Search Results (67)

Based on trade data from 2005 to 2020, this study investigates the driving forces behind China’s grain virtual water (VW) import trade, with a particular focus on the role of the Belt and Road Initiative (BRI). By incorporating economic distance (ED) and institutional distance (ID) into the gravity model framework and applying a high-dimensional fixed-effects Poisson pseudo-maximum likelihood estimation method, the study offers new empirical insights. The results indicate that ED is negatively associated with virtual water trade (VWT) in grains, while ID exhibits an inverted U-shaped relationship with VWT. Furthermore, the BRI significantly moderates the effects of ED and ID, weakening their influence on VWT. Additionally, the initiative demonstrates a clear trade creation effect, promoting increased VW imports. These findings contribute to a deeper understanding of the mechanisms shaping VWT and offer valuable policy guidance for enhancing international cooperation under the BRI framework. Full article

This study explores the effects of sulfur (S) doping and oxygen vacancy (OV) creation on the fundamental properties of TiO₂, which plays a crucial role in photocatalysis applications. Using density functional theory (DFT + U), we investigate how S doping and OV impact the structural, electronic, mechanical, and optical properties of rutile TiO₂. The structural results reveal that the lattice constants of undoped rutile TiO₂ are a = b = 4.63 Å and c = 2.98 Å, which are consistent with reported values. Upon S doping at concentrations of 6.25%, 12.5%, and 18.75%, the lattice constants expand to a = b = 4.89 Å, 5.14 Å, and 5.31 Å, and c = 3.27 Å, 3.69 Å, and 3.82 Å, respectively. This expansion is attributed to the difference in atomic radii between sulfur and oxygen atoms. In contrast, the presence of OV leads to a reduction in the lattice constants, with values of a = b = 4.17 Å and c = 2.82 Å. Our findings on the electronic properties indicate that both S doping and OV contribute to an improvement in the electronic structure, notably shifting the electronic bandgap toward the visible spectrum. Moreover, the mechanical properties show that S doping increases the material’s rigidity, while the introduction of OV results in a reduction of mechanical strength. This highlights a trade-off between improved photocatalytic activity and material durability. Lastly, the optical properties exhibit a red-shift in absorption due to S doping and the formation of OV, offering valuable insights for designing efficient photocatalysts for visible-light-driven applications. Full article

E-participation platforms, such as iVoting and Join in Taiwan, provide digital spaces for citizens to engage in deliberation, voting, and oversight. As a forerunner in Asia, Taiwan has implemented these platforms to enhance participatory democracy. However, there is still limited research on the specific content debated on these platforms. Utilising recent advancements in Natural Language Processing, the content of proposals that users have submitted between 2015 and 2025 is explored. In this study, a pipeline for mining text corpora scraped from these platforms in the context of political analysis is proposed. The pipeline is applied to two datasets which have different characteristics. A topic model for each of the two platforms is generated and later evaluated with OCTIS (Optimizing and Comparing Topic Models Is Simple) and compared to different baselines. Our research highlights the trade-offs between model performance and processing time, emphasizing the balance between accuracy and meaningful topic creation. By integrating a translation pipeline from Chinese to English within the text-mining process, our method also demonstrates a solid approach to overcome language barriers. Consequently, our method is adaptable to e-participation platforms in various languages, providing decision-makers with a more comprehensive tool to understand citizens’ needs and enabling the formulation of more informed and effective policies. Full article

Using carbon emissions trading (CET) to lower carbon emissions and stabilize employment are critical components of both environmental and social sustainability. However, it remains unclear whether there is a conflict between these two objectives. Based on panel data from 267 Chinese cities spanning from 2005 to 2019, this study employs a difference-in-differences (DID) model to evaluate the impact of CET on employment. The results show that CET significantly increases employment by 16.3%. The underlying reason for this growth is that CET boosts output in regulated industries, which not only promotes employment growth in these industries, but also through industrial linkages, and drives significant growth in unregulated sectors, particularly in production and consumption services. Moreover, the study finds that the employment effect is closely related to the characteristics of CET. Higher market liquidity and trading volume amplify CET’s employment impact, while carbon prices exhibit an inverted U-shaped relationship with employment, peaking at around 30 yuan per ton. The findings indicate that CET is a promising policy tool that, if well-designed, can deliver the dual benefits of carbon reduction and job creation. Full article

The development of electricity spot markets necessitates more refined and accurate load forecasting capabilities to enable precise dispatch control and the creation of new trading products. Accurate load forecasting relies on high-quality historical load data, with complete load data serving as the cornerstone for both forecasting and transactions in electricity spot markets. However, historical load data at the distribution network or user level often suffers from anomalies and missing values. Data-driven methods have been widely adopted for anomaly detection due to their independence from prior expert knowledge and precise physical models. Nevertheless, single network architectures struggle to adapt to the diverse load characteristics of distribution networks or users, hindering the effective capture of anomaly patterns. This paper proposes a PLS-VAE-BiLSTM-based method for anomaly identification and correction in load data by combining the strengths of Variational Autoencoders (VAE) and Bidirectional Long Short-Term Memory Networks (BiLSTM). This method begins with data preprocessing, including normalization and preliminary missing value imputation based on Partial Least Squares (PLS). Subsequently, a hybrid VAE-BiLSTM model is constructed and trained on a loaded dataset incorporating influencing factors to learn the relationships between different data features. Anomalies are identified and corrected by calculating the deviation between the model’s reconstructed values and the actual values. Finally, validation on both public and private datasets demonstrates that the PLS-VAE-BiLSTM model achieves average performance metrics of 98.44% precision, 94% recall rate, and 96.05% F1 score. Compared with VAE-LSTM, PSO-PFCM, and WTRR models, the proposed method exhibits superior overall anomaly detection performance. Full article

The continued rise in SMEs’ corruption-related activities results in uncertainty around their ability to sustainably contribute to economic growth, leaving SMEs financially fragile and exposed to problems associated with trade credit management resulting in business exits. Given that little research has been conducted on how corruption affects smaller businesses while corruption’s impact on SMEs’ trade credit management effectiveness remains largely unexamined, the study aims to determine the impact of corruption on SMEs’ trade credit management effectiveness. By addressing this unanswered research gap, SMEs could be better equipped to understand how corruption affects their trade credit management in support of their overall finances. The study employed a quantitative research design with purposive sampling using a survey by administrating 10450 online questionnaires tested by a sample of 450 SMEs across South Africa. The result aligns with expectations around corruption being detrimental to SMEs’ trade credit management effectiveness while also indicating, unexpectedly, SMEs’ willingness to partake in corruption, given that SMEs benefit from increased effectiveness in managing trade credit. The study adds to the existing literature on corruption and SMEs’ trade credit management while also providing anti-corruption recommendations to SMEs that are dependent on trade credit. In so doing, SMEs could be better equipped to understand how corruption affects their trade credit management to support their overall finances contributing to improved SME creation rates and fostering entrepreneurship as a pivotal mechanism for improving South Africa’s sustainable development goals. Full article

Small and medium enterprises (SMEs) are vital to the European economy, but sustaining coopetition networks—collaborative arrangements between competitors—remains challenging. In this study, this gap is addressed by developing a reference model and methodology for coopetition networks explicitly designed for SMEs and grounded in the service-dominant (S-D) logic framework. The model provides a structured approach for managing coopetition across the entire network lifecycle, from initiation to dissolution, emphasizing value co-creation and resource integration. A proof of concept (PoC) was implemented in the Portuguese ornamental stone sector to validate the model, revealing significant improvements in manufacturing effectiveness and demonstrating the model’s practical applicability. The results underscore the potential of coopetition networks to boost SMEs’ competitiveness and performance while identifying key trade-offs and risks, such as knowledge sharing and market cannibalization. Although the model addresses critical challenges, in this study, limitations are acknowledged and areas for future research are suggested, particularly in relation to the long-term sustainability of coopetition and the influence of interpersonal dynamics. Full article

This study examines the impact of international trade activities on employment in the Portuguese textiles and apparel industry from 2010 to 2017. It finds evidence that imports and exports have a persistent, negative, and significant effect on overall job creation, with this impact intensifying over the long-run. Additionally, the increasing elasticity of substitution between imports and exports indicates that private companies of this industry have benefited from a win–win situation characterised by higher production volumes and lower marginal costs. By applying an unsupervised machine-learning method, followed by a discrete choice analysis to infer the firm-level propensity to possess green capital, we identify a phenomenon termed the green international trade paradox. This study also reveals that international trade activities positively influence green job creation in firms lacking green capital if and only if these players are engaged in international markets while negatively affecting firms already endowed with green technologies. As such, empirical results suggest that the export-oriented economic model followed over the last decade by the Portuguese textiles and apparel industry has not necessarily generated new domestic employment opportunities but has significantly altered the magnitude and profile of skill requirements that employers seek to identify in new workforce hires. Full article

This study aims to examine the relationship between customs policy and the economic blocs of which Egypt is a member, with a focus on the theory of New Regionalism and modern trends in customs policies. Egypt joined numerous economic blocs following its accession to the world trade organization (WTO) in 1995, yet this membership has not yielded significant positive impacts on the performance of Egyptian exports or the trade balance. The study utilized panel data analysis of Egypt’s international trade from 2001 to 2023. The results indicate that, despite Egypt’s limited success in reaping the benefits of most economic blocs, largely due to the concentration of Egyptian exports in primary and agricultural products and the low tariff rates, factors such as customs clearance processes, tariff barriers, non-tariff barriers, regional trade agreements, and technology adoption still play a crucial role in influencing trade volume among member countries. The findings highlight the significance of effective customs procedures and the reduction of trade barriers in boosting trade volumes within regional trade agreements. The study proposes a strategy for Egypt’s customs policy to maximize benefits from economic blocs, focusing on four key areas: aligning customs policy planning with targeted export sectors to realize trade creation and trade diversion effects; fully implementing trade facilitation programs and liberalizing customs policy procedures; adopting a national strategy to stimulate high value-added export industries as a long-term solution; and adopting regional trade agreements that support cumulative origin as a short-term solution. Full article

We focus on an integrated sensing and communication (ISAC) system—a single platform equipped with multiple antennas transmitting a waveform to detect targets and communicate with downlink users. Due to spectrum sharing between multiple-input–multiple-output (MIMO) radar and multiuser MIMO (MU-MIMO) communication, beamforming is becoming increasingly important as a technique that enables the creation of directional beams. In this paper, we propose a novel joint transmit beamforming design scheme that employs a beam pattern approximation strategy for radar sensing and utilizes rate-splitting for multiuser communication offering advanced interference management strategies. The optimization problems are formulated from both radar-centric and trade-off viewpoints. First, we propose a radar-centric beamforming scheme to achieve sensing efficiency through beam pattern approximation, while requiring the fairness signal-to-interference-plus-noise ratio (SINR) to be higher than a given threshold to guarantee a minimal level of communication quality, while the obtained performance for the communication system is limited in this scheme. To address this problem, we propose a beamforming design scheme from a trade-off viewpoint that flexibly optimizes both sensing and communication performances with a regularization parameter. Finally, we propose a partial rate-splitting-based beamforming design method aimed at maximizing the effective sensing power, with the constraint of a minimal sum rate for downlink users. Numerical results are provided to assess the effectiveness of all proposed schemes. Full article

In recent years, trade protectionism and unilateralism have prevailed, and countries around the world have imposed restrictions on log exports. It has also become more difficult for China to import wood resources and export deep-processed wood forest products. Based on panel data from 2000 to 2019, this study uses social network analysis to measure the level of the Chinese wood forest product trade network, takes the Chinese free trade agreements (FTAs) as the natural experiment, and uses the multi-stage double-difference method to investigate the impact of the signed FTAs on China’s wood forest product trade. The study finds that the trade network of Chinese wood forest products is becoming increasingly complex, and the central position of China and the Association of Southeast Asian Nations (ASEAN) in the network is increasing year by year. The signing of FTAs has had a significant positive impact on the trade of wood forest products in China and a significant trade creation effect. This finding remains true after conducting the placebo test and propensity score-matched regression control. At the same time, the import of wood forest products in China will have a significant trade transfer effect due to the signing of FTAs, and this will not affect exports. Although FTAs show significant trade creation and trade transfer effects in China’s wood forest product trade, they also increase, to a certain extent, the mismatch of forest resources worldwide. Full article

High-resolution spectroscopy employing spatial heterodyne spectrographs (SHS) holds significant promise for forthcoming space missions, building upon its established track record in science applications. Notably, it offers exceptional performance and cost- effectiveness in the ultraviolet-visual (UV-Vis) region compared to contemporary instruments. SHS instruments provide high-resolution capabilities and substantially larger etendues than similar resolving power instruments. This study introduces a comprehensive Python-based SHS model integrated with a user-friendly web scraping interface for target star selection, parameter generation, and 2D interferogram creation. Our SHS model demonstrates double the resolving power of a grating spectrometer and a throughput comparable to a Fourier transform spectrometer (FTS) but without moving parts, enhancing robustness for deployment in space. The interferogram processing algorithm includes flat-fielding, bias removal, apodization, and an inverse Fourier transform (IFT) for accurate spectrum retrieval. Despite bandwidth limitations due to resolving power constraints, SHS models excel in applications requiring high spectral resolution over narrow wavelength ranges, such as studying isotopic emission lines. The model provides optimization results and trade-offs for system parameters, ensuring precise spectral recovery with realistic signal-to-noise ratio (SNR) values. SHS is versatile and effective for various scientific applications, including investigating atomic and molecular emissions from comets, planetary atmospheres, the Earth’s atmosphere, the Sun, and the interstellar medium (ISM). This research significantly contributes to expediting the development and deployment of SHS instruments, demonstrating their potential across numerous scientific domains. Full article

Approaches to the sustainable development of enterprises are a continuous concern of EU countries, contributing to the achievement of national well-being. Companies today face the combined effects of a multitude of factors that affect their results. This study was carried out to analyse the factors that influence the enterprises in the non-financial sector (industry, trade, and services). The analysis used the related data from eight Eastern European countries in the period of 2018–2020, and includes companies by country, size, number of employees, number of companies by number of employees, turnover, added value and average productivity per employee in generating turnover and added value, respectively. To carry out the study, four research questions and four possible hypotheses were used. For data analysis, generalized linear models were used, and four models were obtained and statistically validated. The obtained results led to the identification of the factors associated with the dependent variables that were the basis for the creation of the models. Full article

This paper underlines the significance of free trade agreements in attracting foreign direct investment and their impact on the operational capacities of local firms in host countries. It argues that free trade agreements do not only eliminate barriers to trade, but they also increase the size of the regional market and improve the business environment, making it more attractive to foreign direct investment, along with all the attributes and spillover effects associated with it. While determining the type of spillover effects of foreign direct investment associated with Oman’s trade agreements, this paper uses the Kruskal–Wallis H-test and 438 samples from companies surveyed between 1 August and 31 October 2023 to assess the impact of spillovers on the performance of the surveyed companies. The results reveal that technology transfer, knowledge transfer, labour productivity, product efficiency, capital investments, and job creation have positive effects on the firms’ operational capacities, with technology transfer having the highest impact (27%), followed by labour productivity and job creation (18%). The spillover effects are almost the same for company size and percentage of ownership. They also identified manufacturing and tourism as priority sectors and the availability of a skilled workforce as a major challenge. These findings make original contribution to the field as this is probably the first study to produce a firm-level analysis of spillover effects of foreign direct investment and trade agreements in the context of Oman and the wider Gulf region. The paper concludes with practical implications for policy makers when negotiating trade agreements and designing investment policies to optimize spillover effects on the performance of their domestic firms. Full article

The worsening climate crisis has increased the urgency of transitioning energy systems from fossil fuels to renewable sources. However, many industrialized countries are struggling to meet their growing demand for renewable energy (RE) through domestic production alone and, therefore, seek to import additional RE using carriers such as hydrogen, ammonia, or metals. The pressing question for RE importers is therefore how to select trading partners, i.e., RE exporting countries. Recent research has identified a plethora of different selection criteria, reflecting the complexity of energy systems and international cooperation. However, there is little guidance on how to reduce this complexity to more manageable levels as well as a lack of tools for effective partner evaluation. This article aims to fill these gaps. It proposes a new multidimensional framework for evaluating and comparing potential RE trading partners based on four dimensions: economy and technology, environment and development, regulation and governance, and innovation and cooperation. Focusing on Germany as an RE importer, an exploratory factor analysis is used to identify a consolidated set of composite selection criteria across these dimensions. The results suggest that Germany’s neighboring developed countries and current net energy exporters, such as Canada and Australia, are among the most attractive RE trading partners for Germany. A dashboard tool has been developed to provide the framework and composite criteria, including adjustable weights to reflect the varying preferences of decision-makers and stakeholders. The framework and the dashboard can provide helpful guidance and transparency for partner selection processes, facilitating the creation of RE trade networks that are essential for a successful energy transition. Full article