Carbon Intensity Measurement and Green Spillover Effects of Generative Artificial Intelligence

Generative Artificial Intelligence is changing production in an accelerating way. However, given that it has a relatively high demand for computing power and energy consumption, it generates a significant amount of greenhouse gas emissions. Whether it will be a “carbon increaser” or a “carbon reducer” in the process of green transition is still an urgent problem to be solved. This paper will study the carbon emission intensity and the green spillover effects of generative artificial intelligence, determine its environmental costs and green benefits, and provide reference materials for policymakers in their green development plans for the digital economy. From a life-cycle perspective, this paper sets the scope of measurement as hardware production, model training and cloud-based inference, and suggests a system for carbon intensity measurement and analytical models. Select representative large-scale models for empirical estimation. At the same time, a theoretical system explores the transmission paths of green spillover effects and carry out corresponding empirical studies. Based on the above data, the carbon intensity of generative artificial intelligence suggests substantial changes in both scale and structure. Most of the carbon emissions occur during the training stage, and although the proportion from the inference stage has been increasing over time, it is still relatively small. The regional energy structure of the models’ deployment appears to be associated with carbon intensity. The results suggest a potential nonlinear pattern between generative artificial intelligence development and regional carbon intensity, although the direction of this relationship remains conditional on energy structure, digital infrastructure, and environmental regulation. The realization of green spillover effects depends on energy structure, digital infrastructure and environmental regulations. Therefore, this paper puts forward some policy recommendations, such as constructing a normalized carbon accounting system, optimizing the spatial arrangement of computing facilities, establishing integrated development mechanisms, enhancing incentive policies, and strengthening international cooperation. The scope of the study is broadened to include the environmental impact of the digital economy, and both theoretical and practical support for the green development of intelligent technologies and low-carbon industrial transformation will be offered.