TY - JOUR
T1 - A global slowdown in the enhancement of urban vegetation growth
AU - Wang, Zhao
AU - Liu, Shuguang
AU - Yuan, Wenping
AU - Wu, Yiping
AU - Smith, Andrew R.
AU - Ning, Ying
AU - Gao, Haiqiang
AU - Feng, Shuailong
AU - Zhao, Shuqing
N1 - Publisher Copyright:
© 2026 The Author(s). Published by Elsevier Inc. on behalf of Youth Innovation Co., Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
PY - 2026
Y1 - 2026
N2 - Cities experience rapid environmental change; thus, vegetation changes in urban areas can provide insights into how vegetation responds to future environmental change. Although previous research has reported a global rise in indirect effects (ωi) of urban vegetation growth, the specific temporal patterns and changes in this growth improvement remain inadequately investigated. To address this, ωi trends over the past two decades were analyzed, and data-driven artificial intelligence models were developed to predict future growth under various climate scenarios in 4,983 cities worldwide. The analysis revealed that ωi exhibited a logistic increase over time, culminating in an approximate 70% enhancement by 2020. However, predictions from the six models indicate a transition to a slower, linear growth pattern moving forward, with additional growth ranging from 16% to 30% between 2020 and 2100. This marks a stark contrast to the rapid growth observed between 2000 and 2020. The analysis identified CO2 as the primary driver of growth enhancement over the past two decades, but its future influence is expected to decline. Instead, land surface temperature, precipitation patterns, and urban population dynamics are projected to become the dominant factors driving future ωᵢ of vegetation growth. These findings offer critical insights into the future acclimation of urban vegetation, supporting improved forecasting and urban planning under global change.
AB - Cities experience rapid environmental change; thus, vegetation changes in urban areas can provide insights into how vegetation responds to future environmental change. Although previous research has reported a global rise in indirect effects (ωi) of urban vegetation growth, the specific temporal patterns and changes in this growth improvement remain inadequately investigated. To address this, ωi trends over the past two decades were analyzed, and data-driven artificial intelligence models were developed to predict future growth under various climate scenarios in 4,983 cities worldwide. The analysis revealed that ωi exhibited a logistic increase over time, culminating in an approximate 70% enhancement by 2020. However, predictions from the six models indicate a transition to a slower, linear growth pattern moving forward, with additional growth ranging from 16% to 30% between 2020 and 2100. This marks a stark contrast to the rapid growth observed between 2000 and 2020. The analysis identified CO2 as the primary driver of growth enhancement over the past two decades, but its future influence is expected to decline. Instead, land surface temperature, precipitation patterns, and urban population dynamics are projected to become the dominant factors driving future ωᵢ of vegetation growth. These findings offer critical insights into the future acclimation of urban vegetation, supporting improved forecasting and urban planning under global change.
KW - Ecology
KW - future projection
KW - indirect effects
KW - long-time vegetation change
KW - urbanization intensity
KW - vegetation growth enhancement
UR - https://www.scopus.com/pages/publications/105042607676
U2 - 10.1016/j.xinn.2026.101459
DO - 10.1016/j.xinn.2026.101459
M3 - 文章
AN - SCOPUS:105042607676
SN - 2666-6758
JO - Innovation
JF - Innovation
M1 - 101459
ER -