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Study on the Evolutionary Characteristics of Spatial and Temporal Patterns and Decoupling Effect of Urban Carbon Emissions in the Yangtze River Delta Region Based on Neural Network Optimized by Aquila Optimizer with Nighttime Light Data

Author

Listed:
  • Xichun Luo

    (The Institute for Sustainable Development, Macau University of Science and Technology, Taipa, Macao 999078, China)

  • Chaoming Cai

    (School of Geography, South China Normal University, Guangzhou 510631, China)

  • Honghao Zhao

    (Department of Decision Sciences, School of Business, Macau University of Science and Technology, Taipa, Macao 999078, China)

Abstract

China produces the largest amount of CO 2 emissions since 2007 and is the second largest economy in the world since 2010, and the Yangtze River Delta (YRD) area plays a crucial role in promoting low-carbon development in China. Analyzing its evolutionary characteristics of spatial and temporal patterns and its decoupling effect is of great importance for the purpose of low-carbon development. However, this analysis relies on the estimation of CO 2 emissions. Recently, neural network-based models are widely used for CO 2 emission estimation. To improve the performance of neural network models, the Aquila Optimizer (AO) algorithm is introduced to optimize the hyper-parameter values in the back-propagation (BP) neural network model in this research due to the appealing searching capability of AO over traditional algorithms. Such a model is referred to as the AO-BP model, and this paper uses the AO-BP model to estimate carbon emissions, compiles a city-level CO 2 emission inventory for the YRD region, and analyzes the spatial dependence, spatial correlation characteristics, and decoupling status of carbon emissions. The results show that the CO 2 emissions in the YRD region show a spatial distribution pattern of “low in the west, high in the east, and developing towards the west”. There exists a spatial dependence of carbon emissions in the cities from 2001 to 2022, except for the year 2000, and the local spatial autocorrelation test shows that high-high is concentrated in Shanghai and Suzhou, and low-low is mainly centered in Anqing, Chizhou, and Huangshan in southern Anhui. Furthermore, there exist significant regional differences in the correlation levels of CO 2 emissions between cities, with a trend of low in the west and high in the east in location, and a decreasing and then increasing trend in time. From 2000 to 2022, the decoupling of carbon emissions and economic growth shows a steadily improving trend.

Suggested Citation

  • Xichun Luo & Chaoming Cai & Honghao Zhao, 2024. "Study on the Evolutionary Characteristics of Spatial and Temporal Patterns and Decoupling Effect of Urban Carbon Emissions in the Yangtze River Delta Region Based on Neural Network Optimized by Aquila," Land, MDPI, vol. 14(1), pages 1-23, December.
    • Handle: RePEc:gam:jlands:v:14:y:2024:i:1:p:51-:d:1556044
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    References listed on IDEAS

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    1. Liu, S. & Xiao, Q., 2021. "An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model," Energy, Elsevier, vol. 224(C).
    2. Feng, Yanchao & Pan, Yuxi & Lu, Shan & Shi, Jiaxin, 2024. "Identifying the multiple nexus between geopolitical risk, energy resilience, and carbon emissions: Evidence from global data," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    3. Liu, Xiaoyu & Duan, Zhiyuan & Shan, Yuli & Duan, Haiyan & Wang, Shuo & Song, Junnian & Wang, Xian'en, 2019. "Low-carbon developments in Northeast China: Evidence from cities," Applied Energy, Elsevier, vol. 236(C), pages 1019-1033.
    4. Zhu Liu & Dabo Guan & Wei Wei & Steven J. Davis & Philippe Ciais & Jin Bai & Shushi Peng & Qiang Zhang & Klaus Hubacek & Gregg Marland & Robert J. Andres & Douglas Crawford-Brown & Jintai Lin & Hongya, 2015. "Reduced carbon emission estimates from fossil fuel combustion and cement production in China," Nature, Nature, vol. 524(7565), pages 335-338, August.
    5. Kakran, Shubham & Sidhu, Arpit & Kumar, Ashish & Ben Youssef, Adel & Lohan, Sheenam, 2023. "Hydrogen energy in BRICS-US: A whirl succeeding fuel treasure," Applied Energy, Elsevier, vol. 334(C).
    6. Xu, Jinghang & Guan, Yuru & Oldfield, Jonathan & Guan, Dabo & Shan, Yuli, 2024. "China carbon emission accounts 2020-2021," Applied Energy, Elsevier, vol. 360(C).
    7. Xie, Pinjie & Gong, Ningyu & Sun, Feihu & Li, Pin & Pan, Xianyou, 2023. "What factors contribute to the extent of decoupling economic growth and energy carbon emissions in China?," Energy Policy, Elsevier, vol. 173(C).
    8. Frances C. Moore & Katherine Lacasse & Katharine J. Mach & Yoon Ah Shin & Louis J. Gross & Brian Beckage, 2022. "Determinants of emissions pathways in the coupled climate–social system," Nature, Nature, vol. 603(7899), pages 103-111, March.
    9. Liang, Xiaoying & Min Fan, & Xiao, Yuting & Yao, Jing, 2022. "Temporal-spatial characteristics of energy-based carbon dioxide emissions and driving factors during 2004–2019, China," Energy, Elsevier, vol. 261(PA).
    10. Yang, Jun & Hao, Yun & Feng, Chao, 2021. "A race between economic growth and carbon emissions: What play important roles towards global low-carbon development?," Energy Economics, Elsevier, vol. 100(C).
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