Kobra Gharali, Saghar Sarshar, Behnam Rafiei
et al.
Canadian remote communities predominantly rely on diesel for electricity generation, resulting in high energy costs, environmental damage, unreliable services, and limitations on community development. To promote the widespread adoption of renewable energy (RE) in remote regions, a comprehensive assessment of their impacts on communities, constraints, and strategies for addressing obstacles is needed. This study reviews RE applications, including geothermal, wind, solar, biomass, and kinetic hydropower, in remote areas of Canada, highlighting resource potential, study methodologies, and associated environmental, economic, social, and policy dimensions. From 120 reviewed publications, hybrid/integrated systems have received the most attention (31 %). Simulation and optimization are the dominant methods (48 % and 45 %, respectively); TRNSYS is the most common simulation tool, while Homer and RETScreen are frequently applied in optimization studies. Adopting RE in remote communities benefits the environment by reducing GHG emissions, local pollutants, and noise, and may contribute to permafrost stability, though risks such as wildlife disturbance and visual impacts require careful siting and design. Economically, high upfront capital costs remain the main barrier, although long-term fuel savings can offset investments, and government incentives and financial support could help overcome this challenge. Socially, RE adoption enhances energy security, improves health and welfare, and creates jobs, but may also displace diesel-related employment, highlighting the importance of local ownership, respect for community values, and youth education in achieving community acceptance. On the policy side, despite growing federal funding, restrictive regulations, low power purchase rates, and policy instability hinder community participation, underscoring the need for supportive and inclusive frameworks.
Propose a deep learning driven multi factor investment model optimization method for risk control. By constructing a deep learning model based on Long Short Term Memory (LSTM) and combining it with a multi factor investment model, we optimize factor selection and weight determination to enhance the model's adaptability and robustness to market changes. Empirical analysis shows that the LSTM model is significantly superior to the benchmark model in risk control indicators such as maximum retracement, Sharp ratio and value at risk (VaR), and shows strong adaptability and robustness in different market environments. Furthermore, the model is applied to the actual portfolio to optimize the asset allocation, which significantly improves the performance of the portfolio, provides investors with more scientific and accurate investment decision-making basis, and effectively balances the benefits and risks.
Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but their effectiveness in financial decision-making remains inadequately evaluated. Current benchmarks primarily assess LLMs' understanding on financial documents rather than the ability to manage assets or dig out trading opportunities in dynamic market conditions. Despite the release of new benchmarks for evaluating diversified tasks on the financial domain, we identified four major problems in these benchmarks, which are data leakage, navel-gazing, over-intervention, and maintenance-hard. To pave the research gap, we introduce DeepFund, a comprehensive arena platform for evaluating LLM-based trading strategies in a live environment. Our approach implements a multi-agent framework where they serve as multiple key roles that realize the real-world investment decision processes. Moreover, we provide a web interface that visualizes LLMs' performance with fund investment metrics across different market conditions, enabling detailed comparative analysis. Through DeepFund, we aim to provide a more realistic and fair assessment on LLM's capabilities in fund investment, offering diversified insights and revealing their potential applications in real-world financial markets. Our code is publicly available at https://github.com/HKUSTDial/DeepFund.
This paper develops a spatial-dynamic framework to analyze the theoretical and quantitative effects of a Land Value Tax (LVT) on urban land markets, capital accumulation, and spatial redistribution. Building upon the Georgist distinction between produced value and unearned rent, the model departs from the static equilibrium tradition by introducing an explicit diffusion process for land values and a local investment dynamic governed by profitability thresholds. Land value $V (x, y, t)$ and built capital $K(x, y, t)$evolve over a two-dimensional urban domain according to coupled nonlinear partial differential equations, incorporating local productivity $A(x, y)$, centrality effects $μ(x, y)$, depreciation $δ$, and fiscal pressure $τ$ . Analytical characterization of the steady states reveals a transcritical bifurcation in the parameter $τ$ , separating inactive (low-investment) and active (self-sustaining) spatial regimes. The equilibrium pair $(V ^*, K^*)$ is shown to exist only when the effective decay rate $α= r + τ- μ(x, y)$ exceeds a profitability threshold $θ= κ+ δ/ I_0$, and becomes locally unstable beyond this boundary. The introduction of diffusion, $D_V ΔV$, stabilizes spatial dynamics and generates continuous gradients of land value and capital intensity, mitigating speculative clustering while preserving productive incentives. Numerical simulations confirm these analytical properties and display the emergence of spatially heterogeneous steady states driven by urban centrality and local productivity. The model also quantifies key aggregate outcomes, including dynamic tax revenues, adjusted capital-to-land ratios, and net present values under spatial heterogeneity and temporal discounting. Sensitivity analyses demonstrate that the main qualitative mechanisms-critical activation, spatial recomposition, and bifurcation structure-remain robust under alternative spatial profiles $(A, μ)$, discretization schemes, and moderate differentiation of the tax rate $τ(x, y)$. From an economic perspective, the results clarify the dual nature of the LVT: while it erodes unproductive rents and speculative land holding, its dynamic incidence on built capital depends on local profitability and financing constraints. The taxation parameter $τ$ thus acts as a control variable in a nonlinear spatial system, shaping transitions between rent-driven and production-driven equilibria. Within a critical range around $τ_c$, the LVT functions as an efficient spatial reallocation operator-reducing inequality in land values and investment density without impairing aggregate productivity. Beyond this range, excessive taxation induces systemic contraction and investment stagnation. Overall, this research bridges static urban tax theory with dynamic spatial economics by formalizing how a land-based fiscal instrument can reshape the geography of value creation through endogenous diffusion and nonlinear feedback. The framework provides a foundation for future extensions involving stochastic shocks, adaptive policy feedbacks, or endogenous public investment, offering a unified quantitative perspective on the dynamic efficiency and spatial equity of land value taxation.
Climate physical risks pose an increasing threat to urban infrastructure, necessitating urgent climate adaptation measures to protect lives and assets. Implementing such measures, including the development of resilient infrastructure and retrofitting existing systems, demands substantial financial investment. Unfortunately, due to the unprofitability stemming from the long-term returns, uncertainty, and complexity of infrastructure adaptation projects and the short-term profit-seeking objectives of private capital, a massive financial gap remains. This study suggests incentivizing private capital to bridge financial gaps through integrated carbon markets. Specifically, the framework combines carbon taxes and carbon markets to involve infrastructure and individuals in the climate mitigation phase, using the funds collected for climate adaptation. Moreover, it integrates lifestyle reformation, environmental mitigation, and infrastructure adaptation to establish harmonized standards and provide circular positive feedback to sustain the markets. We further explore how integrated carbon markets can facilitate fund collection and discuss the challenges of incorporating them into infrastructure climate adaptation. This study aims to foster collaboration between private and public capital to enable a more scientific, rational, and actionable implementation of integrated carbon markets, thus supporting sustainable financial backing for infrastructure climate adaptation
Pablo Azcue, Corina Constantinescu, José Miguel Flores-Contró
et al.
Design and implementation of appropriate social protection strategies is one of the main targets of the United Nation's Sustainable Development Goal (SDG) 1: No Poverty. Cash transfer (CT) programmes are considered one of the main social protection strategies and an instrument for achieving SDG 1. Targeting consists of establishing eligibility criteria for beneficiaries of CT programmes. In low-income countries, where resources are limited, proper targeting of CTs is essential for an efficient use of resources. Given the growing importance of microinsurance as a complementary tool to social protection strategies, this study examines its role as a supplement to CT programmes. In this article, we adopt the piecewise-deterministic Markov process introduced in Kovacevic and Pflug (2011) to model the capital of a household, which when exposed to proportional capital losses (in contrast to the classical Cramér-Lundberg model) can push them into the poverty area. Striving for cost-effective CT programmes, we optimise the expected discounted cost of keeping the household's capital above the poverty line by means of injection of capital (as a direct capital transfer). Using dynamic programming techniques, we derive the Hamilton-Jacobi-Bellman (HJB) equation associated with the optimal control problem of determining the amount of capital to inject over time. We show that this equation admits a viscosity solution that can be approximated numerically. Moreover, in certain special cases, we obtain closed-form expressions for the solution. Numerical examples show that there is an optimal level of injection above the poverty threshold, suggesting that efficient use of resources is achieved when CTs are preventive rather than reactive, since injecting capital into households when their capital levels are above the poverty line is less costly than to do so only when it falls below the threshold.
Competitiveness is imperative for business leadership, as it fosters resilience and the capacity to outperform in a rapidly evolving market. This study explores the evolution of competitiveness factors and identifies those most critical in the current business environment. Competitiveness, traditionally associated with market performance and cost efficiency, has undergone a significant conceptual transformation, increasingly converging with sustainability and digitalization. Using a bibliometric approach, the research analyzed 741 publications indexed in the SCOPUS database, employing tools such as VOSviewer 1.6.20, Bibliometrix, and R 4.4.3 software for keyword co-occurrence mapping and thematic evolution analysis of competitiveness factors. A graphical representation has been employed to convey the results. The findings reveal that innovation remains the most persistent and dominant factor across all periods, underscoring its foundational role in competitive advantage. However, recent years have witnessed a marked rise in sustainability and digital transformation as key drivers of competitiveness. Sustainability, once peripheral, now represents a strategic imperative, reflecting global priorities related to climate change, social responsibility, and regulatory compliance. Similarly, digital transformation has emerged as a motor theme since 2017, highlighting the impact of advanced technologies, such as AI, big data, and automation, on operational efficiency and strategic positioning. Cluster analysis further demonstrates interconnections among these factors, with sustainability and digitalization linked to innovations, information technology, knowledge, human capital, strategy, and investments. The study concludes that competitiveness is no longer defined solely by financial metrics but by the ability to integrate technological, environmental, and social dimensions into business models. These insights provide a comprehensive framework for managers and policymakers to align competitive strategies with emerging global trends, ensuring long-term viability and resilience in dynamic markets.
Abstract Previous studies have suggested that a combined strategy using both emissions abatement and climate adaptation can improve economic outcomes. Here, using a parsimonious economic-climate assessment model, we have shown that, relative to investment in abatement, adaptation has a much shorter timescale for economic return. Adaptation deployed in conjunction with abatement allows earlier benefits compared to investment in abatement alone. Our results provide evidence of greater net benefit with complementary investments in abatement reducing long-term climate damage and investments in adaptation reducing near-term damage. The timescale of return on investment in abatement is strongly influenced by economic discount rates, whereas the timescale of return on investment in adaptation is strongly influenced by the capital depreciation timescale. Higher levels of abatement investment associated with stringent emissions reduction constraints can reduce returns on adaptation investment. Even so, our results indicate greater near-term and long-term net benefits when investing in both abatement and adaptation.
The stock market has become an increasingly popular investment option among new generations, with individuals exploring more complex assets. This rise in retail investors' participation necessitates a deeper understanding of the driving factors behind this trend and the role of financial literacy in enhancing investment decisions. This study aims to investigate how financial literacy influences financial decision-making and stock market participation. By identifying key barriers and motivators, the findings can provide valuable insights for individuals and policymakers to promote informed investing practices. Our research is qualitative in nature, utilizing data collected from social media platforms to analyze real-time investor behavior and attitudes. This approach allows us to capture the nuanced ways in which financial literacy impacts investment choices and participation in the stock market. The findings indicate that financial literacy plays a critical role in stock market participation and financial decision-making. Key barriers to participation include low financial literacy, while increased financial knowledge enhances investment confidence and decision-making. Additionally, behavioral finance factors and susceptibility to financial scams are significantly influenced by levels of financial literacy. These results underscore the importance of targeted financial education programs to improve financial literacy and empower individuals to participate effectively in the stock market.
This paper investigates the optimal investment, consumption, and life insurance strategies for households under the impact of health shock risk. Considering the uncertainty of the future health status of family members, a non-homogeneous Markov process is used to model the health status of the breadwinner. Drawing upon the theory of habit formation, we investigate the influence of different consumption habits on households' investment, consumption, and life insurance strategies. Based on whether the breadwinner is alive or not, we formulate and solve the corresponding Hamilton-Jacobi-Bellman (HJB) equations for the two scenarios of breadwinner survival and breadwinner's demise, respectively, and obtain explicit expressions for the optimal investment, consumption, and life insurance strategies. Through sensitivity analysis, it has been shown that the presence of health shocks within households has a negative impact on investment and consumption decisions, while the formation of consumption habits increases household propensity for precautionary savings.
We explore the nonlinear dynamics of a macroeconomic model with resource constraints. The dynamics is derived from a production function that considers capital and a generalized form of energy as inputs. Energy, the new variable, is depleted during the production process and has to be renewed, whereas capital grows with production and decreases from depreciation. Dependent on time scales and energy related control parameters, we obtain steady states of high or low production, but also sustained oscillations that show properties of business cycles. We also find conditions for the coexistence of stable fixed points and limit cycles. Our model allows to specify investment and saving functions for Kaldor's model of business cycles. We provide evidence for an endogenous origin of business cycles if depleting resources are taken into account.
This article addresses the critical issue of private benefits in corporate governance, focusing on how executives or controlling shareholders exploit their positions for personal gain, often at the expense of shareholder value and corporate performance. This issue is highly relevant, as it directly impacts the alignment between executive and shareholder interests and can lead to inefficiencies in corporate governance. The primary purpose is to examine the various forms of private benefits, such as excessive compensation and self-serving strategic decisions, and to explore governance mechanisms that can mitigate these practices. The choice of private benefits as the research object is justified by their widespread and detrimental impact on corporate performance and sustainability. This article employs conceptual analysis, grounded in agency theory, to investigate how performance-based compensation, independent audit committees, and increased transparency can reduce the extraction of private benefits. Drawing on key studies, it analyzes the ways in which these governance mechanisms align executive actions with long-term corporate goals. The main findings reveal that private benefits manifest in forms like excessive compensation and biased strategic decisions, undermining long-term performance. Strong governance frameworks, including performance-based pay and independent oversight, are essential in curbing such behaviors. The results of this article have practical applications for corporate boards, regulatory bodies, and governance consultants. They can be used to improve governance policies, ensuring that executive compensation and strategic decisions are in line with sustainable corporate practices, benefiting both shareholders and the broader business ecosystem.
Edward J. Oughton, David Amaglobeli, Marian Moszoro
Roughly 3 billion citizens remain offline, equating to approximately 40 percent of the global population. Therefore, providing Internet connectivity is an essential part of the Sustainable Development Goals (SDGs) (Goal 9). In this paper a high-resolution global model is developed to evaluate the necessary investment requirements to achieve affordable universal broadband. The results indicate that approximately $418 billion needs to be mobilized to connect all unconnected citizens globally (targeting 40-50 GB/Month per user with 95 percent reliability). The bulk of additional investment is for emerging market economies (73 percent) and low-income developing countries (24 percent). To our knowledge, the paper contributes the first high-resolution global assessment which quantifies universal broadband investment at the sub-national level to achieve SDG Goal 9.
We investigate optimal carbon abatement in a dynamic general equilibrium climate-economy model with endogenous structural change. By differentiating the production of investment from consumption, we show that social cost of carbon can be conceived as a reduction in physical capital. In addition, we distinguish two final sectors in terms of productivity growth and climate vulnerability. We theoretically show that heterogeneous climate vulnerability results in a climate-induced version of Baumol's cost disease. Further, if climate-vulnerable sectors have high (low) productivity growth, climate impact can either ameliorate (aggravate) the Baumol's cost disease, call for less (more) stringent climate policy. We conclude that carbon abatement should not only factor in unpriced climate capital, but also be tailored to Baumol's cost and climate diseases.
Farhad Billimoria, Filiberto Fele, Iacopo Savelli
et al.
Extreme events, exacerbated by climate change, pose significant risks to the energy system and its consumers. However there are natural limits to the degree of protection that can be delivered from a centralised market architecture. Distributed energy resources provide resilience to the energy system, but their value remains inadequately recognized by regulatory frameworks. We propose an insurance framework to align residual outage risk exposure with locational incentives for distributed investment. We demonstrate that leveraging this framework in large-scale electricity systems could improve consumer welfare outcomes in the face of growing risks from extreme events via investment in distributed energy.
Objective: The purpose of the article is to examine the impact of ground-breaking inventions collectively called the Fourth Industrial Revolution (4IR) or Industry 4.0 on foreign direct investments (FDIs). In particular, the impact of the inventions on offshoring and reshoring (backshoring, nearshoring, insourcing) was analysed in the context of other factors and their potential future course.
Research Design & Methods: The article is theoretical and empirical in nature and based on theoretical literature on the subject, secondary sources containing the results of empirical research, and desk research. Predictive analysis and logical reasoning methods played therefore a very important role, since the studied phenomena are in the process of evolution. The structure of the article consists of findings on the origins and the definitions of offshoring and reshoring and their theoretical foundations, an analysis of the motives, barriers and course of these processes follows, and the presentation of the research results and the final conclusions.
Findings: The implementation of 4IR inventions has the potential to fundamentally change the geography of FDIs and their importance in the world. It is likely to decrease the significance of labour costs as a factor of production location while increasing the role of human capital and technology. As a result, reshoring phenomena will intensify, and many global value chains will be liquidated or shortened. The ambivalent impact of 4IR on the geography of production will be probable, while the trend towards reshoring and spatial dispersion of the production of complete products in the long term may appear to be stronger.
Implications & Recommendations: To a significant degree, Industry 4.0 will probably change the criteria for the implementation of FDIs, their size, and the directions of capital flows. The consequences of this will vary depending on the type of industry and the production carried out within. In particular, the effects will differ when divided between highly developed and developing countries. If the former benefit from the change in business models and strengthen their competitive advantages, the latter may suffer significant losses related to the increase of unemployment and the collapse of their industrialization strategy based on the inflow of FDIs. Therefore, actions by governments and international organizations are necessary to prevent this ‘dark’ scenario from becoming a reality.
Contribution & Value Added: The article presents the theoretical foundations of offshoring and reshoring as well as the analysis and synthesis of their motives and barriers. Their course and recent determinants were characterized. The assessment of the impact of 4IR inventions on offshoring and reshoring and the identification of factors that will affect these two aspects of FDIs positively and negatively in the short, medium, and long term in the future have the greatest importance for the value of the study. Moreover, the study found that the reverse flow of FDIs implies the need to formulate their new paradigm. The former paradigm characterised them in a limited way as a unilateral flow of capital from the country of origin to the host country. The one proposed in the article takes into account also the return movement of capital as an immanent element of FDIs of increasing importance.
The processes of economic development of the Russian regions nowadays represent a deep practical interest in the professional community, which requires further study and observation in dynamics. Due to natural and artificial circumstances and reasons, the regions of the country are developed extremely unevenly. There are no actual systematisation of the economic development of the federal subjects management and territorial clusterisation according to their development indicators. The Lipetsk region historically and today is also no exception. The region is part of the Central Federal District and a significant industrial centre here. More than 18 years ago the industrial special economic zone (hereinafter referred to as ISEZ) “Lipetsk” was created. The Lipetsk region is one of the first regions among various investment ratings in terms of quality and volume of investments, as well as among implemented investment projects, involving domestic and foreign capital. The purpose of this study is to determine the impact of the activities of the ISEZ “Lipetsk” on the socio-economic processes of the region and to study the possibilities of assessing such an impact.
In the knowledge-based economy of the 21stcentury, educated human resources are seen as a capital asset invested in the entity, and human capital theory seeks to explain, from an economic perspective, the phenomena arising from this process. The development of education and scientific spheres requires huge long-term investments, which must be analyzed from a social approach. Neither evaluation experience nor the methods applied provide a clear-cut solution, so evaluating an investment in human capital is a complex problem from both a practical and a scientific point of view. This research started from the hypothesis that investing in human capital will provide competitive advantage and sustainability in the complex world of the economic environment, reviewing the theory and evidence on the economics of human capital. Triangulation was chosen as a research method being considered the most adequate for the expected results and additionally because it allows to identify the most relevant aspects of this field that endorsed the proposed hypothesis. The main conclusion and results are that the emphasis has been placed on human capital just at the personal level, rather than at the level of unity and organization. Furthermore, research into the channels that have a causal effect on development has revealed that education is considered an instrument of developing human capital that promotes direct economic growth. Data analysis demonstrates that the theory of human capital is a convincing explanation for economic growth. In addition, the findings of the research have shown that the debate over equality vs. efficiency in economic development is centred on the human capital dimension.
Quantitative investment is a fundamental financial task that highly relies on accurate stock prediction and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, we observe that the performance of existing DL methods is sensitive to random seeds and network initialization. To design more profitable DL methods, we analyze this phenomenon and find two major limitations of existing works. First, there is a noticeable gap between accurate financial predictions and profitable investment strategies. Second, investment decisions are made based on only one individual predictor without consideration of model uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle these two limitations, we first reformulate quantitative investment as a multi-task learning problem. Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up trading strategy design workflow of successful trading firms. In Stage one, multiple independent trading experts are jointly optimized with an individual uncertainty-aware loss function. In Stage two, we train neural routers (corresponding to the role of a portfolio manager) to dynamically deploy these experts on an as-needed basis. AlphaMix is also a universal framework that is applicable to various backbone network architectures with consistent performance gains. Through extensive experiments on long-term real-world data spanning over five years on two of the most influential financial markets (US and China), we demonstrate that AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
The purpose of the study.It is known that the further development of the Russian regions requires significant investment in all areas of activity. Therefore, the problem of assessing the existing indicators of investment activity, characteristic of each of the regions, is put forward as an urgent one. At the same time, given the wide variety of Russian regions, a comparative analysis of absolute investment volumes is not appropriate. In this regard, we suggest using a comparison of specific indicators for the analysis. The purpose of our study is to assess the levels of specific investment in capital asset per capita in all regions of our country.Materials and methods.The study used the methodological approach proposed by the author, based on the consideration of specific indicators describing investment activity in the regions of Russia. The study included four stages. As initial information, we considered the official statistics provided on the ROSSTAT website, which characterize investments in the regions, as well as the number of their population in 2019. The study conducted a cluster analysis, as well as economic and mathematical modeling of the distribution of the considered indicators by the regions of the country.Results.The cluster analysis allowed us to identify five clusters that unite the regions of Russia with similar values of specific investments per inhabitant of the corresponding region. The first cluster includes four regions, the second cluster - five regions, the third cluster - thirteen regions, the fourth cluster - twenty-six regions and the fifth cluster - thirty-four regions. The cluster analysis showed that in nine regions in 2019, there was a high level of investment, due to the tasks of their strategic development to solve federal problems. For 73 regions there were relatively low values of specific investment, the distribution of empirical data was modeled using the normal distribution function.Conclusion.The scientific novelty of the study is related to the cluster analysis and the study of the distribution of specific investments by region. Regions with high and low values of specific investments in capital asset were identified. It is proved that the values of specific investments have a significant differentiation across the regions of the country. The results of our work have a certain theoretical and practical significance for the government, regional and local authorities. The methodological approach to assessing the level of investment presented in the article can be used in further research.