Liang Ye

The carbon trading pilot policy is a key policy to achieve low-carbon development through market mechanism. Studying the impact of carbon trading on resource allocation efficiency can provide an important empirical basis to achieve energy saving and green development. This paper delves into the influence of carbon trading on resource allocation efficiency by utilizing the multiperiod difference-in-differences based on comprehensive provincial data spanning from 2011 to 2020 in China. The findings show that carbon trading can promote the optimization of capital allocation efficiency but negatively affect the labor allocation efficiency in the pilot areas. This conclusion still holds through various robustness tests. Further research on regional heterogeneity shows that there are significant differences in the impact of resource allocation efficiency and labor allocation efficiency between eastern and western regions. Crucially, the limited scope of the carbon market’s direct influence on labor allocation efficiency is highlighted, demonstrating the need for government intervention to amplify its optimization effects based on the synergistic effect. It provides vital empirical insights for China’s ongoing pursuit of energy-saving and green development strategies, emphasizing the synergies between market mechanisms and government regulation in fostering sustainable economic transformation.

This study extends the existing research on carbon trading policies from the perspective of mediating effects. Based on the difference-in-differences method, this study helps to understand the relationship between China’s carbon trading policies and resource allocation efficiency. The study finds that carbon trading policy promotes the optimization of capital allocation efficiency but does not promote the optimization of labor allocation efficiency. This conclusion has passed a series of robustness tests. Moreover, our analysis shows that carbon trading policies can influence resource allocation efficiency through per capita GDP, foreign direct investment, and innovation levels using multiple mediating models. Factors such as market size, the number of emission entities, and the behavior of market participants affect the resource allocation efficiency in the carbon trading process. Finally, the spatial spillover effect of the carbon trading policy is verified. This paper provides empirical evidence and policy implications for achieving the dual carbon goal and sustainable development.

In rural areas of China, the challenges of efficient and cost-effective distribution are exacerbated by underdeveloped infrastructure and low population density, with last mile logistics distribution posing a significant obstacle. To address the gap in drone application for last mile logistics in rural areas, a truck–drone distribution model was developed based on the specific conditions of rural regions. The improved fuzzy C-means algorithm (FCM) and genetic simulated annealing algorithm (GASA) were employed to tackle real−world cases in rural areas. The focus of the truck–drone system is to optimize the rural logistics distribution process, reduce delivery time, and minimize costs while considering factors such as maximum mileage of trucks and drones as well as customer priority. Compared to traditional methods, this system has demonstrated notable improvements in distribution efficiency and cost reduction, offering valuable insights for practical drone applications in last mile rural logistics.

In this study, we propose a user behavior-based query optimization strategy to address the commonly observed suboptimal query performance and user experience in the field of video retrieval. This strategy primarily involves analyzing user behavioral data during the video retrieval process to expand query keywords, enhancing their relevance and diversity, thus optimizing search results. We achieved this by augmenting the original dataset to generate simulated user behavioral data and Ground Truth data. Subsequently, an automated query expansion algorithm effectively applied this data to video retrieval. Through experimental assessment on the video retrieval dataset (MSR- VTT), we validate the effectiveness of this method. The results demonstrate that this user behavior- driven query expansion approach significantly improves the accuracy of video retrieval and user satisfaction.

Humans are accustomed to autonomously associating prior knowledge with the text in a query when answering questions. However, for machines lacking cognition and common sense, a query is merely a combination of some words. Although we can enrich the semantic information of the given query through language representation or query expansion (QE) , the information contained in the query is still insufficient. In this paper, we propose an effective passage retrieval method named query context expansion-based retrieval (QCER) for open-domain question answering (OpenQA) . QCER associates a query with domain information by adding contextual association information based on the pseudo-relevance feedback (PRF) . QCER uses a dense reader to select top-n expansion terms for QE. We implement QCER by appending reader predictions, theoretically present in candidate passages, as contextual information to the initial query to form the new query. QCER with sparse representations (BM25) can improve retrieval efficiency and accelerate query convergence so that the reader can find the desired answer using fewer relevant passages, e.g., 10 passages, as soon as possible. Moreover, QCER can be easily combined with dense passage retrieval (DPR) to achieve even better performance, as sparse and dense representations are often complementary. Remarkably, we demonstrate that QCER achieves state-of-the-art performance in three tasks, passage retrieval, passage reading, and passage reranking, on the Natural Questions (NQ) and TriviaQA (Trivia) datasets under an extractive QA setup.

Digital technology has energized the development of inclusive finance in China and is beneficial in lowering the threshold and transaction costs of financial services and expanding financial coverage. However, it is a key issue whether digital inclusive finance can help SMEs overcome financing difficulties, obtain liquidity, reduce corporate leverage, and thus achieve sustainable development. By using the data from China’s small- and medium-sized listed companies and an aggregate development index of digital inclusive finance at the county level in China from 2015–2019, this empirical analysis finds that the development of digital inclusive finance can significantly reduce the leverage ratio of SMEs; specifically, the development of digital inclusive finance can cut down the leverage ratio of enterprises through easing financing constraints and reducing finance costs. Heterogeneity analysis shows that digital inclusive finance is more effective in reducing leverage for those low- and medium-leverage and non-private enterprises. Accordingly, it is suggested that the government continue to promote the development of digital inclusive finance, deepen the financial supply-side structural reform, and improve the efficiency of financial recycling. SMEs should speed up digital transformation to enable digital finance to provide precise financing services and achieve high-quality sustainable development. Digital financial institutions should improve the digital inclusive financial system as soon as possible, realize scientific supervision and risk prevention, and promote the sustainable development of digital finance.