There is no doubt that the rational and effective use of visible and thermal infrared image data information to achieve cross-modal complementary fusion is the key to improving the performance of RGB-T salient object detection (SOD). A meticulous analysis of the RGB-T SOD data reveals that it mainly consists of three scenarios in which both modalities (RGB and T) have a significant foreground and only a single modality (RGB or T) is disturbed. However, existing methods are obsessed with pursuing more effective cross-modal fusion based on treating both modalities equally. Obviously, the subjective use of equivalence has two significant limitations. Firstly, it does not allow for practical discrimination of which modality makes the dominant contribution to performance. While both modalities may have visually significant foregrounds, differences in their imaging properties will result in distinct performance contributions. Secondly, in a specific acquisition scenario, a pair of images with two modalities will contribute differently to the final detection performance due to their varying sensitivity to the same background interference. Intelligibly, for the RGB-T saliency detection task, it would be more reasonable to generate exclusive weights for the two modalities and select specific fusion mechanisms based on different weight configurations to perform cross-modal complementary integration. Consequently, we propose a weighted guided optional fusion network (WGOFNet) for RGB-T SOD. Specifically, a feature refinement module is first used to perform an initial refinement of the extracted multilevel features. Subsequently, a weight generation module (WGM) will generate exclusive network performance contribution weights for each of the two modalities, and an optional fusion module (OFM) will rely on this weight to perform particular integration of cross-modal information. Simple cross-level fusion is finally utilized to obtain the final saliency prediction map. Comprehensive experiments on three publicly available benchmark datasets demonstrate the proposed WGOFNet achieves superior performance compared with the state-of-the-art RGB-T SOD methods. The source code is available at: https://github.com/WJ-CV/WGOFNet .

With the fragmentation of global production, the share of intermediate trade in international trade has increased significantly. Nevertheless, more research must be done on how intermediate trade factors influence energy consumption. This paper utilized the multi-regional input-output (MRIO) model to categorize China's energy utilization into six parts based on its position within the global production chain. Additionally, it examined the impact of intermediate factors on China's energy consumption through index decomposition analysis (IDA). The results revealed that: (1) from 2000 to 2014, intermediate factors led to an increase of 1380.96Mtces in China's energy consumption, accounting for around 37% of the national total; (2) the reduction in efficiency is the primary cause of the surge in domestic intermediate energy consumption, while the increase in market proportion is the dominant driver promoting international intermediate energy consumption growth; (3) the international intermediate proportion contributed the most among all intermediate factors. Drawing upon the findings above, this paper proposed policy recommendations to regulate China's rising energy consumption, precisely the escalation of intermediate process energy usage, focusing on the global production chain.

As an important type of knowledge, standards are key factors in economic development and technological innovation. To analyze the impact of participation in standards development on enterprise performance, this study takes China’s high-tech industry as an example. We use the operating data of listed enterprises in the industry in 2019 and conduct the propensity score matching method matching analysis on the entire sample and the classification. The conclusion shows: From an overall point of view, the participation of enterprises in the development of standards has a positive impact on the enterprise’s return on total assets. Specifically, participating in the development of over three standards can also improve the return on total assets. Large enterprises can increase the return on total assets of the enterprise and the return on invested capital. The state-owned enterprises have a positive effect on the return on total assets of the enterprise. Enterprises in the western, central, and eastern region enterprises can increase their net profit, enterprise value and net profit, return on total assets and enterprise value respectively. The enterprises in Beijing-Tianjin-Hebei region, Guangdong-Hong Kong-Macao Greater Bay Area can improve their return on invested capital and enterprise value, average rate of return respectively. The participation in the development of national standards, industry standards and local standards can help increase their return on total assets, the return on total assets and enterprise value, enterprise value respectively. Finally, we suggestions are put forward to enhance enterprises’ enthusiasm to take part in standards development.

ABSTRACT This paper investigates the pattern of the financial development convergence for the top (Europe and Central Asia) and the bottom (South Asia) globalized developing regions from 1984 to 2016. We employ the Philips-Sul club convergence approach to measure the financial development convergence’s speed. The results validate the convergence of financial development in all countries, including the top and bottom of globalized developing regions. Interestingly, the speed of financial development convergence is less in the bottom globalized developing region than in the top globalized developing region. However, these results vary across developing regions in the case of financial institutions and financial markets. Therefore, solid financial market governance can provide a productive and efficient financial system, particularly in the bottom globalized economies.

As an important component of the national innovation system, universities have received widespread attention in recent years regarding their Scientific and Technological Innovation (STI) efficiency. This study considers a new perspective for viewing talent cultivation, scientific research, and social service as parallel subprocesses for universities to perform their functions. We also combine the concept of virtual frontiers to calculate the STI efficiency at universities, avoiding the situation where multiple Decision-making Units (DMUs) cannot distinguish their rankings due to their effectiveness. In addition, we use the virtual frontier method to calculate the Malmquist Productivity Index (MPI), which avoids the inability of DMU to distinguish the Technical Efficiency Change (TEC) due to continuous period effectiveness. Through empirical analysis, we show (1) Although there are significant differences in STI efficiency among universities, it has gradually increased overall, rising from 0.1623 in 2014 to 0.2433 in 2018; (2) The overall MPI of universities is not particularly ideal, with two periods having an MPI less than 1, and only 14 universities having an average MPI exceeding 1 in four periods; (3) The overall average value for TEC is 1.1342, with only one university having a mean value below 1. On the other hand, the overall average for Frontier Shift (FS) is 0.8775, and all universities have FS values below 1. Finally, based on the research results, several practical suggestions are provided.

Climate policies can have a significant impact on the economy. However, these policies have often been associated with uncertainty. Quantitative assessment of the socioeconomic impact of climate policy uncertainty is equally or perhaps more important than looking at the policies themselves. Using a deep learning algorithm—the MacBERT model—this study constructed indices of Chinese climate policy uncertainty (CCPU) at the national, provincial and city levels for the first time. The CCPU indices are based on the text mining of news published by a set of major newspapers in China. A clear upward trend was found in the indices, demonstrating increasing policy uncertainties in China in addressing climate change. There is also evidence of clear regional heterogeneity in subnational indices. The CCPU dataset can provide a useful source of information for government actors, academics and investors in understanding the dynamics of climate policies in China. These indices can also be used to investigate the empirical relationship between climate policy uncertainty and other socioeconomic factors in China.

ABSTRACT Climate change has become the greatest challenge for humanity in the 21st century, and extreme climate risks not only cause substantial direct economic losses, but also affect the operations of micro-enterprises. Based on financial data for Chinese A-share listed companies and meteorological disaster-loss data, this study found that comprehensive meteorological disaster losses significantly reduce firm performance, with rainstorm induced flood (landslide and mud-rock flow) disasters, drought disasters, gale, hail and lightning disasters being the most obvious. Further analysis showed that underperforming and smaller companies are more affected by meteorological disasters and that companies with diversified business operations and more managerial climate attention are less affected by meteorological disasters.

Co-abatement strategies that can reduce both CO2 and air pollutants emissions are essential for the green and low-carbon transition of China’s iron and steel industry. Most previous studies analyzed CO2 and air pollutants reduction policies independently, but the synergies and tradeoffs between the two types of policies have rarely been investigated. By establishing a bottom-up simulation model that considers combinations of different policy objectives, crude steel demand and technology development options, this study couples air pollutants reduction into the carbon neutrality pathways to evaluate the synergistic effects of different abatement strategies in China’s iron and steel industry. The study also incorporates the indirect CO2 and air pollutants emissions from the upstream energy production sectors into pathway analysis, in order to avoid cross-sectoral carbon and air pollution leakages. The results show that the current emissions reduction policies in the iron and steel industry are far from achieving the carbon neutrality goals. To achieve synergistic reduction in CO2 and air pollutants emissions, the short term policies should mainly rely on increasing the share of the scrap-electric arc furnace (EAF), while reducing crude steel demand and promoting breakthrough technologies, particularly hydrogen-based direct reduction, should play a major role in the long term. Meanwhile, China should promote the decarbonization of power generation and hydrogen production to reduce indirect CO2 and air pollutants emissions from the upstream sectors.