Localization is globally accepted as the strategy towards attaining the Sustainable Development Goals (SDGs). In this article, we put forth the South Indian state of Kerala as a true executor of the localization of SDGs owing to her foundational framework of decentralized governance. We attempt to understand how the course of decentralization acts as a development trajectory and how it has paved the way for the effective assimilation of localization principles post-2015 by reviewing the state documents based on the framework propounded by the United Nations. We theorize that the well-established decentralization mechanism, with delegated institutions and functions thereof, encompasses overlapping mandates with the SDGs. Further, through the tools of development plan formulation, good governance, and community participation at decentralized levels, Kerala could easily adapt to localization, concocting output through innovative measures of convergence, monitoring, and incentivization carried out through the pre-existing platforms and processes. The article proves that constant and concerted efforts undertaken by Kerala through her meticulous and action-oriented decentralized system aided the localization of SDGs and provides an answer to the remarkable feat that the state has achieved through the consecutive four times achievements in the state scores of SDG India Index.

The paper assesses the threshold at which climate change impacts banking system stability in selected Sub-Saharan economies by applying the panel threshold regression on data spanning 1996 to 2017. The study found that temperature reported a threshold of −0.7316 ℃. Further, precipitation had a threshold of 7.1646 mm, while the greenhouse gas threshold was 3.6680 GtCO₂eq. In addition, the climate change index recorded a threshold of −0.1751%. Overall, a non-linear relationship was established between climate change variables and banking system stability in selected Sub-Saharan economies. The study recommends that central banks and policymakers propagate the importance of climate change uncertainties and their threshold effects to banking sectors to ensure effective and stable banking system operations.

The policy to accelerate the design of the Detailed Spatial Plan regulation document (RDTR) is a strategic step to enhance ease of doing business and promote sustainable development in Indonesia. Targeting 2036 RDTR sites nationwide, the initiative relies on various policy interventions and technical approaches. However, as of 8 January 2024, only 399 RDTRs (19.59%) were enacted after four years of implementation. This underperformance suggests the need to examine factors influencing the process, including issues at each stage of the RDTR design business process. While often overlooked due to its perceived irrelevance to the core substance of planning, analyzing the process is crucial to addressing operational and procedural challenges. This research identifies critical issues arising from the preparation to the enactment stage of RDTR regulations and proposes necessary policy changes. Using an explanatory approach, the study employs methods such as Analytic Hierarchy Process (AHP), post-review analysis, stakeholder analysis, business process evaluation, and scenario planning. Results show several impediments, including challenges related to commitment, technical and substantive issues, managerial coordination, policy frameworks, ICT support, and data availability. These findings serve as inputs for the development of business process improvement scenarios and reengineering schemes based on Business Process Management principles.

This paper explores the interconnected dynamics between governance, public debt, and domestic investment (also known as gross fixed capital formation (GFCF) in South Africa). It also highlights domestic investment as a key driver of economic growth, noting a consistent decline in investment since the country’s democratic transition in 1994. Moreover, this downward trend is exacerbated by excessive public debt, poor governance, and increased economic risks, discouraging domestic and foreign investments. The analysis incorporates two theoretical perspectives: endogenous growth theory, which stresses the significance of local capital investment and innovation, and institutional governance theory, which focuses on the role of governance in promoting economic development. The study reveals that poor governance, rising debt, and high economic risks have impeded GFCF and economic stability. By utilizing quantitative data from 1995 to 2023, the research concludes that reducing public debt, improving governance, and minimizing economic risk are critical to revitalizing domestic investment in South Africa. These findings suggest that policy reforms centered on good governance, effective debt management, and economic stabilization can stimulate investment, promote growth, and address the country’s economic challenges. This study offers insights into how governance and fiscal policies shape investment and capital formation in a developing nation, providing valuable guidance for policymakers and stakeholders working towards sustainable economic growth in South Africa.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This study investigates the impact of Foreign Direct Investments (FDIs) on wage dynamics in Slovakia and Slovenia, with a particular emphasis on gender-specific effects in post-Communist emerging markets. By analyzing wage outcomes for male and female workers separately, the research reveals potential disparities in FDIs-driven wage growth. Employing econometric techniques and longitudinal data, the study explores the nuanced relationship between FDIs, wage policies, and economic development over time. A temporal lag in FDIs analysis suggests that Slovakia and Slovenia have experienced differing impacts from past foreign capital flows. In Slovakia, significant correlations indicate persistent FDIs influence and a pronounced effect on gender wage disparities. In Slovenia, more moderate correlations and FDIs volatility suggest a less stable relationship between external investment and wage dynamics. The originality of this research lies in its comparative approach, examining two distinct post-Communist nations and identifying unique country-specific patterns and trends. This study contributes to a deeper understanding of FDI’s role in labor market management and its implications for gender equality in two European emerging economies.