae J, Kim Y. Street-level urban heat island mitigation: Assessing the cooling effect of green infrastructure using urban IoT sensor big data[J]. Sustainable Cities and Society, 2024, 100: 105007.
[16] Sun H C, Fung J C H, Chen Y A, et al. Improvement of PM2.5 and O3 forecasting by integration of 3D numerical simulation with deep learning techniques[J]. Sustainable Cities and Society, 2021, 75: 103372.
[17] Li P Y, Wang Z H. Environmental co-benefits of urban greening for mitigating heat and carbon emissions[J]. Journal of Environmental Management, 2021, 293: 112963.
[18] Wang W W, Wang D, Chen H, et al. Identifying urban ventilation corridors through quantitative analysis of ventilation potential and wind characteristics[J]. Building and Environment, 2022, 214: 108943.
[19] Zha Q F, Liu Z, Wang J. Spatial pattern and driving factors of synergistic governance efficiency in pollution reduction and carbon reduction in Chinese cities[J]. Ecological Indicators, 2023, 156: 111198.
[20] Zhou S W, Shi T M, Li S, et al. The impact of urban morphology on multiple ecological effects: Coupling relationships and collaborative optimization strategies[J]. Building Simulation, 2023, 16(8): 1539-1557.
[21] Wang Z H. Compound environmental impact of urban mitigation strategies: Co-benefits, trade-offs, and unintended consequence[J]. Sustainable Cities and Society, 2021, 75: 103284.
[22] He N C, Zeng S B, Jin G. Achieving synergy between carbon mitigation and pollution reduction: Does green finance matter?[J]. Journal of Environmental Management, 2023, 342: 118356.
[23] Jamei E, Thirunavukkarasu G, Chau H W, et al. Investigating the cooling effect of a green roof in Melbourne[J]. Building and Environment, 2023, 246: 110965.
[24] Kashki A, Karami M, Zandi R, et al. Evaluation of the effect of geographical parameters on the formation of the land surface temperature by applying OLS and GWR, A case study Shiraz City, Iran[J]. Urban Climate, 2021, 37: 100832.
[25] Park S, Park J, Lee S. Unpacking the nonlinear relationships and interaction effects between urban environment factors and the urban nighttime heat index[J]. Journal of Cleaner Production, 2023, 428: 139407.
[26] Son T H, Weedon Z, Yigitcanlar T, et al. Algorithmic urban planning for smart and sustainable development: Systematic review of the literature[J]. Sustainable Cities and Society, 2023, 94: 104562.
[27] Ahmed I, van Esch M, van der Hoeven F. Heatwave vulnerability across different spatial scales: Insights from the Dutch built environment[J]. Urban Climate, 2023, 51: 101614.
[28] 黄群芳. 城市空间形态对城市热岛效应的多尺度影响研究进展[J]. 地理科学, 2021, 41(10): 1832-1842.
[29] Sun Y W, Gao C, Li J L, et al. Quantifying the effects of urban form on land surface temperature in subtropical high-density urban areas using machine learning[J]. Remote Sensing, 2019, 11(8): 959.
[30] Li F, Zhou T, Lan F. Relationships between urban form and air quality at different spatial scales: A case study from Northern China[J]. Ecological Indicators, 2021, 121: 107029.
[31] 于晓雨, 许刚, 刘樾, 等. 长江三角洲地区城市建筑三维形态对地表热环境的影响[J]. 中国环境科学, 2021, 41(12): 5806-5816.
[32] Chen Y, Yang J, Yu W B, et al. Relationship between urban spatial form and seasonal land surface temperature under different grid scales[J]. Sustainable Cities and Society, 2023, 89: 104374.
[33] Li S, Zou B, Ma X Y, et al. Improving air quality through urban form optimization: A review study[J]. Building and Environment, 2023, 243: 110685.
[34] 冷红, 赵妍, 袁青. 城市形态调控减碳路径与策略[J]. 城市规划学刊, 2023(1): 54-61.
[35] Samson U E P, Atun F, Zeng Y J, et al. Robust drivers of urban land surface temperature dynamics across diverse landscape characters: An augmented systematic literature review[J]. Ecological Indicators, 2024, 163: 112056./
〈 |
|
〉 |