Research on the control path and countermeasures of net CO2 emissions in central China - represented by Jiangxi Province.

The demand for carbon peaking and 'carbon neutrality' is putting pressure on some provinces in central China that are on the rise in economic development and rely on coal as their main energy source. This article takes Jiangxi Province as an example to study the relationship between net CO2 emissions and 11 indicators, the STIRPAT model is used to perform multivariate linear fitting. The results of regression are used to calculate the peak value of net CO2 emissions under different scenarios, and the fuzzy set qualitative analysis method is used to study the development model of net CO2 emissions in Jiangxi Province and its control path. The results showed that: (1) Per capita GDP has the most significant impact on the net CO2 emissions, while the proportion of total forestry output value, energy conservation, environmental protection expenditures in public expenditures, the number of buses vehicles operating per 10,000 people are negatively correlated with net CO2 emissions. (2) After simulating the possible development scenarios of Jiangxi Province, it can be concluded that there are in three scenarios the net CO2 emissions are most likely to peak before 2030. (3) Configuration analysis results showed that if Jiangxi Province wants to achieve 'carbon neutrality' in 2060, it is necessary to maintain both economic structural adjustment and energy consumption structural adjustment, with carbon intensity reduction as the main goal development model. (4) The central China should give more play to the advantages of the forestry industry and strengthen the energy structure regulation policy.

Efficiency evaluation of green innovation of China's heavy pollution industries based on SBM-Lasso-Tobit model.

Green innovation has become the goal for promoting the transformation and upgrading heavy pollution industries in the context of high-quality development, and the key factor for the success of green innovation is increasing the green innovation efficiency of heavy pollution industries. To understand the current situation of China's industrial innovation and get out of the dilemma, we use non-expected Slacks-based model (SBM) to measure green innovation efficiency in Chinese industry, Lasso regression to screen the influencing factors of heavy pollution industries, tobit regression to study the influence degree and direction of different influencing factors on green innovation efficiency of heavy pollution industry. The results show that: (1) The green innovation efficiency of the 16 heavily polluting industries studied in this paper is generally low; (2) Coordination, green and openness all have a positive impact on the green innovation efficiency of the industry. (3) A certain degree of government scientific research support is conducive to improving the efficiency of industrial green innovation and exceeding the limit will have a restraining effect on enterprise innovation. According to the results, we put forward the corresponding policy implications.

Investigating the Preventive Effects of 99Tc-Methylenediphosphonate on a Glucocorticoid-Induced Osteoporosis Rabbit Model.

BACKGROUND AND OBJECTIVE: Osteoporosis is a worldwide healthcare challenge. Conventional medications for osteoporosis prevention are not clinically effective or associated with gastrointestinal tract adverse effects. The present study aimed to comparatively investigate the effects of technetium-99 conjugated with methylene diphosphonate (99Tc-MDP) and calcium carbonate and alendronate on the prevention and treatment of osteoporosis in glucocorticoid-induced osteoporosis rabbit model through evaluating bone alkaline phosphatase (B-ALP), TRAP-5b levels and histopathological parameters. METHODS: Forty healthy female New Zealand rabbits were randomly divided into five groups (each n=8), including control group (Control Group), osteoporosis model group (GIO Group), osteoporosis model + 99Tc-MDP group (99Tc-MDP Group), osteoporosis model + alendronate group (Alendronate Group), and osteoporosis model + calcium carbonate group (calcium carbonate Group). Animals in each group were treated with corresponding interventions for 14 weeks. The blood samples were collected at the first and 14th week, and B-ALP and TRAP-5b levels were detected by enzyme- linked immunosorbent assay (ELISA). The rabbits were anesthetized at the 14th week, and pathological cytological observation was performed on both femurs. RESULTS: The age and weights of rabbits in different groups had no statistically significant differences (P>0.05). B-ALP levels in serum of all groups except for the Control Group decreased after treatment, but the differences were not statistically significant (P>0.05). TRAP-5b levels in serum of all groups increased after treatment. Specifically, differences in the GIO Group and calcium carbonate group were statistically significant (P<0.05), while differences in 99Tc-MDP Group and alendronate Group were not statistically significant (P<0.05). Pathological sections revealed that the control group presented normal bone tissue morphology. The bone tissue morphology of the 99Tc- MDP group and alendronate group was similar to control group and GIO group. Moreover, the calcium carbonate group and GIO group exhibited similar bone tissue morphology. CONCLUSION: 99Tc-MDP has a preventive effect on the glucocorticoid-induced osteoporotic rabbit model. This osteoporosis preventive effect might be attributed to the capacities of 99Tc-MDP in promoting the osteoblasts generation and inhibiting the generation and reducing the activity of osteoclasts.