66 billion trees have been planted in China's Great Green Wall  and they appear to be growing faster than natural forests 10%

By Brian Owens0%

6/30/2026, 4:08:26 PM

BS Summary: This article contains 24 faulty reasoning types, including Appeal to Authority, Indoctrination, and Post Hoc (False Cause), with Optimism Bias as the most egregious example at 16.4% saturation with 95 hits. Analysis detected 892 faulty-reasoning hits from 581 analyzed words, generating a BS Score of 27.2% and a BS Rank of 10% (19,068 of 21,182 articles). This article is better (less manipulative) than 90.00% of the article peer group.

Trees in China that were planted as part of huge reforestation projects appear to grow faster than those in natural forests, a new study finds. 
This is possibly because the reforestation trees are responding more strongly to the rising atmospheric carbon dioxide, scientists say. 
China is quickly turning green. 
The country has planted 66 billion trees since 1978, with plans for 34 billion more by the middle of this century, as part of its "Great Green Wall" to slow the spread of the Gobi and Taklamakan deserts. 
These new forests absorb large amounts of CO2, but it is unclear exactly how they differ from natural ones, study first author Yuhang Luo, a landscape ecologist at Peking University in Shenzhen, China, told Live Science. 
Luo and his colleagues set out to study how differences between natural and planted forests, including species diversity, tree density and age, might affect how the forests respond to rising CO2 and climate change. 
"Planted forests are widely used in climate mitigation strategies, but most global ecosystem models do not distinguish between forest types or represent age-related dynamics adequately," Luo said. 
"So we felt it was important to clarify how these factors interact — not just for scientific understanding, but also for improving the models and assumptions that underpin real-world forest policy and carbon accounting." 
Most of that difference was due to planted forests being, on average, much younger than the natural ones — and young trees grow faster than old ones. 
But even when comparing forests of similar age and growing conditions, the planted ones still grew 4.6% faster, and the difference was even more pronounced in mixed and evergreen forests. 
This discrepancy peaks in planted forests when trees are around 30 to 40 years old and then declines noticeably after age 40. 
In contrast, natural forests grow more slowly but steadily, so have an advantage over the long term. 
"Planted forests can be a powerful short-term tool for carbon uptake, but this advantage is temporary," Luo said. 
"For long-term carbon storage and resilience, natural forests remain irreplaceable." 
This is largely due to how planted forests are managed. 
They tend to feature fast-growing species like eucalyptus and poplar and are often actively managed, with people removing competing vegetation and even fertilizing them. 
These interventions reduce competition for light, water, and nutrients, amplifying the fertilization effect of rising atmospheric CO2. 
Luo said the findings show that most global climate models are missing something when it comes to understanding how various forest types play a role in carbon sequestration and climate change. 
"Land use management works in more subtle and specific ways than we had assumed," he said. 
"It is not just about planting more trees. 
It is also about when you plant them, what species you choose, and how you manage them over time." 
Luo hopes these findings will help guide reforestation efforts, to ensure we get the most benefit from planting new forests to help mitigate the effects of climate change. 
"Our work offers a more practical guide for forest-based climate action: when to plant, what to plant, how long the benefits last, and what current models are getting wrong. 
We hope that helps people make better decisions," he said. 
*Editor's note: A picture caption in this article was corrected at 5:16 ET on July 1 to say 66 billion trees had been planted. 
* 
Article reasoning-pattern comparisonThis article: 4.6%Brian Owens: 1.2%Live Science: 2.7%Confirmation Bias4.6%This article: 6.5%Brian Owens: 1.6%Live Science: 1.3%Anchoring Bias6.5%This article: 4.3%Brian Owens: 1.1%Live Science: 2.7%Availability Heuristic4.3%This article: 4.1%Brian Owens: 1.0%Live Science: 1.4%Representativeness Heuristic4.1%This article: 2.8%Brian Owens: 0.7%Live Science: 0.4%Hindsight Bias2.8%This article: 3.3%Brian Owens: 3.3%Live Science: 3.1%Overconfidence Bias3.3%This article: 7.6%Brian Owens: 4.0%Live Science: 3.4%Framing Effect7.6%This article: 3.1%Brian Owens: 0.8%Live Science: 0.5%Loss Aversion3.1%This article: 0.0%Brian Owens: 0.0%Live Science: 0.4%Status Quo Bias0.0%This article: 6.5%Brian Owens: 1.6%Live Science: 0.2%Sunk Cost Effect6.5%This article: 16.4%Brian Owens: 4.3%Live Science: 3.6%Optimism Bias16.4%This article: 0.0%Brian Owens: 0.0%Live Science: 1.2%Pessimism Bias0.0%This article: 6.2%Brian Owens: 1.5%Live Science: 3.2%Negativity Bias6.2%This article: 10.8%Brian Owens: 2.7%Live Science: 0.6%Self-Serving Bias10.8%This article: 1.7%Brian Owens: 0.4%Live Science: 0.4%Fundamental Attribution Error1.7%This article: 0.0%Brian Owens: 0.0%Live Science: 0.1%Actor-Observer Bias0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.3%In-Group Bias0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.1%Out-Group Homogeneity Bias0.0%This article: 1.7%Brian Owens: 0.4%Live Science: 1.3%Halo Effect1.7%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%Horn Effect0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%Dunning-Kruger Effect0.0%This article: 5.2%Brian Owens: 1.3%Live Science: 0.9%Recency Bias5.2%This article: 0.0%Brian Owens: 0.0%Live Science: 0.3%Primacy Effect0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.1%Blind-Spot Bias0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%Ad Hominem0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.1%Straw Man0.0%This article: 15.0%Brian Owens: 4.6%Live Science: 4.2%Appeal to Authority15.0%This article: 4.5%Brian Owens: 1.1%Live Science: 1.1%False Dilemma4.5%This article: 0.0%Brian Owens: 0.0%Live Science: 0.4%Slippery Slope0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%Circular Reasoning0.0%This article: 4.3%Brian Owens: 2.2%Live Science: 3.8%Hasty Generalization4.3%This article: 0.0%Brian Owens: 0.0%Live Science: 0.3%Red Herring0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.3%Bandwagon0.0%This article: 5.7%Brian Owens: 1.4%Live Science: 2.4%Appeal to Emotion5.7%This article: 0.0%Brian Owens: 0.0%Live Science: 0.5%Begging the Question0.0%This article: 12.6%Brian Owens: 4.0%Live Science: 2.3%Post Hoc (False Cause)12.6%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%Tu Quoque0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.4%Burden of Proof0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.5%Appeal to Nature0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.3%Composition/Division0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 1.8%Anecdotal0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%No True Scotsman0.0%This article: 5.7%Brian Owens: 1.4%Live Science: 1.7%Ambiguity (Equivocation)5.7%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.1%Middle Ground0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.1%Personal Incredulity0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.1%Special Pleading0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.1%Genetic Fallacy0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 1.5%Unattributed Quote0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 1.0%Quote-first Misdirection0.0%This article: 0.9%Brian Owens: 1.4%Live Science: 3.6%Biased Writer Voice0.9%This article: 13.9%Brian Owens: 3.5%Live Science: 1.0%Indoctrination13.9%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Brian Owens: 0.0%Live Science: 0.0%Politically Right Leaning Bias0.0%This article: 6.2%Brian Owens: 1.5%Live Science: 1.6%Attempt to Sell a Product or S…6.2%

581 words analyzed.

Speakers

1speaker46%attributed speech315writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 22 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 5 words • 100.0% coverageWriter's voice • 38 words • 0.0% coverageYuhang Luo • 36 words • 100.0% coverageWriter's voice • 34 words • 0.0% coverageYuhang Luo • 27 words • 0.0% coverageYuhang Luo • 34 words • 100.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageYuhang Luo • 18 words • 0.0% coverageYuhang Luo • 10 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageYuhang Luo • 31 words • 0.0% coverageYuhang Luo • 16 words • 0.0% coverageYuhang Luo • 8 words • 0.0% coverageYuhang Luo • 19 words • 100.0% coverageYuhang Luo • 28 words • 100.0% coverageYuhang Luo • 29 words • 0.0% coverageYuhang Luo • 10 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverage
Selected voice

Yuhang Luo

100%flagged-word coverage
266 attributed words100% of attributed speech74% writer coverage
0%17.5%35.0%Indoctrination+30.5 ptsWriter: 0.0%Yuhang Luo: 30.5%30.5%Attempt to Sell a Product +13.5 ptsWriter: 0.0%Yuhang Luo: 13.5%13.5%Biased Writer Voice-1.6 ptsWriter: 1.6%Yuhang Luo: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.