Achieving the 2030 and 2050 Paris Agreement targets to improve the global environment and address climate change is critical despite the costs and time requirements to implement possible measures. However, current measures are primarily undertaken from a global macro perspective, and lack established means to examine them from micro, realistic, and practical perspectives. CO₂ direct air capture (DAC) is an innovative negative CO₂ emission technology in its early commercial stages that can help control and mitigate climate change in the long term. Despite technological advances in the past decade, there are still misconceptions about the current and long-term costs of DAC, and energy, water, and area demands. This paper presents a knowledge-based indication method with a prototype system for a DAC location and cost simulator to support early-stage decisions from a micro-local perspective and promote the use of the applicable and scalable DAC technology. Additionally, this study presents a method for determining optimal locations, estimating project costs, and prioritizing projects for early-stage feasibility assessments, policy, and business decisions on DAC investments to accelerate its deployment. The main feature of this approach is to provide a data model and unified cost index ($/CO₂ Ton) to calculate the optimal location and cost projection of DAC implementation based on location characteristics and a quantified industry knowledge base considering various cost-carbon intensity constraints—such as reservoirs, infrastructure, low-carbon electricity, heat, transportation, and atmospheric conditions—that affect the suitability of specific locations.