Brazilian bioenergy generation from cattle and pig manure: An economic life cycle assessment

This article explores the theoretical foundations and historical perspectives of fiscal policy, emphasizing its role in economic stabilization and the management of economic cycles. It traces the evolution of economic ideas, from classical orthodoxy, focused on strict budgetary balance and limited state intervention, to the Keynesian revolution, which positioned fiscal policy as a strategic tool for influencing aggregate demand. The article also examines the main instruments of fiscal policy, including public spending, taxation, and debt, while detailing their effectiveness and limitations in various economic contexts. Finally, particular attention is given to automatic stabilizers and fiscal approaches specific to developing countries, highlighting the importance of balanced management to achieve sustainable growth and macroeconomic stability.

ABSTRACT Air pollution is widely recognized as a major public health concern, and emerging evidence suggests an association with dementia. Establishing a causal relationship, however, is difficult. Economic cycles affect both dementia prevalence and pollution levels: during economic booms, financial resources for treatment rise, but so do air pollution and work-related stress. In South Korea, air quality has generally improved, even as Alzheimer’s cases have increased with population aging, indicating a time-series relationship that biases regression results. Air pollution and vascular dementia also temporarily declined during COVID-19, reflecting omitted variable bias. To address these endogeneity concerns, we use wind speed and direction as instruments for air pollution in South Korea. Our estimates show that higher concentrations of PM10, PM2.5, and NO2 significantly increase dementia cases, with instrumental variable results substantially larger than ordinary least squares, underscoring the importance of correcting for bias. These findings carry important policy implications. Because air pollution is a negative externality, its health consequences—including dementia—extend beyond individual responsibility and represent broader social costs. Reducing pollution could therefore not only improve health outcomes but also ease the considerable economic burden of dementia care. As air pollution disproportionately affects vulnerable groups—individuals with dementia who are unable to sustain employment or income—targeted social support is also essential to address their combined medical and financial challenges. Implications: We underscore the importance of addressing endogeneity issues when evaluating the relationship between air pollution and dementia. Conventional approaches may produce biased estimates due to spurious time-series-correlations and omitted variables. By using wind speed and direction as instruments, we identify LATE-based causal effects of air pollution on the number of dementia patients. Our findings suggest important policy implications: reducing air pollution can lower the substantial social and economic costs associated with dementia. Improved administrative data linking clinical records with environmental exposures would support effective monitoring and policy evaluation. Furthermore, international cooperation is needed to address transboundary nature of air pollution.

Objective: The study examined the effect of deposit insurance on lending behaviour of quoted Deposit Money Banks (DMBs) in Nigeria from 2015-2023. Deposit insurance was proxied by capital adequacy ratio (CAR) and premium rates while lending behavior was proxied by loan-to-deposit ratio. Theoretical Framework: The study was underpinned by the Hands-on theory of deposit insurance. Deposit insurance is supposed to contribute to the maintenance of banking system stability as one of the safety net arrangements. Method: The data was sourced from the individual audited financial reports of the listed DMBs in Nigeria. The study adopted the census approach, in which all the fourteen (14) listed DMBs in Nigeria were involved. Regression model was employed to estimate the relationship between deposit insurance and lending behaviour of listed DMBs in Nigeria. Results and Discussion: The results revealed that CAR had a positive significant effect on loan-to-deposit ratio of listed DMBs in Nigeria. While premium rates had insignificant effect on loan-to-deposit ratio of listed DMBs in Nigeria. Implications of the Research: The study recommended that the Central Bank of Nigeria should maintain and refine capital adequacy requirements in ways that encourage healthy capital buffers without restricting credit growth. Originality/Value: Measures such as countercyclical capital buffers and enhanced stress testing should be integrated to ensure resilience across economic cycles. Also, since the deposit insurance premium rate is risk based, the Nigeria Deposit Insurance Corporation should deepen the implementation of the methodology by assigning a higher weight to capital adequacy. Allowing greater variation in premium rates based on risk profiles will improve incentives for prudence, help reduce latent moral hazard, and improve the effectiveness of deposit insurance as a safety net mechanism within the financial system.

This study aims to reveal the responses of carbon emissions to economic business cycles in the Turkish economy by using the Markov-Switching Autoregressive (MSAR) model.. It is frequently emphasized in the literature that carbon emissions exhibit a nonlinear relationship with economic growth and respond asymmetrically to periods of expansion and contraction. In this context, the study analyzes the cyclical components of annual total and per capita carbon emissions and gross domestic product (GDP) for the period 1970-2023. The findings show that carbon emissions increase during periods of economic expansion but do not decrease at the same rate during recessions. The results reveal that the response of emissions to business cycles is asymmetric and that emission growth is positive but less than one during expansion periods. On the other hand, the sensitivity of emissions is negative during economic recession periods and emissions show a more limited decrease compared to the contraction in GDP. These results have important policy implications for Turkiye’s transition to a low-carbon growth strategy. In particular, stricter regulations should be introduced during periods of economic growth and spesific reduction target should be maintained even during periods of economic recession.

Abstract In normative models a decision-maker is usually assumed to be Bayesian rational, and so to maximize subjective expected utility, within a complete and correctly specified decision model. Following the discussion in Hammond (HEI 179–195, 2007) of Schumpeter’s (Theorie der wirtschaftlichen Entwicklung; Eine Untersuchung über Unternehmergewinn, Kapital, Kredit, Zins und den Konjunkturzyklus, Leipzig, 1911; The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle, 1934) concept of entrepreneurship, as well as Shackle’s (Economica NS, 20:112-117, 1953) concept of potential surprise, we consider enlivened decision trees whose growth over time cannot be accurately modelled in full detail. An enlivened decision tree involves more severe limitations than a mis-specified model, unforeseen contingencies, or unawareness, all of which are typically modelled with reference to a universal state space large enough to encompass any decision model that an agent may consider. We consider a motivating example based on Homer’s classic tale of Odysseus and the Sirens. Though our novel framework transcends standard notions of risk or uncertainty, for finite decision trees that may be truncated because of bounded rationality, an extended and refined form of Bayesian rationality is still possible, with real-valued subjective evaluations instead of consequences attached to terminal nodes where truncations occur. Moreover, these subjective evaluations underlie, for example, the kind of Monte Carlo tree search algorithm used by recent chess-playing software packages. They may also help rationalize the contentious precautionary principle.

The main goal of this paper is to study the impact of digital competencies on economic growth measured by changes in the natural logarithm of GDP per capita. The study covers countries that are currently members of the European Union, and the data used to estimate the model spans the period from 2015 to 2019. To examine the relationship, a panel analysis was used. This method can isolate the impact of countries’ time-invariant characteristics (fixed effects) as well as crises, business cycles, and shocks (time effects) common to all countries. The main explanatory variable for GDP per capita growth was a synthetic digital competence index created for the purposes of this study (Digital_Index). The baseline specification incorporated a number of control variables, which were subsequently removed in alternative specifications to assess changes in the quality of estimation. Models with and without lagged variables were used. A number of models with competency sub-indices and individual Internet and computer skills were tested. For the sake of comparison, OLS models were also estimated. The article indicates that digital competencies alone are not a determinant of economic growth in the short term, but this topic requires further research, the directions of which are pointed out in the conclusions.

This article presents a comprehensive review of the literature on leading indicators of stock market activity in Poland, aimed at identifying the most effective forecasting tools for market trends and economic cycles. The analysis includes publications from the Scopus and Web of Science databases, resulting in a collection of 63 articles, 11 of which directly address the Polish market. The studies are classified into four main groups: market indicators, banking sector indices, macroeconomic variables, and synthetic measures of financial stress and systemic risk. The review highlights the evolution of research approaches: from simple macroeconomic indicators used in the 1990s, through advanced econometric models and stress indices introduced after the 2008 global financial crisis, to sectoral analyses during the COVID-19 pandemic. The results confirm the high predictive value of stock market indices (including the WIG), the role of banking sector indicators in early warning systems, and the need to integrate macroeconomic data with risk analysis based on network models. This synthesis provides a structured source of knowledge for both researchers and market practitioners, and indicates directions for further research and development of economic situation monitoring systems.

This paper examines how loan portfolio heterogeneity shapes the build-up of systemic risk and the design of the countercyclical capital buffer (CCyB) in an emerging, bank-based economy. Using Bolivia as a case study, we show that a uniform CCyB calibrated on the credit-to-GDP gap fails to reflect banks’ differentiated sensitivity to the cycle and may induce moral hazard, competitive distortions and weaker macroprudential effectiveness. We propose a micro-foundation for macroprudential policy by modelling each bank’s loan book as a risky asset in a CAPM-type framework without a risk-free asset. In this setting, the “loan beta” with respect to real GDP growth summarizes the systematic risk taken by each institution and its propensity to amplify or dampen the business cycle. Using quarterly data for 16 banks, we estimate contemporaneous and lagged betas and relate them to business models, portfolio composition and asset quality. The financial cycle lags the real cycle by around four quarters and pro-cyclicality is heterogeneous: universal banks cluster around a beta of one, the SME-oriented banks display betas above two, and microfinance institutions show much lower or even cyclical sensitivities. These results support a proportional, segmented CCyB design, where activation and calibration are anchored in portfolio betas and business models rather than applied uniformly across institutions, thereby strengthening the link between micro-level risk-taking and macroprudential objectives. The framework is tractable and can be generalized to other concentrated banking systems.

We revisit the role of temporary layoffs in the business cycle. While some have emphasized a stabilizing effect due to recall hiring, we quantify from the data an important countercyclical destabilizing effect due to “loss-of-recall,” whereby workers in temporary-layoff unemployment lose their job permanently. We develop a quantitative model allowing for endogenous flows of workers across employment and both temporary-layoff and jobless unemployment. The model captures both pre- and post-pandemic unemployment dynamics, including the contractionary role of loss-of-recall. We use our structural model to show that the Paycheck Protection Program generated sizable employment gains, in part by significantly reducing loss-of-recall. (JEL E24, E32, I12, J41, J63, J64)

Firm dynamics and random search over the business cycle

Economic cycles and the transition to motherhood: Differentiation between natives without a migration background and children of immigrants.

Evolving patterns of hotel agglomeration: Economic cycles and technological influence

A tobacco-rapeseed rotation model for economically sustainable phytoremediation of cadmium-contaminated farmland.

The declining explanatory power of interest rates for stock market and business cycle dynamics

Spanish recessions 1850–2023: A business cycle accounting analysis

This paper focuses on Japan's bubble economy period from the mid-to-late 1980s to the early 1990s, aiming to delve into how macroeconomic prosperity systematically drove a technological paradigm shift in the field of popular music production and thereby gave birth to and defined the iconic music genre of City Pop. Rather than viewing music styles as mere cultural reflections, the study adopts an interdisciplinary approach from the intersection of technological sociology and industrial economics, demonstrating how economic capital, through the acquisition of cutting-edge equipment, restructuring of production processes, and shaping of consumer markets, completely revolutionized the infrastructure of music production. The core empirical section of the paper centers on the technological practices of two arrangers, Motoyoshi Funayama and Kyohei Tsutsumi, analyzing in detail how they, as "digital pioneers" and "fusion masters" respectively, applied computer music systems such as the Fairlight CMI, multi-track digital recording technology, and complex vocal arrangement concepts to their compositions, laying the precise and ornate auditory foundation for City Pop. Meanwhile, the study uses the production cases of top idols Nakajima Miyuki and Momoko Kikuchi as a lens to reveal how, under high-budget production models, resources were channeled into recording, arrangement, and performance to elevate idol products to the level of near-artistry. Ultimately, this paper posits that City Pop represents the "technological realization" of the bubble economy in the acoustic realm, with its rise and fall not only reflecting economic cycles but also leaving a lasting technological legacy and aesthetic symbols. It offers important insights for understanding the deep interplay between economic conditions and cultural and artistic production.

Abstract This paper introduces a weighted output gap measure for Switzerland that combines univariate filters, multivariate filters, and production function approaches. Published quarterly by SECO since 2019:Q4, the series provides a historically consistent and robust indicator of cyclical conditions. Using an inflation forecasting framework, the weighted gap achieves forecasting performance comparable to leading individual methods; although it does not outperform them uniformly, it provides a balanced and reliable signal that mitigates method-specific weaknesses. These properties make it a useful benchmark for business cycle analysis and policy applications.

Purpose This study aims to examine how climate change affects economic growth in Indonesia – the world’s largest Muslim-majority country with a dual banking system – by analyzing the distribution of future growth risks rather than average outcomes. Design/methodology/approach The paper employs a Growth at Risk (GaR) framework, integrating climate variables into ordinary least sqaures and Quantile Regression models using quarterly data from 2008Q1 to 2023Q3. This approach allows the assessment of climate impacts across different states of the economic cycle and forecasting horizons. Findings The results reveal a nonlinear and state-dependent relationship between climate change and economic growth. Climate change has its strongest and statistically significant effects at the lower tail of the growth distribution, where climate-induced fiscal stimulus supports economic recovery during downturns. Research limitations/implications The analysis is conducted at the national level and does not explicitly model differential transmission channels between Islamic and conventional banks, which could be explored in future research. Practical implications The findings suggest that climate-responsive fiscal policy can play a stabilizing role during periods of economic weakness, particularly in dual-banking systems where risk-sharing financial structures may enhance resilience to climate shocks. Social implications By highlighting the role of fiscal responses and inclusive financial systems in mitigating climate-related downturns, the study informs policy strategies aimed at protecting livelihoods and supporting sustainable growth in climate-vulnerable, Muslim-majority economies. Originality/value This study extends the GaR literature by incorporating climate change as a key predictor of growth risk and by contextualizing the analysis within a Muslim-majority, dual-banking economy, offering new insights into the interaction between climate shocks, fiscal policy and financial system structure.

Africa exhibits a wide variation in macroeconomic performance reflecting differences in economic diversification, natural resource dependence, governance systems, political stability, and exposure to global economic cycles. This study investigates the dynamic interactions among key macroeconomic variables, annual inflation rate, exchange rate, foreign direct investment (FDI), and government final expenditure, across eight African economies: Nigeria, South Africa, Egypt, Angola, Morocco, Ethiopia, Tanzania, and Mozambique. Using annual data spanning 1990 to 2024 obtained from the World Development Indicators (WDI), the study employs Vector Autoregression (VAR) models and extends them by integrating machine learning architectures, specifically Random Forest (RF), Multilayer Perceptron (MLP), and XGBoost, to develop VAR–machine learning hybrid forecasting models. The comparative performance of the baseline VAR and hybrid models is evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The findings show substantial forecasting accuracy improvements when machine learning components are incorporated into the VAR framework. For inflation, the VAR+MLP model achieves the most significant reduction in forecasting errors in Nigeria, South Africa, and Morocco, with South Africa’s MAE decreasing from 1.471 (VAR) to 0.888 under the hybrid model. Similarly, FDI predictions improve markedly across nearly all countries, with Tanzania exhibiting a major decline in MAE from 0.534 (VAR) to 0.184 (VAR+XGBoost). For government expenditure, hybrid models outperform VAR in Angola, South Africa, Morocco, and Cameroon, while exchange rate dynamics show mixed outcomes, with traditional VAR excelling in more stable economies such as Nigeria and Morocco. The results demonstrate that hybrid VAR–machine learning models more effectively capture nonlinear macroeconomic relationships and yield superior predictive performance for inflation and FDI, underscoring their relevance for economic planning and policy formulation in African economies.