This study introduces a novel method for integrating green technology innovation into green GDP accounting. It does this by adjusting for environmental pollution and incorporating policy frameworks that consider the effects of global geopolitical risks. Using data from the Anhui Province in 2019, a deep learning-based green GDP accounting model is proposed, which combines environmental costs and economic outputs. The methodology unfolds in two stages: the first stage develops a framework for pollution adjustment across solid, air, and water pollutants to highlight the environmental costs impacting various industries. The second stage applies the long short-term memory (LSTM) algorithm to predict green GDP, demonstrating superior accuracy over conventional methods. Additionally, the study explores the influence of geopolitical uncertainties and policy frameworks on green technology investments, emphasizing strategies for sustainable growth in emerging economies. The findings reveal that pollution-adjusted green GDP closely aligns with traditional green GDP metrics, with the pollution adjustment accounting for 1.96% of green GDP. These results underscore the critical role of green technology and policy in promoting sustainable economic growth amidst global uncertainties.
Citation: Enyang Zhu. Integrating deep learning and policy frameworks for green GDP accounting: A path to sustainable economic growth[J]. AIMS Mathematics, 2025, 10(7): 16927-16956. doi: 10.3934/math.2025761
This study introduces a novel method for integrating green technology innovation into green GDP accounting. It does this by adjusting for environmental pollution and incorporating policy frameworks that consider the effects of global geopolitical risks. Using data from the Anhui Province in 2019, a deep learning-based green GDP accounting model is proposed, which combines environmental costs and economic outputs. The methodology unfolds in two stages: the first stage develops a framework for pollution adjustment across solid, air, and water pollutants to highlight the environmental costs impacting various industries. The second stage applies the long short-term memory (LSTM) algorithm to predict green GDP, demonstrating superior accuracy over conventional methods. Additionally, the study explores the influence of geopolitical uncertainties and policy frameworks on green technology investments, emphasizing strategies for sustainable growth in emerging economies. The findings reveal that pollution-adjusted green GDP closely aligns with traditional green GDP metrics, with the pollution adjustment accounting for 1.96% of green GDP. These results underscore the critical role of green technology and policy in promoting sustainable economic growth amidst global uncertainties.
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Enyang Zhu. Integrating deep learning and policy frameworks for green GDP accounting: A path to sustainable economic growth[J]. AIMS Mathematics, 2025, 10(7): 16927-16956. doi: 10.3934/math.2025761
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