In the wake of growing concerns over climate change and the environmental impact of artificial intelligence (AI) and data centers, this white paper proposes the development and implementation of a Green AI Index. This comprehensive framework aims to evaluate and enhance the sustainability of AI models and data center operations. The paper outlines the current environmental challenges posed by AI, including carbon and water footprints, and provides detailed methodologies for assessing and reducing these impacts. By establishing metrics for energy efficiency, carbon emissions, water usage, and waste management, the Green AI Index seeks to promote best practices in the industry. Furthermore, the paper examines existing policies and regulatory frameworks across the United States, European Union, and China, identifying gaps and proposing recommendations for more robust and enforceable standards. Through a collaborative approach involving government, industry, and academic stakeholders, this initiative strives to foster a more sustainable and resilient digital infrastructure, aligning technological advancement with environmental stewardship.

Thanks to the team work by Pratiyush Singh, Melody Shi, Valerie Zhu and Yunjin Tong at U.S.-Asia Sustainable Development Foundation.

This white paper addresses the critical issue of carbon neutrality within the Chinese manufacturing industry, amidst the broader context of global climate change. It discusses the severe threats posed by climate change to the environment, society, and economy, citing the Intergovernmental Panel on Climate Change (IPCC) report that highlights the potential for catastrophic impacts if global temperatures rise by 1.5°C by 2023. The paper underscores the significant health risks associated with global warming, including the potential for climate change to cause more deaths annually than COVID-19.

Thanks to the team work by Yunjin Tong, Yu Wang, Youjun Guo and Yixin at U.S.-Asia Sustainable Development Foundation.

The invention discloses an enterprise carbon emission monitoring system and method, and relates to the technical field of carbon emission. Through the creation of the invention, a new scheme capable of automatically acquiring, analyzing and displaying the carbon emission information of the manufacturing enterprise in real time is provided, that is, the system comprises a data acquisition layer, a data transmission layer, an edge calculation layer, a data governance fusion layer, a data analysis modeling layer and a data interaction application layer which are arranged from bottom to top, the data acquisition layer comprises a plurality of data acquisition devices arranged in a manufacturing enterprise, and through an automatic acquisition mode of the data acquisition devices, the calculation accuracy can be greatly improved, errors and errors caused by manual filling are avoided, inaccuracy of manual data estimation can also be avoided, and the calculation efficiency is improved. And meanwhile, the data reported by the acquisition equipment and the heterogeneous data obtained by the intelligent combination of the multi-heterogeneous system are directly utilized to carry out carbon checking, so that the real-time and accurate data can be well ensured, the efficiency of enterprise operation checking is also improved, and the cost is reduced.

Thanks for the team work and contribution from Yunjin Tong and Shengtao Gao at CarbonSense Technologies.

The invention discloses an enterprise carbon emission monitoring system and method, and relates to the technical field of carbon emission. Through the creation of the invention, a new scheme capable of automatically acquiring, analyzing and displaying the carbon emission information of the manufacturing enterprise in real time is provided, that is, the system comprises a data acquisition layer, a data transmission layer, an edge calculation layer, a data governance fusion layer, a data analysis modeling layer and a data interaction application layer which are arranged from bottom to top, the data According to the method and the platform for evaluating the carbon neutralization performance of the manufacturing enterprise in the full life cycle, evaluation is performed from two dimensions of enterprise organization full-cycle carbon management evaluation and product full-cycle carbon management evaluation, so that the carbon neutralization performance of the manufacturing enterprise is comprehensively reflected; and the evaluation method model is landed through a big data technology and an artificial intelligence technology, and the performance of the manufacturing enterprise on the low-carbon transformation road is professionally and accurately evaluated through a digital and intelligent mode. According to the method, the characteristic points of the manufacturing industry are effectively evaluated, and the problem of lack of carbon neutralization performance evaluation standards of current manufacturing enterprises is solved. Meanwhile, through an intelligent platform of data mining treatment, fusion analysis and calculation application, the defects that manual information collection consumes long time, the data utilization rate is low, subjective prejudice exists in scoring, and evaluation is incomplete and inaccurate due to data missing are overcome.

Thanks for the team work and contribution from Yunjin Tong and Shengtao Gao at CarbonSense Technologies.