Biodiesel composition largely affects its combustion and emission performance in diesel engines, while the pilot–main injection strategy has the potential to simultaneously improve engine efficiency and alleviate the soot–NO<sub>x</sub> trade-off. Accordingly, soybean oil (SO), animal fat (AF), and waste cooking oil (WCO) biodiesels were numerically investigated under single injection and pilot–main double injection strategies with pilot energy ratios of 5.6%, 10%, and 15% over a range of main injection timings. The CFD framework was validated against the experimental in-cylinder pressure and AHRR, showing good agreement across the tested operating conditions. The results show that advancing the injection timing increases the AHRR peak and MPRR, whereas the pilot–main injection strategy reduces the AHRR peak and generally advances combustion phasing. A stable CA10 at injection timing ranges of 350–358 °CA persists across biodiesels and injection strategies. Emissions correlate with MICT, where NO<sub>x</sub> increases while soot decreases with the rise in MICT, suggesting an intermediate MICT window for a balance between efficiency and emissions. Furthermore, at the respective highest gross ITE of biodiesels, SO provides the most favorable combination of MPRR and efficiency, whereas WCO shows lower NO<sub>x</sub> but stronger indicators of incomplete combustion.
We consider a system consisting of one conservation law and one balance law with a time-dependent source term, and provide a comprehensive analysis of Riemann solutions, including the non-classical overcompressive delta shocks. The minimal yet representative structure of the system captures essential features of transport under density constraints and, despite its simplicity, serves as a versatile prototype for crowd-limited transport processes across diverse contexts, including biological aggregation, ecological dispersal, granular compaction, and traffic congestion. In addition to non-self-similar solutions mentioned above, the associated Riemann problem admits solution structures that traverse vacuum states ($ρ= 0$) and the critical density threshold ($ρ= \barρ$), where mobility vanishes and characteristic speed degenerates. Moreover, the explicit time dependence in the source term leads to the breakdown of self-similarity, resulting in distinct Riemann solutions over successive time intervals and highlighting the dynamic nature of the solution landscape. The theoretical findings are numerically confirmed using the Local Lax-Friedrichs scheme.
Donald Trump skal styre USA fram til 2029. Det er imperativt for norsk sikkerhet at høstens valgvinner i 2025 får forholdet til USA til å fungere, og bygger videre på det vellykkede toppmøtet i det ovale kontoret 24. april. Tett forsvars- og etterretningssamarbeid om Russland i nord, og gode personlige relasjoner gjør Norges utgangspunkt godt. Vi må dog bli mer bevisste på realiseringen av våre egne interesser, i en mer anarkisk verden der andre ikke kan forventes å ivareta vår sikkerhet. Vi må forutsette at norsk utenrikspolitikk faktisk kan forme Trump noe som politiker og tilskynde realiseringen av felles utenrikspolitiske mål. Norsk verdibasert multilateralisme må bestå, men må tilpasses den dominante bilateralisten Trump. Norsk utenrikspolitikk må ta innover seg hva Trumps utenrikspolitikk blir. Vi må unngå unødig friksjon overfor en politiker som kan skifte mening raskt. Empirien fra 2024-valget og fram til innsettelsen indikerer at Trump vil stå for en enda mer transaksjonell utenrikspolitikk enn før, der handel og sikkerhet er helt sammensmeltet. På merkantilistisk vis skisserer Trump ekspansjonistisk at den vestlige hemisfære skal ytterligere domineres av USA gjennom utstrakt bruk av økonomiske maktmidler og voldsomme tollsatser overfor Canada, Panama, Mexico(-golfen) og Grønland. Fra en forsterket regional maktposisjon vil Trump konfrontere Kina. Det betyr at Europa i tiltakende grad må stagge Russland på egen hånd.
Abstract in English
Norway, Trump and the USA Towards 2030
Donald Trump will lead the United States until 2029. It is imperative to Norwegian security that the winner of this fall’s elections can make the US-Norwegian relationship work, building on the successful bilateral meeting in the oval office on April 24th. Furthermore, close defense and intelligence cooperation in the High North as well as good personal relations make for a good start. Norway must, however, become more cognizant of the realization of its own national interests to achieve security in an anarchic world. Norway must assume that its foreign policy can shape both the relationship and Trump as a counterpart in pursuit of common foreign policy goals. Norway must uphold its values-based multilateralism, while adjusting its policy to a dominant bilateralist like Trump. Norway must cater to and acknowledge Trump’s foreign policy, and adjust to avoid unnecessary friction with a politician who can make swift policy shifts. The empirical evidence following Trump’s election indicate that Trump 2.0 will adopt an even more transactional foreign policy, in which trade and security blend completely. Trump’s dominant course seems mercantilist. His rhetoric of expansion in the Western Hemisphere challenges established borders and involves heavy-handed augmentation of formidable economic power. This includes tariffs and outspoken imperial ambitions towards Canada, (the Gulf of) Mexico, the Panama Canal and Greenland. From a strengthened regional position, Trump seeks to confront China in the Indo-Pacific theater. This leaves Europe to balance Russia more on its own.
Optimizing the ecological network is an urgent need to enhance the stability of the ecosystem and maintain regional ecological security. We utilized the PLUS (Patch-generating Land Use Simulation) model to simulate the land use patterns of the Loess Plateau of China under four different development scenarios in 2030, constructed the corresponding ecological network, and evaluated the network structure. The results indicate the following: (1) By 2030, the spatial pattern of ecological network under the four scenarios will be concentrated in the east and west, in the north and south, and the middle of the Loess Plateau. (2) The change of land use pattern driven by a single policy has a trade-off effect on the ecological network and is prone to form the phenomenon of “ecological increase–functional lag”. (3) The regional ecological network layout of “four cores, multiple corridors and multiple sources” was proposed. The results reveal the development trends of land-use change and ecological protection construction under different future development scenarios in the Loess Plateau, which is helpful for decision-makers to balance the relationship between ecological protection and economic development and realize regional sustainable development.
This paper examines the impact of trade openness towards external debt levels in low- and middle-income countries from 1980-2021. Using panel estimation of Fixed Effects (FE) and the General-to-Specific (GETS), the researchers segregated into Model 1 and Model 2, which Model 2 purposely to identify the interaction of international reserves as a potential medium to relate the impact of trade openness and external debt. The finding suggests that independent variables such as GDP growth rate, current account balance, and trade openness are highly significant variables that influence the external debt in the 50 LMICs. The researchers also found a robust interaction between trade openness and international reserves in determining external debt. The result confirms that the positive direct relationship of trade openness increases its coefficient from 0.10% to 0.14% after incorporating the interaction of international reserves in Model 2. The statistical evidence suggests that the inverse interactions of international reserves on trade openness significantly negatively affect external debt. Moreover, the result prevails, continuing to be positive and highly significant. Hence, the researchers can relate that an inverse impact of trade openness on international reserves has negatively affected external debt.
Abstract Our investigations reveal that macroeconomic variables associated with income and exchange rates, commonly included in empirical analyses of Korea's trade balance, remain crucial for explaining Korea's trade flows. This study presents the most comprehensive investigation thus far into the connection between macroeconomic factors and Korea's trade balance with the G-7 countries. It integrates insights from four primary economic theories—elasticity, Keynesian income, absorption, and the monetary approach. Utilizing the Autoregressive Distributed Lag (ARDL) model, we examine the short- and long-term effects of variables such as income, exchange rates, government spending, and interest rates on Korea's bilateral trade flows, specifically emphasizing exports and imports. Our findings validate the persistent significance of income and exchange rates in shaping the trade balance, which is consistent with previous research. Furthermore, the study sheds light on the significant yet varied impacts of government spending and interest rates—factors that have not been studied before—across different trade patterns and timeframes. Particularly, Korean exports to the G-7 nations are notably influenced by changes in government spending and interest rates. In contrast, imports exhibit more pronounced reactions to these variables in the short term than in the long term. This analysis enriches our understanding of the macroeconomic drivers behind Korea's trade balance and provides valuable insights for policy development. JEL Classification: C22, F14, O50
The gravity models has been studied to analyze interaction between two objects such as trade amount between a pair of countries, human migration between a pair of countries and traffic flow between two cities. Particularly in the international trade, predicting trade amount is instrumental to industry and government in business decision making and determining economic policies. Whereas the gravity models well captures such interaction between objects, the model simplifies the interaction to extract essential relationships or needs handcrafted features to drive the models. Recent studies indicate the connection between graph neural networks (GNNs) and the gravity models in international trade. However, to our best knowledge, hardly any previous studies in the this domain directly predicts trade amount by GNNs. We propose GGAE (Gravity-informed Graph Auto-encoder) and its surrogate model, which is inspired by the gravity model, showing trade amount prediction by the gravity model can be formulated as an edge weight prediction problem in GNNs and solved by GGAE and its surrogate model. Furthermore, we conducted experiments to indicate GGAE with GNNs can improve trade amount prediction compared to the traditional gravity model by considering complex relationships.
Ilya Hajiaghayi, MohammadTaghi Hajiaghayi, Gary Peng
et al.
We study bilateral trade with a broker, where a buyer and seller interact exclusively through the broker. The broker strategically maximizes her payoff through arbitrage by trading with the buyer and seller at different prices. We study whether the presence of the broker interferes with the mechanism's gains-from-trade (GFT) achieving a constant-factor approximation to the first-best gains-from-trade (FB). We first show that the GFT achieves a $1 / 36$-approximation to the FB even if the broker runs an optimal posted-pricing mechanism under symmetric agents with monotone-hazard-rate distributions. Beyond posted-pricing mechanisms, even if the broker uses an arbitrary incentive-compatible (IC) and individually-rational (IR) mechanism that maximizes her expected profit, we prove that it induces a $1 / 2$-approximation to the first-best GFT when the buyer and seller's distributions are uniform distributions with arbitrary support. This bound is shown to be tight. We complement such results by proving that if the broker uses an arbitrary profit-maximizing IC and IR mechanism, there exists a family of problem instances under which the approximation factor to the first-best GFT becomes arbitrarily bad. We show that this phenomenon persists even if we restrict one of the buyer's or seller's distributions to have a singleton support, or even in the symmetric setting where the buyer and seller have identical distributions.
The Russia-Ukraine conflict is a growing concern worldwide and poses serious threats to regional and global food security. Using monthly trade data for maize, rice, and wheat from 2016/1 to 2022/12, this paper constructs three international crop trade networks (iCTNs) and an aggregate international food trade network (iFTN). We aim to examine the structural changes following the occurrence of the Russia-Ukraine conflict. We find significant shifts in the number of edges, average degree, density, efficiency, and natural connectivity in the third quarter of 2022, particularly in the international wheat trade network. Additionally, we have shown that political reasons have caused more pronounced changes in the trade connections between the economies of the North Atlantic Treaty Organization and Russia than with Ukraine. This paper could provide insights into the negative impact of geopolitical conflicts on the global food system and encourage a series of effective strategies to mitigate the negative impact of the conflict on global food trade.
Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classification (ROC) algorithm to simultaneously optimize fairness and accuracy while safeguarding protected attributes such as age and gender. Our approach seeks a multi-objective optimization solution that balances accuracy, group fairness loss, and individual fairness loss. The framework integrates fairness objectives without relying on a weighted summation method, instead focusing on directly optimizing the trade-offs. Empirical evaluations on publicly available datasets, including German Credit, Adult Income, and COMPAS, reveal several significant findings: the ROC-based approach demonstrates superior performance, achieving an accuracy of 94.29%, an individual fairness loss of 0.04, and a group fairness loss of 0.06 on the German Credit dataset. These results underscore the effectiveness of our framework, particularly the ROC component, in enhancing both the fairness and performance of machine learning models.
Several researchers have speculated that the model for the Indonesian Trade Balance Value uses a parametric model, but this model has not provided accurate results in determining the actual Indonesian Trade Balance Value. The estimation used is a parametric approach which assumes the data follows a certain pattern. This can result in big mistakes. We propose a nonparametric approach using Kernel functions for data that does not follow a particular pattern and has outliers. The Kernel function used for multivariables is the Gaussian Kernel function with the Priestley-Chao estimator. Analysis of Indonesia’s Trade Balance Data for 2019-2020 using the available data on Indonesia’s Trade Balance Rate, shows that this model is able to estimate with a very small Mean Square Error (MSE) of 0.98 at optimal bandwidth value are h1 =8.72 and h2 = 0.39. Optimum bandwidth selection uses minimum Generalized Cross Validation (GCV). With this bandwidth value, it gives very good estimation results. This model can be used to predict Indonesia’s Trace Balance Accurately on data that does not have a specific pattern and there are outlier data.
Studying intra-industry trade involves theoretical explanations and empirical methods to measure the phenomenon. Indicators have been developed to measure the intensity of intra-industry trade, leading to theoretical models explaining its determinants. It is essential to distinguish between horizontal and vertical differentiation in empirical analyses. The determinants and consequences of intra-industry trade depend on whether the traded products differ in quality. A method for distinguishing between vertical and horizontal differentiation involves comparing exports' unit value to imports for each industry's intra-industry trade. This approach has limitations, leading to the need for an alternative method.
Energy security and energy trade are the cornerstones of global economic and social development. The structural robustness of the international oil trade network (iOTN) plays an important role in the global economy. We integrate the machine learning optimization algorithm, game theory, and utility theory for learning an oil trade decision-making model which contains the benefit endowment and cost endowment of economies in international oil trades. We have reconstructed the network degree, clustering coefficient, and closeness of the iOTN well to verify the effectiveness of the model. In the end, policy simulations based on game theory and agent-based model are carried out in a more realistic environment. We find that the export-oriented economies are more vulnerable to be affected than import-oriented economies after receiving external shocks. Moreover, the impact of the increase and decrease of trade friction costs on the international oil trade is asymmetrical and there are significant differences between international organizations.
We study the problem of social welfare maximization in bilateral trade, where two agents, a buyer and a seller, trade an indivisible item. We consider arguably the simplest form of mechanisms -- the fixed-price mechanisms, where the designer offers trade at a fixed price to the seller and buyer. Besides the simple form, fixed-price mechanisms are also the only DSIC and budget balanced mechanisms in bilateral trade. We obtain improved approximation ratios of fixed-price mechanisms in different settings. In the full prior information setting where the designer has access to the value distributions of both the seller and buyer, we show that the optimal fixed-price mechanism can achieve at least $0.72$ of the optimal welfare, and no fixed-price mechanism can achieve more than $0.7381$ of the optimal welfare. Prior to our result the state of the art approximation ratio was $1 - 1/e + 0.0001 \approx 0.632$. Interestingly, we further show that the optimal approximation ratio achievable with full prior information is identical to the optimal approximation ratio obtainable with only one-sided prior information. We further consider two limited information settings. In the first one, the designer is only given the mean of the buyer's (or the seller's) value. We show that with such minimal information, one can already design a fixed-price mechanism that achieves $2/3$ of the optimal social welfare, which surpasses the previous state of the art ratio even when the designer has access to the full prior information. Furthermore, $2/3$ is the optimal attainable ratio in this setting. In the second one, we assume that the designer has sample access to the value distributions. We propose a new family mechanisms called order statistic mechanisms and provide a complete characterization of their approximation ratios for any fixed number of samples.
The COVID-19 pandemic continues to disrupt the teaching and learning in international higher education. Those of us in the global South have particularly been hard hit, struggling to balance working/ functioning economies, a struggling healthcare system, education, commerce, trade, transport, and the community spread of what was later found to be a very infectious disease (Porter et al., 2021; Rogerson & Rogerson, 2020; Shamasunder et al., 2020). In this paper, we explored and theorised the experiences of academics who taught large classes at a research-intensive university in South Africa. We purposely recruited and interviewed eight academics for this case study. We drew on Chela Sandoval’s (2013) philosophical notion of “decolonial love” to theorise what an inclusive, democratic and ubuntu-orientated teaching of large classes could look like for us in the global South, beyond the pandemic. The findings revealed that academics continue to be frustrated/challenged/made anxious with teaching large classes due to inadequate infrastructure (digital), lack of resources, and general unpreparedness with the virtual/online teaching and learning. The findings also revealed that large classes were problematic as academics struggled to provide critical engagements and discussions during the hard Covid-19 lockdown, and with some lamenting the frustrations of “teaching to themselves” due to the lack of student engagement. We conclude this paper by proposing a decolonial love approach to the online teaching and learning of large classes, underpinned by the ethics of care, compassion and understanding in curriculum imaginations.
Organisms of all species must balance their allocation to growth, survival and recruitment. Among tree species, evolution has resulted in different life‐history strategies for partitioning resources to these key demographic processes. Life‐history strategies in tropical forests have often been shown to align along a trade‐off between fast growth and high survival, that is, the well‐known fast–slow continuum. In addition, an orthogonal trade‐off has been proposed between tall stature—resulting from fast growth and high survival—and recruitment success, that is, a stature−recruitment trade‐off. However, it is not clear whether these two independent dimensions of life‐history variation structure tropical forests worldwide. We used data from 13 large‐scale and long‐term tropical forest monitoring plots in three continents to explore the principal trade‐offs in annual growth, survival and recruitment as well as tree stature. These forests included relatively undisturbed forests as well as typhoon‐disturbed forests. Life‐history variation in 12 forests was structured by two orthogonal trade‐offs, the growth−survival trade‐off and the stature−recruitment trade‐off. Pairwise Procrustes analysis revealed a high similarity of demographic relationships among forests. The small deviations were related to differences between African and Asian plots. Synthesis. The fast–slow continuum and tree stature are two independent dimensions structuring many, but not all tropical tree communities. Our discovery of the consistency of demographic trade‐offs and life‐history strategies across different forest types from three continents substantially improves our ability to predict tropical forest dynamics worldwide.
The most widespread nature-based solution for mitigating climate change is tree planting. When realized as forest restoration in historically forested biomes, it can efficiently contribute to the sequestration of atmospheric carbon and can also entail significant biodiversity and ecosystem service benefits. Conversely, tree planting in naturally open biomes can have adverse effects, of which water shortage due to increased evapotranspiration is among the most alarming ones. Here we assessed how soil texture affects the strength of the trade-off between tree cover and water balance in the forest-steppe biome, where the global pressure for afforestation is threatening with increasing tree cover above historical levels. Here we monitored vertical soil moisture dynamics in four stands in each of the most common forest types of lowland Hungary on well-drained, sandy (natural poplar groves, and Robinia and pine plantations) and on poorly drained, silty-clayey soils (natural oak stands and Robinia plantations), and neighboring grasslands. We found that forests on sand retain moisture in the topsoil (approx. 20 cm) throughout the year, but a thick dry layer develops below that during the vegetation period, significantly impeding groundwater recharge. Neighboring sandy grasslands showed an opposite pattern, with often dry topsoil but intact moisture reserves below, allowing deep percolation. In contrast, forests on silty-clayey soils did not desiccate lower soil layers compared neighboring grasslands, which in turn showed moisture patterns similar to sandy grasslands. We conclude that, in water-limited temperate biomes where landscape-wide water regime depends on deep percolation, soil texture should drive the spatial allocation of tree-based climate mitigation efforts. On sand, the establishment of new forests should be kept to a minimum and grassland restoration should be preferred. The trade-off between water and carbon is less pronounced on silty-clayey soils, making forest patches and wooded rangelands viable targets for both climate mitigation and ecosystem restoration.
Rehab R. Esily, Dalia M. Ibrahiem, Rasha Sameh
et al.
Expanding of complex global supply chains enhances the role of global trade in the deterioration of the environment by production redeployment across nations, which is tightly connected to emission transmission or the carbon trade balance. Although much earlier studies have assessed the link between emissions of carbon dioxide (CO2) and their influenced variables in the past few years, no substantial attention is available in the literature review concerning the influence of carbon trade balance on the environment in N11 economies. Therefore, via economic progress, renewable/fossil energies consumption, financial development, and urbanization growth as control variables, the influence of the carbon trade balance on emissions of CO2 in N11 countries is explored from 1990 to 2020. The Co-integration and causality relationships using Panel PMG ARDL and Granger causality techniques are investigated to reach our goal. All of the variables investigated degrade the environment in the long run, whereas renewables alleviate CO2. As a result, carbon emission countries' regulators should step up their efforts to support green energy subsidies and carbon taxes, as well as, when supply chains outsource emission-intensive production units to partner nations, they should encourage positive externalities of innovative green technologies.
This paper builds a complex network of weighted and directed coal trade based on the international coal trade data in the United Nations trade database from 1999 to 2018, specifically analyzes the trade scale, trade relations, and trade distribution and other characteristics of international coal trade, and determines the main trade core countries and trade hub countries. The results show that the scale of international coal trade has grown steadily, but the transformation of trade relations is complicated. Especially in the context of increasing trade frictions, trade links between countries have decreased significantly, trade balance has declined, and trade agglomeration has increased. Australia, the United States, Japan and other countries are big coal trading countries, while the United States, South Africa, India and other countries are important trading hubs. Based on the theory of competitive advantage, this paper proposes corresponding countermeasures for different countries to enhance their competitive advantages.
Abstract The academic literature on trade-offs in ecosystem management has paid relatively little attention to justice and poverty reduction objectives. The aim of this paper is to highlight the multiple dimensions of trade-offs in ecosystem services management for poverty alleviation, and to support decision-makers in planning for the almost inevitable trade-offs arising from environmental interventions. The paper brings together different dimensions or lenses through which to analyse trade-offs in ecosystem management for poverty alleviation in a low-income country context. Following a literature review of trade-off decisions, the paper introduces the Balance Sheets Approach to structure trade-off analysis and appraise decisions. We apply the Balance Sheets Approach to analyse five case studies set in very different social-ecological systems where trade-offs were pertinent and undermined poverty alleviation. We show how the combination of ‘positive’ approaches, often used at strategic level, with ‘value’ approaches which analyse multiple values, multi-scale governance, power and capacity, is necessary to analyse complex trade-offs. Based on the case studies we identify four lessons for future trade-off analysis in the context of ecosystem management for poverty alleviation in low-income settings.