Search Results (28)

This study explores the challenges and opportunities for embedding the Natural Capital Approach (NCA) in policy processes, especially in the framing of the Environmental Improvement Plan (EIP), which is England’s strategic framework for improving the natural environment, including cleaner air and water, healthy soil, thriving wildlife and climate-adapted landscapes. Semi-structured interviews were undertaken with policymakers working in Defra (Department of Environment, Food and Rural Affairs) and its Arm’s Length Bodies (ALBs) organisations to investigate their views on the barriers and enablers to the adoption of the NCA. It has been widely recognised that the NCA provides unifying concepts that are able to connect economists and ecologists, and it can help to embed nature across government departments and supports to make the business case for nature improvement. On the other hand, there are perceived challenges in mainstreaming the NCA in environmental policy processes. There is some lack of agreement on the usefulness of the approach, problems with the oversuse of monetary valuation in policy appraisal, isolation of work, policy processes and government departments and difficulties in the communication of the benefits of the NCA. Recommendations to overcome the barriers include cross-departmental work placements of natural capital scientists, establishing cross-agency natural capital working goups to work on the use of the NCA to frame environment improvement policies, and prioritising the adoption of deliberative approaches to better understand local values on nature that are difficult or even impossible to monetise. Full article

Climate change is one of the major global challenges of modern times. It also poses a significant threat to price stability—the major objective of modern central banks. It creates the risk of stagflation, as it can lead to price increases (due to the increased frequency of extreme weather events, which will impact food production) and simultaneously weaken economic activity (due to lower productivity resulting from temperature changes). Climate change and political pressure have sparked a lively scientific debate on whether and how central banks should adapt their monetary policy frameworks to support efforts to stop climate change. Although the literature analyzes actions undertaken by monetary authorities in the areas of sustainable finance and climate risk analysis, this research still needs to be developed and disseminated. Therefore, the main aim of this article is to theoretically analyze the integration of climate issues with the monetary policy of modern central banks. This article provides a theoretical and integrative analysis of the role of modern central banks in addressing climate change, with a particular focus on implications for monetary policy. The study is based on a structured critical literature review and desk research, employing a transparent, multi-stage selection and analysis process, based on the PRISMA approach. The article contributes to the existing literature by offering a systematic synthesis of the main approaches to integrating climate-related considerations into central banking. The analysis organizes the literature into distinct analytical strands, including institutional and coordination-based initiatives, theoretical justifications for central bank involvement, debates on mandates and independence and the development of green monetary policy instruments. The findings suggest that the integration of climate considerations into monetary policy is feasible primarily within a risk-based and prudential framework, while more interventionist approaches may generate tensions with the primary objective of price stability. At the same time, the literature reveals persistent trade-offs between market neutrality and active policy intervention, as well as between institutional constraints and policy effectiveness. The study highlights that climate-related measures are often implemented through macroprudential, supervisory and financial stability functions, which complement rather than substitute monetary policy in the strictest sense. The article contributes to a more coherent understanding of the evolving role of central banks in the context of climate change by synthesizing a fragmented body of research and identifying key conceptual tensions that remain unresolved. Full article

This paper presents a Multi-Strategy Market Dynamics Analysis (MSMDA) framework for agent-based economic modeling with reinforcement learning. The primary methodological contribution is an integrated strategy–stability–macro inference pipeline that links population-level strategy evolution to dynamic market stability and model-internal counterfactual policy analysis. The framework is organized into six analytical components: Strategy Temporal Pattern Recognition (STPR), Strategy Transition Detection and Analysis (STDA), Strategy-Macro Causality Analysis (SMCA), the Dynamic Market Stability Index (DMSI), the Adaptive Rationality Equilibrium (ARE), and the Information Asymmetry Propagation (IAP) metric. The method is evaluated within a simulation dataset comprising 447,129 records across four experimental scenarios, 1500 discrete time periods, and 200 heterogeneous firms governed by proximal policy optimization. Results show that competitive strategies dominate market emergence patterns at 60.8% of all observations and achieve superior average profitability of 28.07 monetary units per period, compared with $-4.49$ for dumping strategies and 7.83 for market power strategies. The DMSI reveals a mean stability of 0.372 with standard deviation 0.097, peaking at 0.780 during strategic consolidation and collapsing to zero during a major demand shock. Within the simulated economy, doubly-robust counterfactual analysis projects a 28.4% GDP increase from a market power-to-competition intervention and a 31.2% increase under full ARE optimization at ${\rho }^{*}=0.6$. The ARE further identifies a Pareto-optimal market configuration that jointly maximizes per-firm profit at 229.82 monetary units per period and systemic stability at DMSI $=0.67$, indicating that efficiency and resilience need not conflict in the calibrated simulation environment. To address time-series autocorrelation in bootstrap inference throughout the framework, we employ a moving block bootstrap with data-adaptive block length selection based on the spectral density at frequency zero, providing finite-sample confidence intervals for the reported test statistics and counterfactual projections. Full article

This study examines the influence of diversification on commercial bank performance, specifically considering the role of monetary policy and shifting macroeconomic conditions. PRISMA 2020-based systematic literature review was conducted using Scopus data from 2006–2025. Bibliometric analysis using VOSviewer 1.6.20 and RStudio 4.4.3 was employed to identify key themes and citation patterns. Findings are heterogeneous and context-dependent: diversification can improve profitability and valuations in some settings, yet may increase earnings volatility and reduce risk-adjusted performance in others. In particular, the impact of monetary policy is identified as significant yet mediated by broader macroeconomic factors. These insights assist bank managers in aligning strategies with economic landscapes and aid policymakers in designing regulations adaptive to macroeconomic fluctuations. This study contributes a comprehensive synthesis of diversification strategies, emphasizing the often-overlooked interplay with monetary policy and providing a future research agenda. Full article

Climate change poses significant risks to New Zealand’s coastal agriculture through both slow-onset hazards (e.g., gradual sea level-induced groundwater rise) and sudden-onset hazards (e.g., increasing frequency and severity of storms). These physical changes threaten the productivity and economic viability of coastal farms. However, few studies assess their combined economic impacts in a manner that supports land-use planning. This paper presents a conceptual framework to examine the implications of interacting slow- and sudden-onset climate hazards for New Zealand dairy farms, informed by real-world consultation with subject-matter experts to support assessment. We draw conclusions that illustrate the monetary impacts on farms associated with potential absorptive, adaptive, and transformational responses. The findings highlight the critical role of timing as environmental conditions deteriorate under climate change, as well as the need for policy frameworks that recognise and monetize the contribution of ecosystem services provided by coastal vegetation habitats to social, cultural, and environmental wellbeing. Incorporating these values into present-day financial decision-making is essential for supporting climate-related financial risk reduction and long-term land-use planning. Without such frameworks, the most beneficial land-use transitions are unlikely to be affordable or sustainable in New Zealand, especially towards the year 2100. Full article

This paper examines, through a modified aggregate demand curve, the reduction in equilibrium income caused by the presence of precautionary demand in the money demand function. Specifically, this paper rigorously analyses the transformation of the well-known fiscal and monetary policy multipliers, β and γ, commonly found in macroeconomic theory textbooks. Ceteris paribus, an increase (decrease) in precautionary money demand reduces (increases) equilibrium income, as can be seen through the modified multipliers β and γ. Multiple contingencies that emerged suddenly between 2008 and 2023 may have altered agents’ perceptions regarding precautionary money demand. This work contributes to the adaptation of some well-established tools of macroeconomic theory to address events of this nature. Full article

Landslides rank among the most frequent and devastating natural hazards globally, causing significant loss of life and property. As a result, landslide susceptibility assessment has become a central focus in geohazard research, which is devoted to preventing and alleviating the frequent occurrence of landslides. Numerous analytical models have been applied to evaluate landslide susceptibility, including Frequency Ratio (FR), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and various hybrid and neural network-based approaches. This review synthesizes current progress in integrating Nature-based Solutions (NBS) with modeling and policy frameworks, highlighting their potential to provide cost-effective, sustainable, and adaptive alternatives to conventional landslide mitigation strategies. Based on a systematic review of 127 peer-reviewed publications published between 2023 and 2025, selected from Web of Science, ScienceDirect, MDPI, Springer, and Google Scholar using predefined keywords and screening criteria, this study reveals that the most frequently used conditioning factors in landslide susceptibility modeling are slope (96 times), aspect (77 times), elevation (77 times), and lithology (77 times). Among modeling approaches, Random Forest (RF), Support Vector Machine (SVM), hybrid models, and neural network models consistently demonstrate high predictive performance. Despite the expanding body of literature on NBS, only 2.3% of all NBS-related studies specifically address landslide mitigation. The existing literature primarily concentrates on assessing the biophysical effectiveness of interventions such as vegetation cover, root reinforcement, and forest-based stabilization using a range of predictive modeling techniques. However, significant gaps remain in the integration of economic valuation frameworks, particularly cost–benefit analysis (CBA), to quantify the monetary value of NBS interventions in landslide risk reduction. This highlights a critical area for future research to support evidence-based decision-making and sustainable risk governance. Full article

This paper proposes a novel framework that integrates Deep Learning Neural Networks into the Stochastic Parameterized Expectations Algorithm (DLNN-PEA) to solve nonlinear rational expectations models. This method enhances traditional PEA-based solvers by employing a deep neural expectations operator that captures complex nonlinearities and asymmetries. The DLNN-PEA is implemented in Matlab R2024b. It combines deep learning approximation with the standard expectation-iteration structure of the PEA, replacing the conventional shallow ANN-based operator with a deeper architecture that improves both accuracy and stability. The methodology is applied to the stochastic Neoclassical Growth Model, where the DLNN-PEA is trained to approximate conditional expectations and decision rules under uncertainty. The results show rapid convergence, reduced boundary-related issues, and stable performance even in high-volatility environments. Compared with ANN-PEA, deep architectures exhibit greater robustness and adaptability, making them suitable for economic models characterized by stronger nonlinearities and richer state dynamics. Beyond the benchmark model, the proposed framework is well-suited for medium-scale DSGE models, nonlinear monetary policy environments, and macro-financial simulations involving high-dimensional state spaces. These features make DLNN-PEA a practical tool for applied macroeconomic analysis and model-based policy evaluation. Full article

The Okapi Wildlife Reserve (OWR) in northeastern Democratic Republic of the Congo represents both a biodiversity hotspot and the ancestral homeland of the Indigenous Mbuti and Efe peoples, whose livelihoods and knowledge systems are closely tied to forest resources. This study investigates how Indigenous knowledge and practices contribute to sustainable resource management under conditions of rapid socio-cultural transformation. A mixed-methods approach was applied, combining socio-demographic surveys (n = 80), focus group discussions, floristic inventories, and statistical analyses (ANOVA, logistic regressions, chi-square, MCA). Results show that hunting, fishing, gathering, and honey harvesting remain central livelihood activities, governed by customary taboos and restrictions that act as de facto ecological regulations. Agriculture, recently introduced through intercultural exchange with neighboring Bantu populations, complements rather than replaces traditional practices and demonstrates emerging agroecological hybridization. Nevertheless, evidence of biodiversity decline (including local disappearance of species such as Dioscorea spp.), erosion of intergenerational knowledge transmission, and increased reliance on monetary income indicate vulnerabilities. Multiple Correspondence Analysis revealed a highly structured socio-ecological gradient (98.5% variance explained; Cronbach’s α = 0.977), indicating that perceptions of environmental change are strongly coupled with demographic identity and livelihood strategies. Floristic inventories confirmed significant differences in species abundance across camps (ANOVA, p < 0.001), highlighting site-specific pressures and the protective effect of persistent customary norms. The findings underscore the resilience and adaptability of Indigenous Peoples but also their exposure to ecological and cultural disruptions. We conclude that formal recognition of Indigenous institutions and integration of their knowledge systems into co-management frameworks are essential to strengthen ecological resilience, secure Indigenous rights, and align conservation policies with global biodiversity and climate agendas. Full article

This study examines the effects of climate mitigation, climate change as quantifiable effects of additional extreme weather events, and adaptation investments on economic growth in Germany. First, on the basis of a comprehensive literature review and further considerations, important impact channels are discussed. Second, the macroeconometric national model PANTA RHEI is used to quantify the effects. To this end, scenarios are refined with and without additional climate change, and with and without additional climate protection to achieve national reduction targets until 2045, and defined for the first time with and without adaptation to climate change. This is also the first combination of all three climate dimensions within the model. The results show that, in the model, the quantifiable effects of extreme weather events have a negative impact on GDP that can be reduced by adaptation. By contrast, climate mitigation has a positive effect. As only monetary effects are accounted for, negative effects of climate change and positive impacts of climate policy are underestimated in broader terms. The model results help to understand the interaction between mitigation and adaptation: without mitigation, the impact of the climate crisis will increase significantly. Adaptation measures may then have less impact or even become ineffective. Full article

Effective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. The framework employs Delta-LoRA (low-rank adaptation) for parameter-efficient fine-tuning, updating less than 1% of the total parameters without additional inference overhead. To ensure robust performance across institutions and policy cycles, domain-adversarial neural networks are incorporated to learn domain-invariant representations, and a multi-task learning approach integrates auxiliary hawkish/dovish sentiment signals. Evaluations conducted on a comprehensive dataset comprising Federal Open Market Committee statements and European Central Bank speeches from 1977 to 2024 demonstrate state-of-the-art performance, achieving over 6% improvement in macro-F1 score compared to baseline models while significantly reducing inference latency by 65%. This work offers a powerful and efficient new paradigm for handling ultra-long financial policy texts and demonstrates the effectiveness of integrating advanced sequence modeling, efficient fine-tuning, and domain adaptation techniques for extracting timely economic signals, with the aim to open new avenues for quantitative policy analysis and financial market forecasting. Full article

The evolving macroeconomic landscape, shaped by the global financial crisis and the COVID-19 pandemic, poses significant challenges for economies worldwide. However, Central, Eastern, and Southeastern European (CESEE) countries have demonstrated resilience and rapid recovery during crises, driven by a surge in consumption fueled by domestic credit and robust export growth supported by flexible exchange rates and adaptive monetary policies. Prior to EU accession, substantial foreign direct investment (FDI) during privatization and restructuring facilitated knowledge and technology transfers in CESEE economies. This study examines the interplay of exports, real exchange rates, GDP growth, FDI, inflation, domestic credit, and the human development index (HDI) in the CESEE region from 1995 to 2022, covering the transition period, EU accession, and major crises. Employing a panel ARDL model, we account for asymmetric effects of these variables on exports. The results reveal that GDP, FDI, inflation, domestic credit, and HDI significantly and positively influence exports, with HDI and GDP exerting the strongest effects, underscoring the pivotal roles of human capital and economic growth in enhancing export competitiveness. Conversely, real exchange rate depreciation negatively impacts exports, though non-price factors, such as product quality, mitigate this effect. These findings provide a robust basis for targeted policy measures to strengthen economic resilience and export performance in the CESEE region. Full article

This study investigated the influence of stochastic fluctuations on financial system stability by analyzing both ordinary and fractional-order financial models under noise. The ordinary financial system experiences perturbations due to bounded random disturbances, whereas the fractional-order counterpart models memory-dependent behaviors by incorporating fractional Gaussian noise (FGN) characterized by a Hurst parameter that governs long-term correlations. This study used data generated through MATLAB simulations based on standard financial models from the literature. Numerical simulations compared system behavior in deterministic and noisy environments. The results reveal that ordinary systems experience transient fluctuations, quickly returning to a stable state, whereas fractional systems exhibit persistent deviations due to historical dependencies. This highlights the fundamental difference between integer-order and fractional-order derivatives in financial modeling. Our key findings indicate that noise significantly impacts interest rates, investment needs, price indices, and profit margins, with the fractional system displaying higher sensitivity to external shocks. These insights emphasize the necessity of incorporating memory effects in financial modeling to improve accuracy in predicting market behavior. The study further underscores the importance of adaptive monetary policies and risk management strategies to mitigate financial instability. Future research should explore hybrid models combining short-term stability with long-term memory effects for enhanced financial forecasting and stability analysis. Full article

The environmental impact of beef cattle production varies significantly across farming systems, influenced by factors like feed, management practices, and land use. By applying the LCA perspective with “from cradle to farm gate” boundaries and using the CAP’2ER^® tool, this study evaluates the carbon footprint of two farming models in Italy: a semi-extensive cow-calf beef production and an intensive farm for calf fattening. The carbon footprint was calculated using two functional units: kilograms of live meat gross production (LMGP), and a monetary unit. The first model showed a lower carbon footprint, with 13.4 kg CO₂eq/kg LMGP and 1.96 kg CO₂eq/EUR, compared to the second one 19.2 kg CO₂eq/kg LMGP and 5.20 kg CO₂eq/EUR. The use of monetary value as a functional unit is rarely explored in the literature, since most studies have focused on weight-based metrics, favoring intensive systems with longer lifecycles compared to extensive farming. Furthermore, contrary to findings in the literature for semi-extensive systems like adaptive multi-paddock grazing, the tool used for the calculation did not detect any carbon sequestration. These findings highlight the need for further investigation into diverse functional units to assess the environmental and economic performance of farming systems. Expanding this approach could inform policies and consumer decisions, promoting sustainable beef production aligned with climate goals and the European Green Deal agenda. Full article

This paper explores the mathematical dynamics of consumer spending during a financial crisis using opponent process theory (OPT). Traditionally applied in psychology, OPT explains how initial emotional responses are followed by counteracting reactions to restore equilibrium. This study models the short-term boost in consumer spending and subsequent economic adjustments. Utilizing differential equations to represent these processes, this paper provides insights into the interplay between immediate policy effects and longer-term economic consequences. We focus on the United States (US) response to the 2008 Global Financial Crisis in this study. Results show evidence of diminishing response from prolonged stimuli due to demand saturation, resource allocation inefficiencies, and agent adaptation. Monetary stimuli may inflate debt/prices, outweighing benefits, and structural issues persist despite stimuli. Confidence and expectations impact response because perceived ineffectiveness weakens impact over time. Thus, while stimuli can initially boost activity, their sustained impact demands careful consideration of economic dynamics and agents’ responses. Full article