North Korea's extreme financial exclusion severely hinders economic growth, perpetuating poverty and inequality. This study examines financial exclusion in the country, shaped by its isolationist policies, international sanctions, and a rudimentary financial system. Through a literature review and mixed-method analysis, incorporating data from the World Bank, IMF, academic sources, and NGOs, the research highlights how financial exclusion limits economic development. North Korea's self-reliance ideology, strict internal policies, and global sanctions exacerbate its financial isolation, restricting access to formal banking and financial services. As a result, North Koreans rely on informal financial networks, cross-border trade, remittances, and even cryptocurrencies to navigate these constraints. Additionally, the regime employs sophisticated money-laundering techniques to evade sanctions and integrate illicit funds into the formal economy, funding its nuclear ambitions. The study recommends humanitarian pathways to alleviate financial exclusion, advocating for a conditional easing of sanctions in exchange for compliance with international demands. Strengthening anti-money laundering frameworks is also crucial to curb illicit financial activities. Addressing financial exclusion in North Korea requires a balanced approach that supports the population while enforcing measures against regime-led financial crimes.

This paper presents an analytical framework to assess the probability of achieving nationally determined contributions (NDC). The prediction model based on the Kaya identity is used to simulate the pathway of carbon emission until the target year. Applying the modified STIRPAT framework (named CO-STIRPAT) to data observed in South Korea shows that the probability that the predicted pathway with existing climate technology will stay above the NDC target pathway is significantly high. The result suggests that it is necessary to design a climate policy to improve energy intensity and carbon intensity by accelerating the advance in climate technology.

This paper presents an operational framework for assessing the trajectories of production, energy, emissions, and capital accumulation to ensure the implementation of Nationally Determined Contributions (NDCs). The framework combines widely used methodologies (STIRPAT, system dynamics, and optimization) to simulate the pathways of variables until a target year. The CO-STIRPAT dynamic system allows us to identify the spillover pathways from carbon policy to economic growth based on output optimization principles; to conduct a more systematic analysis of the interconnections between the main drivers that determine carbon emissions; to develop a cost-effective climate policy mix that is a backbone for the right combination of carbon pricing, energy efficiency, and carbon intensity; and to assess NDC targets with respect to ambition gaps, implementation gaps, and feasibility.

The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.