Poverty is a complex and multidimensional social problem faced by almost all countries including Indonesia. Poverty alleviation efforts require accurate and up-to-date data to ensure that social welfare programs can effectively run and target appropriate beneficiaries. Bekasi Regency, as one of the regions that faces significant challenges in managing poverty, needs the right strategy in updating and utilizing poverty data to support social welfare programs. This study analyzes a policy framework for updating and utilizing the poverty data. The data were collected from focus group discussions with experts and stakeholders who were competent in updating and utilizing the poverty data. MULTIPOL method was used to analyze the data. The results of the study show that the best strategy for updating and utilizing poverty data in the scenario is to integrate scenarios with priority policy with digital budgeting policy, as well as priority actions providing incentives. This study makes an important contribution to regional development planning, especially poverty alleviation strategies based on accurate and integrative data.

Future global economic stability is under significant threat from environmental risks. Rural peat areas are particularly susceptible due to their elevated levels of hazard, vulnerability, and limited capacity. Acknowledging these risks is pivotal for fostering a low-carbon development trajectory. This study analyzes the environmental risk in rural peat areas within Riau Province. The PROMETHEE method, incorporating Shannon Entropy weighting for data analysis, was employed. The findings reveal that four regencies are exhibiting favorable environmental risk conditions and five regencies facing adverse conditions. The criteria influencing the environmental risk of rural peat in Riau Province showcase various positive and negative contributions across each regency. The sensitivity analysis underscores the resilience of forest fires and the social forest program in three regencies. Recognizing environmental risk can serve as a foundation for decision-makers to formulate sustainable development policies.

Vulnerability is one of the prominent features of rural areas due to their distinctive characteristics, such as remoteness, geographical conditions, and socio-economic dependence on primary sectors. Addressing the vulnerability of rural areas in terms of the rural development paradigm is both urgent and relevant. This study aims to address this issue using the current state-of-the-art machine learning method, using the socio-ecological framework and integrated vulnerability index of villages in Lampung Province in Indonesia. The study attempts to predict and classify villages' vulnerability to be applied for better planning and rural development. Based on random forest classification and decision tree algorithm, the results show that the village governance system represented by rural water management and the level of education of village leaders are suitable prediction variables related to the low vulnerability index. This study can draw lessons learned to improve rural development in developing countries.