CGE Simulation Analysis on the Labor Transfer, Agricultural Technical Progress, and Economic Development in Chongqing

The basic structure of a CGE model dividing Mainland China into two parts, including Chongqing and rest regions, is described. Based on this CGE model, both the unilateral impact and collaborative impact of two policies, agricultural technical progress and supporting policies for improving rural labor transfer on the economic development in Chongqing, are simulated and analyzed. The results demonstrate that compared with the sum of each unilateral policy effect, the collaboration of two policies has more effective impact on facilitating the labor transfer, promoting regional economic growth, and improving income and welfare of urban and rural residents

Capitalisation of research and development investment and enterprise value: a study on the threshold effect based on level of financialisation

This study uses a mathematical model to explore how enterprises’ financialisation levels affect the role of research and development (R&D) investment capitalisation in enterprise value. We construct a mathematical model involving the financialisation level, capitalised R&D investment, and enterprise value. The sample comprises A-share listed companies that disclosed the capitalisation of R&D investment in the Shanghai and Shenzhen stock markets from 2014 to 2020. The results suggest that R&D investment capitalisation positively impacts enterprise value, especially in the current phase. With financialisation level as the threshold variable, R&D investment capitalisation has a double threshold effect on enterprise value in the current and next phases. Additionally, corporate financial investment behaviour has a timely impact on capitalised R&D investment but does not significantly impact enterprise value in a future phase. Enterprises evidently choose financial investment to enhance enterprise value by increasing capitalised R&D investment. These results can help enterprises formulate financial asset investment strategies and promote their development from virtual to real. The government should standardise enterprises’ financial investment behaviour, prevent excessive financialisation, and promote high-quality development of the real economy

Using Genome and Transcriptome Data From African-Ancestry Female Participants To Identify Putative Breast Cancer Susceptibility Genes

African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes. We built genetic models to predict levels of gene expression, exon junction, and 3\u27 UTR alternative polyadenylation using genomic and transcriptomic data generated in normal breast tissues from 150 AA participants and then used these models to perform association analyses using genomic data from 18,034 cases and 22,104 controls. At Bonferroni-corrected P \u3c 0.05, we identified six genes associated with breast cancer risk, including four genes not previously reported (CTD-3080P12.3, EN1, LINC01956 and NUP210L). Most of these genes showed a stronger association with risk of estrogen-receptor (ER) negative or triple-negative than ER-positive breast cancer. We also replicated the associations with 29 genes reported in previous TWAS at P \u3c 0.05 (one-sided), providing further support for an association of these genes with breast cancer risk. Our study sheds new light on the genetic basis of breast cancer and highlights the value of conducting research in AA populations

CGE Simulation Analysis on the Labor Transfer, Agricultural Technical Progress, and Economic Development in Chongqing

The basic structure of a CGE model dividing Mainland China into two parts, including Chongqing and rest regions, is described. Based on this CGE model, both the unilateral impact and collaborative impact of two policies, agricultural technical progress and supporting policies for improving rural labor transfer on the economic development in Chongqing, are simulated and analyzed. The results demonstrate that compared with the sum of each unilateral policy effect, the collaboration of two policies has more effective impact on facilitating the labor transfer, promoting regional economic growth, and improving income and welfare of urban and rural residents

Community mental health in China: a randomized controlled trial of psychoeducational family interventionfor carers of persons with schizophrenia in a rural area in Chengdu

published_or_final_versionSocial Work and Social AdministrationDoctoralDoctor of Philosoph

Financial Deepening, Spatial Spillover, and Urban–Rural Income Disparity: Evidence from China

Financial development is one of the main sources of economic growth, whether financial deepening can lower the income inequality between urban and rural areas has been the focus of policy makers and researchers. Using data from 31 provinces in China, from 2002 to 2013, this paper examines the impact of financial deepening on income inequality between urban and rural areas. These empirical results show that financial deepening is significantly negatively associated with urban–rural income disparity, that is, for every 1% increase in financial deepening urban–rural income disparity can be reduced by about 0.5%. Further research has investigated that the influence of financial deepening on income disparity has a selective effect. From the decomposition effect of financial deepening, we also find that the proximity effect of the Eastern and Central regions is higher than that of the local effect, while the local effect of the Western region is higher than that of the Eastern and Central regions, but the proximity effect is not significant. The conclusion of this paper is of great significance to further deepen financial reform, improve the quality of financial development, and achieve sustainable development of economy

Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively for mapping forest disturbances across the United States, the ability of this approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest) has yet to be assessed. In this study, the LTSS-VCT approach was applied to examine long-term (up to 24 years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and harvests) in the Greater Yellowstone Ecosystem (GYE). Using high spatial resolution images from Google Earth, we validated the detectable forest recovery status mapped by VCT by year 2011. Validation results show that the VCT was able to map long-term post-disturbance forest recovery with overall accuracy of ~80% for different disturbance types and forest types in the GYE. Harvested areas in the GYE have higher percentages of forest recovery than burned areas by year 2011, and National Forests land generally has higher recovery rates compared with National Parks. The results also indicate that forest recovery is highly related with forest type, elevation and environmental variables such as soil type. Findings from this study can provide valuable insights for ecosystem modeling that aim to predict future carbon dynamics by integrating fine scale forest recovery conditions in GYE, in the face of climate change. With the availability of the VCT product nationwide, this approach can also be applied to examine long-term post-disturbance forest recovery in other study regions across the U.S

Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively for mapping forest disturbances across the United States, the ability of this approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest) has yet to be assessed. In this study, the LTSS-VCT approach was applied to examine long-term (up to 24 years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and harvests) in the Greater Yellowstone Ecosystem (GYE). Using high spatial resolution images from Google Earth, we validated the detectable forest recovery status mapped by VCT by year 2011. Validation results show that the VCT was able to map long-term post-disturbance forest recovery with overall accuracy of ~80% for different disturbance types and forest types in the GYE. Harvested areas in the GYE have higher percentages of forest recovery than burned areas by year 2011, and National Forests land generally has higher recovery rates compared with National Parks. The results also indicate that forest recovery is highly related with forest type, elevation and environmental variables such as soil type. Findings from this study can provide valuable insights for ecosystem modeling that aim to predict future carbon dynamics by integrating fine scale forest recovery conditions in GYE, in the face of climate change. With the availability of the VCT product nationwide, this approach can also be applied to examine long-term post-disturbance forest recovery in other study regions across the U.S