College
College of Engineering & Technology (Batten)
Department
Engineering Management and Systems Engineering
Graduate Level
Doctoral
Graduate Program/Concentration
Systems Engineering
Presentation Type
Oral Presentation
Abstract
Conventional carbon offset programs rely on quantified emissions to determine balancing requirements. While this approach offers a standardized means of measuring carbon output, it often provides industries with a loophole—allowing them to offset their emissions by purchasing equivalent credits for activities such as tree planting rather than tackling inefficiencies at the source. This research proposes a process-based framework called Carbon Accountability Scores (CA scores) to offer a proactive strategy for assessing and reducing carbon footprints. Instead of focusing on the mere balancing of emitted and sequestered carbon, CA scores integrate an organization’s operational processes into the calculation, thereby offering the industry a contextualized score and diagnostic capability to optimize carbon inefficiencies within their scope of operations. This minimizes the opportunities for “greenwashing.”
Unlike traditional carbon credits, which can be acquired to compensate for emissions without requiring process upgrades, CA scores emphasize verified, real-time carbon updates specific to an organization or business process. It is a distilled score matrix based on the ratio of credit and debit scores. While existing scores vet on net emissions or absorption, contextualized CA scores enable optimizing the next-in-line process. Thus, it provides active carbon reduction through engineering management approaches rather than external buyouts or sole reliance on technological improvements. Thus, it incentivizes businesses to adopt better resource management and logistics strategies—such as route optimization and supply chain simplification—thereby strengthening accountability.
CA scores facilitate simulating operational scenarios using Generative AI, allowing methodological improvements between cross-team and cross-organizational process optimizations. Together, these steps reduce the risk of inflated sequestration claims when organizations base carbon reduction on promising future tree growth. This built-in transparency reduces the risk of corruption, where large emitters habitually purchase credits at minimal cost while continuing inefficient activities. The proposed framework acknowledges the complexity of modern industrial processes by factoring in the cumulative emissions of all relevant operations. Organizations can track incremental improvements in their CA scores, providing tangible milestones that motivate continuous refinements. This multi-process reinforcement (feedback) loop contrasts traditional carbon credits' deterministic nature with a more pragmatic-process-oriented carbon offset program.
Keywords
Carbon Credit, Global Warming, Net-Zero Emissions, Climate Change, Greenhouse Gas, Process Optimization, Generative AI, Supply Chain Management, Process Control, Multi-Agent Reinforcement Learning
Included in
Environmental Engineering Commons, Environmental Studies Commons, Industrial Engineering Commons, Operations and Supply Chain Management Commons, Organization Development Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Engineering Commons, Transportation Engineering Commons, Urban Studies and Planning Commons
Carbon Accountability Scores: A Process-Oriented Approach for Carbon Offsetting Using AI Agents
Conventional carbon offset programs rely on quantified emissions to determine balancing requirements. While this approach offers a standardized means of measuring carbon output, it often provides industries with a loophole—allowing them to offset their emissions by purchasing equivalent credits for activities such as tree planting rather than tackling inefficiencies at the source. This research proposes a process-based framework called Carbon Accountability Scores (CA scores) to offer a proactive strategy for assessing and reducing carbon footprints. Instead of focusing on the mere balancing of emitted and sequestered carbon, CA scores integrate an organization’s operational processes into the calculation, thereby offering the industry a contextualized score and diagnostic capability to optimize carbon inefficiencies within their scope of operations. This minimizes the opportunities for “greenwashing.”
Unlike traditional carbon credits, which can be acquired to compensate for emissions without requiring process upgrades, CA scores emphasize verified, real-time carbon updates specific to an organization or business process. It is a distilled score matrix based on the ratio of credit and debit scores. While existing scores vet on net emissions or absorption, contextualized CA scores enable optimizing the next-in-line process. Thus, it provides active carbon reduction through engineering management approaches rather than external buyouts or sole reliance on technological improvements. Thus, it incentivizes businesses to adopt better resource management and logistics strategies—such as route optimization and supply chain simplification—thereby strengthening accountability.
CA scores facilitate simulating operational scenarios using Generative AI, allowing methodological improvements between cross-team and cross-organizational process optimizations. Together, these steps reduce the risk of inflated sequestration claims when organizations base carbon reduction on promising future tree growth. This built-in transparency reduces the risk of corruption, where large emitters habitually purchase credits at minimal cost while continuing inefficient activities. The proposed framework acknowledges the complexity of modern industrial processes by factoring in the cumulative emissions of all relevant operations. Organizations can track incremental improvements in their CA scores, providing tangible milestones that motivate continuous refinements. This multi-process reinforcement (feedback) loop contrasts traditional carbon credits' deterministic nature with a more pragmatic-process-oriented carbon offset program.