Estimating ecological sustainability in the Guangdong-Hong Kong-Macao Greater Bay Area, China: Retrospective analysis and prospective trajectories.

Ecological sustainability Emergy ecological footprint Guangdong-Hong Kong-Macao Greater bay area Remote sensing application

Journal

Journal of environmental management
ISSN: 1095-8630
Titre abrégé: J Environ Manage
Pays: England
ID NLM: 0401664

Informations de publication

Date de publication:
01 Feb 2022
Historique:
received: 22 06 2021
revised: 22 11 2021
accepted: 23 11 2021
pubmed: 4 12 2021
medline: 25 12 2021
entrez: 3 12 2021
Statut: ppublish

Résumé

In recent decades, rapid urbanization and intensified global climate change have resulted in a significant difference of environment and resources distribution on space, which would cause trouble for accurate assessment of regional ecological sustainable development, especially in the large urban agglomerations. The parameters used in previous assessment methods have normally ignored spatial heterogeneity, leading to deviations in the evaluation accuracies against the context above. By incorporating remote sensing technology, this study proposed an improved emergy ecological footprint (EEF) method and a novel ecological sustainability index to comprehensively analyze the variability of ecological security states (ESS) from 1994 to 2018 in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and to predict its sustainable growth potential based on a combined factorial decomposition and scenario analysis. Results showed that the pixel-based emergy analysis revealed significant heterogeneity over time and space under the impact of climate change and intense land use activities during the study period. The emergy carrying capacity per capita (ecc) and the emergy ecological footprint per capita (eef) also showed a significant difference between the nine cities in the GBA. In addition, the traditional EEF method, which does not consider the spatiotemporal variation, has indeed overestimated the GBA's ecc by 15% compared with our results. The ESS of the GBA gradually worsened from slight insecurity in the 1990s to moderate insecurity in 2018. If the current trends in socio-economic activities and climate change continue according to the RCP8.5 scenario in the IPCC, the ESS of the GBA will reach the extreme insecurity state in 2050. However, our scenarios show that industrial structure adjustment, energy structure optimization, and especially biological resource conservation can reduce the EFI by approximately 6.52%, 23.4%, and 30.6%, respectively. Consequently, effective implementation of the above measures can limit the increase both in emergy ecological deficit and emergy ecological footprint intensity (EFI) and, together, contribute to a higher security status in the GBA in 2050.

Identifiants

pubmed: 34861505
pii: S0301-4797(21)02229-5
doi: 10.1016/j.jenvman.2021.114167
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

114167

Informations de copyright

Copyright © 2021. Published by Elsevier Ltd.

Auteurs

Qian Li (Q)

Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China; Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, 650000, China.

Jianping Wu (J)

Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China; Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, 511458, China.

Yongxian Su (Y)

Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China; Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, 511458, China. Electronic address: suyongxian@gdas.ac.cn.

Chaoqun Zhang (C)

Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China; Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, 511458, China.

Xiong Wu (X)

Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China.

Xingping Wen (X)

Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, 650000, China.

Guangqing Huang (G)

Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China; Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, 511458, China.

Yujiao Deng (Y)

Ecological Meteorological Center of Guangdong Province, Guangdong Meteorological Bureau, Guangzhou, 510080, China.

Raffaele Lafortezza (R)

Department of Agricultural and Environmental Sciences, University of Bari "A. Moro", Via Amendola 165/A, 70126, Bari, Italy.

Xiuzhi Chen (X)

Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.

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