Carbon sequestration costs and spatial spillover effects in China's collective forests.

BACKGROUND: Global climate change is one of the major challenges facing the world today, and forests play a crucial role as significant carbon sinks and providers of ecosystem services in mitigating climate change and protecting the environment. China, as one of the largest developing countries globally, owns 60% of its forest resources collectively. Evaluating the carbon sequestration cost of collective forests not only helps assess the contribution of China's forest resources to global climate change mitigation but also provides important evidence for formulating relevant policies and measures. RESULTS: Over the past 30 years, the carbon sequestration cost of collective forests in China has shown an overall upward trend. Except for coastal provinces, southern collective forest areas, as well as some southwestern and northeastern regions, have the advantage of lower carbon sequestration costs. Furthermore, LSTM network predictions indicate that the carbon sequestration cost of collective forests in China will continue to rise. By 2030, the average carbon sequestration cost of collective forests is projected to reach 125 CNY per ton(= 16.06 Euros/t). Additionally, there is spatial correlation in the carbon sequestration cost of collective forests. Timber production, labor costs, and labor prices have negative spatial spillover effects on carbon sequestration costs, while land opportunity costs, forest accumulation, and rural resident consumption have positive spatial spillover effects. CONCLUSION: The results of this study indicate regional disparities in the spatial distribution of carbon sequestration costs of collective forests, with an undeniable upward trend in future cost growth. It is essential to focus on areas with lower carbon sequestration costs and formulate targeted carbon sink economic policies and management measures to maximize the carbon sequestration potential of collective forests and promote the sustainable development of forestry.

Influencing factors and spatiotemporal heterogeneity of net carbon sink of conservation tillage: evidence from China.

Conservation tillage is an important reform of traditional tillage, which has significant carbon sequestration and emission reduction effects. It is important to investigate the influencing factors and spatiotemporal heterogeneity of net carbon sink of conservation tillage for realizing the "dual carbon" target, and facilitating agricultural sustainable development. This study used the coefficient accounting method to calculate the carbon sink and carbon emission of conservation tillage in China from 2000 to 2019, respectively. Based on this, the net carbon sink of conservation tillage was measured. Then, the spatiotemporal heterogeneity of influencing factors on net carbon sink of conservation tillage was analyzed by using the geographically and temporally weighted regression model. The results showed that (1) the net carbon sink of conservation tillage in China was significant and had potential to have a constant rise; (2) spatially, the net carbon sink of conservation tillage changed more variably in longitudinal direction. Specifically, the promotion effect of conservation tillage machinery gradually decreased from west to east. The planting structure and conservation tillage promotion intensity played key roles in improving net carbon sink of conservation tillage. (3) Temporally, the effect of conservation tillage machinery showed positive effect of decreasing yearly, while the positive effect of promotion intensity increased year by year. Planting structure and economic development negatively affected improvement on the net carbon sink of conservation tillage and the negative effect increased year by year. Additionally, the effect of education on the net carbon sink shifted from positive to negative over time. The study aims to provide a reference for the government to promote conservation tillage according to local conditions and to achieve the "dual carbon" target.

Identifying carbon emission characteristics and carbon peak in China based on the perspective of regional clusters.

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region.

The Effect of Non-Cognitive Ability on Farmer's Ecological Protection of Farmland: Evidence from Major Tea Producing Areas in China.

Ecological protection of farmland is an important means to reduce agricultural non-point source pollution and improve the quality of agricultural products. As the main body of current agricultural production and operation, the aging labor force has insufficient cognitive ability and low ability to learn actively, which is not conducive to transforming the green output. However, non-cognitive abilities closely related to the acquired environment can promote the elderly farmers' farmland ecological protection behavior by improving life satisfaction and social adaptability. Based on the above background, using the survey data of 964 farmers in China, the bivariate Probit model was used to empirically test the influence mechanism of non-cognitive ability on the ecological protection behavior of farmland. The study found that non-cognitive ability significantly promoted farmer's ecological protection of farmland in China. Specifically, the variables of non-cognitive ability, social communication ability, active learning ability, self-efficacy, stress resistance, altruistic tendency and individual resilience were found to significantly promote ecological protection of farmland. Mechanism analysis showed that non-cognitive ability promoted the ecological protection behavior of farmland by expanding social capital, information channels and improving technical value perception. A heterogeneity analysis revealed that non-cognitive ability had a greater impact on ecological protection behavior of farmland in the elderly and low-income groups. Therefore, government should attach importance to improving farmers' non-cognitive abilities, further increase technical publicity, and build a communication platform for farmers in order to promote the ecological protection of farmland.

MRA study on variation of the circle of willis in healthy Chinese male adults.

AIM: To investigate the morphology and variation of the circle of Willis (COW) in healthy Chinese male adults. MATERIALS AND METHODS: We analyzed cerebral magnetic resonance angiography (MRA) images of 2,246 healthy subjects using typical magnetic resonance imaging (MRI) and MRA. 3D-time of flight (TOF) MRA method was applied to all subjects and the classification was therefore achieved according to the integrity level of COW and the developmental situation of vessels. RESULTS: The overall incidence of COW integrity was 12.24%, with 7.57% nonvariation integral COW. The incidences of partial integrity and nonintegrity were 70.17% and 17.59%, respectively. The integrity rate of anterior circulation was 78.58%, with a close correlation with A1 segment of the anterior cerebral artery (ACA-A1) developmental condition. The developmental variation rate of ACA-A1 was 28.23% and the variation of the right side was higher than that of the left side. The nonintegrity rate of posterior circulation was 83.93% as the hypoplasia of P1 segment of the posterior cerebral artery (PCA-P1) with an incidence rate of 15.85% for PCA-P1 variation. CONCLUSIONS: The COW variation is a common phenomenon among the healthy subjects. MRA could enable reflecting the physiological morphology of COW in a comprehensive manner.