Disentangling gross primary productivity drivers of forested areas in China and its climate zones from 1990 to 2018

Gross primary productivity plays a critical role in global carbon balance. However, quantifying the effects of different drivers still constitutes a challenge due to the different modeling techniques and data used. This study employs spatiotemporal analysis, machine learning, and statistical approaches to measure the significance of forest gross primary productivity drivers in China and its climate zones from 1990 to 2018. The results show that the annual average forest gross primary productivity in China was 914.74 gC m −2 y −1 during the study period and showed a significantly increasing trend ( p < 0.01) at a rate of 4.09 gC m −2 y −1 . Forest gross primary productivity had a southeast-northwest downward spatiotemporal trend with significantly different distributions within the six climate zones, except in the arid and semi-arid zones. A Random Forest model did better than an eXtreme Gradient Boosting model when 10 explanatory variables were used. These variables included the novel forest fragmentation index and climate zones, which helped explain the effects of forest structure and climate characteristics of the climate zones better. The most important forest gross primary productivity drivers in China were mean annual temperature (26.2 %), mean annual precipitation (18.6 %), solar radiation (11 %), forest fragmentation index (8.8 %), and elevation (8.1 %). Using Chatterjee's correlation coefficient, this study provides, for each climate zone, its unique signature regarding the order and importance of the drivers of forest gross primary productivity. This study helps us understand what factors affect forest gross primary productivity in China and its climate zones better by showing how they work using machine learning. These findings may help China reach its carbon neutrality goals. • Forest gross primary productivity and drivers are quantified for China. • Forest gross primary productivity increased 4.09 gC m −2 y −1 from 1990 to 2018. • Temperature, precipitation and solar radiation are the major drivers. • Climate zones have different dominant forest gross primary productivity drivers.

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Publication Details

Journal
Journal of Cleaner Production
Published
2025-04-29
DOI
https://doi.org/10.1016/j.jclepro.2025.145616
Citations
3
Primary Topic
Forest ecology and management
Type
article
Field-Weighted Citation Impact
6.94

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article

Disentangling gross primary productivity drivers of forested areas in China and its climate zones from 1990 to 2018

Shuangao Wang, Yuhao Shao, Pedro Cabral, Wen Dai et al.
3 citations
Journal of Cleaner Production
Forest ecology and management
6.94
article

Disentangling gross primary productivity drivers of forested areas in China and its climate zones from 1990 to 2018

Shuangao Wang, Yuhao Shao, Pedro Cabral, Wen Dai, Chenxi Zhu, Ana Cristina Costa, Guojie Wang, Wei Liu
article en
3 citations

Abstract

Gross primary productivity plays a critical role in global carbon balance. However, quantifying the effects of different drivers still constitutes a challenge due to the different modeling techniques and data used. This study employs spatiotemporal analysis, machine learning, and statistical approaches to measure the significance of forest gross primary productivity drivers in China and its climate zones from 1990 to 2018. The results show that the annual average forest gross primary productivity in China was 914.74 gC m −2 y −1 during the study period and showed a significantly increasing trend ( p < 0.01) at a rate of 4.09 gC m −2 y −1 . Forest gross primary productivity had a southeast-northwest downward spatiotemporal trend with significantly different distributions within the six climate zones, except in the arid and semi-arid zones. A Random Forest model did better than an eXtreme Gradient Boosting model when 10 explanatory variables were used. These variables included the novel forest fragmentation index and climate zones, which helped explain the effects of forest structure and climate characteristics of the climate zones better. The most important forest gross primary productivity drivers in China were mean annual temperature (26.2 %), mean annual precipitation (18.6 %), solar radiation (11 %), forest fragmentation index (8.8 %), and elevation (8.1 %). Using Chatterjee's correlation coefficient, this study provides, for each climate zone, its unique signature regarding the order and importance of the drivers of forest gross primary productivity. This study helps us understand what factors affect forest gross primary productivity in China and its climate zones better by showing how they work using machine learning. These findings may help China reach its carbon neutrality goals. • Forest gross primary productivity and drivers are quantified for China. • Forest gross primary productivity increased 4.09 gC m −2 y −1 from 1990 to 2018. • Temperature, precipitation and solar radiation are the major drivers. • Climate zones have different dominant forest gross primary productivity drivers.

Journal of Cleaner ProductionVol. 509
Nanjing University of Information Science and Technology (CN), Beijing Academy of Science and Technology (CN), Universidade Nova de Lisboa (PT)
National Natural Science Foundation of China, Nanjing University of Information Science and Technology, Fundação para a Ciência e a Tecnologia
Climate action
Openalex Percentile: Top 4%
Forest ecology and management
6.94
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