Article: AI-based green ecological construction of sustainable environment Journal: International Journal of Environment and Sustainable Development (IJESD) 2026 Vol.25 No.6 pp.108 - 127 Abstract: This paper addresses the critical challenge of integrating artificial intelligence (AI) into green ecological construction for promoting environmental sustainability. It proposes an innovative AI-enhanced slack-based measure data envelopment analysis (SBM-DEA) model to quantitatively evaluate regional ecological efficiency. Focusing on Shenqiu County, a representative agricultural region in central China, the study combines multi-source data - including satellite-derived NDVI, IoT-based PM2.5 monitoring, and socioeconomic inputs - to assess ten townships with diverse economic functions. Results reveal significant efficiency disparities, with only the ecological conservation township achieving full efficiency. Key inefficiency drivers include excessive energy consumption and elevated PM2.5 pollution levels, particularly in industrial port townships. The slack analysis quantifies that these inefficient townships require targeted reductions in PM2.5 of up to 18.5% and in energy use of up to 32.0% to achieve optimal performance. The paper concludes by proposing tailored policy pathways and an AI-driven dynamic governance framework to bridge technical potential with local implementation, offering a scalable model for sustainable ecological management. Inderscience Publishers - linking academia, business and industry through research

Title: AI-based green ecological construction of sustainable environment

Authors: Rui Guo; Yuanyu Zhang; Yinghao Zhao

Addresses: School of Economics and Management, Qinghai Normal University, Xining 810000, Qinghai, China ' School of Economics and Management, Qinghai Normal University, Xining 810000, Qinghai, China ' China Academy of Industrial Internet, Beijing 100102, China

Abstract: This paper addresses the critical challenge of integrating artificial intelligence (AI) into green ecological construction for promoting environmental sustainability. It proposes an innovative AI-enhanced slack-based measure data envelopment analysis (SBM-DEA) model to quantitatively evaluate regional ecological efficiency. Focusing on Shenqiu County, a representative agricultural region in central China, the study combines multi-source data - including satellite-derived NDVI, IoT-based PM2.5 monitoring, and socioeconomic inputs - to assess ten townships with diverse economic functions. Results reveal significant efficiency disparities, with only the ecological conservation township achieving full efficiency. Key inefficiency drivers include excessive energy consumption and elevated PM2.5 pollution levels, particularly in industrial port townships. The slack analysis quantifies that these inefficient townships require targeted reductions in PM2.5 of up to 18.5% and in energy use of up to 32.0% to achieve optimal performance. The paper concludes by proposing tailored policy pathways and an AI-driven dynamic governance framework to bridge technical potential with local implementation, offering a scalable model for sustainable ecological management.

Keywords: sustainable environment; green ecology; artificial intelligence; data envelopment analysis; DEA; decision factor.

DOI: 10.1504/IJESD.2026.154278

International Journal of Environment and Sustainable Development, 2026 Vol.25 No.6, pp.108 - 127

Received: 25 Aug 2025
Accepted: 23 Jan 2026
Published online: 18 Jun 2026 *

Title: AI-based green ecological construction of sustainable environment

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