Forest harvesting restriction and forest restoration in China

Liu, Shilei; Xia, Jun

doi:10.1016/j.forpol.2021.102516

Cited by 22 publications

(14 citation statements)

References 41 publications

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“…A series of policies and regulations have been put forward in 2002 and 2003, including the GTGP, FHPKA, and CFTR. These policies coincided and positively contributed to the increases in both forest loss and gain area, which has been proved by a bunch of studies [3,4,8,62]. The FHPKA and GTGP are two of the China's biggest afforestation projects.…”

Section: Forestry Policies and Forest Loss And Gain Areamentioning

confidence: 83%

Section: Forestry Economy and Forest Lossmentioning

confidence: 99%

“…The economy policy can also indirectly affect forest loss area and explain the different change trends [58,62]. Based on the NFI data, Liu and Xia [62] found that the socioeconomic policies can significantly interact with forestry policy to influence forest loss and gain dynamics in China. Based on the forestry output value data from the NFI, we also conducted a correlation between forestry output value and forest loss area during 1990-2019.…”

Section: Forestry Economy and Forest Lossmentioning

confidence: 99%

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Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

et al. 2022

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China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations.

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Section: Forestry Policies and Forest Loss And Gain Areamentioning

confidence: 83%

Section: Forestry Economy and Forest Lossmentioning

confidence: 99%

Section: Forestry Economy and Forest Lossmentioning

confidence: 99%

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Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

et al. 2022

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“…And, the National Forest Inventory (NFI) is carried out every five years (between 1989 and 1993, 1994 and 1998, 1999 and 2003, 2004 and 2008, 2009 and 2013, and 2014 and 2018). Therefore, a corresponding sum over the entire reference period during which the inventory was conducted must be used for the period corresponding to each inventory rather than provincial figures for a single year [12,30]. National statistical yearbooks, national forestry statistical yearbooks, and national rural statistical yearbooks provide additional province-level data.…”

Section: Datementioning

confidence: 99%

“…This harvesting restriction, however, has generated debate ever since it was implemented [11]. Liu Shilei and Xia Jun [12] found that while the logging quota scheme has effectively halted the country's deforestation and forest degradation from the late 1980s to the early 1990s, it has played a minimal role in promoting forest restoration and regrowth by stifling investment in forest management. China has been planting and repairing forests, and new plants are good at absorbing CO 2 .…”

Section: Introductionmentioning

confidence: 99%

Is Regulation Protection? Forest Logging Quota Impact on Forest Carbon Sinks in China

Zhang,

He,

Huang

et al. 2023

Sustainability

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As the central part of terrestrial ecosystems, forests have an irreplaceable role in regulating climate, prompting various efforts to protect them. Logging regulation is the most commonly used forest conservation strategy. Although the logging permit scheme was written into the Forest Law in China, its effect on forest carbon sequestration has rarely been subject to careful empirical scrutiny. In this paper, we develop and estimate a spatial econometric model to disentangle its potential effects on forest carbon sinks based on a panel dataset of 29 provinces from 1989 to 2018 in China. Our calculations show that China’s forest carbon sinks are still growing and are connected geographically, with a tendency towards “high-high” and “low-low” aggregation. Increasing the logging quota produced a spatial spillover effect that might encourage the formation of forest carbon sinks in nearby areas. It considerably encouraged the expansion of forest carbon sinks. Additional mechanism testing is consistent with the claim that rising logging quotas have significantly boosted the proportion of timber forests in afforestation but had no effect on the movement of rural labor to urban areas. The development of forest carbon sinks is impacted in different ways by various logging quota types, with an increasing tendency for logging quotas to have a more significant contribution. Additionally, the non-collective forest region has a more significant spatial spillover effect of the logging quota on forest carbon sinks. The logging quota scheme should be improved by policymakers, beginning with eliminating tending quotas in the southern collective forest region. After that, the logging quota would gradually be eliminated nationwide, notably for commercial forests.

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Scientization or parametrization? Which system is more advantageous to environmental conservation in the reform of China’s state-owned forest farms?

Jin

Jiang

2023

Environ Sci Pollut Res

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Forest harvesting restriction and forest restoration in China

Cited by 22 publications

References 41 publications

Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

Is Regulation Protection? Forest Logging Quota Impact on Forest Carbon Sinks in China

Scientization or parametrization? Which system is more advantageous to environmental conservation in the reform of China’s state-owned forest farms?

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