Green Ad Tech: A Systems Approach to Carbon-Aware Digital Advertising

Abstract

The digital advertising ecosystem is one of the most latency-sensitive distributed systems on the Internet, fulfilling millions of real-time bidding transactions per day and producing significant greenhouse gas emissions due to server operation, network transmission, and machine learning inference. Combined with highly tight sub-100-ms latency, multifaceted multi-party economic incentives, and increasing regulatory demands that businesses focus on environmental responsibilities, these converging factors present a set of unique carbon reduction challenges to which the carbon-aware computing models have failed to provide sufficient solutions. Green Ad Tech presents a comprehensive systems architecture integrating five coordinated mechanisms: real-time carbon-aware request routing leveraging geographic and temporal variations in grid carbon intensity, lightweight machine learning model switching to reduce inference energy consumption, intelligent edge caching and creative optimization, adaptive auction reweighting through contextual bandit algorithms, and granular per-impression emission attribution aligned with industry measurement standards. The framework formalizes carbon-aware advertising as a constrained multi-objective optimization problem balancing emission minimization against latency service-level objectives, revenue preservation requirements, and data protection compliance constraints. Specialized algorithmic methods to reduce carbon and still be competitive in the auction time window of microseconds allow greedy routing with latency constraints, forecast-based time optimization of deferrable operations, and value-adaptive model selection to be the primary decision primitives. Measurement infrastructure offers per-impression carbon accounting with full provenance metadata, allowing third-party auditing and avoiding greenwashing, and incentive mechanisms can link carbon goals among advertisers, publishers, platforms, and content delivery networks using configurable preferences, eco-labeling, and billing changes. The equity considerations deal with the possible short side of publishers in carbon-intensive areas by providing temporal flexibility, fairness restrictions, and capacity-building investments that do not result in sustainability efforts contributing to global economic inequalities. Privacy and regulatory compliance are considered as hard constraints that cannot be negotiated, and carbon optimization will consider the data protection requirements, such as GDPR restrictions on cross-border processing of data. The framework demonstrates that meaningful emission reductions are achievable within the operational constraints of production advertising systems while maintaining economic viability and stakeholder trust through transparent, auditable measurement aligned with emerging industry standards for media sustainability reporting.