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Towards A Comprehensive Assessment of AI's Environmental Impact

Srija Chakraborty

TL;DR

This work addresses the challenge of quantifying AI's environmental footprint by proposing a spatially explicit monitoring framework that leverages open Earth Observation data to track changes around datacenters. It combines carbon-intensity proxies from ElectricityMaps with satellite-derived metrics such as NDVI, nighttime lights, and UV aerosol index in a Northern Virginia case study to illustrate observable environmental signals associated with AI infrastructure. Key contributions include a methodology for measuring environmental variables around datacenters, a detailed case study demonstrating detectable changes, and concrete data gaps and policy recommendations to move toward a global AI environmental impact inventory. By enabling transparent reporting and policy evaluation, the approach aims to align AI deployment with climate objectives, e.g., the $1.5^ extcircled C$ target, through scalable, open data-driven monitoring tools.

Abstract

Artificial Intelligence, machine learning (AI/ML) has allowed exploring solutions for a variety of environmental and climate questions ranging from natural disasters, greenhouse gas emission, monitoring biodiversity, agriculture, to weather and climate modeling, enabling progress towards climate change mitigation. However, the intersection of AI/ML and environment is not always positive. The recent surge of interest in ML, made possible by processing very large volumes of data, fueled by access to massive compute power, has sparked a trend towards large-scale adoption of AI/ML. This interest places tremendous pressure on natural resources, that are often overlooked and under-reported. There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle for informing policymakers, stakeholders to adequately implement standards and policies and track the policy outcome over time. For these policies to be effective, AI's environmental impact needs to be monitored in a spatially-disaggregated, timely manner across the globe at the key activity sites. This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations. We present a case study around Northern Virginia, United States that hosts a growing number of datacenters and observe changes in multiple satellite-based environmental metrics. We then discuss the steps to expand this methodology for comprehensive assessment of AI's environmental impact across the planet. We also identify data gaps and formulate recommendations for improving the understanding and monitoring AI-induced changes to the environment and climate.

Towards A Comprehensive Assessment of AI's Environmental Impact

TL;DR

This work addresses the challenge of quantifying AI's environmental footprint by proposing a spatially explicit monitoring framework that leverages open Earth Observation data to track changes around datacenters. It combines carbon-intensity proxies from ElectricityMaps with satellite-derived metrics such as NDVI, nighttime lights, and UV aerosol index in a Northern Virginia case study to illustrate observable environmental signals associated with AI infrastructure. Key contributions include a methodology for measuring environmental variables around datacenters, a detailed case study demonstrating detectable changes, and concrete data gaps and policy recommendations to move toward a global AI environmental impact inventory. By enabling transparent reporting and policy evaluation, the approach aims to align AI deployment with climate objectives, e.g., the target, through scalable, open data-driven monitoring tools.

Abstract

Artificial Intelligence, machine learning (AI/ML) has allowed exploring solutions for a variety of environmental and climate questions ranging from natural disasters, greenhouse gas emission, monitoring biodiversity, agriculture, to weather and climate modeling, enabling progress towards climate change mitigation. However, the intersection of AI/ML and environment is not always positive. The recent surge of interest in ML, made possible by processing very large volumes of data, fueled by access to massive compute power, has sparked a trend towards large-scale adoption of AI/ML. This interest places tremendous pressure on natural resources, that are often overlooked and under-reported. There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle for informing policymakers, stakeholders to adequately implement standards and policies and track the policy outcome over time. For these policies to be effective, AI's environmental impact needs to be monitored in a spatially-disaggregated, timely manner across the globe at the key activity sites. This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations. We present a case study around Northern Virginia, United States that hosts a growing number of datacenters and observe changes in multiple satellite-based environmental metrics. We then discuss the steps to expand this methodology for comprehensive assessment of AI's environmental impact across the planet. We also identify data gaps and formulate recommendations for improving the understanding and monitoring AI-induced changes to the environment and climate.
Paper Structure (6 sections, 2 figures)

This paper contains 6 sections, 2 figures.

Table of Contents

  1. Introduction and Background
  2. Assessing the AI Environment Intersection
  3. Assessing Carbon Intensity and Clean Energy Around Datacenter Sites
  4. Integrated Monitoring of Environmental Changes Around AI Datacenter Sites: A Case Study Around Northern Virginia
  5. Gaps and Recommendation for AI-Environment Impact Assessment
  6. Conclusion

Figures (2)

  • Figure 1: Visible Landsat-8 imagery around Arcola in Northern Virginia showing changes in land use with comparison over a decade through (a) before and (b) after images. (c) Trend in the region's vegetation index (Normalized Vegetation Difference Index (NDVI)) shows a decline.
  • Figure 2: Nighttime light (NTL) from VIIRS DNB shows urban infrastructure development and electricity usage. A comparison of DNB-derived NASA Black Marble annual composite data shows the increase in nightlight in the highlighted area around Arcola, VA over a span of eight years comparing (a) before and (b) after images. (c) A time-series of the annual NTL over the same area shows a very high (10X) increase in NTL levels.