1. Report Industry Investment Rating - No relevant content provided. 2. Core Views of the Report - China's real estate and construction industry is in a critical stage of transformation from scale expansion to quality and efficiency improvement, with AI and real - estate technology integration becoming a key driving force [12][17][24]. - The real - estate technology field is moving towards an AI - centered era, and data has become the core asset for the industry's digital transformation [31][110]. - The future of the real - estate industry will focus on asset activation, value reshaping, and ecological co - construction, with competition shifting from resource scale to algorithm density [24][76]. 3. Summary According to the Directory 3.1. KPMG China's "Future 50" Series of Industry Lists - KPMG China has launched the "Future 50" series of industry lists covering multiple sectors, aiming to guide enterprises in strategic choices and help the industry and capital select promising companies. The lists are characterized by professionalism, fairness, and platform - building [30]. 3.2. Overall Overview of the 2025 REBCAI New - Intelligence Practice Case Selection - Since 2021, KPMG China has been involved in real - estate technology selection and research. In 2025, with the development of AI, the industry has entered a new stage. The selection focuses on AI - driven new applications and practices [31]. - The selection process includes case collection and preliminary screening, material review and on - site visits, comprehensive evaluation by the review committee, and result announcement. The core evaluation dimensions are innovation, forward - looking, and growth [40][41][42]. 3.3. Real - Estate Technology Trends and Outlook 3.3.1. Current Situation - AI and large - model development are moving from technological breakthroughs to large - scale applications and ecological co - construction. The real - estate and construction industry has a clearer understanding of the need for AI, but faces challenges in innovation investment due to short - term return and long - term value trade - offs [47]. - Traditional real - estate technology, construction technology, and real - estate asset management technology all face difficulties in transformation, such as data isolation, lack of unified standards, and slow data governance [48][49][50]. 3.3.2. Solutions - Enterprises should focus on user - driven innovation, build a "credible data base", and create a "human - machine collaborative agile organization" [52][57]. - They should also use the method of "trial and error, small - step iteration" to explore effective paths [53]. 3.4. AI Base Capability Improvement from "Traditional Real Estate" to "Corporate Real Estate" and "Pan - Real Estate" 3.4.1. Industry Evolution - The real - estate industry is evolving from "traditional real estate" to "corporate real estate" and then to "pan - real estate", with a shift in value focus and investment logic [72]. 3.4.2. Trends - The AI base is evolving from "tool empowerment" to "ecological reconstruction", becoming a new "operating system" for the industry [73]. - Business value is shifting from "operational efficiency improvement" to "model innovation", with the emergence of new business models and evaluation dimensions [75]. - The core of competition is moving from "resource scale" to "algorithm density", with data quality and algorithm efficiency becoming key competitiveness indicators [76]. 3.5. Starting from First - Party Data 3.5.1. Industry Background - The real - estate industry is in a period of digital transformation, and first - party data has become the cornerstone of this transformation [110]. 3.5.2. Data Trends - Data collection is moving from isolated and fragmented to globally integrated, enabling comprehensive perception [111]. - Data analysis is shifting from descriptive statistics to predictive intelligence, enabling proactive decision - making [112]. - Data application is moving from general solutions to scenario - specific precision, addressing industry - specific problems [114]. - Data - driven goals are expanding from efficiency improvement to value creation, covering multiple dimensions such as asset value and user experience [115]. 3.6. How Today's CRM Moves towards the Future with Users 3.6.1. Challenges of Traditional CRM - Traditional CRM is difficult to meet the full - scenario and full - link service requirements of the real - estate industry, and has limitations in understanding customer needs and linking with other business processes [59][160][161]. 3.6.2. Future Trends - New - type CRM will integrate and analyze customer behavior data to build dynamic user profiles and better understand customers [161]. - It will enhance information collaboration across the entire chain, serving as a core hub for different business stages and providing more convenient services for customers [164]. - It will focus on emotional connection and long - term operation, enhancing customer trust through emotional computing [165]. 3.7. How Future "Good Houses" and "Good Communities" are Cultivated 3.7.1. Industry Trends - The housing construction industry is undergoing a transformation, and real - estate technology is showing three key trends: intelligent construction technology innovation, full - chain digital management, and intelligent operation and human - centered services [194][195][196]. 3.7.2. Leading Practices - In intelligent construction, technologies such as robots and laser scanning are used to improve construction efficiency and quality [201]. - In full - chain solutions, AI digital platforms are built to integrate various processes and promote industry transformation [201]. - In project management, data - driven systems are established to solve traditional management pain points [201]. 3.8. Asset Management Capability in the Era of Stock Assets Empowered by AI 3.8.1. Industry Background - The operation of stock assets has become more important, and the industry needs to balance risk management and asset value enhancement [252][253]. 3.8.2. Trends - Risk tracking management and early - warning granularity are the basis for risk bottom - line management, and data governance is crucial [254]. - KRI (Key Risk Indicator) sorting is a key to balancing the interests of all parties, and asset managers are using digital methods to improve risk control capabilities [255]. - The exploration of full - life - cycle asset value enhancement scenarios is necessary to balance short - term benefits and long - term capabilities, and AI can be used to drive asset management transformation [256]. 3.9. 2025 REBCAI New - Intelligence Practice Case List - The list includes the AI Breakthrough Award cases and AI Momentum Award cases, with details of the enterprises, case names, and corresponding pages [302][304]. 3.10. Annex - The annex includes information on the interview team, report - writing personnel, KPMG's real - estate technology industry insights, and contact information [309][310][317].

Investment Rating - The report does not explicitly provide an investment rating for the banking industry Core Insights - The banking industry is facing increasing uncertainty, necessitating a comprehensive restructuring of risk management processes, with Chief Risk Officers (CROs) playing a crucial role in this transformation [10][11][13] - Effective risk management execution can provide banks with a competitive advantage, enabling them to respond swiftly to market fluctuations and drive continuous improvement across the organization [11] - The report emphasizes the need for banks to adapt to emerging risks, including geopolitical complexities, economic volatility, and the impact of digital transformation on risk management [21][23][24][26] Summary by Sections Introduction - The report highlights the necessity for banks to adjust their risk management cycles to address increasing uncertainties, focusing on risk identification, assessment, monitoring, control, and reporting [10] Drivers of Risk Management Transformation - Key factors driving the transformation of risk management functions include geopolitical challenges, economic fluctuations, and the need for enhanced operational resilience [21][23] - Banks are increasingly recognizing the importance of understanding and adapting to emerging risks, such as supply chain disruptions and cybersecurity threats [23][24] Reshaping Risk Management Functions - The transformation of risk management functions is led by CROs, requiring collaboration among all stakeholders to embrace the evolving role of risk management [36][37] - Banks must modernize their operations and risk management approaches to remain competitive, addressing outdated systems and manual processes [39][40] Future Risk Management Blueprint - The report outlines the importance of integrating data-driven risk management practices, emphasizing the need for improved data quality and infrastructure to support effective risk monitoring and reporting [79][81] - The adoption of artificial intelligence and machine learning is identified as a key technology for enhancing risk management capabilities, although regulatory challenges remain [64][81] Conclusion - The report concludes that banks must proactively adapt their risk management strategies to navigate the complexities of the current environment, aligning risk management with business objectives to drive performance transformation [101]

Economic Performance - In the first three quarters of 2025, China's GDP grew by 5.2% year-on-year, exceeding last year's growth by 0.4 percentage points, indicating good progress towards the annual target of around 5%[8] - In Q3, GDP growth slowed to 4.8%, down 0.4 percentage points from Q2, reflecting a historical low in seasonally adjusted quarter-on-quarter growth[8] - Fixed asset investment decreased by 0.5% year-on-year in the first three quarters, with Q3 showing a significant decline to -6.2%, down 8.3 percentage points from Q2[12] Investment and Consumption - Real estate investment plummeted from -12.1% in Q2 to -19.2% in Q3, significantly dragging down overall fixed asset investment[12] - Social retail sales grew by 4.5% year-on-year in the first three quarters, but Q3 saw a slowdown to 3.5%, a drop of 1.9 percentage points from Q2, primarily due to reduced consumer income growth and insufficient internal demand[11] - Manufacturing investment fell to -1.2% in Q3, marking the first quarterly negative growth since Q3 2020, influenced by external trade uncertainties and the "anti-involution" policy[12] Trade and External Factors - Exports increased by 6.1% year-on-year in the first three quarters, with Q3 growth at 6.5%, supported by a 12.6% increase in exports to non-U.S. markets[13] - The average tariff imposed by the U.S. on China was reduced by 10% to 31%, positively impacting trade expectations for Q4[21] Fiscal and Monetary Policy - The government has implemented 500 billion yuan in policy financial tools and an additional 500 billion yuan in local government debt to support project construction and debt repayment[21] - Public fiscal revenue growth improved, with a cumulative year-on-year increase of 0.5% in the first three quarters, while public expenditure growth slowed to 2.4%[15] - The People's Bank of China emphasized a "moderately loose" monetary policy, with a focus on structural tools to support key sectors such as technology and green development[16]

Group 1: APA Signing Trends - In 2024, China signed a total of 39 APAs, comprising 12 unilateral and 27 bilateral agreements, showing an increase from 36 APAs in 2023[8] - The number of bilateral APAs signed in 2024 included 22 new agreements and 5 renewals, with 70 bilateral APAs in the intention stage, up by 10 from 2023[8] - The completion rate for APAs in China was 24.20% in 2024, significantly higher than the global average of 18.1%, ranking seventh among reported jurisdictions[8] Group 2: International Cooperation and Industry Focus - From 2005 to 2024, 170 bilateral APAs were signed, with 118 (approximately 70%) involving Asian countries, 33 (20%) with European countries, and 18 with North America[8] - The manufacturing sector remains the primary focus for APA services, with 22 cases involving tangible assets, 12 involving intangible assets, and 8 involving services[8] - The transaction net profit method was the most commonly used transfer pricing method, applied 328 times (83.9% of cases), while other methods were also utilized[8] Group 3: Future Outlook and Compliance - The inventory of bilateral APAs reached 177 cases in 2024, an increase of 28 from 2023, indicating a trend towards stricter acceptance and review processes[11] - Companies are encouraged to submit comprehensive and accurate documentation to expedite the APA application process, particularly focusing on value chain and market premium analyses[11] - Chinese tax authorities are actively enhancing international tax cooperation, with 23 transfer pricing MAP cases concluded in 2024, reflecting a commitment to dispute prevention and resolution[11]

Investment Rating - The report indicates a positive outlook for the low-altitude economy, highlighting its potential as a new growth engine for high-quality economic development in China [11][12]. Core Insights - The low-altitude economy is recognized as a strategic emerging industry, officially included in government work reports, marking its importance in national economic strategy [11][12]. - The industry is experiencing a synergistic advancement in technology, industrial cultivation, and large-scale commercialization, with significant growth in sectors such as logistics, agriculture, and tourism [12]. - China is transitioning from a "follower" to a "leader" in global low-altitude economic technology, with a substantial share of global patent applications [11][12]. Summary by Sections Global Low-altitude Economy Development Overview - The global low-altitude economy is evolving from exploration to commercial promotion, with significant advancements in technology and regulatory frameworks [39][40]. - The global drone market is projected to reach $34.4 billion in 2024, with expectations to exceed $40 billion by 2026 [57][58]. Low-altitude Economy as a New Economic Form - The low-altitude economy is characterized by high technological intensity, multi-dimensional spatial features, and significant integration across various industries [21][28]. - It encompasses a wide range of applications, including logistics, agriculture, tourism, and emergency rescue, demonstrating strong industrial extensibility [34]. Key Stages of Low-altitude Economy Enterprise Growth - Enterprises must enhance their "policy decoding ability" to seize opportunities presented by government policies, which are crucial for navigating the development landscape [73][79]. - The report emphasizes the importance of understanding the regulatory framework and adapting to local policy environments for successful implementation [78][79].

Economic Overview - In the first three quarters of 2025, China's GDP grew by 5.2%, an increase of 0.4 percentage points compared to the same period in 2024[67] - The real estate sector is under significant pressure, with a projected debt repayment of 5,257 billion yuan in 2025, leading to an increase in non-performing loans in the banking sector[69] Non-Performing Asset Market Dynamics - In 2024, China disposed of non-performing assets totaling 3.8 trillion yuan, marking the highest annual figure to date, with an estimated stock of non-performing assets reaching approximately 8.5 trillion yuan by the end of 2024[14] - The supply of non-performing assets is expected to grow due to ongoing challenges in the real estate market, local government debt, and the restructuring of small financial institutions[20] Asset Management Companies Performance - By the end of 2024, the total assets of four major asset management companies reached 4.51 trillion yuan, with a year-on-year increase of 2.84%[27] - The operating income of these companies was 248.8 billion yuan in 2024, reflecting a 4.10% increase from the previous year, while net profit rose by 34.97% to 15.5 billion yuan[27] Regulatory Environment - Recent regulatory measures aim to enhance the management of non-performing assets and promote high-quality development within the industry, including the issuance of guidelines for asset management companies[36] - The government has introduced policies to support the restructuring of small financial institutions and improve the efficiency of non-performing asset disposal[37] Challenges and Opportunities - The non-performing asset management industry is transitioning from "risk disposal" to "value reconstruction," necessitating a focus on maximizing asset value through innovative management strategies[24] - The market is witnessing a diversification of asset types and disposal methods, creating new opportunities for asset management companies[24]

Economic Outlook and CEO Confidence - 84% of CEOs in the energy and natural resources sector are optimistic about industry growth, up from 72% last year[12] - 78% of CEOs are confident about their own company's growth prospects, although this is a slight decrease from 82% in 2024[13] - 44% of CEOs expect a slight revenue increase (2.5%-4.99%) this year, compared to 30% last year[13] Artificial Intelligence and Innovation - 80% of CEOs recognize the disruptive potential of artificial intelligence (AI)[10] - 40% of CEOs are actively retraining employees affected by AI to enhance their skills[10] - 66% of CEOs expect to see returns on AI investments within 1-3 years, significantly higher than 15% in 2024[10] Mergers and Acquisitions - 55% of CEOs anticipate "moderate" M&A activity, a significant increase from 38% the previous year[16] - Only 36% of CEOs expect to engage in "major" M&A, down from 58% in 2024[16] ESG and Sustainability - 72% of CEOs have integrated sustainability into their corporate strategy, but only 38% have fully incorporated ESG into capital decisions[54] - 61% of CEOs acknowledge that public debates on sustainability hinder their focus on core tasks[54] Supply Chain Resilience - 34% of CEOs identify supply chain resilience as the primary factor influencing short-term decisions[22] - 61% of stakeholders in the renewable energy sector believe supply chain risks complicate the scaling of renewable projects[19]

Group 1: OECD Taxation Insights - The OECD's latest report highlights China's MAP case closure rate exceeds the global average, showcasing effective tax dispute resolution[3] - China's APA closure rate stands at 24.2%, significantly higher than the global average of 18.1%, ranking seventh among jurisdictions[8] - The tax dispute prevention rate in China is 66.7%, well above the global average of 37.8%, placing it fifth globally[8] Group 2: Global Tax Trends - The total number of global MAP cases increased from 2,782 to 2,980, reflecting a growth rate of 7.12%[8] - The average completion time for MAP cases globally is 27.4 months, with transfer pricing cases reduced from 32 months to 30.9 months[8] - The OECD emphasizes the importance of cooperation among tax authorities to enhance tax certainty and prevent disputes[4] Group 3: Future Outlook - Chinese tax authorities are expected to continue engaging with jurisdictions like Italy, the US, Switzerland, Japan, and South Korea to resolve tax disputes[8] - The OECD's best practices are being integrated into domestic regulations by various jurisdictions, indicating a commitment to improving tax administration[7] - The exploration of multilateral frameworks for tax dispute resolution is gaining traction, with a focus on overcoming domestic legal barriers[7]

Investment Rating - The report does not explicitly state an investment rating for the hydrogen energy industry but emphasizes the potential for growth and innovation through the integration of AI technology. Core Insights - The integration of AI technology in the hydrogen energy sector is seen as a key driver for reducing costs and improving efficiency across the entire industry chain. AI is expected to facilitate breakthroughs in catalyst development, optimize electrolysis parameters, and enhance predictive maintenance, thereby supporting the transition to a low-carbon energy system [8][10][11]. Summary by Sections Section 1: Current State and Future Pathways - Hydrogen energy is recognized as a crucial element for deep decarbonization and energy security, with AI technology emerging as a vital force in driving down costs and enhancing efficiency in the hydrogen industry [8][11]. - The report highlights the urgent need to overcome development bottlenecks in the hydrogen sector, with AI playing a transformative role [8][11]. Section 2: AI's Impact on the Hydrogen Industry Chain - AI is applied across various scenarios in the hydrogen industry, with a focus on catalyst research, predictive maintenance, and optimization of hydrogen production processes. The maturity and value potential of these applications vary significantly [8][9][10]. - In hydrogen production, AI is revolutionizing catalyst development and optimizing electrolysis processes, while predictive maintenance is becoming a hot application area due to its high maturity and value potential [8][9][10]. Section 3: Global Practices of "AI + Hydrogen" - Different countries are adopting varied approaches to integrate AI into hydrogen projects, with Europe leading through policy support and funding, while Asia, particularly China, is establishing a legal framework to promote hydrogen's role in energy management [9][10][11]. - The report notes that the U.S. is making progress in AI-assisted molecular screening and electrolysis optimization, although policy uncertainties remain [9][10]. Section 4: Challenges in AI and Hydrogen Integration - Key challenges include data issues such as insufficient samples and data silos, the gap between laboratory results and industrial application, and the lack of unified standards and regulations [9][10]. - The report also identifies a shortage of interdisciplinary talent and an over-concentration of applications in the transportation sector, which limits the full potential of AI in hydrogen applications [9][10]. Section 5: Recommendations for High-Quality Development - Recommendations include improving data quality, accelerating the conversion of research results to industrial applications, establishing unified standards and regulations, and expanding the application of AI beyond transportation to industrial and building sectors [10][11]. - The report concludes that the synergistic development of AI and hydrogen is a significant trend in the global energy transition, with the potential to release substantial multiplier effects [10][11].

Group 1: Importance of Market Value Management - Market value management is a long-term strategic behavior for state-owned enterprises (SOEs) and is crucial during the "14th Five-Year Plan" period[5] - Effective market value management can provide capital support for the implementation of the "15th Five-Year Plan" and enhance the execution of state-owned enterprise reforms[6] - SOEs play a stabilizing role in the economy, and their market value management can promote healthy and stable development of the capital market[7] Group 2: Current Market Position of SOEs - As of September 2025, there are 1,458 state-controlled listed companies in the A-share market, accounting for approximately 26.8% of the total number of listed companies, with a total market value of about 47.6% of the A-share market[8] - State-controlled listed companies have an average market value exceeding 39 billion yuan, significantly higher than the average of non-state-controlled companies at 157 million yuan[8] - Total assets of state-controlled listed companies account for about 80% of the total assets of A-share listed companies, with an average asset size of 2,594 billion yuan, which is 10.7 times that of non-state-controlled companies[10] Group 3: Challenges in Market Value Management - State-controlled listed companies face multiple challenges in market value management due to their high concentration in traditional industries, which limits growth potential[21] - The effectiveness of market value management tools is underutilized in state-controlled companies, with a lower frequency of mergers and acquisitions compared to private enterprises[26] - The growth of total assets in state-controlled companies does not significantly enhance market value, indicating a disparity in market perception of asset effectiveness[31] Group 4: Recommendations for Improvement - SOEs should establish a collaborative mechanism for market value management between controlling shareholders and listed companies to enhance awareness and effectiveness[35] - It is recommended to utilize "key third parties" to promote market value management efforts and build a long-term assessment system for market value management[35] - A restructuring of underlying valuation logic is necessary to improve the internal value of enterprises, focusing on long-term value creation rather than short-term stock price fluctuations[36]