Enterprise AI Analysis
Financial investments in AI-based technologies and carbon footprint in selected advanced industrial economies
Authored by: Gökhan Konat, Esengül Salihoğlu & Ayşegül Han
Artificial intelligence (AI) is rapidly transforming industries, but its environmental impact, particularly on carbon emissions, remains a critical concern. This study examines how AI investments influence carbon footprints in leading industrial economies, accounting for economic and human development factors.
Executive Impact: Key Findings at a Glance
Our analysis of advanced industrial economies reveals complex interdependencies between AI investments and environmental outcomes. Key insights for strategic decision-making are highlighted below.
32%
Short-Term Imbalances Corrected Annually
0.28%
AI Investments Increase Carbon Emissions
-0.21%
GDP Reduces Carbon Emissions
-0.17%
Renewable Energy Reduces Carbon Emissions
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This study investigates the environmental impact of AI investments, particularly on carbon emissions, in nine technologically advanced economies (South Korea, Japan, Germany, United States, China, Singapore, Sweden, Italy, and France) from 2012-2023. It aims to understand the dynamic interlinkages between technological progress, energy consumption, and environmental outcomes, providing insights for sustainable technological development.
While AI offers significant economic and social advantages, its widespread use generates significant emissions due to computational power requirements and supply-chain intensity. The research utilizes the Panel ARDL-PMG approach to account for both short-run dynamics and long-run equilibrium, including human and economic development indicators.
The study employs the Panel ARDL-PMG approach, utilizing data from 2012–2023 across nine advanced industrial economies. Variables include Carbon Intensity (CI) as the dependent variable, Venture Capital Investments in AI (AIINV) as the main independent variable, and control variables such as per capita GDP, Renewable Energy Consumption (RE), Human Development Index (HDI), and Foreign Direct Investment (FDI).
Before estimation, cross-sectional dependence was assessed using the Pesaran (2004) CD test, followed by unit root tests (Pesaran, 2007 CIPS test) to confirm stationarity. The Westerlund (2008) Durbin–Hausman cointegration test verified the long-run equilibrium relationship. The PMG estimator allows for heterogeneous short-run dynamics while constraining long-run coefficients to be homogeneous, ensuring robust estimation for panels with common long-term tendencies.
The analysis confirms a stable long-run equilibrium, with the Error Correction Term (ECT) of -0.317 indicating that approximately 32% of short-term imbalances are corrected annually. In the long run, per capita GDP and renewable energy consumption significantly reduce carbon emissions.
However, AI investments (AIINV), Foreign Direct Investment (FDI), and the Human Development Index (HDI) are found to increase carbon emissions. This suggests that current AI-driven economic growth and FDI inflows are not sufficiently aligned with energy transition or green innovation, leading to higher environmental pressure. The findings underscore the need for policies promoting eco-efficient technologies to mitigate the carbon-intensive effects of digital transformation.
Policymakers and investors should integrate environmental sustainability criteria into AI and robotics investments. Energy-intensive projects should face carbon-conditional incentives, and the use of renewable energy sources in data centers and cloud computing should be actively promoted.
Investment approval and incentive mechanisms should be based on environmental conditions, particularly in high-carbon emission sectors. Supporting technologies with high energy efficiency and strengthening industry-energy transformation policies are crucial to avoid a 'high-carbon digitalization trap'. Aligning AI with green innovation and sustainable environmental policies is essential for reducing carbon footprints.
Enterprise Process Flow: Econometric Methodology
1. Descriptive Statistics
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2. CSD Test
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3. CIPS Test
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4. Westerlund Durbin-H Test
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5. PARDL-PMG Approach
32%
of short-term imbalances are corrected annually, indicating a gradual adjustment towards long-run equilibrium.
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