Open Economy Research Articles

A semantic segmentation framework with UNet-pyramid for landslide prediction using remote sensing data

Landslides are frequent all over the world, posing serious threats to human life, infrastructure, and economic operations, making them chronic disasters. This study proposes a novel landslide detection methodology that is automated and based on a hybrid deep learning approach. Currently, Deep Learning is constrained by the lack of applicability, lack of data, and low efficiency in landslide detection but with recent advancement in deep learning-based solutions for landslide detection has sparked considerable advantages over traditional techniques. In order to prevent and mitigate disaster, we introduced a hybrid model based on remote sensing technologies such as satellite images. Specifically, the proposed approach consists hybrid U-Net model integrated with a pyramid pooling layer for landslide detection, which uses high-resolution landslide images from the Landslide4Sense dataset. The UNet-Pyramid model has the following modifications: To improve feature acquisition and advancements to strengthen the model’s attention U-Net architecture is integrated with the pyramid pooling layers and OBIA technique. The UNet-Pyramid model was trained and validated using labeled images taken from the Landslide4Sense dataset and the validated set using OBIA to improve its efficacy. The overall Precision, Recall, and F1 Score of the UNet-pyramid model for landslide detection are 91%, 84%, and 87%, respectively.

Optimal Trade Policy in a Ricardian Model with Labor-Market Search-and-Matching Frictions

Abstract I develop a dynamic general-equilibrium model that combines the Diamond–Mortensen–Pissarides labor market with the standard Ricardian model of international trade to investigate the potential role of unemployment as a justification for trade policy implementation. First, I compare a competitive equilibrium and a constrained-efficient equilibrium and then establish the condition in which the competitive equilibrium is constrained-efficient. Second, I show that free trade is optimal only when labor market inefficiency is absent. A small open economy may employ expansionary trade policies (such as export subsidies or reduced import tariffs) if it experiences inefficiently high unemployment. This study provides a rationale behind countries’ tendency to use export subsidies.

Internationalisation as a boost for many firms: evidence from Germany

AbstractThe advantages of globalisation have been increasingly called into question, and protectionist tendencies have entered the stage. So what experiences have firms had after going international in an open economy over the past years? With respect to foreign investor takeovers of initially national firms, we see predominantly positive effects for acquired manufacturing firms in terms of productivity, sales and expenditures on the labour force – likely due to higher employment – in the short and long term. Looking at the results, firm size matters: positive effects are stronger among large firms. In the case of firms starting to invest abroad, positive effects are very rare and limited to short-term sales of acquiring small manufacturing firms. All in all, the largely positive evidence is generally supportive of internationalisation and thus largely contradicts the negative views sometimes present in the public sphere, though even unaffected firms may see themselves as relative losers.

Evolution and Motivation of the Value-Added Trade Pattern of Producer Services Based on a Complex Network

With the acceleration of the digital revolution and free trade, the producer services industry has become a new driving force for countries to enhance their global value chain (GVC). The further integration of GVCs has promoted the formation of a global value network covering a wider range of regions and industries. The way of establishing strong core capabilities and competitive advantages for a country’s producer services industry in the vast value network is of great practical significance for achieving industrial structure transformation and upgrading in an open economy. Therefore, based on the Asian Development Bank Multi-Regional Input–Output Database (ADB-MRIO) and the bilateral export value-added accounting method (WWZ), this study constructs a value network of producer services and analyzes its structural evolution and driving factors. The results show the following: (1) The domestic value-added (DVA) network and foreign value-added (FVA) networks of producer services trade exhibit dense characteristics in terms of trade depth. The DVA network has a small-world topological structure, while the FVA network does not have this feature most years. (2) Western countries, represented by the USA, Germany, and the UK, are located at the hub of the global value network, while China’s network status is rising and gradually occupying a core position not only in the Asian region but also in the world. The value chain of producer services shows a pattern of being led by developed countries and extending to developing countries. (3) The broader tendency of modularity is decreasing, indicating that the development process of the DVA and FVA networks is becoming globalized. The members of the community can not only achieve trade interdependence due to geographical proximity but also break through regional limitations and complement each other. (4) The network evolution process is driven by a variety of factors. Industrial and technological distances are conducive to the expansion of the global value flow in producer services. Geographical and linguistic proximity are the main influencing factors, while the role of institutional proximity tends to be weakened.

Open Economy Macroeconomics: Developments in Theory and Policy Using the TOPSIS Methodology

Open economy macroeconomics examines the complex interplay between a nation's economic policies and global financial markets. This field of study addresses critical issues such as exchange rate determination, balance of payments, and the effects of trade and capital flows on a country's economic performance. The increased integration of economies through globalization has made it essential to understand how domestic economic policies, such as monetary and fiscal policies, interact with international economic environments. This paper explores the key concepts and models in open economy macroeconomics, including the Mundell-Fleming model, purchasing power parity, and the dynamics of exchange rate regimes. The study of open economy macroeconomics has gained prominence with the growing interconnectedness of global markets. Unlike a closed economy, where all economic transactions occur within national borders, an open economy engages in trade and financial transactions with other countries. This openness introduces new challenges and opportunities for policymakers, as domestic economic conditions are increasingly influenced by external factors such as foreign exchange rates, international trade agreements, and global capital flows. Open economy macroeconomics seeks to understand how these external factors affect key economic variables, such as output, inflation, and employment.The significance of studying open economy macroeconomics lies in its profound implications for both national and global economic stability and growth. As economies become increasingly interconnected, understanding the mechanisms by which international trade, capital flows, and exchange rates influence domestic economies is essential for effective policymaking. This field of study is particularly relevant in today’s world, where financial crises, trade wars, and global economic shocks can have ripple effects across borders, impacting nations' economic well-being regardless of their size or level of development.. Free Trade Agreements, Currency Pegging, Capital Controls, Trade Tariffs, Exchange Rate Floating. GDP Growth Rate (%),Trade Balance (Billion $), Inflation Rate (%),Unemployment Rate (%). The results indicate that Free Trade Agreements achieved the highest rank, while Capital Controls had the lowest rank being attained. The value of the dataset for Corporate Open Economy Macroeconomics according to the topsis Method, Integrated Pest Management achieves the highest ranking.

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

In a power-hydrogen coupled integrated energy system (PHCIES), hydrogen fueling stations (HFSs) with solar photovoltaic (PV) systems are crucial devices to support hydrogen demand and maintain a stable and near-zero pollutant emission-based PHCIES operation by producing a pollutant-free hydrogen. However, optimal operation management of HFSs is challenging because the electrolyzers, compressor, hydrogen storage system (HSS), and fuel cell in HFSs are interconnected and change dynamically under various operational environments. To resolve this issue, this paper presents a safe deep reinforcement learning (DRL)-assisted two-stage framework that ensures a reliable and economical PHCIES operation. In the first stage, day-ahead operational schedules of stand-alone PV systems, PV-enabled HFSs, on-load tap changers, and capacitor banks with hourly resolution are coordinated to minimize the system operation cost, electricity arbitrage cost, PV curtailment cost, and real power loss by solving a Volt-VAR control (VVC) optimization problem. In the second stage, a safe DRL algorithm with a 15-min resolution is employed to minimize the real power loss and maximize the HFS profit by rescheduling the reactive power of stand-alone PV systems and real/reactive power and hydrogen of HFSs. The proposed self-tuning adaptive safety module integrated in the DRL method ensures no violations of HSS’ state-of-hydrogen (SOH) and voltage magnitude in the PHCIES during the training process. Numerical examples conducted on a PHCIES (IEEE 33-bus system coupled with two PV-enabled HFSs) demonstrate the effectiveness of the proposed framework in terms of training convergence, SOH/voltage violation, real power loss, and profit of HFSs.

A novel two-stage energy sharing method for data center cluster considering ‘Carbon-Green Certificate’ coupling mechanism

The energy consumption of data centers is on a sharp upward trend, and how to achieve joint optimal scheduling and energy management of multiple data centers is the key to the economic and low-carbon operation of data center prosumers (DCPs). This study introduces shared electric-hydrogen energy storage (SEHES) for DCPs, constructs a data center cluster (DCC) framework, and proposes a two-stage energy sharing model for DCC considering the ‘Carbon-Green Certificate’ coupling mechanism (C-GCCM). First, the DCC framework containing DCC operator (DCCO) and DCPs is established, and the C-GCCM based on conversion coefficients is designed to improve the energy saving and emission reduction level of DCC. In the day-ahead optimization stage, a stochastic multi-objective scheduling model for SEHES considering economy and environmental protection is developed. In the real-time optimization stage, considering the C-GCCM and the difference in on-grid prices, the Stackelberg game model of DCC is constructed according to the internal energy price set by DCCO and the trading strategies of DCPs. Finally, the proposed model is solved using the normal boundary intersection algorithm, the particle swarm algorithm and CPLEX. Simulation results demonstrate that the proposed DCC framework can reduce the daily operating cost of DCPs by 67.05%. The introduction of C-GCCM has resulted in a shift from negative to positive benefits on carbon emission trading for DCC and a 42.33% increase in benefits on green certificate trading.

Distributionally robust optimal configuration of battery energy storage system considering nodal RoCoF security constraints

The large-scale integration of renewable energy source (RES) exacerbates net load fluctuations, reduces system inertia, limits frequency response capabilities, and leads to uneven spatial distribution of inertia resources. To ensure the economic and safe operation of the system, we propose a distributionally robust optimal configuration scheme of battery energy storage system (BESS) considering nodal RoCoF security constraints to address these issues. First, we describe and model the dynamic frequency response characteristics after disturbances, derive the expressions for the system frequency nadir and maximum nodal RoCoF (MN-RoCoF). Second, we address net load uncertainty using the distributionally robust chance constrained (DRCC) approach and incorporate frequency security constraints into the BESS configuration optimization model, embedding the short-term system operation model into the long-term BESS planning. Then, to tackle the highly nonlinear MN-RoCoF constraint, we propose a divide and conquer-based support vector machine (D&C-SVM), to extract the linear relationship between the BESS virtual inertia constant and MN-RoCoF, reformulating the proposed scheme into a mixed-integer second-order cone programming (MISOCP). Finally, we conducted case studies on the IEEE 39-bus and 118-bus test systems. The results verify that the proposed configuration scheme can ensure that the RoCoF at all nodes remains below 1 Hz/s under the preset disturbances. The proposed D&C-SVM demonstrate 99 % accuracy in managing the nonlinear MN-RoCoF constraint. Moreover, the uncertainty in net load is well managed, with all chance constraints satisfied with a confidence level of over 95 %.

Application of monarch butterfly optimization algorithm for solving optimal power flow

This paper proposes a highly flexible, robust, and efficient constraint-handling approach for the solution of the optimal power flow (OPF) problem and this solution lies in the ability to solve the power system problem and avoid the mathematical traps. Centralized control of the power system has become inevitable, in the interest of secure, reliable, and economic operation of the system. In this work, OPF is solved by considering the three distinct objectives, generation cost minimization, power loss minimization, and enhancement of voltage stability index. These three objectives are solved separately by considering the evolutionary-based monarch butterfly optimization (MBO) algorithm. This MBO algorithm is validated on the IEEE 30 bus network and the obtained results are compared with differential evolution, particle swarm optimization, genetic algorithm, and Jaya algorithm. The obtained results reveal that among the various optimization algorithms considered in this work, the MBO evolves as the best algorithm for all three case studies.

The Impact of Export Tax Rebates on Industrial Upgrading: A Literature Review

As China's economy gradually shifts towards a stage of high-quality development, issues such as insufficient demand and overcapacity are becoming increasingly prominent. The development model of extensive economy is no longer sustainable, and the economic operation is facing huge risks and challenges. The adjustment and upgrading of industrial structure has become an inevitable choice. In this context, the export tax rebate policy, as one of the important means to promote foreign trade and industrial upgrading, not only has significant practical value for in-depth exploration and analysis, but also has profound significance for guiding future policy formulation and practice. This article aims to systematically integrate and extract existing research results, deepen the understanding of the mechanism and impact of China's export tax rebate policy in promoting industrial upgrading, and provide some research clues for further research by domestic and foreign scholars.

Analyzing Rupiah-USD Exchange Rate Dynamics: A Study with ARCH and GARCH Models

The study aims to analyze the volatility of the Rupiah-USD exchange rate and predict future fluctuations using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The exchange rate data, spanning from January 2010 to December 2023, is sourced from Bank Indonesia (BI) and adheres to the Jakarta Interbank Spot Dollar Rate (JISDOR) regulations, focusing solely on business days. ARCH and GARCH models are widely applied in financial time series analysis because they capture and forecast time-varying volatility. This study analyzes historical exchange rate data to evaluate the persistence of volatility and detect any structural breaks that could impact future exchange rate behavior. The findings reveal that both models effectively capture the volatility of the Rupiah-USD exchange rate, but the GARCH (1,1) model demonstrates superior forecasting accuracy. This model's ability to account for long-term volatility clustering makes it particularly useful for predicting exchange rate dynamics. The research contributes to a deeper understanding of the factors driving exchange rate fluctuations, offering valuable insights for policymakers, investors, and businesses. These insights can help stakeholders manage exchange rate risks more effectively within Indonesia's open economy, where global financial conditions and external shocks significantly shape currency movements. The study emphasizes the importance of using advanced econometric models for accurate volatility predictions and informed decision-making.

The impact of reserve requirements on macroeconomic and financial stability in a small open economy

The impact of reserve requirements on macroeconomic and financial stability in a small open economy

Reconsidering agricultural productivity, comparative advantage and economic growth

ABSTRACT In this article, we reconsider the work of Matsuyama (Journal of Economic Theory, 58(2): 317–334, 1992). Matsuyama (1992) concluded that, for a small open economy, a negative relationship exists between agricultural productivity and economic growth. Our study questions Matsuyama’s (1992) conclusion. We aim to demonstrate a theoretical possibility in which Matsuyama’s (1992) conclusion for a small open economy does not always hold. We extend Matsuyama’s (1992) model by introducing government services that enhance household utility. Unlike Matsuyama’s (1992) model, our model implies that, even in a small open economy, under certain conditions, a positive relationship may exist between agricultural productivity and economic growth when a small country initially has a comparative advantage in agriculture.

Double-Layer Optimization and Benefit Analysis of Shared Energy Storage Investment Considering Life-Cycle Carbon Emission

As a crucial path to promote the sustainable development of power systems, shared energy storage (SES) is receiving more and more attention. The SES generates carbon emissions during its manufacturing, usage, and recycling process, the neglect of which will introduce a certain extent of errors to the investment of SES, especially in the context of the large-scale integration of renewable energy and dramatic increase in demand for SES capacity. To enhance the accuracy of SES investment, we propose a double-layer optimization model to compute the optimal configuration of a shared energy storage station (SESS) considering its life-cycle carbon emission. First, the service mode, settlement method, profit mechanism, and application scenarios of SESS are introduced. Second, the life-cycle assessment approach is used to calculate the life-cycle carbon emission of SESS, and the uncertainty of supply and demand is considered. Then, a double-layer optimization model that considers the economic operation of multi-microgrid systems and the optimal allocation of SESS is established. The lower-layer model’s Karush–Kuhn–Tucher (KKT) condition is derived to convert the double-layer model into a single-layer one. Finally, a combined heat and power (CHP) three-microgrid system is used to demonstrate the validity of our proposed model, and the economy of SESS investment is analyzed from multiple perspectives. The results show that considering the life-cycle carbon emission of SESS can provide more accurate guidance for investing in and measuring the carbon emission and reduction for SESS.

Joint peak power and carbon emission shaving in active distribution systems using carbon emission flow-based deep reinforcement learning

Distribution optimal power flow (D-OPF) with peak load shaving function is crucial for guaranteeing economical and reliable operations of active distribution grids with various distributed energy resources. However, conventional D-OPF methods reduce only the power operation cost without considering carbon emission reduction, which may lead to a slowdown in achieving global carbon neutrality. To resolve this issue, this study proposes a deep reinforcement learning (DRL)-assisted D-OPF framework realizing dual-peak shaving of power and carbon emission for low-carbon active distribution system operations based on the notion of carbon emission flow (CEF). The proposed framework aims to minimize the total power operation costs of substation and gas-turbine (GT) generators. It also aims to reduce the total carbon emission cost via mitigation of peak power and carbon emission in the CEF-based D-OPF framework with both power and carbon emission peak constraints. A key feature of the proposed framework is the adoption of the DRL method for the CEF-based D-OPF problem to determine economical and eco-friendly peaks of power and carbon emission under dynamically changing distribution system operations. Furthermore, a D-OPF optimization-based reward function for the DRL agent is designed to yield no constraint violations for the D-OPF problem during the agent’s training phase. Numerical examples conducted on the IEEE 33-node and IEEE 69-node distribution systems with GT generators, solar photovoltaic systems, and energy storage systems demonstrate that, in contrast with CEF-free and CEF-integrated optimization methods with fixed power and/or carbon emission peaks, the proposed method further reduces the total carbon emission and cost.

Performance study of integrated compressor/expander based on small-scale compressed air energy storage system

Compressed air energy storage will have good development prospects because of its exceptional safety and reliability, low economic cost, zero carbon emissions, and pollution-free environmentally friendly characteristics, which meet the needs of sustainable and green development. In view of the problems of large volume, great number of equipment, and poor flexibility of traditional compressed air energy storage equipment, this article built a compressed air experimental bench based on pneumatic motor and conducted integrated pneumatic motor compression/expansion experiments. The effects of key parameters such as speed, torque and current on the performance of pneumatic motor under different modes are investigated, providing reference for the application and promotion of small compressed air energy storage systems. The results indicate that in the expansion mode of the pneumatic motor, the total efficiency and power output of the motor can reach up to 14.01 % and 1257.87 W. Its high power output and economic operation are concentrated within the medium range of rotation speed and current, respectively, with a certain cooling effect achieved in this mode. In compression mode, the maximum output power of the motor can reach 3696.33 W, while the energy efficiency is up to 78.39 %. The operating parameters such as torque and current are basically positively correlated with the system performance parameters. The integrated system is flexible, saves space, and has certain feasibility.

Low carbon economic scheduling of integrated energy system with concentrating solar power and multi-stage hydrogen utilization based on ladder-type carbon trading

ABSTRACT As the energy crisis and the greenhouse effect intensify, developing a sustainable and clean energy system is particularly important. How to increase the utilization of clean energy and reduce carbon emissions is a pressing issue. To this end, an integrated energy system (IES) framework incorporating multi-stage hydrogen utilization and concentrated solar power (CSP) has been developed. This study optimizes the scheduling of the IES using a ladder-type carbon trading method to optimize the output of each unit, thereby achieving low-carbon economic operation. The research investigates the conversion and utilization among multiple heterogeneous energy flows, including electricity, heat, gas, and hydrogen, with a focus on the integration of multi-stage hydrogen utilization and CSP heat supply, and the interconnections among different units. Four comparative cases are established to evaluate the proposed IES framework in reducing the totalcost, mitigating renewable energy curtailment, and lowering carbon emissions. The results show that the proposed IES framework presents optimal results, with a total cost reduction of 19.74% and a carbon emission reduction of 36.79 tons, no new energy waste, and high carbon benefits. The proposed IES framework facilitates flexible and efficient distribution of multiple energy types, meeting energy demands while enabling multi-energy complementarity and feasibility.

Central bank balance sheets under foreign exchange accumulation: insights from endogenous money theory and monetary policy implementation

Abstract This article proposes to draw on the recent experiences of many central banks of advanced economies and the evolution of their operational frameworks in the new context of increased domestic liquidity when analyzing the operations of central banks that engage in exchange rate management and increase their official foreign reserves as a result. It argues that the theoretical literature on endogenous money set within the context of an open economy with an exchange rate objective needs to be amended to account for such developments, as it would be entirely possible for a central bank that accumulates substantial foreign reserves to adopt a floor system and thus maintain a near-perfect control over its policy interest rate without the need for any compensating measure. On the grounds of the reverse causation argument, establishing a direct link between the foreign reserves and the monetary base should not entail any quantitative effect on other economic variables.

How does new industrialisation improve manufacturing industry chain resilience?

ABSTRACT As uncertainty in the manufacturing industry chain rises, improving its resilience becomes crucial for ensuring stable global economic operations. Industrialisation fuels modernisation, and China’s new industrialisation is vital for its new era and journey. Firstly, this study uses grounded theory to identify driving factors of manufacturing industry chain resilience (MICR) based on new industrialisation (NI). Secondly, this study analyses the complex causal relationship between NI and MICR from a macro perspective through fsQCA. Thirdly, this study uses Decision-making Trial and Evaluation Laboratory - Interpretative Structural Modeling Method - Bayesian Network (DEMATEL-ISM-BN) to explain the effect mechanism of NI on MICR from a micro perspective. Research findings show that from a macro perspective, MICR is driven by the configuration of six types of NI factors, such as quality priority, open cycle and people-oriented. From the micro perspective, each factor drives MICR from deep to surface, and there is a path that is most likely to cause high MICR. The research results explore the driving factors of MICR based on NI at the present stage, reveal the effect mechanism of NI to improve MICR and provide practical implications for enhancing MICR in the changing environment.

The Crucial Significance of Regulatory Compliance in Business Operations and Supply Chain Management in the South-Western Region of Nigeria

Ensuring adherence to regulations was of utmost importance for firms operating in southwestern Nigeria, especially when it comes to managing the supply chain. This essay examined the crucial importance of regulatory compliance in guaranteeing legal, moral, and environmentally responsible economic operations in the area. The text explored the intricacies of the regulatory environment, which includes federal, state, and municipal rules. It also emphasized the difficulties that companies had, such as limited resources, bureaucratic inefficiency, and frequent changes in regulations. Based on the analysis of the significance of regulatory compliance for risk management, business continuity, and stakeholder confidence, the paper presented findings on the need for proactive compliance methods and cooperative endeavours among stakeholders. Recommendations were given to improve adherence to regulations, which included strengthening the ability to comply, making changes to regulations, adopting new technologies, collaborating between the public and commercial sectors, including stakeholders, implementing incentive systems, and continuously monitoring and evaluating the situation. To cultivate a culture of adherence, encourage moral behaviour, and stimulate sustainable progress in southwestern Nigeria, companies and regulators might adopt these suggestions. Keywords: Regulatory compliance, Business operations, Supply chain management, Risk management, Stakeholder involvement.