Goldman Sachs Maintains an Optimistic Outlook on Reliability and Sustainable Investing in the Asia-Pacific Region

Overview

This report, based on Goldman Sachs' research in the Asia-Pacific region, focuses on key areas of concern for investors, policymakers, and corporations, including energy security, AI-driven data center demand, aging populations, and carbon pricing policies. The report emphasizes the importance of reliability (power, water resources, supply chains, etc.) and affordability, while also pointing out the growth potential of both green and non-green capital expenditures.

Core views

Goldman Sachs believes that investor interest in the theme of reliability continues to rise, especially against the backdrop of rapidly growing AI-driven data center demand. Specifically: 1. Middle East energy disruptions may prompt countries to accelerate investment in energy infrastructure, particularly in redundancy and risk mitigation. 2. The growth in AI-driven data center demand will significantly increase the need for power and water resources; global data center power demand is expected to grow by 220% by 2030, with 60% coming from the U.S. 3. Policymakers are reevaluating the balance between green and non-green investments, especially as energy security becomes a top priority. 4. Sustainable investors are gradually shifting their focus from mere goal-setting to value creation, paying more attention to specific company growth and differentiated returns. 5. Data center construction faces challenges in community relations, including issues such as reduced reliability, higher electricity prices, and restrictions on available water resources. Additionally, the report notes that nuclear energy, as a reliable low-carbon energy source, enjoys broad support, though it remains constrained in the short term by factors such as technology, cost, and public acceptance.

Analysis framework

Goldman Sachs conducted field research and data analysis, combining the AI innovation cycle with the energy supply cycle to assess investment opportunities across different regions and industries. Its analytical approach includes: 1. Comparing policy and investment strategies across various countries and regions regarding energy security, AI demand growth, and aging populations. 2. Using historical data and predictive models to quantify the impact of AI-driven data center demand on power and water resources. 3. Evaluating the growth potential of green and non-green capital expenditures and their impact on investment strategies. 4. Analyzing the role of nuclear energy in the energy mix and the challenges it faces. Through these methods, Goldman Sachs reached a long-term optimistic judgment on the theme of reliability and sustainable investing.

Methodology notes

• Industry/Industrial Analysis FrameworkSupply-demand framework

  This framework focuses on the alignment between supply and demand in the industry.

  The report uses the supply-demand framework to analyze the impact of AI-driven data center demand growth on power and water resources, helping to understand related investment opportunities.

• Valuation MethodDCF Cash Flow Discounting

  Using cash flow discounting to evaluate long-term social value.

  The report calculates the potential social present value brought by AI-driven drug discovery using the DCF method, providing a reference for investors.

• Cycle and Economic Sentiment FrameworkInventory cycle (Kitchin)

  The impact of short-term economic fluctuations on investment strategies.

  The report cites the inventory cycle theory to explain how the AI innovation cycle affects capital expenditures, revealing investment opportunities at different stages.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• US.ASIA

  Benefiting from the Asia-Pacific reliability investment theme

  Strengths

  Covers a wide range of Asia-Pacific markets

  Comparison

  Higher weight in Asian markets compared to other ETFs

  Risks

  Geopolitical risks

• US.BTM

  Benefiting from the growth in AI-driven data center demand

  Strengths

  Focuses on data storage solutions

  Comparison

  More focused on high efficiency compared to traditional data centers

  Risks

  Risk of technological obsolescence

• US.GOOGL

  Benefiting from the AI innovation cycle

  Strengths

  Strong R&D capabilities and capital expenditure

  Comparison

  Higher proportion of AI investment compared to other ultra-large enterprises

  Risks

  Increased market competition

Key data

• Global Data Center Power Demand Growth220%Projected compared to 2023 levels by 2030
• U.S. Data Center Power Demand Share60%The U.S. contributes the largest share to the global data center power demand growth
• AI Server Efficiency Growth RateLow Double DigitsAI server efficiency is expected to maintain low double-digit growth over the next few years
• Ultra-Large Enterprise Annual Capital Expenditure$1.1 Trillion+Estimated total capital expenditure for ultra-large enterprises in 2026

Impact & implications

Goldman Sachs believes that the growth in AI-driven data center demand will have a profound impact on energy and water resource allocation, while driving the growth of both green and non-green capital expenditures. Policymakers and investors need to strike a balance between reliability, affordability, and decarbonization goals. Additionally, the role of nuclear energy in the energy mix is becoming increasingly important, though it still faces technological and public acceptance challenges in the short term.

Risks

• Middle East energy disruptions could lead to energy price volatility
• Community opposition to data center construction may affect project timelines
• Nuclear energy development faces technological and public acceptance challenges

What to watch

• Trends in AI-driven data center demand growth
• Changes in green and non-green capital expenditures
• The degree of policymakers’ emphasis on energy security