The measurement and monitoring of environmental performance are critical components in assessing the sustainability of human activities. Traditional environmental performance indicators (EPIs) have faced challenges in accurately capturing the complex and dynamic interactions within ecosystems, often resulting in oversimplified assessments and inadequate policy responses. In this book chapter, we propose a novel approach to address these limitations by integrating Stochastic Multi-Attribute Analysis (SMAA) into the construction of EPIs. SMAA offers several distinct advantages over traditional methods. Firstly, it embraces uncertainty inherent in environmental data, providing a more robust and reliable assessment of environmental performance. By considering uncertainties in input data, SMAA reduces the risk of biased conclusions and enhances the credibility of EPIs. Secondly, SMAA enables the incorporation of multiple criteria and attributes, allowing for a comprehensive evaluation of environmental performance across various dimensions such as air quality, water resources, biodiversity, and ecosystem services. This multidimensional perspective provides a more holistic understanding of environmental impacts, facilitating informed decision-making and policy formulation. Moreover, SMAA facilitates the integration of stakeholder preferences and values into the assessment process, promoting transparency, accountability, and inclusivity in environmental governance. By engaging stakeholders in the identification and weighting of evaluation criteria, SMAA enhances the legitimacy and acceptability of EPIs, fostering consensus-building and collaborative action towards sustainability goals. Furthermore, SMAA offers flexibility in handling diverse types of data, including qualitative and quantitative information, spatial and temporal data, and expert judgments. This flexibility enables the adaptation of EPIs to different contexts and scales, catering to the specific needs and priorities of stakeholders. Additionally, SMAA provides a systematic framework for sensitivity analysis, allowing for the exploration of the robustness of EPIs to changes in input parameters and assumptions. Through a case study application, we demonstrate the utility and effectiveness of SMAA in improving the construction of EPIs. By addressing the limitations of traditional approaches, SMAA contributes to advancing the field of environmental assessment and management, offering a valuable tool for promoting sustainable development and preserving the integrity of ecosystems for future generations.