Artificial intelligence and sustainable development: A global nonlinear analysis of the moderating roles of human capital and renewable energy

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Artificial intelligence and sustainable development: A global nonlinear analysis of the moderating roles of human capital and renewable energy. / Zhang, C.; Li, R.; Wang, Q.

In: Renewable and Sustainable Energy Reviews, Vol. 228, 01.03.2026.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{f84eff93fd1a4bcd83001778f9e26d17,

title = "Artificial intelligence and sustainable development: A global nonlinear analysis of the moderating roles of human capital and renewable energy",

abstract = "Artificial intelligence (AI) is reshaping the landscape of sustainable development, offering unprecedented opportunities while introducing systemic risks. This study examines the nonlinear and heterogeneous impacts of AI on sustainable development. Using a projection pursuit model based on genetic algorithms, we quantify national AI development levels and integrate quadratic moderation models and curve simulation to trace the dual trajectories of AI impacts on sustainable development. Our findings uncover distinct non-linear patterns: AI exerts an inverted U-shaped effect on HDI, with structural unemployment, algorithmic bias, and privacy erosion emerging in later stages. Conversely, environmental sustainability follows a U-shaped path: foundational AI technologies, over time, significantly reduce carbon intensity, while applied AI may initially increase emissions due to energy-intensive deployment. Crucially, AI can optimize renewable energy efficiency, while labor market imbalances may undermine AI's positive impact on carbon reduction and social welfare. Income-level heterogeneity further reveals that high-income countries are more capable of translating AI into sustainability dividends, while lower-income economies remain constrained by technological bottlenecks and structural mismatches. This study advances “green AI” by uncovering how AI affects sustainable development, emphasizing the need for joint investments in renewables, skilled labor, and governance to maximize benefits and limit risks. {\textcopyright} 2025 Elsevier Ltd",

keywords = "Artificial intelligence, Human capital, Nonlinear effects, Renewable energy, Sustainable development, Carbon, Economic analysis, Employment, Environmental technology, Genetic algorithms, Investments, Nonlinear analysis, Personnel, Algorithmics, Human capitals, Late stage, Model-based OPC, Nonlinear effect, Nonlinear pattern, Projection pursuit models, Renewable energies, Systemic risks, U-shaped",

author = "C. Zhang and R. Li and Q. Wang",

note = "Export Date: 29 March 2026; Cited By: 20; Correspondence Address: R. Li; School of Economics and Management, China University of Petroleum (East China), Qingdao, 266580, China; email: lirr@upc.edu.cn; Q. Wang; Laboratory for Asian Economic Studies, Saint Petersburg State University, Saint Petersburg, 266580, Russian Federation; email: qiangwang7@outlook.com; CODEN: RSERF",

year = "2026",

month = mar,

day = "1",

doi = "10.1016/j.rser.2025.116574",

language = "Английский",

volume = "228",

journal = "Renewable and Sustainable Energy Reviews",

issn = "1364-0321",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Artificial intelligence and sustainable development: A global nonlinear analysis of the moderating roles of human capital and renewable energy

AU - Zhang, C.

AU - Li, R.

AU - Wang, Q.

N1 - Export Date: 29 March 2026; Cited By: 20; Correspondence Address: R. Li; School of Economics and Management, China University of Petroleum (East China), Qingdao, 266580, China; email: lirr@upc.edu.cn; Q. Wang; Laboratory for Asian Economic Studies, Saint Petersburg State University, Saint Petersburg, 266580, Russian Federation; email: qiangwang7@outlook.com; CODEN: RSERF

PY - 2026/3/1

Y1 - 2026/3/1

N2 - Artificial intelligence (AI) is reshaping the landscape of sustainable development, offering unprecedented opportunities while introducing systemic risks. This study examines the nonlinear and heterogeneous impacts of AI on sustainable development. Using a projection pursuit model based on genetic algorithms, we quantify national AI development levels and integrate quadratic moderation models and curve simulation to trace the dual trajectories of AI impacts on sustainable development. Our findings uncover distinct non-linear patterns: AI exerts an inverted U-shaped effect on HDI, with structural unemployment, algorithmic bias, and privacy erosion emerging in later stages. Conversely, environmental sustainability follows a U-shaped path: foundational AI technologies, over time, significantly reduce carbon intensity, while applied AI may initially increase emissions due to energy-intensive deployment. Crucially, AI can optimize renewable energy efficiency, while labor market imbalances may undermine AI's positive impact on carbon reduction and social welfare. Income-level heterogeneity further reveals that high-income countries are more capable of translating AI into sustainability dividends, while lower-income economies remain constrained by technological bottlenecks and structural mismatches. This study advances “green AI” by uncovering how AI affects sustainable development, emphasizing the need for joint investments in renewables, skilled labor, and governance to maximize benefits and limit risks. © 2025 Elsevier Ltd

AB - Artificial intelligence (AI) is reshaping the landscape of sustainable development, offering unprecedented opportunities while introducing systemic risks. This study examines the nonlinear and heterogeneous impacts of AI on sustainable development. Using a projection pursuit model based on genetic algorithms, we quantify national AI development levels and integrate quadratic moderation models and curve simulation to trace the dual trajectories of AI impacts on sustainable development. Our findings uncover distinct non-linear patterns: AI exerts an inverted U-shaped effect on HDI, with structural unemployment, algorithmic bias, and privacy erosion emerging in later stages. Conversely, environmental sustainability follows a U-shaped path: foundational AI technologies, over time, significantly reduce carbon intensity, while applied AI may initially increase emissions due to energy-intensive deployment. Crucially, AI can optimize renewable energy efficiency, while labor market imbalances may undermine AI's positive impact on carbon reduction and social welfare. Income-level heterogeneity further reveals that high-income countries are more capable of translating AI into sustainability dividends, while lower-income economies remain constrained by technological bottlenecks and structural mismatches. This study advances “green AI” by uncovering how AI affects sustainable development, emphasizing the need for joint investments in renewables, skilled labor, and governance to maximize benefits and limit risks. © 2025 Elsevier Ltd

KW - Artificial intelligence

KW - Human capital

KW - Nonlinear effects

KW - Renewable energy

KW - Sustainable development

KW - Carbon

KW - Economic analysis

KW - Employment

KW - Environmental technology

KW - Genetic algorithms

KW - Investments

KW - Nonlinear analysis

KW - Personnel

KW - Algorithmics

KW - Human capitals

KW - Late stage

KW - Model-based OPC

KW - Nonlinear effect

KW - Nonlinear pattern

KW - Projection pursuit models

KW - Renewable energies

KW - Systemic risks

KW - U-shaped

UR - https://www.mendeley.com/catalogue/e2e246be-8169-34bd-a339-3a68e4d3a2e1/

U2 - 10.1016/j.rser.2025.116574

DO - 10.1016/j.rser.2025.116574

M3 - статья

VL - 228

JO - Renewable and Sustainable Energy Reviews

JF - Renewable and Sustainable Energy Reviews

SN - 1364-0321

ER -

ID: 151309389