Economic resilience in the short-run: A dynamic macroeconomic approach

The Covid-19 pandemic has brought about changes in recent times, one of which impacts economic conditions. This study aims to investigate government policies through the National Economic Recovery (PEN) with a macroeconomic approach to the condition of economic resilience. Macroeconomic indicators use interest rates (IR), inflation, IHSG, money supply (MS), and tax revenues to see the effect on economic resilience, which consists of economic growth (GDP), unemployment, and poverty. The data used is a time series with an observation period from January 2017 to June 2021. The analytical method used is the Error Correction Model (ECM). The results show that the policy package through the National Economic Recovery (PEN) effectively overcame the economic downturn during the pandemic, although it is not yet fully optimal. At least policies based on fiscal and monetary can bring Indonesia's economic growth in a positive direction. So that in the long term, unemployment and poverty due to a decrease in people's purchasing power can be overcome.

This paper focuses on the relationship between “urbanization economies” and access to bank credit by assessing the role of product variety and economic complexity in affecting local credit market conditions. Using quarterly data on Italian provinces for 2008–2018 and adopting a dynamic (spatial) econometric approach, the work provides robust evidence highlighting how local economic complexity reduces the barriers to accessing local credit markets. With a greater “qualified” diversification (higher economic complexity), banks access successful projects with greater probability and are more willing to grant credit. The estimation results also reveal the existence of long‐run spatial spillover effects. The empirical findings advocate for a nuanced and context‐specific policy framework. By embracing place‐based and mission‐oriented policies encouraging diversification and knowledge diffusion, regions can enhance their economic resilience and provide tailored support to businesses, ensuring their stability and growth even in the face of challenging credit conditions.

This review paper presents a conceptual framework for enhancing risk assessment models for small and medium-sized enterprises (SMEs) by integrating advanced analytics and machine learning techniques. The framework addresses the limitations of traditional risk models, which often fail to accurately assess the creditworthiness of SMEs due to their reliance on limited and outdated data. By incorporating diverse data sources and employing predictive modeling, the proposed framework offers a more comprehensive and dynamic approach to evaluating SME credit risk. This, in turn, facilitates greater financial inclusion by improving SMEs' access to capital, which is critical to economic growth and resilience in the United States. The paper also explores the implications for financial institutions and policymakers, emphasizing the need for regulatory support and ongoing research to maximize the benefits of these advanced risk assessment models.

This review paper presents a conceptual framework for enhancing risk assessment models for small and medium-sized enterprises (SMEs) by integrating advanced analytics and machine learning techniques. The framework addresses the limitations of traditional risk models, which often fail to accurately assess the creditworthiness of SMEs due to their reliance on limited and outdated data. By incorporating diverse data sources and employing predictive modeling, the proposed framework offers a more comprehensive and dynamic approach to evaluating SME credit risk. This, in turn, facilitates greater financial inclusion by improving SMEs' access to capital, which is critical to economic growth and resilience in the United States. The paper also explores the implications for financial institutions and policymakers, emphasizing the need for regulatory support and ongoing research to maximize the benefits of these advanced risk assessment models.

The tourism industry, as a complex system, plays a significant role in economic growth and sustainable development. This study adopts a system dynamics (SD) approach to investigate the intricate interconnections within smart tourism ecosystems, focussing on how these interactions contribute to sustainable tourism development. By analysing the causal dynamics among components of smart tourism ecosystems, this chapter provides a methodology for examining feedback loops and exploring variable relationships through scenario-based simulations. This approach aids in simplifying the complexity of smart tourism ecosystems, enabling stakeholders to gain a clearer perspective on the system’s dynamics and empowering decision-makers to foster economic resilience and sustainability. The dynamic model proposed emphasises the role of big data analytics and AI in enhancing local economies through smart solutions, laying the foundation for smoother transitions towards smart tourism ecosystems. Information and communication technology (ICT) adoption is highlighted as a means to enrich resident and tourist experiences, promoting seamless co-creation and engagement within smart ecosystems, while smart governance emerges as crucial to achieving sustainability. By addressing the need for informed strategic decisions, the model provides insights for policymakers and business leaders, contributing to a more sustainable tourism industry that supports efficient resource management, improved tourist experiences, and inclusive participation.

After the second world-war there have been theoretically realized the synthesis between the macro-economic and dynamic approaches, in the first place at the neo-Keynesian economists. In this context, a growth-theory have been separated from the development theory, like a part of the contemporary economic science, in contact with other connected sciences, and also with the practice of elaborating measures and their planned and institutionalized implementation, in the sense of economic growth and development. Growth means the global increase of the net domestic product, including structural modifications too. The economic growth expresses those modifications occurring during a certain horizon of time, within a certain area, involving the augmentation of the macro-economic results, closely connected to their determinant factors. Zero-growth is the situation of equal augmentation rhythms of macroeconomic absolutely results and total population, having as effect a constant evolution of macroeconomic results per capita.

Economic modelling approaches to cost estimates for the control of carbon dioxide emissions

98/02597 Economic modelling approaches to cost estimates for the control of carbon dioxide emissions : Zhang, Z. X. and Folmer, H. Energy Economics, 1998, 20, (1), 101–120

Earth observation (EO) technologies, such as very high-resolution optical satellite data available from Maxar, can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public. We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects. We applied our methodology to the COVID-19 pandemic experience in Los Angeles County, California as a case study. We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context. We found that despite limitations, EO data can increase sectoral and temporal resolution, which leads to significant differences from other data sources in terms of direct and total impact results. The findings from this analytical approach have important implications for economic consequence modeling of disasters, as well as providing useful information to policymakers and emergency managers, whose goal is to reduce disaster costs and to improve economic resilience.

In the European literature on the regional and local development, the concept of resilience has progressively gained momentum, eventually overcoming that of competitiveness and posing a critical challenge for the future of territorial studies and the territorialisation of the policy discourse. In the current economic turmoil, the success of an urban and regional economy relies more and more on its capacity to react to sudden shocks in a positive and evolutionary perspective, i.e., in its resilience. Nevertheless, as a recent analysis of the employment dynamics of Italian metro-regions in the period before and after 2008 has demonstrated that the existing taxonomies may be distant from reality and hardly communicable. The paper proposes a taxonomy of regional resilience based on the consideration of the region’s capacity of both improving its employment rate during the pre-crisis period and overcoming the concurrent performance of the nation. Via a shift-share analysis of the employment in Italian metro-regions, the paper investigates the contribution of the sectoral structure of the local labour market in terms of economic resilience. The result is twofold: a geography of the dynamism of the territorial systems in Italy that diverges from some “classic” interpretative frameworks; a novel taxonomic approach to regional resilience.

This paper explores a number of methodological issues that arise in the calculation of equilibrium exchange rates, which are identified in this paper as those real effective exchange rates consistent with macroeconomic equilibrium, i.e., internal and external balance. A partial equilibrium, comparative static analysis is presented and the methodology is applied to the break-up of the Bretton Woods exchange rate system. Then the dynamic interaction between the current account and the stock of net foreign assets is examined. Finally, the analysis uses a more general equilibrium approach by relying on simulations using MULTIMOD, a multicountry econometric model. The paper demonstrates the extent to which the equilibrium exchange rate calculations depend upon alternative assumptions regarding factors that affect internal and external balance. In addition, results obtained using the comparative static and dynamic macroeconomic approaches are compared.

This paper explores a number of methodological issues that arise in the calculation of equilibrium exchange rates, which are identified in this paper as those real effective exchange rates consistent with macroeconomic equilibrium, i.e., internal and external balance. A partial equilibrium, comparative static analysis is presented and the methodology is applied to the break-up of the Bretton Woods exchange rate system. Then the dynamic interaction between the current account and the stock of net foreign assets is examined. Finally, the analysis uses a more general equilibrium approach by relying on simulations using MULTIMOD, a multicountry econometric model. The paper demonstrates the extent to which the equilibrium exchange rate calculations depend upon alternative assumptions regarding factors that affect internal and external balance. In addition, results obtained using the comparative static and dynamic macroeconomic approaches are compared.

This paper explores a number of methodological issues that arise in the calculation of equilibrium exchange rates, which are identified in this paper as those real effective exchange rates consistent with macroeconomic equilibrium, i.e., internal and external balance. A partial equilibrium, comparative static analysis is presented and the methodology is applied to the break-up of the Bretton Woods exchange rate system. Then the dynamic interaction between the current account and the stock of net foreign assets is examined. Finally, the analysis uses a more general equilibrium approach by relying on simulations using MULTIMOD, a multicountry econometric model. The paper demonstrates the extent to which the equilibrium exchange rate calculations depend upon alternative assumptions regarding factors that affect internal and external balance. In addition, results obtained using the comparative static and dynamic macroeconomic approaches are compared.

Despite the fact that the presence of non tradable goods is one of the most frequently advanced reasons for the failure of PPP, the empirical analysis conducted in this paper shows that it explains only a very small portion of the long run behaviour of real exchange rates (RERs) in developed countries: in most cases, there appears to be a very strong long run relationship between RERs calculated on price indexes for tradables and non tradables. As a consequence, deviations from PPP usually appear to be as large for both kinds of goods. To a certain extent, this stylised fact is also verified in the case of the yen/dollar RER, yet formerly known as a typical illustration of the so–called Balassa–Samuelson effect. In this context, so–called macroeconomic approaches of ERERs may be viewed as an alternative to all versions of PPP. We develop a model which combines the contributions of the most fruitful dynamic approaches, namely the NATREX and the BEER. An estimate of this model shows that the main long run determinants of the dollar/euro RER are the rate of consumption and the level of technical progress of the euro area relative to the US.

The urgent need to combat climate change and reduce greenhouse gas emissions underscores the importance of transitioning to renewable energy as a sustainable alternative to fossil‐fuel‐based electricity. This study evaluates a novel financing mechanism for renewable electricity in Iran that leverages profits from the petrochemical industry and carbon tax revenues to support a feed‐in tariff (FIT) model. By reallocating natural gas from inefficient fossil‐fuel power generation to high‐value petrochemical production, the approach enhances economic value, reduces CO₂ emissions, and promotes renewable energy deployment. Iran plans to expand its petrochemical production from 91.5 million tons in 2022 to 183 million tons by 2033, which will drive a 166% increase in demand for fuel and feedstock. Given these resource constraints, integrating renewable electricity into the grid is critical for sustaining industrial growth. System dynamics modeling indicates that carbon tax revenues could reach between $3.8 billion and $37.7 billion by 2033. Meanwhile, the profitability of the petrochemical sector shows wide variability depending on product prices, with a 326% spread between optimistic and pessimistic scenarios. The resulting FIT ranges from 6.24 to 20.29 cents per kilowatt‐hour, with higher carbon taxes being particularly beneficial under low‐price scenarios. This study presents a sustainable, market‐aligned strategy for renewable energy financing that can enhance economic resilience and environmental performance in fossil‐rich nations.