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Working memory (WM) has been extensively studied as a cognitive factor influencing second language acquisition (SLA), but many studies treat it as a static, trait-like capacity. This approach provides limited insight into how WM works across different stages of development, proficiency levels, and learning contexts. This article combines theoretical and empirical research on working memory in SLA from a dynamic and developmental perspective, analyzing how the influence of WM varies over time and interacts with other cognitive and contextual factors. Referencing Baddeley's multicomponent model, complex dynamic systems theory, and longitudinal research, this article examines evidence regarding the role of WM in various language domains, including vocabulary learning, grammar processing, and language production. It also investigates how WM works differently at various proficiency levels and developmental periods, from childhood to adulthood. Evidence suggests that WM plays a very strong role in the early stages of learning when processing is effortful and controlled, but becomes less central as linguistic knowledge becomes automated. The relationship between WM and SLA appears to be bidirectional, with sustained language learning contributing to the development of specific WM components, particularly executive control. The influence of WM varies significantly based on task characteristics, teaching context, and learner profiles. Understanding WM as a dynamic resource rather than a fixed constraint has important pedagogical implications. Instructions that manage cognitive load, provide support, and encourage metacognitive awareness can help learners with diverse cognitive profiles. A developmental approach in WM research requires longitudinal designs and task-aligned methodologies to capture changes over time, thereby advancing theory and practice in SLA.

In 2022, Indonesia will host the G20 Summit with one of the goals agreed upon by G20 members is to reduce carbon emissions produced by each G20 country. Therefore, a model design was made to consider the most optimal policy to overcome this. The research uses a dynamic system model, with the process of identifying and defining problems, system conceptualization, model formulation, model simulation, verification and validation of policy analysis models, and policy implementation. The most optimum result is the imposition of a tax per carbon emission on producing sources with an effectiveness of 20%.