In recent years, the massive trades between China and its neighboring countries have promoted the development of forestry and economic reforms in many countries, but have also posed a certain threat to forest health, which brought further attention to forest health in the border. This paper takes the cross-border between China, Democratic People's Republic of Korea (DPRK)and Russia as the research object. Based on multi-source remote sensing data, we collected forest resource survey data in 2000, 2010, and 2020, and use a combination of analytic hierarchy process and entropy method to determine the index weight and evaluate forest health status. As a result, most of the forest health is in the sub-healthy and moderate health range. The extremely unhealthy forests show a downward trend year by year, and most of them are in the areas of human activity. Except for extremely unhealthy and healthy levels, all grades showed a slow upward trend, which also led to a decrease in the proportion of healthy forests year by year. On a national scale, 50%-60% of the regional forest health in China is in a moderately healthy and sub-healthy state, while unhealthy forest areas account for 18%-21%, and are rising constantly year by year at 18.99%, 19.45%, and 20.76% respectively. The healthy and sub-healthy forest areas in Democratic People's Republic of Korea are on a downward trend in 2010. The sub-healthy forest areas have declined significantly compared with 2000 (25.4%, 7.54%). By 2020, the healthy and sub-healthy forests have continued to decrease by 2.47%. The extremely unhealthy and healthy regional forests in Russia's developed slowly. As a whole, the forest health has declined slightly (0.35%) from 2000 to 2020. In the cross-border region between China, DPRK and Russia, the forest health rank of the three countries is Russia>China>DPRK. This result can be promoted for local development and targeted construction. Through sustainable management and protection, it can provide an effective basis for the healthy development of local or regional forests, and can better protect and manage forest resources.
The cross-border e-commerce platform is a global trade platform that is "free, open, universal and inclusive". On this platform, consumers all over the world can buy and SMEs can sell all over the world, which truly realizes the seamless link of globalization. In the new era, the cross-border e-commerce industry has developed rapidly. In order to promote the development of cross-border e-commerce industry, the original development model must be innovated and optimized, focusing on the optimization of various modules and work processes within the enterprise. Make e-commerce operation more efficient and purchase more convenient, so as to bring users a more convenient and pleasant shopping experience. Research and analyze the cross-border e-commerce business model, first calculate the evaluation results of business pattern innovation elements, and then explain the business pattern innovation under big data analysis technology through actual data. The utilization rate of enterprise resource allocation has increased by 18.47%, and the commercial values have effectively increased by about 27.94%. Therefore, the competitive advantage of e-commerce enterprises has been greatly enhanced.
This paper introduces the concept of technology finance and the combination of science and technology and finance in Guangdong Province, and based on the time series model of Eviews, this paper analyzes the impact of government oriented Technology Finance on technology enterprises in Guangdong Province in recent years, and then obtains the strategy of technology finance to support technology enterprises in Guangdong Province, so as to strengthen the support of technology finance to technology enterprises in Guangdong Province.
With the rapid development of the digital economy, manufacturing enterprises have become the main battlefield for developing the digital economy, and digital transformation is a general trend. Various uncertainties encountered during the process of digital transformation of manufacturing enterprises, how to identify risks in advance in the digital transformation, timely risk avoidance, and risk control are critical to the transformation's success. As a result, establishing a financial early warning system for manufacturing enterprises undergoing digital transformation is critical. This paper introduces non-financial indicators on the basis of financial indicators, takes into account digital transformation, and uses Python crawlers to use text analysis to obtain the digital transformation of 20 manufacturing companies listed on the Shenzhen and Shanghai exchanges that are treated as ST in 2018-2022 and 20 companies in the same industry and of the same scale but with normal finances in that year. The financial indicators were then filtered using the K-S test, t-test, and Mann-Whitney U test, yielding a final set of 18 indicators. The final six principle components were extracted by factor analysis using the KMO and Bartlett tests, and a logistic regression financial risk early warning model was built with them to serve as a guide for manufacturing businesses and their stakeholders going through a digital transition.
This article examines Chinese Manufacturing firms with A-shares listed in Shanghai and Shenzhen between 2010 and 2019 as a study sample It examines the connections and mechanisms between innovation in green technology within corporations, equity concentration, as well as corporate carbon performance using an interaction effect model. It was revealed that although encouraging innovation in green technology within corporations may assist businesses in reducing their corporate carbon performance, equity concentration has a detrimental impact on this connection. Last but not least, the South-North categorization test showed that although green technology innovation and corporate carbon performance. Finally, a significant positive driving impact of green technology innovation on corporate carbon performance in the North was found by the South-North classification test, but this driving impact was not reflected in the South. Therefore, clarifying the association between innovation in green technology within corporations, equity concentration as well as corporate carbon performance can help companies solve many fundamental challenges in carbon emission reduction, bring agile and efficient operation mode for companies, and is important to achieve long-term growth and sustainable change of corporate green innovation capacity.
