Core Insights - The evolution of payment methods reflects societal technological changes, with facial recognition payment emerging as a key infrastructure in the digital economy, raising concerns beyond mere technical security and privacy issues [1][2] Group 1: Governance Principles - Establish a risk-based governance principle focusing on three core aspects: 1. Preventive principle: Regulatory measures should be proactive and adaptable to unknown risks associated with biometric data [3] 2. Proportionality principle: Regulatory intensity must correspond to the risk level of specific applications, avoiding a one-size-fits-all approach [3] 3. Collaborative governance principle: Involvement of multiple stakeholders, including government, industry, and the public, to create a cooperative governance framework [3] Group 2: Regulatory Tools and Mechanisms - Innovate key regulatory tools and mechanisms by: 1. Upgrading the "regulatory sandbox" to test data governance rules and algorithmic ethics comprehensively [4] 2. Establishing mandatory algorithm audits and certifications by independent third parties to ensure fairness and robustness [4] 3. Implementing risk-based tiered management according to transaction amounts and application scenarios, requiring multi-factor authentication for high-risk situations [4] Group 3: Legal and Standard Foundations - Strengthen the legal and standard foundations for governance by: 1. Clarifying legal responsibilities and consumer protection measures, including the principle of "reversed burden of proof" in fraud cases [5] 2. Promoting industry self-regulation and the establishment of high-standard processing norms for biometric information [5] Group 4: External Environment for Governance - Create a favorable external environment for scientific governance by: 1. Enhancing public digital literacy and awareness of risks associated with facial recognition payments [6] 2. Expanding social participation and oversight channels in the formulation of technical standards and regulations [6] - The challenges posed by facial recognition payments necessitate a comprehensive regulatory framework to guide its healthy development, ultimately supporting inclusive finance and social equity [7]

Core Viewpoint - The Ministry of Industry and Information Technology, along with other departments, has issued a "Steel Industry Growth Stabilization Work Plan (2025-2026)" aimed at promoting the stable operation and structural optimization of the steel industry, targeting an average annual growth of around 4% in value-added output from 2025 to 2026 [1][2]. Group 1: Industry Challenges and Goals - The steel industry is currently facing significant challenges, including excessive supply and insufficient effective demand, leading to a supply-demand imbalance that restricts development quality and efficiency [1]. - The plan aims for an average annual growth of approximately 4% in the steel industry's value-added output from 2025 to 2026, with a focus on stabilizing economic benefits and optimizing industry structure [1][2]. Group 2: Implementation Measures - The plan includes specific measures such as capacity reduction and replacement, production regulation, and graded management of the steel industry to optimize supply and demand balance [2][3]. - It emphasizes the need for continuous structural optimization on the supply side and matching with demand-side changes to achieve growth targets [2]. Group 3: Classification and Management - The plan proposes a graded classification management system for steel enterprises, which will provide policy support to compliant enterprises while imposing restrictions on non-compliant ones, ultimately leading to the exit of inefficient capacities [3]. - This classification is expected to enhance the overall competitiveness and standardization of the steel industry, aligning it with high-quality development requirements [3]. Group 4: Innovation and Investment - The plan outlines initiatives to strengthen technological innovation in the industry, enhance high-end product supply capabilities, and improve the resilience and safety of the supply chain [3]. - It also encourages effective investment in upgrading processes and equipment, digital transformation, and green low-carbon modifications [3]. Group 5: Market Expansion and Cooperation - The plan aims to expand the application of steel structures in construction, transportation, and infrastructure to stimulate consumption potential [3]. - It highlights the importance of strengthening collaboration with upstream and downstream enterprises and research institutions to meet the demand for high-strength steel and other specialized products [4].

Core Viewpoint - Guangzhou is actively reforming its engineering construction project bidding system to achieve better quality, economic efficiency, and integrity in the bidding process, addressing the "three difficult problems" of quality, economy, and integrity in engineering bidding [2][5][10]. Group 1: Challenges and Solutions - Challenge: Small and medium-sized enterprises (SMEs) struggle to compete against larger firms due to a "big fish eats small fish" dynamic [3][5]. - Solution: Implementing a "graded classification" management approach to allow for more scientific resource allocation, enabling SMEs to compete on a more level playing field by lowering evaluation thresholds for smaller projects [5][6]. Group 2: Enhancing Competition - Challenge: Insufficient competition leads to poor cost control and potential corruption in bidding processes [6][7]. - Solution: Introducing measures to activate a "catfish effect" to foster healthy competition, including randomizing evaluation rules to reduce collusion between bidders and tenderers [6][7][8]. Group 3: Transparency and Accountability - Challenge: Illegal subcontracting and quality compromises due to lack of oversight [8][9]. - Solution: Strengthening the evaluation of actual construction units and making the management of subcontractors and materials transparent to ensure quality and safety [8][9]. Group 4: Expert Integrity - Challenge: Experts in the bidding process have been compromised, leading to biased evaluations [10][11]. - Solution: Implementing a "lifetime responsibility" system for evaluation experts to ensure accountability and integrity in the evaluation process, alongside measures to prevent collusion [10][12]. Group 5: Efficiency through Technology - Challenge: Traditional manual evaluation methods are inefficient and prone to errors [14][15]. - Solution: Utilizing AI-assisted evaluation systems to enhance efficiency and accuracy in the bidding process, significantly reducing the time required for evaluations [14][15][16].